update get-start.md

docs/guide/get-start.md

@@ -47,7 +47,7 @@ The above configuration declares a dependency on ggml which is downloaded from G
 
 You can add ggml as a submodule of your project. In your project repository:
 
-```bash
+```sh
 git submodule add https://github.com/ggerganov/ggml ggml
 ```
 
@@ -58,25 +58,27 @@ With ggml declared as a dependency, you can use ggml code within your own projec
 As an example, create a file named hello_ggml.cpp in your project directory with the following contents:
 
 ```c++
+#include <vector>
+#include <cstdio>
 
 #include "ggml/ggml.h"
 #include "ggml/ggml-alloc.h"
 #include "ggml/ggml-backend.h"
 
 const struct ggml_init_params params = {
-/*.mem_size =*/ ggml_tensor_overhead() * 10240,
-/*.mem_buffer =*/ nullptr,
-/*.no_alloc =*/ true
+ /*.mem_size =*/ ggml_tensor_overhead() * 10240,
+ /*.mem_buffer =*/ nullptr,
+ /*.no_alloc =*/ true
 };
 int main(int argc, char ** argv) {
- // this get time
+ // init ggml_time total
  ggml_time_init();
  const auto t_main_start_us = ggml_time_us();
  // create ggml ctx
  const auto ctx = ggml_init(params);
  // create ggml cpu backend
  const auto backend = ggml_backend_cpu_init();
- // create a 1d tensor
+ // create 1d tensor
  const auto tensor_a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
  const auto tensor_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
  auto tensor_c = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
@@ -90,54 +92,65 @@ int main(int argc, char ** argv) {
 
  // set value
  {
-
- const auto alloc = ggml_allocr_new_from_buffer(tensor_1d_buffer);
- // this updates the pointers in the tensors to point to the correct location in the buffer
- // this is necessary since the ggml_context is .no_alloc == true
- // note that the buffer can actually be a device buffer, depending on the backend
- ggml_allocr_alloc(alloc, tensor_a);
- ggml_allocr_alloc(alloc, tensor_b);
-
- // in cpu we also can do
- // tensor_a->data = &tensor_data_a.data();
- // tensor_b->data = &tensor_data_b.data();
- ggml_backend_tensor_set(tensor_a, tensor_data_a.data(), 0, ggml_nbytes(tensor_a));
- ggml_backend_tensor_set(tensor_b, tensor_data_b.data(), 0, ggml_nbytes(tensor_b));
- ggml_allocr_free(alloc);
+ const auto alloc = ggml_allocr_new_from_buffer(tensor_1d_buffer);
+ // this updates the pointers in the tensors to point to the correct location in the buffer
+ // this is necessary since the ggml_context is .no_alloc == true
+ // note that the buffer can actually be a device buffer, depending on the backend
+ ggml_allocr_alloc(alloc, tensor_a);
+ ggml_allocr_alloc(alloc, tensor_b);
+
+ // in cpu we also can do
+ // tensor_a->data = &tensor_data_a.data();
+ // tensor_b->data = &tensor_data_b.data();
+ ggml_backend_tensor_set(tensor_a, tensor_data_a.data(), 0, ggml_nbytes(tensor_a));
+ ggml_backend_tensor_set(tensor_b, tensor_data_b.data(), 0, ggml_nbytes(tensor_b));
+ ggml_allocr_free(alloc);
  }
 
  // compute
  {
- const auto compute_tensor_buffer = ggml_backend_alloc_buffer(backend, 656480);
- const auto allocr = ggml_allocr_new_from_buffer(tensor_1d_buffer);
- const auto gf = ggml_new_graph(ctx);
-
- // creat forward
- tensor_c = ggml_add(ctx, tensor_a, tensor_b);
- ggml_build_forward_expand(gf, tensor_c);
-
- // allocate tensors
- ggml_allocr_alloc_graph(allocr, gf);
-
- if (ggml_backend_is_cpu(backend)) {
- ggml_backend_cpu_set_n_threads(backend, 1);
- }
+  const auto compute_tensor_buffer = ggml_backend_alloc_buffer(backend, 656480);
+  const auto allocr = ggml_allocr_new_from_buffer(compute_tensor_buffer);
+  const auto gf = ggml_new_graph(ctx);
+  
+  // creat forward
+  tensor_c = ggml_add(ctx, tensor_a, tensor_b);
+  ggml_build_forward_expand(gf, tensor_c);
+  
+  // allocate tensors
+  ggml_allocr_alloc_graph(allocr, gf);
+  
+  if (ggml_backend_is_cpu(backend)) {
+  ggml_backend_cpu_set_n_threads(backend, 1);
+  }
 
- ggml_backend_graph_compute(backend, gf);
+  ggml_backend_graph_compute(backend, gf);
 
- tensor_data_c.resize(1);
- ggml_backend_tensor_get(gf->nodes[gf->n_nodes - 1], tensor_data_c.data(), 0,
- tensor_data_c.size() * sizeof(float));
- printf("result is = %p \n", tensor_data_c.data());
+ tensor_data_c.resize(1);
+ ggml_backend_tensor_get(gf->nodes[gf->n_nodes - 1], tensor_data_c.data(), 0,
+ tensor_data_c.size() * sizeof(float));
+ printf("result is = [");
+ for (auto i = tensor_data_c.begin(); i != tensor_data_c.end(); ++i) {
+ printf("%f ", *i);
+ }
+ printf("]\n");
+ ggml_allocr_free(allocr);
  }
 
  const auto t_main_end_us = ggml_time_us();
 
  printf("total time = %8.2f ms\n", (t_main_end_us - t_main_start_us) / 1000.0f);
 
-
  ggml_free(ctx);
  ggml_backend_buffer_free(tensor_1d_buffer);
  ggml_backend_free(backend);
 }
-```
+```
+
+The result is 3.000000
+```sh 
+result is = [3.000000]
+total time = 10.93 ms
+Process finished with exit code 0
+```
+