ggml : parallelize ggml_exp

This results in 8% faster token generation for Mamba-130M.

ggml.c

@@ -8414,27 +8414,32 @@ static void ggml_compute_forward_exp_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,
         struct ggml_tensor * dst) {
-    GGML_ASSERT(params->ith == 0);
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
+    GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
     GGML_ASSERT(ggml_are_same_shape(src0, dst));
 
     if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
         return;
     }
 
-    GGML_ASSERT( dst->nb[0] == sizeof(float));
-    GGML_ASSERT(src0->nb[0] == sizeof(float));
+    const int ith = params->ith;
+    const int nth = params->nth;
 
-    GGML_TENSOR_UNARY_OP_LOCALS
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
 
-    for (int64_t i3 = 0; i3 < ne03; i3++) {
-        for (int64_t i2 = 0; i2 < ne02; i2++) {
-            for (int64_t i1 = 0; i1 < ne01; i1++) {
-                float * src_row = (float *) ((char *) src0->data + i1*nb01 + i2*nb02 + i3*nb03);
-                float * dst_row = (float *) ((char *) dst->data  + i1*nb1  + i2*nb2  + i3*nb3);
-                ggml_vec_exp_f32(ne00, dst_row, src_row);
-            }
-        }
-    }
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        ggml_vec_exp_f32(nc,
+                (float *) ((char *) dst->data  + i1*( dst->nb[1])),
+                (float *) ((char *) src0->data + i1*(src0->nb[1])));
+    };
 }
 
 static void ggml_compute_forward_exp(
@@ -16850,13 +16855,13 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
         case GGML_OP_ACC:
+        case GGML_OP_EXP:
             {
                 n_tasks = n_threads;
             } break;
         case GGML_OP_SUB:
         case GGML_OP_SQR:
         case GGML_OP_SQRT:
-        case GGML_OP_EXP:
         case GGML_OP_LOG:
         case GGML_OP_SUM:
         case GGML_OP_SUM_ROWS: