examples: refine tensor_sum_elements(tensor dump) in examples/benchmark/benchmark-matmult.cpp

examples/benchmark/benchmark-matmult.cpp

@@ -1,5 +1,6 @@
 #include "common.h"
 #include "ggml.h"
+#include "ggml-impl.h"
 
 #include <locale.h>
 #include <assert.h>
@@ -32,14 +33,45 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
 }
 
 static float tensor_sum_elements(const ggml_tensor * tensor) {
-    double sum = 0;
+    double sum                  = 0;
+    float  floatvalue           = 0;
+    unsigned short shortvalue   = 0;
+
+    if (tensor->type == GGML_TYPE_I8) {
+        for (int j = 0; j < tensor->ne[1]; j++) {
+            for (int k = 0; k < tensor->ne[0]; k++) {
+                sum += ((int8_t *) tensor->data)[j * tensor->ne[0] + k];
+            }
+        }
+    }
+
+    if (tensor->type == GGML_TYPE_F16) {
+        for (int j = 0; j < tensor->ne[1]; j++) {
+            for (int k = 0; k < tensor->ne[0]; k++) {
+                shortvalue = ((unsigned short *) tensor->data)[j * tensor->ne[0] + k];
+                floatvalue = GGML_FP16_TO_FP32(shortvalue);
+                sum        += floatvalue;
+            }
+        }
+    }
+
     if (tensor->type == GGML_TYPE_F32) {
         for (int j = 0; j < tensor->ne[1]; j++) {
             for (int k = 0; k < tensor->ne[0]; k++) {
-                sum += ((float *) tensor->data)[j*tensor->ne[0] + k];
+                sum += ((float *) tensor->data)[j * tensor->ne[0] + k];
             }
         }
     }
+
+    if (ggml_is_quantized(tensor->type)) {
+        std::vector<float> f32out(ggml_nelements(tensor));
+        ggml_type_traits_t qtype = ggml_internal_get_type_traits(tensor->type);
+        qtype.to_float((void *)tensor->data, f32out.data(), f32out.size());
+        for (const float & value : f32out) {
+            sum += value;
+        }
+    }
+
     return sum;
 }