ikawrakow/ik_llama.cpp
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Add ability to merge up+gate exps to more models

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Kawrakow
2026-01-12 17:00:09 +02:00
parent c03c2d7cc6
commit 60ccbe7bcd
1 changed files with 39 additions and 51 deletions
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90
src/llama-load-tensors.cpp
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@@ -33,6 +33,8 @@ struct create_tensors_helper : public create_tensors_helper_interface {
bool merge_up_gate_exps(const LLM_TN & tn, int i, int bias);
bool create_std_ffn_exps(int64_t n_embd, const LLM_TN & tn, int i, int flags = 0, int n_ff_exps_input = 0);
bool create_tensors() override;
bool create_llama_tensors(const LLM_TN & tn);
@@ -532,9 +534,7 @@ bool create_tensors_helper::create_llama4_tensors(const LLM_TN & tn) {
int n_ff_exp = hparams.n_ff_exp;
layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
// Shared expert
const int64_t n_ff_shexp = n_ff_exp;
@@ -639,9 +639,7 @@ bool create_tensors_helper::create_dbrx_tensors(const LLM_TN & tn) {
layer.attn_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
}
return use_mmap_buffer;
}
@@ -1082,13 +1080,8 @@ bool create_tensors_helper::create_qwen2_moe_tensors(const LLM_TN & tn) {
throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
}
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
// Shared expert branch
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
@@ -1176,17 +1169,8 @@ bool create_tensors_helper::create_qwen3_moe_tensors(const LLM_TN & tn) {
throw std::runtime_error("n_expert_used must be > 0 for QWEN3MOE");
}
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
bool merged = ml.merge_up_gate_exps && merge_up_gate_exps(tn, i, 0);
if (merged) {
use_mmap_buffer = false;
} else {
layer.ffn_up_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
layer.ffn_gate_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
}
layer.ffn_down_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
}
return use_mmap_buffer;
}
@@ -1224,12 +1208,13 @@ bool create_tensors_helper::create_mimo2_tensors(const LLM_TN & tn) {
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
layer.ffn_gate_inp = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_gate_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_exp_probs_b = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_gate_inp = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert},
llama_model_loader::TENSOR_NOT_REQUIRED);
if (layer.ffn_gate_inp) {
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
layer.ffn_exp_probs_b = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert},
llama_model_loader::TENSOR_NOT_REQUIRED);
}
}
return use_mmap_buffer;
}
@@ -1860,9 +1845,7 @@ bool create_tensors_helper::create_deepseek2_tensors(const LLM_TN & tn) {
GGML_ASSERT(n_expert_used > 0);
// MoE branch
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
@@ -1935,17 +1918,11 @@ bool create_tensors_helper::create_glm4_moe_tensors(const LLM_TN & tn) {
layer.ffn_exp_probs_b = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags);
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
layer.ffn_gate_exps = create_tensor(ffn_ctx,
tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
layer.ffn_down_exps = create_tensor(ffn_ctx,
tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
layer.ffn_up_exps = create_tensor(ffn_ctx,
tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i, flags);
// Shared expert
if (n_expert_shared > 0) {
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 = n_ff_exp * n_expert_shared;
layer.ffn_gate_shexp = create_tensor(ffn_ctx,
tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
@@ -2396,9 +2373,7 @@ bool create_tensors_helper::create_dots1_tensors(const LLM_TN & tn) {
throw std::runtime_error("n_expert_used must be > 0");
}
// MoE branch
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
@@ -2451,9 +2426,7 @@ bool create_tensors_helper::create_bailingmoe2_tensors(const LLM_TN & tn) {
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert},
llama_model_loader::TENSOR_NOT_REQUIRED | flags);
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i, flags);
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp}, flags);
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, flags);
@@ -2549,9 +2522,7 @@ bool create_tensors_helper::create_hunyuan_tensors(const LLM_TN & tn) {
layer.ffn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i, 0, n_ff);
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
@@ -2651,9 +2622,7 @@ bool create_tensors_helper::create_minimaxm2_tensors(const LLM_TN & tn) {
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff, n_expert }, 0);
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert }, 0);
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff, n_expert }, 0);
use_mmap_buffer &= !create_std_ffn_exps(n_embd, tn, i, 0, n_ff);
layer.ffn_exp_probs_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, 0);
}
return use_mmap_buffer;
@@ -2746,6 +2715,25 @@ bool create_tensors_helper::merge_up_gate_exps(const LLM_TN & tn, int i, int bia
return true;
}
bool create_tensors_helper::create_std_ffn_exps(int64_t n_embd, const LLM_TN & tn, int i, int flags, int n_ff_exps_input) {
const int64_t n_expert = model.hparams.n_expert;
const int64_t n_expert_used = model.hparams.n_expert_used;
const int64_t n_ff = model.hparams.n_ff();
const int64_t n_ff_exp = n_ff_exps_input > 0 ? n_ff_exps_input : model.hparams.n_ff_exp ? model.hparams.n_ff_exp : n_ff / n_expert_used;
auto & layer = model.layers[i];
auto ffn_ctx = ctx_for_layer_split(i);
bool merged = flags == 0 && ml.merge_up_gate_exps && merge_up_gate_exps(tn, i, 0);
if (!merged) {
layer.ffn_up_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
layer.ffn_gate_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
}
layer.ffn_down_exps = create_tensor(ffn_ctx, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
return merged;
}
bool create_tensors_helper::merge_qkv(const LLM_TN & tn, int i, int bias, bool ignore_attn_scale) {
auto& hparams = model.hparams;
const int64_t n_head = hparams.n_head();