load data for the model

makefile

@@ -1,10 +1,11 @@
 OBJ=./objects
 
 
-all: mkdir simple_rnn test3
+# TODO: I am here
+all: mkdir simple_rnn test3 rnn_generation 
 
-simple_rnn: ${OBJ}/ggml.o  ${OBJ}/simple_rnn.o
- g++ -o simple_rnn_main ${OBJ}/simple_rnn.o ${OBJ}/ggml.o -lm -lpthread
+rnn_generation: ${OBJ}/ggml.o ${OBJ}/rnn_text_gen.o
+ g++ -o rnn_generation_main ${OBJ}/rnn_text_gen.o ${OBJ}/ggml.o -lm -lpthread
 
 test3: mkdir ${OBJ}/ggml.o ${OBJ}/test3.o
  g++ -o test3_main ${OBJ}/test3.o ${OBJ}/ggml.o -lm -pthread
@@ -21,7 +22,12 @@ ${OBJ}/simple_rnn.o: simple_rnn/simple_rnn.cpp
 ${OBJ}/test3.o: tests/test-mul-mat3.cpp
  g++ -I. -o ${OBJ}/test3.o -c tests/test-mul-mat3.cpp
 
+${OBJ}/rnn_text_gen.o: rnn_text_gen/rnn_text_generation.cpp
+ g++ -I. -o ${OBJ}/rnn_text_gen.o -c rnn_text_gen/rnn_text_generation.cpp
+
+
 .PHONY: clean
 
+
 clean:
  rm -f ${OBJ}/*.o 

rnn_text_gen/rnn_text_generation.cpp

@@ -0,0 +1,114 @@
+#include <ggml.h>
+#include <vector>
+#include <string>
+#include <cstdlib>
+#include <iostream>
+#include <fstream>
+
+using namespace std;
+/**
+Layer: my_model/embedding/embeddings:0 with shape (66, 256)
+Layer: my_model/gru/gru_cell/kernel:0 with shape (256, 3072)
+Layer: my_model/gru/gru_cell/recurrent_kernel:0 with shape (1024, 3072)
+Layer: my_model/gru/gru_cell/bias:0 with shape (2, 3072)
+Layer: my_model/dense/kernel:0 with shape (1024, 66)
+Layer: my_model/dense/bias:0 with shape (66,)
+**/
+
+void print_2d(ggml_tensor * ts){
+ for (int i1 = 0; i1 < ts->ne[1]; i1++){
+ for (int i0 = 0; i0 < ts->ne[0]; i0++){
+ cout << ggml_get_f32_1d(ts, i1*ts->ne[0] + i0) << " "; 
+ }
+ cout << endl;
+ } 
+}
+
+
+struct rnn_generator {
+ struct ggml_context * ctx;
+ struct ggml_tensor * embeddings;
+ struct ggml_tensor * cell_kernel;
+ struct ggml_tensor * cell_recurrent_kernel;
+ struct ggml_tensor * cell_bias;
+ struct ggml_tensor * dense_kernel;
+ struct ggml_tensor * dense_bias;
+};
+
+struct rnn_generator load_model(){
+ struct ggml_init_params params = {
+ .mem_size = 128 * 1024 * 1024,
+ .mem_buffer = NULL,
+ .no_alloc = false
+ };
+ struct ggml_context *ctx = ggml_init(params);
+ struct rnn_generator model;
+
+ model.ctx = ctx;
+
+ model.embeddings = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, 256, 66);
+ model.cell_kernel = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, 3072, 256);
+ model.cell_recurrent_kernel = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, 3072, 1024);
+ model.cell_bias = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, 3072, 2);
+ model.dense_kernel = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, 66, 1024);
+ model.dense_bias = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, 66, 1);
+
+ auto fin = ifstream("rnn_text_gen/gru.bin", ios::binary);
+
+ if (! fin) cout << "Error reading file" << endl;
+ int dummy;
+
+ // Read the embeddings
+ for (int i = 0; i < 3; i++) {
+ fin.read((char*)&dummy, sizeof(dummy));
+ cout << dummy << endl;
+ }
+ fin.read((char*)model.embeddings->data, 256*66*sizeof(float));
+
+ // Read the cell kernel
+ for (int i = 0; i < 3; i++) {
+ fin.read((char*)&dummy, sizeof(dummy));
+ cout << dummy << endl;
+ }
+ fin.read((char*)model.cell_kernel->data, 3072*256*sizeof(float));
+
+ // Read the cell recurrent kernel
+ for (int i = 0; i < 3; i++) {
+ fin.read((char*)&dummy, sizeof(dummy));
+ cout << dummy << endl;
+ }
+ fin.read((char*)model.cell_recurrent_kernel->data, 3072*1024*sizeof(float));
+
+ // Read the cell bias
+ for (int i = 0; i < 3; i++) {
+ fin.read((char*)&dummy, sizeof(dummy));
+ cout << dummy << endl;
+ }
+ fin.read((char*)model.cell_bias->data, 3072*2*sizeof(float));
+
+ // Read the dense kernel
+ for (int i = 0; i < 3; i++) {
+ fin.read((char*)&dummy, sizeof(dummy));
+ cout << dummy << endl;
+ }
+ fin.read((char*)model.dense_kernel->data, 66*1024*sizeof(float));
+
+
+ // Read the dense kernel
+ for (int i = 0; i < 2; i++){
+ fin.read((char*)&dummy, sizeof(dummy));
+ cout << dummy << endl;
+ }
+ fin.read((char*)model.dense_bias->data, 66*1*sizeof(float));
+
+ print_2d(model.dense_bias);
+
+ fin.close();
+
+ return model;
+}
+
+int main(){
+
+ struct rnn_generator my_model = load_model();
+}