Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1 | #!/usr/bin/env python3 |
Luke Hutton | 261b7b6 | 2023-01-10 14:50:31 +0000 | [diff] [blame] | 2 | # Copyright (c) 2020-2023, ARM Limited. |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 3 | # SPDX-License-Identifier: Apache-2.0 |
| 4 | import argparse |
| 5 | import os |
| 6 | import re |
| 7 | import traceback |
| 8 | |
| 9 | import numpy as np |
| 10 | |
| 11 | # Level | Level for Humans | Level Description |
| 12 | # -------|------------------|------------------------------------ |
| 13 | # 0 | DEBUG | [Default] Print all messages |
| 14 | # 1 | INFO | Filter out INFO messages |
| 15 | # 2 | WARNING | Filter out INFO & WARNING messages |
| 16 | # 3 | ERROR | Filter out all messages |
| 17 | # Filter tensorflow debug message except errors |
| 18 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" |
| 19 | |
| 20 | # Flake8 E402 - ignore imports not at top of file to allow os.environ setting |
| 21 | import tensorflow as tf # noqa: E402 |
| 22 | from frameworks.write_test_json import write_test_json # noqa: E402 |
| 23 | from frameworks.arg_gen import ArgGen # noqa: E402 |
| 24 | from frameworks.tensor_gen import TGen # noqa: E402 |
Jerry Ge | b1f2501 | 2023-03-03 11:33:51 -0800 | [diff] [blame] | 25 | from frameworks.tensor_gen import ElemSignedness # noqa: E402 |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 26 | from frameworks.test_builder import TBuilder # noqa: E402 |
Jeremy Johnson | 5d1a347 | 2022-03-31 09:50:06 +0100 | [diff] [blame] | 27 | from frameworks.test_gen_utils import ( # noqa: E402 |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 28 | QuantType, |
| 29 | get_tf_dtype, |
| 30 | get_shape_str, |
| 31 | ) # noqa: E402 |
| 32 | from tensorflow.lite.python.interpreter import OpResolverType # noqa: E402 |
| 33 | |
| 34 | # All of the supported frameworks |
| 35 | ALL_FRAMEWORKS = ["tf", "tflite"] |
| 36 | |
| 37 | # Lists of different data types |
| 38 | TYPE_F = [tf.float32] |
| 39 | TYPE_I = [tf.int32] |
| 40 | TYPE_FI = [tf.float32, tf.int32] |
| 41 | TYPE_B = [tf.bool] |
| 42 | TYPE_FIB = [tf.float32, tf.int32, tf.bool] |
| 43 | TYPE_H = [tf.float16] |
| 44 | TYPE_FH = [tf.float32, tf.float16] |
| 45 | TYPE_FHI = [tf.float32, tf.float16, tf.int32] |
| 46 | TYPE_FHIB = [tf.float32, tf.float16, tf.int32, tf.bool] |
| 47 | |
| 48 | # The list of operator tests |
| 49 | # Each dictionary entry for an op is a dictionary with the following required members: |
| 50 | # 'operands': tuple (number_of_placeholder_tensors, number_of_constant_tensors) |
| 51 | # 'build_fcn: tuple (Test builder function, Tensor generator function, |
| 52 | # Argument generator function) |
| 53 | # 'types': list of Tensorflow types that should be tested for this op |
| 54 | # OR |
| 55 | # a dictionary of {'framework_name': [type_list] } for cases where only |
| 56 | # a subset of the types should be tested in each framework. This can also |
| 57 | # be used to restrict an operator to a particular framework. |
| 58 | # |
| 59 | # And optional members: |
| 60 | # 'template': boolean (indicates that this is a templated op which gets further |
| 61 | # processing in createDynamicOpLists) |
| 62 | # 'bias': boolean indicating that there is a bias component to be generated |
| 63 | # 'qtypes': List of QuantType quantized types to generate for this op |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 64 | # 'rank': tuple (lowest rank, highest rank). Dimension range of input tensor. |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 65 | |
| 66 | TF_OP_LIST = { |
| 67 | "add": { |
| 68 | "operands": (2, 0), |
| 69 | "build_fcn": (TBuilder.Add, TGen.tgBFuzz, ArgGen.agNone), |
| 70 | "types": { |
| 71 | "tf": TYPE_FI, |
| 72 | "tflite": list( |
| 73 | TYPE_FI + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 74 | ), |
| 75 | }, |
| 76 | }, |
| 77 | "sub": { |
| 78 | "operands": (2, 0), |
| 79 | "build_fcn": (TBuilder.Sub, TGen.tgBFuzz, ArgGen.agNone), |
| 80 | "types": { |
| 81 | "tf": TYPE_FI, |
| 82 | "tflite": list(TYPE_FI + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 83 | # QuantType.ALL_I16 fail in TFLite conversion |
| 84 | }, |
| 85 | }, |
| 86 | "mul": { |
| 87 | "operands": (2, 0), |
| 88 | "build_fcn": (TBuilder.Mul, TGen.tgBFuzz, ArgGen.agNone), |
| 89 | "types": { |
| 90 | "tf": TYPE_FI, |
| 91 | "tflite": list( |
| 92 | TYPE_FI + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 93 | ), |
| 94 | }, |
| 95 | }, |
| 96 | "exp": { |
| 97 | "operands": (1, 0), |
| 98 | "build_fcn": (TBuilder.Exp, TGen.tgBasic, ArgGen.agNone), |
| 99 | "types": TYPE_F, |
| 100 | }, |
| 101 | "rcp": { |
| 102 | "operands": (1, 0), |
| 103 | "build_fcn": (TBuilder.Rcp, TGen.tgBasic, ArgGen.agNone), |
| 104 | "types": TYPE_F, |
| 105 | }, |
| 106 | "relu": { |
| 107 | "operands": (1, 0), |
| 108 | "build_fcn": (TBuilder.Relu, TGen.tgBasic, ArgGen.agNone), |
| 109 | "types": { |
| 110 | "tf": TYPE_F, |
| 111 | "tflite": list( |
| 112 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 113 | ), |
| 114 | }, |
| 115 | }, |
Jerry Ge | 9391243 | 2022-07-22 10:29:13 -0700 | [diff] [blame] | 116 | "relu1": { |
| 117 | "operands": (1, 0), |
| 118 | "build_fcn": (TBuilder.Relu1, TGen.tgBasic, ArgGen.agNone), |
| 119 | "types": { |
| 120 | "tf": [], |
| 121 | "tflite": list(TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 122 | }, |
| 123 | }, |
Jerry Ge | 2eea5bf | 2022-10-11 16:27:05 +0000 | [diff] [blame] | 124 | "relu0To1": { |
| 125 | "operands": (1, 0), |
| 126 | "build_fcn": (TBuilder.Relu0To1, TGen.tgBasic, ArgGen.agNone), |
| 127 | "types": { |
| 128 | "tf": [], |
| 129 | "tflite": list(TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 130 | }, |
| 131 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 132 | "relu6": { |
| 133 | "operands": (1, 0), |
| 134 | "build_fcn": (TBuilder.Relu6, TGen.tgBasic, ArgGen.agNone), |
| 135 | "types": { |
| 136 | "tf": TYPE_F, |
| 137 | "tflite": list( |
| 138 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 139 | ), |
| 140 | }, |
| 141 | }, |
| 142 | "leaky_relu": { |
| 143 | "operands": (1, 0), |
| 144 | "build_fcn": (TBuilder.LeakyRelu, TGen.tgBasic, ArgGen.agFloat), |
| 145 | "types": { |
| 146 | "tf": TYPE_F, |
| 147 | "tflite": list( |
| 148 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 149 | ), |
| 150 | }, |
| 151 | }, |
TatWai Chong | 41a04fe | 2022-11-03 21:44:32 +0000 | [diff] [blame] | 152 | "prelu": { |
| 153 | "operands": (1, 0), |
| 154 | "build_fcn": (TBuilder.Prelu, TGen.tgBasic, ArgGen.agNone), |
| 155 | "types": { |
| 156 | "tflite": list(TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 157 | }, |
| 158 | }, |
TatWai Chong | 473eb38 | 2022-08-02 04:21:30 +0000 | [diff] [blame] | 159 | "gelu": { |
| 160 | "operands": (1, 0), |
| 161 | "build_fcn": (TBuilder.Gelu, TGen.tgBasic, ArgGen.agNone), |
| 162 | "types": { |
| 163 | # Need compiler support for tf.Erf. |
| 164 | # "tf": TYPE_F, |
| 165 | "tflite": list( |
| 166 | # Only float32, int8 and uint8 supported currently |
| 167 | TYPE_F |
| 168 | + [QuantType.ALL_U8, QuantType.ALL_I8] |
| 169 | ), |
| 170 | }, |
| 171 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 172 | "concat": { |
| 173 | "operands": (2, 0), |
| 174 | "build_fcn": (TBuilder.Concat, TGen.tgBasic, ArgGen.agAxes), |
| 175 | "types": TYPE_FI, |
| 176 | }, |
| 177 | "bitwise_and": { |
| 178 | "operands": (2, 0), |
| 179 | "build_fcn": (TBuilder.BitwiseAnd, TGen.tgBFuzz, ArgGen.agNone), |
| 180 | "types": {"tf": TYPE_I}, # Not supported in TF Lite |
| 181 | }, |
| 182 | "bitwise_or": { |
| 183 | "operands": (2, 0), |
| 184 | "build_fcn": (TBuilder.BitwiseOr, TGen.tgBFuzz, ArgGen.agNone), |
| 185 | "types": {"tf": TYPE_I}, # Not supported in TF Lite |
| 186 | }, |
| 187 | "bitwise_not": { |
| 188 | "operands": (1, 0), |
| 189 | "build_fcn": (TBuilder.BitwiseNot, TGen.tgBFuzz, ArgGen.agNone), |
| 190 | "types": {"tf": TYPE_I}, # Not supported in TF Lite |
| 191 | }, |
| 192 | "bitwise_xor": { |
| 193 | "operands": (2, 0), |
| 194 | "build_fcn": (TBuilder.BitwiseXor, TGen.tgBFuzz, ArgGen.agNone), |
| 195 | "types": {"tf": TYPE_I}, # Not supported in TF Lite |
| 196 | }, |
| 197 | "logical_and": { |
| 198 | "operands": (2, 0), |
| 199 | "build_fcn": (TBuilder.LogicalAnd, TGen.tgBFuzz, ArgGen.agNone), |
| 200 | "types": TYPE_B, |
| 201 | }, |
| 202 | "logical_or": { |
| 203 | "operands": (2, 0), |
| 204 | "build_fcn": (TBuilder.LogicalOr, TGen.tgBFuzz, ArgGen.agNone), |
| 205 | "types": TYPE_B, |
| 206 | }, |
| 207 | "logical_not": { |
| 208 | "operands": (1, 0), |
| 209 | "build_fcn": (TBuilder.LogicalNot, TGen.tgBFuzz, ArgGen.agNone), |
| 210 | "types": TYPE_B, |
| 211 | }, |
| 212 | "reduce_any": { |
| 213 | "operands": (1, 0), |
| 214 | "build_fcn": (TBuilder.ReduceAny, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
| 215 | "types": TYPE_B, |
| 216 | }, |
| 217 | "reduce_all": { |
| 218 | "operands": (1, 0), |
| 219 | "build_fcn": (TBuilder.ReduceAll, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
TatWai Chong | 6ad6d6e | 2022-11-11 15:28:50 -0800 | [diff] [blame] | 220 | "types": TYPE_B, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 221 | }, |
| 222 | "reduce_min": { |
| 223 | "operands": (1, 0), |
| 224 | "build_fcn": (TBuilder.ReduceMin, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
| 225 | "types": { |
| 226 | "tf": TYPE_FI, |
| 227 | "tflite": list(TYPE_FI + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 228 | }, |
| 229 | }, |
| 230 | "reduce_max": { |
| 231 | "operands": (1, 0), |
| 232 | "build_fcn": (TBuilder.ReduceMax, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
| 233 | "types": { |
| 234 | "tf": TYPE_FI, |
| 235 | "tflite": list(TYPE_FI + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 236 | }, |
| 237 | }, |
| 238 | "reduce_sum": { |
| 239 | "operands": (1, 0), |
| 240 | "build_fcn": (TBuilder.ReduceSum, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
| 241 | "types": { |
| 242 | "tf": TYPE_F, |
| 243 | # v2 converter doesn't recognize quantized reduce_sum |
| 244 | # "tflite": list(TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 245 | "tflite": TYPE_F, |
| 246 | }, |
| 247 | }, |
| 248 | "reduce_mean": { |
| 249 | "operands": (1, 0), |
| 250 | "build_fcn": (TBuilder.ReduceMean, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
| 251 | "types": { |
| 252 | "tf": TYPE_F, |
| 253 | "tflite": list( |
| 254 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 255 | ), |
| 256 | }, |
| 257 | }, |
| 258 | "reduce_product": { |
| 259 | "operands": (1, 0), |
| 260 | "build_fcn": (TBuilder.ReduceProduct, TGen.tgBasic, ArgGen.agAxesListKeepdims), |
| 261 | "types": TYPE_F, |
| 262 | }, |
| 263 | "min": { |
| 264 | "operands": (2, 0), |
| 265 | "build_fcn": (TBuilder.Min, TGen.tgBFuzz, ArgGen.agNone), |
| 266 | "types": TYPE_FI, |
| 267 | }, |
| 268 | "max": { |
| 269 | "operands": (2, 0), |
| 270 | "build_fcn": (TBuilder.Max, TGen.tgBFuzz, ArgGen.agNone), |
| 271 | "types": TYPE_FI, |
| 272 | }, |
| 273 | "pow": { |
| 274 | "operands": (2, 0), |
| 275 | "build_fcn": (TBuilder.Pow, TGen.tgBFuzz, ArgGen.agNone), |
| 276 | # Technically, integer is supported, but only for positive exponents. |
| 277 | # Needs a random argument generator. |
| 278 | "types": TYPE_F, |
| 279 | }, |
| 280 | "abs": { |
| 281 | "operands": (1, 0), |
| 282 | "build_fcn": (TBuilder.Abs, TGen.tgBasic, ArgGen.agNone), |
| 283 | "types": TYPE_F, |
| 284 | }, |
| 285 | "ceil": { |
| 286 | "operands": (1, 0), |
| 287 | "build_fcn": (TBuilder.Ceil, TGen.tgBasic, ArgGen.agNone), |
| 288 | "types": TYPE_F, |
| 289 | }, |
| 290 | "floor": { |
| 291 | "operands": (1, 0), |
| 292 | "build_fcn": (TBuilder.Floor, TGen.tgBasic, ArgGen.agNone), |
| 293 | "types": TYPE_F, |
| 294 | }, |
| 295 | "log": { |
| 296 | "operands": (1, 0), |
| 297 | "build_fcn": (TBuilder.Log, TGen.tgBasic, ArgGen.agNone), |
| 298 | "types": TYPE_F, |
| 299 | }, |
| 300 | "negate": { |
| 301 | "operands": (1, 0), |
| 302 | "build_fcn": (TBuilder.Negate, TGen.tgBasic, ArgGen.agNone), |
| 303 | "types": TYPE_F, |
| 304 | }, |
| 305 | "rsqrt": { |
| 306 | "operands": (1, 0), |
Jerry Ge | b1f2501 | 2023-03-03 11:33:51 -0800 | [diff] [blame] | 307 | "build_fcn": (TBuilder.Rsqrt, TGen.tgBasicPositive, ArgGen.agNone), |
| 308 | "types": { |
| 309 | "tf": TYPE_F, |
| 310 | "tflite": list(TYPE_F + [QuantType.ALL_I8]), |
| 311 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 312 | }, |
TatWai Chong | d713a4d | 2022-11-10 13:54:28 -0800 | [diff] [blame] | 313 | "sign": { |
| 314 | "operands": (1, 0), |
| 315 | "build_fcn": (TBuilder.Sign, TGen.tgBasic, ArgGen.agNone), |
| 316 | "types": { |
| 317 | "tf": TYPE_F, |
| 318 | }, |
| 319 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 320 | "sigmoid": { |
| 321 | "operands": (1, 0), |
| 322 | "build_fcn": (TBuilder.Sigmoid, TGen.tgBasic, ArgGen.agNone), |
| 323 | "types": { |
| 324 | "tf": TYPE_F, |
| 325 | "tflite": list( |
| 326 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 327 | ), |
| 328 | }, |
| 329 | }, |
| 330 | "tanh": { |
| 331 | "operands": (1, 0), |
| 332 | "build_fcn": (TBuilder.Tanh, TGen.tgBasic, ArgGen.agNone), |
| 333 | "types": { |
| 334 | "tf": TYPE_F, |
| 335 | "tflite": list( |
| 336 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 337 | ), |
| 338 | }, |
| 339 | }, |
Luke Hutton | 4160186 | 2022-12-06 17:29:15 +0000 | [diff] [blame] | 340 | "sin": { |
| 341 | "operands": (1, 0), |
| 342 | "build_fcn": (TBuilder.Sin, TGen.tgBasic, ArgGen.agNone), |
Luke Hutton | 657af86 | 2022-12-16 16:30:45 +0000 | [diff] [blame] | 343 | "types": TYPE_F, |
Luke Hutton | 4160186 | 2022-12-06 17:29:15 +0000 | [diff] [blame] | 344 | }, |
| 345 | "cos": { |
| 346 | "operands": (1, 0), |
| 347 | "build_fcn": (TBuilder.Cos, TGen.tgBasic, ArgGen.agNone), |
Luke Hutton | 657af86 | 2022-12-16 16:30:45 +0000 | [diff] [blame] | 348 | "types": TYPE_F, |
Luke Hutton | 4160186 | 2022-12-06 17:29:15 +0000 | [diff] [blame] | 349 | }, |
Luke Hutton | 2138a19 | 2022-12-15 11:01:39 +0000 | [diff] [blame] | 350 | "atan2": { |
| 351 | "operands": (2, 0), |
| 352 | "build_fcn": (TBuilder.Atan2, TGen.tgBasic, ArgGen.agNone), |
| 353 | "types": { |
| 354 | "tflite": TYPE_F, |
| 355 | }, |
| 356 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 357 | "square": { |
| 358 | "operands": (1, 0), |
| 359 | "build_fcn": (TBuilder.Square, TGen.tgBasic, ArgGen.agNone), |
| 360 | "types": TYPE_F, |
| 361 | }, |
| 362 | "squared_difference": { |
| 363 | "operands": (2, 0), |
| 364 | "build_fcn": (TBuilder.SquaredDifference, TGen.tgBFuzz, ArgGen.agNone), |
| 365 | "types": TYPE_F, |
| 366 | }, |
| 367 | "equal": { |
| 368 | "operands": (2, 0), |
| 369 | "build_fcn": (TBuilder.Equal, TGen.tgBFuzz, ArgGen.agNone), |
| 370 | "types": TYPE_FI, |
| 371 | }, |
| 372 | "greater_equal": { |
| 373 | "operands": (2, 0), |
| 374 | "build_fcn": (TBuilder.GreaterEqual, TGen.tgBFuzz, ArgGen.agNone), |
| 375 | "types": TYPE_FI, |
| 376 | }, |
| 377 | "greater": { |
| 378 | "operands": (2, 0), |
| 379 | "build_fcn": (TBuilder.Greater, TGen.tgBFuzz, ArgGen.agNone), |
| 380 | "types": TYPE_FI, |
| 381 | }, |
| 382 | "less": { |
| 383 | "operands": (2, 0), |
| 384 | "build_fcn": (TBuilder.Less, TGen.tgBFuzz, ArgGen.agNone), |
| 385 | "types": TYPE_FI, |
| 386 | }, |
| 387 | "less_equal": { |
| 388 | "operands": (2, 0), |
| 389 | "build_fcn": (TBuilder.LessEqual, TGen.tgBFuzz, ArgGen.agNone), |
| 390 | "types": TYPE_FI, |
| 391 | }, |
| 392 | "conv2d_TEMPLATE": { |
| 393 | "operands": (1, 1), |
| 394 | "build_fcn": (TBuilder.Conv2d, TGen.tgConv2d, ArgGen.agConv2d), |
| 395 | "types": { |
| 396 | "tf": [tf.float32], |
| 397 | "tflite": [ |
| 398 | tf.float32, |
| 399 | QuantType.CONV_U8_U8, |
| 400 | QuantType.CONV_I8_I8, |
| 401 | QuantType.CONV_I16_I8, |
| 402 | ], |
| 403 | }, |
| 404 | "template": True, |
| 405 | }, |
| 406 | "conv2d_relu_TEMPLATE": { |
| 407 | "operands": (1, 2), |
| 408 | "build_fcn": (TBuilder.Conv2dRelu, TGen.tgConv2d, ArgGen.agNone), |
| 409 | "types": { |
| 410 | "tf": [tf.float32], |
| 411 | "tflite": [ |
| 412 | tf.float32, |
| 413 | QuantType.CONV_U8_U8, |
| 414 | QuantType.CONV_I8_I8, |
| 415 | QuantType.CONV_I16_I8, |
| 416 | ], |
| 417 | }, |
| 418 | "template": True, |
| 419 | }, |
| 420 | "conv2d_relu6_TEMPLATE": { |
| 421 | "operands": (1, 2), |
| 422 | "build_fcn": (TBuilder.Conv2dRelu6, TGen.tgConv2d, ArgGen.agNone), |
| 423 | "types": { |
| 424 | "tf": [tf.float32], |
| 425 | "tflite": [ |
| 426 | tf.float32, |
| 427 | QuantType.CONV_U8_U8, |
| 428 | QuantType.CONV_I8_I8, |
| 429 | QuantType.CONV_I16_I8, |
| 430 | ], |
| 431 | }, |
| 432 | "template": True, |
| 433 | }, |
| 434 | "conv2d_relu_n1_to_1_TEMPLATE": { |
| 435 | "operands": (1, 2), |
| 436 | "build_fcn": (TBuilder.Conv2dReluN1To1, TGen.tgConv2d, ArgGen.agNone), |
| 437 | "types": { |
| 438 | "tf": [tf.float32], |
| 439 | "tflite": [ |
| 440 | tf.float32, |
| 441 | QuantType.CONV_U8_U8, |
| 442 | QuantType.CONV_I8_I8, |
| 443 | QuantType.CONV_I16_I8, |
| 444 | ], |
| 445 | }, |
| 446 | "template": True, |
| 447 | }, |
| 448 | # This test is converted as: |
| 449 | # tfl.conv2d(){fused_activation_function="NONE"} + tfl.tanh() |
| 450 | # TODO: anyway to generate tfl.conv2d(){fused_activation_function="TANH"}? |
| 451 | "conv2d_tanh_TEMPLATE": { |
| 452 | "operands": (1, 2), |
| 453 | "build_fcn": (TBuilder.Conv2dTanh, TGen.tgConv2d, ArgGen.agNone), |
| 454 | "types": { |
| 455 | "tf": [tf.float32], |
| 456 | "tflite": [ |
| 457 | tf.float32, |
| 458 | QuantType.CONV_U8_U8, |
| 459 | QuantType.CONV_I8_I8, |
| 460 | QuantType.CONV_I16_I8, |
| 461 | ], |
| 462 | }, |
| 463 | "template": True, |
| 464 | }, |
| 465 | "conv2d_bias_TEMPLATE": { |
| 466 | "operands": (1, 2), |
| 467 | "build_fcn": (TBuilder.Conv2dWithBias, TGen.tgConv2d, ArgGen.agConv2d), |
| 468 | "types": { |
| 469 | "tf": [tf.float32], |
| 470 | "tflite": [ |
| 471 | tf.float32, |
| 472 | QuantType.CONV_U8_U8, |
| 473 | QuantType.CONV_I8_I8, |
| 474 | QuantType.CONV_I16_I8, |
| 475 | ], |
| 476 | }, |
| 477 | "bias": True, |
| 478 | "template": True, |
| 479 | }, |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 480 | "conv3d_TEMPLATE": { |
| 481 | "operands": (1, 1), |
| 482 | "build_fcn": (TBuilder.Conv3d, TGen.tgConv3d, ArgGen.agConv3d), |
| 483 | "types": { |
| 484 | "tf": [tf.float32], |
| 485 | "tflite": [ |
| 486 | tf.float32, |
| 487 | QuantType.CONV_U8_U8, |
| 488 | QuantType.CONV_I8_I8, |
| 489 | # Quantization to 16x8-bit not yet supported by tflite. |
| 490 | ], |
| 491 | }, |
| 492 | "template": True, |
| 493 | "rank": (1, 5), |
| 494 | }, |
| 495 | "conv3d_bias_TEMPLATE": { |
| 496 | "operands": (1, 2), |
| 497 | "build_fcn": (TBuilder.Conv3dWithBias, TGen.tgConv3d, ArgGen.agConv3d), |
| 498 | "types": { |
| 499 | "tf": [tf.float32], |
| 500 | "tflite": [ |
| 501 | tf.float32, |
| 502 | QuantType.CONV_U8_U8, |
| 503 | QuantType.CONV_I8_I8, |
| 504 | # Quantization to 16x8-bit not yet supported by tflite. |
| 505 | ], |
| 506 | }, |
| 507 | "bias": True, |
| 508 | "template": True, |
| 509 | "rank": (1, 5), |
| 510 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 511 | "depthwise_conv2d_TEMPLATE": { |
| 512 | "operands": (1, 1), |
| 513 | "build_fcn": ( |
| 514 | TBuilder.DepthwiseConv2d, |
| 515 | TGen.tgDepthwiseConv2d, |
| 516 | ArgGen.agDepthwiseConv2d, |
| 517 | ), |
| 518 | "types": { |
| 519 | "tf": [tf.float32], |
| 520 | "tflite": [ |
| 521 | tf.float32, |
| 522 | QuantType.CONV_U8_U8, |
| 523 | QuantType.CONV_I8_I8, |
| 524 | QuantType.CONV_I16_I8, |
| 525 | ], |
| 526 | }, |
| 527 | "template": True, |
| 528 | }, |
| 529 | "depthwise_conv2d_bias_TEMPLATE": { |
| 530 | "operands": (1, 2), |
| 531 | "build_fcn": ( |
| 532 | TBuilder.DepthwiseConv2dWithBias, |
| 533 | TGen.tgDepthwiseConv2d, |
| 534 | ArgGen.agDepthwiseConv2d, |
| 535 | ), |
| 536 | "types": { |
| 537 | "tf": [tf.float32], |
| 538 | "tflite": [ |
| 539 | tf.float32, |
| 540 | QuantType.CONV_U8_U8, |
| 541 | QuantType.CONV_I8_I8, |
| 542 | QuantType.CONV_I16_I8, |
| 543 | ], |
| 544 | }, |
| 545 | "bias": True, |
| 546 | "template": True, |
| 547 | }, |
| 548 | "transpose_conv2d_TEMPLATE": { |
| 549 | "operands": (1, 1), |
| 550 | "build_fcn": ( |
| 551 | TBuilder.TransposeConv2d, |
| 552 | TGen.tgTransposeConv2d, |
| 553 | ArgGen.agTransposeConv2d, |
| 554 | ), |
| 555 | "types": { |
| 556 | "tf": [tf.float32], |
| 557 | "tflite": [ |
| 558 | tf.float32, |
| 559 | QuantType.CONV_U8_U8, |
| 560 | QuantType.CONV_I8_I8, |
| 561 | QuantType.CONV_I16_I8, |
| 562 | ], |
| 563 | }, |
| 564 | "template": True, |
| 565 | }, |
| 566 | "argmax": { |
| 567 | "operands": (1, 0), |
| 568 | "build_fcn": (TBuilder.Argmax, TGen.tgBasic, ArgGen.agAxes), |
| 569 | "types": {"tf": TYPE_F}, |
| 570 | }, |
| 571 | "avg_pool2d": { |
| 572 | "operands": (1, 0), |
| 573 | "build_fcn": (TBuilder.AvgPool2d, TGen.tgPooling, ArgGen.agPooling), |
| 574 | "types": { |
| 575 | "tf": TYPE_F, |
| 576 | "tflite": list( |
| 577 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 578 | ), |
| 579 | }, |
| 580 | }, |
| 581 | "max_pool2d": { |
| 582 | "operands": (1, 0), |
| 583 | "build_fcn": (TBuilder.MaxPool2d, TGen.tgPooling, ArgGen.agPooling), |
| 584 | "types": { |
| 585 | "tf": TYPE_F, |
| 586 | "tflite": list(TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 587 | # ALL_I16 not supported yet |
| 588 | # In tensorflow/compiler/mlir/lite/ir/tfl_ops.td, |
| 589 | # QI16 is missing from MaxPoolOperandAndResultConstraints |
| 590 | # If adding QI16 back this test can run through. |
| 591 | }, |
| 592 | }, |
| 593 | "reshape": { |
| 594 | "operands": (1, 0), |
| 595 | "build_fcn": (TBuilder.Reshape, TGen.tgBasic, ArgGen.agReshape), |
| 596 | "types": TYPE_FI, |
| 597 | }, |
| 598 | "transpose": { |
| 599 | "operands": (1, 0), |
| 600 | "build_fcn": (TBuilder.Transpose, TGen.tgBasic, ArgGen.agTranspose), |
| 601 | "types": TYPE_FI, |
| 602 | }, |
| 603 | "slice": { |
| 604 | "operands": (1, 0), |
| 605 | "build_fcn": (TBuilder.Slice, TGen.tgBasic, ArgGen.agSlice), |
| 606 | "types": TYPE_FI, |
| 607 | }, |
| 608 | "strided_slice": { |
| 609 | "operands": (1, 0), |
| 610 | "build_fcn": (TBuilder.StridedSlice, TGen.tgBasic, ArgGen.agStridedSlice), |
| 611 | "types": TYPE_FI, |
| 612 | }, |
| 613 | "select": { |
| 614 | "operands": (3, 0), |
| 615 | "build_fcn": (TBuilder.Select, TGen.tgSelect, ArgGen.agNone), |
| 616 | "types": TYPE_FI, |
| 617 | }, |
| 618 | "addn": { |
| 619 | "operands": (4, 0), |
| 620 | "build_fcn": (TBuilder.Addn, TGen.tgBasic, ArgGen.agNone), |
| 621 | "types": TYPE_FI, |
| 622 | }, |
| 623 | "concatv2": { |
| 624 | "operands": (4, 0), |
| 625 | "build_fcn": (TBuilder.Concatv2, TGen.tgBasic, ArgGen.agAxes), |
| 626 | "types": TYPE_FI, |
| 627 | }, |
| 628 | "stack": { |
| 629 | "operands": (4, 0), |
| 630 | "build_fcn": (TBuilder.Stack, TGen.tgBasic, ArgGen.agStack), |
| 631 | "types": TYPE_FI, |
| 632 | }, |
| 633 | "unstack": { |
| 634 | "operands": (1, 0), |
| 635 | "build_fcn": (TBuilder.Unstack, TGen.tgPooling, ArgGen.agAxes), |
| 636 | "types": TYPE_F, |
| 637 | }, |
TatWai Chong | f7008da | 2022-09-09 09:35:40 +0000 | [diff] [blame] | 638 | "mirrorpad": { |
| 639 | "operands": (1, 0), |
| 640 | "build_fcn": (TBuilder.MirrorPad, TGen.tgBasic, ArgGen.agMirrorPad), |
| 641 | "types": TYPE_FI, |
| 642 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 643 | "pad": { |
| 644 | "operands": (1, 0), |
| 645 | "build_fcn": (TBuilder.Pad, TGen.tgBasic, ArgGen.agPad), |
TatWai Chong | 2226f90 | 2023-02-22 18:38:01 -0800 | [diff] [blame] | 646 | "types": { |
| 647 | "tf": TYPE_F, |
| 648 | "tflite": list(TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8]), |
| 649 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 650 | }, |
| 651 | "expand_dims": { |
| 652 | "operands": (1, 0), |
| 653 | "build_fcn": (TBuilder.ExpandDims, TGen.tgBasic, ArgGen.agStack), |
| 654 | "types": TYPE_FI, |
| 655 | }, |
| 656 | "shape": { |
| 657 | "operands": (1, 0), |
| 658 | "build_fcn": (TBuilder.Shape, TGen.tgBasic, ArgGen.agNone), |
| 659 | "types": TYPE_FI, |
| 660 | }, |
| 661 | "rank": { |
| 662 | "operands": (1, 0), |
| 663 | "build_fcn": (TBuilder.Rank, TGen.tgBasic, ArgGen.agNone), |
| 664 | "types": TYPE_FI, |
| 665 | }, |
| 666 | "fill": { |
| 667 | "operands": (1, 0), |
| 668 | "build_fcn": (TBuilder.Fill, TGen.tgBasic, ArgGen.agFill), |
| 669 | "types": TYPE_FI, |
| 670 | }, |
| 671 | "elu": { |
| 672 | "operands": (1, 0), |
| 673 | "build_fcn": (TBuilder.Elu, TGen.tgBasic, ArgGen.agNone), |
| 674 | "types": TYPE_F, |
| 675 | }, |
| 676 | "softmax": { |
| 677 | "operands": (1, 0), |
| 678 | "build_fcn": (TBuilder.Softmax, TGen.tgBasic, ArgGen.agNone), |
| 679 | "types": { |
| 680 | "tf": TYPE_F, |
| 681 | "tflite": list( |
| 682 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 683 | ), |
| 684 | }, |
| 685 | }, |
| 686 | "log_softmax": { |
| 687 | "operands": (1, 0), |
| 688 | "build_fcn": (TBuilder.LogSoftmax, TGen.tgBasic, ArgGen.agNone), |
| 689 | "types": TYPE_F, |
| 690 | }, |
| 691 | "matmul": { |
| 692 | "operands": (2, 0), |
| 693 | "build_fcn": (TBuilder.MatMul, TGen.tgMatmul, ArgGen.agNone), |
| 694 | "types": { |
| 695 | "tf": TYPE_F, |
| 696 | "tflite": list( |
| 697 | TYPE_F |
| 698 | + [QuantType.ALL_U8, QuantType.ALL_I8] |
| 699 | # 16 bits matmul fail to convert |
| 700 | ), |
| 701 | }, |
| 702 | }, |
| 703 | "add_scalar": { |
| 704 | "operands": (1, 0), |
| 705 | "build_fcn": (TBuilder.AddScalar, TGen.tgBasic, ArgGen.agNone), |
| 706 | "types": TYPE_F, |
| 707 | }, |
| 708 | "add_1d": { |
| 709 | "operands": (2, 0), |
| 710 | "build_fcn": (TBuilder.Add1d, TGen.tgBasic, ArgGen.agNone), |
| 711 | "types": TYPE_F, |
| 712 | }, |
| 713 | "split": { |
| 714 | "operands": (1, 0), |
| 715 | "build_fcn": (TBuilder.Split, TGen.tgBasic, ArgGen.agSplit), |
| 716 | "types": TYPE_FI, |
| 717 | }, |
| 718 | "tile": { |
| 719 | "operands": (1, 0), |
| 720 | "build_fcn": (TBuilder.Tile, TGen.tgBasic, ArgGen.agTile), |
| 721 | "types": TYPE_FI, |
| 722 | }, |
| 723 | "reverse": { |
| 724 | "operands": (1, 0), |
| 725 | "build_fcn": (TBuilder.Reverse, TGen.tgBasic, ArgGen.agAxes), |
| 726 | "types": {"tf": TYPE_FI}, |
| 727 | }, |
| 728 | "gather": { |
| 729 | "operands": (1, 0), |
| 730 | "build_fcn": (TBuilder.Gather, TGen.tgBasic, ArgGen.agGather), |
| 731 | "types": TYPE_FI, |
| 732 | }, |
| 733 | "gather_nd": { |
| 734 | "operands": (1, 0), |
| 735 | "build_fcn": (TBuilder.GatherNd, TGen.tgBasic, ArgGen.agGatherND), |
| 736 | "types": TYPE_FI, |
| 737 | }, |
| 738 | "scatter_nd": { |
| 739 | "operands": (1, 0), |
| 740 | "build_fcn": (TBuilder.ScatterNd, TGen.tgBasic, ArgGen.agScatterND), |
| 741 | "types": TYPE_FI, |
| 742 | }, |
| 743 | "space_to_batch": { |
| 744 | "operands": (1, 0), |
| 745 | "build_fcn": (TBuilder.SpaceToBatch, TGen.tgBasic, ArgGen.agSpaceToBatch), |
| 746 | "types": TYPE_F, |
| 747 | }, |
| 748 | "batch_to_space": { |
| 749 | "operands": (1, 0), |
| 750 | "build_fcn": (TBuilder.BatchToSpace, TGen.tgBasic, ArgGen.agBatchToSpace), |
| 751 | "types": TYPE_F, |
| 752 | }, |
| 753 | "space_to_depth": { |
| 754 | "operands": (1, 0), |
| 755 | "build_fcn": (TBuilder.SpaceToDepth, TGen.tgBasic, ArgGen.agSpaceToDepth), |
| 756 | "types": TYPE_F, |
| 757 | }, |
| 758 | "depth_to_space": { |
| 759 | "operands": (1, 0), |
| 760 | "build_fcn": (TBuilder.DepthToSpace, TGen.tgBasic, ArgGen.agDepthToSpace), |
| 761 | "types": TYPE_F, |
| 762 | }, |
| 763 | "one_hot": { |
| 764 | "operands": (3, 1), |
| 765 | "build_fcn": (TBuilder.OneHot, TGen.tgOneHot, ArgGen.agOneHot), |
| 766 | "types": TYPE_FI, |
| 767 | }, |
| 768 | "fakequant": { |
| 769 | "operands": (1, 0), |
| 770 | "build_fcn": ( |
| 771 | TBuilder.Fakequant, |
| 772 | TGen.tgBasic, |
| 773 | ArgGen.agFakequant, |
| 774 | ), |
| 775 | "types": {"tf": TYPE_F}, |
| 776 | }, |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 777 | "resize": { |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 778 | "operands": (1, 0), |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 779 | "build_fcn": (TBuilder.Resize, TGen.tgPooling, ArgGen.agResize), |
TatWai Chong | f732609 | 2022-06-08 12:17:14 -0700 | [diff] [blame] | 780 | "types": { |
| 781 | "tf": TYPE_F, |
| 782 | "tflite": list( |
| 783 | TYPE_F + [QuantType.ALL_U8, QuantType.ALL_I8, QuantType.ALL_I16] |
| 784 | ), |
| 785 | }, |
| 786 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 787 | "left_shift": { |
| 788 | "operands": (1, 0), |
| 789 | "build_fcn": (TBuilder.LeftShift, TGen.tgBasic, ArgGen.agShift), |
| 790 | "types": {"tf": [tf.int32]}, |
| 791 | }, |
| 792 | "right_shift": { |
| 793 | "operands": (1, 0), |
| 794 | "build_fcn": (TBuilder.RightShift, TGen.tgBasic, ArgGen.agShift), |
| 795 | "types": { |
| 796 | "tf": [ |
| 797 | tf.int32, |
| 798 | ] |
| 799 | }, |
| 800 | }, |
Jerry Ge | 9e94af8 | 2022-10-27 09:57:00 -0700 | [diff] [blame] | 801 | "while": { |
| 802 | "operands": (1, 0), |
| 803 | "build_fcn": (TBuilder.While, TGen.tgBasic, ArgGen.agNone), |
| 804 | "types": { |
| 805 | "tflite": list(TYPE_F), |
| 806 | }, |
| 807 | }, |
| 808 | "lstm": { |
| 809 | "operands": (1, 0), |
| 810 | "build_fcn": (TBuilder.LSTM, TGen.tgRecurrent, ArgGen.agNone), |
| 811 | "types": { |
| 812 | "tflite": [ |
| 813 | tf.float32, |
| 814 | # tf.int32 |
| 815 | ] |
| 816 | }, |
| 817 | }, |
| 818 | "gru": { |
| 819 | "operands": (1, 0), |
| 820 | "build_fcn": (TBuilder.GRU, TGen.tgRecurrent, ArgGen.agNone), |
| 821 | "types": { |
| 822 | "tflite": [ |
| 823 | tf.float32, |
| 824 | # tf.int32 |
| 825 | ] |
| 826 | }, |
| 827 | }, |
| 828 | "rnn": { |
| 829 | "operands": (1, 0), |
| 830 | "build_fcn": (TBuilder.RNN, TGen.tgRecurrent, ArgGen.agNone), |
| 831 | "types": { |
| 832 | "tflite": [ |
| 833 | tf.float32, |
| 834 | ] |
| 835 | }, |
| 836 | }, |
Luke Hutton | 261b7b6 | 2023-01-10 14:50:31 +0000 | [diff] [blame] | 837 | "rfft2d": { |
| 838 | "operands": (1, 0), |
| 839 | "build_fcn": (TBuilder.RFFT2d, TGen.tgRFFT2d, ArgGen.agRFFT2d), |
| 840 | "types": { |
| 841 | "tflite": TYPE_F, |
| 842 | }, |
| 843 | }, |
Luke Hutton | 714aa60 | 2023-02-08 19:45:26 +0000 | [diff] [blame^] | 844 | "real": { |
| 845 | "operands": (1, 0), |
| 846 | "build_fcn": (TBuilder.Real, TGen.tgComplexComponents, ArgGen.agNone), |
| 847 | "types": { |
| 848 | "tflite": [tf.complex64], |
| 849 | }, |
| 850 | }, |
| 851 | "imag": { |
| 852 | "operands": (1, 0), |
| 853 | "build_fcn": (TBuilder.Imag, TGen.tgComplexComponents, ArgGen.agNone), |
| 854 | "types": { |
| 855 | "tflite": [tf.complex64], |
| 856 | }, |
| 857 | }, |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 858 | } |
| 859 | |
| 860 | # Shapes to be tested; default can be overwritten |
| 861 | shape_list = [ |
| 862 | (1,), |
| 863 | (64,), |
| 864 | (14, 19), |
| 865 | (13, 21, 3), |
Luke Hutton | 261b7b6 | 2023-01-10 14:50:31 +0000 | [diff] [blame] | 866 | (1, 8, 16), |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 867 | (1, 4, 4, 4), |
| 868 | (1, 8, 4, 17), |
| 869 | (1, 4, 8, 19), |
| 870 | (1, 32, 32, 8), |
| 871 | (1, 7, 7, 9), |
TatWai Chong | 0cef07e | 2023-02-27 13:22:52 -0800 | [diff] [blame] | 872 | (3, 1, 1, 7), |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 873 | (2, 2, 7, 7, 2), |
| 874 | (1, 4, 8, 21, 17), |
| 875 | (3, 32, 16, 16, 5), |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 876 | ] |
| 877 | |
| 878 | |
| 879 | def gen_rand_shapes(args): |
| 880 | """Overwrite the global shape list with a new list of random shapes""" |
| 881 | global shape_list |
| 882 | |
| 883 | rng = np.random.default_rng(args.random_seed) |
| 884 | |
| 885 | # Don't let things get too big... cap the maximum volume, but let |
| 886 | # an individual dimension be 1..47 |
| 887 | max_total_volume = 32 * 32 * 4 |
| 888 | |
| 889 | shape_list = [] |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 890 | # Only iterate over ranks 2, 3, 4, and 5 |
| 891 | for rank in range(2, 6): |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 892 | for n in range(args.random_shapes): |
| 893 | new_shape = rng.integers(1, 48, size=rank) |
| 894 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 895 | # Set the batch dimension on 4D or 5D objects to 1 |
| 896 | if rank == 4 or rank == 5: |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 897 | new_shape[0] = 1 |
| 898 | |
| 899 | # Limit the total shape volume and throw out any |
| 900 | # shapes that wouldn't leave at least size=2 in some non-batch dimension |
| 901 | volume = 1 |
| 902 | skip_shape = False |
| 903 | for i in range(rank): |
| 904 | |
| 905 | volume *= new_shape[i] |
| 906 | |
| 907 | # Reduce the shape, while it's larger than the maximum volume |
| 908 | while volume > max_total_volume: |
| 909 | new_shape[i] = new_shape[i] // 2 |
| 910 | volume = volume // 2 |
| 911 | |
| 912 | # Now an untenable dimension size? Skip this one. |
| 913 | if new_shape[i] < 1: |
| 914 | skip_shape = True |
| 915 | |
| 916 | if not skip_shape: |
| 917 | shape_list.append(tuple(new_shape)) |
| 918 | |
| 919 | |
| 920 | # Construct, run and save a whole tensorflow tf.function to a protobuf file |
| 921 | # or convert to .tflite if it's quantized unit test |
| 922 | def run_unit_test( |
| 923 | op_name, |
| 924 | args, |
| 925 | test_dir, |
| 926 | curr_shape, |
| 927 | addl_args, |
| 928 | dtype, |
| 929 | excluded_framework_list, |
| 930 | quantized_inference_dtype, |
| 931 | result_name, |
| 932 | seed, |
| 933 | ): |
| 934 | |
| 935 | try: |
| 936 | op = TF_OP_LIST[op_name] |
| 937 | op_fcn, tensor_gen_fcn, arg_gen_fcn = op["build_fcn"] |
| 938 | |
| 939 | # Get and seed a random number generator for this test |
| 940 | rng = np.random.default_rng(seed) |
| 941 | |
| 942 | # return placeholders=(str: name, np.array: value) |
| 943 | # consts=(str: name, np.array: value) |
| 944 | placeholders, consts = tensor_gen_fcn(op, curr_shape, dtype, rng) |
| 945 | |
| 946 | # if test doesn't have any placeholders/consts, terminated |
| 947 | if len(placeholders) == 0 and len(consts) == 0: |
| 948 | return True |
| 949 | |
| 950 | if not args.quiet: |
| 951 | print(" {} ".format(test_dir)) |
| 952 | |
| 953 | try: |
| 954 | os.mkdir(test_dir) |
| 955 | except FileExistsError: |
| 956 | pass |
| 957 | |
| 958 | const_nodes = [value for name, value in consts] |
| 959 | |
| 960 | num_placeholders = len(placeholders) |
| 961 | # if test is quantized, create tensor quantization metadata info for |
| 962 | # each input tensor, based on different quantized type |
| 963 | if quantized_inference_dtype: |
| 964 | is_quantized = True |
| 965 | # TODO: support INT8 IFM x INT4 weight later |
| 966 | if quantized_inference_dtype == QuantType.ALL_U8: |
| 967 | qzero = [128] * num_placeholders |
| 968 | numpy_dtype = [np.uint8] * num_placeholders |
| 969 | tflite_inference_dtype = tf.uint8 |
| 970 | elif quantized_inference_dtype == QuantType.ALL_I8: |
| 971 | qzero = [0] * num_placeholders |
| 972 | numpy_dtype = [np.int8] * num_placeholders |
| 973 | tflite_inference_dtype = tf.int8 |
| 974 | elif quantized_inference_dtype == QuantType.ALL_I16: |
| 975 | qzero = [0] * num_placeholders |
| 976 | numpy_dtype = [np.int16] * num_placeholders |
| 977 | tflite_inference_dtype = tf.int16 |
| 978 | elif quantized_inference_dtype == QuantType.CONV_U8_U8: |
| 979 | assert ( |
| 980 | num_placeholders == 1 |
| 981 | ), "Unsupported number of placeholders for Convolution: {}".format( |
| 982 | num_placeholders |
| 983 | ) |
| 984 | qzero = [128] * num_placeholders |
| 985 | if num_placeholders == 2: |
| 986 | numpy_dtype = [np.uint8, np.uint8] |
| 987 | else: |
| 988 | numpy_dtype = [np.uint8, np.uint8, np.int32] |
| 989 | tflite_inference_dtype = tf.uint8 |
| 990 | elif quantized_inference_dtype == QuantType.CONV_I8_I8: |
| 991 | assert ( |
| 992 | num_placeholders == 1 |
| 993 | ), "Unsupported number of placeholders for Convolution: {}".format( |
| 994 | num_placeholders |
| 995 | ) |
| 996 | qzero = [0] * num_placeholders |
| 997 | if num_placeholders == 2: |
| 998 | numpy_dtype = [np.int8, np.int8] |
| 999 | else: |
| 1000 | numpy_dtype = [np.int8, np.int8, np.int32] |
| 1001 | tflite_inference_dtype = tf.int8 |
| 1002 | elif quantized_inference_dtype == QuantType.CONV_I16_I8: |
| 1003 | assert ( |
| 1004 | num_placeholders == 1 |
| 1005 | ), "Unsupported number of placeholders for Convolution: {}".format( |
| 1006 | num_placeholders |
| 1007 | ) |
| 1008 | if num_placeholders == 2: |
| 1009 | qzero = [0, 0] |
| 1010 | numpy_dtype = [np.int16, np.int8] |
| 1011 | else: |
| 1012 | qzero = [0, 0, 0] |
| 1013 | numpy_dtype = [ |
| 1014 | np.int16, |
| 1015 | np.int8, |
| 1016 | np.int64, |
| 1017 | ] # np.int64 to represent 40 bits accumulator |
| 1018 | tflite_inference_dtype = tf.int16 |
| 1019 | else: |
| 1020 | raise Exception( |
| 1021 | "Unsupported fakequant dtype: {}".format(quantized_inference_dtype) |
| 1022 | ) |
| 1023 | |
| 1024 | else: |
| 1025 | is_quantized = False |
| 1026 | |
| 1027 | tf_model_filename = None |
| 1028 | tf_result_npy_filename = None |
| 1029 | tf_result_name = None |
| 1030 | |
| 1031 | tflite_model_filename = None |
| 1032 | tflite_result_npy_filename = None |
| 1033 | tflite_result_name = None |
| 1034 | |
| 1035 | placeholder_names = [] |
| 1036 | placeholder_vals = [] |
| 1037 | placeholder_signatures = () |
| 1038 | placeholder_npy_filenames = [] |
| 1039 | placeholder_shapes = [] |
| 1040 | |
| 1041 | for idx, (name, val) in enumerate(placeholders): |
| 1042 | placeholder_names.append(name) |
| 1043 | placeholder_signatures = placeholder_signatures + ( |
| 1044 | tf.TensorSpec(shape=val.shape, dtype=val.dtype, name=name), |
| 1045 | ) |
| 1046 | placeholder_npy_filenames.append("{}.npy".format(name.split(":")[0])) |
| 1047 | placeholder_shapes.append(val.shape) |
| 1048 | |
| 1049 | # Get test builder class |
| 1050 | fcn_node = op_fcn(*const_nodes, *addl_args, result_name) |
| 1051 | concrete_function = tf.function(input_signature=placeholder_signatures)( |
| 1052 | fcn_node.eval |
| 1053 | ).get_concrete_function() |
| 1054 | |
| 1055 | if is_quantized: |
| 1056 | |
| 1057 | assert dtype is tf.float32, "quantized test must come from float32 graph" |
| 1058 | |
| 1059 | # 1. Quantize float placeholder npy to quantized to feed the graph |
| 1060 | for idx, (name, val) in enumerate(placeholders): |
| 1061 | |
| 1062 | # we use np.amin()/np.amax() to determine dynamic range |
| 1063 | # for quantized test |
| 1064 | zeropoint = 0 |
| 1065 | scale = 1.0 |
| 1066 | if numpy_dtype[idx] != np.int64: |
| 1067 | qmin = np.iinfo(numpy_dtype[idx]).min |
| 1068 | qmax = np.iinfo(numpy_dtype[idx]).max |
| 1069 | num_bits = np.iinfo(numpy_dtype[idx]).bits |
| 1070 | # 40 bit is represented as np.int64 |
| 1071 | else: |
| 1072 | num_bits = 40 |
| 1073 | qmin = -(1 << num_bits) |
| 1074 | qmax = (1 << num_bits) - 1 |
| 1075 | |
| 1076 | min_val = np.amin(val) |
| 1077 | max_val = np.amax(val) |
| 1078 | |
| 1079 | # for single value tensor, we set scale equal to the abs(value), |
| 1080 | # and fix zeropoint to 128 |
| 1081 | # if val > 0, it'll be represented as 129, |
| 1082 | # where val = (129 - 128) * val |
| 1083 | # if val < 0, it'll be represented as 127, |
| 1084 | # where val = (127 - 128) * (-val) |
| 1085 | # if val == 0, it'll be represted as 128, with range [-128.0, 128.0] |
| 1086 | # and let quantized 1 represent the value |
| 1087 | # also adjust effective min/max consequently |
| 1088 | if max_val == min_val: |
| 1089 | if max_val != 0: |
| 1090 | scale = abs(max_val) |
| 1091 | else: |
| 1092 | scale = 1.0 |
| 1093 | min_val = float(qmin - qzero[idx]) * scale |
| 1094 | max_val = float(qmax - qzero[idx]) * scale |
| 1095 | else: |
| 1096 | scale = (max_val - min_val) / float(qmax - qmin) |
| 1097 | zeropoint = int(round((-min_val) / scale)) + qmin |
| 1098 | |
| 1099 | # run through tf.fakequant first to assure quantization error aligned |
| 1100 | fakequant_val = tf.quantization.fake_quant_with_min_max_args( |
| 1101 | val, |
| 1102 | min=min_val, |
| 1103 | max=max_val, |
| 1104 | num_bits=num_bits, |
| 1105 | name="gen_quant_npy", |
| 1106 | ) |
| 1107 | |
| 1108 | quant_val = np.round(fakequant_val / scale).astype(np.int32) + zeropoint |
| 1109 | |
| 1110 | # very few unit tests after TF hash may/2020, this quantized |
| 1111 | # value for some reason exceed [0, 255] range |
| 1112 | saved_val = np.clip(quant_val, qmin, qmax).astype(numpy_dtype[idx]) |
| 1113 | |
| 1114 | # saved all quantized tensor as np.int32 |
| 1115 | # since TOSA numpy Cpp API only supports int32 |
| 1116 | np.save( |
| 1117 | os.path.join(test_dir, placeholder_npy_filenames[idx]), |
| 1118 | saved_val.astype(np.int32), |
| 1119 | False, |
| 1120 | ) |
| 1121 | |
| 1122 | placeholder_vals.append(tf.convert_to_tensor(saved_val)) |
| 1123 | |
| 1124 | # 2. Convert the model to quantized TFLite flatbuffer |
| 1125 | module = tf.Module() |
| 1126 | converter = tf.lite.TFLiteConverter.from_concrete_functions( |
| 1127 | [concrete_function], module |
| 1128 | ) |
| 1129 | converter.optimizations = [tf.lite.Optimize.DEFAULT] |
| 1130 | converter.experimental_new_converter = True |
| 1131 | |
| 1132 | # use MLIR-based post-quantizer |
| 1133 | converter.experimental_new_quantizer = True |
| 1134 | |
| 1135 | flag = ( |
| 1136 | tf.lite.OpsSet.EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8 # noqa: E501 |
| 1137 | ) |
| 1138 | if tflite_inference_dtype == tf.int16: |
| 1139 | converter.target_spec.supported_ops = [flag] |
| 1140 | |
| 1141 | def input_stats(): |
Jerry Ge | b1f2501 | 2023-03-03 11:33:51 -0800 | [diff] [blame] | 1142 | ## Rsqrt can only handle positive numbers |
| 1143 | elem_signedness = ElemSignedness.ALL_RANGE |
| 1144 | if op_name == "rsqrt": |
| 1145 | elem_signedness = ElemSignedness.POSITIVE |
| 1146 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1147 | for i in range(0, args.num_samples): |
| 1148 | a = [ |
Jerry Ge | b1f2501 | 2023-03-03 11:33:51 -0800 | [diff] [blame] | 1149 | TGen.getRand(shape, tf.float32, rng, elem_signedness) |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1150 | for shape in placeholder_shapes |
| 1151 | ] |
| 1152 | yield a |
| 1153 | |
| 1154 | converter.representative_dataset = input_stats |
| 1155 | converter.inference_input_type = tflite_inference_dtype |
| 1156 | converter.inference_output_type = tflite_inference_dtype |
| 1157 | |
| 1158 | tflite_model = converter.convert() |
| 1159 | |
| 1160 | tflite_model_filename = "model.tflite" |
| 1161 | |
| 1162 | # Write out converted model to disk |
| 1163 | with open(os.path.join(test_dir, tflite_model_filename), "wb") as f: |
| 1164 | f.write(tflite_model) |
| 1165 | |
| 1166 | else: # is_quantized is False |
| 1167 | |
| 1168 | # 1. Saved out numpy array directly |
| 1169 | for idx, (name, val) in enumerate(placeholders): |
| 1170 | placeholder_vals.append(tf.convert_to_tensor(val)) |
Luke Hutton | 714aa60 | 2023-02-08 19:45:26 +0000 | [diff] [blame^] | 1171 | |
| 1172 | # Complex tensors are expected to be repsesented by a |
| 1173 | # single floating point tensor of shape [?, ..., ?, 2]. |
| 1174 | if val.dtype == np.complex64: |
| 1175 | val_shape = val.shape + (2,) |
| 1176 | val = val.view(np.float32) |
| 1177 | val = val.reshape(val_shape) |
| 1178 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1179 | np.save( |
| 1180 | os.path.join(test_dir, placeholder_npy_filenames[idx]), val, False |
| 1181 | ) |
| 1182 | |
| 1183 | # 2.a Saved out .pb if framework includes tensorflow |
| 1184 | if "tf" not in excluded_framework_list: |
| 1185 | # Write out graph as protobuf to disk |
| 1186 | tf_model_filename = "model.pb" |
| 1187 | tf.io.write_graph( |
| 1188 | concrete_function.graph, test_dir, tf_model_filename, True |
| 1189 | ) |
| 1190 | |
| 1191 | # 2.b Saved out .tflite if framework includes tflite |
| 1192 | if "tflite" not in excluded_framework_list: |
| 1193 | # Convert the model to TFLite flatbuffer |
| 1194 | module = tf.Module() |
| 1195 | converter = tf.lite.TFLiteConverter.from_concrete_functions( |
| 1196 | [concrete_function], module |
| 1197 | ) |
| 1198 | |
| 1199 | converter.experimental_new_converter = True |
| 1200 | |
| 1201 | # Even it's non-quantized int32 test, this needs to be set to tf.float32 |
| 1202 | converter.inference_input_type = tf.float32 |
| 1203 | converter.inference_output_type = tf.float32 |
| 1204 | tflite_model = converter.convert() |
| 1205 | |
| 1206 | # Write out converted model to disk |
| 1207 | tflite_model_filename = "model.tflite" |
| 1208 | with open(os.path.join(test_dir, tflite_model_filename), "wb") as f: |
| 1209 | f.write(tflite_model) |
| 1210 | |
| 1211 | # Get TF reference result if .pb is specified |
| 1212 | if tf_model_filename: |
| 1213 | tf_result_npy_filename = "tf_result.npy" |
| 1214 | tf_result = concrete_function(*placeholder_vals) |
| 1215 | np.save(os.path.join(test_dir, tf_result_npy_filename), tf_result, False) |
| 1216 | |
| 1217 | tf_result_name = result_name |
| 1218 | |
| 1219 | # Get TFLite inference result if .tflite is specified |
| 1220 | if tflite_model_filename: |
| 1221 | tflite_result_npy_filename = "tflite_result.npy" |
| 1222 | |
Luke Hutton | 5c84421 | 2023-01-27 14:17:52 +0000 | [diff] [blame] | 1223 | ops_with_optimized_only_kernel = ["elu", "ceil", "gather", "rfft2d"] |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1224 | |
| 1225 | if args.tflite_kernel_mode == "optimized" or ( |
| 1226 | op_name in ops_with_optimized_only_kernel |
| 1227 | ): |
| 1228 | interpreter = tf.lite.Interpreter( |
| 1229 | model_path=os.path.join(test_dir, tflite_model_filename) |
| 1230 | ) |
| 1231 | elif args.tflite_kernel_mode == "reference": |
| 1232 | interpreter = tf.lite.Interpreter( |
| 1233 | model_path=os.path.join(test_dir, tflite_model_filename), |
| 1234 | experimental_op_resolver_type=OpResolverType.BUILTIN_REF, |
| 1235 | ) |
| 1236 | else: |
| 1237 | assert 0, "unknown tflite interpreter mode {}".format( |
| 1238 | args.tflite_kernel_mode |
| 1239 | ) |
| 1240 | interpreter.allocate_tensors() |
| 1241 | |
| 1242 | input_details = interpreter.get_input_details() |
| 1243 | output_details = interpreter.get_output_details() |
| 1244 | |
| 1245 | assert len(input_details) == len( |
| 1246 | placeholder_vals |
| 1247 | ), "number of placeholder mismatch" |
| 1248 | |
| 1249 | for idx, val in enumerate(placeholder_vals): |
| 1250 | interpreter.set_tensor(input_details[idx]["index"], val.numpy()) |
| 1251 | |
| 1252 | interpreter.invoke() |
| 1253 | tflite_result = interpreter.get_tensor(output_details[0]["index"]) |
| 1254 | |
| 1255 | np.save( |
| 1256 | os.path.join(test_dir, tflite_result_npy_filename), tflite_result, False |
| 1257 | ) |
| 1258 | |
| 1259 | # Result tensor name would change after converting to TFLite flatbuffer |
| 1260 | # Overwrite the information from TFLite models directly. |
| 1261 | # Assume single result tensor now |
| 1262 | tflite_result_name = output_details[0]["name"] |
| 1263 | |
| 1264 | # Write out test descriptor |
| 1265 | write_test_json( |
| 1266 | filename=os.path.join(test_dir, "test.json"), |
| 1267 | tf_model_filename=tf_model_filename, |
| 1268 | tf_result_npy_filename=tf_result_npy_filename, |
| 1269 | tf_result_name=tf_result_name, |
| 1270 | tflite_model_filename=tflite_model_filename, |
| 1271 | tflite_result_npy_filename=tflite_result_npy_filename, |
| 1272 | tflite_result_name=tflite_result_name, |
| 1273 | ifm_name=placeholder_names, |
| 1274 | ifm_file=placeholder_npy_filenames, |
| 1275 | ifm_shape=placeholder_shapes, |
| 1276 | framework_exclusions=excluded_framework_list, |
| 1277 | quantized=is_quantized, |
| 1278 | ) |
| 1279 | except Exception as e: |
| 1280 | msg = "Error running task: {}".format(e) |
| 1281 | print(msg) |
| 1282 | print( |
| 1283 | "".join( |
| 1284 | traceback.format_exception(etype=type(e), value=e, tb=e.__traceback__) |
| 1285 | ) |
| 1286 | ) |
| 1287 | return False |
| 1288 | return True |
| 1289 | |
| 1290 | |
| 1291 | def build_const_net( |
| 1292 | args, |
| 1293 | curr_shape, |
| 1294 | op_name, |
| 1295 | dtype, |
| 1296 | excluded_framework_list, |
| 1297 | quantized_inference_dtype, |
| 1298 | result_name, |
| 1299 | seed, |
| 1300 | rng, |
| 1301 | filter, |
| 1302 | unit_test_args, |
| 1303 | ): |
| 1304 | |
| 1305 | if quantized_inference_dtype: |
| 1306 | quant_dtype = get_tf_dtype(quantized_inference_dtype) |
| 1307 | test_dir = "test_{}_{}".format(op_name, get_shape_str(curr_shape, quant_dtype)) |
| 1308 | else: |
| 1309 | test_dir = "test_{}_{}".format(op_name, get_shape_str(curr_shape, dtype)) |
| 1310 | test_dir = os.path.join(args.output_dir, test_dir) |
| 1311 | |
| 1312 | # If the operator has an additional function to generate arguments, call it |
| 1313 | # here and iterate through the argument list that it generates |
| 1314 | op = TF_OP_LIST[op_name] |
| 1315 | op_fcn, tensor_gen_fcn, arg_gen_fcn = op["build_fcn"] |
| 1316 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 1317 | try: |
| 1318 | rank_lo, rank_hi = op["rank"] |
| 1319 | except KeyError: |
| 1320 | # Set testing rank to (1, 4) in default. |
| 1321 | rank_lo = 1 |
| 1322 | rank_hi = 4 |
| 1323 | |
| 1324 | if len(curr_shape) not in range(rank_lo, rank_hi + 1): |
| 1325 | return |
| 1326 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1327 | addl_args_tuple = arg_gen_fcn(op, curr_shape, rng) |
| 1328 | for desc, addl_args in addl_args_tuple: |
Jeremy Johnson | 0e6218e | 2022-05-05 17:08:04 +0100 | [diff] [blame] | 1329 | # Only filter on the full test_name, not the output directory |
| 1330 | _, test_name = os.path.split(test_dir + desc) |
| 1331 | if not filter or filter.search(test_name): |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1332 | unit_test_args.append( |
| 1333 | [ |
| 1334 | op_name, |
| 1335 | args, |
| 1336 | test_dir + desc, |
| 1337 | curr_shape, |
| 1338 | addl_args, |
| 1339 | dtype, |
| 1340 | excluded_framework_list, |
| 1341 | quantized_inference_dtype, |
| 1342 | result_name, |
| 1343 | seed, |
| 1344 | ] |
| 1345 | ) |
| 1346 | |
| 1347 | |
| 1348 | # python hash is not reproducible, create hash for our purpose |
| 1349 | def op_name_hash(op_name): |
| 1350 | result = 0xDEADBEEF |
| 1351 | for ch in op_name: |
| 1352 | if result & 1: |
| 1353 | result = (ord(ch) << 24) ^ (result >> 1) ^ 0x82608EDB |
| 1354 | else: |
| 1355 | result = (ord(ch) << 24) ^ (result >> 1) |
| 1356 | |
| 1357 | return result |
| 1358 | |
| 1359 | |
| 1360 | def generate_op_tests(args, op_name, shape_list, result_name, filter, unit_test_args): |
| 1361 | |
| 1362 | if not args.quiet: |
| 1363 | print( |
| 1364 | "Generating tests for {} ".format( |
| 1365 | op_name |
| 1366 | ) |
| 1367 | ) |
| 1368 | |
| 1369 | op = TF_OP_LIST[op_name] |
| 1370 | |
| 1371 | # Seed the RNG so that we get the same random tests for each test each time |
| 1372 | # If the number of tests for a given generation function changes, the tests |
| 1373 | # for that operator may also change accordingly, but this will at least keep |
| 1374 | # down churn across operators. |
| 1375 | |
| 1376 | bounded_hash_val = (args.random_seed + op_name_hash(op_name)) % np.iinfo( |
| 1377 | np.int32 |
| 1378 | ).max |
| 1379 | rng = np.random.default_rng(bounded_hash_val) |
| 1380 | |
| 1381 | # this is a dictionary with 'tf' and 'tflite' as key |
| 1382 | # and value being the data types we want to test under these framework |
| 1383 | |
| 1384 | if isinstance(op["types"], dict): |
| 1385 | try: |
| 1386 | tf_dtypes = op["types"]["tf"] |
| 1387 | except KeyError: |
| 1388 | tf_dtypes = [] |
| 1389 | try: |
| 1390 | tflite_dtypes = op["types"]["tflite"] |
| 1391 | except KeyError: |
| 1392 | tflite_dtypes = [] |
| 1393 | elif isinstance(op["types"], list): |
| 1394 | tf_dtypes = op["types"] |
| 1395 | tflite_dtypes = op["types"] |
| 1396 | |
| 1397 | tf_nonquantized_dtypes = tf_dtypes # tf doesn't support quantized data types |
| 1398 | tflite_quantized_dtypes = [] |
| 1399 | tflite_nonquantized_dtypes = [] |
| 1400 | for dtype in tflite_dtypes: |
| 1401 | if isinstance(dtype, QuantType): |
| 1402 | tflite_quantized_dtypes.append(dtype) |
| 1403 | else: |
| 1404 | tflite_nonquantized_dtypes.append(dtype) |
| 1405 | |
| 1406 | nonquantized_dtypes_set = set(tf_nonquantized_dtypes).union( |
| 1407 | set(tflite_nonquantized_dtypes) |
| 1408 | ) |
| 1409 | nonquantized_dtypes = list(nonquantized_dtypes_set) |
| 1410 | quantized_dtypes = tflite_quantized_dtypes |
| 1411 | |
| 1412 | # populate non quantized unit test arguments |
| 1413 | for dtype in nonquantized_dtypes: |
| 1414 | |
| 1415 | excluded_framework_set = set(ALL_FRAMEWORKS) |
| 1416 | if dtype in tf_nonquantized_dtypes: |
| 1417 | excluded_framework_set.remove("tf") |
| 1418 | if dtype in tflite_nonquantized_dtypes: |
| 1419 | excluded_framework_set.remove("tflite") |
| 1420 | excluded_framework_list = list(excluded_framework_set) |
| 1421 | |
| 1422 | for curr_shape in shape_list: |
| 1423 | build_const_net( |
| 1424 | args, |
| 1425 | curr_shape, |
| 1426 | op_name, |
| 1427 | dtype, |
| 1428 | excluded_framework_list, |
| 1429 | None, |
| 1430 | result_name, |
| 1431 | bounded_hash_val, |
| 1432 | rng, |
| 1433 | filter, |
| 1434 | unit_test_args, |
| 1435 | ) |
| 1436 | |
| 1437 | # populate quantized unit test arguments |
| 1438 | # must exclude 'tf' and source dtype being tf.float32 |
| 1439 | for dtype in quantized_dtypes: |
| 1440 | for curr_shape in shape_list: |
| 1441 | build_const_net( |
| 1442 | args, |
| 1443 | curr_shape, |
| 1444 | op_name, |
| 1445 | tf.float32, |
| 1446 | ["tf"], |
| 1447 | dtype, |
| 1448 | result_name, |
| 1449 | bounded_hash_val, |
| 1450 | rng, |
| 1451 | filter, |
| 1452 | unit_test_args, |
| 1453 | ) |
| 1454 | |
| 1455 | return unit_test_args |
| 1456 | |
| 1457 | |
| 1458 | def createDynamicOpLists(): |
| 1459 | """The templated operators are conv2d-style operators with a number of kernel |
| 1460 | sizes. Since the operator is unchanged, we generate the range of kernel |
| 1461 | sizes here in this loop and remove the original templates from the list. |
| 1462 | |
| 1463 | This could be expanded to non-conv2d-style operators in the future.""" |
| 1464 | |
| 1465 | # Dynamically create op lists for convolutions with a list of kernel sizes |
| 1466 | KERNELS = [ |
| 1467 | [1, 1], |
| 1468 | [3, 3], |
| 1469 | [5, 5], |
| 1470 | ] |
| 1471 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 1472 | # dim = [D, H, W] |
| 1473 | KERNELS_3D = [ |
| 1474 | [1, 1, 1], |
| 1475 | [2, 3, 3], |
| 1476 | [3, 5, 5], |
| 1477 | ] |
| 1478 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1479 | TEMPLATE_LIST = [ |
| 1480 | "conv2d", |
| 1481 | "conv2d_bias", |
| 1482 | "conv2d_relu", |
| 1483 | "conv2d_relu6", |
| 1484 | "conv2d_relu_n1_to_1", |
| 1485 | "conv2d_tanh", |
| 1486 | "depthwise_conv2d", |
| 1487 | "depthwise_conv2d_bias", |
| 1488 | "transpose_conv2d", |
| 1489 | ] |
| 1490 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 1491 | TEMPLATE_LIST_CONV3D = [ |
| 1492 | "conv3d", |
| 1493 | "conv3d_bias", |
| 1494 | ] |
| 1495 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1496 | for t in TEMPLATE_LIST: |
| 1497 | for k in KERNELS: |
| 1498 | testName = "{}_{}x{}".format(t, k[0], k[1]) |
| 1499 | TF_OP_LIST[testName] = TF_OP_LIST["{}_TEMPLATE".format(t)].copy() |
| 1500 | TF_OP_LIST[testName]["filter"] = k |
| 1501 | TF_OP_LIST[testName]["template"] = False |
| 1502 | |
TatWai Chong | fd62905 | 2022-07-25 04:01:58 +0000 | [diff] [blame] | 1503 | # The existing operators don't support the dimension of kernel that is higher than 2. |
| 1504 | for t in TEMPLATE_LIST_CONV3D: |
| 1505 | for k in KERNELS_3D: |
| 1506 | testName = "{}_{}x{}x{}".format(t, k[0], k[1], k[2]) |
| 1507 | TF_OP_LIST[testName] = TF_OP_LIST["{}_TEMPLATE".format(t)].copy() |
| 1508 | TF_OP_LIST[testName]["filter"] = k |
| 1509 | TF_OP_LIST[testName]["template"] = False |
| 1510 | |
Jeremy Johnson | 015c355 | 2022-02-23 12:15:03 +0000 | [diff] [blame] | 1511 | # Delete any templates after having created any dynamic ops |
| 1512 | # This is a two-pass operation because it's bad practice to delete |
| 1513 | # keys from dictionaries while iterating |
| 1514 | keyList = [] |
| 1515 | for k in TF_OP_LIST: |
| 1516 | try: |
| 1517 | if TF_OP_LIST[k]["template"]: |
| 1518 | keyList.append(k) |
| 1519 | continue |
| 1520 | except KeyError: |
| 1521 | pass |
| 1522 | |
| 1523 | for k in keyList: |
| 1524 | del TF_OP_LIST[k] |
| 1525 | |
| 1526 | |
| 1527 | def main(): |
| 1528 | parser = argparse.ArgumentParser() |
| 1529 | parser.add_argument( |
| 1530 | "--seed", dest="random_seed", default=42, type=int, help="Random seed" |
| 1531 | ) |
| 1532 | parser.add_argument( |
| 1533 | "--random-shapes", |
| 1534 | dest="random_shapes", |
| 1535 | default=0, |
| 1536 | type=int, |
| 1537 | help=( |
| 1538 | "Use N random shapes of each rank for generating tests," |
| 1539 | "seeded with random seed" |
| 1540 | ), |
| 1541 | ) |
| 1542 | parser.add_argument( |
| 1543 | "-o", |
| 1544 | "--output-dir", |
| 1545 | dest="output_dir", |
| 1546 | default=".", |
| 1547 | type=str, |
| 1548 | help="Test output directory path prefix", |
| 1549 | ) |
| 1550 | parser.add_argument( |
| 1551 | "-q", |
| 1552 | "--quiet", |
| 1553 | dest="quiet", |
| 1554 | default=False, |
| 1555 | action="store_true", |
| 1556 | help="Do not print test names", |
| 1557 | ) |
| 1558 | parser.add_argument( |
| 1559 | "-j", "--jobs", dest="jobs", type=int, default=1, help="Number of parallel jobs" |
| 1560 | ) |
| 1561 | parser.add_argument( |
| 1562 | "-m", |
| 1563 | "--tflite-kernel-mode", |
| 1564 | dest="tflite_kernel_mode", |
| 1565 | type=str, |
| 1566 | choices=["reference", "optimized"], |
| 1567 | default="reference", |
| 1568 | help="TFLite interpreter kernel mode", |
| 1569 | ) |
| 1570 | parser.add_argument( |
| 1571 | "--num-samples", |
| 1572 | dest="num_samples", |
| 1573 | default=200, |
| 1574 | type=int, |
| 1575 | help="Number of input samples for post-training quantization", |
| 1576 | ) |
| 1577 | parser.add_argument( |
| 1578 | "--filter", |
| 1579 | dest="filter", |
| 1580 | default="", |
| 1581 | type=str, |
| 1582 | help="Filter test names by this expression", |
| 1583 | ) |
| 1584 | args = parser.parse_args() |
| 1585 | |
| 1586 | # Turn the filter into a re object if present |
| 1587 | filter = None |
| 1588 | if args.filter != "": |
| 1589 | filter = re.compile(args.filter) |
| 1590 | |
| 1591 | # Autodetect CPU count |
| 1592 | if args.jobs <= 0: |
| 1593 | args.jobs = os.cpu_count() |
| 1594 | |
| 1595 | # Disable TF info messages |
| 1596 | os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" |
| 1597 | |
| 1598 | try: |
| 1599 | os.makedirs(args.output_dir) |
| 1600 | except FileExistsError: |
| 1601 | pass |
| 1602 | |
| 1603 | if args.random_shapes: |
| 1604 | gen_rand_shapes(args) |
| 1605 | |
| 1606 | # Build dynamic ops |
| 1607 | createDynamicOpLists() |
| 1608 | |
| 1609 | # Generate the test list and arguments to run_unit_test() |
| 1610 | unit_test_args = [] |
| 1611 | |
| 1612 | for op in TF_OP_LIST: |
| 1613 | generate_op_tests(args, op, shape_list, "result", filter, unit_test_args) |
| 1614 | |
| 1615 | errors = 0 |
| 1616 | for t in unit_test_args: |
| 1617 | if not run_unit_test(*t): |
| 1618 | errors = errors + 1 |
| 1619 | |
| 1620 | if not args.quiet: |
| 1621 | print("\nAll tasks done - with {} errors".format(errors)) |
| 1622 | |
| 1623 | return 1 if errors else 0 |
| 1624 | |
| 1625 | |
| 1626 | if __name__ == "__main__": |
| 1627 | exit(main()) |