remember to copy back the last_eigenvector

Author: ngxson

examples/control-vector-generator/pca.hpp

@@ -62,9 +62,6 @@ struct pca_model {
     struct ggml_tensor * dev_square;
     struct ggml_tensor * dev_eigenvector;
 
-    // tensors to store output data on host
-    struct ggml_tensor * host_eigenvector;
-
     pca_model(struct ggml_tensor * t_input) {
 #ifdef GGML_USE_CUDA
         fprintf(stderr, "%s: using CUDA backend\n", __func__);
@@ -129,17 +126,16 @@ struct pca_model {
         }
 
         // init host context
-        struct ggml_init_params host_params = {
-            /*.mem_size   =*/ (n_embd * sizeof(float) + ggml_tensor_overhead()) * 2u,
-            /*.mem_buffer =*/ NULL,
-            /*.no_alloc   =*/ false,
-        };
-        ctx_host = ggml_init(host_params);
-        host_eigenvector = ggml_new_tensor_1d(ctx_host, GGML_TYPE_F32, n_embd);
+        //struct ggml_init_params host_params = {
+        //    /*.mem_size   =*/ (n_embd * sizeof(float) + ggml_tensor_overhead()) * 2u,
+        //    /*.mem_buffer =*/ NULL,
+        //    /*.no_alloc   =*/ false,
+        //};
+        //ctx_host = ggml_init(host_params);
+        //host_eigenvector = ggml_new_tensor_1d(ctx_host, GGML_TYPE_F32, n_embd);
     }
 
     ~pca_model() {
-        ggml_free(ctx_host);
         ggml_free(ctx);
         ggml_backend_buffer_free(buffer);
         ggml_backend_free(backend);
@@ -299,6 +295,14 @@ static void power_iteration(
             ggml_backend_tensor_set(model.dev_square, tmp_buf.data(), 0, tmp_buf.size());
         }
 
+        {
+            // copy last eigen vector and store as input for next iteration
+            GGML_ASSERT(last_eigenvector != NULL);
+            std::vector<uint8_t> tmp_buf(ggml_nbytes(last_eigenvector));
+            ggml_backend_tensor_get(last_eigenvector, tmp_buf.data(), 0, tmp_buf.size());
+            ggml_backend_tensor_set(model.dev_eigenvector, tmp_buf.data(), 0, tmp_buf.size());
+        }
+
         printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n",
             __func__, params.i_layer+1, params.n_layers, iter, n_iters, params.n_batch);
     }