Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1 | #!/usr/bin/env python3 |
| 2 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3 | # Copyright (c) 2020-2021, ARM Limited. |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | # you may not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
| 16 | |
| 17 | |
| 18 | import numpy as np |
| 19 | import argparse |
| 20 | import sys |
| 21 | import re |
| 22 | import os |
| 23 | import subprocess |
| 24 | import shlex |
| 25 | import json |
| 26 | import glob |
| 27 | import math |
| 28 | import queue |
| 29 | import threading |
| 30 | import traceback |
| 31 | import math |
| 32 | |
| 33 | from enum import IntEnum, Enum, unique |
| 34 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 35 | # Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH |
| 36 | parent_dir = os.path.dirname(os.path.realpath(__file__)) |
| 37 | sys.path.append( |
| 38 | os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") |
| 39 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 40 | import tosa_serializer as ts |
| 41 | from tosa_serializer import * |
| 42 | import tosa |
| 43 | |
| 44 | # Convenience variables to the flatc-generated types that should be enums, but aren't |
| 45 | DType = tosa.DType.DType() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 46 | Op = tosa.Op.Op() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 47 | ResizeMode = tosa.ResizeMode.ResizeMode() |
| 48 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 49 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 50 | class TosaQuantGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 51 | """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" |
| 52 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 53 | def __init__(self): |
| 54 | pass |
| 55 | |
| 56 | @staticmethod |
| 57 | def needsQinfo(op, dtype): |
Jared Smolens | 2a76ad2 | 2021-03-04 11:18:54 -0800 | [diff] [blame] | 58 | if dtype == DType.INT8 or dtype == DType.INT16: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 59 | return True |
| 60 | return False |
| 61 | |
| 62 | @staticmethod |
| 63 | def qgUnary(testGen, op, dtype): |
| 64 | qinfo = ts.TosaSerializerQuantInfo() |
| 65 | if TosaQuantGen.needsQinfo(op, dtype): |
| 66 | qinfo.UnaryQuantInfo(testGen.randInt(), testGen.randInt()) |
| 67 | else: |
| 68 | qinfo.UnaryQuantInfo(0, 0) |
| 69 | return qinfo |
| 70 | |
| 71 | @staticmethod |
| 72 | def qgConv(testGen, op, dtype): |
| 73 | qinfo = ts.TosaSerializerQuantInfo() |
| 74 | if TosaQuantGen.needsQinfo(op, dtype): |
| 75 | qinfo.ConvQuantInfo(testGen.randInt(), testGen.randInt()) |
| 76 | else: |
| 77 | qinfo.ConvQuantInfo(0, 0) |
| 78 | return qinfo |
| 79 | |
| 80 | @staticmethod |
| 81 | def qgMatmul(testGen, op, dtype): |
| 82 | qinfo = ts.TosaSerializerQuantInfo() |
| 83 | if TosaQuantGen.needsQinfo(op, dtype): |
| 84 | qinfo.MatMulQuantInfo(testGen.randInt(), testGen.randInt()) |
| 85 | else: |
| 86 | qinfo.MatMulQuantInfo(0, 0) |
| 87 | return qinfo |
| 88 | |
| 89 | @staticmethod |
| 90 | def qgPad(testGen, op, dtype): |
| 91 | qinfo = ts.TosaSerializerQuantInfo() |
| 92 | if TosaQuantGen.needsQinfo(op, dtype): |
| 93 | qinfo.PadQuantInfo(testGen.randInt()) |
| 94 | else: |
| 95 | qinfo.PadQuantInfo(0) |
| 96 | return qinfo |
| 97 | |
| 98 | @staticmethod |
| 99 | def computeMultiplierAndShift(scaleFp, scale32): |
| 100 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 101 | # Provide a floating-point scaling factor and the scale32 parameter |
| 102 | # to compute the multiplier and shift |
| 103 | |
| 104 | if scale32: |
| 105 | scaleBits = 31 |
| 106 | else: |
| 107 | scaleBits = 15 |
| 108 | |
| 109 | m, shift = math.frexp(scaleFp) |
| 110 | |
| 111 | if scaleFp < 0.0: |
| 112 | m = -m |
| 113 | |
| 114 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 115 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 116 | |
| 117 | if multiplier == (1 << scaleBits): |
| 118 | multiplier = multiplier // 2 |
| 119 | shift = shift + 1 |
| 120 | |
| 121 | shift = (-shift) + scaleBits |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 122 | # print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 123 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 124 | assert multiplier <= (1 << scaleBits) |
| 125 | assert shift >= 0 and shift <= 63 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 126 | |
| 127 | return multiplier, shift |
| 128 | |
| 129 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 130 | class TosaTensorGen: |
| 131 | """Tensor generators create a shape list for the placeholder and const tensor |
| 132 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 133 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 134 | def __init__(self): |
| 135 | pass |
| 136 | |
| 137 | @staticmethod |
| 138 | def tgBasic(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 139 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 140 | shape = testGen.makeShape(rank) |
| 141 | |
| 142 | shape_list = [] |
| 143 | for i in range(pl + const): |
| 144 | shape_list.append(shape.copy()) |
| 145 | |
| 146 | return shape_list |
| 147 | |
| 148 | @staticmethod |
| 149 | def tgNHWC(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 150 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 151 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 152 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 153 | |
| 154 | shape = testGen.makeShape(rank) |
| 155 | |
| 156 | # Constrict the batch size? |
| 157 | if testGen.args.max_batch_size: |
| 158 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
| 159 | |
| 160 | shape_list = [] |
| 161 | for i in range(pl + const): |
| 162 | shape_list.append(shape.copy()) |
| 163 | |
| 164 | return shape_list |
| 165 | |
| 166 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 167 | def tgScatter(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 168 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 169 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 170 | assert pl == 2 |
| 171 | assert const == 0 |
| 172 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 173 | |
| 174 | values_in_shape = testGen.makeShape(rank) |
| 175 | |
| 176 | # Constrict the batch size? |
| 177 | if testGen.args.max_batch_size: |
| 178 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 179 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 180 | W = testGen.randInt( |
| 181 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 182 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 183 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 184 | |
| 185 | shape_list = [] |
| 186 | shape_list.append(values_in_shape.copy()) |
| 187 | shape_list.append(input_shape.copy()) |
| 188 | |
| 189 | return shape_list |
| 190 | |
| 191 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 192 | def tgBroadcastFuzz(testGen, op, rank): |
| 193 | shape = testGen.makeShape(rank) |
| 194 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 195 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 196 | |
| 197 | shape_list = [] |
| 198 | |
| 199 | # Choose one of the inputs to broadcast |
| 200 | bcast_idx = testGen.randInt(0, pl + const) |
| 201 | for i in range(pl + const): |
| 202 | shape_bcast = shape.copy() |
| 203 | |
| 204 | # If the chosen input, pick a random index to broadcast |
| 205 | if i == bcast_idx: |
| 206 | fuzz_idx = testGen.randInt(0, rank) |
| 207 | shape_bcast[fuzz_idx] = 1 |
| 208 | |
| 209 | shape_list.append(shape_bcast) |
| 210 | |
| 211 | return shape_list |
| 212 | |
| 213 | @staticmethod |
| 214 | def tgConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 215 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 216 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 217 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 218 | |
| 219 | # IFM dimensions are NHWC |
| 220 | ifm_shape = testGen.makeShape(rank) |
| 221 | |
| 222 | # Constrict the batch size? |
| 223 | if testGen.args.max_batch_size: |
| 224 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 225 | |
| 226 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 227 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 228 | |
| 229 | # Generate a random OFM depth |
| 230 | ofm_depth = testGen.makeShape(1)[0] |
| 231 | |
| 232 | # The filter dimensions are OHWI |
| 233 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 234 | |
| 235 | # The bias is OC |
| 236 | bias_shape = np.asarray([ofm_depth]) |
| 237 | |
| 238 | return [ifm_shape, filter_shape, bias_shape] |
| 239 | |
| 240 | @staticmethod |
| 241 | def tgTransposeConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 242 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 243 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 244 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 245 | |
| 246 | # IFM dimensions are NHWC |
| 247 | ifm_shape = testGen.makeShape(rank) |
| 248 | |
| 249 | # Constrict the batch size? |
| 250 | if testGen.args.max_batch_size: |
| 251 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 252 | |
| 253 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 254 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 255 | |
| 256 | # Generate a random OFM depth |
| 257 | ofm_depth = testGen.makeShape(1)[0] |
| 258 | |
| 259 | # The filter dimensions are OHWI |
| 260 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 261 | |
| 262 | return [ifm_shape, filter_shape] |
| 263 | |
| 264 | @staticmethod |
| 265 | def tgDepthwiseConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 266 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 267 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 268 | assert rank == 4 |
| 269 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 270 | |
| 271 | # IFM dimensions are NHWC |
| 272 | ifm_shape = testGen.makeShape(rank) |
| 273 | |
| 274 | # Constrict the batch size? |
| 275 | if testGen.args.max_batch_size: |
| 276 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 277 | |
| 278 | # Get the filter height/width from the operator parameters |
| 279 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 280 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 281 | |
| 282 | # Generate a random OFM depth, but don't let it get too big because |
| 283 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 284 | filter_m = ( |
| 285 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 286 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 287 | |
| 288 | # The filter dimensions are HWCM |
| 289 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 290 | |
| 291 | # The bias is M * C |
| 292 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 293 | |
| 294 | return [ifm_shape, filter_shape, bias_shape] |
| 295 | |
| 296 | @staticmethod |
| 297 | def tgFullyConnected(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 298 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 299 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 300 | assert rank == 2 |
| 301 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 302 | |
| 303 | input_shape = testGen.makeShape(rank) |
| 304 | filter_oc = testGen.makeShape(1)[0] |
| 305 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 306 | |
| 307 | bias_shape = np.asarray([filter_oc]) |
| 308 | |
| 309 | return [input_shape, filter_shape, bias_shape] |
| 310 | |
| 311 | @staticmethod |
| 312 | def tgMatmul(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 313 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 314 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 315 | assert rank == 2 |
| 316 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 317 | |
| 318 | a_shape = testGen.makeShape(rank) |
| 319 | b_oc = testGen.makeShape(1)[0] |
| 320 | b_shape = np.asarray([a_shape[1], b_oc]) |
| 321 | |
| 322 | return [a_shape, b_shape] |
| 323 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 324 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 325 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 326 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 327 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 328 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 329 | as arguments to the operator build function.""" |
| 330 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 331 | def __init__(self): |
| 332 | pass |
| 333 | |
| 334 | @staticmethod |
| 335 | def agNone(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 336 | """A trivial argument generator for operators that don't take any |
| 337 | non-tensor arguments""" |
| 338 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 339 | |
| 340 | @staticmethod |
| 341 | def agAxis(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 342 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 343 | axes = [] |
| 344 | |
| 345 | shape = shapeList[0] |
| 346 | |
| 347 | for a in range(0, len(shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 348 | axes.append(("axis_{}".format(a), [a])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 349 | return axes |
| 350 | |
| 351 | @staticmethod |
| 352 | def agConv2D(testGen, opName, shapeList, dtype): |
| 353 | arg_list = [] |
| 354 | |
| 355 | ifm_shape = shapeList[0] |
| 356 | filter_shape = shapeList[1] |
| 357 | |
| 358 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 359 | assert len(ifm_shape) == 4 |
| 360 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 361 | |
| 362 | maxStride = testGen.args.max_conv_stride |
| 363 | maxPadding = testGen.args.max_conv_padding + 1 |
| 364 | maxDilation = testGen.args.max_conv_dilation |
| 365 | |
| 366 | # Strides, padding, dilations |
| 367 | for stride in range(0, maxStride ** 2): |
| 368 | for padding in range(0, (maxPadding) ** 4): |
| 369 | for dilation in range(0, maxDilation ** 2): |
| 370 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 371 | s = [stride // maxStride + 1, stride % maxStride + 1] |
| 372 | p = [ |
| 373 | (padding // (maxPadding * 4)) % maxPadding, |
| 374 | (padding // (maxPadding * 2)) % maxPadding, |
| 375 | (padding // (maxPadding * 1)) % maxPadding, |
| 376 | padding % maxPadding, |
| 377 | ] |
| 378 | d = [dilation // maxDilation + 1, dilation % maxDilation + 1] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 379 | |
| 380 | # 4 padding parameters for regular conv2d |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 381 | arg_list.append( |
| 382 | ( |
| 383 | "st{}{}_pad{}{}{}{}_dilat{}{}".format( |
| 384 | s[0], s[1], p[0], p[1], p[2], p[3], d[0], d[1] |
| 385 | ), |
| 386 | [s, p, d], |
| 387 | ) |
| 388 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 389 | return arg_list |
| 390 | |
| 391 | @staticmethod |
| 392 | def agTransposeConv2D(testGen, opName, shapeList, dtype): |
| 393 | arg_list = [] |
| 394 | |
| 395 | ifm_shape = shapeList[0] |
| 396 | filter_shape = shapeList[1] |
| 397 | |
| 398 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 399 | assert len(ifm_shape) == 4 |
| 400 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 401 | |
| 402 | maxStride = testGen.args.max_conv_stride |
| 403 | maxPadding = testGen.args.max_conv_padding + 1 |
| 404 | maxDilation = testGen.args.max_conv_dilation |
| 405 | |
| 406 | # Strides, padding, dilations |
| 407 | for stride in range(0, maxStride ** 2): |
| 408 | for out_padding in range(0, (maxPadding) ** 2): |
| 409 | for dilation in range(0, maxDilation ** 2): |
| 410 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 411 | s = [stride // maxStride + 1, stride % maxStride + 1] |
| 412 | p = [ |
| 413 | (out_padding // (maxPadding * 1)) % maxPadding, |
| 414 | out_padding % maxPadding, |
| 415 | ] |
| 416 | d = [dilation // maxDilation + 1, dilation % maxDilation + 1] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 417 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 418 | oh = ( |
| 419 | ifm_shape[1] |
| 420 | - filter_shape[1] |
| 421 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 422 | + 2 * p[0] |
| 423 | ) // s[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 424 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 425 | ow = ( |
| 426 | ifm_shape[2] |
| 427 | - filter_shape[2] |
| 428 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 429 | + 2 * p[1] |
| 430 | ) // s[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 431 | |
| 432 | # Output shape |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 433 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 434 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 435 | arg_list.append( |
| 436 | ( |
| 437 | "st{}{}_outpad{}{}_dilat{}{}_os{}x{}x{}x{}".format( |
| 438 | s[0], |
| 439 | s[1], |
| 440 | p[0], |
| 441 | p[1], |
| 442 | d[0], |
| 443 | d[1], |
| 444 | os[0], |
| 445 | os[1], |
| 446 | os[2], |
| 447 | os[3], |
| 448 | ), |
| 449 | [s, p, d, os], |
| 450 | ) |
| 451 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 452 | |
| 453 | return arg_list |
| 454 | |
| 455 | @staticmethod |
| 456 | def agPad(testGen, opName, shapeList, dtype): |
| 457 | arg_list = [] |
| 458 | rank = len(shapeList[0]) |
| 459 | |
| 460 | # Exhaustively test combinations of 0/1 padding on each side of each dimension |
| 461 | # This process might need some revision for >1 padding, but use rank**2 as a bitmask |
| 462 | # for now |
| 463 | for v in range(rank ** 2): |
| 464 | |
| 465 | # Create a flat arraypadding4D |
| 466 | paddings = np.zeros((rank * 2), dtype=np.int32) |
| 467 | |
| 468 | # Fill in the 1's |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 469 | for r in range(rank * 2): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 470 | if (v >> r) & 1: |
| 471 | paddings[r] = 1 |
| 472 | |
| 473 | # Reshape back to a 2D array |
| 474 | paddings = paddings.reshape((rank, 2)) |
| 475 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 476 | arg_list.append(("pad{0:b}".format(v), [paddings])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 477 | |
| 478 | return arg_list |
| 479 | |
| 480 | @staticmethod |
| 481 | def agPooling(testGen, opName, shapeList, dtype): |
| 482 | arg_list = [] |
| 483 | |
| 484 | shape = shapeList[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 485 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 486 | |
| 487 | maxStride = testGen.args.max_pooling_stride |
| 488 | maxKernel = testGen.args.max_pooling_kernel |
| 489 | maxPadding = testGen.args.max_pooling_padding + 1 |
| 490 | |
| 491 | for kernel in range(0, maxKernel ** 2): |
| 492 | for stride in range(0, maxStride ** 2): |
| 493 | for padding in range(0, maxPadding ** 4): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 494 | s = [stride // maxStride + 1, stride % maxStride + 1] |
| 495 | k = [(kernel // maxKernel) + 2, (kernel % maxKernel) + 2] |
| 496 | p = [ |
| 497 | (padding // (maxPadding * 4)) % maxPadding, |
| 498 | (padding // (maxPadding * 2)) % maxPadding, |
| 499 | (padding // (maxPadding * 1)) % maxPadding, |
| 500 | padding % maxPadding, |
| 501 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 502 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 503 | arg_list.append( |
| 504 | ( |
| 505 | "st{}{}_kern{}{}_pad{}{}{}{}".format( |
| 506 | s[0], s[1], k[0], k[1], p[0], p[1], p[2], p[3] |
| 507 | ), |
| 508 | [k, s, p], |
| 509 | ) |
| 510 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 511 | return arg_list |
| 512 | |
| 513 | @staticmethod |
| 514 | def agCast(testGen, opName, shapeList, inDtype): |
| 515 | arg_list = [] |
| 516 | |
| 517 | # Enumerate the output types here |
| 518 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 519 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 520 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 521 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 522 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 523 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 524 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 525 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 526 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 527 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 528 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 529 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 530 | |
| 531 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 532 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 533 | |
| 534 | return arg_list |
| 535 | |
| 536 | @staticmethod |
| 537 | def agRescale(testGen, opName, shapeList, inDtype): |
| 538 | arg_list = [] |
| 539 | |
| 540 | # Enumerate the output types here |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 541 | for dtype in [DType.INT8, DType.INT16, DType.INT32]: |
| 542 | for scale32 in [False, True]: |
| 543 | for double_round in [False, True]: |
| 544 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 545 | |
| 546 | if inDtype == DType.INT48 and scale32: |
| 547 | # Illegal condition. Must be scale32=False |
| 548 | continue |
| 549 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 550 | arg_list.append( |
| 551 | ( |
| 552 | "out{}_sc{}_dr{}_pc{}".format( |
| 553 | DTypeNames[dtype], |
| 554 | int(scale32), |
| 555 | int(double_round), |
| 556 | int(per_channel), |
| 557 | ), |
| 558 | [dtype, scale32, double_round, per_channel], |
| 559 | ) |
| 560 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 561 | |
| 562 | return arg_list |
| 563 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 564 | @staticmethod |
| 565 | def agMul(testGen, opName, shapeList, dtype): |
| 566 | arg_list = [] |
| 567 | |
| 568 | if dtype is DType.INT32: |
| 569 | for p in range(testGen.args.num_rand_permutations): |
| 570 | |
| 571 | shift = testGen.randInt(0, 32) |
| 572 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 573 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 574 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 575 | arg_list.append(("shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 576 | |
| 577 | return arg_list |
| 578 | |
| 579 | @staticmethod |
| 580 | def agArithmeticRightShift(testGen, opName, shapeList, dtype): |
| 581 | arg_list = [] |
| 582 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 583 | arg_list.append(("roundTrue", [True])) |
| 584 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 585 | |
| 586 | return arg_list |
| 587 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 588 | # Helper function for reshape. Gets some factors of a larger number. |
| 589 | @staticmethod |
| 590 | def getFactors(val, start=1): |
| 591 | factors = [] |
| 592 | |
| 593 | for i in range(start, int(np.sqrt(val))): |
| 594 | if (val % i) == 0: |
| 595 | factors.append(i) |
| 596 | |
| 597 | return factors |
| 598 | |
| 599 | @staticmethod |
| 600 | def agReshape(testGen, opName, shapeList, dtype): |
| 601 | arg_list = [] |
| 602 | |
| 603 | origShape = shapeList[0] |
| 604 | |
| 605 | totalElements = 1 |
| 606 | for s in origShape: |
| 607 | totalElements *= s |
| 608 | |
| 609 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 610 | factors = TosaArgGen.getFactors(totalElements) |
| 611 | |
| 612 | for p in range(testGen.args.num_rand_permutations): |
| 613 | newRank = testGen.randInt(1, 6) |
| 614 | newShape = [] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 615 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 616 | continue |
| 617 | |
| 618 | remainingElements = totalElements |
| 619 | shuffledFactors = testGen.rng.permutation(factors) |
| 620 | for i in range(newRank): |
| 621 | # pick rank-1 factors |
| 622 | newShape.append(shuffledFactors[0]) |
| 623 | remainingElements = remainingElements // shuffledFactors[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 624 | shuffledFactors = testGen.rng.permutation( |
| 625 | TosaArgGen.getFactors(remainingElements) |
| 626 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 627 | newShape.append(remainingElements) |
| 628 | |
| 629 | # Toss in a -1 sometimes |
| 630 | minusOne = testGen.randInt(0, newRank * 4) |
| 631 | if minusOne < newRank: |
| 632 | newShape[minusOne] = -1 |
| 633 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 634 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 635 | |
| 636 | return arg_list |
| 637 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 638 | @staticmethod |
| 639 | def agTranspose(testGen, opName, shapeList, dtype): |
| 640 | arg_list = [] |
| 641 | |
| 642 | ifm_shape = shapeList[0] |
| 643 | |
| 644 | perms = range(len(ifm_shape)) |
| 645 | for p in range(testGen.args.num_rand_permutations): |
| 646 | perms = np.int32(testGen.rng.permutation(perms)).tolist() |
| 647 | |
| 648 | # Avoid duplicates |
| 649 | found = False |
| 650 | for name, other_perm in arg_list: |
| 651 | if other_perm[0] == perms: |
| 652 | found = True |
| 653 | break |
| 654 | |
| 655 | if not found: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 656 | arg_list.append(("perm{}".format(p), [perms])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 657 | |
| 658 | return arg_list |
| 659 | |
| 660 | @staticmethod |
| 661 | def agSlice(testGen, opName, shapeList, dtype): |
| 662 | arg_list = [] |
| 663 | |
| 664 | ifm_shape = shapeList[0] |
| 665 | rank = len(ifm_shape) |
| 666 | |
| 667 | for p in range(testGen.args.num_rand_permutations): |
| 668 | begin = [] |
| 669 | size = [] |
| 670 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 671 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 672 | |
| 673 | for i in range(rank): |
| 674 | if ifm_shape[i] > 1: |
| 675 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 676 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 677 | |
| 678 | # Invalid slice size? |
| 679 | if size[i] == 0: |
| 680 | valid = False |
| 681 | else: |
| 682 | begin.append(0) |
| 683 | size.append(1) |
| 684 | |
| 685 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 686 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 687 | return arg_list |
| 688 | |
| 689 | @staticmethod |
| 690 | def agTile(testGen, opName, shapeList, dtype): |
| 691 | arg_list = [] |
| 692 | |
| 693 | ifm_shape = shapeList[0] |
| 694 | rank = len(ifm_shape) |
| 695 | |
| 696 | for p in range(testGen.args.num_rand_permutations): |
| 697 | |
| 698 | # Pick a few random, but small multiple values |
| 699 | # because otherwise this has a tendency to generate |
| 700 | # enormous tensors |
| 701 | multiples = [] |
| 702 | for i in range(rank): |
| 703 | multiples.append(testGen.randInt(1, 4)) |
| 704 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 705 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 706 | |
| 707 | return arg_list |
| 708 | |
| 709 | @staticmethod |
| 710 | def agResize(testGen, opName, shapeList, dtype): |
| 711 | arg_list = [] |
| 712 | |
| 713 | ifm_shape = shapeList[0] |
| 714 | |
| 715 | for m in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
| 716 | |
| 717 | # Exclude illegal {mode, type} configurations. Pick legal output types |
| 718 | if m == ResizeMode.NEAREST and dtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 719 | outputDTypeList = [DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 720 | elif m == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 721 | outputDTypeList = [DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 722 | elif m == ResizeMode.BILINEAR and dtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 723 | outputDTypeList = [DType.INT8] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 724 | elif m == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 725 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 726 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 727 | outputDTypeList = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 728 | else: |
| 729 | continue |
| 730 | |
| 731 | for outputDType in outputDTypeList: |
| 732 | for perm in range(testGen.args.num_rand_permutations): |
| 733 | |
| 734 | # Randomly generate legal output dimensions and shift |
| 735 | # and then compute the stride and offset based on them |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 736 | output_dims = [testGen.randInt(1), testGen.randInt(1)] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 737 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 738 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 739 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 740 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 741 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 742 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 743 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 744 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 745 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 746 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 747 | if outputDType == DType.FLOAT: |
| 748 | shift = 0 |
| 749 | stride = [0, 0] |
| 750 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 751 | stride_fp = [fp_stride_y, fp_stride_x] |
| 752 | offset_fp = [fp_offset_y, fp_offset_x] |
| 753 | arg_list.append( |
| 754 | ( |
| 755 | "mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}".format( |
| 756 | m, |
| 757 | output_dims[0], |
| 758 | output_dims[1], |
| 759 | testGen.typeStr(outputDType), |
| 760 | stride_fp[0], |
| 761 | stride_fp[1], |
| 762 | offset_fp[0], |
| 763 | offset_fp[1], |
| 764 | ), |
| 765 | [ |
| 766 | m, |
| 767 | stride, |
| 768 | offset, |
| 769 | shift, |
| 770 | stride_fp, |
| 771 | offset_fp, |
| 772 | output_dims, |
| 773 | dtype, |
| 774 | outputDType, |
| 775 | ], |
| 776 | ) |
| 777 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 778 | else: |
| 779 | shift = 11 |
| 780 | unit = float(1 << shift) |
| 781 | stride_y = int(round(fp_stride_y * unit)) |
| 782 | stride_x = int(round(fp_stride_x * unit)) |
| 783 | offset_y = int(round(fp_offset_y * unit)) |
| 784 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 785 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 786 | while ( |
| 787 | stride_y >= 32768 |
| 788 | or stride_x >= 32768 |
| 789 | or offset_y >= 32768 |
| 790 | or offset_x >= 32768 |
| 791 | or offset_y < -32768 |
| 792 | or offset_x < -32768 |
| 793 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 794 | shift = shift - 1 |
| 795 | unit = float(1 << shift) |
| 796 | stride_y = int(round(fp_stride_y * unit)) |
| 797 | stride_x = int(round(fp_stride_x * unit)) |
| 798 | offset_y = int(round(fp_offset_y * unit)) |
| 799 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 800 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 801 | stride = [stride_y, stride_x] |
| 802 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 803 | |
| 804 | stride_fp = [0.0, 0.0] |
| 805 | offset_fp = [0.0, 0.0] |
| 806 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 807 | arg_list.append( |
| 808 | ( |
| 809 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
| 810 | m, |
| 811 | shift, |
| 812 | output_dims[0], |
| 813 | output_dims[1], |
| 814 | testGen.typeStr(outputDType), |
| 815 | stride[0], |
| 816 | stride[1], |
| 817 | offset[0], |
| 818 | offset[1], |
| 819 | ), |
| 820 | [ |
| 821 | m, |
| 822 | stride, |
| 823 | offset, |
| 824 | shift, |
| 825 | stride_fp, |
| 826 | offset_fp, |
| 827 | output_dims, |
| 828 | dtype, |
| 829 | outputDType, |
| 830 | ], |
| 831 | ) |
| 832 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 833 | |
| 834 | return arg_list |
| 835 | |
| 836 | def agCondIf(testGen, opName, shapeList, dtype): |
| 837 | # CondIf generates the condition values here. |
| 838 | # Convert to tensors in the build function, along with the |
| 839 | # then and else blocks |
| 840 | arg_list = [] |
| 841 | |
| 842 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 843 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 844 | |
| 845 | return arg_list |
| 846 | |
| 847 | def agWhileLoop(testGen, opName, shapeList, dtype): |
| 848 | # While loop: 0 iterations, 1, more than 1 |
| 849 | arg_list = [] |
| 850 | |
| 851 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 852 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 853 | |
| 854 | return arg_list |
| 855 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 856 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 857 | class TosaTestGen: |
| 858 | def __init__(self, args): |
| 859 | self.args = args |
| 860 | self.basePath = args.output_dir |
| 861 | self.random_seed = args.random_seed |
| 862 | self.ser = None |
| 863 | self.rng = np.random.default_rng(self.random_seed) |
| 864 | self.createDynamicOpLists() |
| 865 | self.initOpListDefaults() |
| 866 | self.quantGen = TosaQuantGen() |
| 867 | # Force makeShape to do a specific starting shape |
| 868 | self.targetted_shape = None |
| 869 | |
| 870 | def createSerializer(self, opName, testPath): |
| 871 | self.testPath = os.path.join(opName, testPath) |
| 872 | |
| 873 | fullPath = os.path.join(self.basePath, self.testPath) |
| 874 | os.makedirs(fullPath, exist_ok=True) |
| 875 | self.ser = ts.TosaSerializer(fullPath) |
| 876 | |
| 877 | def getSerializer(self): |
| 878 | return self.ser |
| 879 | |
| 880 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 881 | with open( |
| 882 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 883 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 884 | fd.write(self.ser.serialize()) |
| 885 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 886 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 887 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 888 | |
| 889 | def getRandTensor(self, shape, dtype): |
| 890 | RAND_SHIFT_FACTOR = 0.5 |
| 891 | RAND_SCALE_FACTOR = 4.0 |
| 892 | |
| 893 | if dtype == DType.BOOL: |
| 894 | np_dt = np.bool |
| 895 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 896 | elif dtype == DType.INT4: |
| 897 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
| 898 | elif dtype == DType.INT8: |
| 899 | return np.int32(self.rng.integers(low=-127, high=128, size=shape)) |
| 900 | elif dtype == DType.INT16: |
| 901 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 902 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 903 | return np.int32( |
| 904 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 905 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 906 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 907 | return np.int64( |
| 908 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 909 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 910 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 911 | return np.float32( |
| 912 | self.rng.random(size=shape) - RAND_SHIFT_FACTOR * RAND_SCALE_FACTOR |
| 913 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 914 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 915 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 916 | |
| 917 | def buildPlaceholderTensors(self, shape_list, dtype): |
| 918 | placeholders = [] |
| 919 | |
| 920 | for shape in shape_list: |
| 921 | arr = self.getRandTensor(shape, dtype) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 922 | placeholders.append(self.ser.addPlaceholder(shape, dtype, arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 923 | |
| 924 | return placeholders |
| 925 | |
| 926 | def buildConstTensors(self, shape_list, dtype): |
| 927 | consts = [] |
| 928 | |
| 929 | for shape in shape_list: |
| 930 | arr = self.getRandTensor(shape, dtype) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 931 | consts.append(self.ser.addConst(shape, dtype, arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 932 | |
| 933 | return consts |
| 934 | |
| 935 | def makeShape(self, rank): |
| 936 | if self.targetted_shape: |
| 937 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 938 | return np.int32( |
| 939 | self.rng.integers( |
| 940 | low=self.args.tensor_shape_range[0], |
| 941 | high=self.args.tensor_shape_range[1], |
| 942 | size=rank, |
| 943 | ) |
| 944 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 945 | |
| 946 | def setTargetShape(self, shape): |
| 947 | self.targetted_shape = shape |
| 948 | |
| 949 | def randInt(self, low=0, high=256): |
| 950 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 951 | |
| 952 | def getRandNumberDType(self, dtype): |
| 953 | if dtype == DType.FLOAT: |
| 954 | return self.rng.random() |
| 955 | elif dtype == DType.BOOL: |
| 956 | return self.rng.choice([False, True]) |
| 957 | elif dtype == DType.INT4: |
| 958 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 959 | elif dtype == DType.INT8: |
| 960 | low, high = (-127, 128) |
| 961 | elif dtype == DType.INT16: |
| 962 | low, high = (-32768, 32768) |
| 963 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 964 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 965 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 966 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 967 | # Special size |
| 968 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 969 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 970 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 971 | |
| 972 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 973 | |
| 974 | def shapeStr(self, shape): |
| 975 | |
| 976 | sStr = [] |
| 977 | # Convert to strings |
| 978 | for i in shape: |
| 979 | sStr.append(str(i)) |
| 980 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 981 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 982 | |
| 983 | def typeStr(self, t): |
| 984 | if t == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 985 | return "b" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 986 | elif t == DType.INT4: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 987 | return "i4" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 988 | elif t == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 989 | return "i8" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 990 | elif t == DType.UINT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 991 | return "u8" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 992 | elif t == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 993 | return "i16" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 994 | elif t == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 995 | return "i32" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 996 | elif t == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 997 | return "i48" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 998 | elif t == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 999 | return "float" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1000 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1001 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1002 | |
| 1003 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1004 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1005 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1006 | return 4 |
| 1007 | elif t == DType.INT8: |
| 1008 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1009 | elif t == DType.UINT8: |
| 1010 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1011 | elif t == DType.INT16: |
| 1012 | return 16 |
| 1013 | elif t == DType.INT32: |
| 1014 | return 32 |
| 1015 | elif t == DType.INT48: |
| 1016 | return 48 |
| 1017 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1018 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1019 | |
| 1020 | # Argument generators |
| 1021 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 1022 | # Where the string descriptor is used to generate the test name and |
| 1023 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 1024 | # build function |
| 1025 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1026 | def build_unary(self, op, a, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1027 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1028 | self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) |
| 1029 | return result_tens |
| 1030 | |
| 1031 | def build_binary_broadcast(self, op, a, b): |
| 1032 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1033 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1034 | return result_tens |
| 1035 | |
| 1036 | def build_binary_nonbroadcast(self, op, a, b): |
| 1037 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
| 1038 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1039 | return result_tens |
| 1040 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1041 | def build_arithmetic_right_shift(self, op, a, b, round): |
| 1042 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1043 | |
| 1044 | attr = ts.TosaSerializerAttribute() |
| 1045 | attr.ArithmeticRightShiftAttribute(round) |
| 1046 | |
| 1047 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
| 1048 | return result_tens |
| 1049 | |
| 1050 | def build_mul(self, op, a, b, shift): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1051 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1052 | |
| 1053 | # Special for multiply: |
| 1054 | # Force the result to INT32 for INT types |
| 1055 | if a.dtype != DType.FLOAT: |
| 1056 | result_tens.setDtype(DType.INT32) |
| 1057 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1058 | attr = ts.TosaSerializerAttribute() |
| 1059 | attr.MulAttribute(shift) |
| 1060 | |
| 1061 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1062 | return result_tens |
| 1063 | |
| 1064 | def build_table(self, op, a): |
| 1065 | # Constant size, random values |
| 1066 | table_arr = self.getRandTensor([513], DType.INT16) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1067 | table_tens = self.ser.addConst(table_arr.shape, DType.INT16, table_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1068 | |
| 1069 | result_tens = OutputShaper.tableOp(self.ser, a, table_tens) |
| 1070 | self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None) |
| 1071 | |
| 1072 | return result_tens |
| 1073 | |
| 1074 | def build_select(self, op, cond, a, b): |
| 1075 | |
| 1076 | # Replace the cond tensor with a boolean tensor since it probably |
| 1077 | # has the wrong dtype |
| 1078 | t = self.buildPlaceholderTensors([cond.shape], DType.BOOL) |
| 1079 | cond = t[0] |
| 1080 | |
| 1081 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
| 1082 | self.ser.addOperator(op, [cond.name, a.name, b.name], [result_tens.name]) |
| 1083 | |
| 1084 | return result_tens |
| 1085 | |
| 1086 | def build_comparison(self, op, a, b): |
| 1087 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
| 1088 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1089 | return result_tens |
| 1090 | |
| 1091 | def build_argmax(self, op, a, axis): |
| 1092 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 1093 | |
| 1094 | attr = ts.TosaSerializerAttribute() |
| 1095 | attr.AxisAttribute(axis) |
| 1096 | |
| 1097 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1098 | return result_tens |
| 1099 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1100 | def build_pool2d(self, op, input, kernel, stride, pad, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1101 | result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) |
| 1102 | |
| 1103 | attr = ts.TosaSerializerAttribute() |
| 1104 | attr.Pool2dAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1105 | |
| 1106 | self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo) |
| 1107 | return result_tens |
| 1108 | |
| 1109 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1110 | assert len(padding) == 4 |
| 1111 | result_tens = OutputShaper.conv2dOp( |
| 1112 | self.ser, ifm, filter, strides, padding, dilations |
| 1113 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1114 | |
| 1115 | attr = ts.TosaSerializerAttribute() |
| 1116 | attr.Conv2dAttribute(padding, strides, dilations) |
| 1117 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1118 | self.ser.addOperator( |
| 1119 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1120 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1121 | return result_tens |
| 1122 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1123 | def build_transpose_conv2d( |
| 1124 | self, op, ifm, filter, stride, outpad, dilation, output_shape, qinfo |
| 1125 | ): |
| 1126 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1127 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 1128 | |
| 1129 | attr = ts.TosaSerializerAttribute() |
| 1130 | attr.TransposeConv2DAttribute(outpad, stride, dilation, output_shape) |
| 1131 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1132 | # Create bias here since the acc_t depends on (but isn't the same as) the input dtype |
| 1133 | # The bias is OC |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1134 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1135 | bias_type = DType.INT32 |
| 1136 | elif ifm.dtype == DType.INT16: |
| 1137 | bias_type = DType.INT48 |
| 1138 | elif ifm.dtype == DType.FLOAT: |
| 1139 | bias_type = DType.FLOAT |
| 1140 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1141 | raise Exception( |
| 1142 | "Unsupported dtype for transpose_conv2d: {}".format(ifm.dtype) |
| 1143 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1144 | |
| 1145 | bias_arr = self.getRandTensor([filter.shape[0]], bias_type) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1146 | bias_tens = self.ser.addConst([filter.shape[0]], bias_type, bias_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1147 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1148 | self.ser.addOperator( |
| 1149 | op, [ifm.name, filter.name, bias_tens.name], [result_tens.name], attr, qinfo |
| 1150 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1151 | return result_tens |
| 1152 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1153 | def build_depthwise_conv2d( |
| 1154 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 1155 | ): |
| 1156 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 1157 | self.ser, ifm, filter, strides, padding, dilations |
| 1158 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1159 | |
| 1160 | attr = ts.TosaSerializerAttribute() |
| 1161 | attr.Conv2dAttribute(padding, strides, dilations) |
| 1162 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1163 | self.ser.addOperator( |
| 1164 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1165 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1166 | return result_tens |
| 1167 | |
| 1168 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 1169 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 1170 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1171 | self.ser.addOperator( |
| 1172 | op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo |
| 1173 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1174 | return result_tens |
| 1175 | |
| 1176 | def build_matmul(self, op, a, b, qinfo): |
| 1177 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
| 1178 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], None, qinfo) |
| 1179 | return result_tens |
| 1180 | |
| 1181 | def build_reduce(self, op, a, axis): |
| 1182 | result_tens = OutputShaper.reduceOp(self.ser, a, axis) |
| 1183 | |
| 1184 | attr = ts.TosaSerializerAttribute() |
| 1185 | attr.AxisAttribute(axis) |
| 1186 | |
| 1187 | self.ser.addOperator(op, [a.name], result_tens.name, attr) |
| 1188 | return result_tens |
| 1189 | |
| 1190 | def build_clamp(self, op, a): |
| 1191 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1192 | |
| 1193 | attr = ts.TosaSerializerAttribute() |
| 1194 | |
| 1195 | # Get two random ints |
| 1196 | v = [self.randInt(), self.randInt()] |
| 1197 | |
| 1198 | if a.dtype == DType.FLOAT: |
| 1199 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 1200 | else: |
| 1201 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 1202 | |
| 1203 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1204 | return result_tens |
| 1205 | |
| 1206 | def build_leaky_relu(self, op, a): |
| 1207 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1208 | attr = ts.TosaSerializerAttribute() |
| 1209 | |
| 1210 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 1211 | |
| 1212 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1213 | return result_tens |
| 1214 | |
| 1215 | # Needs an additional type/input |
| 1216 | def build_prelu(self, op, a): |
| 1217 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1218 | |
| 1219 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1220 | return result_tens |
| 1221 | |
| 1222 | def build_relun(self, op, a): |
| 1223 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1224 | |
| 1225 | attr = ts.TosaSerializerAttribute() |
| 1226 | |
| 1227 | if a.dtype == DType.FLOAT: |
| 1228 | attr.ReluNAttribute(0, self.getRandNumberDType(a.dtype)) |
| 1229 | else: |
| 1230 | attr.ReluNAttribute(self.getRandNumberDType(a.dtype), 0) |
| 1231 | |
| 1232 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1233 | return result_tens |
| 1234 | |
| 1235 | def build_sigmoid(self, op, a): |
| 1236 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1237 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1238 | return result_tens |
| 1239 | |
| 1240 | def build_tanh(self, op, a): |
| 1241 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1242 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1243 | return result_tens |
| 1244 | |
| 1245 | def build_concat(self, op, a, b, axis): |
| 1246 | result_tens = OutputShaper.concatOp(self.ser, a, b, axis) |
| 1247 | |
| 1248 | attr = ts.TosaSerializerAttribute() |
| 1249 | attr.AxisAttribute(axis) |
| 1250 | |
| 1251 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
| 1252 | |
| 1253 | def build_pad(self, op, a, padding, qinfo): |
| 1254 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 1255 | |
| 1256 | # Need to turn the padding array into a TOSA tensor here. |
| 1257 | # This is one of the few tensor operands that does not get |
| 1258 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1259 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1260 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1261 | self.ser.addOperator( |
| 1262 | op, [a.name, padding_tens.name], [result_tens.name], None, qinfo |
| 1263 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1264 | |
| 1265 | def build_reshape(self, op, a, newShape): |
| 1266 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 1267 | |
| 1268 | attr = ts.TosaSerializerAttribute() |
| 1269 | attr.ReshapeAttribute(newShape) |
| 1270 | |
| 1271 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1272 | return result_tens |
| 1273 | |
| 1274 | def build_reverse(self, op, a, axis): |
| 1275 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1276 | |
| 1277 | attr = ts.TosaSerializerAttribute() |
| 1278 | attr.AxisAttribute(axis) |
| 1279 | |
| 1280 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1281 | return result_tens |
| 1282 | |
| 1283 | def build_transpose(self, op, a, perms): |
| 1284 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 1285 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1286 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1287 | |
| 1288 | self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name]) |
| 1289 | return result_tens |
| 1290 | |
| 1291 | def build_slice(self, op, a, begin, size): |
| 1292 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 1293 | |
| 1294 | attr = ts.TosaSerializerAttribute() |
| 1295 | attr.SliceAttribute(begin, size) |
| 1296 | |
| 1297 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1298 | return result_tens |
| 1299 | |
| 1300 | def build_tile(self, op, a, multiples): |
| 1301 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 1302 | |
| 1303 | attr = ts.TosaSerializerAttribute() |
| 1304 | attr.TileAttribute(multiples) |
| 1305 | |
| 1306 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1307 | return result_tens |
| 1308 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1309 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1310 | |
| 1311 | # Create a new indicies tensor |
| 1312 | # here with data that doesn't exceed the dimensions of the values tensor |
| 1313 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1314 | K = values.shape[1] # K |
| 1315 | W = self.randInt( |
| 1316 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 1317 | ) # W |
| 1318 | indicies_arr = np.int32( |
| 1319 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 1320 | ) # (N, W) |
| 1321 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1322 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1323 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1324 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1325 | self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1326 | |
| 1327 | return result_tens |
| 1328 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1329 | def build_scatter(self, op, values_in, input): |
| 1330 | |
| 1331 | # Create a new indicies tensor |
| 1332 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 1333 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1334 | K = values_in.shape[1] # K |
| 1335 | W = input.shape[1] # W |
| 1336 | indicies_arr = np.int32( |
| 1337 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 1338 | ) # (N, W) |
| 1339 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1340 | |
| 1341 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 1342 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1343 | self.ser.addOperator( |
| 1344 | op, [values_in.name, indicies.name, input.name], [result_tens.name] |
| 1345 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1346 | |
| 1347 | return result_tens |
| 1348 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1349 | def build_resize( |
| 1350 | self, |
| 1351 | op, |
| 1352 | input, |
| 1353 | mode, |
| 1354 | stride, |
| 1355 | offset, |
| 1356 | shift, |
| 1357 | stride_fp, |
| 1358 | offset_fp, |
| 1359 | output_dims, |
| 1360 | input_dtype, |
| 1361 | output_dtype, |
| 1362 | ): |
| 1363 | result_tens = OutputShaper.resizeOp( |
| 1364 | self.ser, |
| 1365 | input, |
| 1366 | mode, |
| 1367 | stride, |
| 1368 | offset, |
| 1369 | shift, |
| 1370 | stride_fp, |
| 1371 | offset_fp, |
| 1372 | output_dims, |
| 1373 | input_dtype, |
| 1374 | output_dtype, |
| 1375 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1376 | |
| 1377 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1378 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1379 | attr.ResizeAttribute( |
| 1380 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 1381 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1382 | |
| 1383 | self.ser.addOperator(op, [input.name], [result_tens.name], attr) |
| 1384 | return result_tens |
| 1385 | |
| 1386 | def build_identityn(self, op, val, val2): |
| 1387 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1388 | result_tens = OutputShaper.unaryOp(self.ser, val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1389 | result_tens2 = OutputShaper.unaryOp(self.ser, val2) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1390 | self.ser.addOperator( |
| 1391 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 1392 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1393 | return result_tens |
| 1394 | |
| 1395 | def build_placeholder(self, op, val): |
| 1396 | # Add an identity op to avoid warning in the reference model |
| 1397 | return self.build_unary(Op.IDENTITY, val) |
| 1398 | |
| 1399 | # Type Conversion |
| 1400 | def build_cast(self, op, val, out_dtype): |
| 1401 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 1402 | self.ser.addOperator(op, [val.name], [result_tens.name]) |
| 1403 | return result_tens |
| 1404 | |
| 1405 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel): |
| 1406 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 1407 | |
| 1408 | if per_channel: |
| 1409 | nc = val.shape[-1] |
| 1410 | else: |
| 1411 | nc = 1 |
| 1412 | |
| 1413 | in_type_width = self.typeWidth(val.dtype) |
| 1414 | out_type_width = self.typeWidth(out_dtype) |
| 1415 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1416 | if val.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1417 | input_zp = self.randInt() |
| 1418 | in_type_width = in_type_width + 1 |
| 1419 | else: |
| 1420 | input_zp = 0 |
| 1421 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1422 | if out_dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1423 | output_zp = self.randInt() |
| 1424 | out_type_width = out_type_width + 1 |
| 1425 | else: |
| 1426 | output_zp = 0 |
| 1427 | |
| 1428 | # Calculate scale based on: |
| 1429 | # scale = a *(2^output_width)/(2^input_width)) |
| 1430 | |
| 1431 | a = np.float32(self.rng.random(size=[nc])) |
| 1432 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 1433 | |
| 1434 | if scale32: |
| 1435 | pass |
| 1436 | # Cap the scaling at 2^15 - 1 for scale16 |
| 1437 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 1438 | else: |
| 1439 | # Cap the scaling at 2^15 - 1 for scale16 |
| 1440 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 1441 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1442 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1443 | |
| 1444 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 1445 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 1446 | |
| 1447 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1448 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 1449 | scale_arr[i], scale32 |
| 1450 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1451 | if shift_arr[i] < 2 or shift_arr[i] > 62: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1452 | self.ser.setExpectedFailure(True, "OpRescale: invalid shift value") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1453 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1454 | # print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1455 | |
| 1456 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1457 | attr.RescaleAttribute( |
| 1458 | input_zp, |
| 1459 | output_zp, |
| 1460 | multiplier_arr, |
| 1461 | shift_arr, |
| 1462 | scale32, |
| 1463 | double_round, |
| 1464 | per_channel, |
| 1465 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1466 | |
| 1467 | self.ser.addOperator(op, [val.name], [result_tens.name], attr) |
| 1468 | return result_tens |
| 1469 | |
| 1470 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 1471 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 1472 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 1473 | # and fill them with const nodes for the body. |
| 1474 | |
| 1475 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1476 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1477 | |
| 1478 | # Make then/else tensors |
| 1479 | out_shape = then_tens.shape |
| 1480 | then_arr = np.int32(self.rng.integers(0, 255, size=out_shape)) |
| 1481 | else_arr = np.int32(self.rng.integers(0, 255, size=out_shape)) |
| 1482 | |
| 1483 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1484 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1485 | |
| 1486 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1487 | then_block = "THEN_BLOCK" |
| 1488 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1489 | attr = ts.TosaSerializerAttribute() |
| 1490 | attr.CondIfAttribute(then_block, else_block) |
| 1491 | |
| 1492 | # Finally, build the op and the two blocks |
| 1493 | self.ser.addOperator(op, [cond_tens.name], [result_tens.name], attr) |
| 1494 | |
| 1495 | self.ser.startBasicBlock(then_block) |
| 1496 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1497 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1498 | self.ser.addOutputTensor(then_tens) |
| 1499 | |
| 1500 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1501 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1502 | self.ser.addOutputTensor(else_tens) |
| 1503 | |
| 1504 | return result_tens |
| 1505 | |
| 1506 | def build_cond_if_binary(self, op, a, b, cond): |
| 1507 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 1508 | # alternately add or subtract them based on the condition |
| 1509 | |
| 1510 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1511 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1512 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1513 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1514 | self.ser.currBasicBlock.addOutput(result_tens.name) |
| 1515 | |
| 1516 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1517 | then_block = "THEN_BLOCK" |
| 1518 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1519 | attr = ts.TosaSerializerAttribute() |
| 1520 | attr.CondIfAttribute(then_block, else_block) |
| 1521 | |
| 1522 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1523 | self.ser.addOperator( |
| 1524 | op, [cond_tens.name, a.name, b.name], [result_tens.name], attr |
| 1525 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1526 | |
| 1527 | self.ser.startBasicBlock(then_block) |
| 1528 | self.ser.addInputTensor(a) |
| 1529 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1530 | then_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1531 | self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) |
| 1532 | |
| 1533 | self.ser.startBasicBlock(else_block) |
| 1534 | self.ser.addInputTensor(a) |
| 1535 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1536 | else_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1537 | self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) |
| 1538 | |
| 1539 | return result_tens |
| 1540 | |
| 1541 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1542 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1543 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1544 | cond_block = "COND_BLOCK" |
| 1545 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1546 | |
| 1547 | attr = ts.TosaSerializerAttribute() |
| 1548 | attr.WhileLoopAttribute(cond_block, body_block) |
| 1549 | |
| 1550 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1551 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1552 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1553 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1554 | |
| 1555 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1556 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 1557 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 1558 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1559 | |
| 1560 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1561 | self.ser.addOperator( |
| 1562 | op, |
| 1563 | [iter.name, a.name, acc.name], |
| 1564 | [iter_out.name, a_out.name, acc_out.name], |
| 1565 | attr, |
| 1566 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1567 | |
| 1568 | # COND block (input: iter, output: cond_tens ) |
| 1569 | self.ser.startBasicBlock(cond_block) |
| 1570 | self.ser.addInputTensor(iter) |
| 1571 | self.ser.addInputTensor(a) |
| 1572 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1573 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 1574 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 1575 | self.ser.addOperator(Op.GREATER, [iter.name, zero_tens.name], [cond_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1576 | |
| 1577 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 1578 | # Note that local intermediate tensors need to be declared here for the outputs |
| 1579 | self.ser.startBasicBlock(body_block) |
| 1580 | self.ser.addInputTensor(iter) |
| 1581 | self.ser.addInputTensor(a) |
| 1582 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1583 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 1584 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 1585 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1586 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 1587 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 1588 | self.ser.addOutputTensor(iter_body_out) |
| 1589 | self.ser.addOutputTensor(a) |
| 1590 | self.ser.addOutputTensor(acc_body_out) |
| 1591 | |
| 1592 | return acc_out |
| 1593 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1594 | def genOpTestList( |
| 1595 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None |
| 1596 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1597 | |
| 1598 | try: |
| 1599 | op = self.TOSA_OP_LIST[opName] |
| 1600 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1601 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1602 | |
| 1603 | # Initialize a new random number generator |
| 1604 | self.rng = np.random.default_rng(self.random_seed) |
| 1605 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1606 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1607 | |
| 1608 | # Generate the lists of arguments |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1609 | rmin, rmax = op["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1610 | |
| 1611 | # Test list consists of a tuple of: |
| 1612 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 1613 | testList = [] |
| 1614 | |
| 1615 | if not shapeFilter: |
| 1616 | shapeFilter = [None] |
| 1617 | |
| 1618 | for r in range(rmin, rmax + 1): |
| 1619 | |
| 1620 | # Filter out the rank? |
| 1621 | if rankFilter is not None and r not in rankFilter: |
| 1622 | continue |
| 1623 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1624 | for t in op["types"]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1625 | |
| 1626 | # Filter tests based on dtype? |
| 1627 | if dtypeFilter is not None: |
| 1628 | if t not in dtypeFilter: |
| 1629 | continue |
| 1630 | |
| 1631 | # Create the placeholder and const tensors |
| 1632 | for shape in shapeFilter: |
| 1633 | # A None shape chooses a random shape of a given rank |
| 1634 | |
| 1635 | # Filter out by rank |
| 1636 | if shape is not None and len(shape) != r: |
| 1637 | continue |
| 1638 | |
| 1639 | self.setTargetShape(shape) |
| 1640 | shapeList = tgen_fcn(self, op, r) |
| 1641 | |
| 1642 | shapeStr = self.shapeStr(shapeList[0]) |
| 1643 | typeStr = self.typeStr(t) |
| 1644 | |
| 1645 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 1646 | argList = [] |
| 1647 | if agen_fcn: |
| 1648 | argList = agen_fcn(self, opName, shapeList, t) |
| 1649 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1650 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1651 | |
| 1652 | for argStr, args in argList: |
| 1653 | if argStr: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1654 | testStr = "{}_{}_{}_{}".format( |
| 1655 | opName, shapeStr, typeStr, argStr |
| 1656 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1657 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1658 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1659 | |
| 1660 | testList.append((opName, testStr, t, shapeList, args)) |
| 1661 | |
| 1662 | return testList |
| 1663 | |
| 1664 | def serializeTest(self, opName, testStr, dtype, shapeList, testArgs): |
| 1665 | try: |
| 1666 | op = self.TOSA_OP_LIST[opName] |
| 1667 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1668 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1669 | |
| 1670 | # Create a serializer |
| 1671 | self.createSerializer(opName, testStr) |
| 1672 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1673 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
| 1674 | pCount, cCount = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1675 | |
| 1676 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1677 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1678 | except KeyError: |
| 1679 | qgen = None |
| 1680 | |
| 1681 | # Build the random tensor operands and the test |
| 1682 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1683 | |
| 1684 | # If test is ArithmeticRightShift, force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1685 | if op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 1686 | assert ( |
| 1687 | pCount == 2 and cCount == 0 |
| 1688 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1689 | |
| 1690 | placeholders = [] |
| 1691 | for idx, shape in enumerate(shapeList[:]): |
| 1692 | if idx == 1: |
| 1693 | if dtype == DType.INT8: |
| 1694 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
| 1695 | elif dtype == DType.INT16: |
| 1696 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
| 1697 | elif dtype == DType.INT32: |
| 1698 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 1699 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1700 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1701 | else: |
| 1702 | arr = self.getRandTensor(shapeList[0], dtype) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1703 | placeholders.append(self.ser.addPlaceholder(shape, dtype, arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1704 | |
| 1705 | tens.extend(placeholders) |
| 1706 | else: |
| 1707 | tens.extend(self.buildPlaceholderTensors(shapeList[0:pCount], dtype)) |
| 1708 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1709 | |
| 1710 | if qgen is not None: |
| 1711 | qinfo = qgen(self, op, dtype) |
| 1712 | else: |
| 1713 | qinfo = None |
| 1714 | |
| 1715 | try: |
| 1716 | if qinfo is not None: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1717 | resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1718 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1719 | resultName = build_fcn(self, op["op"], *tens, *testArgs) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1720 | except TypeError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1721 | print( |
| 1722 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 1723 | build_fcn, tens, testArgs |
| 1724 | ) |
| 1725 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1726 | raise e |
| 1727 | |
| 1728 | # Save the serialized test |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1729 | self.serialize("test") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1730 | |
| 1731 | def createDynamicOpLists(self): |
| 1732 | |
| 1733 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1734 | KERNELS = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1735 | |
| 1736 | for k in KERNELS: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1737 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 1738 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 1739 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 1740 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1741 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1742 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 1743 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 1744 | "depthwise_conv2d_TEMPLATE" |
| 1745 | ].copy() |
| 1746 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 1747 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1748 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1749 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 1750 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 1751 | "transpose_conv2d_TEMPLATE" |
| 1752 | ].copy() |
| 1753 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 1754 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1755 | |
| 1756 | # Delete any templates after having created any dynamic ops |
| 1757 | # This is a two-pass operation because it's bad practice to delete |
| 1758 | # keys from dictionaries while iterating |
| 1759 | keyList = [] |
| 1760 | for k in self.TOSA_OP_LIST: |
| 1761 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1762 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1763 | keyList.append(k) |
| 1764 | continue |
| 1765 | except KeyError: |
| 1766 | pass |
| 1767 | |
| 1768 | for k in keyList: |
| 1769 | del self.TOSA_OP_LIST[k] |
| 1770 | |
| 1771 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1772 | """Fill in default fields for ops if they aren't already specified. |
| 1773 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1774 | for op in self.TOSA_OP_LIST: |
| 1775 | |
| 1776 | # Required fields |
| 1777 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1778 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1779 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1780 | raise Exception( |
| 1781 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 1782 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1783 | |
| 1784 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1785 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1786 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1787 | raise Exception( |
| 1788 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 1789 | op |
| 1790 | ) |
| 1791 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1792 | |
| 1793 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1794 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1795 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1796 | raise Exception( |
| 1797 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 1798 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1799 | |
| 1800 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1801 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1802 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1803 | raise Exception( |
| 1804 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 1805 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1806 | |
| 1807 | # Put in default rank range, if missing |
| 1808 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1809 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1810 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1811 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1812 | |
| 1813 | # Tensor operator list |
| 1814 | # 'op': op name |
| 1815 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1816 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 1817 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1818 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 1819 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1820 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1821 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1822 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 1823 | TYPE_INT_FP = [DType.INT8, DType.INT16, DType.INT32, DType.FLOAT] # Excludes INT4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1824 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1825 | TYPE_BOOL = [DType.BOOL] |
| 1826 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 1827 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 1828 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1829 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1830 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1831 | |
| 1832 | DEFAULT_RANK_RANGE = (1, 4) |
| 1833 | |
| 1834 | TOSA_OP_LIST = { |
| 1835 | # Binary ops |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1836 | "add": { |
| 1837 | "op": Op.ADD, |
| 1838 | "operands": (2, 0), |
| 1839 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1840 | "types": TYPE_FI32, |
| 1841 | }, |
| 1842 | "arithmetic_right_shift": { |
| 1843 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 1844 | "operands": (2, 0), |
| 1845 | "build_fcn": ( |
| 1846 | build_arithmetic_right_shift, |
| 1847 | TosaTensorGen.tgBroadcastFuzz, |
| 1848 | TosaArgGen.agArithmeticRightShift, |
| 1849 | ), |
| 1850 | "types": TYPE_INT, |
| 1851 | }, |
| 1852 | "bitwise_and": { |
| 1853 | "op": Op.BITWISE_AND, |
| 1854 | "operands": (2, 0), |
| 1855 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1856 | "types": TYPE_INT, |
| 1857 | }, |
| 1858 | "bitwise_or": { |
| 1859 | "op": Op.BITWISE_OR, |
| 1860 | "operands": (2, 0), |
| 1861 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1862 | "types": TYPE_INT, |
| 1863 | }, |
| 1864 | "bitwise_xor": { |
| 1865 | "op": Op.BITWISE_XOR, |
| 1866 | "operands": (2, 0), |
| 1867 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1868 | "types": TYPE_INT, |
| 1869 | }, |
| 1870 | "logical_and": { |
| 1871 | "op": Op.LOGICAL_AND, |
| 1872 | "operands": (2, 0), |
| 1873 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1874 | "types": TYPE_BOOL, |
| 1875 | }, |
| 1876 | "logical_left_shift": { |
| 1877 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 1878 | "operands": (2, 0), |
| 1879 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1880 | "types": TYPE_INT, |
| 1881 | }, |
| 1882 | "logical_right_shift": { |
| 1883 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 1884 | "operands": (2, 0), |
| 1885 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1886 | "types": TYPE_INT, |
| 1887 | }, |
| 1888 | "logical_or": { |
| 1889 | "op": Op.LOGICAL_OR, |
| 1890 | "operands": (2, 0), |
| 1891 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1892 | "types": TYPE_BOOL, |
| 1893 | }, |
| 1894 | "logical_xor": { |
| 1895 | "op": Op.LOGICAL_XOR, |
| 1896 | "operands": (2, 0), |
| 1897 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1898 | "types": TYPE_BOOL, |
| 1899 | }, |
| 1900 | "max": { |
| 1901 | "op": Op.MAXIMUM, |
| 1902 | "operands": (2, 0), |
| 1903 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1904 | "types": TYPE_FI32, |
| 1905 | }, |
| 1906 | "min": { |
| 1907 | "op": Op.MINIMUM, |
| 1908 | "operands": (2, 0), |
| 1909 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1910 | "types": TYPE_FI32, |
| 1911 | }, |
| 1912 | "mul": { |
| 1913 | "op": Op.MUL, |
| 1914 | "operands": (2, 0), |
| 1915 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 1916 | "types": TYPE_INT_FP, |
| 1917 | }, |
| 1918 | "pow": { |
| 1919 | "op": Op.POW, |
| 1920 | "operands": (2, 0), |
| 1921 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 1922 | "types": TYPE_FP, |
| 1923 | }, |
| 1924 | "sub": { |
| 1925 | "op": Op.SUB, |
| 1926 | "operands": (2, 0), |
| 1927 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 1928 | "types": TYPE_FI32, |
| 1929 | }, |
| 1930 | "table": { |
| 1931 | "op": Op.TABLE, |
| 1932 | # Use the automatic generation functions to create the input array |
| 1933 | # but create the table tensor in the build function, as it may be |
| 1934 | # a different type from the input |
| 1935 | "operands": (1, 0), |
| 1936 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
| 1937 | "types": [DType.INT16], |
| 1938 | }, |
| 1939 | "argmax": { |
| 1940 | "op": Op.ARGMAX, |
| 1941 | "operands": (1, 0), |
| 1942 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 1943 | "types": TYPE_NARROW_INT_FP, |
| 1944 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1945 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1946 | "conv2d_TEMPLATE": { |
| 1947 | "op": Op.CONV2D, |
| 1948 | "operands": (1, 2), |
| 1949 | "rank": (4, 4), |
| 1950 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), |
| 1951 | "qgen": TosaQuantGen.qgConv, |
| 1952 | "types": TYPE_NARROW_INT_FP, |
| 1953 | "template": True, |
| 1954 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1955 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1956 | "depthwise_conv2d_TEMPLATE": { |
| 1957 | "op": Op.DEPTHWISE_CONV2D, |
| 1958 | "operands": (1, 2), |
| 1959 | "filter": [1, 1], |
| 1960 | "rank": (4, 4), |
| 1961 | "build_fcn": ( |
| 1962 | build_depthwise_conv2d, |
| 1963 | TosaTensorGen.tgDepthwiseConv2D, |
| 1964 | TosaArgGen.agConv2D, |
| 1965 | ), |
| 1966 | "qgen": TosaQuantGen.qgConv, |
| 1967 | "types": TYPE_NARROW_INT_FP, |
| 1968 | "template": True, |
| 1969 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1970 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 1971 | "transpose_conv2d_TEMPLATE": { |
| 1972 | "op": Op.TRANSPOSE_CONV2D, |
| 1973 | "operands": (1, 1), |
| 1974 | "rank": (4, 4), |
| 1975 | "build_fcn": ( |
| 1976 | build_transpose_conv2d, |
| 1977 | TosaTensorGen.tgTransposeConv2D, |
| 1978 | TosaArgGen.agTransposeConv2D, |
| 1979 | ), |
| 1980 | "qgen": TosaQuantGen.qgConv, |
| 1981 | "types": TYPE_FP, |
| 1982 | "template": True, |
| 1983 | }, |
| 1984 | "fully_connected": { |
| 1985 | "op": Op.FULLY_CONNECTED, |
| 1986 | "operands": (2, 0), |
| 1987 | "rank": (2, 2), |
| 1988 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 1989 | "qgen": TosaQuantGen.qgConv, |
| 1990 | "types": TYPE_NARROW_INT_FP, |
| 1991 | }, |
| 1992 | "matmul": { |
| 1993 | "op": Op.MATMUL, |
| 1994 | "operands": (2, 0), |
| 1995 | "rank": (2, 2), |
| 1996 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 1997 | "qgen": TosaQuantGen.qgMatmul, |
| 1998 | "types": TYPE_NARROW_INT_FP, |
| 1999 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2000 | # Unary operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2001 | "abs": { |
| 2002 | "op": Op.ABS, |
| 2003 | "operands": (1, 0), |
| 2004 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2005 | "types": TYPE_FI32, |
| 2006 | }, |
| 2007 | "bitwise_not": { |
| 2008 | "op": Op.BITWISE_NOT, |
| 2009 | "operands": (1, 0), |
| 2010 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2011 | "types": TYPE_INT, |
| 2012 | }, |
| 2013 | "ceil": { |
| 2014 | "op": Op.CEIL, |
| 2015 | "operands": (1, 0), |
| 2016 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2017 | "types": TYPE_FP, |
| 2018 | }, |
| 2019 | "clz": { |
| 2020 | "op": Op.CLZ, |
| 2021 | "operands": (1, 0), |
| 2022 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2023 | "types": [DType.INT32], |
| 2024 | }, |
| 2025 | "exp": { |
| 2026 | "op": Op.EXP, |
| 2027 | "operands": (1, 0), |
| 2028 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2029 | "types": TYPE_FP, |
| 2030 | }, |
| 2031 | "floor": { |
| 2032 | "op": Op.FLOOR, |
| 2033 | "operands": (1, 0), |
| 2034 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2035 | "types": TYPE_FP, |
| 2036 | }, |
| 2037 | "log": { |
| 2038 | "op": Op.LOG, |
| 2039 | "operands": (1, 0), |
| 2040 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2041 | "types": TYPE_FP, |
| 2042 | }, |
| 2043 | "floor": { |
| 2044 | "op": Op.FLOOR, |
| 2045 | "operands": (1, 0), |
| 2046 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2047 | "types": TYPE_FP, |
| 2048 | }, |
| 2049 | "logical_not": { |
| 2050 | "op": Op.LOGICAL_NOT, |
| 2051 | "operands": (1, 0), |
| 2052 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2053 | "types": TYPE_BOOL, |
| 2054 | }, |
| 2055 | "negate": { |
| 2056 | "op": Op.NEGATE, |
| 2057 | "operands": (1, 0), |
| 2058 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2059 | "qgen": TosaQuantGen.qgUnary, |
| 2060 | "types": TYPE_INT_FP, |
| 2061 | }, |
| 2062 | "reciprocal": { |
| 2063 | "op": Op.RECIPROCAL, |
| 2064 | "operands": (1, 0), |
| 2065 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2066 | "types": TYPE_FP, |
| 2067 | }, |
| 2068 | "rsqrt": { |
| 2069 | "op": Op.RSQRT, |
| 2070 | "operands": (1, 0), |
| 2071 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2072 | "types": TYPE_FP, |
| 2073 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2074 | # Ternary operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2075 | "select": { |
| 2076 | "op": Op.SELECT, |
| 2077 | "operands": (3, 0), |
| 2078 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 2079 | "types": TYPE_FIB, |
| 2080 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2081 | # Comparison operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2082 | "equal": { |
| 2083 | "op": Op.EQUAL, |
| 2084 | "operands": (2, 0), |
| 2085 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2086 | "types": TYPE_FI32, |
| 2087 | }, |
| 2088 | "greater_equal": { |
| 2089 | "op": Op.GREATER_EQUAL, |
| 2090 | "operands": (2, 0), |
| 2091 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2092 | "types": TYPE_FI32, |
| 2093 | }, |
| 2094 | "greater": { |
| 2095 | "op": Op.GREATER, |
| 2096 | "operands": (2, 0), |
| 2097 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2098 | "types": TYPE_FI32, |
| 2099 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2100 | # Pooling operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2101 | "avg_pool2d": { |
| 2102 | "op": Op.AVG_POOL2D, |
| 2103 | "operands": (1, 0), |
| 2104 | "rank": (4, 4), |
| 2105 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2106 | "qgen": TosaQuantGen.qgUnary, |
| 2107 | "types": TYPE_NARROW_INT_FP, |
| 2108 | }, |
| 2109 | "max_pool2d": { |
| 2110 | "op": Op.MAX_POOL2D, |
| 2111 | "operands": (1, 0), |
| 2112 | "rank": (4, 4), |
| 2113 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2114 | "types": TYPE_NARROW_INT_FP, |
| 2115 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2116 | # Reduce operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2117 | "reduce_any": { |
| 2118 | "op": Op.REDUCE_ANY, |
| 2119 | "operands": (1, 0), |
| 2120 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2121 | "types": TYPE_BOOL, |
| 2122 | }, |
| 2123 | "reduce_all": { |
| 2124 | "op": Op.REDUCE_ALL, |
| 2125 | "operands": (1, 0), |
| 2126 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2127 | "types": TYPE_BOOL, |
| 2128 | }, |
| 2129 | "reduce_max": { |
| 2130 | "op": Op.REDUCE_MAX, |
| 2131 | "operands": (1, 0), |
| 2132 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2133 | "types": TYPE_INT_FP, |
| 2134 | }, |
| 2135 | "reduce_min": { |
| 2136 | "op": Op.REDUCE_MAX, |
| 2137 | "operands": (1, 0), |
| 2138 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2139 | "types": TYPE_INT_FP, |
| 2140 | }, |
| 2141 | "reduce_product": { |
| 2142 | "op": Op.REDUCE_PRODUCT, |
| 2143 | "operands": (1, 0), |
| 2144 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2145 | "types": TYPE_FP, |
| 2146 | }, |
| 2147 | "reduce_sum": { |
| 2148 | "op": Op.REDUCE_SUM, |
| 2149 | "operands": (1, 0), |
| 2150 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2151 | "types": TYPE_FI32, |
| 2152 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2153 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2154 | "clamp": { |
| 2155 | "op": Op.CLAMP, |
| 2156 | "operands": (1, 0), |
| 2157 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 2158 | "types": TYPE_NARROW_INT_FP, |
| 2159 | }, |
| 2160 | "relun": { |
| 2161 | "op": Op.RELUN, |
| 2162 | "operands": (1, 0), |
| 2163 | "build_fcn": (build_relun, TosaTensorGen.tgBasic, None), |
| 2164 | "types": TYPE_FI32, |
| 2165 | }, |
| 2166 | "sigmoid": { |
| 2167 | "op": Op.SIGMOID, |
| 2168 | "operands": (1, 0), |
| 2169 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 2170 | "types": TYPE_FP, |
| 2171 | }, |
| 2172 | "tanh": { |
| 2173 | "op": Op.TANH, |
| 2174 | "operands": (1, 0), |
| 2175 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 2176 | "types": TYPE_FP, |
| 2177 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2178 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2179 | "concat": { |
| 2180 | "op": Op.CONCAT, |
| 2181 | "operands": (2, 0), |
| 2182 | "build_fcn": (build_concat, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2183 | "types": TYPE_FIB, |
| 2184 | }, |
| 2185 | "pad": { |
| 2186 | "op": Op.PAD, |
| 2187 | "operands": (1, 0), |
| 2188 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 2189 | "qgen": TosaQuantGen.qgPad, |
| 2190 | "types": TYPE_FIB, |
| 2191 | }, |
| 2192 | "reshape": { |
| 2193 | "op": Op.RESHAPE, |
| 2194 | "operands": (1, 0), |
| 2195 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 2196 | "types": TYPE_FIB, |
| 2197 | }, |
| 2198 | "reverse": { |
| 2199 | "op": Op.REVERSE, |
| 2200 | "operands": (1, 0), |
| 2201 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2202 | "types": TYPE_FIB, |
| 2203 | }, |
| 2204 | "slice": { |
| 2205 | "op": Op.SLICE, |
| 2206 | "operands": (1, 0), |
| 2207 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 2208 | "types": TYPE_FIB, |
| 2209 | }, |
| 2210 | "tile": { |
| 2211 | "op": Op.TILE, |
| 2212 | "operands": (1, 0), |
| 2213 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 2214 | "types": TYPE_FIB, |
| 2215 | }, |
| 2216 | "transpose": { |
| 2217 | "op": Op.TRANSPOSE, |
| 2218 | "operands": (1, 0), |
| 2219 | "rank": (2, 4), # Do not allow tranpose on rank=1 |
| 2220 | "build_fcn": ( |
| 2221 | build_transpose, |
| 2222 | TosaTensorGen.tgBasic, |
| 2223 | TosaArgGen.agTranspose, |
| 2224 | ), |
| 2225 | "types": TYPE_FIB, |
| 2226 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2227 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2228 | "gather": { |
| 2229 | "op": Op.GATHER, |
| 2230 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 2231 | "operands": (1, 0), |
| 2232 | "rank": (3, 3), |
| 2233 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 2234 | "types": TYPE_INT_FP, |
| 2235 | }, |
| 2236 | "scatter": { |
| 2237 | "op": Op.SCATTER, |
| 2238 | # Only specify 'values_in' tensor here. |
| 2239 | #'indices' and 'input' are generated in op building stage |
| 2240 | "operands": (2, 0), |
| 2241 | "rank": (3, 3), |
| 2242 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 2243 | "types": TYPE_INT_FP, |
| 2244 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2245 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2246 | "resize": { |
| 2247 | "op": Op.RESIZE, |
| 2248 | "operands": (1, 0), |
| 2249 | "rank": (4, 4), |
| 2250 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 2251 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
| 2252 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2253 | # Data nodes |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2254 | "placeholder": { |
| 2255 | "op": Op.PLACEHOLDER, |
| 2256 | "operands": (1, 0), |
| 2257 | "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), |
| 2258 | "types": TYPE_FIB, |
| 2259 | }, |
| 2260 | "const": { |
| 2261 | "op": Op.CONST, |
| 2262 | "operands": (1, 0), |
| 2263 | "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), |
| 2264 | "types": TYPE_FIB, |
| 2265 | }, |
| 2266 | "identity": { |
| 2267 | "op": Op.IDENTITY, |
| 2268 | "operands": (1, 0), |
| 2269 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2270 | "types": TYPE_FIB, |
| 2271 | }, |
| 2272 | "identityn": { |
| 2273 | "op": Op.IDENTITYN, |
| 2274 | "operands": (2, 0), |
| 2275 | "build_fcn": (build_identityn, TosaTensorGen.tgBasic, None), |
| 2276 | "types": TYPE_FIB, |
| 2277 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2278 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2279 | "cast": { |
| 2280 | "op": Op.CAST, |
| 2281 | "operands": (1, 0), |
| 2282 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 2283 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 2284 | }, |
| 2285 | "rescale": { |
| 2286 | "op": Op.RESCALE, |
| 2287 | "operands": (1, 0), |
| 2288 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
| 2289 | "types": [DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
| 2290 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2291 | # Custom |
| 2292 | # Not implemented. |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2293 | # Control flow |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2294 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 2295 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 2296 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2297 | "cond_if_const": { |
| 2298 | "op": Op.COND_IF, |
| 2299 | "operands": (0, 2), |
| 2300 | "build_fcn": ( |
| 2301 | build_cond_if_const, |
| 2302 | TosaTensorGen.tgBasic, |
| 2303 | TosaArgGen.agCondIf, |
| 2304 | ), |
| 2305 | "types": [DType.BOOL], |
| 2306 | }, |
| 2307 | "cond_if_binary": { |
| 2308 | "op": Op.COND_IF, |
| 2309 | "operands": (2, 0), |
| 2310 | "build_fcn": ( |
| 2311 | build_cond_if_binary, |
| 2312 | TosaTensorGen.tgBasic, |
| 2313 | TosaArgGen.agCondIf, |
| 2314 | ), |
| 2315 | "types": TYPE_FI32, |
| 2316 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2317 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2318 | "while_loop": { |
| 2319 | "op": Op.WHILE_LOOP, |
| 2320 | "operands": (0, 1), |
| 2321 | "build_fcn": ( |
| 2322 | build_while_loop, |
| 2323 | TosaTensorGen.tgBasic, |
| 2324 | TosaArgGen.agWhileLoop, |
| 2325 | ), |
| 2326 | "types": [DType.INT32], |
| 2327 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2328 | } |
| 2329 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2330 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2331 | class OutputShaper: |
| 2332 | # Methods in this class compute the expected output shape and datatype |
| 2333 | # for common classes of operations |
| 2334 | def __init__(self): |
| 2335 | pass |
| 2336 | |
| 2337 | # These methods return arguments that can be used for |
| 2338 | # creating a new output tensor |
| 2339 | @staticmethod |
| 2340 | def binaryBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2341 | assert len(a.shape) == len(b.shape) |
| 2342 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2343 | |
| 2344 | shape = [] |
| 2345 | for i in range(len(a.shape)): |
| 2346 | if a.shape[i] == 1: |
| 2347 | shape.append(b.shape[i]) |
| 2348 | else: |
| 2349 | shape.append(a.shape[i]) |
| 2350 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2351 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2352 | |
| 2353 | @staticmethod |
| 2354 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2355 | assert len(a.shape) == len(b.shape) |
| 2356 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2357 | |
| 2358 | shape = [] |
| 2359 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2360 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2361 | shape.append(a.shape[i]) |
| 2362 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2363 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2364 | |
| 2365 | @staticmethod |
| 2366 | def unaryOp(ser, a): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2367 | return ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2368 | |
| 2369 | @staticmethod |
| 2370 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2371 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 2372 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2373 | |
| 2374 | shape = [] |
| 2375 | for i in range(len(a.shape)): |
| 2376 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 2377 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2378 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2379 | |
| 2380 | @staticmethod |
| 2381 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2382 | assert len(a.shape) == len(b.shape) |
| 2383 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2384 | |
| 2385 | # Do broadcast |
| 2386 | shape = [] |
| 2387 | for i in range(len(a.shape)): |
| 2388 | if a.shape[i] == 1: |
| 2389 | shape.append(b.shape[i]) |
| 2390 | else: |
| 2391 | shape.append(a.shape[i]) |
| 2392 | |
| 2393 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2394 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2395 | |
| 2396 | @staticmethod |
| 2397 | def reduceOp(ser, a, axis): |
| 2398 | |
| 2399 | shape = a.shape.copy() |
| 2400 | |
| 2401 | shape[axis] = 1 |
| 2402 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2403 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2404 | |
| 2405 | @staticmethod |
| 2406 | def argmaxOp(ser, a, axis): |
| 2407 | shape = a.shape.copy() |
| 2408 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2409 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2410 | |
| 2411 | @staticmethod |
| 2412 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 2413 | |
| 2414 | # IFM: NHWC |
| 2415 | # Filter: OHWI |
| 2416 | # OFM: NHWC |
| 2417 | |
| 2418 | if len(padding) == 2: |
| 2419 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 2420 | # From H,W to T,B,L,R |
| 2421 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 2422 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2423 | h = ( |
| 2424 | ifm.shape[1] |
| 2425 | - filter.shape[1] |
| 2426 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 2427 | + padding[0] |
| 2428 | + padding[1] |
| 2429 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2430 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2431 | w = ( |
| 2432 | ifm.shape[2] |
| 2433 | - filter.shape[2] |
| 2434 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 2435 | + padding[2] |
| 2436 | + padding[3] |
| 2437 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2438 | |
| 2439 | if h <= 0 or w <= 0: |
| 2440 | # Invalid test parameters? |
| 2441 | h = 0 |
| 2442 | w = 0 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2443 | ser.setExpectedFailure(True, "Invalid combination of conv2d parameters") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2444 | |
| 2445 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 2446 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2447 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2448 | out_dtype = DType.INT32 |
| 2449 | elif ifm.dtype == DType.INT16: |
| 2450 | out_dtype = DType.INT48 |
| 2451 | elif ifm.dtype == DType.FLOAT: |
| 2452 | out_dtype = DType.FLOAT |
| 2453 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2454 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2455 | |
Jared Smolens | 2a76ad2 | 2021-03-04 11:18:54 -0800 | [diff] [blame] | 2456 | if ifm.dtype == DType.INT16: |
| 2457 | ser.setExpectedFailure(True, "INT16 support is in progress") |
| 2458 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2459 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2460 | |
| 2461 | @staticmethod |
| 2462 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 2463 | # IFM: NHWC |
| 2464 | # Filter: HWCM |
| 2465 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2466 | h = ( |
| 2467 | ifm.shape[1] |
| 2468 | - filter.shape[0] |
| 2469 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 2470 | + padding[0] |
| 2471 | + padding[1] |
| 2472 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2473 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2474 | w = ( |
| 2475 | ifm.shape[2] |
| 2476 | - filter.shape[1] |
| 2477 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 2478 | + padding[2] |
| 2479 | + padding[3] |
| 2480 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2481 | |
| 2482 | if h <= 0 or w <= 0: |
| 2483 | # Invalid test parameters? |
| 2484 | h = 0 |
| 2485 | w = 0 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2486 | ser.setExpectedFailure(True, "Invalid combination of conv2d parameters") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2487 | |
| 2488 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 2489 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2490 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2491 | out_dtype = DType.INT32 |
| 2492 | elif ifm.dtype == DType.INT16: |
| 2493 | out_dtype = DType.INT48 |
| 2494 | elif ifm.dtype == DType.FLOAT: |
| 2495 | out_dtype = DType.FLOAT |
| 2496 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2497 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2498 | |
Jared Smolens | 2a76ad2 | 2021-03-04 11:18:54 -0800 | [diff] [blame] | 2499 | if ifm.dtype == DType.INT16: |
| 2500 | ser.setExpectedFailure(True, "INT16 support is in progress") |
| 2501 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2502 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2503 | |
| 2504 | @staticmethod |
| 2505 | def pool2dOp(ser, ifm, kernel, stride, pad): |
| 2506 | # input: NHWC |
| 2507 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 2508 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 2509 | |
| 2510 | if h <= 0 or w <= 0: |
| 2511 | # Invalid test parameters? |
| 2512 | h = 0 |
| 2513 | w = 0 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2514 | ser.setExpectedFailure(True, "Invalid combination of pooling parameters") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2515 | |
| 2516 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2517 | return ser.addOutput(ofm_shape, ifm.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2518 | |
| 2519 | @staticmethod |
| 2520 | def fullyConnectedOp(ser, input, filter): |
| 2521 | # input: N, IC |
| 2522 | # filter: OC, IC |
| 2523 | # output: N, OC |
| 2524 | |
| 2525 | output_shape = [input.shape[0], filter.shape[0]] |
| 2526 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2527 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2528 | out_dtype = DType.INT32 |
| 2529 | elif input.dtype == DType.INT16: |
| 2530 | out_dtype = DType.INT48 |
| 2531 | elif input.dtype == DType.FLOAT: |
| 2532 | out_dtype = DType.FLOAT |
| 2533 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2534 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2535 | |
Jared Smolens | 2a76ad2 | 2021-03-04 11:18:54 -0800 | [diff] [blame] | 2536 | if input.dtype == DType.INT16: |
| 2537 | ser.setExpectedFailure(True, "INT16 support is in progress") |
| 2538 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2539 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2540 | |
| 2541 | @staticmethod |
| 2542 | def matmulOp(ser, a, b): |
| 2543 | # a: M, K |
| 2544 | # b: K, N |
| 2545 | # out: M, N |
| 2546 | |
| 2547 | output_shape = [a.shape[0], b.shape[1]] |
| 2548 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2549 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2550 | out_dtype = DType.INT32 |
| 2551 | elif a.dtype == DType.INT16: |
| 2552 | out_dtype = DType.INT48 |
| 2553 | elif a.dtype == DType.FLOAT: |
| 2554 | out_dtype = DType.FLOAT |
| 2555 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2556 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2557 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2558 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2559 | |
| 2560 | @staticmethod |
| 2561 | def concatOp(ser, a, b, axis): |
| 2562 | |
| 2563 | output_shape = a.shape.copy() |
| 2564 | output_shape[axis] = a.shape[axis] + b.shape[axis] |
| 2565 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2566 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2567 | |
| 2568 | @staticmethod |
| 2569 | def padOp(ser, a, padding): |
| 2570 | |
| 2571 | output_shape = a.shape.copy() |
| 2572 | |
| 2573 | for i in range(len(output_shape)): |
| 2574 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 2575 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2576 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2577 | |
| 2578 | @staticmethod |
| 2579 | def reshapeOp(ser, a, shape): |
| 2580 | output_shape = shape.copy() |
| 2581 | |
| 2582 | totalElements = 1 |
| 2583 | for i in a.shape: |
| 2584 | totalElements *= i |
| 2585 | |
| 2586 | # If there are any -1 elements, figure out what that dimension must be |
| 2587 | totalOutputElements = 1 |
| 2588 | for i in output_shape: |
| 2589 | if i != -1: |
| 2590 | totalOutputElements *= i |
| 2591 | |
| 2592 | # And fill it in |
| 2593 | for i in range(len(output_shape)): |
| 2594 | if output_shape[i] == -1: |
| 2595 | output_shape[i] = totalElements // totalOutputElements |
| 2596 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2597 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2598 | |
| 2599 | @staticmethod |
| 2600 | def sliceOp(ser, a, begin, size): |
| 2601 | |
| 2602 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2603 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2604 | |
| 2605 | @staticmethod |
| 2606 | def tileOp(ser, a, multiples): |
| 2607 | |
| 2608 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2609 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2610 | |
| 2611 | for i in range(len(output_shape)): |
| 2612 | output_shape[i] = a.shape[i] * multiples[i] |
| 2613 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2614 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2615 | |
| 2616 | @staticmethod |
| 2617 | def transposeOp(ser, a, perms): |
| 2618 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2619 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2620 | |
| 2621 | for i in range(len(output_shape)): |
| 2622 | output_shape[i] = a.shape[perms[i]] |
| 2623 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2624 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2625 | |
| 2626 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2627 | def gatherOp(ser, values, indices): |
| 2628 | assert len(values.shape) == 3 |
| 2629 | assert len(indices.shape) == 2 |
| 2630 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2631 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2632 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 2633 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2634 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2635 | |
| 2636 | @staticmethod |
| 2637 | def scatterOp(ser, values_in, indices, input): |
| 2638 | assert len(values_in.shape) == 3 |
| 2639 | assert len(indices.shape) == 2 |
| 2640 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2641 | assert values_in.shape[0] == indices.shape[0] # N |
| 2642 | assert input.shape[1] == indices.shape[1] # W |
| 2643 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2644 | |
| 2645 | output_shape = values_in.shape |
| 2646 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2647 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2648 | |
| 2649 | @staticmethod |
| 2650 | def tableOp(ser, input, table): |
| 2651 | # Same shape as the input, but with the type of the table. |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2652 | return ser.addOutput(input.shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2653 | |
| 2654 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2655 | def resizeOp( |
| 2656 | ser, |
| 2657 | input, |
| 2658 | mode, |
| 2659 | stride, |
| 2660 | offset, |
| 2661 | shift, |
| 2662 | stride_fp, |
| 2663 | offset_fp, |
| 2664 | output_dims, |
| 2665 | input_dtype, |
| 2666 | output_dtype, |
| 2667 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2668 | |
| 2669 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] |
| 2670 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2671 | if input_dtype == DType.FLOAT: |
| 2672 | if stride_fp[0] <= 0 or stride_fp[1] <= 0: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2673 | ser.setExpectedFailure(True, "Negative or zero stride") |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2674 | else: |
| 2675 | if stride[0] <= 0 or stride[1] <= 0: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2676 | ser.setExpectedFailure(True, "Negative or zero stride") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2677 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2678 | if mode == ResizeMode.BILINEAR: |
| 2679 | if input_dtype == DType.INT8: |
| 2680 | if output_dtype != DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2681 | ser.setExpectedFailure(True, "Invalid output data type") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2682 | elif input_dtype == DType.INT16: |
| 2683 | if output_dtype != DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2684 | ser.setexpectedfailure(true, "Invalid output data type") |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2685 | elif input_dtype == DType.FLOAT: |
| 2686 | if output_dtype != DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2687 | ser.setexpectedfailure(true, "Invalid output data type") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2688 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2689 | ser.setexpectedfailure(true, "Invalid input data type") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2690 | |
| 2691 | elif mode == ResizeMode.NEAREST: |
| 2692 | if input_dtype == DType.INT8: |
| 2693 | if output_dtype != DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2694 | ser.setExpectedFailure(True, "Invalid output data type") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2695 | elif input_dtype == DType.INT16: |
| 2696 | if output_dtype != DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2697 | ser.setexpectedfailure(true, "Invalid output data type") |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2698 | elif input_dtype == DType.FLOAT: |
| 2699 | if output_dtype != DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2700 | ser.setexpectedfailure(true, "Invalid output data type") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2701 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2702 | ser.setexpectedfailure(true, "Invalid input data type") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2703 | |
| 2704 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2705 | ser.setexpectedfailure(true, "Invalid resize mode") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2706 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2707 | return ser.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2708 | |
| 2709 | @staticmethod |
| 2710 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2711 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2712 | |
| 2713 | @staticmethod |
| 2714 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2715 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2716 | out_dtype = DType.INT32 |
| 2717 | elif ifm.dtype == DType.INT16: |
| 2718 | out_dtype = DType.INT48 |
| 2719 | elif ifm.dtype == DType.FLOAT: |
| 2720 | out_dtype = DType.FLOAT |
| 2721 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2722 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2723 | |
| 2724 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2725 | ser.setExpectedFailure(True, "Negative output shape") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2726 | |
Jared Smolens | 2a76ad2 | 2021-03-04 11:18:54 -0800 | [diff] [blame] | 2727 | if ifm.dtype == DType.INT16: |
| 2728 | ser.setExpectedFailure(True, "INT16 support is in progress") |
| 2729 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame^] | 2730 | return ser.addOutput(output_shape, out_dtype) |