| 1 | /** |
|---|---|
| 2 | * Copyright (c) Glow Contributors. See CONTRIBUTORS file. |
| 3 | * |
| 4 | * Licensed under the Apache License, Version 2.0 (the "License"); |
| 5 | * you may not use this file except in compliance with the License. |
| 6 | * You may obtain a copy of the License at |
| 7 | * |
| 8 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 9 | * |
| 10 | * Unless required by applicable law or agreed to in writing, software |
| 11 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 13 | * See the License for the specific language governing permissions and |
| 14 | * limitations under the License. |
| 15 | */ |
| 16 | #include "NodeBuilder.h" |
| 17 | |
| 18 | #include <fstream> |
| 19 | #include <iostream> |
| 20 | |
| 21 | int main(int argc, char **argv) { |
| 22 | if (argc != 6) { |
| 23 | std::cerr << "Usage: "<< argv[0] |
| 24 | << " output.h output.cpp output.def import.h export.h\n"; |
| 25 | return -1; |
| 26 | } |
| 27 | |
| 28 | std::cout << "Writing node descriptors to:\n\t"<< argv[1] << "\n\t" |
| 29 | << argv[2] << "\n\t"<< argv[3] << "\n\t"<< argv[4] << "\n\t" |
| 30 | << argv[5] << "\n"; |
| 31 | |
| 32 | std::ofstream hFile(argv[1]); |
| 33 | std::ofstream cFile(argv[2]); |
| 34 | std::ofstream dFile(argv[3]); |
| 35 | std::ofstream iFile(argv[4]); |
| 36 | std::ofstream eFile(argv[5]); |
| 37 | |
| 38 | Builder BB(hFile, cFile, dFile, iFile, eFile); |
| 39 | |
| 40 | //===--------------------------------------------------------------------===// |
| 41 | // Input/Output nodes |
| 42 | //===--------------------------------------------------------------------===// |
| 43 | |
| 44 | BB.includeHeader("glow/Graph/Nodes.h"); |
| 45 | |
| 46 | BB.declareNode("Storage"); |
| 47 | BB.declareNode("Constant"); |
| 48 | BB.declareNode("Placeholder"); |
| 49 | |
| 50 | BB.newNode("Save") |
| 51 | .addInput("Input") |
| 52 | .addInput("Output") |
| 53 | .addExtraMethod("Placeholder *getPlaceholder() const;", |
| 54 | "Placeholder *SaveNode::getPlaceholder() const { return " |
| 55 | "llvm::cast<Placeholder>(Output_.getNode()); };") |
| 56 | .addOverwrittenInput("Output") |
| 57 | .setHasSideEffects(true) |
| 58 | .dataParallel() |
| 59 | .skipAutogenSerialization() |
| 60 | .setDocstring("Specifies a node whose Input will be copied to Output." |
| 61 | "This node prevents graph optimizations from eliminating " |
| 62 | "this node and all of its ancestor nodes. Generally " |
| 63 | "intended to save the final result of a network."); |
| 64 | |
| 65 | //===--------------------------------------------------------------------===// |
| 66 | // Convolution / Pool / FC |
| 67 | //===--------------------------------------------------------------------===// |
| 68 | |
| 69 | BB.newNode("Pad") |
| 70 | .addInput("Input") |
| 71 | .addMember(MemberType::Enum, "Mode") |
| 72 | .addMember(MemberType::VectorSigned, "Pads") |
| 73 | .addMember(MemberType::Float, "Value") |
| 74 | .addResultFromCtorArg() |
| 75 | .setDocstring( |
| 76 | "Performs padding of a given input tensor. The Padding information " |
| 77 | "must be specified for each dimension of the tensor in Pads (start " |
| 78 | "and end padding). In case the padding is negative, it means that " |
| 79 | "the tensor must be cropped. Mode defines how extra padding elements " |
| 80 | "are created. Supported modes are defined in the PaddingMode enum: " |
| 81 | "CONSTANT, REFLECT, EDGE. Value is only used with the CONSTANT " |
| 82 | "mode."); |
| 83 | |
| 84 | BB.newNode("Convolution") |
| 85 | .addInput("Input") |
| 86 | .addInput("Filter") |
| 87 | .addInput("Bias") |
| 88 | .addMember(MemberType::VectorUnsigned, "Kernels") |
| 89 | .addMember(MemberType::VectorUnsigned, "Strides") |
| 90 | .addMember(MemberType::VectorUnsigned, "Pads", /* addSetter */ true) |
| 91 | .addMember(MemberType::Unsigned, "Group", /* addSetter */ true) |
| 92 | .addMember(MemberType::VectorUnsigned, "Dilation") |
| 93 | .addMember(MEMBER_TYPE_INFO(glow::ConvolutionLayout), "Layout") |
| 94 | .addFusedActivation() |
| 95 | .addResultFromCtorArg() |
| 96 | .addGradient() |
| 97 | .setDocstring( |
| 98 | "Performs 2D Convolution using a given Input, Filter, and " |
| 99 | "Bias tensors, as well as provided Kernels, Strides, Pads, " |
| 100 | "Group and Dilation. Supported Layouts are defined in the " |
| 101 | "ConvolutionLayout enum: NHWC and NCHW. Supported FusedActivations " |
| 102 | "are defined in the FusedActivation enum."); |
| 103 | |
| 104 | BB.newNode("ChannelwiseQuantizedConvolution") |
| 105 | .addInput("Input") |
| 106 | .addInput("Filter") |
| 107 | .addInput("Bias") |
| 108 | .addInput("FilterScales") |
| 109 | .addInput("FilterOffsets") |
| 110 | .addInput("BiasScales") |
| 111 | .addInput("BiasOffsets") |
| 112 | .addMember(MemberType::VectorUnsigned, "Kernels", /* addSetter */ true) |
| 113 | .addMember(MemberType::VectorUnsigned, "Strides") |
| 114 | .addMember(MemberType::VectorUnsigned, "Pads", /* addSetter */ true) |
| 115 | .addMember(MemberType::Unsigned, "Group", /* addSetter */ true) |
| 116 | .addMember(MemberType::VectorUnsigned, "Dilation") |
| 117 | .addFusedActivation() |
| 118 | .addResultFromCtorArg() |
| 119 | .setDocstring( |
| 120 | "Performs 2D Convolution using a given Input, Filter, and " |
| 121 | "Bias tensors, as well as provided Kernels, Strides, Pads, " |
| 122 | "and Group. The filter channel wise quantization parameters " |
| 123 | "are provided by FilterScales and FilterOffsets while the " |
| 124 | "bias channel wise quantization parameters are provided by " |
| 125 | "BiasScales and BiasOffsets."); |
| 126 | |
| 127 | BB.newNode("ConvTranspose") |
| 128 | .addInput("Input") |
| 129 | .addInput("Filter") |
| 130 | .addInput("Bias") |
| 131 | .addMember(MemberType::VectorUnsigned, "Kernels") |
| 132 | .addMember(MemberType::VectorUnsigned, "Strides") |
| 133 | .addMember(MemberType::VectorUnsigned, "Pads") |
| 134 | .addMember(MemberType::Unsigned, "Group") |
| 135 | .addMember(MemberType::VectorUnsigned, "Dilation") |
| 136 | .addResultFromCtorArg() |
| 137 | .setDocstring("Performs 2D Transposed Convolution using a given Input," |
| 138 | "Filter, and Bias tensors, as well as provided Kernels," |
| 139 | "Strides, Pads, and Group."); |
| 140 | |
| 141 | BB.newNode("Convolution3D") |
| 142 | .addInput("Input") |
| 143 | .addInput("Filter") |
| 144 | .addInput("Bias") |
| 145 | .addMember(MemberType::VectorUnsigned, "Kernels") |
| 146 | .addMember(MemberType::VectorUnsigned, "Strides") |
| 147 | .addMember(MemberType::VectorUnsigned, "Pads") |
| 148 | .addMember(MemberType::Unsigned, "Group") |
| 149 | .addResultFromCtorArg() |
| 150 | .addGradient() |
| 151 | .setDocstring("Performs 3D Convolution using a given Input, Filter, and " |
| 152 | "Bias tensors, as well as provided Kernels, Strides, Pads, " |
| 153 | "and Group."); |
| 154 | |
| 155 | BB.newNode("MaxPool") |
| 156 | .addInput("Input") |
| 157 | .addMember(MemberType::VectorUnsigned, "Kernels") |
| 158 | .addMember(MemberType::VectorUnsigned, "Strides") |
| 159 | .addMember(MemberType::VectorUnsigned, "Pads", /* addSetter */ true) |
| 160 | .addMember(MemberType::Enum, "Layout") |
| 161 | .addResultFromCtorArg("Result") |
| 162 | .addResultFromCtorArg("Argmax") |
| 163 | .addGradient() |
| 164 | .setDocstring( |
| 165 | "Performs a Max Pool with Argmax operation on the Input " |
| 166 | "given provided Kernels, Strides, and Pads. Argmax is a flattened " |
| 167 | "index corresponding to respective max element. Supported layouts " |
| 168 | "are defined in the ConvolutionLayout enum: NHWC and NCHW."); |
| 169 | |
| 170 | BB.newNode("ArgMax") |
| 171 | .addInput("Input") |
| 172 | .addMember(MemberType::Unsigned, "Axis") |
| 173 | .addMember(MemberType::Boolean, "KeepDims") |
| 174 | .addResultFromCtorArg() |
| 175 | .setDocstring("Finds index of a maximum element along Axis. " |
| 176 | "If KeepDims is not true, the axis is removed from output"); |
| 177 | |
| 178 | BB.newNode("ArgMin") |
| 179 | .addInput("Input") |
| 180 | .addMember(MemberType::Unsigned, "Axis") |
| 181 | .addMember(MemberType::Boolean, "KeepDims") |
| 182 | .addResultFromCtorArg() |
| 183 | .setDocstring("Finds index of a minimum element along Axis. " |
| 184 | "If KeepDims is not true, the axis is removed from output"); |
| 185 | |
| 186 | BB.newNode("AvgPool") |
| 187 | .addInput("Input") |
| 188 | .addMember(MemberType::VectorUnsigned, "Kernels") |
| 189 | .addMember(MemberType::VectorUnsigned, "Strides") |
| 190 | .addMember(MemberType::VectorUnsigned, "Pads", /* addSetter */ true) |
| 191 | .addMember(MemberType::Enum, "Layout") |
| 192 | .addMember(MemberType::Boolean, "CountIncludePads") |
| 193 | .addResultFromCtorArg() |
| 194 | .addGradient() |
| 195 | .setDocstring( |
| 196 | "Performs an Average Pool operation on the Input given " |
| 197 | "provided Kernels, Strides, and Pads. Supported layouts are defined " |
| 198 | "in the ConvolutionLayout enum: NHWC, NCHW, NTHWC and NCTHW."); |
| 199 | |
| 200 | BB.newNode("AdaptiveAvgPool") |
| 201 | .addInput("Input") |
| 202 | .addResultFromCtorArg() |
| 203 | .addGradient() |
| 204 | .setDocstring( |
| 205 | "Performs an Adaptive Average Pool operation on the Input given"); |
| 206 | |
| 207 | BB.newNode("Gemm") |
| 208 | .addInput("A") |
| 209 | .addInput("B") |
| 210 | .addInput("C") |
| 211 | .addMember(MemberType::Float, "Alpha") |
| 212 | .addMember(MemberType::Float, "Beta") |
| 213 | .addMember(MemberType::Boolean, "TransposeA") |
| 214 | .addMember(MemberType::Boolean, "TransposeB") |
| 215 | .addResultFromCtorArg() |
| 216 | .setDocstring( |
| 217 | "Computes Y = Alpha * A * B + Beta * C where Alpha, Beta are scalars " |
| 218 | "and A, B, C are matrices. If TransposeA or TransposeB is used then " |
| 219 | "A or B is additionally transposed."); |
| 220 | |
| 221 | BB.newNode("FullyConnected") |
| 222 | .addInput("Input") |
| 223 | .addInput("Weights") |
| 224 | .addInput("Bias") |
| 225 | .addResultFromCtorArg() |
| 226 | .addGradient() |
| 227 | .setDocstring("Creates a FullyConnected node where the Input tensor and " |
| 228 | "Weights tensor are multiplied, and then the Bias tensor " |
| 229 | "is added to it, producing the Output."); |
| 230 | |
| 231 | BB.newNode("RowwiseQuantizedFullyConnected") |
| 232 | .addInput("Input") |
| 233 | .addInput("Weights") |
| 234 | .addInput("Scales") |
| 235 | .addInput("Offsets") |
| 236 | .addInput("Bias") |
| 237 | .addResultFromCtorArg() |
| 238 | .setDocstring( |
| 239 | "Creates a RowwiseQuantizedFullyConnected node where the Input " |
| 240 | "matrix and the transpose of Weights matrix are multiplied, and " |
| 241 | "then the Bias vector is broadcast-added to the result. Input, " |
| 242 | "Bias and Result are regularly quantized, while Weights use row-wise" |
| 243 | "quantization."); |
| 244 | |
| 245 | BB.newNode("DynamicQuantizedFullyConnected") |
| 246 | .addInput("Input") |
| 247 | .addInput("Weights") |
| 248 | .addInput("Bias") |
| 249 | .addMember(MemberType::Boolean, "IsSymmetric") |
| 250 | .addMember(MemberType::Boolean, "IsPerBatchElement") |
| 251 | .addResultFromCtorArg() |
| 252 | .setDocstring( |
| 253 | "Creates a DynamicQuantizedFullyConnectedNode which implement the " |
| 254 | "functionality of dynamic_quantization => quantized_fc => " |
| 255 | "dequantize, which support symmteric/asymmetric quantization. " |
| 256 | "Quantize parameters are automatically selected from range of input, " |
| 257 | "while weights are pre-quantized to int8 and bias are whether float " |
| 258 | "or int32"); |
| 259 | |
| 260 | BB.newNode("DynamicRowwiseQuantizedFullyConnected") |
| 261 | .addInput("Input") |
| 262 | .addInput("Weights") |
| 263 | .addInput("Bias") |
| 264 | .addInput("Scales") |
| 265 | .addInput("Offsets") |
| 266 | .addMember(MemberType::Boolean, "IsSymmetric") |
| 267 | .addMember(MemberType::Boolean, "IsPerBatchElement") |
| 268 | .addResultFromCtorArg() |
| 269 | .setDocstring( |
| 270 | "Creates a DynamicRowwiseQuantizedFullyConnectedNode which implement " |
| 271 | "the functionality of dynamic_quantization => quantized_fc => " |
| 272 | "dequantize, which support symmteric/asymmetric quantization. " |
| 273 | "Quantize parameters are automatically selected from range of input, " |
| 274 | "while weights are pre-rowwise-quantized to int8, whose rowwise " |
| 275 | "params stored in Scales and Offsets, and bias are whether float " |
| 276 | "or int32"); |
| 277 | //===--------------------------------------------------------------------===// |
| 278 | // Normalization |
| 279 | //===--------------------------------------------------------------------===// |
| 280 | |
| 281 | BB.newNode("BatchNormalization") |
| 282 | .addInput("Input") |
| 283 | .addInput("Scale") |
| 284 | .addInput("Bias") |
| 285 | .addInput("Mean") |
| 286 | .addInput("Var") |
| 287 | .addMember(MemberType::Unsigned, "ChannelIdx") |
| 288 | .addMember(MemberType::Float, "Epsilon") |
| 289 | .addMember(MemberType::Float, "Momentum") |
| 290 | .addResultFromCtorArg() |
| 291 | .addGradient() |
| 292 | .setDocstring("Performs batch normalization on the Input tensor with the " |
| 293 | "provided Scale, Bias, Mean, Var, ChannelIdx, Epsilon, and " |
| 294 | "Momentum. Similar to Caffe2 SpatialBN, and ONNX " |
| 295 | "BatchNormalization operator."); |
| 296 | |
| 297 | BB.newNode("InstanceNormalization") |
| 298 | .addInput("Input") |
| 299 | .addInput("Scale") |
| 300 | .addInput("Bias") |
| 301 | .addMember(MemberType::Unsigned, "ChannelIdx") |
| 302 | .addMember(MemberType::Float, "Epsilon") |
| 303 | .addResult("Input.getType()") |
| 304 | .setDocstring("Performs instance normalization on the Input tensor with " |
| 305 | "the provided Scale, Bias, Epsilon. Similar to ONNX " |
| 306 | "InstanceNormalization operator."); |
| 307 | |
| 308 | BB.newNode("MeanVarNormalization") |
| 309 | .addInput("Input") |
| 310 | .addInput("Mean") |
| 311 | .addInput("Var") |
| 312 | .addMember(MemberType::Unsigned, "ChannelIdx") |
| 313 | .addMember(MemberType::Float, "Momentum") |
| 314 | .addResult("Mean.getType()", "NewMean") |
| 315 | .addResult("Var.getType()", "NewVar") |
| 316 | .setDocstring("Calculates new normalized mean and variance based on the " |
| 317 | "input mean, variance, and input."); |
| 318 | |
| 319 | BB.newNode("LocalResponseNormalization") |
| 320 | .addInput("Input") |
| 321 | .addMember(MemberType::Unsigned, "HalfWindowSize") |
| 322 | .addMember(MemberType::Float, "Alpha") |
| 323 | .addMember(MemberType::Float, "Beta") |
| 324 | .addMember(MemberType::Float, "K") |
| 325 | .addResult("Input.getType()") |
| 326 | .addGradient() |
| 327 | .setDocstring("Performs local response normalization on the Input tensor " |
| 328 | "with the provided Scale, Bias, Mean, Var, ChannelIdx, " |
| 329 | "Epsilon, and Momentum. Similar to Caffe2 and ONNX LRN."); |
| 330 | |
| 331 | BB.newNode("LayerNormalization") |
| 332 | .addInput("Input") |
| 333 | .addInput("Scale") |
| 334 | .addInput("Bias") |
| 335 | .addMember(MemberType::Float, "Epsilon") |
| 336 | .addResultFromCtorArg() |
| 337 | .setDocstring("Performs layer normalization on the Input tensor with the " |
| 338 | "provided Scale, Bias, and Epsilon. Layer sizes are " |
| 339 | "determined by the dimensions of Scale and Bias. Similar " |
| 340 | "to PyTorch layer_norm."); |
| 341 | |
| 342 | BB.newNode("BatchBoxCox") |
| 343 | .addInput("Input") |
| 344 | .addInput("Lambda1") |
| 345 | .addInput("Lambda2") |
| 346 | .addMember(MemberType::Float, "Epsilon") |
| 347 | .addResult("Input.getType()") |
| 348 | .setDocstring("Apply box-cox transform for each column for each column " |
| 349 | "in NxD input tensor"); |
| 350 | |
| 351 | BB.newNode("VectorNorm") |
| 352 | .addInput("Input") |
| 353 | .addMember(MemberType::Unsigned, "Axis") |
| 354 | .addMember(MemberType::Unsigned, "P") |
| 355 | .addResultFromCtorArg() |
| 356 | .setDocstring("Performs L2 norm of the Input operand based on Axis."); |
| 357 | |
| 358 | //===--------------------------------------------------------------------===// |
| 359 | // Bucketing |
| 360 | //===--------------------------------------------------------------------===// |
| 361 | |
| 362 | BB.newNode("Bucketize") |
| 363 | .addInput("Input") |
| 364 | .addMember(MemberType::VectorFloat, "Boundaries") |
| 365 | .addResultFromCtorArg() |
| 366 | .setDocstring("Performs bucketization on the input given Boundaries"); |
| 367 | |
| 368 | //===--------------------------------------------------------------------===// |
| 369 | // Loss operations |
| 370 | //===--------------------------------------------------------------------===// |
| 371 | |
| 372 | BB.newNode("SoftMax") |
| 373 | .addInput("Input") |
| 374 | .addInput("Selected") |
| 375 | .addResultFromCtorArg() |
| 376 | .addGradient() |
| 377 | .setDocstring("Performs SoftMax normalization on the Input tensor."); |
| 378 | |
| 379 | BB.newNode("LogSoftMax") |
| 380 | .addInput("Input") |
| 381 | .addInput("Selected") |
| 382 | .addResultFromCtorArg() |
| 383 | .addGradient() |
| 384 | .setDocstring("Performs LogSoftMax normalization on the Input tensor."); |
| 385 | |
| 386 | BB.newNode("CrossEntropyLoss") |
| 387 | .addInput("P") |
| 388 | .addInput("Labels") |
| 389 | .addResultFromCtorArg("CE") |
| 390 | .addGradient() |
| 391 | .setDocstring("Computes the average cross entropy loss of the input."); |
| 392 | |
| 393 | BB.newNode("Regression") |
| 394 | .addInput("Input") |
| 395 | .addInput("Expected") |
| 396 | .addResult("Input.getType()") |
| 397 | .addGradient() |
| 398 | .setDocstring( |
| 399 | "Takes an Input tensor and creates a regression output layer."); |
| 400 | |
| 401 | BB.newNode("SigmoidCrossEntropyWithLogits") |
| 402 | .addInput("Logits") |
| 403 | .addInput("Targets") |
| 404 | .addResultFromCtorArg() |
| 405 | .setDocstring("Computes the sigmoid cross entropy between two inputs."); |
| 406 | |
| 407 | //===--------------------------------------------------------------------===// |
| 408 | // Arithmetic |
| 409 | //===--------------------------------------------------------------------===// |
| 410 | |
| 411 | BB.newNode("Add") |
| 412 | .addInput("LHS") |
| 413 | .addInput("RHS") |
| 414 | .addResultFromCtorArg() |
| 415 | .dataParallel() |
| 416 | .addGradient() |
| 417 | .setDocstring("Performs Add on the LHS and RHS operands."); |
| 418 | |
| 419 | BB.newNode("Mul") |
| 420 | .addInput("LHS") |
| 421 | .addInput("RHS") |
| 422 | .addResultFromCtorArg() |
| 423 | .dataParallel() |
| 424 | .addGradient() |
| 425 | .setDocstring("Performs Mul on the LHS and RHS operands."); |
| 426 | |
| 427 | BB.newNode("Sub") |
| 428 | .addInput("LHS") |
| 429 | .addInput("RHS") |
| 430 | .addResultFromCtorArg() |
| 431 | .dataParallel() |
| 432 | .addGradient() |
| 433 | .setDocstring("Performs Sub on the LHS and RHS operands."); |
| 434 | |
| 435 | BB.newNode("Div") |
| 436 | .addInput("LHS") |
| 437 | .addInput("RHS") |
| 438 | .addResultFromCtorArg() |
| 439 | .dataParallel() |
| 440 | .addGradient() |
| 441 | .setDocstring("Performs Div on the LHS and RHS operands."); |
| 442 | |
| 443 | BB.newNode("FloorDiv") |
| 444 | .addInput("LHS") |
| 445 | .addInput("RHS") |
| 446 | .addMember(MemberType::Boolean, "Truncate") |
| 447 | .addResultFromCtorArg() |
| 448 | .dataParallel() |
| 449 | .setDocstring( |
| 450 | "Performs Div on the LHS and RHS operands, then Floor. If Truncate " |
| 451 | "is set to true then truncate the quotient to zero instead."); |
| 452 | |
| 453 | BB.newNode("Fmod") |
| 454 | .addInput("LHS") |
| 455 | .addInput("RHS") |
| 456 | .addResultFromCtorArg() |
| 457 | .dataParallel() |
| 458 | .setDocstring("Computes the element-wise remainder of division."); |
| 459 | |
| 460 | BB.newNode("Max") |
| 461 | .addInput("LHS") |
| 462 | .addInput("RHS") |
| 463 | .addResultFromCtorArg() |
| 464 | .dataParallel() |
| 465 | .setDocstring("Performs Max on the LHS and RHS operands."); |
| 466 | |
| 467 | BB.newNode("Min") |
| 468 | .addInput("LHS") |
| 469 | .addInput("RHS") |
| 470 | .addResultFromCtorArg() |
| 471 | .dataParallel() |
| 472 | .setDocstring("Performs Min on the LHS and RHS operands."); |
| 473 | |
| 474 | BB.newNode("CmpEQ") |
| 475 | .addInput("LHS") |
| 476 | .addInput("RHS") |
| 477 | .addResultFromCtorArg() |
| 478 | .dataParallel() |
| 479 | .setDocstring("Performs an element-wise EQUAL comparison between the " |
| 480 | "LHS and RHS operands."); |
| 481 | |
| 482 | BB.newNode("CmpNEQ") |
| 483 | .addInput("LHS") |
| 484 | .addInput("RHS") |
| 485 | .addResultFromCtorArg() |
| 486 | .dataParallel() |
| 487 | .setDocstring("Performs an element-wise NOT EQUAL comparison between " |
| 488 | "the LHS and RHS operands."); |
| 489 | |
| 490 | BB.newNode("CmpLT") |
| 491 | .addInput("LHS") |
| 492 | .addInput("RHS") |
| 493 | .addResultFromCtorArg() |
| 494 | .dataParallel() |
| 495 | .setDocstring("Performs an element-wise LESS THAN comparison between " |
| 496 | "the LHS and RHS operands."); |
| 497 | |
| 498 | BB.newNode("CmpLTE") |
| 499 | .addInput("LHS") |
| 500 | .addInput("RHS") |
| 501 | .addResultFromCtorArg() |
| 502 | .dataParallel() |
| 503 | .setDocstring("Performs an element-wise LESS THAN OR EQUAL comparison " |
| 504 | "between the LHS and RHS operands."); |
| 505 | |
| 506 | BB.newNode("Pow") |
| 507 | .addInput("LHS") |
| 508 | .addInput("RHS") |
| 509 | .addResultFromCtorArg() |
| 510 | .dataParallel() |
| 511 | .setDocstring("Performs elementwise pow(LHS, RHS)."); |
| 512 | |
| 513 | BB.newNode("And") |
| 514 | .addInput("LHS") |
| 515 | .addInput("RHS") |
| 516 | .addResultFromCtorArg() |
| 517 | .dataParallel() |
| 518 | .setDocstring("Performs an element-wise logical AND between the LHS and " |
| 519 | "RHS operands."); |
| 520 | |
| 521 | BB.newNode("BitwiseAnd") |
| 522 | .addInput("LHS") |
| 523 | .addInput("RHS") |
| 524 | .addResultFromCtorArg() |
| 525 | .dataParallel() |
| 526 | .setDocstring("Performs an element-wise bitwise AND between the LHS and " |
| 527 | "RHS operands."); |
| 528 | |
| 529 | BB.newNode("Or") |
| 530 | .addInput("LHS") |
| 531 | .addInput("RHS") |
| 532 | .addResultFromCtorArg() |
| 533 | .dataParallel() |
| 534 | .setDocstring("Performs an element-wise logical OR between the LHS and " |
| 535 | "RHS operands."); |
| 536 | |
| 537 | BB.newNode("BitwiseOr") |
| 538 | .addInput("LHS") |
| 539 | .addInput("RHS") |
| 540 | .addResultFromCtorArg() |
| 541 | .dataParallel() |
| 542 | .setDocstring("Performs an element-wise bitwise OR between the LHS and " |
| 543 | "RHS operands."); |
| 544 | |
| 545 | BB.newNode("Xor") |
| 546 | .addInput("LHS") |
| 547 | .addInput("RHS") |
| 548 | .addResultFromCtorArg() |
| 549 | .dataParallel() |
| 550 | .setDocstring("Performs an element-wise logical XOR between the LHS and " |
| 551 | "RHS operands."); |
| 552 | |
| 553 | BB.newNode("BitwiseXor") |
| 554 | .addInput("LHS") |
| 555 | .addInput("RHS") |
| 556 | .addResultFromCtorArg() |
| 557 | .dataParallel() |
| 558 | .setDocstring("Performs an element-wise bitwise XOR between the LHS and " |
| 559 | "RHS operands."); |
| 560 | |
| 561 | BB.newNode("Not") |
| 562 | .addInput("Input") |
| 563 | .addResultFromCtorArg() |
| 564 | .dataParallel() |
| 565 | .setDocstring("Performs an element-wise logical NOT of the Input " |
| 566 | "operand."); |
| 567 | |
| 568 | BB.newNode("BitwiseNot") |
| 569 | .addInput("Input") |
| 570 | .addResultFromCtorArg() |
| 571 | .dataParallel() |
| 572 | .setDocstring("Performs an element-wise bitwise NOT of the Input " |
| 573 | "operand."); |
| 574 | |
| 575 | BB.newNode("Neg") |
| 576 | .addInput("Input") |
| 577 | .addResultFromCtorArg() |
| 578 | .dataParallel() |
| 579 | .setDocstring("Performs an element-wise negation (sign flip) of the " |
| 580 | "Input operand."); |
| 581 | |
| 582 | BB.newNode("Abs") |
| 583 | .addInput("Input") |
| 584 | .addResultFromCtorArg() |
| 585 | .dataParallel() |
| 586 | .setDocstring("Performs an element-wise ABS(x) of the Input operand."); |
| 587 | |
| 588 | BB.newNode("Floor") |
| 589 | .addInput("Input") |
| 590 | .addResultFromCtorArg() |
| 591 | .dataParallel() |
| 592 | .setDocstring("Performs an element-wise FLOOR(x) of the Input operand."); |
| 593 | |
| 594 | BB.newNode("Sign") |
| 595 | .addInput("Input") |
| 596 | .addResultFromCtorArg() |
| 597 | .dataParallel() |
| 598 | .setDocstring("Performs an element-wise Sign(x) of the Input operand"); |
| 599 | |
| 600 | BB.newNode("Ceil") |
| 601 | .addInput("Input") |
| 602 | .addResultFromCtorArg() |
| 603 | .dataParallel() |
| 604 | .setDocstring("Performs an element-wise CEIL(x) of the Input operand."); |
| 605 | |
| 606 | BB.newNode("Round") |
| 607 | .addInput("Input") |
| 608 | .addResultFromCtorArg() |
| 609 | .dataParallel() |
| 610 | .setDocstring("Performs an element-wise ROUND(x) of the Input operand."); |
| 611 | |
| 612 | BB.newNode("Truncate") |
| 613 | .addInput("Input") |
| 614 | .addResultFromCtorArg() |
| 615 | .dataParallel() |
| 616 | .setDocstring( |
| 617 | "Performs an element-wise TRUNCATE(x) of the Input operand."); |
| 618 | |
| 619 | BB.newNode("Sqrt") |
| 620 | .addInput("Input") |
| 621 | .addResultFromCtorArg() |
| 622 | .dataParallel() |
| 623 | .setDocstring("Performs an element-wise SQRT(x) of the Input operand."); |
| 624 | |
| 625 | BB.newNode("Rsqrt") |
| 626 | .addInput("Input") |
| 627 | .addResultFromCtorArg() |
| 628 | .dataParallel() |
| 629 | .setDocstring("Performs an element-wise RSQRT(x) = 1 / SQRT(x) of the " |
| 630 | "Input operand."); |
| 631 | |
| 632 | BB.newNode("Reciprocal") |
| 633 | .addInput("Input") |
| 634 | .addResultFromCtorArg() |
| 635 | .dataParallel() |
| 636 | .setDocstring("Performs an element-wise RECIPROCAL(x) = 1 / x of the " |
| 637 | "Input operand."); |
| 638 | |
| 639 | BB.newNode("Sin") |
| 640 | .addInput("Input") |
| 641 | .addResultFromCtorArg() |
| 642 | .dataParallel() |
| 643 | .setDocstring("Performs an element-wise SIN(x) of the Input operand."); |
| 644 | |
| 645 | BB.newNode("Cos") |
| 646 | .addInput("Input") |
| 647 | .addResultFromCtorArg() |
| 648 | .dataParallel() |
| 649 | .setDocstring("Performs an element-wise COS(x) of the Input operand."); |
| 650 | |
| 651 | // clang-format off |
| 652 | BB.newNode("Log") |
| 653 | .addInput("Input") |
| 654 | .addResultFromCtorArg() |
| 655 | .dataParallel() |
| 656 | .setDocstring("Performs element-wise natural log to the Input."); |
| 657 | |
| 658 | BB.newNode("Acos") |
| 659 | .addInput("Input") |
| 660 | .addResultFromCtorArg() |
| 661 | .dataParallel() |
| 662 | .setDocstring("Performs an element-wise Arccosine(x) of the Input operand."); |
| 663 | |
| 664 | BB.newNode("Asin") |
| 665 | .addInput("Input") |
| 666 | .addResultFromCtorArg() |
| 667 | .dataParallel() |
| 668 | .setDocstring("Performs an element-wise Arcsine(x) of the Input operand."); |
| 669 | |
| 670 | BB.newNode("Atan") |
| 671 | .addInput("Input") |
| 672 | .addResultFromCtorArg() |
| 673 | .dataParallel() |
| 674 | .setDocstring("Performs an element-wise Arctan(x) of the Input operand."); |
| 675 | |
| 676 | BB.newNode("Erf") |
| 677 | .addInput("Input") |
| 678 | .addResultFromCtorArg() |
| 679 | .dataParallel() |
| 680 | .setDocstring("Performs an element-wise Erf(x) of the Input operand."); |
| 681 | |
| 682 | BB.newNode("Exp") |
| 683 | .addInput("Input") |
| 684 | .addResultFromCtorArg() |
| 685 | .dataParallel() |
| 686 | .setDocstring("Performs element-wise exponential to the Input."); |
| 687 | // clang-format on |
| 688 | |
| 689 | BB.newNode("Logit") |
| 690 | .addInput("Input") |
| 691 | .addMember(MemberType::Float, "Epsilon") |
| 692 | .addResultFromCtorArg() |
| 693 | .dataParallel() |
| 694 | .setDocstring("Computes elementwise: result = log(input / (1 - input))."); |
| 695 | |
| 696 | BB.newNode("NonZero") |
| 697 | .addInput("Cond") |
| 698 | .addResultFromCtorArg() |
| 699 | .dataParallel() |
| 700 | .setDocstring("Selects indices of the true elements in Cond"); |
| 701 | |
| 702 | BB.newNode("Select") |
| 703 | .addInput("Cond") |
| 704 | .addInput("LHS") |
| 705 | .addInput("RHS") |
| 706 | .addResultFromCtorArg() |
| 707 | .dataParallel() |
| 708 | .setDocstring("Selects between values on the LHS or RHS, depending on " |
| 709 | "the value of Cond. Cond is generated by the compare " |
| 710 | "instruction, and is target- and type-specific."); |
| 711 | |
| 712 | BB.newNode("BatchedAdd") |
| 713 | .addInput("Batch") |
| 714 | .addInput("Slice") |
| 715 | .addResultFromCtorArg() |
| 716 | .setDocstring( |
| 717 | "Adds the 'Slice' operand to each one of the slices in the batch."); |
| 718 | |
| 719 | BB.newNode("BatchedMul") |
| 720 | .addInput("Batch") |
| 721 | .addInput("Slice") |
| 722 | .addResultFromCtorArg() |
| 723 | .setDocstring("Multiplies the 'Slice' operand to each one of the slices " |
| 724 | "in the batch."); |
| 725 | |
| 726 | BB.newNode("MatMul") |
| 727 | .addInput("LHS") |
| 728 | .addInput("RHS") |
| 729 | .addResultFromCtorArg() |
| 730 | .setDocstring("Performs matrix multiplication between the LHS and RHS." |
| 731 | "Example: (A, Z) x (Z, B) => (A, B)"); |
| 732 | |
| 733 | BB.newNode("BatchMatMul") |
| 734 | .addInput("LHS") |
| 735 | .addInput("RHS") |
| 736 | .addResultFromCtorArg() |
| 737 | .setDocstring("Performs batch matrix multiplication between the LHS and " |
| 738 | "RHS. The operands are a stack of two dimensional " |
| 739 | "matrices. Example: (N, A, Z) x (N, Z, B) => (N, A, B)"); |
| 740 | |
| 741 | BB.newNode("BatchedReduceAdd") |
| 742 | .addInput("Batch") |
| 743 | .addMember(MemberType::Unsigned, "Axis") |
| 744 | .addResultFromCtorArg() |
| 745 | .setDocstring("Accumulates all of the layers in the batch and produce a " |
| 746 | "tensor that has the same dimensions as the input tensor " |
| 747 | "without the first dimension."); |
| 748 | |
| 749 | BB.newNode("BatchedReduceSumSquare") |
| 750 | .addInput("Batch") |
| 751 | .addMember(MemberType::Unsigned, "Axis") |
| 752 | .addResultFromCtorArg() |
| 753 | .setDocstring( |
| 754 | "Accumulates squares of all of the layers in the batch and produce a " |
| 755 | "tensor that has the same dimensions as the input tensor " |
| 756 | "without the first dimension."); |
| 757 | |
| 758 | BB.newNode("BatchedReduceMean") |
| 759 | .addInput("Batch") |
| 760 | .addMember(MemberType::VectorUnsigned, "Axes") |
| 761 | .addResultFromCtorArg() |
| 762 | .setDocstring("Performs Average Mean operation on the Input given " |
| 763 | "Axes."); |
| 764 | |
| 765 | BB.newNode("BatchedReduceMin") |
| 766 | .addInput("Batch") |
| 767 | .addMember(MemberType::VectorUnsigned, "Axes") |
| 768 | .addResultFromCtorArg() |
| 769 | .setDocstring("Performs Reduce Min operation on the Input given " |
| 770 | "Axes."); |
| 771 | |
| 772 | BB.newNode("BatchedReduceMax") |
| 773 | .addInput("Batch") |
| 774 | .addMember(MemberType::VectorUnsigned, "Axes") |
| 775 | .addResultFromCtorArg() |
| 776 | .setDocstring("Performs Reduce Max operation on the Input given " |
| 777 | "Axes."); |
| 778 | |
| 779 | BB.newNode("BatchedReduceProd") |
| 780 | .addInput("Batch") |
| 781 | .addMember(MemberType::Unsigned, "Axis") |
| 782 | .addResultFromCtorArg() |
| 783 | .setDocstring("Accumulates the product all of the layers in the batch " |
| 784 | " and produce a tensor that has the same dimensions as " |
| 785 | " the input tensor without the first dimension."); |
| 786 | |
| 787 | BB.newNode("ChannelShuffle") |
| 788 | .addInput("Input") |
| 789 | .addMember(MemberType::Unsigned, "Group") |
| 790 | .addMember(MemberType::Unsigned, "Kernel") |
| 791 | .addResultFromCtorArg() |
| 792 | .setDocstring("Performs Channel shuffle."); |
| 793 | |
| 794 | BB.newNode("CumSum") |
| 795 | .addInput("Input") |
| 796 | .addMember(MemberType::Int64, "Dim") |
| 797 | .addMember(MemberType::Unsigned, "Exclusive") |
| 798 | .addMember(MemberType::Unsigned, "Reverse") |
| 799 | .addResultFromCtorArg() |
| 800 | .dataParallel() |
| 801 | .setDocstring("Performs a Cumulative Sum operation over a 1D vector with " |
| 802 | "flags for working in exclusive mode and in reverse. In " |
| 803 | "each case the output size is the same as in input size." |
| 804 | "e.g (default) [1, 2, 3, 4] -> [1, 3, 6, 10]. " |
| 805 | "(exclusive) [1, 2, 3, 4] -> [0, 1, 3, 6]. " |
| 806 | "(reverse) [1, 2, 3, 4] -> [10, 9, 7, 4]. "); |
| 807 | |
| 808 | BB.newNode("LengthsSum") |
| 809 | .addInput("Data") |
| 810 | .addInput("Lengths") |
| 811 | .addResultFromCtorArg() |
| 812 | .setDocstring("Sums slices of the outermost dimension of Data in groups " |
| 813 | "defined by Lengths. The first Lengths[0] slices are " |
| 814 | "added together and stored in Result[0], the subsequent " |
| 815 | "Lengths[1] slices are added together and stored in " |
| 816 | "Result[1], etc."); |
| 817 | |
| 818 | BB.newNode("SparseLengthsSum") |
| 819 | .addInput("Data") |
| 820 | .addInput("Indices") |
| 821 | .addInput("Lengths") |
| 822 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 823 | .addMember(MemberType::Float, "AvgLength") |
| 824 | .addResultFromCtorArg() |
| 825 | .addGradient() |
| 826 | .setDocstring("Gathers slices of the outer-most dimension of Data " |
| 827 | "indexed by Indices vector, and then accumulates them into " |
| 828 | "len(Lengths) entries: first Lengths[0] slices are " |
| 829 | "aggregated to Result[0], next Lengths[1] slices are " |
| 830 | "aggregated to Result[1], etc. I.e. sum(Lengths) must be " |
| 831 | "equal to len(Indices)."); |
| 832 | |
| 833 | BB.newNode("SparseLengthsWeightedSum") |
| 834 | .addInput("Data") |
| 835 | .addInput("Weights") |
| 836 | .addInput("Indices") |
| 837 | .addInput("Lengths") |
| 838 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 839 | .addMember(MemberType::Float, "AvgLength") |
| 840 | .addResultFromCtorArg() |
| 841 | .addGradient() |
| 842 | .setDocstring("Gathers slices of the outer-most dimension of Data " |
| 843 | "indexed by Indices vector, and then accumulates them into " |
| 844 | "len(Lengths) entries: first Lengths[0] slices are " |
| 845 | "aggregated to Result[0], next Lengths[1] slices are " |
| 846 | "aggregated to Result[1], etc. I.e. sum(Lengths) must be " |
| 847 | "equal to len(Indices). Before doing aggregation, each " |
| 848 | "individual slice is scaled by its weight: Result[0] = " |
| 849 | "Weights[0] * Slice(0) + Weights[1] * Slice(1) + ... " |
| 850 | "It implies that len(Weights) == len(Indices)."); |
| 851 | |
| 852 | BB.newNode("Embedding") |
| 853 | .addInput("Weights") |
| 854 | .addInput("Indices") |
| 855 | .addMember(MemberType::Int64, "PadIdx") |
| 856 | .addMember(MemberType::Boolean, "Scale") |
| 857 | .addMember(MemberType::Boolean, "Sparse") |
| 858 | .addResultFromCtorArg() |
| 859 | .setDocstring("Gathers slices of the outer-most dimension of Weights " |
| 860 | "indexed by Indices tensor."); |
| 861 | |
| 862 | BB.newNode("EmbeddingBag") |
| 863 | .addInput("Data") |
| 864 | .addInput("Weights") |
| 865 | .addInput("Indices") |
| 866 | .addInput("Offsets") |
| 867 | .addMember(MemberType::Boolean, "HasEndOffset") |
| 868 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 869 | .addMember(MemberType::Float, "AvgLength") |
| 870 | .addResultFromCtorArg() |
| 871 | .setDocstring( |
| 872 | "Gathers slices of the outer-most dimension of Data " |
| 873 | "indexed by Indices vector, and then accumulates them into " |
| 874 | "len(Offsets) entries: first slice between Offsets[0] and Offsets[1] " |
| 875 | "(or total length if there's only one elem in Offsets) are " |
| 876 | "aggregated to Result[0], etc. I.e. largest offset must be " |
| 877 | "less than or equal to len(Indices). Before doing aggregation, each " |
| 878 | "individual slice is scaled by its weight: Result[0] = " |
| 879 | "Weights[0] * Slice(0) + Weights[1] * Slice(1) + ... " |
| 880 | "It implies that len(Weights) == len(Indices)."); |
| 881 | |
| 882 | BB.newNode("EmbeddingBagByteRowwiseOffsets") |
| 883 | .addInput("Data") |
| 884 | .addInput("Weights") |
| 885 | .addInput("Indices") |
| 886 | .addInput("Offsets") |
| 887 | .addMember(MemberType::Boolean, "UseFP16Accumulation", |
| 888 | /* addSetter */ true) |
| 889 | .addMember(MemberType::Boolean, "HasEndOffset") |
| 890 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 891 | .addMember(MemberType::Float, "AvgLength") |
| 892 | .addResultFromCtorArg() |
| 893 | .setDocstring("Same as FusedRowwiseQuantizedSparseLengthsWeightedSum but " |
| 894 | "using offsets instead of lengths."); |
| 895 | |
| 896 | BB.newNode("RowwiseQuantizedSparseLengthsWeightedSum") |
| 897 | .addInput("Data") |
| 898 | .addInput("Scales") |
| 899 | .addInput("Offsets") |
| 900 | .addInput("Weights") |
| 901 | .addInput("Indices") |
| 902 | .addInput("Lengths") |
| 903 | .addMember(MemberType::Boolean, "UseFP16Accumulation", |
| 904 | /* addSetter */ true) |
| 905 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 906 | .addMember(MemberType::Float, "AvgLength") |
| 907 | .addResultFromCtorArg() |
| 908 | .setDocstring("Gathers slices of the outer-most dimension of Data " |
| 909 | "indexed by Indices vector, and then accumulates them into " |
| 910 | "len(Lengths) entries: first Lengths[0] slices are " |
| 911 | "aggregated to Result[0], next Lengths[1] slices are " |
| 912 | "aggregated to Result[1], etc. I.e. sum(Lengths) must be " |
| 913 | "equal to len(Indices). Before doing aggregation, each " |
| 914 | "individual slice is scaled by its weight: Result[0] = " |
| 915 | "Weights[0] * Slice(0) + Weights[1] * Slice(1) + ... " |
| 916 | "It implies that len(Weights) == len(Indices). The input " |
| 917 | "data is rowwise-quantized, where the Scales and Offsets " |
| 918 | "are 1D tensors of length equal to the first dim of Data."); |
| 919 | |
| 920 | BB.newNode("FusedRowwiseQuantizedSparseLengthsWeightedSum") |
| 921 | .addInput("Data") |
| 922 | .addInput("Weights") |
| 923 | .addInput("Indices") |
| 924 | .addInput("Lengths") |
| 925 | .addMember(MemberType::Boolean, "UseFP16Accumulation", |
| 926 | /* addSetter */ true) |
| 927 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 928 | .addMember(MemberType::Float, "AvgLength") |
| 929 | .addResultFromCtorArg() |
| 930 | .setDocstring("Gathers slices of the outer-most dimension of Data " |
| 931 | "indexed by Indices vector, and then accumulates them into " |
| 932 | "len(Lengths) entries: first Lengths[0] slices are " |
| 933 | "aggregated to Result[0], next Lengths[1] slices are " |
| 934 | "aggregated to Result[1], etc. I.e. sum(Lengths) must be " |
| 935 | "equal to len(Indices). Before doing aggregation, each " |
| 936 | "individual slice is scaled by its weight: Result[0] = " |
| 937 | "Weights[0] * Slice(0) + Weights[1] * Slice(1) + ... " |
| 938 | "It implies that len(Weights) == len(Indices). The input " |
| 939 | "data is fused rowwise-quantized, where the Scales and " |
| 940 | "Offsets are appended to the end of each row. Thus, Data " |
| 941 | "must be a two-dimensional tensor."); |
| 942 | |
| 943 | BB.newNode("FusedRowwiseQuantizedSparseLengthsSum") |
| 944 | .addInput("Data") |
| 945 | .addInput("Indices") |
| 946 | .addInput("Lengths") |
| 947 | .addMember(MemberType::Boolean, "UseFP16Accumulation", |
| 948 | /* addSetter */ true) |
| 949 | .addMember(MEMBER_TYPE_INFO(glow::LengthsMode), "LengthsMode") |
| 950 | .addMember(MemberType::Float, "AvgLength") |
| 951 | .addResultFromCtorArg() |
| 952 | .setDocstring("Gathers slices of the outer-most dimension of Data " |
| 953 | "indexed by Indices vector, and then accumulates them into " |
| 954 | "len(Lengths) entries: first Lengths[0] slices are " |
| 955 | "aggregated to Result[0], next Lengths[1] slices are " |
| 956 | "aggregated to Result[1], etc. I.e. sum(Lengths) must be " |
| 957 | "equal to len(Indices). The input " |
| 958 | "data is fused rowwise-quantized, where the Scales and " |
| 959 | "Offsets are appended to the end of each row. Thus, Data " |
| 960 | "must be a two-dimensional tensor."); |
| 961 | |
| 962 | BB.newNode("LengthsToRanges") |
| 963 | .addInput("Lengths") |
| 964 | .addResultFromCtorArg() |
| 965 | .setDocstring("Given a vector of segment lengths, calculates offsets of " |
| 966 | "each segment and packs them next to the lengths. For the " |
| 967 | "input vector of length N the output is a Nx2 matrix with " |
| 968 | "(offset, lengths) packaged for each segment."); |
| 969 | |
| 970 | BB.newNode("LengthsRangeFill") |
| 971 | .addInput("Lengths") |
| 972 | .addResultFromCtorArg() |
| 973 | .setDocstring( |
| 974 | "Converts an input Lengths 1D vector into a range sequence."); |
| 975 | |
| 976 | BB.newNode("BatchSparseToDense") |
| 977 | .addInput("Lengths") |
| 978 | .addInput("Indices") |
| 979 | .addInput("Values") |
| 980 | .addMember(MemberType::Float, "DefaultValue") |
| 981 | .addMember(MemberType::Unsigned, "DenseLastDim") |
| 982 | .addResultFromCtorArg() |
| 983 | .setDocstring( |
| 984 | "Converts the sparse representation specified by " |
| 985 | "(Lengths, Indices, Values) into a dense one. In the dense " |
| 986 | "representation, elements of the lengths vector represent the number " |
| 987 | "of indices in the corresponding batch, where each batch " |
| 988 | "contains each value from Values at the " |
| 989 | "corresponding index specified in Indices, and is filled with " |
| 990 | "DefaultValue otherwise. Within each batch, Indices shouldn't " |
| 991 | "contain duplicate indices."); |
| 992 | |
| 993 | BB.newNode("FillExamplesWithIndicator") |
| 994 | .addInput("Data") |
| 995 | .addInput("Indicator") |
| 996 | .addResultFromCtorArg() |
| 997 | .setDocstring("Inserts zeros into data along axis=0 for indices where " |
| 998 | "indicator is zero."); |
| 999 | |
| 1000 | BB.newNode("SparseToDenseMask") |
| 1001 | .addInput("Indices") |
| 1002 | .addInput("Values") |
| 1003 | .addInput("DefaultValue") |
| 1004 | .addInput("Lengths") |
| 1005 | .addMember(MemberType::VectorDimT, "Mask") |
| 1006 | .addResultFromCtorArg() |
| 1007 | .setDocstring( |
| 1008 | "Converts the sparse representation specified by the pair " |
| 1009 | "(Indices, Values) into a dense one, where compacted tensor only " |
| 1010 | "contains IDs from given Mask. Indices cannot contain duplicate " |
| 1011 | "values. Lengths is used to distinguish elements from different " |
| 1012 | "examples of one batch. That is, first Lengths[0] index-value pairs " |
| 1013 | "belong to batch's example 0, next Lengths[1] pairs belong to " |
| 1014 | "example 1, and so on."); |
| 1015 | |
| 1016 | // clang-format off |
| 1017 | BB.newNode("IsNaN") |
| 1018 | .addInput("Input") |
| 1019 | .addResultFromCtorArg() |
| 1020 | .dataParallel() |
| 1021 | .setDocstring("Determines whether each element of the Input is NaN and " |
| 1022 | "generates a mask that can be consumed by a Select node."); |
| 1023 | // clang-format on |
| 1024 | |
| 1025 | BB.newNode("ReplaceNaN") |
| 1026 | .addInput("Input") |
| 1027 | .addMember(MemberType::Float, "Value") |
| 1028 | .addResultFromCtorArg() |
| 1029 | .setDocstring("Replaces NaNs found in Input with Value."); |
| 1030 | |
| 1031 | BB.newNode("Modulo") |
| 1032 | .addInput("Input") |
| 1033 | .addMember(MemberType::Int64, "Divisor") |
| 1034 | .addMember(MemberType::Boolean, "SignFollowDivisor") |
| 1035 | .addResultFromCtorArg() |
| 1036 | .dataParallel() |
| 1037 | .setDocstring("Performs elementwise modulo operation on the input where " |
| 1038 | "each element in the output is the corresponding element " |
| 1039 | "in the input data modulo Divisor."); |
| 1040 | |
| 1041 | BB.newNode("BatchedPairwiseDotProduct") |
| 1042 | .addMember(MemberType::VectorNodeValue, "Inputs") |
| 1043 | .addResultFromCtorArg() |
| 1044 | .setDocstring( |
| 1045 | "Performs batched pairwise dot products of the input vectors"); |
| 1046 | |
| 1047 | BB.newNode("BatchedPairwiseDotProductGrad") |
| 1048 | .addInput("OutputGrad") |
| 1049 | .hasExtraResults() |
| 1050 | .addMember(MemberType::VectorNodeValue, "OriginalInputs") |
| 1051 | .setDocstring( |
| 1052 | "Performs the gradient operation for BatchedPairwiseDotProduct"); |
| 1053 | |
| 1054 | BB.newNode("BatchedUnaryEmbeddingsBags") |
| 1055 | .addInput("Weights") |
| 1056 | .addInput("TableOffsets") |
| 1057 | .addInput("Offsets") |
| 1058 | .addInput("Indices") |
| 1059 | .addResultFromCtorArg() |
| 1060 | .setDocstring("Sum weight embeddings according to offsets and indices"); |
| 1061 | |
| 1062 | BB.newNode("IntNBitSplitEmbeddingBags") |
| 1063 | .addInput("DevWeights") |
| 1064 | .addInput("UvmWeights") |
| 1065 | .addInput("WeightsPlacements") |
| 1066 | .addInput("WeightsOffsets") |
| 1067 | .addInput("WeightsTys") |
| 1068 | .addInput("DimOffsets") |
| 1069 | .addInput("Indices") |
| 1070 | .addInput("Offsets") |
| 1071 | .addMember(MemberType::Int64, "TotalDims") |
| 1072 | .addMember(MEMBER_TYPE_INFO(glow::SplitEmbeddingPoolingMode), |
| 1073 | "PoolingMode") |
| 1074 | .addMember(MEMBER_TYPE_INFO(glow::SplitEmbeddingSparseType), |
| 1075 | "OutputDType") |
| 1076 | .addResultFromCtorArg() |
| 1077 | .setDocstring("Table based batched embeddingbags with quantization " |
| 1078 | "support. Experimental only and subject to change."); |
| 1079 | |
| 1080 | BB.newNode("IntNBitSplitEmbeddingWeightedBags") |
| 1081 | .addInput("DevWeights") |
| 1082 | .addInput("UvmWeights") |
| 1083 | .addInput("WeightsPlacements") |
| 1084 | .addInput("WeightsOffsets") |
| 1085 | .addInput("WeightsTys") |
| 1086 | .addInput("DimOffsets") |
| 1087 | .addInput("Indices") |
| 1088 | .addInput("Offsets") |
| 1089 | .addInput("IndiceWeight") |
| 1090 | .addMember(MemberType::Int64, "TotalDims") |
| 1091 | .addMember(MEMBER_TYPE_INFO(glow::SplitEmbeddingPoolingMode), |
| 1092 | "PoolingMode") |
| 1093 | .addMember(MEMBER_TYPE_INFO(glow::SplitEmbeddingSparseType), |
| 1094 | "OutputDType") |
| 1095 | .addResultFromCtorArg() |
| 1096 | .setDocstring( |
| 1097 | "Table based batched embeddingbags with quantization support and " |
| 1098 | "indice weights. Experimental only and subject to change."); |
| 1099 | |
| 1100 | //===--------------------------------------------------------------------===// |
| 1101 | // Fillers |
| 1102 | //===--------------------------------------------------------------------===// |
| 1103 | |
| 1104 | BB.newNode("GaussianFill") |
| 1105 | .addInput("Input") |
| 1106 | .addMember(MemberType::Float, "Mean") |
| 1107 | .addMember(MemberType::Float, "Scale") |
| 1108 | .addMember(MemberType::Float, "Seed") |
| 1109 | .addResultFromCtorArg() |
| 1110 | .setDocstring("Fills an output tensor with samples drawn from a normal " |
| 1111 | "distribution specified by the mean and standard deviation " |
| 1112 | "arguments. The output tensor shape is determined by the " |
| 1113 | "input shape if provided, and shape otherwise"); |
| 1114 | |
| 1115 | //===--------------------------------------------------------------------===// |
| 1116 | // Non-linearities |
| 1117 | //===--------------------------------------------------------------------===// |
| 1118 | |
| 1119 | BB.newNode("Relu") |
| 1120 | .addInput("Input") |
| 1121 | .addResultFromCtorArg() |
| 1122 | .dataParallel() |
| 1123 | .addGradient() |
| 1124 | .setDocstring( |
| 1125 | "Applies ReLU, max(0, x), to each element in the Input tensor."); |
| 1126 | |
| 1127 | BB.newNode("HardSwish") |
| 1128 | .addInput("Input") |
| 1129 | .addResultFromCtorArg() |
| 1130 | .dataParallel() |
| 1131 | .setDocstring("Applies HardSwish to each element in the Input tensor."); |
| 1132 | |
| 1133 | BB.newNode("Gelu") |
| 1134 | .addInput("Input") |
| 1135 | .addResultFromCtorArg() |
| 1136 | .dataParallel() |
| 1137 | .setDocstring("Applies GeLU, to each element in the Input tensor."); |
| 1138 | |
| 1139 | BB.newNode("Clip") |
| 1140 | .addInput("Input") |
| 1141 | .addMember(MemberType::Float, "Min") |
| 1142 | .addMember(MemberType::Float, "Max") |
| 1143 | .addResultFromCtorArg() |
| 1144 | .dataParallel() |
| 1145 | .setDocstring("Clip range of inputs to lie in [Min, Max]."); |
| 1146 | |
| 1147 | BB.newNode("PRelu") |
| 1148 | .addInput("Input") |
| 1149 | .addInput("Slope") |
| 1150 | .addResultFromCtorArg() |
| 1151 | .dataParallel() |
| 1152 | .setDocstring("Applies PReLU, slope * min(0, x) + max(0, x), to each " |
| 1153 | "element in the Input tensor."); |
| 1154 | |
| 1155 | BB.newNode("Sigmoid") |
| 1156 | .addInput("Input") |
| 1157 | .addResultFromCtorArg() |
| 1158 | .dataParallel() |
| 1159 | .addGradient() |
| 1160 | .setDocstring("Applies Sigmoid, 1 / (1 + exp(-x)), to each element in " |
| 1161 | "the Input tensor."); |
| 1162 | |
| 1163 | BB.newNode("Swish") |
| 1164 | .addInput("Input") |
| 1165 | .addResultFromCtorArg() |
| 1166 | .dataParallel() |
| 1167 | .setDocstring("Applies Swish, X * Sigmoid(X), to each element in " |
| 1168 | "the Input tensor."); |
| 1169 | |
| 1170 | BB.newNode("Tanh") |
| 1171 | .addInput("Input") |
| 1172 | .addResultFromCtorArg() |
| 1173 | .dataParallel() |
| 1174 | .addGradient() |
| 1175 | .setDocstring("Applies hyperbolic tangent to each element in the Input " |
| 1176 | "tensor."); |
| 1177 | |
| 1178 | BB.newNode("LeakyRelu") |
| 1179 | .addInput("Input") |
| 1180 | .addMember(MemberType::Float, "Alpha") |
| 1181 | .addResultFromCtorArg() |
| 1182 | .dataParallel() |
| 1183 | .setDocstring( |
| 1184 | "Applies LeakyReLU = x for positive x and alpha * x for negative x " |
| 1185 | "to each element in the Input tensor."); |
| 1186 | |
| 1187 | BB.newNode("SoftPlus") |
| 1188 | .addInput("Input") |
| 1189 | .addResultFromCtorArg() |
| 1190 | .dataParallel() |
| 1191 | .setDocstring("Performs SoftPlus, ln(exp(x) + 1), to each element in the " |
| 1192 | "Input tensor."); |
| 1193 | |
| 1194 | //===--------------------------------------------------------------------===// |
| 1195 | // Shape transformations |
| 1196 | //===--------------------------------------------------------------------===// |
| 1197 | |
| 1198 | BB.newNode("Reshape") |
| 1199 | .addInput("Input") |
| 1200 | .addMember(MemberType::VectorDimT, "Dims") |
| 1201 | .addMember(MemberType::String, "Layout") |
| 1202 | .addResultFromCtorArg() |
| 1203 | .setDocstring("Reshape the Input tensor to shape Dims."); |
| 1204 | |
| 1205 | BB.newNode("Transpose") |
| 1206 | .addInput("Input") |
| 1207 | .addMember(MemberType::VectorUnsigned, "Shuffle") |
| 1208 | .addMember(MemberType::String, "Layout") |
| 1209 | .addResultFromCtorArg() |
| 1210 | .setDocstring("Transpose the Input tensor based on the vector Shuffle, " |
| 1211 | "which assigns a new axis for each dimension in Input."); |
| 1212 | |
| 1213 | BB.newNode("Concat") |
| 1214 | .addMember(MemberType::VectorNodeValue, "Inputs") |
| 1215 | .addMember(MemberType::Unsigned, "Dim") |
| 1216 | .addResultFromCtorArg() |
| 1217 | .setDocstring("The concat operator adds two tensors together.\nThe " |
| 1218 | "parameter 'dim' specifies the dimension to use when " |
| 1219 | "joining the tensors."); |
| 1220 | |
| 1221 | BB.newNode("Slice") |
| 1222 | .addInput("Input") |
| 1223 | .addMember(MemberType::VectorDimT, "Start") |
| 1224 | .addResultFromCtorArg() |
| 1225 | .setDocstring("Produces a slice of the Input tensor. The Start vector " |
| 1226 | "defines the starting indices for each dimension from " |
| 1227 | "which the slice should be taken. The end index for each " |
| 1228 | "dimension is determined from the input type's shape."); |
| 1229 | |
| 1230 | BB.newNode("InsertTensor") |
| 1231 | .addInput("Big") |
| 1232 | .addInput("Small") |
| 1233 | .addMember(MemberType::VectorDimT, "Start") |
| 1234 | .addMember(MemberType::Unsigned, "Count") |
| 1235 | .addMember(MemberType::Unsigned, "Axis") |
| 1236 | .addResult("Big.getType()") |
| 1237 | .setDocstring("Insert tensor Small into tensor Big given indices Start. " |
| 1238 | "Small is inserted Count times along Axis. The resulting " |
| 1239 | "Tensor will have the same type as the input Big tensor."); |
| 1240 | |
| 1241 | // TODO: Rename "BatchDims" member to "Axis". This was attempted in #5565 but |
| 1242 | // some internal FB tests failed. The member needs to be renamed because that |
| 1243 | // is the true meaning of the member and that is what the implementation does |
| 1244 | // according to both Caffe2, ONNX and TFLite operator definitions. |
| 1245 | // https://github.com/onnx/onnx/blob/master/docs/Operators.md#gather |
| 1246 | // https://www.tensorflow.org/mlir/tfl_ops#tflgather_tflgatherop |
| 1247 | BB.newNode("Gather") |
| 1248 | .addInput("Data") |
| 1249 | .addInput("Indices") |
| 1250 | .addMember(MemberType::Unsigned, "BatchDims") |
| 1251 | .addResultFromCtorArg() |
| 1252 | .setDocstring("Gathers entries of the outer-most dimension of Data " |
| 1253 | "indexed by Indices, and concatenates them. Output tensor " |
| 1254 | "will have dimensions: {I_0, I_1, ... I_n, D_1, D_2, ... " |
| 1255 | "D_m}, where D_i and I_j denote Data and Indices " |
| 1256 | "dimensions respectively. If axis is not zero, the " |
| 1257 | "gather operator will treat the first axis as the " |
| 1258 | "batch and will concat the result of the gather operation " |
| 1259 | "on each sample in the batch."); |
| 1260 | |
| 1261 | BB.newNode("GatherND") |
| 1262 | .addInput("Data") |
| 1263 | .addInput("Indices") |
| 1264 | .addMember(MemberType::Unsigned, "BatchDims") |
| 1265 | .addResultFromCtorArg() |
| 1266 | .setDocstring( |
| 1267 | "Given Data tensor of rank r >= 1, Indices tensor of rank q >= 1 " |
| 1268 | "This operator gathers slices of Data into " |
| 1269 | "an output tensor of rank q + r - Indices_shape[-1] - 1 ."); |
| 1270 | |
| 1271 | BB.newNode("GatherElements") |
| 1272 | .addInput("Data") |
| 1273 | .addInput("Indices") |
| 1274 | .addMember(MemberType::Unsigned, "Dim") |
| 1275 | .addResultFromCtorArg() |
| 1276 | .setDocstring( |
| 1277 | "GatherElements takes inputs data and indices of the same rank r " |
| 1278 | ">= 1 and an attribute axis specified by dim. It is an indexing" |
| 1279 | "operation that produces its output by indexing into the " |
| 1280 | "input data tensor at index positions determined by elements of the " |
| 1281 | "indices tensor. Its output shape is the same as the shape of " |
| 1282 | "indices and consists of one value (gathered from the data) for each " |
| 1283 | "element in indices."); |
| 1284 | |
| 1285 | BB.newNode("GatherRanges") |
| 1286 | .addInput("Data") |
| 1287 | .addInput("Ranges") |
| 1288 | .addResultFromCtorArg("Output") |
| 1289 | .addResultFromCtorArg("Lengths") |
| 1290 | .setDocstring("Gathers entries of Data into Output in groups specified " |
| 1291 | "by the elements of Ranges. Each element of Ranges " |
| 1292 | "contains a list of pairs of indices of the form (index, " |
| 1293 | "length) which specify which entries of data to gather. " |
| 1294 | "The ordering of elements in Ranges and of pairs within an " |
| 1295 | "element is preserved in Output. Lengths contains the " |
| 1296 | "lengths of the ranges gathered by each list of pairs in " |
| 1297 | "Ranges."); |
| 1298 | |
| 1299 | BB.newNode("ScatterData") |
| 1300 | .addInput("Data") |
| 1301 | .addInput("Indices") |
| 1302 | .addInput("Slices") |
| 1303 | .addMember(MemberType::Boolean, "Cumulative") |
| 1304 | .addResult("Data.getType()") |
| 1305 | .setDocstring( |
| 1306 | "Copies each slice from Slices into Data at the " |
| 1307 | "corresponding index in Indices. For example, given input " |
| 1308 | "Data {{1,2},{3,4},{5,6}}, Slices {{-3,-4}}, and Indices " |
| 1309 | "{{1}}, the result is {{1,2},{-3,-4},{5,6}}. It also supports " |
| 1310 | "multi-dimensional indices. For example, given input Data " |
| 1311 | "{{1,2},{3,4},{5,6}}, Slices {-3,-4}, and Indices {{1,0},{1,1}} also " |
| 1312 | "produces {{1,2},{-3,-4},{5,6}}. If Cumulative is true, the node " |
| 1313 | "adds values from Slices to Data instead of copying. For example, " |
| 1314 | "given input Data {{1,2},{3,4},{5,6}}, Slices {{-3,-4}}, and Indices " |
| 1315 | "{1}, the result is {{1,2},{0,0},{5,6}}. If an index is specified " |
| 1316 | "several times, its updates will be added several times as well."); |
| 1317 | |
| 1318 | BB.newNode("Tile") |
| 1319 | .addInput("Input") |
| 1320 | .addMember(MemberType::Unsigned, "Count") |
| 1321 | .addMember(MemberType::Unsigned, "Axis") |
| 1322 | .addResultFromCtorArg() |
| 1323 | .setDocstring("Tile an Input tensor Count times along Axis."); |
| 1324 | |
| 1325 | BB.newNode("BatchOneHot") |
| 1326 | .addInput("Data") |
| 1327 | .addInput("Lengths") |
| 1328 | .addInput("Values") |
| 1329 | .addResultFromCtorArg() |
| 1330 | .setDocstring("Expands each row of the Data to a row of zeros and ones, " |
| 1331 | "according to One Hot Encoding. i-th element of Result's " |
| 1332 | "row is one iff Values[i] equals to the corresponding " |
| 1333 | "element of Data."); |
| 1334 | |
| 1335 | BB.newNode("SpaceToDepth") |
| 1336 | .addInput("Input") |
| 1337 | .addMember(MemberType::Unsigned, "BlockSize") |
| 1338 | .addResultFromCtorArg() |
| 1339 | .setDocstring("Given Input tensor of [N,H,W,C], where N is the batch " |
| 1340 | "axis, C is the channel or depth, H is the height and W is " |
| 1341 | "the width. This produces Output tensor of [N, " |
| 1342 | "H/BlockSize, W/BlockSize, C * " |
| 1343 | "BlockSize * BlockSize]."); |
| 1344 | |
| 1345 | BB.newNode("ResizeNearest") |
| 1346 | .addInput("Input") |
| 1347 | .addMember(MemberType::VectorFloat, "Scale") |
| 1348 | .addResultFromCtorArg() |
| 1349 | .setDocstring( |
| 1350 | "Given Input tensor of 3D, 4D, 5D or 6D, generates an " |
| 1351 | "Output tensor with resized spatial dimensions using nearest " |
| 1352 | "neighbor interpolation. The Output tensor is of shape " |
| 1353 | "floor(input_dimension * scale)"); |
| 1354 | |
| 1355 | BB.newNode("ResizeBilinear") |
| 1356 | .addInput("Input") |
| 1357 | .addMember(MemberType::VectorFloat, "Scale") |
| 1358 | .addResultFromCtorArg() |
| 1359 | .setDocstring( |
| 1360 | "Given Input tensor of [N,H,W,C], where N is the batch, C is the " |
| 1361 | "channel or depth, H is the height and W is the width, Generates an " |
| 1362 | "Output tensor with resized spatial dimensions using bilinear " |
| 1363 | "neighbor interpolation. The Output tensor is of shape " |
| 1364 | "floor(input_dimension * scale)"); |
| 1365 | |
| 1366 | BB.newNode("Broadcast") |
| 1367 | .addInput("Input") |
| 1368 | .addMember(MemberType::Unsigned, "Axis") |
| 1369 | .addMember(MemberType::VectorDimT, "TargetDim") |
| 1370 | .addResultFromCtorArg() |
| 1371 | .setDocstring( |
| 1372 | "Broadcast the Input tensor to TargetDim using Axis to indicate the " |
| 1373 | "offset between Input dimension and TargetDim"); |
| 1374 | |
| 1375 | BB.newNode("SparseLabelSplit") |
| 1376 | .addInput("Lengths") |
| 1377 | .addInput("Indices") |
| 1378 | .addInput("Values") |
| 1379 | .addMember(MemberType::Unsigned, "NumLabels") |
| 1380 | .addResultFromCtorArg("LabelValues") |
| 1381 | .addResultFromCtorArg("ExampleIds") |
| 1382 | .addResultFromCtorArg("GradientOffsetMap") |
| 1383 | .setDocstring("TODO"); |
| 1384 | |
| 1385 | //===--------------------------------------------------------------------===// |
| 1386 | // Reorder transformations |
| 1387 | //===--------------------------------------------------------------------===// |
| 1388 | |
| 1389 | BB.newNode("Flip") |
| 1390 | .addInput("Input") |
| 1391 | .addMember(MemberType::Unsigned, "Axis") |
| 1392 | .addResultFromCtorArg() |
| 1393 | .setDocstring( |
| 1394 | "Reverse the order of elements in a tensor along the given axis. The " |
| 1395 | "shape of the tensor is preserved, but the elements are reordered. " |
| 1396 | "The node is inspired from Python numpy."); |
| 1397 | |
| 1398 | //===--------------------------------------------------------------------===// |
| 1399 | // Nodes used for network training |
| 1400 | //===--------------------------------------------------------------------===// |
| 1401 | |
| 1402 | BB.newNode("Splat") |
| 1403 | .addMember(MemberType::Float, "Value") |
| 1404 | .addResultFromCtorArg() |
| 1405 | .setDocstring("Generate a tensor of a specific type filled with 'Value'." |
| 1406 | "Splat always keep floating point value internally but can" |
| 1407 | "quantize it based on the output type."); |
| 1408 | |
| 1409 | // clang-format off |
| 1410 | BB.newNode("Touch") |
| 1411 | .addResultFromCtorArg() |
| 1412 | .setDocstring( |
| 1413 | "Generate a tensor of a specific type without initializing " |
| 1414 | "it. This is useful when filling a big tensor entirely with " |
| 1415 | "multiple small slices using InsertTensor nodes such that " |
| 1416 | "the big tensor is not required to be initialized (filled) " |
| 1417 | "with some value prior to insertion. This node is intended " |
| 1418 | "to remove the overhead associated with the initialization " |
| 1419 | "in situations where it is not required."); |
| 1420 | // clang-format on |
| 1421 | |
| 1422 | BB.newNode("SGD") |
| 1423 | .addInput("Gradient") |
| 1424 | .addInput("Weight") |
| 1425 | .addMember(MemberType::Float, "L1Decay") |
| 1426 | .addMember(MemberType::Float, "L2Decay") |
| 1427 | .addMember(MemberType::Float, "LearningRate") |
| 1428 | .addMember(MemberType::Float, "Momentum") |
| 1429 | .addMember(MemberType::Unsigned, "BatchSize") |
| 1430 | .addResult("Weight.getType()", "UpdatedWeight") |
| 1431 | .setHasSideEffects(true) |
| 1432 | .setDocstring("Stochastic Gradient Descent node used during training. " |
| 1433 | "Produces the updated weight that needs to be used " |
| 1434 | "instead of Weight for the next iteration."); |
| 1435 | |
| 1436 | //===--------------------------------------------------------------------===// |
| 1437 | // Nodes used for debugging/profiling/printing |
| 1438 | //===--------------------------------------------------------------------===// |
| 1439 | |
| 1440 | BB.newNode("TraceEvent") |
| 1441 | .addInput("Data") |
| 1442 | .addMember(MemberType::String, "EventName") |
| 1443 | .addMember(MemberType::String, "EventType") |
| 1444 | .addMember(MemberType::Unsigned, "Index") |
| 1445 | .setHasSideEffects(true) |
| 1446 | .setDocstring("Inserts a TraceEvent for profiling."); |
| 1447 | |
| 1448 | //===--------------------------------------------------------------------===// |
| 1449 | // Nodes used by quantization. |
| 1450 | //===--------------------------------------------------------------------===// |
| 1451 | |
| 1452 | BB.newNode("QuantizationProfile") |
| 1453 | .addInput("Input") |
| 1454 | .addInput("Histogram") |
| 1455 | .addInput("ComputationInfo") |
| 1456 | .addMember(MemberType::String, "ProfiledNodeName") |
| 1457 | .addMember(MemberType::Unsigned, "ProfiledOutputNumber") |
| 1458 | .addExtraMethod( |
| 1459 | "Placeholder *getHistogramPlaceholder() const ;\n", |
| 1460 | "Placeholder *QuantizationProfileNode::getHistogramPlaceholder() " |
| 1461 | "const { return " |
| 1462 | "llvm::cast<Placeholder>(Histogram_.getNode()); };\n") |
| 1463 | .addExtraMethod( |
| 1464 | "Placeholder *getComputationInfoPlaceholder() const;\n", |
| 1465 | "Placeholder " |
| 1466 | "*QuantizationProfileNode::getComputationInfoPlaceholder() const " |
| 1467 | "{ " |
| 1468 | "return llvm::cast<Placeholder>(ComputationInfo_.getNode()); };\n") |
| 1469 | .addOverwrittenInput("ComputationInfo") |
| 1470 | .addOverwrittenInput("Histogram") |
| 1471 | .setHasSideEffects(true) |
| 1472 | .setDocstring( |
| 1473 | "Generate profile (distribution of values) of the Input " |
| 1474 | "tensor. This data is used for quantization of the tensor " |
| 1475 | "later on. ProfiledNodeName contains the name of the node " |
| 1476 | "which is profiled by the QuantizationProfile node. " |
| 1477 | "ProfiledNodeName is helpful as lowering might transform the " |
| 1478 | "original graph. " |
| 1479 | "ProfiledOutputNumber contains the position of the node's output " |
| 1480 | "which gets profiled."); |
| 1481 | |
| 1482 | BB.newNode("IntLookupTable") |
| 1483 | .addInput("Input") |
| 1484 | .addInput("Mapping") |
| 1485 | .addResultFromCtorArg() |
| 1486 | .dataParallel() |
| 1487 | .setDocstring("Simple mapping between quantized numbers." |
| 1488 | "This can be used as quantized sigmoid or tanh functions."); |
| 1489 | |
| 1490 | BB.newNode("Quantize") |
| 1491 | .addInput("Input") |
| 1492 | .addResultFromCtorArg() |
| 1493 | .dataParallel() |
| 1494 | .setDocstring("Quantize floating point tensor. This operation converts " |
| 1495 | "floating point numbers to integers based on the given " |
| 1496 | "Scale and Offset. Scale and Offset are deduced from the " |
| 1497 | "type of the output." |
| 1498 | "x_q = clip(round(x/Scale) + Offset, -128, 127)"); |
| 1499 | |
| 1500 | BB.newNode("Dequantize") |
| 1501 | .addInput("Input") |
| 1502 | .addResultFromCtorArg() |
| 1503 | .dataParallel() |
| 1504 | .setDocstring("Convert quantized input tensor into the float " |
| 1505 | "representation. x = Scale * (x_q - Offset)."); |
| 1506 | |
| 1507 | BB.newNode("RescaleQuantized") |
| 1508 | .addInput("Input") |
| 1509 | .addResultFromCtorArg() |
| 1510 | .dataParallel() |
| 1511 | .setDocstring("Rescale the input quantized tensor to a new Scale and " |
| 1512 | "Offset. The new Scale and Offset are specified by the " |
| 1513 | "output type passed to the constructor"); |
| 1514 | |
| 1515 | //===--------------------------------------------------------------------===// |
| 1516 | // Nodes used by RNN |
| 1517 | //===--------------------------------------------------------------------===// |
| 1518 | |
| 1519 | BB.newNode("TopK") |
| 1520 | .addInput("Input") |
| 1521 | .addMember(MemberType::Unsigned, "K") |
| 1522 | .addResultFromCtorArg("Values") |
| 1523 | .addResultFromCtorArg("Indices") |
| 1524 | .setDocstring("Finds the top K maximal elements for each vector in the " |
| 1525 | "tensor. Vectors are defined as the last dimension in the " |
| 1526 | "tensor. The input shape {D_0, D_1, ... D_n} results in " |
| 1527 | "the outputs {D_0, D_1, ... D_n-1, K}, sorted in " |
| 1528 | "non-decreasing order."); |
| 1529 | |
| 1530 | BB.newNode("LSTMUnit") |
| 1531 | .addInput("Input") |
| 1532 | .addInput("C") |
| 1533 | .addResult("C.getType()", "newC") |
| 1534 | .addResult("C.getType()", "newH") |
| 1535 | .setDocstring( |
| 1536 | "A LSTM unit node, take Input as I, F, G, O," |
| 1537 | "takes F from forget gate, I from input gate," |
| 1538 | "O from output gate, G from cell gate and C from cell state. " |
| 1539 | "Calulates newC = sigmoid(F) * C + sigmoid(I) * tanh(G), " |
| 1540 | "newH = tanh(newC) * sigmoid(O)."); |
| 1541 | |
| 1542 | //===--------------------------------------------------------------------===// |
| 1543 | // Conversions |
| 1544 | //===--------------------------------------------------------------------===// |
| 1545 | |
| 1546 | BB.newNode("ConvertTo") |
| 1547 | .addInput("Input") |
| 1548 | .addResultFromCtorArg() |
| 1549 | .dataParallel() |
| 1550 | .setDocstring( |
| 1551 | "Convert the input from its current type to the destination " |
| 1552 | "type. The input and output types must have the same shapes. " |
| 1553 | "Moreover the input and output types must not be quantized types. " |
| 1554 | "Quantized types should use the appropriate Quantize, Dequantize, " |
| 1555 | "and Rescale nodes."); |
| 1556 | |
| 1557 | //===--------------------------------------------------------------------===// |
| 1558 | // Custom kernels invocations |
| 1559 | //===--------------------------------------------------------------------===// |
| 1560 | BB.newNode("ExternalFunctionCall") |
| 1561 | .addMember(MemberType::VectorNodeValue, "Inputs") |
| 1562 | // For now use single output. |
| 1563 | .addResultFromCtorArg() |
| 1564 | .addMember(MemberType::String, "FunctionName") |
| 1565 | // Examples are function source code, binary, or as needed. |
| 1566 | // The use of the following two fields will vary depending |
| 1567 | // on which kind of external function is used. |
| 1568 | .addMember(MemberType::String, "FunctionImpl") |
| 1569 | // Function kind, e.g. CUDA, function pointer, binary, backend-specific |
| 1570 | // source code. |
| 1571 | .addMember(MemberType::String, "FunctionKind") |
| 1572 | .skipAutogenSerialization() |
| 1573 | .setHasSideEffects(true) |
| 1574 | .setDocstring("This is a node representing an external function call. " |
| 1575 | "One possible use of this capability is to pass a source " |
| 1576 | "code for a function/kernel. When processing this node, a " |
| 1577 | "backend can compile and execute the source code. This " |
| 1578 | "node can also be used to pass binary or pointers to " |
| 1579 | "executable code. The semantics and implementation of this " |
| 1580 | "node not standardized and is very backend-specific."); |
| 1581 | |
| 1582 | //===--------------------------------------------------------------------===// |
| 1583 | // Pre Processing |
| 1584 | //===--------------------------------------------------------------------===// |
| 1585 | |
| 1586 | BB.newNode("AudioSpectrogram") |
| 1587 | .addInput("Input") |
| 1588 | .addInput("Window") |
| 1589 | .addInput("TwiddleFactors") |
| 1590 | .addInput("BitReverseIndices") |
| 1591 | .addInput("ComplexToRealWeights") |
| 1592 | .addMember(MemberType::Unsigned, "WindowSize") |
| 1593 | .addMember(MemberType::Unsigned, "WindowStride") |
| 1594 | .addMember(MemberType::Boolean, "MagnitudeSquared") |
| 1595 | .addResultFromCtorArg("Spectrogram") |
| 1596 | .setDocstring("Computes the spectrogram of a mono audio signal using " |
| 1597 | "given window size and stride. The FFT length used to " |
| 1598 | "compute the spectrogram is the next power of 2 (for a " |
| 1599 | "window size of 640 the FFT length is 1024). The length " |
| 1600 | "of each spectrogram window is FFT_length / 2 + 1. " |
| 1601 | "This node is inspired from TensorFlow."); |
| 1602 | |
| 1603 | BB.newNode("MFCC") |
| 1604 | .addInput("Spectrogram") |
| 1605 | .addInput("MelWeights") |
| 1606 | .addInput("MelRanges") |
| 1607 | .addInput("DctMat") |
| 1608 | .addMember(MemberType::Float, "SampleRate") |
| 1609 | .addMember(MemberType::Float, "LowerFrequency") |
| 1610 | .addMember(MemberType::Float, "UpperFrequency") |
| 1611 | .addMember(MemberType::Unsigned, "FilterBankCount") |
| 1612 | .addMember(MemberType::Unsigned, "NumCoefficients") |
| 1613 | .addResultFromCtorArg("Coefficients") |
| 1614 | .setDocstring("Computes the MFCC (Mel Frequency Cepstral Coefficient) " |
| 1615 | "for the given spectrogram. This node is mostly used as " |
| 1616 | "feature extractor for voice/speech audio data in " |
| 1617 | "voice command or keyword spotting applications. The input " |
| 1618 | "is assumed to be a power spectrogram and not a magnitude." |
| 1619 | "This node is inspired from TensorFlow."); |
| 1620 | |
| 1621 | //===--------------------------------------------------------------------===// |
| 1622 | // Post Processing |
| 1623 | //===--------------------------------------------------------------------===// |
| 1624 | |
| 1625 | BB.newNode("NonMaxSuppression") |
| 1626 | .addInput("Boxes") |
| 1627 | .addInput("Scores") |
| 1628 | .addMember(MemberType::Unsigned, "CenterPointBox") |
| 1629 | .addMember(MemberType::Unsigned, "MaxOutputBoxesPerClass") |
| 1630 | .addMember(MemberType::Float, "IouThreshold") |
| 1631 | .addMember(MemberType::Float, "ScoreThreshold") |
| 1632 | .addMember(MemberType::Boolean, "IsTFVersion4") |
| 1633 | .addResultFromCtorArg("Indices") |
| 1634 | .addResultFromCtorArg("NumberOfSelectedIndices") |
| 1635 | .setDocstring("This is a mix of ONNX and TF NMSv4. It supports multiple " |
| 1636 | "classes and does per class NMS. It also supports TF NMS " |
| 1637 | "V4 by outputting indices and scalar tensor with number of " |
| 1638 | "valid indices. It pads the rest with global MIN box."); |
| 1639 | |
| 1640 | BB.newNode("TFLiteDetectionPostProcess") |
| 1641 | .addInput("Boxes") |
| 1642 | .addInput("Scores") |
| 1643 | .addInput("Anchors") |
| 1644 | .addMember(MemberType::Unsigned, "NumClasses") |
| 1645 | .addMember(MemberType::Unsigned, "MaxDetections") |
| 1646 | .addMember(MemberType::Unsigned, "MaxClassesPerDetection") |
| 1647 | .addMember(MemberType::Unsigned, "MaxDetectionsPerClass") |
| 1648 | .addMember(MemberType::Float, "IouThreshold") |
| 1649 | .addMember(MemberType::Float, "ScoreThreshold") |
| 1650 | .addMember(MemberType::Float, "XScale") |
| 1651 | .addMember(MemberType::Float, "YScale") |
| 1652 | .addMember(MemberType::Float, "HScale") |
| 1653 | .addMember(MemberType::Float, "WScale") |
| 1654 | .addMember(MemberType::Boolean, "RegularNMS") |
| 1655 | .addResultFromCtorArg("DetectionBoxes") |
| 1656 | .addResultFromCtorArg("DetectionClasses") |
| 1657 | .addResultFromCtorArg("DetectionScores") |
| 1658 | .addResultFromCtorArg("NumDetections") |
| 1659 | .setDocstring( |
| 1660 | "This node is a TensorFlowLite version of NonMaxSuppresion. The node " |
| 1661 | "has the following inputs: Boxes with size [N, B, 4], Scores with " |
| 1662 | "size [N, B, C] and Anchors with size [B, 4] where N is the batch " |
| 1663 | "size, B is the number of boxes and C is the number of classes. " |
| 1664 | "The node has the following attributes (parameters): " |
| 1665 | "NumClasses - Number of classes (without the background class). " |
| 1666 | "MaxDetections - The maximum number of detections. " |
| 1667 | "MaxClassesPerDetection - Maximum classes per detection (Fast NMS). " |
| 1668 | "MaxDetectionsPerClass - Maximum detections per class (Regular NMS). " |
| 1669 | "IouThreshold - Detection threshold for IoU metric. " |
| 1670 | "ScoreThreshold - Detection threshold for scores. " |
| 1671 | "XScale - X scale used for decoding the boxes. " |
| 1672 | "YScale - Y scale used for decoding the boxes. " |
| 1673 | "HScale - H scale used for decoding the boxes. " |
| 1674 | "WScale - W scale used for decoding the boxes. " |
| 1675 | "RegularNMS - Whether the NMS is 'Regular' or 'Fast'. " |
| 1676 | "The node will have the following outputs: " |
| 1677 | "DetectionBoxes - the chosen boxes (float). " |
| 1678 | "DetectionClasses - the classes of the chosen boxes (int32). " |
| 1679 | "DetectionScores - the scores of the chosen boxes (float). " |
| 1680 | "NumDetections - number of chose boxes (int32). " |
| 1681 | "The first three output tensors will be allocated using the maximum " |
| 1682 | "number of possible detections (worst case scenario) but the actual " |
| 1683 | "usage will be given by the 'NumDetections' output. "); |
| 1684 | |
| 1685 | //===--------------------------------------------------------------------===// |
| 1686 | // Region of Interest nodes |
| 1687 | //===--------------------------------------------------------------------===// |
| 1688 | |
| 1689 | BB.newNode("ROIAlign") |
| 1690 | .addInput("FeatureMap") |
| 1691 | .addInput("Boxes") |
| 1692 | .addInput("BatchIndices") |
| 1693 | .addMember(MemberType::Enum, "Mode") |
| 1694 | .addMember(MemberType::Unsigned, "OutputHeight") |
| 1695 | .addMember(MemberType::Unsigned, "OutputWidth") |
| 1696 | .addMember(MemberType::Unsigned, "SamplingRatio") |
| 1697 | .addMember(MemberType::Float, "SpatialScale") |
| 1698 | .addMember(MemberType::Boolean, "Aligned") |
| 1699 | .addMember(MemberType::Boolean, "Rotated") |
| 1700 | .addResultFromCtorArg() |
| 1701 | .setDocstring( |
| 1702 | "Performs region of interest align (ROI) operator. " |
| 1703 | "FeatureMap - a tensor of [N,H,W,C]. N is the batch, C is the " |
| 1704 | "channel, H is the height, W is the width. " |
| 1705 | "Boxes - a tensor of [K,4] or [K,5] with format " |
| 1706 | "[[optinal_batch_index] x0, y0, x1, y1]. K is the number of boxes. " |
| 1707 | "BatchIndices - a tensor of [K,]. If N > 1 and Box shape is [K,4], " |
| 1708 | "BatchIndices must be valid. " |
| 1709 | "Output is a tensor with shape [K, OutputHeight, OutputWidth, C]. " |
| 1710 | "Aligned - if true, coordinates are aligned to a center of a pixel."); |
| 1711 | |
| 1712 | BB.newNode("BBoxTransform") |
| 1713 | .addInput("Rois") |
| 1714 | .addInput("Deltas") |
| 1715 | .addInput("ImInfo") |
| 1716 | .addMember(MemberType::VectorFloat, "Weights") |
| 1717 | .addMember(MemberType::Boolean, "ApplyScale") |
| 1718 | .addMember(MemberType::Boolean, "Rotated") |
| 1719 | .addMember(MemberType::Boolean, "AngleBoundOn") |
| 1720 | .addMember(MemberType::Int64, "AngleBoundLo") |
| 1721 | .addMember(MemberType::Int64, "AngleBoundHi") |
| 1722 | .addMember(MemberType::Float, "ClipAngleThresh") |
| 1723 | .addMember(MemberType::Boolean, "LegacyPlusOne") |
| 1724 | .addResultFromCtorArg("BoxOut") |
| 1725 | .addResultFromCtorArg("RoiBatchSplits") |
| 1726 | .setDocstring( |
| 1727 | "Transform proposal bounding boxes to target bounding box using " |
| 1728 | "bounding box regression deltas. " |
| 1729 | "Rois tensor's format is: " |
| 1730 | "<[optional_batch_index], x1, y1, x2, y2>, shape (M, 4) or (M, 5) " |
| 1731 | "where M is the number of Rois. " |
| 1732 | "For rotated boxes, this would have an additional angle (in degrees) " |
| 1733 | "in the format <[optional_batch_id], ctr_x, ctr_y, w, h, angle> " |
| 1734 | "Deltas are of shape (M, K*4) with format <dx, dy, dw, dh>, " |
| 1735 | "where K is the number of classes. " |
| 1736 | "For rotated Rois: shape (M, K*5), format <dx, dy, dw, dh, da>. " |
| 1737 | "ImInfo is of shape <batch_size, 3> with format <img_height, " |
| 1738 | "img_width, img_scale>." |
| 1739 | "If proposals from multiple images in a batch are present, they " |
| 1740 | "should be grouped sequentially and in incremental order."); |
| 1741 | |
| 1742 | BB.newNode("CollectRpnProposals") |
| 1743 | .addMember(MemberType::VectorNodeValue, "RoisIn") |
| 1744 | .addMember(MemberType::VectorNodeValue, "RoisProbsIn") |
| 1745 | .addMember(MemberType::Int64, "RpnMaxLevel") |
| 1746 | .addMember(MemberType::Int64, "RpnMinLevel") |
| 1747 | .addMember(MemberType::Unsigned, "RpnPostNmsTopN") |
| 1748 | .addResultFromCtorArg() |
| 1749 | .setDocstring( |
| 1750 | "Given RpnMinLevel, RpnMaxLevel and RpnPostNmsTopN " |
| 1751 | "CollectRpnProposals merges RoisIn based on " |
| 1752 | "RoisProbsIn and returns top proposals limited to " |
| 1753 | "RpnPostNmsTopN total, size (n x B), where B is " |
| 1754 | "box dimensions and based on dimension of input rois. " |
| 1755 | "Format for upright boxes is (image_index, x1, y1, x2, y2)." |
| 1756 | "Format for rotated boxes (image_index, ctr_x, ctr_y, w, h, angle)" |
| 1757 | "RpnPostNmsTopN should be greater than zero"); |
| 1758 | |
| 1759 | //===--------------------------------------------------------------------===// |
| 1760 | // Lookup Table Operators |
| 1761 | //===--------------------------------------------------------------------===// |
| 1762 | |
| 1763 | BB.newNode("LookupTable") |
| 1764 | // Input to the function. |
| 1765 | .addInput("Input") |
| 1766 | // Table containing the coefficients for interpolation. |
| 1767 | .addInput("Table") |
| 1768 | // Table containing the index mapping to find the right entry in the main |
| 1769 | // table. |
| 1770 | .addInput("TableIdx") |
| 1771 | .addMember(MEMBER_TYPE_INFO(glow::LUTOperator), "Operator") |
| 1772 | .addMember(MemberType::VectorFloat, "OperatorArgs") |
| 1773 | .addResultFromCtorArg() |
| 1774 | .dataParallel() |
| 1775 | .setDocstring( |
| 1776 | "LookupTable based data-parallel operation." |
| 1777 | "Given an interpolation table and and index table, " |
| 1778 | "return interpolated approximations for arbitrary functions."); |
| 1779 | |
| 1780 | //===--------------------------------------------------------------------===// |
| 1781 | // Backend-Specific Nodes |
| 1782 | //===--------------------------------------------------------------------===// |
| 1783 | |
| 1784 | #include "glow/NodeGenIncludes.h" |
| 1785 | |
| 1786 | return 0; |
| 1787 | } |
| 1788 |
Generated on 2022-Jan-12 from project glow revision 00b843b
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