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 |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 32 | import itertools |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 33 | |
| 34 | from enum import IntEnum, Enum, unique |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 35 | from tosa_ref_run import TosaReturnCode |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 36 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 37 | # Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH |
| 38 | parent_dir = os.path.dirname(os.path.realpath(__file__)) |
| 39 | sys.path.append( |
| 40 | os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") |
| 41 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 42 | import tosa_serializer as ts |
| 43 | from tosa_serializer import * |
| 44 | import tosa |
| 45 | |
| 46 | # Convenience variables to the flatc-generated types that should be enums, but aren't |
| 47 | DType = tosa.DType.DType() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 48 | Op = tosa.Op.Op() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 49 | ResizeMode = tosa.ResizeMode.ResizeMode() |
| 50 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 51 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 52 | class TosaQuantGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 53 | """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" |
| 54 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 55 | def __init__(self): |
| 56 | pass |
| 57 | |
| 58 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 59 | def getQinfo(testGen, dtype): |
| 60 | if dtype == DType.INT8: |
| 61 | return testGen.randInt(-128, 128) |
| 62 | if dtype == DType.UINT8: |
| 63 | return testGen.randInt(0, 256) |
| 64 | return 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 65 | |
| 66 | @staticmethod |
| 67 | def qgUnary(testGen, op, dtype): |
| 68 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 69 | qinfo.UnaryQuantInfo( |
| 70 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 71 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 72 | return qinfo |
| 73 | |
| 74 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 75 | def qgConv(testGen, op, dtype_or_dtypeList): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 76 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 77 | if isinstance(dtype_or_dtypeList, list): |
| 78 | # a list of [input, weights, accumulator] dtypes |
| 79 | dtypeList = dtype_or_dtypeList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 80 | else: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 81 | # an int, [input, weights, accumulator] dtypes are the same |
| 82 | dtypeList = [dtype_or_dtypeList] * 3 |
| 83 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 84 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 85 | qinfo.ConvQuantInfo(input_zp, weights_zp) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 86 | return qinfo |
| 87 | |
| 88 | @staticmethod |
| 89 | def qgMatmul(testGen, op, dtype): |
| 90 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 91 | qinfo.MatMulQuantInfo( |
| 92 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 93 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 94 | return qinfo |
| 95 | |
| 96 | @staticmethod |
| 97 | def qgPad(testGen, op, dtype): |
| 98 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 99 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 100 | return qinfo |
| 101 | |
| 102 | @staticmethod |
| 103 | def computeMultiplierAndShift(scaleFp, scale32): |
| 104 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 105 | # Provide a floating-point scaling factor and the scale32 parameter |
| 106 | # to compute the multiplier and shift |
| 107 | |
| 108 | if scale32: |
| 109 | scaleBits = 31 |
| 110 | else: |
| 111 | scaleBits = 15 |
| 112 | |
| 113 | m, shift = math.frexp(scaleFp) |
| 114 | |
| 115 | if scaleFp < 0.0: |
| 116 | m = -m |
| 117 | |
| 118 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 119 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 120 | |
| 121 | if multiplier == (1 << scaleBits): |
| 122 | multiplier = multiplier // 2 |
| 123 | shift = shift + 1 |
| 124 | |
| 125 | shift = (-shift) + scaleBits |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 126 | #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
| 127 | |
| 128 | # Adjust multiplier such that shift is in allowed value range. |
| 129 | if shift == 0: |
| 130 | multiplier = multiplier // 4 |
| 131 | shift = shift + 2 |
| 132 | elif shift == 1: |
| 133 | multiplier = multiplier // 2 |
| 134 | shift = shift + 1 |
| 135 | elif shift == 63: |
| 136 | multiplier = multiplier * 2 |
| 137 | shift = shift - 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 138 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 139 | assert multiplier <= (1 << scaleBits) |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 140 | assert shift >= 2 and shift <= 62 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 141 | |
| 142 | return multiplier, shift |
| 143 | |
| 144 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 145 | class TosaTensorGen: |
| 146 | """Tensor generators create a shape list for the placeholder and const tensor |
| 147 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 148 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 149 | def __init__(self): |
| 150 | pass |
| 151 | |
| 152 | @staticmethod |
| 153 | def tgBasic(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 154 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 155 | shape = testGen.makeShape(rank) |
| 156 | |
| 157 | shape_list = [] |
| 158 | for i in range(pl + const): |
| 159 | shape_list.append(shape.copy()) |
| 160 | |
| 161 | return shape_list |
| 162 | |
| 163 | @staticmethod |
| 164 | def tgNHWC(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 165 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 166 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 167 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 168 | |
| 169 | shape = testGen.makeShape(rank) |
| 170 | |
| 171 | # Constrict the batch size? |
| 172 | if testGen.args.max_batch_size: |
| 173 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
| 174 | |
| 175 | shape_list = [] |
| 176 | for i in range(pl + const): |
| 177 | shape_list.append(shape.copy()) |
| 178 | |
| 179 | return shape_list |
| 180 | |
| 181 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 182 | def tgScatter(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 183 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 184 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 185 | assert pl == 2 |
| 186 | assert const == 0 |
| 187 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 188 | |
| 189 | values_in_shape = testGen.makeShape(rank) |
| 190 | |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 191 | # ignore max batch size if target shape is set |
| 192 | if testGen.args.max_batch_size and not testGen.args.target_shapes: |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 193 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 194 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 195 | W = testGen.randInt( |
| 196 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 197 | ) |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 198 | # Constrict W if one dimension is too large to keep tensor size reasonable |
| 199 | if max(values_in_shape) > 5000: |
| 200 | W = testGen.randInt(0, 16) |
| 201 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 202 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 203 | |
| 204 | shape_list = [] |
| 205 | shape_list.append(values_in_shape.copy()) |
| 206 | shape_list.append(input_shape.copy()) |
| 207 | |
| 208 | return shape_list |
| 209 | |
| 210 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 211 | def tgBroadcastFuzz(testGen, op, rank): |
| 212 | shape = testGen.makeShape(rank) |
| 213 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 214 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 215 | |
| 216 | shape_list = [] |
| 217 | |
| 218 | # Choose one of the inputs to broadcast |
| 219 | bcast_idx = testGen.randInt(0, pl + const) |
| 220 | for i in range(pl + const): |
| 221 | shape_bcast = shape.copy() |
| 222 | |
| 223 | # If the chosen input, pick a random index to broadcast |
| 224 | if i == bcast_idx: |
| 225 | fuzz_idx = testGen.randInt(0, rank) |
| 226 | shape_bcast[fuzz_idx] = 1 |
| 227 | |
| 228 | shape_list.append(shape_bcast) |
| 229 | |
| 230 | return shape_list |
| 231 | |
| 232 | @staticmethod |
| 233 | def tgConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 234 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 235 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 236 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 237 | |
| 238 | # IFM dimensions are NHWC |
| 239 | ifm_shape = testGen.makeShape(rank) |
| 240 | |
| 241 | # Constrict the batch size? |
| 242 | if testGen.args.max_batch_size: |
| 243 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 244 | |
| 245 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 246 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 247 | |
| 248 | # Generate a random OFM depth |
| 249 | ofm_depth = testGen.makeShape(1)[0] |
| 250 | |
| 251 | # The filter dimensions are OHWI |
| 252 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 253 | |
| 254 | # The bias is OC |
| 255 | bias_shape = np.asarray([ofm_depth]) |
| 256 | |
| 257 | return [ifm_shape, filter_shape, bias_shape] |
| 258 | |
| 259 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 260 | def tgConv3D(testGen, op, rank): |
| 261 | pl, const = op["operands"] |
| 262 | |
| 263 | assert rank == 5 |
| 264 | |
| 265 | # IFM dimensions are NDHWC |
| 266 | ifm_shape = testGen.makeShape(rank) |
| 267 | |
| 268 | # Constrict the batch size? |
| 269 | if testGen.args.max_batch_size: |
| 270 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 271 | |
| 272 | # Get the filter depth/height/width from the operator parameters |
| 273 | filter_dhw = op["filter"] |
| 274 | |
| 275 | # Generate a random OFM channel |
| 276 | ofm_channel = testGen.makeShape(1)[0] |
| 277 | |
| 278 | # The filter dimensions are ODHWI |
| 279 | filter_shape = np.asarray( |
| 280 | [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] |
| 281 | ) |
| 282 | |
| 283 | # The bias is OC |
| 284 | bias_shape = np.asarray([ofm_channel]) |
| 285 | |
| 286 | return [ifm_shape, filter_shape, bias_shape] |
| 287 | |
| 288 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 289 | def tgTransposeConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 290 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 291 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 292 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 293 | |
| 294 | # IFM dimensions are NHWC |
| 295 | ifm_shape = testGen.makeShape(rank) |
| 296 | |
| 297 | # Constrict the batch size? |
| 298 | if testGen.args.max_batch_size: |
| 299 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 300 | |
| 301 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 302 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 303 | |
| 304 | # Generate a random OFM depth |
| 305 | ofm_depth = testGen.makeShape(1)[0] |
| 306 | |
| 307 | # The filter dimensions are OHWI |
| 308 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 309 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 310 | # The bias is OC |
| 311 | bias_shape = np.asarray([ofm_depth]) |
| 312 | |
| 313 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 314 | |
| 315 | @staticmethod |
| 316 | def tgDepthwiseConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 317 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 318 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 319 | assert rank == 4 |
| 320 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 321 | |
| 322 | # IFM dimensions are NHWC |
| 323 | ifm_shape = testGen.makeShape(rank) |
| 324 | |
| 325 | # Constrict the batch size? |
| 326 | if testGen.args.max_batch_size: |
| 327 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 328 | |
| 329 | # Get the filter height/width from the operator parameters |
| 330 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 331 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 332 | |
| 333 | # Generate a random OFM depth, but don't let it get too big because |
| 334 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 335 | filter_m = ( |
| 336 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 337 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 338 | |
| 339 | # The filter dimensions are HWCM |
| 340 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 341 | |
| 342 | # The bias is M * C |
| 343 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 344 | |
| 345 | return [ifm_shape, filter_shape, bias_shape] |
| 346 | |
| 347 | @staticmethod |
| 348 | def tgFullyConnected(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 349 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 350 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 351 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 352 | |
| 353 | input_shape = testGen.makeShape(rank) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 354 | filter_oc = testGen.rng.integers( |
| 355 | low=testGen.args.tensor_shape_range[0], |
| 356 | high=testGen.args.tensor_shape_range[1], |
| 357 | size=1, |
| 358 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 359 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 360 | |
| 361 | bias_shape = np.asarray([filter_oc]) |
| 362 | |
| 363 | return [input_shape, filter_shape, bias_shape] |
| 364 | |
| 365 | @staticmethod |
| 366 | def tgMatmul(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 367 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 368 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 369 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 370 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 371 | |
| 372 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 373 | # Get a random number for b_oc even if target shape is defined |
| 374 | b_oc = np.int32( |
| 375 | testGen.rng.integers( |
| 376 | low=testGen.args.tensor_shape_range[0], |
| 377 | high=testGen.args.tensor_shape_range[1], |
| 378 | size=1, |
| 379 | ) |
| 380 | )[0] |
| 381 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 382 | if max(a_shape) > 1000: |
| 383 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 384 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 385 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 386 | return [a_shape, b_shape] |
| 387 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 388 | @staticmethod |
| 389 | def tgConcat(testGen, opName, rank): |
| 390 | pl, const = opName["operands"] |
| 391 | shape = testGen.makeShape(rank) |
| 392 | |
| 393 | # Create extra tensors to concat. |
| 394 | # Take into account value of pl when getting maximum number of concats |
| 395 | num_tensors = testGen.randInt(0, 4) |
| 396 | shape_list = [] |
| 397 | for i in range(pl + const + num_tensors): |
| 398 | shape_list.append(shape.copy()) |
| 399 | |
| 400 | return shape_list |
| 401 | |
| 402 | @staticmethod |
| 403 | def tgConcatConstInput(testGen, shapeList, axis): |
| 404 | # Split concat shape along axis to allow for multiple const inputs |
| 405 | # without making too many large tensors |
| 406 | shape = shapeList[0] |
| 407 | if len(shapeList) == 2 or shape[axis] < len(shapeList): |
| 408 | return shapeList |
| 409 | |
| 410 | new_shapeList = [shape.copy()] |
| 411 | length_on_axis = shape[axis] |
| 412 | remaining_length = length_on_axis |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 413 | for i in range(len(shapeList) - 2): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 414 | # Calculate split on axis and remaining value |
| 415 | split_shape_val = int(shape[axis] / 2) |
| 416 | remaining_length = remaining_length - split_shape_val |
| 417 | |
| 418 | # Append new shape, and set remaining shape |
| 419 | shape[axis] = split_shape_val |
| 420 | new_shapeList.append(shape.copy()) |
| 421 | shape[axis] = remaining_length |
| 422 | if i == len(shapeList) - 3: |
| 423 | new_shapeList.append(shape.copy()) |
| 424 | |
| 425 | return new_shapeList |
| 426 | |
| 427 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 428 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 429 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 430 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 431 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 432 | as arguments to the operator build function.""" |
| 433 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 434 | def __init__(self): |
| 435 | pass |
| 436 | |
| 437 | @staticmethod |
| 438 | def agNone(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 439 | """A trivial argument generator for operators that don't take any |
| 440 | non-tensor arguments""" |
| 441 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 442 | |
| 443 | @staticmethod |
| 444 | def agAxis(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 445 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 446 | axes = [] |
| 447 | |
| 448 | shape = shapeList[0] |
| 449 | |
| 450 | for a in range(0, len(shape)): |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 451 | axes.append(("axis{}".format(a), [a])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 452 | return axes |
| 453 | |
| 454 | @staticmethod |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 455 | def agConv(testGen, opName, shapeList, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 456 | arg_list = [] |
| 457 | |
| 458 | ifm_shape = shapeList[0] |
| 459 | filter_shape = shapeList[1] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 460 | # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) |
| 461 | k = [int(x) for x in opName.split("_")[-1].split("x")] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 462 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 463 | # Check the rank |
| 464 | rank = 5 if opName.startswith("conv3d") else 4 |
| 465 | assert len(ifm_shape) == rank |
| 466 | assert len(filter_shape) == rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 467 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 468 | # kernel rank omits batch and channels |
| 469 | k_rank = rank - 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 470 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 471 | # Generate comprehensive argument lists |
| 472 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 473 | paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} |
| 474 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 475 | strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| 476 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 477 | dilations = {x for x in itertools.product(*([d_vals] * k_rank))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 478 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 479 | # add some oversize argument values |
| 480 | if max(ifm_shape) < 64: |
| 481 | bigPadding = 9 |
| 482 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) |
| 483 | bigStride = 8 |
| 484 | strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) |
| 485 | bigDilation = 7 |
| 486 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 487 | |
| 488 | # There are too many parameter combinations, so generate them sparsely |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 489 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 490 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 491 | if sparsity < 13: |
| 492 | sparsity = 1 |
| 493 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 494 | sparsity += 1 |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 495 | n = 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 496 | for s in sorted(list(strides)): |
| 497 | for p in sorted(list(paddings)): |
| 498 | for d in sorted(list(dilations)): |
| 499 | if (n % sparsity == 0 |
| 500 | # padding must not exceed the kernel size ? |
| 501 | # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 502 | # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) |
| 503 | # the padded shape must exceed the kernel size |
| 504 | and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] |
| 505 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) |
| 506 | # the padded shape must exceed the dilation |
| 507 | and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] |
| 508 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) |
| 509 | ): |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 510 | arg_list.append( |
| 511 | ( |
| 512 | "st{}_pad{}_dilat{}".format( |
| 513 | "".join([str(x) for x in s]), |
| 514 | "".join([str(x) for x in p]), |
| 515 | "".join([str(x) for x in d]), |
| 516 | ), |
| 517 | [s, p, d], |
| 518 | ) |
| 519 | ) |
| 520 | n += 1 |
| 521 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 522 | return arg_list |
| 523 | |
| 524 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 525 | def agTransposeConv2D(testGen, opName, shapeList, dtype): |
| 526 | arg_list = [] |
| 527 | |
| 528 | ifm_shape = shapeList[0] |
| 529 | filter_shape = shapeList[1] |
| 530 | |
| 531 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 532 | assert len(ifm_shape) == 4 |
| 533 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 534 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 535 | # Generate comprehensive argument lists |
| 536 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 537 | paddings = {x for x in itertools.product(*([p_vals] * 2))} |
| 538 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 539 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 540 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 541 | dilations = {x for x in itertools.product(*([d_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 542 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 543 | # add some oversize argument values |
| 544 | if max(ifm_shape) < 64: |
| 545 | bigPadding = 9 |
| 546 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) |
| 547 | bigStride = 8 |
| 548 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 549 | bigDilation = 7 |
| 550 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 551 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 552 | # There are too many parameter combinations, so generate them sparsely |
| 553 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 554 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 555 | if sparsity < 13: |
| 556 | sparsity = 1 |
| 557 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 558 | sparsity += 1 |
| 559 | n = 0 |
| 560 | for s in sorted(list(strides)): |
| 561 | for p in sorted(list(paddings)): |
| 562 | for d in sorted(list(dilations)): |
| 563 | if n % sparsity == 0: |
| 564 | # Determine the output shape |
| 565 | oh = ( |
| 566 | ifm_shape[1] |
| 567 | - filter_shape[1] |
| 568 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 569 | + 2 * p[0] |
| 570 | ) // s[0] + 1 |
| 571 | ow = ( |
| 572 | ifm_shape[2] |
| 573 | - filter_shape[2] |
| 574 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 575 | + 2 * p[1] |
| 576 | ) // s[1] + 1 |
| 577 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| 578 | arg_list.append( |
| 579 | ( |
| 580 | "st{}_pad{}_dilat{}_os{}".format( |
| 581 | "".join([str(x) for x in s]), |
| 582 | "".join([str(x) for x in p]), |
| 583 | "".join([str(x) for x in d]), |
| 584 | "x".join([str(x) for x in os]), |
| 585 | ), |
| 586 | [s, p, d, os], |
| 587 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 588 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 589 | n += 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 590 | |
| 591 | return arg_list |
| 592 | |
| 593 | @staticmethod |
| 594 | def agPad(testGen, opName, shapeList, dtype): |
| 595 | arg_list = [] |
| 596 | rank = len(shapeList[0]) |
| 597 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 598 | # Exhaustively test combinations of padding on each side of each dimension |
| 599 | # - the range of padding values is defined by pad_min and pad_max |
| 600 | # - for padding >9, the name format needs to be more distinctive |
| 601 | pad_min, pad_max = 0, 1 |
| 602 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
| 603 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 604 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 605 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 606 | for paddings in shape_pad_values: |
| 607 | name = "pad" |
| 608 | for r in range(rank): |
| 609 | before, after = paddings[r] |
| 610 | name = f"{name}{before}{after}" |
| 611 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 612 | |
| 613 | return arg_list |
| 614 | |
| 615 | @staticmethod |
| 616 | def agPooling(testGen, opName, shapeList, dtype): |
| 617 | arg_list = [] |
| 618 | |
| 619 | shape = shapeList[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 620 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 621 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 622 | # Generate comprehensive argument lists |
| 623 | p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] |
| 624 | paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| 625 | s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] |
| 626 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 627 | k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] |
| 628 | kernels = {x for x in itertools.product(*([k_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 629 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 630 | # add some oversize argument values |
| 631 | bigStride = 7 |
| 632 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 633 | bigKernel = 6 |
| 634 | kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) |
| 635 | if max(shape) < 64: |
| 636 | # padding must be less than the kernel size |
| 637 | bigPadding = bigKernel - 1 |
| 638 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 639 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 640 | # There are too many parameter combinations, so generate them sparsely |
| 641 | sparsity = len(paddings) * len(strides) * len(kernels) // 500 + 1 |
| 642 | n = 0 |
| 643 | for s in sorted(list(strides)): |
| 644 | for p in sorted(list(paddings)): |
| 645 | for k in sorted(list(kernels)): |
| 646 | if (n % sparsity == 0 |
| 647 | # padding must not exceed the kernel size |
| 648 | and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 649 | # the padded shape must exceed the kernel size |
| 650 | and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] |
| 651 | ): |
| 652 | arg_list.append( |
| 653 | ( |
| 654 | "st{}_kern{}_pad{}".format( |
| 655 | "".join([str(x) for x in s]), |
| 656 | "".join([str(x) for x in k]), |
| 657 | "".join([str(x) for x in p]), |
| 658 | ), |
| 659 | [s, p, k], |
| 660 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 661 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 662 | n += 1 |
| 663 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 664 | return arg_list |
| 665 | |
| 666 | @staticmethod |
| 667 | def agCast(testGen, opName, shapeList, inDtype): |
| 668 | arg_list = [] |
| 669 | |
| 670 | # Enumerate the output types here |
| 671 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 672 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 673 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 674 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 675 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 676 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 677 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 678 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 679 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 680 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 681 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 682 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 683 | |
| 684 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 685 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 686 | |
| 687 | return arg_list |
| 688 | |
| 689 | @staticmethod |
| 690 | def agRescale(testGen, opName, shapeList, inDtype): |
| 691 | arg_list = [] |
| 692 | |
| 693 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 694 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 695 | if inDtype == DType.UINT8 and dtype != DType.INT8: |
| 696 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 697 | continue |
| 698 | if inDtype != DType.INT8 and dtype == DType.UINT8: |
| 699 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 700 | continue |
| 701 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 702 | for scale32 in [False, True]: |
| 703 | for double_round in [False, True]: |
| 704 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 705 | |
| 706 | if inDtype == DType.INT48 and scale32: |
| 707 | # Illegal condition. Must be scale32=False |
| 708 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 709 | if double_round and not scale32: |
| 710 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 711 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 712 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 713 | arg_list.append( |
| 714 | ( |
| 715 | "out{}_sc{}_dr{}_pc{}".format( |
| 716 | DTypeNames[dtype], |
| 717 | int(scale32), |
| 718 | int(double_round), |
| 719 | int(per_channel), |
| 720 | ), |
| 721 | [dtype, scale32, double_round, per_channel], |
| 722 | ) |
| 723 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 724 | |
| 725 | return arg_list |
| 726 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 727 | @staticmethod |
| 728 | def agMul(testGen, opName, shapeList, dtype): |
| 729 | arg_list = [] |
| 730 | |
| 731 | if dtype is DType.INT32: |
| 732 | for p in range(testGen.args.num_rand_permutations): |
| 733 | |
| 734 | shift = testGen.randInt(0, 32) |
| 735 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 736 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 737 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 738 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 739 | |
| 740 | return arg_list |
| 741 | |
| 742 | @staticmethod |
| 743 | def agArithmeticRightShift(testGen, opName, shapeList, dtype): |
| 744 | arg_list = [] |
| 745 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 746 | arg_list.append(("roundTrue", [True])) |
| 747 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 748 | |
| 749 | return arg_list |
| 750 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 751 | # Helper function for reshape. Gets some factors of a larger number. |
| 752 | @staticmethod |
| 753 | def getFactors(val, start=1): |
| 754 | factors = [] |
| 755 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 756 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 757 | if (val % i) == 0: |
| 758 | factors.append(i) |
| 759 | |
| 760 | return factors |
| 761 | |
| 762 | @staticmethod |
| 763 | def agReshape(testGen, opName, shapeList, dtype): |
| 764 | arg_list = [] |
| 765 | |
| 766 | origShape = shapeList[0] |
| 767 | |
| 768 | totalElements = 1 |
| 769 | for s in origShape: |
| 770 | totalElements *= s |
| 771 | |
| 772 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 773 | factors = TosaArgGen.getFactors(totalElements) |
| 774 | |
| 775 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 776 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 777 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 778 | continue |
| 779 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 780 | found = True |
| 781 | # escape_counter breaks while loop if it continues on for too long |
| 782 | escape_counter = 0 |
| 783 | while found: |
| 784 | newShape = [] |
| 785 | # Generate newShape ensuring it isn't a duplicate |
| 786 | remainingElements = totalElements |
| 787 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 788 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 789 | # pick rank-1 factors |
| 790 | newShape.append(shuffledFactors[0]) |
| 791 | remainingElements = remainingElements // shuffledFactors[0] |
| 792 | shuffledFactors = testGen.rng.permutation( |
| 793 | TosaArgGen.getFactors(remainingElements) |
| 794 | ) |
| 795 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 796 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 797 | # Toss in a -1 sometimes |
| 798 | minusOne = testGen.randInt(0, newRank * 4) |
| 799 | if minusOne < newRank: |
| 800 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 801 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 802 | # Check for duplicates |
| 803 | found = False |
| 804 | for name, other_shape in arg_list: |
| 805 | if other_shape[0] == newShape: |
| 806 | found = True |
| 807 | break |
| 808 | |
| 809 | escape_counter += 1 |
| 810 | if escape_counter >= 100: |
| 811 | break |
| 812 | |
| 813 | if not found: |
| 814 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 815 | |
| 816 | return arg_list |
| 817 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 818 | @staticmethod |
| 819 | def agTranspose(testGen, opName, shapeList, dtype): |
| 820 | arg_list = [] |
| 821 | |
| 822 | ifm_shape = shapeList[0] |
| 823 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 824 | # Get all permutations |
| 825 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 826 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 827 | # Limit to possible permutations from shape dimension or argument setting |
| 828 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 829 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 830 | # Get random permutation generator that uses all permutations |
| 831 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 832 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 833 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 834 | arg_list = [ |
| 835 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 836 | for p in range(limit) |
| 837 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 838 | return arg_list |
| 839 | |
| 840 | @staticmethod |
| 841 | def agSlice(testGen, opName, shapeList, dtype): |
| 842 | arg_list = [] |
| 843 | |
| 844 | ifm_shape = shapeList[0] |
| 845 | rank = len(ifm_shape) |
| 846 | |
| 847 | for p in range(testGen.args.num_rand_permutations): |
| 848 | begin = [] |
| 849 | size = [] |
| 850 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 851 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 852 | |
| 853 | for i in range(rank): |
| 854 | if ifm_shape[i] > 1: |
| 855 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 856 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 857 | |
| 858 | # Invalid slice size? |
| 859 | if size[i] == 0: |
| 860 | valid = False |
| 861 | else: |
| 862 | begin.append(0) |
| 863 | size.append(1) |
| 864 | |
| 865 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 866 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 867 | return arg_list |
| 868 | |
| 869 | @staticmethod |
| 870 | def agTile(testGen, opName, shapeList, dtype): |
| 871 | arg_list = [] |
| 872 | |
| 873 | ifm_shape = shapeList[0] |
| 874 | rank = len(ifm_shape) |
| 875 | |
| 876 | for p in range(testGen.args.num_rand_permutations): |
| 877 | |
| 878 | # Pick a few random, but small multiple values |
| 879 | # because otherwise this has a tendency to generate |
| 880 | # enormous tensors |
| 881 | multiples = [] |
| 882 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame] | 883 | if ifm_shape[i] > 1000: |
| 884 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 885 | multiples.append(1) |
| 886 | elif max(ifm_shape) > 1000: |
| 887 | multiples.append(2) |
| 888 | else: |
| 889 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 890 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 891 | |
| 892 | return arg_list |
| 893 | |
| 894 | @staticmethod |
| 895 | def agResize(testGen, opName, shapeList, dtype): |
| 896 | arg_list = [] |
| 897 | |
| 898 | ifm_shape = shapeList[0] |
| 899 | |
| 900 | for m in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
| 901 | |
| 902 | # Exclude illegal {mode, type} configurations. Pick legal output types |
| 903 | if m == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 904 | outputDTypeList = [DType.INT8] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 905 | elif m == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 906 | outputDTypeList = [DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 907 | elif m == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 908 | outputDTypeList = [DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 909 | elif m == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 910 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 911 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 912 | outputDTypeList = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 913 | else: |
| 914 | continue |
| 915 | |
| 916 | for outputDType in outputDTypeList: |
| 917 | for perm in range(testGen.args.num_rand_permutations): |
| 918 | |
| 919 | # Randomly generate legal output dimensions and shift |
| 920 | # and then compute the stride and offset based on them |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 921 | output_dims = [testGen.randInt(1), testGen.randInt(1)] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 922 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 923 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 924 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 925 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 926 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 927 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 928 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 929 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 930 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 931 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 932 | if outputDType == DType.FLOAT: |
| 933 | shift = 0 |
| 934 | stride = [0, 0] |
| 935 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 936 | stride_fp = [fp_stride_y, fp_stride_x] |
| 937 | offset_fp = [fp_offset_y, fp_offset_x] |
| 938 | arg_list.append( |
| 939 | ( |
| 940 | "mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}".format( |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 941 | "N" if m == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 942 | output_dims[0], |
| 943 | output_dims[1], |
| 944 | testGen.typeStr(outputDType), |
| 945 | stride_fp[0], |
| 946 | stride_fp[1], |
| 947 | offset_fp[0], |
| 948 | offset_fp[1], |
| 949 | ), |
| 950 | [ |
| 951 | m, |
| 952 | stride, |
| 953 | offset, |
| 954 | shift, |
| 955 | stride_fp, |
| 956 | offset_fp, |
| 957 | output_dims, |
| 958 | dtype, |
| 959 | outputDType, |
| 960 | ], |
| 961 | ) |
| 962 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 963 | else: |
| 964 | shift = 11 |
| 965 | unit = float(1 << shift) |
| 966 | stride_y = int(round(fp_stride_y * unit)) |
| 967 | stride_x = int(round(fp_stride_x * unit)) |
| 968 | offset_y = int(round(fp_offset_y * unit)) |
| 969 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 970 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 971 | while ( |
| 972 | stride_y >= 32768 |
| 973 | or stride_x >= 32768 |
| 974 | or offset_y >= 32768 |
| 975 | or offset_x >= 32768 |
| 976 | or offset_y < -32768 |
| 977 | or offset_x < -32768 |
| 978 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 979 | shift = shift - 1 |
| 980 | unit = float(1 << shift) |
| 981 | stride_y = int(round(fp_stride_y * unit)) |
| 982 | stride_x = int(round(fp_stride_x * unit)) |
| 983 | offset_y = int(round(fp_offset_y * unit)) |
| 984 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 985 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 986 | stride = [stride_y, stride_x] |
| 987 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 988 | |
| 989 | stride_fp = [0.0, 0.0] |
| 990 | offset_fp = [0.0, 0.0] |
| 991 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 992 | arg_list.append( |
| 993 | ( |
| 994 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 995 | "N" if m == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 996 | shift, |
| 997 | output_dims[0], |
| 998 | output_dims[1], |
| 999 | testGen.typeStr(outputDType), |
| 1000 | stride[0], |
| 1001 | stride[1], |
| 1002 | offset[0], |
| 1003 | offset[1], |
| 1004 | ), |
| 1005 | [ |
| 1006 | m, |
| 1007 | stride, |
| 1008 | offset, |
| 1009 | shift, |
| 1010 | stride_fp, |
| 1011 | offset_fp, |
| 1012 | output_dims, |
| 1013 | dtype, |
| 1014 | outputDType, |
| 1015 | ], |
| 1016 | ) |
| 1017 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1018 | |
| 1019 | return arg_list |
| 1020 | |
| 1021 | def agCondIf(testGen, opName, shapeList, dtype): |
| 1022 | # CondIf generates the condition values here. |
| 1023 | # Convert to tensors in the build function, along with the |
| 1024 | # then and else blocks |
| 1025 | arg_list = [] |
| 1026 | |
| 1027 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1028 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1029 | |
| 1030 | return arg_list |
| 1031 | |
| 1032 | def agWhileLoop(testGen, opName, shapeList, dtype): |
| 1033 | # While loop: 0 iterations, 1, more than 1 |
| 1034 | arg_list = [] |
| 1035 | |
| 1036 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1037 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1038 | |
| 1039 | return arg_list |
| 1040 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1041 | class TosaInvalidValidator: |
| 1042 | |
| 1043 | @staticmethod |
| 1044 | def ivWrongDataTypeOrModeResize(**kwargs): |
| 1045 | input_dtype = kwargs["input_dtype"] |
| 1046 | args = kwargs["args"] |
| 1047 | mode = args[0] |
| 1048 | stride = args[1] |
| 1049 | stride_fp = args[4] |
| 1050 | output_dtype = args[8] |
| 1051 | |
| 1052 | if mode == ResizeMode.BILINEAR: |
| 1053 | # Invalid output data type / Invalid input datatype |
| 1054 | return ( |
| 1055 | not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or |
| 1056 | not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or |
| 1057 | not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or |
| 1058 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1059 | ) |
| 1060 | elif mode == ResizeMode.NEAREST: |
| 1061 | # Invalid output data type / Invalid input datatype |
| 1062 | return ( |
| 1063 | (input_dtype != output_dtype) or |
| 1064 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1065 | ) |
| 1066 | else: |
| 1067 | # Invalid resize mode |
| 1068 | return True |
| 1069 | |
| 1070 | @staticmethod |
| 1071 | def ivBadStride(**kwargs): |
| 1072 | input_dtype = kwargs["input_dtype"] |
| 1073 | args = kwargs["args"] |
| 1074 | stride_x = args[1][0] |
| 1075 | stride_y = args[1][1] |
| 1076 | stride_fp_x = args[4][0] |
| 1077 | stride_fp_y = args[4][1] |
| 1078 | |
| 1079 | if input_dtype == DType.FLOAT: |
| 1080 | if stride_fp_x <= 0 or stride_fp_y <= 0: |
| 1081 | # Negative or zero stride |
| 1082 | return True |
| 1083 | else: |
| 1084 | if stride_x <= 0 or stride_y <= 0: |
| 1085 | # Negative or zero stride |
| 1086 | return True |
| 1087 | return False |
| 1088 | |
| 1089 | |
| 1090 | |
| 1091 | |
| 1092 | @staticmethod |
| 1093 | def ivHeightWidthSmallerZero(**kwargs): |
| 1094 | opName = kwargs['opName'] |
| 1095 | |
| 1096 | inputShapes = kwargs['shapeList'] |
| 1097 | input = inputShapes[0] |
| 1098 | if not opName.endswith("pool2d"): |
| 1099 | filter = inputShapes[1] |
| 1100 | |
| 1101 | args = kwargs['args'] |
| 1102 | strides = args[0] |
| 1103 | padding = args[1] |
| 1104 | dilations = args[2] |
| 1105 | if opName.endswith("pool2d"): |
| 1106 | kernel = args[2] |
| 1107 | |
| 1108 | if opName.startswith('conv2d'): |
| 1109 | h = ( |
| 1110 | input[1] |
| 1111 | - filter[1] |
| 1112 | - (filter[1] - 1) * (dilations[0] - 1) |
| 1113 | + padding[0] |
| 1114 | + padding[1] |
| 1115 | ) // strides[0] + 1 |
| 1116 | |
| 1117 | w = ( |
| 1118 | input[2] |
| 1119 | - filter[2] |
| 1120 | - (filter[2] - 1) * (dilations[1] - 1) |
| 1121 | + padding[2] |
| 1122 | + padding[3] |
| 1123 | ) // strides[1] + 1 |
| 1124 | elif opName.startswith("depthwise_conv2d"): |
| 1125 | h = ( |
| 1126 | input[1] |
| 1127 | - filter[0] |
| 1128 | - (filter[0] - 1) * (dilations[0] - 1) |
| 1129 | + padding[0] |
| 1130 | + padding[1] |
| 1131 | ) // strides[0] + 1 |
| 1132 | |
| 1133 | w = ( |
| 1134 | input[2] |
| 1135 | - filter[1] |
| 1136 | - (filter[1] - 1) * (dilations[1] - 1) |
| 1137 | + padding[2] |
| 1138 | + padding[3] |
| 1139 | ) // strides[1] + 1 |
| 1140 | elif opName.endswith("pool2d"): |
| 1141 | h = (input[1] + padding[0] + padding[1] + strides[0] - kernel[0]) // strides[0] |
| 1142 | w = (input[2] + padding[2] + padding[3] + strides[1] - kernel[1]) // strides[1] |
| 1143 | else: |
| 1144 | assert False, "Unrecognized Op" |
| 1145 | |
| 1146 | if h <= 0 or w <= 0: |
| 1147 | # Invalid parameter combination |
| 1148 | return True |
| 1149 | return False |
| 1150 | |
| 1151 | @staticmethod |
| 1152 | def ivNonPositiveOutputShape(**kwargs): |
| 1153 | args = kwargs['args'] |
| 1154 | output_shape = args[3] |
| 1155 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
| 1156 | # Negative output shape |
| 1157 | return True |
| 1158 | return False |
| 1159 | |
| 1160 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1161 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1162 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1163 | # Maximum rank of tensor supported by test generator. |
| 1164 | TOSA_TENSOR_MAX_RANK = 6 |
| 1165 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1166 | def __init__(self, args): |
| 1167 | self.args = args |
| 1168 | self.basePath = args.output_dir |
| 1169 | self.random_seed = args.random_seed |
| 1170 | self.ser = None |
| 1171 | self.rng = np.random.default_rng(self.random_seed) |
| 1172 | self.createDynamicOpLists() |
| 1173 | self.initOpListDefaults() |
| 1174 | self.quantGen = TosaQuantGen() |
| 1175 | # Force makeShape to do a specific starting shape |
| 1176 | self.targetted_shape = None |
| 1177 | |
| 1178 | def createSerializer(self, opName, testPath): |
| 1179 | self.testPath = os.path.join(opName, testPath) |
| 1180 | |
| 1181 | fullPath = os.path.join(self.basePath, self.testPath) |
| 1182 | os.makedirs(fullPath, exist_ok=True) |
| 1183 | self.ser = ts.TosaSerializer(fullPath) |
| 1184 | |
| 1185 | def getSerializer(self): |
| 1186 | return self.ser |
| 1187 | |
| 1188 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1189 | with open( |
| 1190 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 1191 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1192 | fd.write(self.ser.serialize()) |
| 1193 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1194 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 1195 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1196 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1197 | def resetRNG(self, seed=None): |
| 1198 | if seed == None: |
| 1199 | seed = self.random_seed + 1 |
| 1200 | self.rng = np.random.default_rng(seed) |
| 1201 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1202 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1203 | if dtype == DType.BOOL: |
| 1204 | np_dt = np.bool |
| 1205 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1206 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1207 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1208 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1209 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1210 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 1211 | elif dtype == DType.UINT8: |
| 1212 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1213 | elif dtype == DType.INT16: |
| 1214 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 1215 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1216 | return np.int32( |
| 1217 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 1218 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1219 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1220 | return np.int64( |
| 1221 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 1222 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1223 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1224 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1225 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1226 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1227 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1228 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1229 | placeholders = [] |
| 1230 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1231 | assert len(shape_list) == len(dtype_list) |
| 1232 | |
| 1233 | for idx, shape in enumerate(shape_list): |
| 1234 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1235 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1236 | |
| 1237 | return placeholders |
| 1238 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1239 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1240 | consts = [] |
| 1241 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1242 | assert len(shape_list) == len(dtype_list) |
| 1243 | |
| 1244 | for idx, shape in enumerate(shape_list): |
| 1245 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1246 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1247 | |
| 1248 | return consts |
| 1249 | |
| 1250 | def makeShape(self, rank): |
| 1251 | if self.targetted_shape: |
| 1252 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1253 | return np.int32( |
| 1254 | self.rng.integers( |
| 1255 | low=self.args.tensor_shape_range[0], |
| 1256 | high=self.args.tensor_shape_range[1], |
| 1257 | size=rank, |
| 1258 | ) |
| 1259 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1260 | |
| 1261 | def setTargetShape(self, shape): |
| 1262 | self.targetted_shape = shape |
| 1263 | |
| 1264 | def randInt(self, low=0, high=256): |
| 1265 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 1266 | |
| 1267 | def getRandNumberDType(self, dtype): |
| 1268 | if dtype == DType.FLOAT: |
| 1269 | return self.rng.random() |
| 1270 | elif dtype == DType.BOOL: |
| 1271 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1272 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1273 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1274 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1275 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1276 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1277 | elif dtype == DType.INT16: |
| 1278 | low, high = (-32768, 32768) |
| 1279 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1280 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1281 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1282 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1283 | # Special size |
| 1284 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 1285 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1286 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1287 | |
| 1288 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 1289 | |
| 1290 | def shapeStr(self, shape): |
| 1291 | |
| 1292 | sStr = [] |
| 1293 | # Convert to strings |
| 1294 | for i in shape: |
| 1295 | sStr.append(str(i)) |
| 1296 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1297 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1298 | |
| 1299 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1300 | if isinstance(t, list): |
| 1301 | assert len(t) >= 2 |
| 1302 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1303 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1304 | if t == DType.BOOL: |
| 1305 | return "b" |
| 1306 | elif t == DType.INT4: |
| 1307 | return "i4" |
| 1308 | elif t == DType.INT8: |
| 1309 | return "i8" |
| 1310 | elif t == DType.UINT8: |
| 1311 | return "u8" |
| 1312 | elif t == DType.INT16: |
| 1313 | return "i16" |
| 1314 | elif t == DType.INT32: |
| 1315 | return "i32" |
| 1316 | elif t == DType.INT48: |
| 1317 | return "i48" |
| 1318 | elif t == DType.FLOAT: |
| 1319 | return "float" |
| 1320 | else: |
| 1321 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1322 | |
| 1323 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1324 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1325 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1326 | return 4 |
| 1327 | elif t == DType.INT8: |
| 1328 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1329 | elif t == DType.UINT8: |
| 1330 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1331 | elif t == DType.INT16: |
| 1332 | return 16 |
| 1333 | elif t == DType.INT32: |
| 1334 | return 32 |
| 1335 | elif t == DType.INT48: |
| 1336 | return 48 |
| 1337 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1338 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1339 | |
| 1340 | # Argument generators |
| 1341 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 1342 | # Where the string descriptor is used to generate the test name and |
| 1343 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 1344 | # build function |
| 1345 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1346 | def build_unary(self, op, a, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1347 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1348 | self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) |
| 1349 | return result_tens |
| 1350 | |
| 1351 | def build_binary_broadcast(self, op, a, b): |
| 1352 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1353 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1354 | return result_tens |
| 1355 | |
| 1356 | def build_binary_nonbroadcast(self, op, a, b): |
| 1357 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
| 1358 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1359 | return result_tens |
| 1360 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1361 | def build_arithmetic_right_shift(self, op, a, b, round): |
| 1362 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1363 | |
| 1364 | attr = ts.TosaSerializerAttribute() |
| 1365 | attr.ArithmeticRightShiftAttribute(round) |
| 1366 | |
| 1367 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
| 1368 | return result_tens |
| 1369 | |
| 1370 | def build_mul(self, op, a, b, shift): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1371 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1372 | |
| 1373 | # Special for multiply: |
| 1374 | # Force the result to INT32 for INT types |
| 1375 | if a.dtype != DType.FLOAT: |
| 1376 | result_tens.setDtype(DType.INT32) |
| 1377 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1378 | attr = ts.TosaSerializerAttribute() |
| 1379 | attr.MulAttribute(shift) |
| 1380 | |
| 1381 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1382 | return result_tens |
| 1383 | |
| 1384 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1385 | # Constant size depending on type, random values |
| 1386 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1387 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1388 | table_arr = self.getRandTensor([513], table_dtype) |
| 1389 | else: |
| 1390 | assert a.dtype == DType.INT8 |
| 1391 | table_dtype = DType.INT8 |
| 1392 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1393 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1394 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 1395 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1396 | self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None) |
| 1397 | |
| 1398 | return result_tens |
| 1399 | |
| 1400 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1401 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
| 1402 | self.ser.addOperator(op, [cond.name, a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1403 | return result_tens |
| 1404 | |
| 1405 | def build_comparison(self, op, a, b): |
| 1406 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
| 1407 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1408 | return result_tens |
| 1409 | |
| 1410 | def build_argmax(self, op, a, axis): |
| 1411 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 1412 | |
| 1413 | attr = ts.TosaSerializerAttribute() |
| 1414 | attr.AxisAttribute(axis) |
| 1415 | |
| 1416 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1417 | return result_tens |
| 1418 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1419 | def build_pool2d(self, op, input, stride, pad, kernel, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1420 | result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) |
| 1421 | |
| 1422 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1423 | attr.PoolAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1424 | |
| 1425 | self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo) |
| 1426 | return result_tens |
| 1427 | |
| 1428 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1429 | assert len(padding) == 4 |
| 1430 | result_tens = OutputShaper.conv2dOp( |
| 1431 | self.ser, ifm, filter, strides, padding, dilations |
| 1432 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1433 | |
| 1434 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1435 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1436 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1437 | self.ser.addOperator( |
| 1438 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1439 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1440 | return result_tens |
| 1441 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 1442 | def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
| 1443 | assert len(padding) == 6 |
| 1444 | result_tens = OutputShaper.conv3dOp( |
| 1445 | self.ser, ifm, filter, strides, padding, dilations |
| 1446 | ) |
| 1447 | |
| 1448 | attr = ts.TosaSerializerAttribute() |
| 1449 | attr.ConvAttribute(padding, strides, dilations) |
| 1450 | |
| 1451 | self.ser.addOperator( |
| 1452 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1453 | ) |
| 1454 | return result_tens |
| 1455 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1456 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1457 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1458 | ): |
| 1459 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1460 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 1461 | |
| 1462 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1463 | attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1464 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1465 | self.ser.addOperator( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1466 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1467 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1468 | return result_tens |
| 1469 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1470 | def build_depthwise_conv2d( |
| 1471 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 1472 | ): |
| 1473 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 1474 | self.ser, ifm, filter, strides, padding, dilations |
| 1475 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1476 | |
| 1477 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1478 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1479 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1480 | self.ser.addOperator( |
| 1481 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1482 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1483 | return result_tens |
| 1484 | |
| 1485 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 1486 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 1487 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1488 | self.ser.addOperator( |
| 1489 | op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo |
| 1490 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1491 | return result_tens |
| 1492 | |
| 1493 | def build_matmul(self, op, a, b, qinfo): |
| 1494 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
| 1495 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], None, qinfo) |
| 1496 | return result_tens |
| 1497 | |
| 1498 | def build_reduce(self, op, a, axis): |
| 1499 | result_tens = OutputShaper.reduceOp(self.ser, a, axis) |
| 1500 | |
| 1501 | attr = ts.TosaSerializerAttribute() |
| 1502 | attr.AxisAttribute(axis) |
| 1503 | |
| 1504 | self.ser.addOperator(op, [a.name], result_tens.name, attr) |
| 1505 | return result_tens |
| 1506 | |
| 1507 | def build_clamp(self, op, a): |
| 1508 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1509 | |
| 1510 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1511 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1512 | |
| 1513 | if a.dtype == DType.FLOAT: |
| 1514 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 1515 | else: |
| 1516 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 1517 | |
| 1518 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1519 | return result_tens |
| 1520 | |
| 1521 | def build_leaky_relu(self, op, a): |
| 1522 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1523 | attr = ts.TosaSerializerAttribute() |
| 1524 | |
| 1525 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 1526 | |
| 1527 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1528 | return result_tens |
| 1529 | |
| 1530 | # Needs an additional type/input |
| 1531 | def build_prelu(self, op, a): |
| 1532 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1533 | |
| 1534 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1535 | return result_tens |
| 1536 | |
| 1537 | def build_relun(self, op, a): |
| 1538 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1539 | |
| 1540 | attr = ts.TosaSerializerAttribute() |
| 1541 | |
| 1542 | if a.dtype == DType.FLOAT: |
| 1543 | attr.ReluNAttribute(0, self.getRandNumberDType(a.dtype)) |
| 1544 | else: |
| 1545 | attr.ReluNAttribute(self.getRandNumberDType(a.dtype), 0) |
| 1546 | |
| 1547 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1548 | return result_tens |
| 1549 | |
| 1550 | def build_sigmoid(self, op, a): |
| 1551 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1552 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1553 | return result_tens |
| 1554 | |
| 1555 | def build_tanh(self, op, a): |
| 1556 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1557 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1558 | return result_tens |
| 1559 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1560 | def build_concat(self, op, *a): |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1561 | assert type(a[-1]) == int |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1562 | |
| 1563 | # To store variable length list of input tensors we need to store axis along with it |
| 1564 | axis = a[-1] |
| 1565 | a = a[:-1] |
| 1566 | |
| 1567 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1568 | |
| 1569 | attr = ts.TosaSerializerAttribute() |
| 1570 | attr.AxisAttribute(axis) |
| 1571 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1572 | input_tensor_names = [] |
| 1573 | for tensor in a: |
| 1574 | input_tensor_names.append(tensor.name) |
| 1575 | |
| 1576 | self.ser.addOperator(op, input_tensor_names, [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1577 | |
| 1578 | def build_pad(self, op, a, padding, qinfo): |
| 1579 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 1580 | |
| 1581 | # Need to turn the padding array into a TOSA tensor here. |
| 1582 | # This is one of the few tensor operands that does not get |
| 1583 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1584 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1585 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1586 | self.ser.addOperator( |
| 1587 | op, [a.name, padding_tens.name], [result_tens.name], None, qinfo |
| 1588 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1589 | |
| 1590 | def build_reshape(self, op, a, newShape): |
| 1591 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 1592 | |
| 1593 | attr = ts.TosaSerializerAttribute() |
| 1594 | attr.ReshapeAttribute(newShape) |
| 1595 | |
| 1596 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1597 | return result_tens |
| 1598 | |
| 1599 | def build_reverse(self, op, a, axis): |
| 1600 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1601 | |
| 1602 | attr = ts.TosaSerializerAttribute() |
| 1603 | attr.AxisAttribute(axis) |
| 1604 | |
| 1605 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1606 | return result_tens |
| 1607 | |
| 1608 | def build_transpose(self, op, a, perms): |
| 1609 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 1610 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1611 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1612 | |
| 1613 | self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name]) |
| 1614 | return result_tens |
| 1615 | |
| 1616 | def build_slice(self, op, a, begin, size): |
| 1617 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 1618 | |
| 1619 | attr = ts.TosaSerializerAttribute() |
| 1620 | attr.SliceAttribute(begin, size) |
| 1621 | |
| 1622 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1623 | return result_tens |
| 1624 | |
| 1625 | def build_tile(self, op, a, multiples): |
| 1626 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 1627 | |
| 1628 | attr = ts.TosaSerializerAttribute() |
| 1629 | attr.TileAttribute(multiples) |
| 1630 | |
| 1631 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1632 | return result_tens |
| 1633 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1634 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1635 | |
| 1636 | # Create a new indicies tensor |
| 1637 | # here with data that doesn't exceed the dimensions of the values tensor |
| 1638 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1639 | K = values.shape[1] # K |
| 1640 | W = self.randInt( |
| 1641 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 1642 | ) # W |
| 1643 | indicies_arr = np.int32( |
| 1644 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 1645 | ) # (N, W) |
| 1646 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1647 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1648 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1649 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1650 | self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1651 | |
| 1652 | return result_tens |
| 1653 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1654 | def build_scatter(self, op, values_in, input): |
| 1655 | |
| 1656 | # Create a new indicies tensor |
| 1657 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 1658 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1659 | K = values_in.shape[1] # K |
| 1660 | W = input.shape[1] # W |
| 1661 | indicies_arr = np.int32( |
| 1662 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 1663 | ) # (N, W) |
| 1664 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1665 | |
| 1666 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 1667 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1668 | self.ser.addOperator( |
| 1669 | op, [values_in.name, indicies.name, input.name], [result_tens.name] |
| 1670 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1671 | |
| 1672 | return result_tens |
| 1673 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1674 | def build_resize( |
| 1675 | self, |
| 1676 | op, |
| 1677 | input, |
| 1678 | mode, |
| 1679 | stride, |
| 1680 | offset, |
| 1681 | shift, |
| 1682 | stride_fp, |
| 1683 | offset_fp, |
| 1684 | output_dims, |
| 1685 | input_dtype, |
| 1686 | output_dtype, |
| 1687 | ): |
| 1688 | result_tens = OutputShaper.resizeOp( |
| 1689 | self.ser, |
| 1690 | input, |
| 1691 | mode, |
| 1692 | stride, |
| 1693 | offset, |
| 1694 | shift, |
| 1695 | stride_fp, |
| 1696 | offset_fp, |
| 1697 | output_dims, |
| 1698 | input_dtype, |
| 1699 | output_dtype, |
| 1700 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1701 | |
| 1702 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1703 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1704 | attr.ResizeAttribute( |
| 1705 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 1706 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1707 | |
| 1708 | self.ser.addOperator(op, [input.name], [result_tens.name], attr) |
| 1709 | return result_tens |
| 1710 | |
| 1711 | def build_identityn(self, op, val, val2): |
| 1712 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1713 | result_tens = OutputShaper.unaryOp(self.ser, val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1714 | result_tens2 = OutputShaper.unaryOp(self.ser, val2) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1715 | self.ser.addOperator( |
| 1716 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 1717 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1718 | return result_tens |
| 1719 | |
| 1720 | def build_placeholder(self, op, val): |
| 1721 | # Add an identity op to avoid warning in the reference model |
| 1722 | return self.build_unary(Op.IDENTITY, val) |
| 1723 | |
| 1724 | # Type Conversion |
| 1725 | def build_cast(self, op, val, out_dtype): |
| 1726 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 1727 | self.ser.addOperator(op, [val.name], [result_tens.name]) |
| 1728 | return result_tens |
| 1729 | |
| 1730 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel): |
| 1731 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 1732 | |
| 1733 | if per_channel: |
| 1734 | nc = val.shape[-1] |
| 1735 | else: |
| 1736 | nc = 1 |
| 1737 | |
| 1738 | in_type_width = self.typeWidth(val.dtype) |
| 1739 | out_type_width = self.typeWidth(out_dtype) |
| 1740 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1741 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 1742 | input_zp = self.randInt(-128, 128) |
| 1743 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1744 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 1745 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1746 | in_type_width = in_type_width + 1 |
| 1747 | else: |
| 1748 | input_zp = 0 |
| 1749 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1750 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 1751 | output_zp = self.randInt(-128, 128) |
| 1752 | out_type_width = out_type_width + 1 |
| 1753 | elif out_dtype == DType.UINT8: |
| 1754 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1755 | out_type_width = out_type_width + 1 |
| 1756 | else: |
| 1757 | output_zp = 0 |
| 1758 | |
| 1759 | # Calculate scale based on: |
| 1760 | # scale = a *(2^output_width)/(2^input_width)) |
| 1761 | |
| 1762 | a = np.float32(self.rng.random(size=[nc])) |
| 1763 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 1764 | |
| 1765 | if scale32: |
| 1766 | pass |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1767 | # Cap the scaling at 2^31 - 1 for scale32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1768 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 1769 | else: |
| 1770 | # Cap the scaling at 2^15 - 1 for scale16 |
| 1771 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 1772 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1773 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1774 | |
| 1775 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 1776 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 1777 | |
| 1778 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1779 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 1780 | scale_arr[i], scale32 |
| 1781 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1782 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1783 | # 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] | 1784 | |
| 1785 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1786 | attr.RescaleAttribute( |
| 1787 | input_zp, |
| 1788 | output_zp, |
| 1789 | multiplier_arr, |
| 1790 | shift_arr, |
| 1791 | scale32, |
| 1792 | double_round, |
| 1793 | per_channel, |
| 1794 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1795 | |
| 1796 | self.ser.addOperator(op, [val.name], [result_tens.name], attr) |
| 1797 | return result_tens |
| 1798 | |
| 1799 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 1800 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 1801 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 1802 | # and fill them with const nodes for the body. |
| 1803 | |
| 1804 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1805 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1806 | |
| 1807 | # Make then/else tensors |
| 1808 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1809 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 1810 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1811 | |
| 1812 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1813 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1814 | |
| 1815 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1816 | then_block = "THEN_BLOCK" |
| 1817 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1818 | attr = ts.TosaSerializerAttribute() |
| 1819 | attr.CondIfAttribute(then_block, else_block) |
| 1820 | |
| 1821 | # Finally, build the op and the two blocks |
| 1822 | self.ser.addOperator(op, [cond_tens.name], [result_tens.name], attr) |
| 1823 | |
| 1824 | self.ser.startBasicBlock(then_block) |
| 1825 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1826 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1827 | self.ser.addOutputTensor(then_tens) |
| 1828 | |
| 1829 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1830 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1831 | self.ser.addOutputTensor(else_tens) |
| 1832 | |
| 1833 | return result_tens |
| 1834 | |
| 1835 | def build_cond_if_binary(self, op, a, b, cond): |
| 1836 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 1837 | # alternately add or subtract them based on the condition |
| 1838 | |
| 1839 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1840 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1841 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1842 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1843 | self.ser.currBasicBlock.addOutput(result_tens.name) |
| 1844 | |
| 1845 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1846 | then_block = "THEN_BLOCK" |
| 1847 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1848 | attr = ts.TosaSerializerAttribute() |
| 1849 | attr.CondIfAttribute(then_block, else_block) |
| 1850 | |
| 1851 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1852 | self.ser.addOperator( |
| 1853 | op, [cond_tens.name, a.name, b.name], [result_tens.name], attr |
| 1854 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1855 | |
| 1856 | self.ser.startBasicBlock(then_block) |
| 1857 | self.ser.addInputTensor(a) |
| 1858 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1859 | then_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1860 | self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) |
| 1861 | |
| 1862 | self.ser.startBasicBlock(else_block) |
| 1863 | self.ser.addInputTensor(a) |
| 1864 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1865 | else_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1866 | self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) |
| 1867 | |
| 1868 | return result_tens |
| 1869 | |
| 1870 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1871 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1872 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1873 | cond_block = "COND_BLOCK" |
| 1874 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1875 | |
| 1876 | attr = ts.TosaSerializerAttribute() |
| 1877 | attr.WhileLoopAttribute(cond_block, body_block) |
| 1878 | |
| 1879 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1880 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1881 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1882 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1883 | |
| 1884 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1885 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 1886 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 1887 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1888 | |
| 1889 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1890 | self.ser.addOperator( |
| 1891 | op, |
| 1892 | [iter.name, a.name, acc.name], |
| 1893 | [iter_out.name, a_out.name, acc_out.name], |
| 1894 | attr, |
| 1895 | ) |
Kevin Cheng | b227ae5 | 2021-09-02 13:43:17 -0700 | [diff] [blame] | 1896 | self.ser.addOutputTensor(acc_out) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1897 | |
| 1898 | # COND block (input: iter, output: cond_tens ) |
| 1899 | self.ser.startBasicBlock(cond_block) |
| 1900 | self.ser.addInputTensor(iter) |
| 1901 | self.ser.addInputTensor(a) |
| 1902 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1903 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 1904 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 1905 | 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] | 1906 | |
| 1907 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 1908 | # Note that local intermediate tensors need to be declared here for the outputs |
| 1909 | self.ser.startBasicBlock(body_block) |
| 1910 | self.ser.addInputTensor(iter) |
| 1911 | self.ser.addInputTensor(a) |
| 1912 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1913 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 1914 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 1915 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1916 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 1917 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 1918 | self.ser.addOutputTensor(iter_body_out) |
| 1919 | self.ser.addOutputTensor(a) |
| 1920 | self.ser.addOutputTensor(acc_body_out) |
| 1921 | |
| 1922 | return acc_out |
| 1923 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1924 | def genOpTestList( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1925 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1926 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1927 | |
| 1928 | try: |
| 1929 | op = self.TOSA_OP_LIST[opName] |
| 1930 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1931 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1932 | |
| 1933 | # Initialize a new random number generator |
| 1934 | self.rng = np.random.default_rng(self.random_seed) |
| 1935 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1936 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1937 | |
| 1938 | # Generate the lists of arguments |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1939 | rmin, rmax = op["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1940 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1941 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 1942 | default_test_rank_range = range(1, 5) |
| 1943 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1944 | # Test list consists of a tuple of: |
| 1945 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 1946 | testList = [] |
| 1947 | |
| 1948 | if not shapeFilter: |
| 1949 | shapeFilter = [None] |
| 1950 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1951 | # Positive test loop |
| 1952 | if testType in ['positive', 'both']: |
| 1953 | for r in range(rmin, rmax + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1954 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1955 | # Filter out the rank? |
| 1956 | if rankFilter is not None and r not in rankFilter: |
| 1957 | continue |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 1958 | if opName.startswith("conv3d"): |
| 1959 | assert r == 5, "conv3d test must have input rank == 5" |
| 1960 | elif ( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1961 | rankFilter is None |
| 1962 | and shapeFilter[0] is None |
| 1963 | and r not in default_test_rank_range |
| 1964 | ): |
| 1965 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1966 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1967 | for t in op["types"]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1968 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1969 | # Filter tests based on dtype? |
| 1970 | if dtypeFilter is not None: |
| 1971 | if not ( |
| 1972 | t in dtypeFilter |
| 1973 | or (isinstance(t, list) and t[0] in dtypeFilter) |
| 1974 | ): |
| 1975 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1976 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1977 | # Create the placeholder and const tensors |
| 1978 | for shape in shapeFilter: |
| 1979 | # A None shape chooses a random shape of a given rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1980 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1981 | # Filter out by rank |
| 1982 | if shape is not None and len(shape) != r: |
| 1983 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1984 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1985 | self.setTargetShape(shape) |
| 1986 | shapeList = tgen_fcn(self, op, r) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1987 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1988 | shapeStr = self.shapeStr(shapeList[0]) |
| 1989 | typeStr = self.typeStr(t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1990 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1991 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 1992 | argList = [] |
| 1993 | if agen_fcn: |
| 1994 | argList = agen_fcn(self, opName, shapeList, t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1995 | else: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1996 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1997 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1998 | for argStr, args in argList: |
| 1999 | if argStr: |
| 2000 | testStr = "{}_{}_{}_{}".format( |
| 2001 | opName, shapeStr, typeStr, argStr |
| 2002 | ) |
| 2003 | else: |
| 2004 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
| 2005 | |
| 2006 | testList.append((opName, testStr, t, shapeList, args)) |
| 2007 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2008 | # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| 2009 | if "invalid_test_validators" in op: |
| 2010 | invalid_test_validators = op["invalid_test_validators"] |
| 2011 | clean_testList = [] |
| 2012 | for test in testList: |
| 2013 | for validator_fcn in invalid_test_validators: |
| 2014 | remove_test = False |
| 2015 | if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[3], args=test[4]): |
| 2016 | remove_test = True |
| 2017 | if not remove_test: |
| 2018 | clean_testList.append(test) |
| 2019 | testList = clean_testList |
| 2020 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2021 | # Reset RNG so both positive and negative tests are reproducible |
| 2022 | self.resetRNG() |
| 2023 | # Negative test loop |
| 2024 | if testType in ['negative', 'both']: |
| 2025 | print("Negative tests unsupported") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2026 | |
| 2027 | return testList |
| 2028 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2029 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2030 | try: |
| 2031 | op = self.TOSA_OP_LIST[opName] |
| 2032 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2033 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2034 | |
| 2035 | # Create a serializer |
| 2036 | self.createSerializer(opName, testStr) |
| 2037 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2038 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
| 2039 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2040 | num_operands = pCount + cCount |
| 2041 | |
| 2042 | if isinstance(dtype_or_dtypeList, list): |
| 2043 | dtypeList = dtype_or_dtypeList |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2044 | elif op["op"] == Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2045 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2046 | else: |
| 2047 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 2048 | |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2049 | if op["op"] != Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2050 | assert ( |
| 2051 | len(shapeList) == num_operands |
| 2052 | ), "shapeList length {} must match number of operands {}".format( |
| 2053 | len(shapeList), num_operands |
| 2054 | ) |
| 2055 | assert ( |
| 2056 | len(dtypeList) == num_operands |
| 2057 | ), "dtypeList length {} must match number of operands {}".format( |
| 2058 | len(dtypeList), num_operands |
| 2059 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2060 | |
| 2061 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2062 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2063 | except KeyError: |
| 2064 | qgen = None |
| 2065 | |
| 2066 | # Build the random tensor operands and the test |
| 2067 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2068 | |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 2069 | if (op["op"] == Op.ADD or op["op"] == Op.SUB) and dtypeList[0] == DType.INT32: |
| 2070 | # Make sure the operation does not cause value saturation - where |
| 2071 | # the number wraps due to limited number of bits to store the answer |
| 2072 | assert ( |
| 2073 | pCount == 2 and cCount == 0 |
| 2074 | ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
| 2075 | |
| 2076 | placeholders = [] |
| 2077 | add = (op["op"] == Op.ADD) |
| 2078 | a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2079 | b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2080 | if add: |
| 2081 | res_arr = np.add(a_arr, b_arr, dtype=np.int64) |
| 2082 | else: |
| 2083 | res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) |
| 2084 | |
| 2085 | # Work out the saturation limits |
| 2086 | max_i32 = (1 << 31)-1 |
| 2087 | min_i32 = -(1 << 31) |
| 2088 | max_arr = np.full(shapeList[1], max_i32) |
| 2089 | min_arr = np.full(shapeList[1], min_i32) |
| 2090 | |
| 2091 | # Find how much values exceed the maximum/minimums |
| 2092 | sat_max_arr = np.maximum(res_arr - max_arr, 0) |
| 2093 | sat_min_arr = np.minimum(res_arr - min_arr, 0) |
| 2094 | |
| 2095 | if not add: |
| 2096 | # Swap saturation values and negate values as we need to perform opposite operations |
| 2097 | sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr |
| 2098 | |
| 2099 | # Create new array of unsaturated values by clipping values as needed |
| 2100 | b_unsat_arr = b_arr |
| 2101 | if (sat_max_arr != 0).any(): |
| 2102 | # Clip values that cause saturation |
| 2103 | b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) |
| 2104 | # Reduce axes in unsaturated tensor to match original tensor |
| 2105 | for axis, dim in enumerate(b_arr.shape): |
| 2106 | if dim != b_unsat_arr.shape[axis]: |
| 2107 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2108 | b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) |
| 2109 | |
| 2110 | if (sat_min_arr != 0).any(): |
| 2111 | # Clip values that cause saturation |
| 2112 | b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) |
| 2113 | # Reduce axes in unsaturated tensor to match original tensor |
| 2114 | for axis, dim in enumerate(b_arr.shape): |
| 2115 | if dim != b_unsat_arr.shape[axis]: |
| 2116 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2117 | b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) |
| 2118 | |
| 2119 | placeholders.append( |
| 2120 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2121 | ) |
| 2122 | placeholders.append( |
| 2123 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| 2124 | ) |
| 2125 | |
| 2126 | tens.extend(placeholders) |
| 2127 | elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 2128 | # Force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2129 | assert ( |
| 2130 | pCount == 2 and cCount == 0 |
| 2131 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2132 | |
| 2133 | placeholders = [] |
| 2134 | for idx, shape in enumerate(shapeList[:]): |
| 2135 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2136 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2137 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2138 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2139 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2140 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2141 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 2142 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2143 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2144 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2145 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2146 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2147 | |
| 2148 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 2149 | elif op["op"] == Op.SELECT: |
| 2150 | # Set datatype of condition tensor to boolean |
| 2151 | dtypeList[0] = DType.BOOL |
| 2152 | tens.extend( |
| 2153 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2154 | ) |
| 2155 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2156 | elif op["op"] == Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2157 | assert ( |
| 2158 | pCount == 2 and cCount == 0 |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2159 | ), "Op.INTDIV must have 2 placeholders, 0 consts" |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2160 | |
| 2161 | placeholders = [] |
| 2162 | |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2163 | # Two invalid cases for Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2164 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2165 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2166 | while True: |
| 2167 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2168 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2169 | |
| 2170 | if (divisor_arr == 0).any(): |
| 2171 | continue |
| 2172 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2173 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2174 | continue |
| 2175 | |
| 2176 | break |
| 2177 | |
| 2178 | placeholders.append( |
| 2179 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 2180 | ) |
| 2181 | placeholders.append( |
| 2182 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 2183 | ) |
| 2184 | |
| 2185 | tens.extend(placeholders) |
| 2186 | elif op["op"] == Op.MUL: |
| 2187 | assert ( |
| 2188 | pCount == 2 and cCount == 0 |
| 2189 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 2190 | |
| 2191 | if dtypeList[0] == DType.FLOAT: |
| 2192 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 2193 | else: |
| 2194 | placeholders = [] |
| 2195 | |
| 2196 | # Make sure multiply result in int32 range |
| 2197 | shift = testArgs[0] |
| 2198 | if dtypeList[0] == DType.INT8: |
| 2199 | num_bits = 8 |
| 2200 | elif dtypeList[0] == DType.INT16: |
| 2201 | num_bits = 16 |
| 2202 | elif dtypeList[0] == DType.INT32: |
| 2203 | num_bits = 32 |
| 2204 | else: |
| 2205 | raise Exception("OpMul: invalid input dtype") |
| 2206 | |
| 2207 | for idx, shape in enumerate(shapeList[:]): |
| 2208 | low = -(2 ** (num_bits - 1)) |
| 2209 | high = (2 ** (num_bits - 1)) - 1 |
| 2210 | |
| 2211 | a_arr = np.int32( |
| 2212 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 2213 | ) |
| 2214 | b_arr = np.int32( |
| 2215 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 2216 | ) |
| 2217 | |
| 2218 | i = 0 |
| 2219 | while True: |
| 2220 | |
| 2221 | a_arr_64 = a_arr.astype(np.int64) |
| 2222 | b_arr_64 = b_arr.astype(np.int64) |
| 2223 | |
| 2224 | if shift > 0: |
| 2225 | rounding = 1 << (shift - 1) |
| 2226 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 2227 | else: |
| 2228 | result_arr = a_arr_64 * b_arr_64 |
| 2229 | |
| 2230 | if (result_arr > -(2 ** 31)).all() and ( |
| 2231 | result_arr <= ((2 ** 31) - 1) |
| 2232 | ).all(): |
| 2233 | break |
| 2234 | |
| 2235 | i = i + 1 |
| 2236 | a_arr = a_arr // 2 |
| 2237 | b_arr = b_arr // 2 |
| 2238 | |
| 2239 | placeholders.append( |
| 2240 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2241 | ) |
| 2242 | placeholders.append( |
| 2243 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 2244 | ) |
| 2245 | |
| 2246 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2247 | elif op["op"] == Op.CONCAT: |
| 2248 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 2249 | if count < 1: |
| 2250 | count = 1 |
| 2251 | if self.args.num_const_inputs_concat == 0: |
| 2252 | count = len(shapeList) |
| 2253 | |
| 2254 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 2255 | tens.extend( |
| 2256 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 2257 | ) |
| 2258 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2259 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2260 | tens.extend( |
| 2261 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2262 | ) |
| 2263 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2264 | |
| 2265 | if qgen is not None: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 2266 | qinfo = qgen(self, op, dtype_or_dtypeList) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2267 | else: |
| 2268 | qinfo = None |
| 2269 | |
| 2270 | try: |
| 2271 | if qinfo is not None: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2272 | resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2273 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2274 | resultName = build_fcn(self, op["op"], *tens, *testArgs) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2275 | except TypeError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2276 | print( |
| 2277 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 2278 | build_fcn, tens, testArgs |
| 2279 | ) |
| 2280 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2281 | raise e |
| 2282 | |
| 2283 | # Save the serialized test |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2284 | self.serialize("test") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2285 | |
| 2286 | def createDynamicOpLists(self): |
| 2287 | |
| 2288 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2289 | KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2290 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2291 | for k in KERNELS_2D: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2292 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 2293 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 2294 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2295 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2296 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2297 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 2298 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 2299 | "depthwise_conv2d_TEMPLATE" |
| 2300 | ].copy() |
| 2301 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2302 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2303 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2304 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 2305 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 2306 | "transpose_conv2d_TEMPLATE" |
| 2307 | ].copy() |
| 2308 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2309 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2310 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2311 | KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| 2312 | for k in KERNELS_3D: |
| 2313 | testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| 2314 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| 2315 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2316 | self.TOSA_OP_LIST[testName]["template"] = False |
| 2317 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2318 | # Delete any templates after having created any dynamic ops |
| 2319 | # This is a two-pass operation because it's bad practice to delete |
| 2320 | # keys from dictionaries while iterating |
| 2321 | keyList = [] |
| 2322 | for k in self.TOSA_OP_LIST: |
| 2323 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2324 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2325 | keyList.append(k) |
| 2326 | continue |
| 2327 | except KeyError: |
| 2328 | pass |
| 2329 | |
| 2330 | for k in keyList: |
| 2331 | del self.TOSA_OP_LIST[k] |
| 2332 | |
| 2333 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2334 | """Fill in default fields for ops if they aren't already specified. |
| 2335 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2336 | for op in self.TOSA_OP_LIST: |
| 2337 | |
| 2338 | # Required fields |
| 2339 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2340 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2341 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2342 | raise Exception( |
| 2343 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 2344 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2345 | |
| 2346 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2347 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2348 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2349 | raise Exception( |
| 2350 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 2351 | op |
| 2352 | ) |
| 2353 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2354 | |
| 2355 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2356 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2357 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2358 | raise Exception( |
| 2359 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 2360 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2361 | |
| 2362 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2363 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2364 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2365 | raise Exception( |
| 2366 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 2367 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2368 | |
| 2369 | # Put in default rank range, if missing |
| 2370 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2371 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2372 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2373 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2374 | |
| 2375 | # Tensor operator list |
| 2376 | # 'op': op name |
| 2377 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2378 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 2379 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2380 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 2381 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2382 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2383 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2384 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 2385 | 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] | 2386 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2387 | TYPE_BOOL = [DType.BOOL] |
| 2388 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 2389 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 2390 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2391 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2392 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2393 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2394 | TYPE_CONV = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2395 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2396 | [DType.INT8, DType.INT8, DType.INT32], |
| 2397 | [DType.INT16, DType.INT8, DType.INT48], |
| 2398 | DType.FLOAT, |
| 2399 | ] |
| 2400 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2401 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2402 | |
| 2403 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2404 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2405 | "argmax": { |
| 2406 | "op": Op.ARGMAX, |
| 2407 | "operands": (1, 0), |
| 2408 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2409 | "types": TYPE_NARROW_INT_FP, |
| 2410 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2411 | "avg_pool2d": { |
| 2412 | "op": Op.AVG_POOL2D, |
| 2413 | "operands": (1, 0), |
| 2414 | "rank": (4, 4), |
| 2415 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2416 | "qgen": TosaQuantGen.qgUnary, |
| 2417 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2418 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2419 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2420 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2421 | "conv2d_TEMPLATE": { |
| 2422 | "op": Op.CONV2D, |
| 2423 | "operands": (1, 2), |
| 2424 | "rank": (4, 4), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 2425 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2426 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2427 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2428 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2429 | "template": True, |
| 2430 | }, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2431 | # Templated operator. Filled in by createDynamicOpLists |
| 2432 | "conv3d_TEMPLATE": { |
| 2433 | "op": Op.CONV3D, |
| 2434 | "operands": (1, 2), |
| 2435 | "rank": (5, 5), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 2436 | "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2437 | "qgen": TosaQuantGen.qgConv, |
| 2438 | "types": TYPE_CONV, |
| 2439 | "template": True, |
| 2440 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2441 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2442 | "depthwise_conv2d_TEMPLATE": { |
| 2443 | "op": Op.DEPTHWISE_CONV2D, |
| 2444 | "operands": (1, 2), |
| 2445 | "filter": [1, 1], |
| 2446 | "rank": (4, 4), |
| 2447 | "build_fcn": ( |
| 2448 | build_depthwise_conv2d, |
| 2449 | TosaTensorGen.tgDepthwiseConv2D, |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame^] | 2450 | TosaArgGen.agConv, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2451 | ), |
| 2452 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2453 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2454 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2455 | "template": True, |
| 2456 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2457 | "fully_connected": { |
| 2458 | "op": Op.FULLY_CONNECTED, |
| 2459 | "operands": (1, 2), |
| 2460 | "rank": (2, 2), |
| 2461 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 2462 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2463 | "types": TYPE_CONV, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2464 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2465 | "matmul": { |
| 2466 | "op": Op.MATMUL, |
| 2467 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 2468 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2469 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 2470 | "qgen": TosaQuantGen.qgMatmul, |
| 2471 | "types": TYPE_NARROW_INT_FP, |
| 2472 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2473 | "max_pool2d": { |
| 2474 | "op": Op.MAX_POOL2D, |
| 2475 | "operands": (1, 0), |
| 2476 | "rank": (4, 4), |
| 2477 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2478 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2479 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2480 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2481 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2482 | "transpose_conv2d_TEMPLATE": { |
| 2483 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2484 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2485 | "rank": (4, 4), |
| 2486 | "build_fcn": ( |
| 2487 | build_transpose_conv2d, |
| 2488 | TosaTensorGen.tgTransposeConv2D, |
| 2489 | TosaArgGen.agTransposeConv2D, |
| 2490 | ), |
| 2491 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2492 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2493 | "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2494 | "template": True, |
| 2495 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2496 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2497 | "clamp": { |
| 2498 | "op": Op.CLAMP, |
| 2499 | "operands": (1, 0), |
| 2500 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 2501 | "types": TYPE_NARROW_INT_FP, |
| 2502 | }, |
| 2503 | "relun": { |
| 2504 | "op": Op.RELUN, |
| 2505 | "operands": (1, 0), |
| 2506 | "build_fcn": (build_relun, TosaTensorGen.tgBasic, None), |
| 2507 | "types": TYPE_FI32, |
| 2508 | }, |
| 2509 | "sigmoid": { |
| 2510 | "op": Op.SIGMOID, |
| 2511 | "operands": (1, 0), |
| 2512 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 2513 | "types": TYPE_FP, |
| 2514 | }, |
| 2515 | "tanh": { |
| 2516 | "op": Op.TANH, |
| 2517 | "operands": (1, 0), |
| 2518 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 2519 | "types": TYPE_FP, |
| 2520 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2521 | # Elementwise Binary Operators |
| 2522 | "add": { |
| 2523 | "op": Op.ADD, |
| 2524 | "operands": (2, 0), |
| 2525 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2526 | "types": TYPE_FI32, |
| 2527 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2528 | "arithmetic_right_shift": { |
| 2529 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 2530 | "operands": (2, 0), |
| 2531 | "build_fcn": ( |
| 2532 | build_arithmetic_right_shift, |
| 2533 | TosaTensorGen.tgBroadcastFuzz, |
| 2534 | TosaArgGen.agArithmeticRightShift, |
| 2535 | ), |
| 2536 | "types": TYPE_INT, |
| 2537 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2538 | "bitwise_and": { |
| 2539 | "op": Op.BITWISE_AND, |
| 2540 | "operands": (2, 0), |
| 2541 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2542 | "types": TYPE_INT, |
| 2543 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2544 | "bitwise_or": { |
| 2545 | "op": Op.BITWISE_OR, |
| 2546 | "operands": (2, 0), |
| 2547 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2548 | "types": TYPE_INT, |
| 2549 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2550 | "bitwise_xor": { |
| 2551 | "op": Op.BITWISE_XOR, |
| 2552 | "operands": (2, 0), |
| 2553 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2554 | "types": TYPE_INT, |
| 2555 | }, |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2556 | "intdiv": { |
| 2557 | "op": Op.INTDIV, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2558 | "operands": (2, 0), |
| 2559 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2560 | "types": [DType.INT32], |
| 2561 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2562 | "logical_and": { |
| 2563 | "op": Op.LOGICAL_AND, |
| 2564 | "operands": (2, 0), |
| 2565 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2566 | "types": TYPE_BOOL, |
| 2567 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2568 | "logical_left_shift": { |
| 2569 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 2570 | "operands": (2, 0), |
| 2571 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2572 | "types": TYPE_INT, |
| 2573 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2574 | "logical_right_shift": { |
| 2575 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 2576 | "operands": (2, 0), |
| 2577 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2578 | "types": TYPE_INT, |
| 2579 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2580 | "logical_or": { |
| 2581 | "op": Op.LOGICAL_OR, |
| 2582 | "operands": (2, 0), |
| 2583 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2584 | "types": TYPE_BOOL, |
| 2585 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2586 | "logical_xor": { |
| 2587 | "op": Op.LOGICAL_XOR, |
| 2588 | "operands": (2, 0), |
| 2589 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2590 | "types": TYPE_BOOL, |
| 2591 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2592 | "maximum": { |
| 2593 | "op": Op.MAXIMUM, |
| 2594 | "operands": (2, 0), |
| 2595 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2596 | "types": TYPE_FI32, |
| 2597 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2598 | "minimum": { |
| 2599 | "op": Op.MINIMUM, |
| 2600 | "operands": (2, 0), |
| 2601 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2602 | "types": TYPE_FI32, |
| 2603 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2604 | "mul": { |
| 2605 | "op": Op.MUL, |
| 2606 | "operands": (2, 0), |
| 2607 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 2608 | "types": TYPE_INT_FP, |
| 2609 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2610 | "pow": { |
| 2611 | "op": Op.POW, |
| 2612 | "operands": (2, 0), |
| 2613 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 2614 | "types": TYPE_FP, |
| 2615 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2616 | "sub": { |
| 2617 | "op": Op.SUB, |
| 2618 | "operands": (2, 0), |
| 2619 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2620 | "types": TYPE_FI32, |
| 2621 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2622 | "table": { |
| 2623 | "op": Op.TABLE, |
| 2624 | # Use the automatic generation functions to create the input array |
| 2625 | # but create the table tensor in the build function, as it may be |
| 2626 | # a different type from the input |
| 2627 | "operands": (1, 0), |
| 2628 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2629 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2630 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2631 | # Elementwise Unary operators |
| 2632 | "abs": { |
| 2633 | "op": Op.ABS, |
| 2634 | "operands": (1, 0), |
| 2635 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2636 | "types": TYPE_FI32, |
| 2637 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2638 | "bitwise_not": { |
| 2639 | "op": Op.BITWISE_NOT, |
| 2640 | "operands": (1, 0), |
| 2641 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2642 | "types": TYPE_INT, |
| 2643 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2644 | "ceil": { |
| 2645 | "op": Op.CEIL, |
| 2646 | "operands": (1, 0), |
| 2647 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2648 | "types": TYPE_FP, |
| 2649 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2650 | "clz": { |
| 2651 | "op": Op.CLZ, |
| 2652 | "operands": (1, 0), |
| 2653 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2654 | "types": [DType.INT32], |
| 2655 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2656 | "exp": { |
| 2657 | "op": Op.EXP, |
| 2658 | "operands": (1, 0), |
| 2659 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2660 | "types": TYPE_FP, |
| 2661 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2662 | "floor": { |
| 2663 | "op": Op.FLOOR, |
| 2664 | "operands": (1, 0), |
| 2665 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2666 | "types": TYPE_FP, |
| 2667 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2668 | "log": { |
| 2669 | "op": Op.LOG, |
| 2670 | "operands": (1, 0), |
| 2671 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2672 | "types": TYPE_FP, |
| 2673 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2674 | "logical_not": { |
| 2675 | "op": Op.LOGICAL_NOT, |
| 2676 | "operands": (1, 0), |
| 2677 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2678 | "types": TYPE_BOOL, |
| 2679 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2680 | "negate": { |
| 2681 | "op": Op.NEGATE, |
| 2682 | "operands": (1, 0), |
| 2683 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2684 | "qgen": TosaQuantGen.qgUnary, |
| 2685 | "types": TYPE_INT_FP, |
| 2686 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2687 | "reciprocal": { |
| 2688 | "op": Op.RECIPROCAL, |
| 2689 | "operands": (1, 0), |
| 2690 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2691 | "types": TYPE_FP, |
| 2692 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2693 | "rsqrt": { |
| 2694 | "op": Op.RSQRT, |
| 2695 | "operands": (1, 0), |
| 2696 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2697 | "types": TYPE_FP, |
| 2698 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2699 | # Elementwise Ternary operators |
| 2700 | "select": { |
| 2701 | "op": Op.SELECT, |
| 2702 | "operands": (3, 0), |
| 2703 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 2704 | "types": TYPE_FIB, |
| 2705 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2706 | # Comparison operators |
| 2707 | "equal": { |
| 2708 | "op": Op.EQUAL, |
| 2709 | "operands": (2, 0), |
| 2710 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2711 | "types": TYPE_FI32, |
| 2712 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2713 | "greater_equal": { |
| 2714 | "op": Op.GREATER_EQUAL, |
| 2715 | "operands": (2, 0), |
| 2716 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2717 | "types": TYPE_FI32, |
| 2718 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2719 | "greater": { |
| 2720 | "op": Op.GREATER, |
| 2721 | "operands": (2, 0), |
| 2722 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2723 | "types": TYPE_FI32, |
| 2724 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2725 | # Reduction operators |
| 2726 | "reduce_all": { |
| 2727 | "op": Op.REDUCE_ALL, |
| 2728 | "operands": (1, 0), |
| 2729 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2730 | "types": TYPE_BOOL, |
| 2731 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2732 | "reduce_any": { |
| 2733 | "op": Op.REDUCE_ANY, |
| 2734 | "operands": (1, 0), |
| 2735 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2736 | "types": TYPE_BOOL, |
| 2737 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2738 | "reduce_max": { |
| 2739 | "op": Op.REDUCE_MAX, |
| 2740 | "operands": (1, 0), |
| 2741 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2742 | "types": TYPE_INT_FP, |
| 2743 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2744 | "reduce_min": { |
| 2745 | "op": Op.REDUCE_MAX, |
| 2746 | "operands": (1, 0), |
| 2747 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2748 | "types": TYPE_INT_FP, |
| 2749 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2750 | "reduce_product": { |
| 2751 | "op": Op.REDUCE_PRODUCT, |
| 2752 | "operands": (1, 0), |
| 2753 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2754 | "types": TYPE_FP, |
| 2755 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2756 | "reduce_sum": { |
| 2757 | "op": Op.REDUCE_SUM, |
| 2758 | "operands": (1, 0), |
| 2759 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2760 | "types": TYPE_FI32, |
| 2761 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2762 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2763 | "concat": { |
| 2764 | "op": Op.CONCAT, |
| 2765 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2766 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2767 | "types": TYPE_FIB, |
| 2768 | }, |
| 2769 | "pad": { |
| 2770 | "op": Op.PAD, |
| 2771 | "operands": (1, 0), |
| 2772 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 2773 | "qgen": TosaQuantGen.qgPad, |
| 2774 | "types": TYPE_FIB, |
| 2775 | }, |
| 2776 | "reshape": { |
| 2777 | "op": Op.RESHAPE, |
| 2778 | "operands": (1, 0), |
| 2779 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 2780 | "types": TYPE_FIB, |
| 2781 | }, |
| 2782 | "reverse": { |
| 2783 | "op": Op.REVERSE, |
| 2784 | "operands": (1, 0), |
| 2785 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2786 | "types": TYPE_FIB, |
| 2787 | }, |
| 2788 | "slice": { |
| 2789 | "op": Op.SLICE, |
| 2790 | "operands": (1, 0), |
| 2791 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 2792 | "types": TYPE_FIB, |
| 2793 | }, |
| 2794 | "tile": { |
| 2795 | "op": Op.TILE, |
| 2796 | "operands": (1, 0), |
| 2797 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 2798 | "types": TYPE_FIB, |
| 2799 | }, |
| 2800 | "transpose": { |
| 2801 | "op": Op.TRANSPOSE, |
| 2802 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 2803 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2804 | "build_fcn": ( |
| 2805 | build_transpose, |
| 2806 | TosaTensorGen.tgBasic, |
| 2807 | TosaArgGen.agTranspose, |
| 2808 | ), |
| 2809 | "types": TYPE_FIB, |
| 2810 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2811 | # Data nodes |
| 2812 | "const": { |
| 2813 | "op": Op.CONST, |
| 2814 | "operands": (1, 0), |
| 2815 | "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), |
| 2816 | "types": TYPE_FIB, |
| 2817 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2818 | "identity": { |
| 2819 | "op": Op.IDENTITY, |
| 2820 | "operands": (1, 0), |
| 2821 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2822 | "types": TYPE_FIB, |
| 2823 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2824 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2825 | "gather": { |
| 2826 | "op": Op.GATHER, |
| 2827 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 2828 | "operands": (1, 0), |
| 2829 | "rank": (3, 3), |
| 2830 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 2831 | "types": TYPE_INT_FP, |
| 2832 | }, |
| 2833 | "scatter": { |
| 2834 | "op": Op.SCATTER, |
| 2835 | # Only specify 'values_in' tensor here. |
| 2836 | #'indices' and 'input' are generated in op building stage |
| 2837 | "operands": (2, 0), |
| 2838 | "rank": (3, 3), |
| 2839 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 2840 | "types": TYPE_INT_FP, |
| 2841 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2842 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2843 | "resize": { |
| 2844 | "op": Op.RESIZE, |
| 2845 | "operands": (1, 0), |
| 2846 | "rank": (4, 4), |
| 2847 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 2848 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2849 | "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2850 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2851 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2852 | "cast": { |
| 2853 | "op": Op.CAST, |
| 2854 | "operands": (1, 0), |
| 2855 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 2856 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 2857 | }, |
| 2858 | "rescale": { |
| 2859 | "op": Op.RESCALE, |
| 2860 | "operands": (1, 0), |
| 2861 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2862 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2863 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2864 | # Custom |
| 2865 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2866 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2867 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 2868 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 2869 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2870 | "cond_if_const": { |
| 2871 | "op": Op.COND_IF, |
| 2872 | "operands": (0, 2), |
| 2873 | "build_fcn": ( |
| 2874 | build_cond_if_const, |
| 2875 | TosaTensorGen.tgBasic, |
| 2876 | TosaArgGen.agCondIf, |
| 2877 | ), |
| 2878 | "types": [DType.BOOL], |
| 2879 | }, |
| 2880 | "cond_if_binary": { |
| 2881 | "op": Op.COND_IF, |
| 2882 | "operands": (2, 0), |
| 2883 | "build_fcn": ( |
| 2884 | build_cond_if_binary, |
| 2885 | TosaTensorGen.tgBasic, |
| 2886 | TosaArgGen.agCondIf, |
| 2887 | ), |
| 2888 | "types": TYPE_FI32, |
| 2889 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2890 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2891 | "while_loop": { |
| 2892 | "op": Op.WHILE_LOOP, |
| 2893 | "operands": (0, 1), |
| 2894 | "build_fcn": ( |
| 2895 | build_while_loop, |
| 2896 | TosaTensorGen.tgBasic, |
| 2897 | TosaArgGen.agWhileLoop, |
| 2898 | ), |
| 2899 | "types": [DType.INT32], |
| 2900 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2901 | } |
| 2902 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2903 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2904 | class OutputShaper: |
| 2905 | # Methods in this class compute the expected output shape and datatype |
| 2906 | # for common classes of operations |
| 2907 | def __init__(self): |
| 2908 | pass |
| 2909 | |
| 2910 | # These methods return arguments that can be used for |
| 2911 | # creating a new output tensor |
| 2912 | @staticmethod |
| 2913 | def binaryBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2914 | assert len(a.shape) == len(b.shape) |
| 2915 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2916 | |
| 2917 | shape = [] |
| 2918 | for i in range(len(a.shape)): |
| 2919 | if a.shape[i] == 1: |
| 2920 | shape.append(b.shape[i]) |
| 2921 | else: |
| 2922 | shape.append(a.shape[i]) |
| 2923 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2924 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2925 | |
| 2926 | @staticmethod |
| 2927 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2928 | assert len(a.shape) == len(b.shape) |
| 2929 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2930 | |
| 2931 | shape = [] |
| 2932 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2933 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2934 | shape.append(a.shape[i]) |
| 2935 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2936 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2937 | |
| 2938 | @staticmethod |
| 2939 | def unaryOp(ser, a): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2940 | return ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2941 | |
| 2942 | @staticmethod |
| 2943 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2944 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 2945 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2946 | |
| 2947 | shape = [] |
| 2948 | for i in range(len(a.shape)): |
| 2949 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 2950 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2951 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2952 | |
| 2953 | @staticmethod |
| 2954 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2955 | assert len(a.shape) == len(b.shape) |
| 2956 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2957 | |
| 2958 | # Do broadcast |
| 2959 | shape = [] |
| 2960 | for i in range(len(a.shape)): |
| 2961 | if a.shape[i] == 1: |
| 2962 | shape.append(b.shape[i]) |
| 2963 | else: |
| 2964 | shape.append(a.shape[i]) |
| 2965 | |
| 2966 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2967 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2968 | |
| 2969 | @staticmethod |
| 2970 | def reduceOp(ser, a, axis): |
| 2971 | |
| 2972 | shape = a.shape.copy() |
| 2973 | |
| 2974 | shape[axis] = 1 |
| 2975 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2976 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2977 | |
| 2978 | @staticmethod |
| 2979 | def argmaxOp(ser, a, axis): |
| 2980 | shape = a.shape.copy() |
| 2981 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2982 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2983 | |
| 2984 | @staticmethod |
| 2985 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 2986 | |
| 2987 | # IFM: NHWC |
| 2988 | # Filter: OHWI |
| 2989 | # OFM: NHWC |
| 2990 | |
| 2991 | if len(padding) == 2: |
| 2992 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 2993 | # From H,W to T,B,L,R |
| 2994 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 2995 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2996 | h = ( |
| 2997 | ifm.shape[1] |
| 2998 | - filter.shape[1] |
| 2999 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3000 | + padding[0] |
| 3001 | + padding[1] |
| 3002 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3003 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3004 | w = ( |
| 3005 | ifm.shape[2] |
| 3006 | - filter.shape[2] |
| 3007 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3008 | + padding[2] |
| 3009 | + padding[3] |
| 3010 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3011 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3012 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 3013 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3014 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3015 | out_dtype = DType.INT32 |
| 3016 | elif ifm.dtype == DType.INT16: |
| 3017 | out_dtype = DType.INT48 |
| 3018 | elif ifm.dtype == DType.FLOAT: |
| 3019 | out_dtype = DType.FLOAT |
| 3020 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3021 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3022 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3023 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3024 | |
| 3025 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3026 | def conv3dOp(ser, ifm, filter, strides, padding, dilations): |
| 3027 | |
| 3028 | # IFM: NDHWC |
| 3029 | # Filter: ODHWI |
| 3030 | # OFM: NDHWC |
| 3031 | |
| 3032 | d = ( |
| 3033 | ifm.shape[1] |
| 3034 | - filter.shape[1] |
| 3035 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3036 | + padding[0] |
| 3037 | + padding[1] |
| 3038 | ) // strides[0] + 1 |
| 3039 | |
| 3040 | h = ( |
| 3041 | ifm.shape[2] |
| 3042 | - filter.shape[2] |
| 3043 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3044 | + padding[2] |
| 3045 | + padding[3] |
| 3046 | ) // strides[1] + 1 |
| 3047 | |
| 3048 | w = ( |
| 3049 | ifm.shape[3] |
| 3050 | - filter.shape[3] |
| 3051 | - (filter.shape[3] - 1) * (dilations[2] - 1) |
| 3052 | + padding[4] |
| 3053 | + padding[5] |
| 3054 | ) // strides[2] + 1 |
| 3055 | |
| 3056 | ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| 3057 | |
| 3058 | if ifm.dtype == DType.INT8: |
| 3059 | out_dtype = DType.INT32 |
| 3060 | elif ifm.dtype == DType.INT16: |
| 3061 | out_dtype = DType.INT48 |
| 3062 | elif ifm.dtype == DType.FLOAT: |
| 3063 | out_dtype = DType.FLOAT |
| 3064 | else: |
| 3065 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
| 3066 | |
| 3067 | return ser.addOutput(ofm_shape, out_dtype) |
| 3068 | |
| 3069 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3070 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3071 | # IFM: NHWC |
| 3072 | # Filter: HWCM |
| 3073 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3074 | h = ( |
| 3075 | ifm.shape[1] |
| 3076 | - filter.shape[0] |
| 3077 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 3078 | + padding[0] |
| 3079 | + padding[1] |
| 3080 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3081 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3082 | w = ( |
| 3083 | ifm.shape[2] |
| 3084 | - filter.shape[1] |
| 3085 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 3086 | + padding[2] |
| 3087 | + padding[3] |
| 3088 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3089 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3090 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 3091 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3092 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3093 | out_dtype = DType.INT32 |
| 3094 | elif ifm.dtype == DType.INT16: |
| 3095 | out_dtype = DType.INT48 |
| 3096 | elif ifm.dtype == DType.FLOAT: |
| 3097 | out_dtype = DType.FLOAT |
| 3098 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3099 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3100 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3101 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3102 | |
| 3103 | @staticmethod |
| 3104 | def pool2dOp(ser, ifm, kernel, stride, pad): |
| 3105 | # input: NHWC |
| 3106 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 3107 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 3108 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3109 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3110 | return ser.addOutput(ofm_shape, ifm.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3111 | |
| 3112 | @staticmethod |
| 3113 | def fullyConnectedOp(ser, input, filter): |
| 3114 | # input: N, IC |
| 3115 | # filter: OC, IC |
| 3116 | # output: N, OC |
| 3117 | |
| 3118 | output_shape = [input.shape[0], filter.shape[0]] |
| 3119 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3120 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3121 | out_dtype = DType.INT32 |
| 3122 | elif input.dtype == DType.INT16: |
| 3123 | out_dtype = DType.INT48 |
| 3124 | elif input.dtype == DType.FLOAT: |
| 3125 | out_dtype = DType.FLOAT |
| 3126 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3127 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3128 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3129 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3130 | |
| 3131 | @staticmethod |
| 3132 | def matmulOp(ser, a, b): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3133 | # a: N, H, C |
| 3134 | # b: N, C, W |
| 3135 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3136 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3137 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3138 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3139 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3140 | out_dtype = DType.INT32 |
| 3141 | elif a.dtype == DType.INT16: |
| 3142 | out_dtype = DType.INT48 |
| 3143 | elif a.dtype == DType.FLOAT: |
| 3144 | out_dtype = DType.FLOAT |
| 3145 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3146 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3147 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3148 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3149 | |
| 3150 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3151 | def concatOp(ser, axis, *a): |
| 3152 | input1 = a[0] |
| 3153 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3154 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3155 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3156 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3157 | output_shape[axis] = input1.shape[axis] |
| 3158 | |
| 3159 | for tensor in remaining_inputs: |
| 3160 | output_shape[axis] += tensor.shape[axis] |
| 3161 | |
| 3162 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3163 | |
| 3164 | @staticmethod |
| 3165 | def padOp(ser, a, padding): |
| 3166 | |
| 3167 | output_shape = a.shape.copy() |
| 3168 | |
| 3169 | for i in range(len(output_shape)): |
| 3170 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 3171 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3172 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3173 | |
| 3174 | @staticmethod |
| 3175 | def reshapeOp(ser, a, shape): |
| 3176 | output_shape = shape.copy() |
| 3177 | |
| 3178 | totalElements = 1 |
| 3179 | for i in a.shape: |
| 3180 | totalElements *= i |
| 3181 | |
| 3182 | # If there are any -1 elements, figure out what that dimension must be |
| 3183 | totalOutputElements = 1 |
| 3184 | for i in output_shape: |
| 3185 | if i != -1: |
| 3186 | totalOutputElements *= i |
| 3187 | |
| 3188 | # And fill it in |
| 3189 | for i in range(len(output_shape)): |
| 3190 | if output_shape[i] == -1: |
| 3191 | output_shape[i] = totalElements // totalOutputElements |
| 3192 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3193 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3194 | |
| 3195 | @staticmethod |
| 3196 | def sliceOp(ser, a, begin, size): |
| 3197 | |
| 3198 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3199 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3200 | |
| 3201 | @staticmethod |
| 3202 | def tileOp(ser, a, multiples): |
| 3203 | |
| 3204 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3205 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3206 | |
| 3207 | for i in range(len(output_shape)): |
| 3208 | output_shape[i] = a.shape[i] * multiples[i] |
| 3209 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3210 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3211 | |
| 3212 | @staticmethod |
| 3213 | def transposeOp(ser, a, perms): |
| 3214 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3215 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3216 | |
| 3217 | for i in range(len(output_shape)): |
| 3218 | output_shape[i] = a.shape[perms[i]] |
| 3219 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3220 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3221 | |
| 3222 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3223 | def gatherOp(ser, values, indices): |
| 3224 | assert len(values.shape) == 3 |
| 3225 | assert len(indices.shape) == 2 |
| 3226 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3227 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3228 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 3229 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3230 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3231 | |
| 3232 | @staticmethod |
| 3233 | def scatterOp(ser, values_in, indices, input): |
| 3234 | assert len(values_in.shape) == 3 |
| 3235 | assert len(indices.shape) == 2 |
| 3236 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3237 | assert values_in.shape[0] == indices.shape[0] # N |
| 3238 | assert input.shape[1] == indices.shape[1] # W |
| 3239 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3240 | |
| 3241 | output_shape = values_in.shape |
| 3242 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3243 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3244 | |
| 3245 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3246 | def tableOp(ser, input, table_dtype): |
| 3247 | # Same shape as the input, but dtype dependent on table dtype |
| 3248 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 3249 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 3250 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3251 | |
| 3252 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3253 | def resizeOp( |
| 3254 | ser, |
| 3255 | input, |
| 3256 | mode, |
| 3257 | stride, |
| 3258 | offset, |
| 3259 | shift, |
| 3260 | stride_fp, |
| 3261 | offset_fp, |
| 3262 | output_dims, |
| 3263 | input_dtype, |
| 3264 | output_dtype, |
| 3265 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3266 | |
| 3267 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] |
| 3268 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3269 | return ser.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3270 | |
| 3271 | @staticmethod |
| 3272 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3273 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3274 | |
| 3275 | @staticmethod |
| 3276 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3277 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3278 | out_dtype = DType.INT32 |
| 3279 | elif ifm.dtype == DType.INT16: |
| 3280 | out_dtype = DType.INT48 |
| 3281 | elif ifm.dtype == DType.FLOAT: |
| 3282 | out_dtype = DType.FLOAT |
| 3283 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3284 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3285 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3286 | return ser.addOutput(output_shape, out_dtype) |