rename register weight

src/dataloader/weights_loader.cpp

@@ -42,7 +42,7 @@ Loader::Loader(infiniDevice_t dev, const std::vector<int> &dev_ids) : _device(de
         RUN_INFINI(infinirtStreamCreate(&_streams[rank]));
     }
 }
-void Loader::resigter(const std::string &name, std::shared_ptr<Tensor> tensor, int rank, DistributionType dist_type) {
+void Loader::register_weight(const std::string &name, std::shared_ptr<Tensor> tensor, int rank, DistributionType dist_type) {
     _weights_maps[rank][name] = std::make_shared<Weight>(tensor, rank, _dev_ids.size(), dist_type);
 }
 void Loader::load(const std::string &name, const void *host_data) {

src/dataloader/weights_loader.hpp

@@ -48,7 +48,7 @@ public:
     /// @param tensor
     /// @param rank the rank of the weight tensor (default 0)
     /// @param dist_type either FULL, or distributed by ROW or COLUMN (default FULL)
-    void resigter(const std::string &name, std::shared_ptr<Tensor> tensor, int rank = 0, DistributionType dist_type = DistributionType::FULL);
+    void register_weight(const std::string &name, std::shared_ptr<Tensor> tensor, int rank = 0, DistributionType dist_type = DistributionType::FULL);
     void load(const std::string &name, const void *host_data);
     void finalize();
     std::shared_ptr<Tensor> get(const std::string &name, int rank = 0);

src/models/jiuge_awq/jiuge_awq_weight.cpp

@@ -66,15 +66,15 @@ JiugeAWQWeights::JiugeAWQWeights(
         _device_weights[i] = weight;
 
         auto w_in_embd = Tensor::weight(nullptr, dt_logits, {dvoc, d});
-        this->resigter("model.embed_tokens.weight", w_in_embd, i);
+        this->register_weight("model.embed_tokens.weight", w_in_embd, i);
         weight->w_in_embd = w_in_embd;
 
         auto w_out_norm = Tensor::weight(nullptr, dt_norm_w, {d});
-        this->resigter("model.norm.weight", w_out_norm, i);
+        this->register_weight("model.norm.weight", w_out_norm, i);
         weight->w_out_norm = w_out_norm;
 
         auto w_out_embd = Tensor::weight(nullptr, dt_logits, {dvoc, d})->permute({1, 0});
-        this->resigter("lm_head.weight", w_out_embd, i);
+        this->register_weight("lm_head.weight", w_out_embd, i);
         weight->w_out_embd = w_out_embd;
 
         weight->sin_table = getSinTable(dctx, dh, meta->theta);
@@ -82,21 +82,21 @@ JiugeAWQWeights::JiugeAWQWeights(
 
         for (size_t layer = 0; layer < nlayer; layer++) {
 
-#define RIGISTER_LAYER_WEIGHT(W_NAME, W_VAR, W_SHAPE, W_DTYPE, W_DIST_TYPE)               \
-    auto W_VAR = Tensor::weight(nullptr, W_DTYPE, W_SHAPE);                               \
-    this->resigter(W_NAME, W_VAR, i, infinicore::weights::DistributionType::W_DIST_TYPE); \
+#define RIGISTER_LAYER_WEIGHT(W_NAME, W_VAR, W_SHAPE, W_DTYPE, W_DIST_TYPE)                      \
+    auto W_VAR = Tensor::weight(nullptr, W_DTYPE, W_SHAPE);                                      \
+    this->register_weight(W_NAME, W_VAR, i, infinicore::weights::DistributionType::W_DIST_TYPE); \
     weight->W_VAR.push_back(W_VAR);
 
             RIGISTER_LAYER_WEIGHT("model.layers." + std::to_string(layer) + ".input_layernorm.weight", w_attn_norm, {d}, dt_norm_w, FULL);
 
-#define REGISTER_LAYER_QUANT_WEIGHT(W_NAME, W_VAR, W_IN, W_OUT, W_DIST_TYPE)                              \
-    auto W_VAR = std::make_shared<QuantInt4Weight>();                                                     \
-    W_VAR->w = Tensor::weight(nullptr, INFINI_DTYPE_I32, {W_IN, (W_OUT)*nbit / 32});                      \
-    this->resigter(W_NAME + ".qweight", W_VAR->w, i, infinicore::weights::DistributionType::W_DIST_TYPE); \
-    W_VAR->s = Tensor::weight(nullptr, INFINI_DTYPE_F16, {(W_IN) / quant_group_size, (W_OUT)});           \
-    this->resigter(W_NAME + ".scales", W_VAR->s, i, infinicore::weights::DistributionType::W_DIST_TYPE);  \
-    W_VAR->z = Tensor::weight(nullptr, INFINI_DTYPE_I32, {(W_IN) / quant_group_size, (W_OUT)*nbit / 32}); \
-    this->resigter(W_NAME + ".qzeros", W_VAR->z, i, infinicore::weights::DistributionType::W_DIST_TYPE);  \
+#define REGISTER_LAYER_QUANT_WEIGHT(W_NAME, W_VAR, W_IN, W_OUT, W_DIST_TYPE)                                     \
+    auto W_VAR = std::make_shared<QuantInt4Weight>();                                                            \
+    W_VAR->w = Tensor::weight(nullptr, INFINI_DTYPE_I32, {W_IN, (W_OUT)*nbit / 32});                             \
+    this->register_weight(W_NAME + ".qweight", W_VAR->w, i, infinicore::weights::DistributionType::W_DIST_TYPE); \
+    W_VAR->s = Tensor::weight(nullptr, INFINI_DTYPE_F16, {(W_IN) / quant_group_size, (W_OUT)});                  \
+    this->register_weight(W_NAME + ".scales", W_VAR->s, i, infinicore::weights::DistributionType::W_DIST_TYPE);  \
+    W_VAR->z = Tensor::weight(nullptr, INFINI_DTYPE_I32, {(W_IN) / quant_group_size, (W_OUT)*nbit / 32});        \
+    this->register_weight(W_NAME + ".qzeros", W_VAR->z, i, infinicore::weights::DistributionType::W_DIST_TYPE);  \
     weight->W_VAR.push_back(W_VAR);
 
             REGISTER_LAYER_QUANT_WEIGHT("model.layers." + std::to_string(layer) + ".self_attn.q_proj", w_attn_q, d, nh * dh, COLUMN);