| Line | Branch | Exec | Source |
|---|---|---|---|
| 1 | submodule(athena__base_layer) athena__base_layer_submodule | ||
| 2 | !! Submodule containing the implementation of the base layer types | ||
| 3 | !! | ||
| 4 | !! This submodule contains the implementation of the base layer types | ||
| 5 | !! used in the ATHENA library. The base layer types are the abstract | ||
| 6 | !! types from which all other layer types are derived. The submodule | ||
| 7 | !! contains the implementation of the procedures that are common to | ||
| 8 | !! all layer types, such as setting the input shape, getting the | ||
| 9 | !! number of parameters, and printing the layer to a file. | ||
| 10 | !! | ||
| 11 | !! The following procedures are based on code from the neural-fortran library | ||
| 12 | !! https://github.com/modern-fortran/neural-fortran/blob/main/src/nf/nf_layer.f90 | ||
| 13 | !! procedures: | ||
| 14 | !! - get_num_params* | ||
| 15 | !! - get_params* | ||
| 16 | !! - set_params* | ||
| 17 | !! - get_gradients* | ||
| 18 | !! - set_gradients* | ||
| 19 | use coreutils, only: stop_program, print_warning | ||
| 20 | |||
| 21 | contains | ||
| 22 | |||
| 23 | !############################################################################### | ||
| 24 | 5 | module function get_attributes_base(this) result(attributes) | |
| 25 | !! Get the attributes of the layer (for ONNX export) | ||
| 26 | implicit none | ||
| 27 | |||
| 28 | ! Arguments | ||
| 29 | class(base_layer_type), intent(in) :: this | ||
| 30 | !! Instance of the layer | ||
| 31 | type(onnx_attribute_type), allocatable, dimension(:) :: attributes | ||
| 32 | !! Attributes of the layer | ||
| 33 | |||
| 34 | ! Allocate attributes array | ||
| 35 |
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5 | allocate(attributes(0)) |
| 36 | ! attributes(0)%name = this%name | ||
| 37 | ! attributes(0)%val = this%get_type_name() | ||
| 38 | ! attributes(0)%type = "" | ||
| 39 | |||
| 40 | 5 | end function get_attributes_base | |
| 41 | !------------------------------------------------------------------------------- | ||
| 42 | 2 | module function get_attributes_conv(this) result(attributes) | |
| 43 | !! Get the attributes of a convolutional layer (for ONNX export) | ||
| 44 | implicit none | ||
| 45 | |||
| 46 | ! Arguments | ||
| 47 | class(conv_layer_type), intent(in) :: this | ||
| 48 | !! Instance of the layer | ||
| 49 | type(onnx_attribute_type), allocatable, dimension(:) :: attributes | ||
| 50 | !! Attributes of the layer | ||
| 51 | |||
| 52 | ! Local variables | ||
| 53 | character(256) :: buffer, fmt | ||
| 54 | !! Buffer for formatting | ||
| 55 | |||
| 56 | ! Allocate attributes array | ||
| 57 |
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8 | allocate(attributes(3)) |
| 58 |
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2 | attributes(1)%name = "kernel_shape" |
| 59 | 2 | write(fmt,'("(",I0,"(1X,I0))")') size(this%knl) | |
| 60 | 2 | write(buffer,fmt) this%knl | |
| 61 |
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2 | attributes(1)%val = trim(adjustl(buffer)) |
| 62 |
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2 | attributes(1)%type = "ints" |
| 63 | |||
| 64 |
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2 | attributes(2)%name = "strides" |
| 65 | 2 | write(fmt,'("(",I0,"(1X,I0))")') size(this%stp) | |
| 66 | 2 | write(buffer,fmt) this%stp | |
| 67 |
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2 | attributes(2)%val = trim(adjustl(buffer)) |
| 68 |
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2 | attributes(2)%type = "ints" |
| 69 | |||
| 70 |
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2 | attributes(3)%name = "dilations" |
| 71 | 2 | write(fmt,'("(",I0,"(1X,I0))")') size(this%dil) | |
| 72 | 2 | write(buffer,fmt) this%dil | |
| 73 |
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2 | attributes(3)%val = trim(adjustl(buffer)) |
| 74 |
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2 | attributes(3)%type = "ints" |
| 75 | |||
| 76 | 2 | end function get_attributes_conv | |
| 77 | !------------------------------------------------------------------------------- | ||
| 78 | 2 | module function get_attributes_pool(this) result(attributes) | |
| 79 | !! Get the attributes of a pooling layer (for ONNX export) | ||
| 80 | implicit none | ||
| 81 | |||
| 82 | ! Arguments | ||
| 83 | class(pool_layer_type), intent(in) :: this | ||
| 84 | !! Instance of the layer | ||
| 85 | type(onnx_attribute_type), allocatable, dimension(:) :: attributes | ||
| 86 | !! Attributes of the layer | ||
| 87 | |||
| 88 | ! Local variables | ||
| 89 | character(256) :: buffer, fmt | ||
| 90 | !! Buffer for formatting | ||
| 91 | |||
| 92 | ! Allocate attributes array | ||
| 93 |
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6 | allocate(attributes(2)) |
| 94 |
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2 | attributes(1)%name = "kernel_shape" |
| 95 | 2 | write(fmt,'("(",I0,"(1X,I0))")') size(this%pool) | |
| 96 | 2 | write(buffer,fmt) this%pool | |
| 97 |
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2 | attributes(1)%val = trim(adjustl(buffer)) |
| 98 |
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2 | attributes(1)%type = "ints" |
| 99 | |||
| 100 |
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2 | attributes(2)%name = "strides" |
| 101 | 2 | write(fmt,'("(",I0,"(1X,I0))")') size(this%strd) | |
| 102 | 2 | write(buffer,fmt) this%strd | |
| 103 |
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2 | attributes(2)%val = trim(adjustl(buffer)) |
| 104 |
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2 | attributes(2)%type = "ints" |
| 105 | |||
| 106 | 2 | end function get_attributes_pool | |
| 107 | !------------------------------------------------------------------------------- | ||
| 108 | ✗ | module function get_attributes_batch(this) result(attributes) | |
| 109 | !! Get the attributes of a batch normalisation layer (for ONNX export) | ||
| 110 | implicit none | ||
| 111 | |||
| 112 | ! Arguments | ||
| 113 | class(batch_layer_type), intent(in) :: this | ||
| 114 | !! Instance of the layer | ||
| 115 | type(onnx_attribute_type), allocatable, dimension(:) :: attributes | ||
| 116 | !! Attributes of the layer | ||
| 117 | |||
| 118 | ! Local variables | ||
| 119 | character(256) :: buffer, fmt | ||
| 120 | !! Buffer for formatting | ||
| 121 | |||
| 122 | ! Allocate attributes array | ||
| 123 | ✗ | allocate(attributes(4)) | |
| 124 | ✗ | attributes(1)%name = "epsilon" | |
| 125 | ✗ | write(buffer,'("(",F0.6,")")') this%epsilon | |
| 126 | ✗ | attributes(1)%val = trim(adjustl(buffer)) | |
| 127 | ✗ | attributes(1)%type = "float" | |
| 128 | |||
| 129 | ✗ | attributes(2)%name = "momentum" | |
| 130 | ✗ | write(buffer,'("(",F0.6,")")') this%momentum | |
| 131 | ✗ | attributes(2)%val = trim(adjustl(buffer)) | |
| 132 | ✗ | attributes(2)%type = "float" | |
| 133 | |||
| 134 | ✗ | attributes(3)%name = "scale" | |
| 135 | ✗ | write(fmt,'("(",I0,"(1X,F0.6))")') this%num_channels | |
| 136 | ✗ | write(buffer,fmt) this%params(1)%val(1:this%num_channels,1) | |
| 137 | ✗ | attributes(3)%val = trim(adjustl(buffer)) | |
| 138 | ✗ | attributes(3)%type = "float" | |
| 139 | |||
| 140 | ✗ | attributes(4)%name = "B" | |
| 141 | ✗ | write(fmt,'("(",I0,"(1X,F0.6))")') this%num_channels | |
| 142 | ✗ | write(buffer,fmt) this%params(1)%val(this%num_channels+1:2*this%num_channels,1) | |
| 143 | ✗ | attributes(4)%val = trim(adjustl(buffer)) | |
| 144 | ✗ | attributes(4)%type = "float" | |
| 145 | |||
| 146 | ✗ | end function get_attributes_batch | |
| 147 | !############################################################################### | ||
| 148 | |||
| 149 | |||
| 150 | !############################################################################### | ||
| 151 | ✗ | module subroutine build_from_onnx_base( & | |
| 152 | this, node, initialisers, value_info, verbose & | ||
| 153 | ) | ||
| 154 | !! Build layer from ONNX node and initialiser | ||
| 155 | implicit none | ||
| 156 | |||
| 157 | ! Arguments | ||
| 158 | class(base_layer_type), intent(inout) :: this | ||
| 159 | !! Instance of the layer | ||
| 160 | type(onnx_node_type), intent(in) :: node | ||
| 161 | !! ONNX node | ||
| 162 | type(onnx_initialiser_type), dimension(:), intent(in) :: initialisers | ||
| 163 | !! ONNX initialisers | ||
| 164 | type(onnx_tensor_type), dimension(:), intent(in) :: value_info | ||
| 165 | !! ONNX value info | ||
| 166 | integer, intent(in) :: verbose | ||
| 167 | !! Verbosity level | ||
| 168 | |||
| 169 | write(0,*) "build_from_onnx_base: " // & | ||
| 170 | ✗ | trim(this%name) // " layer cannot be built from ONNX" | |
| 171 | |||
| 172 | ✗ | end subroutine build_from_onnx_base | |
| 173 | !############################################################################### | ||
| 174 | |||
| 175 | |||
| 176 | !############################################################################### | ||
| 177 | ✗ | module subroutine emit_onnx_nodes_base( & | |
| 178 | this, prefix, & | ||
| 179 | nodes, num_nodes, max_nodes, & | ||
| 180 | inits, num_inits, max_inits, & | ||
| 181 | input_name, is_last_layer, format & | ||
| 182 | ) | ||
| 183 | !! Default implementation: no-op (standard layers are handled by write_onnx) | ||
| 184 | implicit none | ||
| 185 | |||
| 186 | ! Arguments | ||
| 187 | class(base_layer_type), intent(in) :: this | ||
| 188 | !! Instance of the layer | ||
| 189 | character(*), intent(in) :: prefix | ||
| 190 | !! Prefix for node names | ||
| 191 | type(onnx_node_type), intent(inout), dimension(:) :: nodes | ||
| 192 | !! ONNX nodes | ||
| 193 | integer, intent(inout) :: num_nodes | ||
| 194 | !! Number of ONNX nodes | ||
| 195 | integer, intent(in) :: max_nodes | ||
| 196 | !! Maximum number of ONNX nodes | ||
| 197 | type(onnx_initialiser_type), intent(inout), dimension(:) :: inits | ||
| 198 | !! ONNX initialisers | ||
| 199 | integer, intent(inout) :: num_inits | ||
| 200 | !! Number of ONNX initialisers | ||
| 201 | integer, intent(in) :: max_inits | ||
| 202 | !! Maximum number of ONNX initialisers | ||
| 203 | character(*), optional, intent(in) :: input_name | ||
| 204 | !! Name of the input tensor from the previous layer | ||
| 205 | logical, optional, intent(in) :: is_last_layer | ||
| 206 | !! Whether this is the last non-input layer | ||
| 207 | integer, optional, intent(in) :: format | ||
| 208 | !! Export format selector | ||
| 209 | |||
| 210 | ! Default: do nothing. Standard layers are handled directly by write_onnx. | ||
| 211 | ✗ | end subroutine emit_onnx_nodes_base | |
| 212 | !############################################################################### | ||
| 213 | |||
| 214 | |||
| 215 | !############################################################################### | ||
| 216 | ✗ | module subroutine emit_onnx_graph_inputs_base( & | |
| 217 | this, prefix, & | ||
| 218 | graph_inputs, num_inputs & | ||
| 219 | ) | ||
| 220 | !! Default implementation: no-op (standard layers don't add graph inputs) | ||
| 221 | implicit none | ||
| 222 | |||
| 223 | ! Arguments | ||
| 224 | class(base_layer_type), intent(in) :: this | ||
| 225 | !! Instance of the layer | ||
| 226 | character(*), intent(in) :: prefix | ||
| 227 | !! Prefix for input names | ||
| 228 | type(onnx_tensor_type), intent(inout), dimension(:) :: graph_inputs | ||
| 229 | !! ONNX graph inputs | ||
| 230 | integer, intent(inout) :: num_inputs | ||
| 231 | !! Number of ONNX graph inputs | ||
| 232 | |||
| 233 | ! Default: do nothing. Standard input layers are handled directly. | ||
| 234 | ✗ | end subroutine emit_onnx_graph_inputs_base | |
| 235 | !############################################################################### | ||
| 236 | |||
| 237 | |||
| 238 | !############################################################################### | ||
| 239 | ✗ | module subroutine set_rank_base(this, input_rank, output_rank) | |
| 240 | !! Set the input and output ranks of the layer | ||
| 241 | implicit none | ||
| 242 | |||
| 243 | ! Arguments | ||
| 244 | class(base_layer_type), intent(inout) :: this | ||
| 245 | !! Instance of the layer | ||
| 246 | integer, intent(in) :: input_rank | ||
| 247 | !! Input rank | ||
| 248 | integer, intent(in) :: output_rank | ||
| 249 | !! Output rank | ||
| 250 | |||
| 251 | !--------------------------------------------------------------------------- | ||
| 252 | ! Set input and output ranks | ||
| 253 | !--------------------------------------------------------------------------- | ||
| 254 | ✗ | call stop_program("set_rank_base: this layer cannot have its rank set") | |
| 255 | |||
| 256 | ✗ | end subroutine set_rank_base | |
| 257 | !############################################################################### | ||
| 258 | |||
| 259 | |||
| 260 | !############################################################################### | ||
| 261 |
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420 | module subroutine set_shape_base(this, input_shape) |
| 262 | !! Set the input shape of the layer | ||
| 263 | implicit none | ||
| 264 | |||
| 265 | ! Arguments | ||
| 266 | class(base_layer_type), intent(inout) :: this | ||
| 267 | !! Instance of the layer | ||
| 268 | integer, dimension(:), intent(in) :: input_shape | ||
| 269 | !! Input shape | ||
| 270 | character(len=100) :: err_msg | ||
| 271 | !! Error message | ||
| 272 | |||
| 273 | !--------------------------------------------------------------------------- | ||
| 274 | ! initialise input shape | ||
| 275 | !--------------------------------------------------------------------------- | ||
| 276 |
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420 | if(size(input_shape,dim=1).eq.this%input_rank)then |
| 277 |
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1267 | this%input_shape = input_shape |
| 278 | else | ||
| 279 | write(err_msg,'("Invalid size of input_shape in ",A,& | ||
| 280 | &" expected (",I0,"), got (",I0,")")') & | ||
| 281 | ✗ | trim(this%name), this%input_rank, size(input_shape,dim=1) | |
| 282 | ✗ | call stop_program(err_msg) | |
| 283 | ✗ | return | |
| 284 | end if | ||
| 285 | |||
| 286 | end subroutine set_shape_base | ||
| 287 | !############################################################################### | ||
| 288 | |||
| 289 | |||
| 290 | !############################################################################### | ||
| 291 | 61 | module subroutine extract_output_base(this, output) | |
| 292 | !! Get the output of the layer | ||
| 293 | implicit none | ||
| 294 | |||
| 295 | ! Arguments | ||
| 296 | class(base_layer_type), intent(in) :: this | ||
| 297 | !! Instance of the layer | ||
| 298 | real(real32), allocatable, dimension(..), intent(out) :: output | ||
| 299 | !! Output of the Layer | ||
| 300 | |||
| 301 |
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183 | if(size(this%output).gt.1)then |
| 302 | call print_warning("extract_output_base: output has more than one"& | ||
| 303 | ✗ | &" sample, cannot extract") | |
| 304 | ✗ | return | |
| 305 | end if | ||
| 306 | |||
| 307 |
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61 | call this%output(1,1)%extract(output) |
| 308 | |||
| 309 | end subroutine extract_output_base | ||
| 310 | !############################################################################### | ||
| 311 | |||
| 312 | |||
| 313 | !############################################################################### | ||
| 314 | ✗ | pure module function get_num_params_base(this) result(num_params) | |
| 315 | !! Get the number of parameters in the layer | ||
| 316 | implicit none | ||
| 317 | |||
| 318 | ! Arguments | ||
| 319 | class(base_layer_type), intent(in) :: this | ||
| 320 | !! Instance of the layer | ||
| 321 | integer :: num_params | ||
| 322 | !! Number of parameters | ||
| 323 | |||
| 324 | ! No parameters in the base layer | ||
| 325 | ✗ | num_params = 0 | |
| 326 | |||
| 327 | ✗ | end function get_num_params_base | |
| 328 | !------------------------------------------------------------------------------- | ||
| 329 | 67 | pure module function get_num_params_conv(this) result(num_params) | |
| 330 | !! Get the number of parameters in convolutional layer | ||
| 331 | implicit none | ||
| 332 | |||
| 333 | ! Arguments | ||
| 334 | class(conv_layer_type), intent(in) :: this | ||
| 335 | !! Instance of the layer | ||
| 336 | integer :: num_params | ||
| 337 | !! Number of parameters | ||
| 338 | |||
| 339 | ! num_filters x num_channels x kernel_size + num_biases | ||
| 340 | ! num_biases = num_filters | ||
| 341 | 268 | num_params = this%num_filters * this%num_channels * product(this%knl) + & | |
| 342 |
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223 | this%num_filters |
| 343 | |||
| 344 | 67 | end function get_num_params_conv | |
| 345 | !------------------------------------------------------------------------------- | ||
| 346 | 18 | pure module function get_num_params_batch(this) result(num_params) | |
| 347 | !! Get the number of parameters in batch normalisation layer | ||
| 348 | implicit none | ||
| 349 | |||
| 350 | ! Arguments | ||
| 351 | class(batch_layer_type), intent(in) :: this | ||
| 352 | !! Instance of the layer | ||
| 353 | integer :: num_params | ||
| 354 | !! Number of parameters | ||
| 355 | |||
| 356 | ! num_filters x num_channels x kernel_size + num_biases | ||
| 357 | ! num_biases = num_filters | ||
| 358 | 18 | num_params = 2 * this%num_channels | |
| 359 | |||
| 360 | 18 | end function get_num_params_batch | |
| 361 | !############################################################################### | ||
| 362 | |||
| 363 | |||
| 364 | !############################################################################### | ||
| 365 | ✗ | module subroutine forward_base(this, input) | |
| 366 | !! Forward pass for the layer | ||
| 367 | implicit none | ||
| 368 | |||
| 369 | ! Arguments | ||
| 370 | class(base_layer_type), intent(inout) :: this | ||
| 371 | !! Instance of the layer | ||
| 372 | class(array_type), dimension(:,:), intent(in) :: input | ||
| 373 | !! Input data | ||
| 374 | |||
| 375 | ! Local variables | ||
| 376 | integer :: i, j | ||
| 377 | !! Loop indices | ||
| 378 | |||
| 379 | ✗ | do i = 1, size(input, 1) | |
| 380 | ✗ | do j = 1, size(input, 2) | |
| 381 | ✗ | if(.not.input(i,j)%allocated)then | |
| 382 | ✗ | call stop_program('Input to input layer not allocated') | |
| 383 | ✗ | return | |
| 384 | end if | ||
| 385 | ✗ | this%output(i,j) = input(i,j) | |
| 386 | end do | ||
| 387 | end do | ||
| 388 | |||
| 389 | end subroutine forward_base | ||
| 390 | !------------------------------------------------------------------------------- | ||
| 391 | ✗ | module function forward_eval_base(this, input) result(output) | |
| 392 | !! Forward pass of layer and return output for evaluation | ||
| 393 | implicit none | ||
| 394 | |||
| 395 | ! Arguments | ||
| 396 | class(base_layer_type), intent(inout), target :: this | ||
| 397 | !! Instance of the layer | ||
| 398 | class(array_type), dimension(:,:), intent(in) :: input | ||
| 399 | !! Input data | ||
| 400 | type(array_type), pointer :: output(:,:) | ||
| 401 | !! Output data | ||
| 402 | |||
| 403 | ✗ | call this%forward(input) | |
| 404 | ✗ | output => this%output | |
| 405 | ✗ | end function forward_eval_base | |
| 406 | !############################################################################### | ||
| 407 | |||
| 408 | |||
| 409 | !############################################################################### | ||
| 410 | ✗ | module subroutine set_graph_base(this, graph) | |
| 411 | !! Set the graph structure of the input data | ||
| 412 | implicit none | ||
| 413 | |||
| 414 | ! Arguments | ||
| 415 | class(base_layer_type), intent(inout) :: this | ||
| 416 | !! Instance of the layer | ||
| 417 | type(graph_type), dimension(:), intent(in) :: graph | ||
| 418 | !! Graph structure of input data | ||
| 419 | |||
| 420 | ! Local variables | ||
| 421 | integer :: s | ||
| 422 | !! Loop index | ||
| 423 | |||
| 424 | ✗ | if(allocated(this%graph))then | |
| 425 | ✗ | if(size(this%graph).ne.size(graph))then | |
| 426 | ✗ | deallocate(this%graph) | |
| 427 | ✗ | allocate(this%graph(size(graph))) | |
| 428 | end if | ||
| 429 | else | ||
| 430 | ✗ | allocate(this%graph(size(graph))) | |
| 431 | end if | ||
| 432 | ✗ | do s = 1, size(graph) | |
| 433 | ✗ | this%graph(s)%adj_ia = graph(s)%adj_ia | |
| 434 | ✗ | this%graph(s)%adj_ja = graph(s)%adj_ja | |
| 435 | ✗ | this%graph(s)%edge_weights = graph(s)%edge_weights | |
| 436 | ✗ | this%graph(s)%num_edges = graph(s)%num_edges | |
| 437 | ✗ | this%graph(s)%num_vertices = graph(s)%num_vertices | |
| 438 | end do | ||
| 439 | |||
| 440 | ✗ | end subroutine set_graph_base | |
| 441 | !############################################################################### | ||
| 442 | |||
| 443 | |||
| 444 | !############################################################################### | ||
| 445 | ✗ | module subroutine nullify_graph_base(this) | |
| 446 | !! Nullify the forward pass data of the layer to free memory | ||
| 447 | implicit none | ||
| 448 | |||
| 449 | ! Arguments | ||
| 450 | class(base_layer_type), intent(inout) :: this | ||
| 451 | !! Instance of the layer | ||
| 452 | |||
| 453 | ! Local variables | ||
| 454 | integer :: i, j | ||
| 455 | !! Loop indices | ||
| 456 | |||
| 457 | ✗ | do i = 1, size(this%output,1) | |
| 458 | ✗ | do j = 1, size(this%output,2) | |
| 459 | ✗ | call this%output(i,j)%nullify_graph() | |
| 460 | end do | ||
| 461 | end do | ||
| 462 | |||
| 463 | ✗ | end subroutine nullify_graph_base | |
| 464 | !############################################################################### | ||
| 465 | |||
| 466 | |||
| 467 | !############################################################################### | ||
| 468 | 11 | module subroutine reduce_learnable(this, input) | |
| 469 | !! Merge two learnable layers via summation | ||
| 470 | implicit none | ||
| 471 | |||
| 472 | ! Arguments | ||
| 473 | class(learnable_layer_type), intent(inout) :: this | ||
| 474 | !! Instance of the layer | ||
| 475 | class(learnable_layer_type), intent(in) :: input | ||
| 476 | !! Instance of a layer | ||
| 477 | |||
| 478 | ! Local variables | ||
| 479 | integer :: i | ||
| 480 | !! Loop index | ||
| 481 | |||
| 482 |
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11 | if(allocated(this%params).and.allocated(input%params))then |
| 483 |
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11 | if(size(this%params).ne.size(input%params))then |
| 484 | ✗ | call stop_program("reduce_learnable: incompatible parameter sizes") | |
| 485 | ✗ | return | |
| 486 | end if | ||
| 487 |
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39 | do i = 1, size(this%params,1) |
| 488 |
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28 | this%params(i) = this%params(i) + input%params(i) |
| 489 |
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56 | if(associated(this%params(i)%grad).and.& |
| 490 |
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39 | associated(input%params(i)%grad))then |
| 491 | ✗ | this%params(i)%grad = this%params(i)%grad + & | |
| 492 | ✗ | input%params(i)%grad | |
| 493 | end if | ||
| 494 | end do | ||
| 495 | else | ||
| 496 | ✗ | call stop_program("reduce_learnable: unallocated parameter arrays") | |
| 497 | ✗ | return | |
| 498 | end if | ||
| 499 | |||
| 500 | end subroutine reduce_learnable | ||
| 501 | !############################################################################### | ||
| 502 | |||
| 503 | |||
| 504 | !############################################################################### | ||
| 505 | 12 | module function add_learnable(a, b) result(output) | |
| 506 | !! Add two learnable layers together | ||
| 507 | implicit none | ||
| 508 | |||
| 509 | ! Arguments | ||
| 510 | class(learnable_layer_type), intent(in) :: a, b | ||
| 511 | !! Instances of layers | ||
| 512 | class(learnable_layer_type), allocatable :: output | ||
| 513 | !! Output layer | ||
| 514 | |||
| 515 | ! Local variables | ||
| 516 | integer :: i | ||
| 517 | !! Loop index | ||
| 518 | |||
| 519 |
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12 | output = a |
| 520 |
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12 | if(allocated(a%params).and.allocated(b%params))then |
| 521 |
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12 | if(size(a%params).ne.size(b%params))then |
| 522 | ✗ | call stop_program("add_learnable: incompatible parameter sizes") | |
| 523 | ✗ | return | |
| 524 | end if | ||
| 525 |
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39 | do i = 1, size(a%params,1) |
| 526 |
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27 | output%params(i)%grad => null() |
| 527 |
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27 | output%params(i) = a%params(i) + b%params(i) |
| 528 |
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54 | if(associated(a%params(i)%grad).and.& |
| 529 |
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39 | associated(b%params(i)%grad))then |
| 530 |
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3 | allocate(output%params(i)%grad) |
| 531 | 12 | output%params(i)%grad = a%params(i)%grad + & | |
| 532 |
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3 | b%params(i)%grad |
| 533 | end if | ||
| 534 | end do | ||
| 535 | else | ||
| 536 | ✗ | call stop_program("add_learnable: unallocated parameter arrays") | |
| 537 | ✗ | return | |
| 538 | end if | ||
| 539 | |||
| 540 | 12 | end function add_learnable | |
| 541 | !############################################################################### | ||
| 542 | |||
| 543 | |||
| 544 | !############################################################################### | ||
| 545 |
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17 | pure module function get_params(this) result(params) |
| 546 | !! Get the learnable parameters of the layer | ||
| 547 | !! | ||
| 548 | !! This function returns the learnable parameters of the layer | ||
| 549 | !! as a single array. | ||
| 550 | !! This has been modified from the neural-fortran library | ||
| 551 | implicit none | ||
| 552 | |||
| 553 | ! Arguments | ||
| 554 | class(learnable_layer_type), intent(in) :: this | ||
| 555 | !! Instance of the layer | ||
| 556 | real(real32), dimension(this%num_params) :: params | ||
| 557 | !! Learnable parameters | ||
| 558 | |||
| 559 | ! Local variables | ||
| 560 | integer :: i, start_idx, end_idx | ||
| 561 | !! Loop indices | ||
| 562 | |||
| 563 | 17 | start_idx = 0 | |
| 564 | 17 | end_idx = 0 | |
| 565 |
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58 | do i = 1, size(this%params) |
| 566 | 41 | start_idx = end_idx + 1 | |
| 567 |
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41 | end_idx = start_idx + size(this%params(i)%val,1) - 1 |
| 568 |
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|
4938 | params(start_idx:end_idx) = this%params(i)%val(:,1) |
| 569 | end do | ||
| 570 | |||
| 571 |
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17 | end function get_params |
| 572 | !############################################################################### | ||
| 573 | |||
| 574 | |||
| 575 | !############################################################################### | ||
| 576 | 11 | module subroutine set_params(this, params) | |
| 577 | !! Set the learnable parameters of the layer | ||
| 578 | !! | ||
| 579 | !! This function sets the learnable parameters of the layer | ||
| 580 | !! from a single array. | ||
| 581 | !! This has been modified from the neural-fortran library | ||
| 582 | implicit none | ||
| 583 | |||
| 584 | ! Arguments | ||
| 585 | class(learnable_layer_type), intent(inout) :: this | ||
| 586 | !! Instance of the layer | ||
| 587 | real(real32), dimension(this%num_params), intent(in) :: params | ||
| 588 | !! Learnable parameters | ||
| 589 | |||
| 590 | ! Local variables | ||
| 591 | integer :: i, start_idx, end_idx | ||
| 592 | !! Loop indices | ||
| 593 | |||
| 594 |
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11 | if(.not.allocated(this%params))then |
| 595 | ✗ | call stop_program("set_params: params not allocated") | |
| 596 | ✗ | return | |
| 597 | end if | ||
| 598 | 11 | start_idx = 0 | |
| 599 | 11 | end_idx = 0 | |
| 600 |
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36 | do i = 1, size(this%params) |
| 601 | 25 | start_idx = end_idx + 1 | |
| 602 |
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25 | end_idx = start_idx + size(this%params(i)%val,1) - 1 |
| 603 |
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2581 | this%params(i)%val(:,1) = params(start_idx:end_idx) |
| 604 | end do | ||
| 605 | |||
| 606 | end subroutine set_params | ||
| 607 | !############################################################################### | ||
| 608 | |||
| 609 | |||
| 610 | !############################################################################### | ||
| 611 |
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35 | pure module function get_gradients(this, clip_method) result(gradients) |
| 612 | !! Get the gradients of the layer | ||
| 613 | !! | ||
| 614 | !! This function returns the gradients of the layer as a single array. | ||
| 615 | !! This has been modified from the neural-fortran library | ||
| 616 | use athena__clipper, only: clip_type | ||
| 617 | implicit none | ||
| 618 | |||
| 619 | ! Arguments | ||
| 620 | class(learnable_layer_type), intent(in) :: this | ||
| 621 | !! Instance of the layer | ||
| 622 | type(clip_type), optional, intent(in) :: clip_method | ||
| 623 | !! Method to clip the gradients | ||
| 624 | real(real32), dimension(this%num_params) :: gradients | ||
| 625 | !! Gradients of the layer | ||
| 626 | |||
| 627 | ! Local variables | ||
| 628 | integer :: i, start_idx, end_idx | ||
| 629 | !! Loop indices | ||
| 630 | |||
| 631 |
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35 | if(.not.allocated(this%params))then |
| 632 | ✗ | return | |
| 633 | end if | ||
| 634 | 35 | start_idx = 0 | |
| 635 | 35 | end_idx = 0 | |
| 636 |
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101 | do i = 1, size(this%params) |
| 637 | 66 | start_idx = end_idx + 1 | |
| 638 |
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66 | end_idx = start_idx + size(this%params(i)%val,1) - 1 |
| 639 |
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101 | if(.not.associated(this%params(i)%grad))then |
| 640 |
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2340 | gradients(start_idx:end_idx) = 0._real32 |
| 641 | else | ||
| 642 |
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7388 | gradients(start_idx:end_idx) = this%params(i)%grad%val(:,1) |
| 643 | end if | ||
| 644 | end do | ||
| 645 | |||
| 646 |
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35 | if(present(clip_method)) call clip_method%apply(size(gradients),gradients) |
| 647 | |||
| 648 |
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35 | end function get_gradients |
| 649 | !############################################################################### | ||
| 650 | |||
| 651 | |||
| 652 | !############################################################################### | ||
| 653 | 23 | module subroutine set_gradients(this, gradients) | |
| 654 | !! Set the gradients of the layer | ||
| 655 | !! | ||
| 656 | !! This function sets the gradients of the layer from a single array. | ||
| 657 | !! This has been modified from the neural-fortran library | ||
| 658 | implicit none | ||
| 659 | |||
| 660 | ! Arguments | ||
| 661 | class(learnable_layer_type), intent(inout) :: this | ||
| 662 | !! Instance of the layer | ||
| 663 | real(real32), dimension(..), intent(in) :: gradients | ||
| 664 | !! Gradients of the layer | ||
| 665 | |||
| 666 | ! Local variables | ||
| 667 | integer :: i, start_idx, end_idx | ||
| 668 | !! Loop indices | ||
| 669 | |||
| 670 | 23 | start_idx = 0 | |
| 671 | 23 | end_idx = 0 | |
| 672 | select rank(gradients) | ||
| 673 | rank(0) | ||
| 674 |
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40 | do i = 1, size(this%params) |
| 675 |
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25 | if(.not.associated(this%params(i)%grad))then |
| 676 |
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9 | this%params(i)%grad => this%params(i)%create_result() |
| 677 | end if | ||
| 678 |
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2408 | this%params(i)%grad%val(:,1) = gradients |
| 679 | end do | ||
| 680 | rank(1) | ||
| 681 |
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24 | do i = 1, size(this%params) |
| 682 |
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16 | if(.not.associated(this%params(i)%grad))then |
| 683 |
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13 | this%params(i)%grad => this%params(i)%create_result() |
| 684 | end if | ||
| 685 | 16 | start_idx = end_idx + 1 | |
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16 | end_idx = start_idx + size(this%params(i)%val,1) - 1 |
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2274 | this%params(i)%grad%val(:,1) = gradients(start_idx:end_idx) |
| 688 | end do | ||
| 689 | end select | ||
| 690 | |||
| 691 | 23 | end subroutine set_gradients | |
| 692 | !############################################################################### | ||
| 693 | |||
| 694 |
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23 | end submodule athena__base_layer_submodule |
| 695 |