Merge pull request #119 from BerkeleyLab/bugfixes-for-nag

Bugfixes for nag

example/learn-addition.f90

@@ -22,12 +22,12 @@ program learn_addition
   !! This trains a neural network to learn the following six polynomial functions of its eight inputs.
   use inference_engine_m, only : &
     inference_engine_t, trainable_engine_t, mini_batch_t, tensor_t, input_output_pair_t, shuffle, relu_t
-  use sourcery_m, only : string_t, file_t, command_line_t, bin_t, csv
+  use sourcery_m, only : string_t, file_t, command_line_t, bin_t
   use assert_m, only : assert, intrinsic_array_t
   use addition_m, only : y
   implicit none
 
-  type(string_t) intial_network_file, final_network_file
+  type(string_t) final_network_file
   type(command_line_t) command_line
 
   final_network_file = string_t(command_line%flag_value("--output-file"))
@@ -47,7 +47,9 @@ program learn_addition
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))
@@ -122,7 +124,7 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     real, intent(in) :: perturbation_magnitude
     integer, parameter :: n(*) = [8, 64, 64, 64, 6]
     integer, parameter :: n_max = maxval(n), layers = size(n)
-    integer j, k, l
+    integer k, l
     real, allocatable :: identity(:,:,:), w_harvest(:,:,:), b_harvest(:,:)
 
     identity =  reshape( [( [(e(k,n_max), k=1,n_max)], l = 1, layers-1 )], [n_max, n_max, layers-1])
@@ -144,4 +146,4 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     end associate
   end function
 
-end program
+end program

example/learn-exponentiation.f90

@@ -22,12 +22,12 @@ program learn_exponentiation
   !! This trains a neural network to learn the following six polynomial functions of its eight inputs.
   use inference_engine_m, only : &
     inference_engine_t, trainable_engine_t, mini_batch_t, tensor_t, input_output_pair_t, shuffle, relu_t
-  use sourcery_m, only : string_t, file_t, command_line_t, bin_t, csv
+  use sourcery_m, only : string_t, file_t, command_line_t, bin_t
   use assert_m, only : assert, intrinsic_array_t
   use exponentiation_m, only : y
   implicit none
 
-  type(string_t) intial_network_file, final_network_file
+  type(string_t) final_network_file
   type(command_line_t) command_line
 
   final_network_file = string_t(command_line%flag_value("--output-file"))
@@ -47,7 +47,9 @@ program learn_exponentiation
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))
@@ -60,10 +62,10 @@ program learn_exponentiation
         inputs = [(tensor_t(real([(j*i, j = 1,num_inputs)])/(num_inputs*num_pairs)), i = 1, num_pairs)]
         desired_outputs = y(inputs)
         output_sizes = [(size(desired_outputs(i)%values()),i=1,size(desired_outputs))]
-        call assert(all([num_outputs==output_sizes]), "fit-polynomials: # outputs", intrinsic_array_t([num_outputs,output_sizes])) 
+        call assert(all([num_outputs==output_sizes]), "fit-polynomials: # outputs", intrinsic_array_t([num_outputs,output_sizes]))
       end block
       input_output_pairs = input_output_pair_t(inputs, desired_outputs)
-      block 
+      block
         integer b
         bins = [(bin_t(num_items=num_pairs, num_bins=num_mini_batches, bin_number=b), b = 1, num_mini_batches)]
       end block
@@ -122,7 +124,7 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     real, intent(in) :: perturbation_magnitude
     integer, parameter :: n(*) = [8, 64, 64, 64, 6] ! nodes per layer (first layer = input, last layer = output)
     integer, parameter :: n_max = maxval(n), layers = size(n)
-    integer j, k, l
+    integer k, l
     real, allocatable :: identity(:,:,:), w_harvest(:,:,:), b_harvest(:,:)
 
     identity =  reshape( [( [(e(k,n_max), k=1,n_max)], l = 1, layers-1 )], [n_max, n_max, layers-1])

example/learn-microphysics-procedures.f90

@@ -39,7 +39,9 @@ program learn_microphysics_procedures
     integer, parameter :: nodes_per_layer(*) = [2, 72, 2]
     real, parameter :: cost_tolerance = 1.E-08
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     open(newunit=network_unit, file=network_file%string(), form='formatted', status='old', iostat=io_status, action='read')
 

example/learn-multiplication.f90

@@ -22,12 +22,12 @@ program learn_multiplication
   !! This trains a neural network to learn the following six polynomial functions of its eight inputs.
   use inference_engine_m, only : &
     inference_engine_t, trainable_engine_t, mini_batch_t, tensor_t, input_output_pair_t, shuffle, relu_t
-  use sourcery_m, only : string_t, file_t, command_line_t, bin_t, csv
+  use sourcery_m, only : string_t, file_t, command_line_t, bin_t
   use assert_m, only : assert, intrinsic_array_t
   use multiply_inputs, only : y
   implicit none
 
-  type(string_t) intial_network_file, final_network_file
+  type(string_t) final_network_file
   type(command_line_t) command_line
 
   final_network_file = string_t(command_line%flag_value("--output-file"))
@@ -47,7 +47,9 @@ program learn_multiplication
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))
@@ -122,7 +124,7 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     real, intent(in) :: perturbation_magnitude
     integer, parameter :: n(*) = [8, 64, 64, 64, 6]
     integer, parameter :: n_max = maxval(n), layers = size(n)
-    integer j, k, l
+    integer k, l
     real, allocatable :: identity(:,:,:), w_harvest(:,:,:), b_harvest(:,:)
 
     identity =  reshape( [( [(e(k,n_max), k=1,n_max)], l = 1, layers-1 )], [n_max, n_max, layers-1])
@@ -144,4 +146,4 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     end associate
   end function
 
-end program
+end program

example/learn-power-series.f90

@@ -22,12 +22,12 @@ program learn_power_series
   !! This trains a neural network to learn the following six polynomial functions of its eight inputs.
   use inference_engine_m, only : &
     inference_engine_t, trainable_engine_t, mini_batch_t, tensor_t, input_output_pair_t, shuffle, relu_t
-  use sourcery_m, only : string_t, file_t, command_line_t, bin_t, csv
+  use sourcery_m, only : string_t, file_t, command_line_t, bin_t
   use assert_m, only : assert, intrinsic_array_t
   use power_series, only : y
   implicit none
 
-  type(string_t) intial_network_file, final_network_file
+  type(string_t) final_network_file
   type(command_line_t) command_line
 
   final_network_file = string_t(command_line%flag_value("--output-file"))
@@ -47,7 +47,9 @@ program learn_power_series
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.05)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))
@@ -124,7 +126,7 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     real, intent(in) :: perturbation_magnitude
     integer, parameter :: n(*) = [8, 196, 196, 196, 196, 6]
     integer, parameter :: n_max = maxval(n), layers = size(n)
-    integer j, k, l
+    integer k, l
     real, allocatable :: identity(:,:,:), w_harvest(:,:,:), b_harvest(:,:)
 
     identity =  reshape( [( [(e(k,n_max), k=1,n_max)], l = 1, layers-1 )], [n_max, n_max, layers-1])
@@ -146,4 +148,4 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     end associate
   end function
 
-end program
+end program

example/learn-saturated-mixing-ratio.f90

@@ -38,7 +38,9 @@ program train_saturated_mixture_ratio
     integer, parameter :: nodes_per_layer(*) = [2, 72, 1]
     real, parameter :: cost_tolerance = 1.E-08
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     open(newunit=network_unit, file=network_file%string(), form='formatted', status='old', iostat=io_status, action='read')
 

example/train-and-write.f90

@@ -14,7 +14,7 @@ program train_and_write
   use assert_m, only : assert, intrinsic_array_t
   implicit none
 
-  type(string_t) intial_network_file, final_network_file
+  type(string_t) final_network_file
   type(command_line_t) command_line
 
   final_network_file = string_t(command_line%flag_value("--output-file"))
@@ -34,7 +34,9 @@ program train_and_write
     type(bin_t), allocatable :: bins(:)
     real, allocatable :: cost(:), random_numbers(:)
 
+#ifndef NAGFOR
     call random_init(image_distinct=.true., repeatable=.true.)
+#endif
 
     trainable_engine = perturbed_identity_network(perturbation_magnitude=0.2)
     call output(trainable_engine%to_inference_engine(), string_t("initial-network.json"))
@@ -124,4 +126,4 @@ function perturbed_identity_network(perturbation_magnitude) result(trainable_eng
     end associate
   end function
 
-end program
+end program

src/inference_engine/activation_strategy_m.f90

@@ -15,7 +15,7 @@ module activation_strategy_m
   type, abstract :: activation_strategy_t
   contains
      procedure(activation_i), nopass, deferred :: activation
-     procedure(function_name_i), nopass, deferred :: function_name
+     procedure(function_name_i), deferred :: function_name
   end type
 
   abstract interface
@@ -27,9 +27,10 @@ elemental function activation_i(x) result(y)
       real(rkind) y
     end function
 
-    elemental function function_name_i() result(string)
-      import string_t
+    elemental function function_name_i(self) result(string)
+      import string_t, activation_strategy_t
       implicit none
+      class(activation_strategy_t), intent(in) :: self
       type(string_t) string
     end function
 

src/inference_engine/inference_engine_s.f90

@@ -12,8 +12,8 @@
   implicit none
 
   interface assert_consistency
-    module procedure inference_engine_consistency
-    module procedure difference_consistency
+    procedure inference_engine_consistency
+    procedure difference_consistency
   end interface
 
 contains
@@ -53,7 +53,7 @@
 
   end procedure
 
-  pure module subroutine inference_engine_consistency(self)
+  pure subroutine inference_engine_consistency(self)
 
     type(inference_engine_t), intent(in) :: self
 
@@ -78,7 +78,7 @@ pure module subroutine inference_engine_consistency(self)
 
   end subroutine
 
-  pure module subroutine difference_consistency(self)
+  pure subroutine difference_consistency(self)
 
     type(difference_t), intent(in) :: self
 

src/inference_engine/relu_m.f90

@@ -13,7 +13,7 @@ module relu_m
   contains
      procedure, nopass :: activation
      procedure, nopass :: activation_derivative
-     procedure, nopass :: function_name
+     procedure :: function_name
   end type
 
   interface
@@ -30,8 +30,9 @@ elemental module function activation_derivative(x) result(y)
       real(rkind) y
     end function
 
-    elemental module function function_name() result(string)
+    elemental module function function_name(self) result(string)
       implicit none
+      class(relu_t), intent(in) :: self
       type(string_t) string
     end function
 

src/inference_engine/sigmoid_m.f90

@@ -13,7 +13,7 @@ module sigmoid_m
   contains
      procedure, nopass :: activation
      procedure, nopass :: activation_derivative
-     procedure, nopass :: function_name
+     procedure :: function_name
   end type
 
   interface
@@ -30,8 +30,9 @@ elemental module function activation_derivative(x) result(y)
       real(rkind) y
     end function
 
-    elemental module function function_name() result(string)
+    elemental module function function_name(self) result(string)
       implicit none
+      class(sigmoid_t), intent(in) :: self
       type(string_t) string
     end function
 

src/inference_engine/step_m.f90

@@ -12,7 +12,7 @@ module step_m
   type, extends(activation_strategy_t) :: step_t
   contains
      procedure, nopass :: activation
-     procedure, nopass :: function_name
+     procedure :: function_name
   end type
 
   interface
@@ -23,8 +23,9 @@ elemental module function activation(x) result(y)
       real(rkind) y
     end function
 
-    elemental module function function_name() result(string)
+    elemental module function function_name(self) result(string)
       implicit none
+      class(step_t), intent(in) :: self
       type(string_t) string
     end function
 

src/inference_engine/swish_m.f90

@@ -13,7 +13,7 @@ module swish_m
   contains
      procedure, nopass :: activation
      procedure, nopass :: activation_derivative
-     procedure, nopass :: function_name
+     procedure :: function_name
   end type
 
   interface
@@ -30,8 +30,9 @@ elemental module function activation_derivative(x) result(y)
       real(rkind) y
     end function
 
-    elemental module function function_name() result(string)
+    elemental module function function_name(self) result(string)
       implicit none
+      class(swish_t), intent(in) :: self
       type(string_t) string
     end function
 

src/inference_engine/trainable_engine_m.f90

@@ -103,7 +103,7 @@ elemental module function num_layers(self) result(n_layers)
       integer n_layers
     end function
 
-    pure module function to_inference_engine(self) result(inference_engine)
+    module function to_inference_engine(self) result(inference_engine)
       implicit none
       class(trainable_engine_t), intent(in) :: self
       type(inference_engine_t) :: inference_engine

src/inference_engine/trainable_engine_s.f90

@@ -3,8 +3,6 @@
 submodule(trainable_engine_m) trainable_engine_s
   use assert_m, only : assert
   use intrinsic_array_m, only : intrinsic_array_t
-  use input_output_pair_m, only : input_output_pair_t
-  use sigmoid_m, only : sigmoid_t
   use tensor_m, only : tensor_t
   implicit none
 
@@ -186,7 +184,7 @@
               real, parameter :: obeta(*) = [1._rkind - beta(1), 1._rkind - beta(2)]
               real, parameter :: epsilon = real(1.D-08,rkind)
 
-              adjust_weights_and_biases: &
+              adam_adjust_weights_and_biases: &
               do concurrent(l = 1:output_layer)
                 dcdw(1:n(l),1:n(l-1),l) = dcdw(1:n(l),1:n(l-1),l)/(mini_batch_size)
                 vdw(1:n(l),1:n(l-1),l)  = beta(1)*vdw(1:n(l),1:n(l-1),l) + obeta(1)*dcdw(1:n(l),1:n(l-1),l)
@@ -202,7 +200,7 @@
                 vdbc(1:n(l),l) = vdb(1:n(l),l)/(1._rkind - beta(1)**num_mini_batches)
                 sdbc(1:n(l),l) = sdb(1:n(l),l)/(1._rkind - beta(2)**num_mini_batches)
                 b(1:n(l),l) = b(1:n(l),l) - alpha*vdbc(1:n(l),l)/(sqrt(sdbc(1:n(l),l))+epsilon) ! Adjust weights
-              end do adjust_weights_and_biases
+              end do adam_adjust_weights_and_biases
             end block
           else
             adjust_weights_and_biases: &
@@ -233,6 +231,9 @@
   end procedure
 
   module procedure to_inference_engine
+    ! assignment-stmt disallows the procedure from being pure because it might
+    ! deallocate polymorphic allocatable subcomponent `activation_strategy_`
+    ! TODO: consider how this affects design
     inference_engine = inference_engine_t(metadata = self%metadata_, weights = self%w, biases = self%b, nodes = self%n)
   end procedure