Feature: make assertions removable

include/compound_assertions.h

@@ -0,0 +1,23 @@
+! Copyright (c), The Regents of the University of California
+! Terms of use are as specified in LICENSE.txt
+
+! This file provides preprocessor-based macros to call procedures that exist
+! solely to make calls to 'assert' and that therefore will be eliminated completely
+! whenever the ASSERTIONS macro is defined to a value other than 0.
+
+! Enable repeated includes to toggle assertions based on current settings:
+#undef call_assert_consistency
+#undef call_assert_conformable
+
+#ifndef ASSERTIONS
+! Assertions are off by default
+#define ASSERTIONS 0
+#endif
+
+#if ASSERTIONS
+# define call_assert_consistency(obj) call assert_consistency(obj)
+# define call_assert_conformable(lhs,rhs) call assert_conformable(lhs,rhs)
+#else
+# define call_assert_consistency(obj)
+# define call_assert_conformable(lhs,rhs)
+#endif

src/fiats/activation_m.f90

@@ -1,3 +1,6 @@
+! Copyright (c), The Regents of the University of California
+! Terms of use are as specified in LICENSE.txt
+
 module activation_m
   use iso_c_binding, only :  c_int
   use julienne_m, only : string_t

src/fiats/activation_s.f90

@@ -1,5 +1,8 @@
+! Copyright (c), The Regents of the University of California
+! Terms of use are as specified in LICENSE.txt
+
 submodule(activation_m) activation_s
-  use assert_m, only : assert
+  use assert_m
   implicit none
 
   real            , parameter :: pi    = 3.141592653589793

src/fiats/hyperparameters_s.f90

@@ -1,7 +1,10 @@
 ! Copyright (c), The Regents of the University of California
 ! Terms of use are as specified in LICENSE.txt
+
+#include "assert_macros.h"
+
 submodule(hyperparameters_m) hyperparameters_s
-  use assert_m, only : assert
+  use assert_m
   implicit none
 
   character(len=*), parameter :: mini_batches_key  = "mini-batches"
@@ -26,8 +29,7 @@
 
     real, parameter :: tolerance = 1.E-08
 
-    call assert(allocated(lhs%optimizer_) .and. allocated(rhs%optimizer_), &
-      "hyperparameters_s(default_real_equals): allocated optimizers")
+    call_assert(allocated(lhs%optimizer_) .and. allocated(rhs%optimizer_))
 
     lhs_equals_rhs = &
       lhs%mini_batches_ == rhs%mini_batches_ .and. &
@@ -40,8 +42,7 @@
 
     double precision, parameter :: tolerance = 1.D-15
 
-    call assert(allocated(lhs%optimizer_) .and. allocated(rhs%optimizer_), &
-      "hyperparameters_s(double_precisionequals): allocated optimizers")
+    call_assert(allocated(lhs%optimizer_) .and. allocated(rhs%optimizer_))
 
     lhs_equals_rhs = &
       lhs%mini_batches_ == rhs%mini_batches_ .and. &
@@ -66,7 +67,7 @@
       end if
     end do
 
-    call assert(hyperparameters_key_found, "hyperparameters_s(default_real_from_json): hyperparameters_found")
+    call_assert(hyperparameters_key_found)
   end procedure
 
   module procedure double_precision_from_json
@@ -85,7 +86,7 @@
       end if
     end do
 
-    call assert(hyperparameters_key_found, "hyperparameters_s(double_precision_from_json): hyperparameters_found")
+    call_assert(hyperparameters_key_found)
   end procedure
 
   module procedure default_real_to_json

src/fiats/layer_s.f90

@@ -1,7 +1,10 @@
 ! Copyright (c), The Regents of the University of California
 ! Terms of use are as specified in LICENSE.txt
+
+#include "assert_macros.h"
+
 submodule(layer_m) layer_s
-  use assert_m, only : assert
+  use assert_m
   implicit none
 
 contains
@@ -77,7 +80,7 @@
       num_hidden_layers =>  hidden_layers%count_layers(), &
       num_output_layers => output_layer%count_layers() &
     )   
-      call assert(num_output_layers==1, "neural_network_s(default_real_neural_network): 1 output layer", num_output_layers)
+      call_assert(num_output_layers==1)
 
       associate(nodes => [num_inputs, neurons_per_hidden_layer, num_outputs])
         associate(n_max => maxval(nodes))
@@ -149,7 +152,7 @@
       num_hidden_layers =>  hidden_layers%count_layers(), &
       num_output_layers => output_layer%count_layers() &
     )   
-      call assert(num_output_layers==1, "neural_network_s(double_precision_neural_network): 1 output layer", num_output_layers)
+      call_assert(num_output_layers==1)
 
       associate(nodes => [num_inputs, neurons_per_hidden_layer, num_outputs])
         associate(n_max => maxval(nodes))

src/fiats/metadata_s.f90

@@ -1,7 +1,10 @@
 ! Copyright (c), The Regents of the University of California
 ! Terms of use are as specified in LICENSE.txt
+
+#include "assert_macros.h"
+
 submodule(metadata_m) metadata_s
-  use assert_m, only : assert
+  use assert_m
   implicit none
 
 contains
@@ -25,7 +28,7 @@
   module procedure from_json
     integer l
 
-    call assert(lines(1)%get_json_key() == "metadata", "metadata_s(from_json): metadata found")
+    call_assert(lines(1)%get_json_key() == "metadata")
 
     do l = 2, size(lines)-1
       associate(key => lines(l)%get_json_key())
@@ -52,7 +55,7 @@
   module procedure double_precision_from_json
     integer l
 
-    call assert(lines(1)%get_json_key() == "metadata", "metadata_s(double_precision_from_json): metadata found")
+    call_assert(lines(1)%get_json_key() == "metadata")
 
     do l = 2, size(lines)-1
       associate(key => lines(l)%get_json_key())

src/fiats/network_configuration_s.F90

@@ -1,7 +1,10 @@
 ! Copyright (c), The Regents of the University of California
 ! Terms of use are as specified in LICENSE.txt
+
+#include "assert_macros.h"
+
 submodule(network_configuration_m) network_configuration_s
-  use assert_m, only : assert
+  use assert_m
   use julienne_formats_m, only : csv
   implicit none
 
@@ -19,8 +22,7 @@
 
   module procedure equals
 
-    call assert(allocated(lhs%activation_name_) .and. allocated(rhs%activation_name_), &
-     "network_configuration_s(equals): allocated({lhs,rhs}%activation_name_)")
+    call_assert(allocated(lhs%activation_name_) .and. allocated(rhs%activation_name_))
 
     lhs_equals_rhs = &
       lhs%skip_connections_ .eqv. rhs%skip_connections_ .and. &
@@ -45,7 +47,7 @@
       end if
     end do
 
-    call assert(network_configuration_key_found, "network_configuration_s(from_json): network_configuration_found")
+    call_assert(network_configuration_key_found)
   end procedure
 
   module procedure from_double_precision_string_json
@@ -64,8 +66,7 @@
       end if
     end do
 
-    call assert(network_configuration_key_found, &
-      "network_configuration_s(from_double_precision_string_json): network_configuration_found")
+    call_assert(network_configuration_key_found)
   end procedure
 
   module procedure to_json

src/fiats/neural_network_m.f90

@@ -16,6 +16,7 @@ module neural_network_m
   public :: neural_network_t
   public :: unmapped_network_t
   public :: workspace_t
+  public :: assert_consistency
 
   type neural_network_t(k)
     !! Encapsulate the information needed to perform inference
@@ -35,12 +36,9 @@ module neural_network_m
     generic :: num_inputs               => default_real_num_inputs,              double_precision_num_inputs
     generic :: num_outputs              => default_real_num_outputs,             double_precision_num_outputs
     generic :: nodes_per_layer          => default_real_nodes_per_layer,         double_precision_nodes_per_layer
-    generic :: assert_conformable_with  => default_real_assert_conformable_with, double_precision_assert_conformable_with
     generic :: skip                     => default_real_skip,                    double_precision_skip
     generic :: activation_function_name => default_real_activation_name,         double_precision_activation_name
     generic :: learn                    => default_real_learn
-    generic :: assert_consistency       => default_real_consistency,             double_precision_consistency
-    procedure, private, non_overridable :: default_real_consistency,             double_precision_consistency
     procedure, private, non_overridable :: default_real_approximately_equal,     double_precision_approximately_equal
     procedure, private, non_overridable :: default_real_infer,                   double_precision_infer
     procedure, private, non_overridable :: default_real_learn
@@ -51,7 +49,6 @@ module neural_network_m
     procedure, private, non_overridable :: default_real_num_inputs,              double_precision_num_inputs
     procedure, private, non_overridable :: default_real_num_outputs,             double_precision_num_outputs
     procedure, private, non_overridable :: default_real_nodes_per_layer,         double_precision_nodes_per_layer
-    procedure, private, non_overridable :: default_real_assert_conformable_with, double_precision_assert_conformable_with
     procedure, private, non_overridable :: default_real_skip,                    double_precision_skip
     procedure, private, non_overridable :: default_real_activation_name,         double_precision_activation_name
   end type
@@ -190,18 +187,6 @@ impure elemental module function double_precision_to_json(self) result(json_file
       type(file_t) json_file
     end function
 
-    elemental module subroutine default_real_assert_conformable_with(self, neural_network)
-      implicit none
-      class(neural_network_t), intent(in) :: self
-      type(neural_network_t), intent(in) :: neural_network
-    end subroutine
-
-    elemental module subroutine double_precision_assert_conformable_with(self, neural_network)
-      implicit none
-      class(neural_network_t(double_precision)), intent(in) :: self
-      type(neural_network_t(double_precision)), intent(in) :: neural_network
-    end subroutine
-
     elemental module function default_real_infer(self, inputs) result(outputs)
       implicit none
       class(neural_network_t), intent(in) :: self
@@ -298,6 +283,10 @@ pure module subroutine default_real_learn(self, mini_batches_arr, cost, adam, le
       type(workspace_t), intent(inout) :: workspace
     end subroutine
 
+  end interface
+
+  interface assert_consistency
+
     pure module subroutine default_real_consistency(self)
       implicit none
       class(neural_network_t), intent(in) :: self