File size: 11,184 Bytes
a98d378 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 5e61f61..9861e32 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -441,6 +441,12 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
+ { LLM_TENSOR_FFN_GATE_EXPS_TIER1, "blk.%d.ffn_gate_exps_tier1" },
+ { LLM_TENSOR_FFN_UP_EXPS_TIER1, "blk.%d.ffn_up_exps_tier1" },
+ { LLM_TENSOR_FFN_DOWN_EXPS_TIER1, "blk.%d.ffn_down_exps_tier1" },
+ { LLM_TENSOR_FFN_GATE_EXPS_TIER2, "blk.%d.ffn_gate_exps_tier2" },
+ { LLM_TENSOR_FFN_UP_EXPS_TIER2, "blk.%d.ffn_up_exps_tier2" },
+ { LLM_TENSOR_FFN_DOWN_EXPS_TIER2, "blk.%d.ffn_down_exps_tier2" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_GATE_UP_EXPS, "blk.%d.ffn_gate_up_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
@@ -889,6 +895,12 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_DEC_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
{LLM_TENSOR_ENC_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_GET_ROWS}},
{LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+ {LLM_TENSOR_FFN_GATE_EXPS_TIER1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+ {LLM_TENSOR_FFN_UP_EXPS_TIER1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+ {LLM_TENSOR_FFN_DOWN_EXPS_TIER1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+ {LLM_TENSOR_FFN_GATE_EXPS_TIER2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+ {LLM_TENSOR_FFN_UP_EXPS_TIER2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
+ {LLM_TENSOR_FFN_DOWN_EXPS_TIER2, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_GATE_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index ca7d55a..9026666 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -469,6 +469,12 @@ enum llm_tensor {
LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
+ LLM_TENSOR_FFN_GATE_EXPS_TIER1,
+ LLM_TENSOR_FFN_UP_EXPS_TIER1,
+ LLM_TENSOR_FFN_DOWN_EXPS_TIER1,
+ LLM_TENSOR_FFN_GATE_EXPS_TIER2,
+ LLM_TENSOR_FFN_UP_EXPS_TIER2,
+ LLM_TENSOR_FFN_DOWN_EXPS_TIER2,
LLM_TENSOR_FFN_GATE_UP_EXPS,
LLM_TENSOR_FFN_DOWN_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
diff --git a/src/llama-model.h b/src/llama-model.h
index 0d7352a..024952b 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -329,6 +329,12 @@ struct llama_layer {
struct ggml_tensor * ffn_gate_exps = nullptr;
struct ggml_tensor * ffn_down_exps = nullptr;
struct ggml_tensor * ffn_up_exps = nullptr;
+ struct ggml_tensor * ffn_gate_exps_tier1 = nullptr;
+ struct ggml_tensor * ffn_up_exps_tier1 = nullptr;
+ struct ggml_tensor * ffn_down_exps_tier1 = nullptr;
+ struct ggml_tensor * ffn_gate_exps_tier2 = nullptr;
+ struct ggml_tensor * ffn_up_exps_tier2 = nullptr;
+ struct ggml_tensor * ffn_down_exps_tier2 = nullptr;
struct ggml_tensor * ffn_gate_up_exps = nullptr;
struct ggml_tensor * ffn_gate_inp_b = nullptr;
struct ggml_tensor * ffn_gate_exps_b = nullptr;
diff --git a/src/models/qwen4exp.cpp b/src/models/qwen4exp.cpp
index acfdd5b..1fcb5c7 100644
--- a/src/models/qwen4exp.cpp
+++ b/src/models/qwen4exp.cpp
@@ -198,9 +198,32 @@ void llama_model_qwen4exp::load_arch_tensors(llama_model_loader & ml) {
layer.ple_conv1d = create_tensor(tn(LLM_TENSOR_PLE_CONV1D, "weight", il), { hparams.ple_conv_kernel, hc_dim }, 0);
}
- layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, n_expert }, 0);
- layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, 0);
- create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, 0);
+ const auto * t1 = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXPS_TIER1, "weight", il).str().c_str());
+ const bool mixed = t1 != nullptr;
+ const auto * router_meta = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il).str().c_str());
+ if (!router_meta) throw std::runtime_error("missing expert router tensor");
+ const int64_t layer_experts = mixed ? router_meta->ne[1] : n_expert;
+ if (layer_experts < n_expert_used || layer_experts > n_expert) throw std::runtime_error("invalid layer expert count");
+ layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", il), { n_embd, layer_experts }, 0);
+ if (mixed) {
+ const auto * t0 = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", il).str().c_str());
+ const auto * t2 = ml.get_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXPS_TIER2, "weight", il).str().c_str());
+ if (!t0 || !t2 || n_ff_exp != 640) throw std::runtime_error("missing mixed-tier expert tensors");
+ const int64_t n0 = t0->ne[2], n1 = t1->ne[2], n2 = t2->ne[2];
+ if (n0 < 1 || n0 > 64 || n1 < 1 || n1 > 64 || n2 < 1 || n2 > 64 || n0+n1+n2 != layer_experts)
+ throw std::runtime_error("invalid mixed-tier bank sizes");
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n0 }, 0);
+ create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n0, 0);
+ layer.ffn_gate_exps_tier1 = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS_TIER1, "weight", il), { n_embd, n_ff_exp, n1 }, 0);
+ layer.ffn_up_exps_tier1 = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS_TIER1, "weight", il), { n_embd, n_ff_exp, n1 }, 0);
+ layer.ffn_down_exps_tier1 = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS_TIER1, "weight", il), { 768, n_embd, n1 }, 0);
+ layer.ffn_gate_exps_tier2 = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS_TIER2, "weight", il), { n_embd, n_ff_exp, n2 }, 0);
+ layer.ffn_up_exps_tier2 = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS_TIER2, "weight", il), { n_embd, n_ff_exp, n2 }, 0);
+ layer.ffn_down_exps_tier2 = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS_TIER2, "weight", il), { n_ff_exp, n_embd, n2 }, 0);
+ } else {
+ layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", il), { n_ff_exp, n_embd, n_expert }, 0);
+ create_tensor_gate_up_exps(layer, il, n_embd, n_ff_exp, n_expert, 0);
+ }
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", il), { n_embd }, 0);
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", il), { n_embd, n_ff_shexp }, 0);
@@ -904,8 +927,52 @@ ggml_tensor * llama_model_qwen4exp::graph::build_layer_attn_linear(
ggml_tensor * llama_model_qwen4exp::graph::build_layer_ffn(ggml_tensor * cur, const int il) {
GGML_ASSERT(model.layers[il].ffn_gate_inp != nullptr);
- ggml_tensor * moe_out =
- build_moe_ffn(cur,
+ ggml_tensor * moe_out = nullptr;
+ if (model.layers[il].ffn_gate_exps_tier1 != nullptr) {
+ const auto & layer = model.layers[il];
+ const int64_t n_tokens = cur->ne[1];
+ ggml_tensor * logits = build_lora_mm(layer.ffn_gate_inp, cur);
+ ggml_tensor * probs = ggml_soft_max(ctx0, logits);
+ cb(probs, "ffn_moe_probs", il);
+ ggml_tensor * ids = ggml_argsort_top_k(ctx0, probs, n_expert_used);
+ cb(ids, "ffn_moe_topk", il);
+ ggml_tensor * selected = ggml_get_rows(ctx0, ggml_reshape_3d(ctx0, probs, 1, layer.ffn_gate_inp->ne[1], n_tokens), ids);
+ selected = ggml_reshape_2d(ctx0, selected, n_expert_used, n_tokens);
+ ggml_tensor * denom = ggml_clamp(ctx0, ggml_sum_rows(ctx0, selected), 6.103515625e-5f, INFINITY);
+ selected = ggml_div(ctx0, selected, denom);
+ if (hparams.expert_weights_scale != 0.0f && hparams.expert_weights_scale != 1.0f) {
+ selected = ggml_scale(ctx0, selected, hparams.expert_weights_scale);
+ }
+ ggml_tensor * ids_f = ggml_cast(ctx0, ids, GGML_TYPE_F32);
+ ggml_tensor * inp = ggml_reshape_3d(ctx0, cur, cur->ne[0], 1, n_tokens);
+ ggml_tensor * weighted = nullptr;
+ ggml_tensor * ups[] = {layer.ffn_up_exps, layer.ffn_up_exps_tier1, layer.ffn_up_exps_tier2};
+ ggml_tensor * gates[] = {layer.ffn_gate_exps, layer.ffn_gate_exps_tier1, layer.ffn_gate_exps_tier2};
+ ggml_tensor * downs[] = {layer.ffn_down_exps, layer.ffn_down_exps_tier1, layer.ffn_down_exps_tier2};
+ int64_t tier_start = 0;
+ for (int tier = 0; tier < 3; ++tier) {
+ const int64_t tier_size = ups[tier]->ne[2];
+ const float base = float(tier_start);
+ ggml_tensor * local_f = ggml_scale_bias(ctx0, ids_f, 1.0f, -base);
+ ggml_tensor * local = ggml_cast(ctx0, ggml_clamp(ctx0, local_f, 0.0f, float(tier_size - 1)), GGML_TYPE_I32);
+ ggml_tensor * low = ggml_step(ctx0, ggml_scale_bias(ctx0, local_f, 1.0f, 0.5f));
+ ggml_tensor * high = ggml_step(ctx0, ggml_scale_bias(ctx0, local_f, -1.0f, float(tier_size) - 0.5f));
+ tier_start += tier_size;
+ ggml_tensor * mask = ggml_mul(ctx0, low, high);
+ ggml_tensor * weights = ggml_reshape_3d(ctx0, ggml_mul(ctx0, selected, mask), 1, n_expert_used, n_tokens);
+ ggml_tensor * up = build_lora_mm_id(ups[tier], inp, local);
+ ggml_tensor * gate = build_lora_mm_id(gates[tier], inp, local);
+ ggml_tensor * act = ggml_swiglu_split(ctx0, gate, up);
+ if (tier == 1) act = ggml_pad(ctx0, act, 128, 0, 0, 0);
+ ggml_tensor * result = ggml_mul(ctx0, build_lora_mm_id(downs[tier], act, local), weights);
+ weighted = weighted ? ggml_add(ctx0, weighted, result) : result;
+ }
+ for (int i = 0; i < n_expert_used; ++i) {
+ ggml_tensor * piece = ggml_view_2d(ctx0, weighted, cur->ne[0], n_tokens, weighted->nb[2], i*weighted->nb[1]);
+ moe_out = moe_out ? ggml_add(ctx0, moe_out, piece) : piece;
+ }
+ } else {
+ moe_out = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
model.layers[il].ffn_gate_exps,
@@ -919,6 +986,7 @@ ggml_tensor * llama_model_qwen4exp::graph::build_layer_ffn(ggml_tensor * cur, co
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
model.layers[il].ffn_down_exps_s);
+ }
cb(moe_out, "ffn_moe_out", il);
// shared experts, as in the Qwen3Next reference
|