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fix merge

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This commit is contained in:
Xuan Son Nguyen
2026-10-02 01:24:55 +02:00
parent 4732881d35
commit 335afadd7a
6 changed files with 25 additions and 31 deletions
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gguf-py/gguf/tensor_mapping.py
+8 -2
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@@ -1314,6 +1314,14 @@ class TensorNameMap:
"joint_head.option_norm", # clef
),
MODEL_TENSOR.DECISION_SCORER: (
"joint_head.residual_scorer.0", # clef
),
MODEL_TENSOR.DECISION_SCORER_OUT: (
"joint_head.residual_scorer.3", # clef
),
MODEL_TENSOR.ENC_ATTN_NORM: (
"encoder.block.{bid}.layer.0.layer_norm", # t5
),
@@ -1515,13 +1523,11 @@ class TensorNameMap:
"dense", # neobert
"head.dense", # modern-bert
"scorer.1", # laya
"joint_head.residual_scorer.0", # clef
),
MODEL_TENSOR.CLS_OUT: (
"classifier.out_proj", # roberta
"scorer.3", # laya
"joint_head.residual_scorer.3", # clef
),
MODEL_TENSOR.CLS_NORM: (
src/llama-arch.cpp
+4
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@@ -614,6 +614,8 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_DECISION_FIELD_NORM, "decision.field_norm" },
{ LLM_TENSOR_DECISION_OPTION_NORM, "decision.option_norm" },
{ LLM_TENSOR_DECISION_SCALES, "decision.scales" },
{ LLM_TENSOR_DECISION_SCORER, "decision.scorer" },
{ LLM_TENSOR_DECISION_SCORER_OUT, "decision.scorer_out" },
{ LLM_TENSOR_DEC_ATTN_NORM, "dec.blk.%d.attn_norm" },
{ LLM_TENSOR_DEC_ATTN_Q, "dec.blk.%d.attn_q" },
{ LLM_TENSOR_DEC_ATTN_K, "dec.blk.%d.attn_k" },
@@ -771,6 +773,8 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_DECISION_FIELD_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_DECISION_OPTION_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_DECISION_SCALES, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_DECISION_SCORER, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_DECISION_SCORER_OUT, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
{LLM_TENSOR_ENC_OUTPUT_NORM, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
{LLM_TENSOR_ROPE_FREQS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
{LLM_TENSOR_ROPE_FACTORS_LONG, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
src/llama-arch.h
+2
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@@ -661,6 +661,8 @@ enum llm_tensor {
LLM_TENSOR_DECISION_FIELD_NORM,
LLM_TENSOR_DECISION_OPTION_NORM,
LLM_TENSOR_DECISION_SCALES,
LLM_TENSOR_DECISION_SCORER,
LLM_TENSOR_DECISION_SCORER_OUT,
LLM_TENSOR_ENC_ATTN_NORM,
LLM_TENSOR_ENC_ATTN_Q,
LLM_TENSOR_ENC_ATTN_K,
src/models/clef.cpp
+7 -6
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@@ -90,10 +90,11 @@ void llama_model_clef::load_arch_tensors(llama_model_loader & ml) {
scales = create_tensor(tn(LLM_TENSOR_DECISION_SCALES), {3}, 0);
type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd_h, 3}, 0);
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {4 * n_embd_h, n_embd_h}, 0);
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd_h}, 0);
cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd_h, 1}, 0);
cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, 0);
scorer = create_tensor(tn(LLM_TENSOR_DECISION_SCORER, "weight"), {4 * n_embd_h, n_embd_h}, 0);
scorer_b = create_tensor(tn(LLM_TENSOR_DECISION_SCORER, "bias"), {n_embd_h}, 0);
scorer_out = create_tensor(tn(LLM_TENSOR_DECISION_SCORER_OUT, "weight"), {n_embd_h, 1}, 0);
scorer_out_b = create_tensor(tn(LLM_TENSOR_DECISION_SCORER_OUT, "bias"), {1}, 0);
}
std::unique_ptr<llm_graph_context> llama_model_clef::build_arch_graph(const llm_graph_params & params) const {
@@ -598,9 +599,9 @@ ggml_tensor * llama_model_clef::graph::build_head(ggml_tensor * hidden, input_de
features = ggml_concat(ctx0, features, ggml_mul(ctx0, field, options), 0);
features = ggml_concat(ctx0, features, ggml_abs(ctx0, ggml_sub(ctx0, field, options)), 0);
ggml_tensor * residual = ggml_add(ctx0, ggml_mul_mat(ctx0, model.cls, features), model.cls_b);
ggml_tensor * residual = ggml_add(ctx0, ggml_mul_mat(ctx0, model.scorer, features), model.scorer_b);
residual = ggml_gelu_erf(ctx0, residual);
residual = ggml_add(ctx0, ggml_mul_mat(ctx0, model.cls_out, residual), model.cls_out_b); // [1, n_options]
residual = ggml_add(ctx0, ggml_mul_mat(ctx0, model.scorer_out, residual), model.scorer_out_b); // [1, n_options]
auto scale = [&](int i) {
return ggml_view_1d(ctx0, model.scales, 1, i * ggml_element_size(model.scales));
src/models/models.h
+4
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@@ -2445,6 +2445,10 @@ struct llama_model_clef : public llama_model_qwen35 {
ggml_tensor * proj_option_context = nullptr;
ggml_tensor * proj_option_lexical = nullptr;
ggml_tensor * scales = nullptr; // prior scale, joint scale, residual gate
ggml_tensor * scorer = nullptr;
ggml_tensor * scorer_b = nullptr;
ggml_tensor * scorer_out = nullptr;
ggml_tensor * scorer_out_b = nullptr;
class input_decision;
tools/server/server-decision.cpp
-23
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@@ -534,29 +534,6 @@ static const std::string CLEF_PIECE_STATE = "<<clef:state>>";
static const std::string CLEF_PIECE_QUESTION = "<<clef:question>>";
static const std::string CLEF_PIECE_OPTION = "<<clef:option>>";
// same value with the keys of all objects sorted
static json decision_sort_keys(const json & val) {
if (val.is_array()) {
json out = json::array();
for (const auto & item : val) {
out.push_back(decision_sort_keys(item));
}
return out;
}
if (val.is_object()) {
std::map<std::string, json> sorted;
for (const auto & [key, item] : val.items()) {
sorted[key] = decision_sort_keys(item);
}
json out = json::object();
for (const auto & [key, item] : sorted) {
out[key] = item;
}
return out;
}
return val;
}
// strings are used as is, other values are compact JSON with sorted keys
static std::string clef_render(const json & val) {
return val.is_string() ? val.get<std::string>() : decision_sort_keys(val).dump();