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arch/*.cpp + gguf-py: mechanical rename n_ff_exp->n_ff_exp_impl, n_expert_used->n_expert_used_impl

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All non-nemotron arch files continue using the scalar impl member directly.
Behaviour is identical: the impl value is the broadcast value from the GGUF scalar.

gguf_writer: add_expert_feed_forward_length and add_expert_used_count now accept
int | Sequence[int], mirroring add_feed_forward_length, so converters can write
per-layer arrays with the same existing GGUF keys.
This commit is contained in:
Yaniss
2026-07-26 11:30:37 +02:00
parent b1878a1017
commit 8f009f54be
38 changed files with 96 additions and 90 deletions
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gguf-py/gguf/gguf_writer.py
+10 -4
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@@ -724,8 +724,11 @@ class GGUFWriter:
else:
self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_feed_forward_length(self, length: int | Sequence[int]) -> None:
if isinstance(length, int):
self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
else:
self.add_array(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_shared_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
@@ -833,8 +836,11 @@ class GGUFWriter:
def add_expert_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
def add_expert_used_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
def add_expert_used_count(self, count: int | Sequence[int]) -> None:
if isinstance(count, int):
self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
else:
self.add_array(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count)
def add_expert_shared_count(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count)
src/models/afmoe.cpp
+2 -2
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@@ -3,7 +3,7 @@
void llama_model_afmoe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
@@ -52,7 +52,7 @@ void llama_model_afmoe::load_arch_tensors(llama_model_loader &) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
src/models/bailingmoe.cpp
+2 -2
View File
@@ -3,7 +3,7 @@
void llama_model_bailingmoe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
@@ -19,7 +19,7 @@ void llama_model_bailingmoe::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/bailingmoe2.cpp
+2 -2
View File
@@ -3,7 +3,7 @@
void llama_model_bailingmoe2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
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);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
@@ -24,7 +24,7 @@ void llama_model_bailingmoe2::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/bert.cpp
+1 -1
View File
@@ -182,7 +182,7 @@ llama_model_bert::graph::graph(const llama_model & model, const llm_graph_params
nullptr,
model.layers[il].ffn_down_exps,
nullptr,
hparams.n_expert, hparams.n_expert_used,
hparams.n_expert, hparams.n_expert_used_impl,
LLM_FFN_GELU, false,
hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
src/models/cohere2moe.cpp
+3 -3
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@@ -13,7 +13,7 @@ void llama_model_cohere2moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
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);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
@@ -88,7 +88,7 @@ void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
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 {
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, 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);
@@ -112,7 +112,7 @@ void llama_model_cohere2moe::load_arch_tensors(llama_model_loader & ml) {
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, flags);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff;
// Routed experts
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
src/models/deepseek.cpp
+3 -3
View File
@@ -3,11 +3,11 @@
void llama_model_deepseek::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
switch (hparams.n_ff_exp) {
switch (hparams.n_ff_exp_impl) {
case 1408: type = LLM_TYPE_16B; break;
case 1792: type = LLM_TYPE_20B; break;
default: type = LLM_TYPE_UNKNOWN;
@@ -19,7 +19,7 @@ void llama_model_deepseek::load_arch_tensors(llama_model_loader &) {
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/deepseek2.cpp
+2 -2
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@@ -15,7 +15,7 @@ void llama_model_deepseek2::load_arch_hparams(llama_model_loader & ml) {
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_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
@@ -69,7 +69,7 @@ void llama_model_deepseek2::load_arch_tensors(llama_model_loader &) {
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_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/deepseek2ocr.cpp
+2 -2
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@@ -4,7 +4,7 @@ void llama_model_deepseek2ocr::load_arch_hparams(llama_model_loader & ml) {
// similar to deepseek2, but without MLA
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
@@ -25,7 +25,7 @@ void llama_model_deepseek2ocr::load_arch_tensors(llama_model_loader &) {
const int64_t n_expert_shared = hparams.n_expert_shared;
// similar to deepseek2, but without MLA
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/deepseek32.cpp
+4 -4
View File
@@ -4,14 +4,14 @@
#include "llama-kv-cache-dsa.h"
void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
hparams.f_norm_eps = 1e-6; // eps for layer norm
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_USED_COUNT, hparams.n_expert_used_impl);
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, false);
@@ -22,7 +22,7 @@ void llama_model_deepseek32::load_arch_hparams(llama_model_loader & ml) {
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_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
// DSA parameters
@@ -66,7 +66,7 @@ void llama_model_deepseek32::load_arch_tensors(llama_model_loader &) {
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_ff_exp = hparams.n_ff_exp_impl;
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);
src/models/deepseek4.cpp
+3 -3
View File
@@ -20,7 +20,7 @@ void llama_model_deepseek4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm);
@@ -65,7 +65,7 @@ void llama_model_deepseek4::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t q_lora_rank = hparams.n_lora_q;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_embd_head = hparams.n_embd_head_k();
@@ -1150,7 +1150,7 @@ llama_model_deepseek4::graph::graph(const llama_model & model, const llm_graph_p
layer.ffn_gate_exps,
layer.ffn_down_exps,
exp_probs_b,
n_expert, hparams.n_expert_used,
n_expert, hparams.n_expert_used_impl,
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
src/models/dots1.cpp
+2 -2
View File
@@ -3,7 +3,7 @@
void llama_model_dots1::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
@@ -19,7 +19,7 @@ void llama_model_dots1::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_expert_shared = hparams.n_expert_shared;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/ernie4-5.cpp
+2 -2
View File
@@ -6,7 +6,7 @@ void llama_model_ernie4_5::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
if (arch == LLM_ARCH_ERNIE4_5_MOE) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
@@ -47,7 +47,7 @@ void llama_model_ernie4_5::load_arch_tensors(llama_model_loader &) {
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
if (arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
int n_ff_exp = hparams.n_ff_exp;
int n_ff_exp = hparams.n_ff_exp_impl;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
src/models/exaone-moe.cpp
+2 -2
View File
@@ -13,7 +13,7 @@ void llama_model_exaone_moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
@@ -33,7 +33,7 @@ void llama_model_exaone_moe::load_arch_hparams(llama_model_loader & ml) {
void llama_model_exaone_moe::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
const int64_t n_ff_shexp = hparams.n_ff_shexp > 0 ? hparams.n_ff_shexp : n_ff_exp;
const int64_t head_dim = hparams.n_embd_head_k();
const int64_t n_qo_dim = n_head * head_dim;
src/models/gemma4.cpp
+2 -2
View File
@@ -11,7 +11,7 @@ void llama_model_gemma4::load_arch_hparams(llama_model_loader & ml) {
hparams.f_attention_scale = 1.0f; // Gemma4 uses self.scaling = 1.0 (no pre-attn scaling)
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer);
@@ -32,7 +32,7 @@ void llama_model_gemma4::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const uint32_t n_embd_per_layer = hparams.n_embd_per_layer;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
if (n_embd_head_k != n_embd_head_v) {
throw std::runtime_error("Gemma 4 requires n_embd_head_k == n_embd_head_v");
src/models/glm-dsa.cpp
+4 -4
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@@ -27,13 +27,13 @@ const std::array<uint32_t, LLAMA_MAX_LAYERS> GLM_5_2_DEFAULT_INDEXER_TYPES = {
};
void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
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_USED_COUNT, hparams.n_expert_used_impl);
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, false);
@@ -44,7 +44,7 @@ void llama_model_glm_dsa::load_arch_hparams(llama_model_loader & ml) {
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_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
// DSA parameters
@@ -96,7 +96,7 @@ void llama_model_glm_dsa::load_arch_tensors(llama_model_loader &) {
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_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/glm4-moe.cpp
+4 -4
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@@ -1,13 +1,13 @@
#include "models.h"
void llama_model_glm4_moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
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_USED_COUNT, hparams.n_expert_used_impl);
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, false);
@@ -37,7 +37,7 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
GGML_ASSERT(hparams.n_expert_used_impl > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
@@ -86,7 +86,7 @@ void llama_model_glm4_moe::load_arch_tensors(llama_model_loader &) {
layer.ffn_exp_probs_b = create_tensor(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;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
layer.ffn_gate_exps = create_tensor(
tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
src/models/grok.cpp
+2 -2
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@@ -12,7 +12,7 @@ void llama_model_grok::load_arch_hparams(llama_model_loader & ml) {
hparams.f_final_logit_softcapping = 0.0f;
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale, false);
ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale, false);
ml.get_key(LLM_KV_ATTENTION_OUTPUT_SCALE, hparams.f_attn_out_scale, false);
@@ -50,7 +50,7 @@ void llama_model_grok::load_arch_tensors(llama_model_loader &) {
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*/; // grok-1 n_ff_exp == n_ff
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff/* / n_expert_used*/; // grok-1 n_ff_exp == n_ff
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
src/models/grovemoe.cpp
+2 -2
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@@ -1,7 +1,7 @@
#include "models.h"
void llama_model_grovemoe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_CHUNK_FEED_FORWARD_LENGTH, hparams.n_ff_chexp, false);
ml.get_key(LLM_KV_EXPERT_GROUP_SCALE, hparams.expert_group_scale);
ml.get_key(LLM_KV_EXPERTS_PER_GROUP, hparams.n_group_experts);
@@ -46,7 +46,7 @@ void llama_model_grovemoe::load_arch_tensors(llama_model_loader &) {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
const int64_t n_ff_chexp = hparams.n_ff_chexp ? hparams.n_ff_chexp : n_embd_head_k;
const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
src/models/hunyuan-moe.cpp
+1 -1
View File
@@ -2,7 +2,7 @@
void llama_model_hunyuan_moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
switch (hparams.n_layer()) {
src/models/hy-v3.cpp
+2 -2
View File
@@ -2,7 +2,7 @@
void llama_model_hy_v3::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
@@ -45,7 +45,7 @@ void llama_model_hy_v3::load_arch_tensors(llama_model_loader & ml) {
auto load_block = [&](int i, int flags) {
auto & layer = layers[i];
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / (n_expert_used > 0 ? n_expert_used : 1);
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / (n_expert_used > 0 ? n_expert_used : 1);
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff_exp;
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
src/models/kimi-linear.cpp
+3 -3
View File
@@ -19,7 +19,7 @@ void llama_model_kimi_linear::load_arch_hparams(llama_model_loader & ml) {
}
// MoE parameters - Kimi uses moe_intermediate_size = 1024
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
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, false);
@@ -137,7 +137,7 @@ void llama_model_kimi_linear::load_arch_tensors(llama_model_loader &) {
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
// MoE intermediate size (different from dense FFN)
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
// Kimi uses n_layer_dense_lead to determine which layers use dense FFN vs MoE
// first_k_dense_replace = 1 means layer 0 uses dense FFN, layers 1+ use MoE
@@ -504,7 +504,7 @@ llama_model_kimi_linear::graph::graph(const llama_model & model, const llm_graph
layer.ffn_down_exps,
layer.ffn_exp_probs_b,
hparams.n_expert,
hparams.n_expert_used,
hparams.n_expert_used_impl,
LLM_FFN_SILU, true,
hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
src/models/lfm2.cpp
+3 -3
View File
@@ -51,9 +51,9 @@ void llama_model_lfm2::load_arch_tensors(llama_model_loader &) {
if (is_moe_layer) {
GGML_ASSERT(n_expert && n_expert_used);
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, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp_impl, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp_impl, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp_impl, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
} else { // dense
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
src/models/lfm2moe.cpp
+4 -4
View File
@@ -6,7 +6,7 @@ void llama_model_lfm2moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_SHORTCONV_L_CACHE, hparams.n_shortconv_l_cache);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
for (uint32_t il = 0; il < hparams.n_layer(); ++il) {
@@ -42,9 +42,9 @@ void llama_model_lfm2moe::load_arch_tensors(llama_model_loader &) {
if (is_moe_layer) {
GGML_ASSERT(n_expert && n_expert_used);
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, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp, n_expert}, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, hparams.n_ff_exp_impl, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {hparams.n_ff_exp_impl, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, hparams.n_ff_exp_impl, n_expert}, 0);
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
} else { // dense
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
src/models/llada-moe.cpp
+2 -2
View File
@@ -1,7 +1,7 @@
#include "models.h"
void llama_model_llada_moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
// diffusion language model uses non-causal attention
@@ -39,7 +39,7 @@ void llama_model_llada_moe::load_arch_tensors(llama_model_loader &) {
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 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_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
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);
src/models/llama4.cpp
+2 -2
View File
@@ -2,7 +2,7 @@
void llama_model_llama4::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
@@ -75,7 +75,7 @@ void llama_model_llama4::load_arch_tensors(llama_model_loader &) {
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
if (is_moe_layer) {
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
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);
src/models/mellum.cpp
+2 -2
View File
@@ -2,7 +2,7 @@
void llama_model_mellum::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
if (hparams.n_swa > 0) {
@@ -61,7 +61,7 @@ void llama_model_mellum::load_arch_tensors(llama_model_loader &) {
throw std::runtime_error("n_expert_used must be > 0 for Mellum");
}
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
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);
src/models/mimo2.cpp
+2 -2
View File
@@ -5,7 +5,7 @@ void llama_model_mimo2::load_arch_hparams(llama_model_loader & ml) {
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
@@ -58,7 +58,7 @@ void llama_model_mimo2::load_arch_tensors(llama_model_loader &) {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED | skip);
// MoE branch
int64_t n_ff_exp = hparams.n_ff_exp;
int64_t n_ff_exp = hparams.n_ff_exp_impl;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED | skip);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED | skip);
src/models/minimax-m2.cpp
+1 -1
View File
@@ -2,7 +2,7 @@
void llama_model_minimax_m2::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
switch (hparams.n_layer()) {
src/models/openai-moe.cpp
+2 -2
View File
@@ -2,7 +2,7 @@
void llama_model_openai_moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
@@ -24,7 +24,7 @@ void llama_model_openai_moe::load_arch_hparams(llama_model_loader & ml) {
void llama_model_openai_moe::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
src/models/qwen2moe.cpp
+2 -2
View File
@@ -1,7 +1,7 @@
#include "models.h"
void llama_model_qwen2moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, 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);
@@ -42,7 +42,7 @@ void llama_model_qwen2moe::load_arch_tensors(llama_model_loader &) {
}
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
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);
src/models/qwen35moe.cpp
+3 -3
View File
@@ -2,7 +2,7 @@
#include "llama-memory-recurrent.h"
void llama_model_qwen35moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, 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);
@@ -57,7 +57,7 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
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_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
// Calculate dimensions from hyperparameters
@@ -109,7 +109,7 @@ void llama_model_qwen35moe::load_arch_tensors(llama_model_loader & ml) {
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_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
// MTP block looks like a full-attention Qwen3.5 decoder block with MoE FFN.
src/models/qwen3moe.cpp
+2 -2
View File
@@ -1,7 +1,7 @@
#include "models.h"
void llama_model_qwen3moe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer()) {
@@ -47,7 +47,7 @@ void llama_model_qwen3moe::load_arch_tensors(llama_model_loader &) {
}
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
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);
src/models/qwen3next.cpp
+2 -2
View File
@@ -2,7 +2,7 @@
#include "llama-memory-recurrent.h"
void llama_model_qwen3next::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, 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);
@@ -46,7 +46,7 @@ void llama_model_qwen3next::load_arch_tensors(llama_model_loader &) {
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;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
// Calculate dimensions from hyperparameters
const int64_t head_k_dim = hparams.ssm_d_state;
src/models/qwen3vlmoe.cpp
+2 -2
View File
@@ -3,7 +3,7 @@
void llama_model_qwen3vlmoe::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer()) {
@@ -49,7 +49,7 @@ void llama_model_qwen3vlmoe::load_arch_tensors(llama_model_loader &) {
}
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
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);
src/models/rnd1.cpp
+2 -2
View File
@@ -1,7 +1,7 @@
#include "models.h"
void llama_model_rnd1::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer()) {
@@ -49,7 +49,7 @@ void llama_model_rnd1::load_arch_tensors(llama_model_loader &) {
}
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
const int64_t n_ff_exp = hparams.n_ff_exp_impl ? hparams.n_ff_exp_impl : n_ff / n_expert_used;
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);
src/models/smallthinker.cpp
+2 -2
View File
@@ -18,7 +18,7 @@ void llama_model_smallthinker::load_arch_hparams(llama_model_loader & ml) {
hparams.n_no_rope_layer_step = hparams.n_layer();
}
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
@@ -57,7 +57,7 @@ void llama_model_smallthinker::load_arch_tensors(llama_model_loader &) {
GGML_ASSERT(n_expert_used > 0 && "n_expert_used must be > 0 for SMALLTHINKER");
// MoE branch
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
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);
src/models/step35.cpp
+3 -3
View File
@@ -9,7 +9,7 @@ void llama_model_step35::load_arch_hparams(llama_model_loader & ml) {
hparams.n_rot_full = hparams.n_rot_full / 2;
// MoE + SWA parameters
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_impl);
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
@@ -99,7 +99,7 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
// MoE routed experts + selection bias (router_bias)
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED);
@@ -150,7 +150,7 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
// MoE routed experts + selection bias (router_bias)
const int64_t n_ff_exp = hparams.n_ff_exp;
const int64_t n_ff_exp = hparams.n_ff_exp_impl;
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, TENSOR_NOT_REQUIRED);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, TENSOR_NOT_REQUIRED);