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 |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 45 | from tosa_error_if import ErrorIf |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 46 | |
| 47 | # Convenience variables to the flatc-generated types that should be enums, but aren't |
| 48 | DType = tosa.DType.DType() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 49 | Op = tosa.Op.Op() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 50 | ResizeMode = tosa.ResizeMode.ResizeMode() |
| 51 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 52 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 53 | class TosaQuantGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 54 | """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" |
| 55 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 56 | def __init__(self): |
| 57 | pass |
| 58 | |
| 59 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 60 | def getQinfo(testGen, dtype): |
| 61 | if dtype == DType.INT8: |
| 62 | return testGen.randInt(-128, 128) |
| 63 | if dtype == DType.UINT8: |
| 64 | return testGen.randInt(0, 256) |
| 65 | return 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 66 | |
| 67 | @staticmethod |
| 68 | def qgUnary(testGen, op, dtype): |
| 69 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 70 | qinfo.UnaryQuantInfo( |
| 71 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 72 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 73 | return qinfo |
| 74 | |
| 75 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 76 | def qgConv(testGen, op, dtype_or_dtypeList): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 77 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 78 | if isinstance(dtype_or_dtypeList, list): |
| 79 | # a list of [input, weights, accumulator] dtypes |
| 80 | dtypeList = dtype_or_dtypeList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 81 | else: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 82 | # an int, [input, weights, accumulator] dtypes are the same |
| 83 | dtypeList = [dtype_or_dtypeList] * 3 |
| 84 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 85 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 86 | qinfo.ConvQuantInfo(input_zp, weights_zp) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 87 | return qinfo |
| 88 | |
| 89 | @staticmethod |
| 90 | def qgMatmul(testGen, op, dtype): |
| 91 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 92 | qinfo.MatMulQuantInfo( |
| 93 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 94 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 95 | return qinfo |
| 96 | |
| 97 | @staticmethod |
| 98 | def qgPad(testGen, op, dtype): |
| 99 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 100 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 101 | return qinfo |
| 102 | |
| 103 | @staticmethod |
| 104 | def computeMultiplierAndShift(scaleFp, scale32): |
| 105 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 106 | # Provide a floating-point scaling factor and the scale32 parameter |
| 107 | # to compute the multiplier and shift |
| 108 | |
| 109 | if scale32: |
| 110 | scaleBits = 31 |
| 111 | else: |
| 112 | scaleBits = 15 |
| 113 | |
| 114 | m, shift = math.frexp(scaleFp) |
| 115 | |
| 116 | if scaleFp < 0.0: |
| 117 | m = -m |
| 118 | |
| 119 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 120 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 121 | |
| 122 | if multiplier == (1 << scaleBits): |
| 123 | multiplier = multiplier // 2 |
| 124 | shift = shift + 1 |
| 125 | |
| 126 | shift = (-shift) + scaleBits |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 127 | #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
| 128 | |
| 129 | # Adjust multiplier such that shift is in allowed value range. |
| 130 | if shift == 0: |
| 131 | multiplier = multiplier // 4 |
| 132 | shift = shift + 2 |
| 133 | elif shift == 1: |
| 134 | multiplier = multiplier // 2 |
| 135 | shift = shift + 1 |
| 136 | elif shift == 63: |
| 137 | multiplier = multiplier * 2 |
| 138 | shift = shift - 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 139 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 140 | assert multiplier <= (1 << scaleBits) |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 141 | assert shift >= 2 and shift <= 62 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 142 | |
| 143 | return multiplier, shift |
| 144 | |
| 145 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 146 | class TosaTensorGen: |
| 147 | """Tensor generators create a shape list for the placeholder and const tensor |
| 148 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 149 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 150 | def __init__(self): |
| 151 | pass |
| 152 | |
| 153 | @staticmethod |
| 154 | def tgBasic(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 155 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 156 | shape = testGen.makeShape(rank) |
| 157 | |
| 158 | shape_list = [] |
| 159 | for i in range(pl + const): |
| 160 | shape_list.append(shape.copy()) |
| 161 | |
| 162 | return shape_list |
| 163 | |
| 164 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 165 | def tgNHWC(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 166 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 167 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 168 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 169 | |
| 170 | shape = testGen.makeShape(rank) |
| 171 | |
| 172 | # Constrict the batch size? |
| 173 | if testGen.args.max_batch_size: |
| 174 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
| 175 | |
| 176 | shape_list = [] |
| 177 | for i in range(pl + const): |
| 178 | shape_list.append(shape.copy()) |
| 179 | |
| 180 | return shape_list |
| 181 | |
| 182 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 183 | def tgScatter(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 184 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 185 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 186 | assert pl == 2 |
| 187 | assert const == 0 |
| 188 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 189 | |
| 190 | values_in_shape = testGen.makeShape(rank) |
| 191 | |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 192 | # ignore max batch size if target shape is set |
| 193 | if testGen.args.max_batch_size and not testGen.args.target_shapes: |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 194 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 195 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 196 | W = testGen.randInt( |
| 197 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 198 | ) |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 199 | # Constrict W if one dimension is too large to keep tensor size reasonable |
| 200 | if max(values_in_shape) > 5000: |
| 201 | W = testGen.randInt(0, 16) |
| 202 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 203 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 204 | |
| 205 | shape_list = [] |
| 206 | shape_list.append(values_in_shape.copy()) |
| 207 | shape_list.append(input_shape.copy()) |
| 208 | |
| 209 | return shape_list |
| 210 | |
| 211 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 212 | def tgBroadcastFuzz(testGen, op, rank): |
| 213 | shape = testGen.makeShape(rank) |
| 214 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 215 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 216 | |
| 217 | shape_list = [] |
| 218 | |
| 219 | # Choose one of the inputs to broadcast |
| 220 | bcast_idx = testGen.randInt(0, pl + const) |
| 221 | for i in range(pl + const): |
| 222 | shape_bcast = shape.copy() |
| 223 | |
| 224 | # If the chosen input, pick a random index to broadcast |
| 225 | if i == bcast_idx: |
| 226 | fuzz_idx = testGen.randInt(0, rank) |
| 227 | shape_bcast[fuzz_idx] = 1 |
| 228 | |
| 229 | shape_list.append(shape_bcast) |
| 230 | |
| 231 | return shape_list |
| 232 | |
| 233 | @staticmethod |
| 234 | def tgConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 235 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 236 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 237 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 238 | |
| 239 | # IFM dimensions are NHWC |
| 240 | ifm_shape = testGen.makeShape(rank) |
| 241 | |
| 242 | # Constrict the batch size? |
| 243 | if testGen.args.max_batch_size: |
| 244 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 245 | |
| 246 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 247 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 248 | |
| 249 | # Generate a random OFM depth |
| 250 | ofm_depth = testGen.makeShape(1)[0] |
| 251 | |
| 252 | # The filter dimensions are OHWI |
| 253 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 254 | |
| 255 | # The bias is OC |
| 256 | bias_shape = np.asarray([ofm_depth]) |
| 257 | |
| 258 | return [ifm_shape, filter_shape, bias_shape] |
| 259 | |
| 260 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 261 | def tgConv3D(testGen, op, rank): |
| 262 | pl, const = op["operands"] |
| 263 | |
| 264 | assert rank == 5 |
| 265 | |
| 266 | # IFM dimensions are NDHWC |
| 267 | ifm_shape = testGen.makeShape(rank) |
| 268 | |
| 269 | # Constrict the batch size? |
| 270 | if testGen.args.max_batch_size: |
| 271 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 272 | |
| 273 | # Get the filter depth/height/width from the operator parameters |
| 274 | filter_dhw = op["filter"] |
| 275 | |
| 276 | # Generate a random OFM channel |
| 277 | ofm_channel = testGen.makeShape(1)[0] |
| 278 | |
| 279 | # The filter dimensions are ODHWI |
| 280 | filter_shape = np.asarray( |
| 281 | [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] |
| 282 | ) |
| 283 | |
| 284 | # The bias is OC |
| 285 | bias_shape = np.asarray([ofm_channel]) |
| 286 | |
| 287 | return [ifm_shape, filter_shape, bias_shape] |
| 288 | |
| 289 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 290 | def tgTransposeConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 291 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 292 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 293 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 294 | |
| 295 | # IFM dimensions are NHWC |
| 296 | ifm_shape = testGen.makeShape(rank) |
| 297 | |
| 298 | # Constrict the batch size? |
| 299 | if testGen.args.max_batch_size: |
| 300 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 301 | |
| 302 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 303 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 304 | |
| 305 | # Generate a random OFM depth |
| 306 | ofm_depth = testGen.makeShape(1)[0] |
| 307 | |
| 308 | # The filter dimensions are OHWI |
| 309 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 310 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 311 | # The bias is OC |
| 312 | bias_shape = np.asarray([ofm_depth]) |
| 313 | |
| 314 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 315 | |
| 316 | @staticmethod |
| 317 | def tgDepthwiseConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 318 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 319 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 320 | assert rank == 4 |
| 321 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 322 | |
| 323 | # IFM dimensions are NHWC |
| 324 | ifm_shape = testGen.makeShape(rank) |
| 325 | |
| 326 | # Constrict the batch size? |
| 327 | if testGen.args.max_batch_size: |
| 328 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 329 | |
| 330 | # Get the filter height/width from the operator parameters |
| 331 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 332 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 333 | |
| 334 | # Generate a random OFM depth, but don't let it get too big because |
| 335 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 336 | filter_m = ( |
| 337 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 338 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 339 | |
| 340 | # The filter dimensions are HWCM |
| 341 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 342 | |
| 343 | # The bias is M * C |
| 344 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 345 | |
| 346 | return [ifm_shape, filter_shape, bias_shape] |
| 347 | |
| 348 | @staticmethod |
| 349 | def tgFullyConnected(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 350 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 351 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 352 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 353 | |
| 354 | input_shape = testGen.makeShape(rank) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 355 | filter_oc = testGen.rng.integers( |
| 356 | low=testGen.args.tensor_shape_range[0], |
| 357 | high=testGen.args.tensor_shape_range[1], |
| 358 | size=1, |
| 359 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 360 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 361 | |
| 362 | bias_shape = np.asarray([filter_oc]) |
| 363 | |
| 364 | return [input_shape, filter_shape, bias_shape] |
| 365 | |
| 366 | @staticmethod |
| 367 | def tgMatmul(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 368 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 369 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 370 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 371 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 372 | |
| 373 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 374 | # Get a random number for b_oc even if target shape is defined |
| 375 | b_oc = np.int32( |
| 376 | testGen.rng.integers( |
| 377 | low=testGen.args.tensor_shape_range[0], |
| 378 | high=testGen.args.tensor_shape_range[1], |
| 379 | size=1, |
| 380 | ) |
| 381 | )[0] |
| 382 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 383 | if max(a_shape) > 1000: |
| 384 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 385 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 386 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 387 | return [a_shape, b_shape] |
| 388 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 389 | @staticmethod |
| 390 | def tgConcat(testGen, opName, rank): |
| 391 | pl, const = opName["operands"] |
| 392 | shape = testGen.makeShape(rank) |
| 393 | |
| 394 | # Create extra tensors to concat. |
| 395 | # Take into account value of pl when getting maximum number of concats |
| 396 | num_tensors = testGen.randInt(0, 4) |
| 397 | shape_list = [] |
| 398 | for i in range(pl + const + num_tensors): |
| 399 | shape_list.append(shape.copy()) |
| 400 | |
| 401 | return shape_list |
| 402 | |
| 403 | @staticmethod |
| 404 | def tgConcatConstInput(testGen, shapeList, axis): |
| 405 | # Split concat shape along axis to allow for multiple const inputs |
| 406 | # without making too many large tensors |
| 407 | shape = shapeList[0] |
| 408 | if len(shapeList) == 2 or shape[axis] < len(shapeList): |
| 409 | return shapeList |
| 410 | |
| 411 | new_shapeList = [shape.copy()] |
| 412 | length_on_axis = shape[axis] |
| 413 | remaining_length = length_on_axis |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 414 | for i in range(len(shapeList) - 2): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 415 | # Calculate split on axis and remaining value |
| 416 | split_shape_val = int(shape[axis] / 2) |
| 417 | remaining_length = remaining_length - split_shape_val |
| 418 | |
| 419 | # Append new shape, and set remaining shape |
| 420 | shape[axis] = split_shape_val |
| 421 | new_shapeList.append(shape.copy()) |
| 422 | shape[axis] = remaining_length |
| 423 | if i == len(shapeList) - 3: |
| 424 | new_shapeList.append(shape.copy()) |
| 425 | |
| 426 | return new_shapeList |
| 427 | |
| 428 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 429 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 430 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 431 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 432 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 433 | as arguments to the operator build function.""" |
| 434 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 435 | def __init__(self): |
| 436 | pass |
| 437 | |
| 438 | @staticmethod |
| 439 | def agNone(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 440 | """A trivial argument generator for operators that don't take any |
| 441 | non-tensor arguments""" |
| 442 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 443 | |
| 444 | @staticmethod |
| 445 | def agAxis(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 446 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 447 | axes = [] |
| 448 | |
| 449 | shape = shapeList[0] |
| 450 | |
| 451 | for a in range(0, len(shape)): |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 452 | axes.append(("axis{}".format(a), [a])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 453 | return axes |
| 454 | |
| 455 | @staticmethod |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 456 | def agConv(testGen, opName, shapeList, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 457 | arg_list = [] |
| 458 | |
| 459 | ifm_shape = shapeList[0] |
| 460 | filter_shape = shapeList[1] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 461 | # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) |
| 462 | k = [int(x) for x in opName.split("_")[-1].split("x")] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 463 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 464 | # Check the rank |
| 465 | rank = 5 if opName.startswith("conv3d") else 4 |
| 466 | assert len(ifm_shape) == rank |
| 467 | assert len(filter_shape) == rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 468 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 469 | # kernel rank omits batch and channels |
| 470 | k_rank = rank - 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 471 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 472 | # Generate comprehensive argument lists |
| 473 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 474 | paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} |
| 475 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 476 | strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| 477 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 478 | dilations = {x for x in itertools.product(*([d_vals] * k_rank))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 479 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 480 | # add some oversize argument values |
| 481 | if max(ifm_shape) < 64: |
| 482 | bigPadding = 9 |
| 483 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) |
| 484 | bigStride = 8 |
| 485 | strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) |
| 486 | bigDilation = 7 |
| 487 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 488 | |
| 489 | # There are too many parameter combinations, so generate them sparsely |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 490 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 491 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 492 | if sparsity < 13: |
| 493 | sparsity = 1 |
| 494 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 495 | sparsity += 1 |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 496 | n = 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 497 | for s in sorted(list(strides)): |
| 498 | for p in sorted(list(paddings)): |
| 499 | for d in sorted(list(dilations)): |
| 500 | if (n % sparsity == 0 |
| 501 | # padding must not exceed the kernel size ? |
| 502 | # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 503 | # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) |
| 504 | # the padded shape must exceed the kernel size |
| 505 | and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] |
| 506 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) |
| 507 | # the padded shape must exceed the dilation |
| 508 | and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] |
| 509 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) |
| 510 | ): |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 511 | arg_list.append( |
| 512 | ( |
| 513 | "st{}_pad{}_dilat{}".format( |
| 514 | "".join([str(x) for x in s]), |
| 515 | "".join([str(x) for x in p]), |
| 516 | "".join([str(x) for x in d]), |
| 517 | ), |
| 518 | [s, p, d], |
| 519 | ) |
| 520 | ) |
| 521 | n += 1 |
| 522 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 523 | return arg_list |
| 524 | |
| 525 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 526 | def agTransposeConv2D(testGen, opName, shapeList, dtype): |
| 527 | arg_list = [] |
| 528 | |
| 529 | ifm_shape = shapeList[0] |
| 530 | filter_shape = shapeList[1] |
| 531 | |
| 532 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 533 | assert len(ifm_shape) == 4 |
| 534 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 535 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 536 | # Generate comprehensive argument lists |
| 537 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 538 | paddings = {x for x in itertools.product(*([p_vals] * 2))} |
| 539 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 540 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 541 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 542 | dilations = {x for x in itertools.product(*([d_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 543 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 544 | # add some oversize argument values |
| 545 | if max(ifm_shape) < 64: |
| 546 | bigPadding = 9 |
| 547 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) |
| 548 | bigStride = 8 |
| 549 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 550 | bigDilation = 7 |
| 551 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 552 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 553 | # There are too many parameter combinations, so generate them sparsely |
| 554 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 555 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 556 | if sparsity < 13: |
| 557 | sparsity = 1 |
| 558 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 559 | sparsity += 1 |
| 560 | n = 0 |
| 561 | for s in sorted(list(strides)): |
| 562 | for p in sorted(list(paddings)): |
| 563 | for d in sorted(list(dilations)): |
| 564 | if n % sparsity == 0: |
| 565 | # Determine the output shape |
| 566 | oh = ( |
| 567 | ifm_shape[1] |
| 568 | - filter_shape[1] |
| 569 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 570 | + 2 * p[0] |
| 571 | ) // s[0] + 1 |
| 572 | ow = ( |
| 573 | ifm_shape[2] |
| 574 | - filter_shape[2] |
| 575 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 576 | + 2 * p[1] |
| 577 | ) // s[1] + 1 |
| 578 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| 579 | arg_list.append( |
| 580 | ( |
| 581 | "st{}_pad{}_dilat{}_os{}".format( |
| 582 | "".join([str(x) for x in s]), |
| 583 | "".join([str(x) for x in p]), |
| 584 | "".join([str(x) for x in d]), |
| 585 | "x".join([str(x) for x in os]), |
| 586 | ), |
| 587 | [s, p, d, os], |
| 588 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 589 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 590 | n += 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 591 | |
| 592 | return arg_list |
| 593 | |
| 594 | @staticmethod |
| 595 | def agPad(testGen, opName, shapeList, dtype): |
| 596 | arg_list = [] |
| 597 | rank = len(shapeList[0]) |
| 598 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 599 | # Exhaustively test combinations of padding on each side of each dimension |
| 600 | # - the range of padding values is defined by pad_min and pad_max |
| 601 | # - for padding >9, the name format needs to be more distinctive |
| 602 | pad_min, pad_max = 0, 1 |
| 603 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
| 604 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 605 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 606 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 607 | for paddings in shape_pad_values: |
| 608 | name = "pad" |
| 609 | for r in range(rank): |
| 610 | before, after = paddings[r] |
| 611 | name = f"{name}{before}{after}" |
| 612 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 613 | |
| 614 | return arg_list |
| 615 | |
| 616 | @staticmethod |
| 617 | def agPooling(testGen, opName, shapeList, dtype): |
| 618 | arg_list = [] |
| 619 | |
| 620 | shape = shapeList[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 621 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 622 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 623 | # Generate comprehensive argument lists |
| 624 | p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] |
| 625 | paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| 626 | s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] |
| 627 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 628 | k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] |
| 629 | kernels = {x for x in itertools.product(*([k_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 630 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 631 | # add some oversize argument values |
| 632 | bigStride = 7 |
| 633 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 634 | bigKernel = 6 |
| 635 | kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) |
| 636 | if max(shape) < 64: |
| 637 | # padding must be less than the kernel size |
| 638 | bigPadding = bigKernel - 1 |
| 639 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 640 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 641 | # There are too many parameter combinations, so generate them sparsely |
| 642 | sparsity = len(paddings) * len(strides) * len(kernels) // 500 + 1 |
| 643 | n = 0 |
| 644 | for s in sorted(list(strides)): |
| 645 | for p in sorted(list(paddings)): |
| 646 | for k in sorted(list(kernels)): |
| 647 | if (n % sparsity == 0 |
| 648 | # padding must not exceed the kernel size |
| 649 | and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 650 | # the padded shape must exceed the kernel size |
| 651 | and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] |
| 652 | ): |
| 653 | arg_list.append( |
| 654 | ( |
| 655 | "st{}_kern{}_pad{}".format( |
| 656 | "".join([str(x) for x in s]), |
| 657 | "".join([str(x) for x in k]), |
| 658 | "".join([str(x) for x in p]), |
| 659 | ), |
| 660 | [s, p, k], |
| 661 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 662 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 663 | n += 1 |
| 664 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 665 | return arg_list |
| 666 | |
| 667 | @staticmethod |
| 668 | def agCast(testGen, opName, shapeList, inDtype): |
| 669 | arg_list = [] |
| 670 | |
| 671 | # Enumerate the output types here |
| 672 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 673 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 674 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 675 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 676 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 677 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 678 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 679 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 680 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 681 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 682 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 683 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 684 | |
| 685 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 686 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 687 | |
| 688 | return arg_list |
| 689 | |
| 690 | @staticmethod |
| 691 | def agRescale(testGen, opName, shapeList, inDtype): |
| 692 | arg_list = [] |
| 693 | |
| 694 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 695 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 696 | if inDtype == DType.UINT8 and dtype != DType.INT8: |
| 697 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 698 | continue |
| 699 | if inDtype != DType.INT8 and dtype == DType.UINT8: |
| 700 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 701 | continue |
| 702 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 703 | for scale32 in [False, True]: |
| 704 | for double_round in [False, True]: |
| 705 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 706 | |
| 707 | if inDtype == DType.INT48 and scale32: |
| 708 | # Illegal condition. Must be scale32=False |
| 709 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 710 | if double_round and not scale32: |
| 711 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 712 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 713 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 714 | arg_list.append( |
| 715 | ( |
| 716 | "out{}_sc{}_dr{}_pc{}".format( |
| 717 | DTypeNames[dtype], |
| 718 | int(scale32), |
| 719 | int(double_round), |
| 720 | int(per_channel), |
| 721 | ), |
| 722 | [dtype, scale32, double_round, per_channel], |
| 723 | ) |
| 724 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 725 | |
| 726 | return arg_list |
| 727 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 728 | @staticmethod |
| 729 | def agMul(testGen, opName, shapeList, dtype): |
| 730 | arg_list = [] |
| 731 | |
| 732 | if dtype is DType.INT32: |
| 733 | for p in range(testGen.args.num_rand_permutations): |
| 734 | |
| 735 | shift = testGen.randInt(0, 32) |
| 736 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 737 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 738 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 739 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 740 | |
| 741 | return arg_list |
| 742 | |
| 743 | @staticmethod |
| 744 | def agArithmeticRightShift(testGen, opName, shapeList, dtype): |
| 745 | arg_list = [] |
| 746 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 747 | arg_list.append(("roundTrue", [True])) |
| 748 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 749 | |
| 750 | return arg_list |
| 751 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 752 | # Helper function for reshape. Gets some factors of a larger number. |
| 753 | @staticmethod |
| 754 | def getFactors(val, start=1): |
| 755 | factors = [] |
| 756 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 757 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 758 | if (val % i) == 0: |
| 759 | factors.append(i) |
| 760 | |
| 761 | return factors |
| 762 | |
| 763 | @staticmethod |
| 764 | def agReshape(testGen, opName, shapeList, dtype): |
| 765 | arg_list = [] |
| 766 | |
| 767 | origShape = shapeList[0] |
| 768 | |
| 769 | totalElements = 1 |
| 770 | for s in origShape: |
| 771 | totalElements *= s |
| 772 | |
| 773 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 774 | factors = TosaArgGen.getFactors(totalElements) |
| 775 | |
| 776 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 777 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 778 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 779 | continue |
| 780 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 781 | found = True |
| 782 | # escape_counter breaks while loop if it continues on for too long |
| 783 | escape_counter = 0 |
| 784 | while found: |
| 785 | newShape = [] |
| 786 | # Generate newShape ensuring it isn't a duplicate |
| 787 | remainingElements = totalElements |
| 788 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 789 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 790 | # pick rank-1 factors |
| 791 | newShape.append(shuffledFactors[0]) |
| 792 | remainingElements = remainingElements // shuffledFactors[0] |
| 793 | shuffledFactors = testGen.rng.permutation( |
| 794 | TosaArgGen.getFactors(remainingElements) |
| 795 | ) |
| 796 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 797 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 798 | # Toss in a -1 sometimes |
| 799 | minusOne = testGen.randInt(0, newRank * 4) |
| 800 | if minusOne < newRank: |
| 801 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 802 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 803 | # Check for duplicates |
| 804 | found = False |
| 805 | for name, other_shape in arg_list: |
| 806 | if other_shape[0] == newShape: |
| 807 | found = True |
| 808 | break |
| 809 | |
| 810 | escape_counter += 1 |
| 811 | if escape_counter >= 100: |
| 812 | break |
| 813 | |
| 814 | if not found: |
| 815 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 816 | |
| 817 | return arg_list |
| 818 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 819 | @staticmethod |
| 820 | def agTranspose(testGen, opName, shapeList, dtype): |
| 821 | arg_list = [] |
| 822 | |
| 823 | ifm_shape = shapeList[0] |
| 824 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 825 | # Get all permutations |
| 826 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 827 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 828 | # Limit to possible permutations from shape dimension or argument setting |
| 829 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 830 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 831 | # Get random permutation generator that uses all permutations |
| 832 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 833 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 834 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 835 | arg_list = [ |
| 836 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 837 | for p in range(limit) |
| 838 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 839 | return arg_list |
| 840 | |
| 841 | @staticmethod |
| 842 | def agSlice(testGen, opName, shapeList, dtype): |
| 843 | arg_list = [] |
| 844 | |
| 845 | ifm_shape = shapeList[0] |
| 846 | rank = len(ifm_shape) |
| 847 | |
| 848 | for p in range(testGen.args.num_rand_permutations): |
| 849 | begin = [] |
| 850 | size = [] |
| 851 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 852 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 853 | |
| 854 | for i in range(rank): |
| 855 | if ifm_shape[i] > 1: |
| 856 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 857 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 858 | |
| 859 | # Invalid slice size? |
| 860 | if size[i] == 0: |
| 861 | valid = False |
| 862 | else: |
| 863 | begin.append(0) |
| 864 | size.append(1) |
| 865 | |
| 866 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 867 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 868 | return arg_list |
| 869 | |
| 870 | @staticmethod |
| 871 | def agTile(testGen, opName, shapeList, dtype): |
| 872 | arg_list = [] |
| 873 | |
| 874 | ifm_shape = shapeList[0] |
| 875 | rank = len(ifm_shape) |
| 876 | |
| 877 | for p in range(testGen.args.num_rand_permutations): |
| 878 | |
| 879 | # Pick a few random, but small multiple values |
| 880 | # because otherwise this has a tendency to generate |
| 881 | # enormous tensors |
| 882 | multiples = [] |
| 883 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame] | 884 | if ifm_shape[i] > 1000: |
| 885 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 886 | multiples.append(1) |
| 887 | elif max(ifm_shape) > 1000: |
| 888 | multiples.append(2) |
| 889 | else: |
| 890 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 891 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 892 | |
| 893 | return arg_list |
| 894 | |
| 895 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 896 | def agResize(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 897 | arg_list = [] |
| 898 | |
| 899 | ifm_shape = shapeList[0] |
| 900 | |
| 901 | for m in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
| 902 | |
| 903 | # Exclude illegal {mode, type} configurations. Pick legal output types |
| 904 | if m == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 905 | outputDTypeList = [DType.INT8] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 906 | elif m == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 907 | outputDTypeList = [DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 908 | elif m == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 909 | outputDTypeList = [DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 910 | elif m == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 911 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 912 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 913 | outputDTypeList = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 914 | else: |
| 915 | continue |
| 916 | |
| 917 | for outputDType in outputDTypeList: |
| 918 | for perm in range(testGen.args.num_rand_permutations): |
| 919 | |
| 920 | # Randomly generate legal output dimensions and shift |
| 921 | # and then compute the stride and offset based on them |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 922 | # A output_dim of 1 will cause offset to exceed allowed range |
| 923 | # so minimum value 2 produced below |
| 924 | output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1] |
| 925 | while ((float(ifm_shape[1]) / float(output_dims[0])) >= 16): |
| 926 | output_dims[0] += 1 |
| 927 | while ((float(ifm_shape[2]) / float(output_dims[1])) >= 16): |
| 928 | output_dims[1] += 1 |
| 929 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 930 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 931 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 932 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 933 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 934 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 935 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 936 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 937 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 938 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 939 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 940 | if outputDType == DType.FLOAT: |
| 941 | shift = 0 |
| 942 | stride = [0, 0] |
| 943 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 944 | stride_fp = [fp_stride_y, fp_stride_x] |
| 945 | offset_fp = [fp_offset_y, fp_offset_x] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 946 | |
| 947 | if error_name is not None: |
| 948 | shift, stride, stride_fp, offset, offset_fp = TosaErrorIfArgGen.eiResizeErrorIf( |
| 949 | testGen, |
| 950 | error_name, |
| 951 | shapeList, |
| 952 | outputDType, |
| 953 | shift, |
| 954 | stride, |
| 955 | stride_fp, |
| 956 | offset, |
| 957 | offset_fp |
| 958 | ) |
| 959 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 960 | arg_list.append( |
| 961 | ( |
| 962 | "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] | 963 | "N" if m == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 964 | output_dims[0], |
| 965 | output_dims[1], |
| 966 | testGen.typeStr(outputDType), |
| 967 | stride_fp[0], |
| 968 | stride_fp[1], |
| 969 | offset_fp[0], |
| 970 | offset_fp[1], |
| 971 | ), |
| 972 | [ |
| 973 | m, |
| 974 | stride, |
| 975 | offset, |
| 976 | shift, |
| 977 | stride_fp, |
| 978 | offset_fp, |
| 979 | output_dims, |
| 980 | dtype, |
| 981 | outputDType, |
| 982 | ], |
| 983 | ) |
| 984 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 985 | else: |
| 986 | shift = 11 |
| 987 | unit = float(1 << shift) |
| 988 | stride_y = int(round(fp_stride_y * unit)) |
| 989 | stride_x = int(round(fp_stride_x * unit)) |
| 990 | offset_y = int(round(fp_offset_y * unit)) |
| 991 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 992 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 993 | while ( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 994 | stride_y >= (16 << shift) |
| 995 | or stride_x >= (16 << shift) |
| 996 | or offset_y >= (16 << shift) |
| 997 | or offset_x >= (16 << shift) |
| 998 | or offset_y <= (-16 << shift) |
| 999 | or offset_x <= (-16 << shift) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1000 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1001 | shift = shift - 1 |
| 1002 | unit = float(1 << shift) |
| 1003 | stride_y = int(round(fp_stride_y * unit)) |
| 1004 | stride_x = int(round(fp_stride_x * unit)) |
| 1005 | offset_y = int(round(fp_offset_y * unit)) |
| 1006 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1007 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1008 | stride = [stride_y, stride_x] |
| 1009 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1010 | |
| 1011 | stride_fp = [0.0, 0.0] |
| 1012 | offset_fp = [0.0, 0.0] |
| 1013 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1014 | if error_name is not None: |
| 1015 | shift, stride, stride_fp, offset, offset_fp = TosaErrorIfArgGen.eiResizeErrorIf( |
| 1016 | testGen, |
| 1017 | error_name, |
| 1018 | shapeList, |
| 1019 | outputDType, |
| 1020 | shift, |
| 1021 | stride, |
| 1022 | stride_fp, |
| 1023 | offset, |
| 1024 | offset_fp |
| 1025 | ) |
| 1026 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1027 | arg_list.append( |
| 1028 | ( |
| 1029 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 1030 | "N" if m == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1031 | shift, |
| 1032 | output_dims[0], |
| 1033 | output_dims[1], |
| 1034 | testGen.typeStr(outputDType), |
| 1035 | stride[0], |
| 1036 | stride[1], |
| 1037 | offset[0], |
| 1038 | offset[1], |
| 1039 | ), |
| 1040 | [ |
| 1041 | m, |
| 1042 | stride, |
| 1043 | offset, |
| 1044 | shift, |
| 1045 | stride_fp, |
| 1046 | offset_fp, |
| 1047 | output_dims, |
| 1048 | dtype, |
| 1049 | outputDType, |
| 1050 | ], |
| 1051 | ) |
| 1052 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1053 | |
| 1054 | return arg_list |
| 1055 | |
| 1056 | def agCondIf(testGen, opName, shapeList, dtype): |
| 1057 | # CondIf generates the condition values here. |
| 1058 | # Convert to tensors in the build function, along with the |
| 1059 | # then and else blocks |
| 1060 | arg_list = [] |
| 1061 | |
| 1062 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1063 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1064 | |
| 1065 | return arg_list |
| 1066 | |
| 1067 | def agWhileLoop(testGen, opName, shapeList, dtype): |
| 1068 | # While loop: 0 iterations, 1, more than 1 |
| 1069 | arg_list = [] |
| 1070 | |
| 1071 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1072 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1073 | |
| 1074 | return arg_list |
| 1075 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1076 | class TosaErrorIfArgGen: |
| 1077 | |
| 1078 | @staticmethod |
| 1079 | def eiResizeErrorIf(testGen, error_name, shapeList, outputDType, shift, stride, stride_fp, offset, offset_fp): |
| 1080 | |
| 1081 | if outputDType == DType.FLOAT: |
| 1082 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1083 | stride_fp = testGen.rng.random(size=[2]) - 2 |
| 1084 | elif error_name == ErrorIf.ShiftNotZero: |
| 1085 | shift = testGen.rng.integers(1, 5) |
| 1086 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1087 | shape = shapeList[0] |
| 1088 | transform_height = testGen.rng.choice([False, True]) |
| 1089 | if transform_height: |
| 1090 | stride_fp[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1091 | else: |
| 1092 | stride_fp[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1093 | else: |
| 1094 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1095 | stride = np.int16(testGen.rng.integers(-1, 1, size=[2])) |
| 1096 | elif error_name == ErrorIf.ShiftSmallerOne: |
| 1097 | shift = testGen.rng.integers(-3, 1) |
| 1098 | if shift <= 0: |
| 1099 | stride = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1100 | offset = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1101 | else: |
| 1102 | stride = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1103 | offset = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1104 | elif error_name == ErrorIf.ShiftLargerEleven: |
| 1105 | shift = np.int16(testGen.rng.integers(12, 15)) |
| 1106 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1107 | shape = shapeList[0] |
| 1108 | transform_height = testGen.rng.choice([False, True]) |
| 1109 | if transform_height: |
| 1110 | stride[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1111 | else: |
| 1112 | stride[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1113 | elif error_name == ErrorIf.StrideLargerEqualMax: |
| 1114 | stride = [(16 << shift) + 1, (16 << shift) + 1] |
| 1115 | elif error_name == ErrorIf.OffsetLargerEqualMax: |
| 1116 | offset = [(16 << shift) + 1, (16 << shift) + 1] |
| 1117 | elif error_name == ErrorIf.OffsetSmallerEqualMin: |
| 1118 | offset = [(-16 << shift) - 1, (-16 << shift) - 1] |
| 1119 | |
| 1120 | return shift, stride, stride_fp, offset, offset_fp |
| 1121 | |
| 1122 | |
| 1123 | class TosaErrorValidator: |
| 1124 | |
| 1125 | |
| 1126 | @staticmethod |
| 1127 | def evMaxDimExceeded(check=False, **kwargs): |
| 1128 | error_name = ErrorIf.MaxDimExceeded |
| 1129 | param_reqs = {"rank": [4,4], "dtype": [DType.INT8], "shape": [[1, 16584, 5, 1]]} |
| 1130 | error_result = False |
| 1131 | error_reason = "At least one maximum dimension is larger than 16384" |
| 1132 | |
| 1133 | if check: |
| 1134 | input_shape = kwargs['input_shape'].shape |
| 1135 | output_shape = kwargs['output_shape'] # Note this is just (OH, OW) |
| 1136 | if ((input_shape[1] > 16384) or |
| 1137 | (input_shape[2] > 16384) or |
| 1138 | (output_shape[0] > 16384) or |
| 1139 | (output_shape[1] > 16384)): |
| 1140 | error_result = True |
| 1141 | |
| 1142 | info_dict = { |
| 1143 | "error_name": error_name, |
| 1144 | "error_result": error_result, |
| 1145 | "error_reason": error_reason, |
| 1146 | "param_reqs": param_reqs |
| 1147 | } |
| 1148 | return info_dict |
| 1149 | |
| 1150 | @staticmethod |
| 1151 | def evStrideSmallerEqualZero(check=False, **kwargs): |
| 1152 | error_name = ErrorIf.StrideSmallerEqualZero |
| 1153 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1154 | error_result = False |
| 1155 | error_reason = "Stride value smaller than or equal zero" |
| 1156 | |
| 1157 | if check: |
| 1158 | input_dtype = kwargs['input_dtype'] |
| 1159 | if input_dtype == DType.FLOAT: |
| 1160 | stride = kwargs['stride_fp'] |
| 1161 | else: |
| 1162 | stride = kwargs['stride'] |
| 1163 | |
| 1164 | if min(stride) <= 0: |
| 1165 | error_result = True |
| 1166 | |
| 1167 | info_dict = { |
| 1168 | "error_name": error_name, |
| 1169 | "error_result": error_result, |
| 1170 | "error_reason": error_reason, |
| 1171 | "param_reqs": param_reqs |
| 1172 | } |
| 1173 | return info_dict |
| 1174 | |
| 1175 | @staticmethod |
| 1176 | def evStrideLargerEqualMax(check=False, **kwargs): |
| 1177 | error_name = ErrorIf.StrideLargerEqualMax |
| 1178 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1179 | error_result = False |
| 1180 | error_reason = "Stride value larger than or equal to maximum value" |
| 1181 | |
| 1182 | if check: |
| 1183 | shift = kwargs['shift'] |
| 1184 | input_dtype = kwargs['input_dtype'] |
| 1185 | stride = kwargs['stride'] |
| 1186 | if input_dtype in [DType.INT8, DType.INT16]: |
| 1187 | if shift >= 0 and (stride[0] >= (16 << shift) or stride[1] >= (16 << shift)): |
| 1188 | error_result = True |
| 1189 | elif shift < 0 and (stride[0] >= (16 >> -shift) or stride[1] >= (16 >> -shift)): |
| 1190 | error_result = True |
| 1191 | |
| 1192 | info_dict = { |
| 1193 | "error_name": error_name, |
| 1194 | "error_result": error_result, |
| 1195 | "error_reason": error_reason, |
| 1196 | "param_reqs": param_reqs |
| 1197 | } |
| 1198 | return info_dict |
| 1199 | |
| 1200 | |
| 1201 | @staticmethod |
| 1202 | def evStrideLargerDimension(check=False, **kwargs): |
| 1203 | error_name = ErrorIf.StrideLargerDimension |
| 1204 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1205 | error_result = False |
| 1206 | error_reason = "Stride value larger than or equal to H/W dimension" |
| 1207 | |
| 1208 | if check: |
| 1209 | shape = kwargs['input_shape'].shape |
| 1210 | input_dtype = kwargs['input_dtype'] |
| 1211 | stride = kwargs['stride_fp'] |
| 1212 | |
| 1213 | if input_dtype == DType.FLOAT and (stride[0] > shape[1]) or (stride[1] > shape[2]): |
| 1214 | error_result = True |
| 1215 | |
| 1216 | info_dict = { |
| 1217 | "error_name": error_name, |
| 1218 | "error_result": error_result, |
| 1219 | "error_reason": error_reason, |
| 1220 | "param_reqs": param_reqs |
| 1221 | } |
| 1222 | return info_dict |
| 1223 | |
| 1224 | |
| 1225 | @staticmethod |
| 1226 | def evOffsetSmallerEqualMin(check=False, **kwargs): |
| 1227 | error_name = ErrorIf.OffsetSmallerEqualMin |
| 1228 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1229 | error_result = False |
| 1230 | error_reason = "Offset value smaller than or equal to minimum value" |
| 1231 | |
| 1232 | if check: |
| 1233 | shift = kwargs['shift'] |
| 1234 | input_dtype = kwargs['input_dtype'] |
| 1235 | if input_dtype == DType.FLOAT: |
| 1236 | offset = kwargs['offset_fp'] |
| 1237 | else: |
| 1238 | offset = kwargs['offset'] |
| 1239 | |
| 1240 | if shift >= 0 and (offset[0] <= (-16 << shift) or offset[1] <= (-16 << shift)): |
| 1241 | error_result = True |
| 1242 | elif shift < 0 and (offset[0] <= (-16 >> -shift) or offset[1] <= (-16 >> -shift)): |
| 1243 | error_result = True |
| 1244 | |
| 1245 | info_dict = { |
| 1246 | "error_name": error_name, |
| 1247 | "error_result": error_result, |
| 1248 | "error_reason": error_reason, |
| 1249 | "param_reqs": param_reqs |
| 1250 | } |
| 1251 | return info_dict |
| 1252 | |
| 1253 | @staticmethod |
| 1254 | def evOffsetLargerEqualMax(check=False, **kwargs): |
| 1255 | error_name = ErrorIf.OffsetLargerEqualMax |
| 1256 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1257 | error_result = False |
| 1258 | error_reason = "Offset value larger than or equal to maximum value" |
| 1259 | |
| 1260 | if check: |
| 1261 | shift = kwargs['shift'] |
| 1262 | input_dtype = kwargs['input_dtype'] |
| 1263 | if input_dtype == DType.FLOAT: |
| 1264 | offset = kwargs['offset_fp'] |
| 1265 | else: |
| 1266 | offset = kwargs['offset'] |
| 1267 | |
| 1268 | if shift >= 0: |
| 1269 | if offset[0] >= (16 << shift) or offset[1] >= (16 << shift): |
| 1270 | error_result = True |
| 1271 | |
| 1272 | if shift >= 0 and (offset[0] >= (16 << shift) or offset[1] >= (16 << shift)): |
| 1273 | error_result = True |
| 1274 | elif shift < 0 and (offset[0] >= (16 >> -shift) or offset[1] >= (16 >> -shift)): |
| 1275 | error_result = True |
| 1276 | |
| 1277 | info_dict = { |
| 1278 | "error_name": error_name, |
| 1279 | "error_result": error_result, |
| 1280 | "error_reason": error_reason, |
| 1281 | "param_reqs": param_reqs |
| 1282 | } |
| 1283 | return info_dict |
| 1284 | |
| 1285 | @staticmethod |
| 1286 | def evShiftNotZero(check=False, **kwargs): |
| 1287 | error_name = ErrorIf.ShiftNotZero |
| 1288 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1289 | error_result = False |
| 1290 | error_reason = "Shift value must be zero for float input" |
| 1291 | |
| 1292 | if check: |
| 1293 | shift = kwargs['shift'] |
| 1294 | input_dtype = kwargs['input_dtype'] |
| 1295 | if input_dtype == DType.FLOAT and shift != 0: |
| 1296 | error_result = True |
| 1297 | |
| 1298 | info_dict = { |
| 1299 | "error_name": error_name, |
| 1300 | "error_result": error_result, |
| 1301 | "error_reason": error_reason, |
| 1302 | "param_reqs": param_reqs |
| 1303 | } |
| 1304 | return info_dict |
| 1305 | |
| 1306 | |
| 1307 | @staticmethod |
| 1308 | def evShiftSmallerOne(check=False, **kwargs): |
| 1309 | error_name = ErrorIf.ShiftSmallerOne |
| 1310 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1311 | error_result = False |
| 1312 | error_reason = "Shift value smaller than one" |
| 1313 | |
| 1314 | if check: |
| 1315 | shift = kwargs['shift'] |
| 1316 | input_dtype = kwargs['input_dtype'] |
| 1317 | if shift < 1 and input_dtype != DType.FLOAT: |
| 1318 | error_result = True |
| 1319 | |
| 1320 | info_dict = { |
| 1321 | "error_name": error_name, |
| 1322 | "error_result": error_result, |
| 1323 | "error_reason": error_reason, |
| 1324 | "param_reqs": param_reqs |
| 1325 | } |
| 1326 | return info_dict |
| 1327 | |
| 1328 | @staticmethod |
| 1329 | def evShiftLargerEleven(check=False, **kwargs): |
| 1330 | error_name = ErrorIf.ShiftLargerEleven |
| 1331 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1332 | error_result = False |
| 1333 | error_reason = "Shift value larger than eleven" |
| 1334 | |
| 1335 | if check: |
| 1336 | shift = kwargs['shift'] |
| 1337 | if shift > 11: |
| 1338 | error_result = True |
| 1339 | |
| 1340 | info_dict = { |
| 1341 | "error_name": error_name, |
| 1342 | "error_result": error_result, |
| 1343 | "error_reason": error_reason, |
| 1344 | "param_reqs": param_reqs |
| 1345 | } |
| 1346 | return info_dict |
| 1347 | |
| 1348 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1349 | class TosaInvalidValidator: |
| 1350 | |
| 1351 | @staticmethod |
| 1352 | def ivWrongDataTypeOrModeResize(**kwargs): |
| 1353 | input_dtype = kwargs["input_dtype"] |
| 1354 | args = kwargs["args"] |
| 1355 | mode = args[0] |
| 1356 | stride = args[1] |
| 1357 | stride_fp = args[4] |
| 1358 | output_dtype = args[8] |
| 1359 | |
| 1360 | if mode == ResizeMode.BILINEAR: |
| 1361 | # Invalid output data type / Invalid input datatype |
| 1362 | return ( |
| 1363 | not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or |
| 1364 | not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or |
| 1365 | not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or |
| 1366 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1367 | ) |
| 1368 | elif mode == ResizeMode.NEAREST: |
| 1369 | # Invalid output data type / Invalid input datatype |
| 1370 | return ( |
| 1371 | (input_dtype != output_dtype) or |
| 1372 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1373 | ) |
| 1374 | else: |
| 1375 | # Invalid resize mode |
| 1376 | return True |
| 1377 | |
| 1378 | @staticmethod |
| 1379 | def ivBadStride(**kwargs): |
| 1380 | input_dtype = kwargs["input_dtype"] |
| 1381 | args = kwargs["args"] |
| 1382 | stride_x = args[1][0] |
| 1383 | stride_y = args[1][1] |
| 1384 | stride_fp_x = args[4][0] |
| 1385 | stride_fp_y = args[4][1] |
| 1386 | |
| 1387 | if input_dtype == DType.FLOAT: |
| 1388 | if stride_fp_x <= 0 or stride_fp_y <= 0: |
| 1389 | # Negative or zero stride |
| 1390 | return True |
| 1391 | else: |
| 1392 | if stride_x <= 0 or stride_y <= 0: |
| 1393 | # Negative or zero stride |
| 1394 | return True |
| 1395 | return False |
| 1396 | |
| 1397 | |
| 1398 | |
| 1399 | |
| 1400 | @staticmethod |
| 1401 | def ivHeightWidthSmallerZero(**kwargs): |
| 1402 | opName = kwargs['opName'] |
| 1403 | |
| 1404 | inputShapes = kwargs['shapeList'] |
| 1405 | input = inputShapes[0] |
| 1406 | if not opName.endswith("pool2d"): |
| 1407 | filter = inputShapes[1] |
| 1408 | |
| 1409 | args = kwargs['args'] |
| 1410 | strides = args[0] |
| 1411 | padding = args[1] |
| 1412 | dilations = args[2] |
| 1413 | if opName.endswith("pool2d"): |
| 1414 | kernel = args[2] |
| 1415 | |
| 1416 | if opName.startswith('conv2d'): |
| 1417 | h = ( |
| 1418 | input[1] |
| 1419 | - filter[1] |
| 1420 | - (filter[1] - 1) * (dilations[0] - 1) |
| 1421 | + padding[0] |
| 1422 | + padding[1] |
| 1423 | ) // strides[0] + 1 |
| 1424 | |
| 1425 | w = ( |
| 1426 | input[2] |
| 1427 | - filter[2] |
| 1428 | - (filter[2] - 1) * (dilations[1] - 1) |
| 1429 | + padding[2] |
| 1430 | + padding[3] |
| 1431 | ) // strides[1] + 1 |
| 1432 | elif opName.startswith("depthwise_conv2d"): |
| 1433 | h = ( |
| 1434 | input[1] |
| 1435 | - filter[0] |
| 1436 | - (filter[0] - 1) * (dilations[0] - 1) |
| 1437 | + padding[0] |
| 1438 | + padding[1] |
| 1439 | ) // strides[0] + 1 |
| 1440 | |
| 1441 | w = ( |
| 1442 | input[2] |
| 1443 | - filter[1] |
| 1444 | - (filter[1] - 1) * (dilations[1] - 1) |
| 1445 | + padding[2] |
| 1446 | + padding[3] |
| 1447 | ) // strides[1] + 1 |
| 1448 | elif opName.endswith("pool2d"): |
| 1449 | h = (input[1] + padding[0] + padding[1] + strides[0] - kernel[0]) // strides[0] |
| 1450 | w = (input[2] + padding[2] + padding[3] + strides[1] - kernel[1]) // strides[1] |
| 1451 | else: |
| 1452 | assert False, "Unrecognized Op" |
| 1453 | |
| 1454 | if h <= 0 or w <= 0: |
| 1455 | # Invalid parameter combination |
| 1456 | return True |
| 1457 | return False |
| 1458 | |
| 1459 | @staticmethod |
| 1460 | def ivNonPositiveOutputShape(**kwargs): |
| 1461 | args = kwargs['args'] |
| 1462 | output_shape = args[3] |
| 1463 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
| 1464 | # Negative output shape |
| 1465 | return True |
| 1466 | return False |
| 1467 | |
| 1468 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1469 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1470 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1471 | # Maximum rank of tensor supported by test generator. |
| 1472 | TOSA_TENSOR_MAX_RANK = 6 |
| 1473 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1474 | def __init__(self, args): |
| 1475 | self.args = args |
| 1476 | self.basePath = args.output_dir |
| 1477 | self.random_seed = args.random_seed |
| 1478 | self.ser = None |
| 1479 | self.rng = np.random.default_rng(self.random_seed) |
| 1480 | self.createDynamicOpLists() |
| 1481 | self.initOpListDefaults() |
| 1482 | self.quantGen = TosaQuantGen() |
| 1483 | # Force makeShape to do a specific starting shape |
| 1484 | self.targetted_shape = None |
| 1485 | |
| 1486 | def createSerializer(self, opName, testPath): |
| 1487 | self.testPath = os.path.join(opName, testPath) |
| 1488 | |
| 1489 | fullPath = os.path.join(self.basePath, self.testPath) |
| 1490 | os.makedirs(fullPath, exist_ok=True) |
| 1491 | self.ser = ts.TosaSerializer(fullPath) |
| 1492 | |
| 1493 | def getSerializer(self): |
| 1494 | return self.ser |
| 1495 | |
| 1496 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1497 | with open( |
| 1498 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 1499 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1500 | fd.write(self.ser.serialize()) |
| 1501 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1502 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 1503 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1504 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1505 | def resetRNG(self, seed=None): |
| 1506 | if seed == None: |
| 1507 | seed = self.random_seed + 1 |
| 1508 | self.rng = np.random.default_rng(seed) |
| 1509 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1510 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1511 | if dtype == DType.BOOL: |
| 1512 | np_dt = np.bool |
| 1513 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1514 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1515 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1516 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1517 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1518 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 1519 | elif dtype == DType.UINT8: |
| 1520 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1521 | elif dtype == DType.INT16: |
| 1522 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 1523 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1524 | return np.int32( |
| 1525 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 1526 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1527 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1528 | return np.int64( |
| 1529 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 1530 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1531 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1532 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1533 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1534 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1535 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1536 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1537 | placeholders = [] |
| 1538 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1539 | assert len(shape_list) == len(dtype_list) |
| 1540 | |
| 1541 | for idx, shape in enumerate(shape_list): |
| 1542 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1543 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1544 | |
| 1545 | return placeholders |
| 1546 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1547 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1548 | consts = [] |
| 1549 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1550 | assert len(shape_list) == len(dtype_list) |
| 1551 | |
| 1552 | for idx, shape in enumerate(shape_list): |
| 1553 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1554 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1555 | |
| 1556 | return consts |
| 1557 | |
| 1558 | def makeShape(self, rank): |
| 1559 | if self.targetted_shape: |
| 1560 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1561 | return np.int32( |
| 1562 | self.rng.integers( |
| 1563 | low=self.args.tensor_shape_range[0], |
| 1564 | high=self.args.tensor_shape_range[1], |
| 1565 | size=rank, |
| 1566 | ) |
| 1567 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1568 | |
| 1569 | def setTargetShape(self, shape): |
| 1570 | self.targetted_shape = shape |
| 1571 | |
| 1572 | def randInt(self, low=0, high=256): |
| 1573 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 1574 | |
| 1575 | def getRandNumberDType(self, dtype): |
| 1576 | if dtype == DType.FLOAT: |
| 1577 | return self.rng.random() |
| 1578 | elif dtype == DType.BOOL: |
| 1579 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1580 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1581 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1582 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1583 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1584 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1585 | elif dtype == DType.INT16: |
| 1586 | low, high = (-32768, 32768) |
| 1587 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1588 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1589 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1590 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1591 | # Special size |
| 1592 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 1593 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1594 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1595 | |
| 1596 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 1597 | |
| 1598 | def shapeStr(self, shape): |
| 1599 | |
| 1600 | sStr = [] |
| 1601 | # Convert to strings |
| 1602 | for i in shape: |
| 1603 | sStr.append(str(i)) |
| 1604 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1605 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1606 | |
| 1607 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1608 | if isinstance(t, list): |
| 1609 | assert len(t) >= 2 |
| 1610 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1611 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1612 | if t == DType.BOOL: |
| 1613 | return "b" |
| 1614 | elif t == DType.INT4: |
| 1615 | return "i4" |
| 1616 | elif t == DType.INT8: |
| 1617 | return "i8" |
| 1618 | elif t == DType.UINT8: |
| 1619 | return "u8" |
| 1620 | elif t == DType.INT16: |
| 1621 | return "i16" |
| 1622 | elif t == DType.INT32: |
| 1623 | return "i32" |
| 1624 | elif t == DType.INT48: |
| 1625 | return "i48" |
| 1626 | elif t == DType.FLOAT: |
| 1627 | return "float" |
| 1628 | else: |
| 1629 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1630 | |
| 1631 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1632 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1633 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1634 | return 4 |
| 1635 | elif t == DType.INT8: |
| 1636 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1637 | elif t == DType.UINT8: |
| 1638 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1639 | elif t == DType.INT16: |
| 1640 | return 16 |
| 1641 | elif t == DType.INT32: |
| 1642 | return 32 |
| 1643 | elif t == DType.INT48: |
| 1644 | return 48 |
| 1645 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1646 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1647 | |
| 1648 | # Argument generators |
| 1649 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 1650 | # Where the string descriptor is used to generate the test name and |
| 1651 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 1652 | # build function |
| 1653 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1654 | def build_unary(self, op, a, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1655 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1656 | self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) |
| 1657 | return result_tens |
| 1658 | |
| 1659 | def build_binary_broadcast(self, op, a, b): |
| 1660 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1661 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1662 | return result_tens |
| 1663 | |
| 1664 | def build_binary_nonbroadcast(self, op, a, b): |
| 1665 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
| 1666 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1667 | return result_tens |
| 1668 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1669 | def build_arithmetic_right_shift(self, op, a, b, round): |
| 1670 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1671 | |
| 1672 | attr = ts.TosaSerializerAttribute() |
| 1673 | attr.ArithmeticRightShiftAttribute(round) |
| 1674 | |
| 1675 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
| 1676 | return result_tens |
| 1677 | |
| 1678 | def build_mul(self, op, a, b, shift): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1679 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1680 | |
| 1681 | # Special for multiply: |
| 1682 | # Force the result to INT32 for INT types |
| 1683 | if a.dtype != DType.FLOAT: |
| 1684 | result_tens.setDtype(DType.INT32) |
| 1685 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1686 | attr = ts.TosaSerializerAttribute() |
| 1687 | attr.MulAttribute(shift) |
| 1688 | |
| 1689 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1690 | return result_tens |
| 1691 | |
| 1692 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1693 | # Constant size depending on type, random values |
| 1694 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1695 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1696 | table_arr = self.getRandTensor([513], table_dtype) |
| 1697 | else: |
| 1698 | assert a.dtype == DType.INT8 |
| 1699 | table_dtype = DType.INT8 |
| 1700 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1701 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1702 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 1703 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1704 | self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None) |
| 1705 | |
| 1706 | return result_tens |
| 1707 | |
| 1708 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1709 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
| 1710 | 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] | 1711 | return result_tens |
| 1712 | |
| 1713 | def build_comparison(self, op, a, b): |
| 1714 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
| 1715 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1716 | return result_tens |
| 1717 | |
| 1718 | def build_argmax(self, op, a, axis): |
| 1719 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 1720 | |
| 1721 | attr = ts.TosaSerializerAttribute() |
| 1722 | attr.AxisAttribute(axis) |
| 1723 | |
| 1724 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1725 | return result_tens |
| 1726 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1727 | def build_pool2d(self, op, input, stride, pad, kernel, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1728 | result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) |
| 1729 | |
| 1730 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1731 | attr.PoolAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1732 | |
| 1733 | self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo) |
| 1734 | return result_tens |
| 1735 | |
| 1736 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1737 | assert len(padding) == 4 |
| 1738 | result_tens = OutputShaper.conv2dOp( |
| 1739 | self.ser, ifm, filter, strides, padding, dilations |
| 1740 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1741 | |
| 1742 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1743 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1744 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1745 | self.ser.addOperator( |
| 1746 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1747 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1748 | return result_tens |
| 1749 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 1750 | def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
| 1751 | assert len(padding) == 6 |
| 1752 | result_tens = OutputShaper.conv3dOp( |
| 1753 | self.ser, ifm, filter, strides, padding, dilations |
| 1754 | ) |
| 1755 | |
| 1756 | attr = ts.TosaSerializerAttribute() |
| 1757 | attr.ConvAttribute(padding, strides, dilations) |
| 1758 | |
| 1759 | self.ser.addOperator( |
| 1760 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1761 | ) |
| 1762 | return result_tens |
| 1763 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1764 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1765 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1766 | ): |
| 1767 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1768 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 1769 | |
| 1770 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1771 | attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1772 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1773 | self.ser.addOperator( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1774 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1775 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1776 | return result_tens |
| 1777 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1778 | def build_depthwise_conv2d( |
| 1779 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 1780 | ): |
| 1781 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 1782 | self.ser, ifm, filter, strides, padding, dilations |
| 1783 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1784 | |
| 1785 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1786 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1787 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1788 | self.ser.addOperator( |
| 1789 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1790 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1791 | return result_tens |
| 1792 | |
| 1793 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 1794 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 1795 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1796 | self.ser.addOperator( |
| 1797 | op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo |
| 1798 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1799 | return result_tens |
| 1800 | |
| 1801 | def build_matmul(self, op, a, b, qinfo): |
| 1802 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
| 1803 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], None, qinfo) |
| 1804 | return result_tens |
| 1805 | |
| 1806 | def build_reduce(self, op, a, axis): |
| 1807 | result_tens = OutputShaper.reduceOp(self.ser, a, axis) |
| 1808 | |
| 1809 | attr = ts.TosaSerializerAttribute() |
| 1810 | attr.AxisAttribute(axis) |
| 1811 | |
| 1812 | self.ser.addOperator(op, [a.name], result_tens.name, attr) |
| 1813 | return result_tens |
| 1814 | |
| 1815 | def build_clamp(self, op, a): |
| 1816 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1817 | |
| 1818 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1819 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1820 | |
| 1821 | if a.dtype == DType.FLOAT: |
| 1822 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 1823 | else: |
| 1824 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 1825 | |
| 1826 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1827 | return result_tens |
| 1828 | |
| 1829 | def build_leaky_relu(self, op, a): |
| 1830 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1831 | attr = ts.TosaSerializerAttribute() |
| 1832 | |
| 1833 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 1834 | |
| 1835 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1836 | return result_tens |
| 1837 | |
| 1838 | # Needs an additional type/input |
| 1839 | def build_prelu(self, op, a): |
| 1840 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1841 | |
| 1842 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1843 | return result_tens |
| 1844 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1845 | def build_sigmoid(self, op, a): |
| 1846 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1847 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1848 | return result_tens |
| 1849 | |
| 1850 | def build_tanh(self, op, a): |
| 1851 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1852 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1853 | return result_tens |
| 1854 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1855 | def build_concat(self, op, *a): |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1856 | assert type(a[-1]) == int |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1857 | |
| 1858 | # To store variable length list of input tensors we need to store axis along with it |
| 1859 | axis = a[-1] |
| 1860 | a = a[:-1] |
| 1861 | |
| 1862 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1863 | |
| 1864 | attr = ts.TosaSerializerAttribute() |
| 1865 | attr.AxisAttribute(axis) |
| 1866 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1867 | input_tensor_names = [] |
| 1868 | for tensor in a: |
| 1869 | input_tensor_names.append(tensor.name) |
| 1870 | |
| 1871 | self.ser.addOperator(op, input_tensor_names, [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1872 | |
| 1873 | def build_pad(self, op, a, padding, qinfo): |
| 1874 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 1875 | |
| 1876 | # Need to turn the padding array into a TOSA tensor here. |
| 1877 | # This is one of the few tensor operands that does not get |
| 1878 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1879 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1880 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1881 | self.ser.addOperator( |
| 1882 | op, [a.name, padding_tens.name], [result_tens.name], None, qinfo |
| 1883 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1884 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1885 | |
| 1886 | def build_reshape(self, op, a, newShape): |
| 1887 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 1888 | |
| 1889 | attr = ts.TosaSerializerAttribute() |
| 1890 | attr.ReshapeAttribute(newShape) |
| 1891 | |
| 1892 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1893 | return result_tens |
| 1894 | |
| 1895 | def build_reverse(self, op, a, axis): |
| 1896 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1897 | |
| 1898 | attr = ts.TosaSerializerAttribute() |
| 1899 | attr.AxisAttribute(axis) |
| 1900 | |
| 1901 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1902 | return result_tens |
| 1903 | |
| 1904 | def build_transpose(self, op, a, perms): |
| 1905 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 1906 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1907 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1908 | |
| 1909 | self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name]) |
| 1910 | return result_tens |
| 1911 | |
| 1912 | def build_slice(self, op, a, begin, size): |
| 1913 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 1914 | |
| 1915 | attr = ts.TosaSerializerAttribute() |
| 1916 | attr.SliceAttribute(begin, size) |
| 1917 | |
| 1918 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1919 | return result_tens |
| 1920 | |
| 1921 | def build_tile(self, op, a, multiples): |
| 1922 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 1923 | |
| 1924 | attr = ts.TosaSerializerAttribute() |
| 1925 | attr.TileAttribute(multiples) |
| 1926 | |
| 1927 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1928 | return result_tens |
| 1929 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1930 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1931 | |
| 1932 | # Create a new indicies tensor |
| 1933 | # here with data that doesn't exceed the dimensions of the values tensor |
| 1934 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1935 | K = values.shape[1] # K |
| 1936 | W = self.randInt( |
| 1937 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 1938 | ) # W |
| 1939 | indicies_arr = np.int32( |
| 1940 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 1941 | ) # (N, W) |
| 1942 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1943 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1944 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1945 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1946 | self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1947 | |
| 1948 | return result_tens |
| 1949 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1950 | def build_scatter(self, op, values_in, input): |
| 1951 | |
| 1952 | # Create a new indicies tensor |
| 1953 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 1954 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1955 | K = values_in.shape[1] # K |
| 1956 | W = input.shape[1] # W |
| 1957 | indicies_arr = np.int32( |
| 1958 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 1959 | ) # (N, W) |
| 1960 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1961 | |
| 1962 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 1963 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1964 | self.ser.addOperator( |
| 1965 | op, [values_in.name, indicies.name, input.name], [result_tens.name] |
| 1966 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1967 | |
| 1968 | return result_tens |
| 1969 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1970 | def build_resize( |
| 1971 | self, |
| 1972 | op, |
| 1973 | input, |
| 1974 | mode, |
| 1975 | stride, |
| 1976 | offset, |
| 1977 | shift, |
| 1978 | stride_fp, |
| 1979 | offset_fp, |
| 1980 | output_dims, |
| 1981 | input_dtype, |
| 1982 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1983 | validator_fcns, |
| 1984 | error_name = None, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1985 | ): |
| 1986 | result_tens = OutputShaper.resizeOp( |
| 1987 | self.ser, |
| 1988 | input, |
| 1989 | mode, |
| 1990 | stride, |
| 1991 | offset, |
| 1992 | shift, |
| 1993 | stride_fp, |
| 1994 | offset_fp, |
| 1995 | output_dims, |
| 1996 | input_dtype, |
| 1997 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1998 | error_name |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1999 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2000 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2001 | # Check ERROR_IF statements |
| 2002 | for val_fcn in validator_fcns: |
| 2003 | val_result = val_fcn( |
| 2004 | check=True, |
| 2005 | shift=shift, |
| 2006 | input_dtype=input_dtype, |
| 2007 | input_shape=input, |
| 2008 | output_shape=output_dims, |
| 2009 | offset=offset, |
| 2010 | offset_fp=offset_fp, |
| 2011 | stride=stride, |
| 2012 | stride_fp=stride_fp) |
| 2013 | |
| 2014 | validator_name = val_result['error_name'] |
| 2015 | error_result = val_result['error_result'] |
| 2016 | error_reason = val_result['error_reason'] |
| 2017 | |
| 2018 | if error_result: |
| 2019 | if error_name == validator_name: |
| 2020 | self.ser.setExpectedReturnCode(2, error_reason) |
| 2021 | else: |
| 2022 | print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}") |
| 2023 | return None # Return None to delete test if wrong ERROR_IF is hit |
| 2024 | |
| 2025 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2026 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2027 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2028 | attr.ResizeAttribute( |
| 2029 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 2030 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2031 | |
| 2032 | self.ser.addOperator(op, [input.name], [result_tens.name], attr) |
| 2033 | return result_tens |
| 2034 | |
| 2035 | def build_identityn(self, op, val, val2): |
| 2036 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2037 | result_tens = OutputShaper.unaryOp(self.ser, val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2038 | result_tens2 = OutputShaper.unaryOp(self.ser, val2) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2039 | self.ser.addOperator( |
| 2040 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 2041 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2042 | return result_tens |
| 2043 | |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame^] | 2044 | def build_const(self, op, val): |
| 2045 | self.ser.addOutputTensor(val) |
| 2046 | return val |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2047 | |
| 2048 | # Type Conversion |
| 2049 | def build_cast(self, op, val, out_dtype): |
| 2050 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 2051 | self.ser.addOperator(op, [val.name], [result_tens.name]) |
| 2052 | return result_tens |
| 2053 | |
| 2054 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel): |
| 2055 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 2056 | |
| 2057 | if per_channel: |
| 2058 | nc = val.shape[-1] |
| 2059 | else: |
| 2060 | nc = 1 |
| 2061 | |
| 2062 | in_type_width = self.typeWidth(val.dtype) |
| 2063 | out_type_width = self.typeWidth(out_dtype) |
| 2064 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2065 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2066 | input_zp = self.randInt(-128, 128) |
| 2067 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2068 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2069 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2070 | in_type_width = in_type_width + 1 |
| 2071 | else: |
| 2072 | input_zp = 0 |
| 2073 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2074 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2075 | output_zp = self.randInt(-128, 128) |
| 2076 | out_type_width = out_type_width + 1 |
| 2077 | elif out_dtype == DType.UINT8: |
| 2078 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2079 | out_type_width = out_type_width + 1 |
| 2080 | else: |
| 2081 | output_zp = 0 |
| 2082 | |
| 2083 | # Calculate scale based on: |
| 2084 | # scale = a *(2^output_width)/(2^input_width)) |
| 2085 | |
| 2086 | a = np.float32(self.rng.random(size=[nc])) |
| 2087 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 2088 | |
| 2089 | if scale32: |
| 2090 | pass |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2091 | # Cap the scaling at 2^31 - 1 for scale32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2092 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 2093 | else: |
| 2094 | # Cap the scaling at 2^15 - 1 for scale16 |
| 2095 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 2096 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2097 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2098 | |
| 2099 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 2100 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 2101 | |
| 2102 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2103 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 2104 | scale_arr[i], scale32 |
| 2105 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2106 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2107 | # 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] | 2108 | |
| 2109 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2110 | attr.RescaleAttribute( |
| 2111 | input_zp, |
| 2112 | output_zp, |
| 2113 | multiplier_arr, |
| 2114 | shift_arr, |
| 2115 | scale32, |
| 2116 | double_round, |
| 2117 | per_channel, |
| 2118 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2119 | |
| 2120 | self.ser.addOperator(op, [val.name], [result_tens.name], attr) |
| 2121 | return result_tens |
| 2122 | |
| 2123 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 2124 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 2125 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 2126 | # and fill them with const nodes for the body. |
| 2127 | |
| 2128 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2129 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2130 | |
| 2131 | # Make then/else tensors |
| 2132 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2133 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 2134 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2135 | |
| 2136 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2137 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2138 | |
| 2139 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2140 | then_block = "THEN_BLOCK" |
| 2141 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2142 | attr = ts.TosaSerializerAttribute() |
| 2143 | attr.CondIfAttribute(then_block, else_block) |
| 2144 | |
| 2145 | # Finally, build the op and the two blocks |
| 2146 | self.ser.addOperator(op, [cond_tens.name], [result_tens.name], attr) |
| 2147 | |
| 2148 | self.ser.startBasicBlock(then_block) |
| 2149 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2150 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2151 | self.ser.addOutputTensor(then_tens) |
| 2152 | |
| 2153 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2154 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2155 | self.ser.addOutputTensor(else_tens) |
| 2156 | |
| 2157 | return result_tens |
| 2158 | |
| 2159 | def build_cond_if_binary(self, op, a, b, cond): |
| 2160 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 2161 | # alternately add or subtract them based on the condition |
| 2162 | |
| 2163 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2164 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2165 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2166 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2167 | |
| 2168 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2169 | then_block = "THEN_BLOCK" |
| 2170 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2171 | attr = ts.TosaSerializerAttribute() |
| 2172 | attr.CondIfAttribute(then_block, else_block) |
| 2173 | |
| 2174 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2175 | self.ser.addOperator( |
| 2176 | op, [cond_tens.name, a.name, b.name], [result_tens.name], attr |
| 2177 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2178 | |
| 2179 | self.ser.startBasicBlock(then_block) |
| 2180 | self.ser.addInputTensor(a) |
| 2181 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2182 | then_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2183 | self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) |
| 2184 | |
| 2185 | self.ser.startBasicBlock(else_block) |
| 2186 | self.ser.addInputTensor(a) |
| 2187 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2188 | else_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2189 | self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) |
| 2190 | |
| 2191 | return result_tens |
| 2192 | |
| 2193 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2194 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2195 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2196 | cond_block = "COND_BLOCK" |
| 2197 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2198 | |
| 2199 | attr = ts.TosaSerializerAttribute() |
| 2200 | attr.WhileLoopAttribute(cond_block, body_block) |
| 2201 | |
| 2202 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2203 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2204 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2205 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2206 | |
| 2207 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2208 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 2209 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 2210 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2211 | |
| 2212 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2213 | self.ser.addOperator( |
| 2214 | op, |
| 2215 | [iter.name, a.name, acc.name], |
| 2216 | [iter_out.name, a_out.name, acc_out.name], |
| 2217 | attr, |
| 2218 | ) |
Kevin Cheng | b227ae5 | 2021-09-02 13:43:17 -0700 | [diff] [blame] | 2219 | self.ser.addOutputTensor(acc_out) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2220 | |
| 2221 | # COND block (input: iter, output: cond_tens ) |
| 2222 | self.ser.startBasicBlock(cond_block) |
| 2223 | self.ser.addInputTensor(iter) |
| 2224 | self.ser.addInputTensor(a) |
| 2225 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2226 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 2227 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 2228 | 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] | 2229 | |
| 2230 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 2231 | # Note that local intermediate tensors need to be declared here for the outputs |
| 2232 | self.ser.startBasicBlock(body_block) |
| 2233 | self.ser.addInputTensor(iter) |
| 2234 | self.ser.addInputTensor(a) |
| 2235 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2236 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 2237 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 2238 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2239 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 2240 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 2241 | self.ser.addOutputTensor(iter_body_out) |
| 2242 | self.ser.addOutputTensor(a) |
| 2243 | self.ser.addOutputTensor(acc_body_out) |
| 2244 | |
| 2245 | return acc_out |
| 2246 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2247 | def genOpTestList( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2248 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2249 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2250 | |
| 2251 | try: |
| 2252 | op = self.TOSA_OP_LIST[opName] |
| 2253 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2254 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2255 | |
| 2256 | # Initialize a new random number generator |
| 2257 | self.rng = np.random.default_rng(self.random_seed) |
| 2258 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2259 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2260 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2261 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 2262 | default_test_rank_range = range(1, 5) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2263 | if not shapeFilter: |
| 2264 | shapeFilter = [None] |
| 2265 | |
| 2266 | # Generate the lists of arguments |
| 2267 | rmin, rmax = op["rank"] |
| 2268 | if rankFilter is not None: |
| 2269 | cleanRankFilter = [] |
| 2270 | # Ensure rankFilter values are allowed by operator |
| 2271 | for rank in rankFilter: |
| 2272 | if rank >= rmin and rank <= rmax: |
| 2273 | cleanRankFilter.append(rank) |
| 2274 | rankFilter = cleanRankFilter |
| 2275 | elif rankFilter is None and shapeFilter[0] is None: |
| 2276 | cleanRankFilter = [] |
| 2277 | # Ensure default behaviour is bounded by default range or by operator, whichever is smaller. |
| 2278 | rankRange = range(rmin, rmax + 1) |
| 2279 | for rank in rankRange: |
| 2280 | if rank >= min(default_test_rank_range) and rank <= max(default_test_rank_range): |
| 2281 | cleanRankFilter.append(rank) |
| 2282 | rankFilter = cleanRankFilter |
| 2283 | else: |
| 2284 | rankFilter = range(rmin, rmax + 1) |
| 2285 | |
| 2286 | dtypes = op["types"] |
| 2287 | if dtypeFilter is not None: |
| 2288 | cleanDtypeFilter = [] |
| 2289 | # Ensure filtered dtypes are allowed by operator |
| 2290 | for dtype in dtypeFilter: |
| 2291 | if dtype in dtypes: |
| 2292 | cleanDtypeFilter.append(dtype) |
| 2293 | dtypeFilter = cleanDtypeFilter |
| 2294 | else: |
| 2295 | dtypeFilter = dtypes |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2296 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2297 | # Test list consists of a tuple of: |
| 2298 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 2299 | testList = [] |
| 2300 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2301 | # Positive test loop |
| 2302 | if testType in ['positive', 'both']: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2303 | for r in rankFilter: |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2304 | if opName.startswith("conv3d"): |
| 2305 | assert r == 5, "conv3d test must have input rank == 5" |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2306 | for t in dtypeFilter: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2307 | # Create the placeholder and const tensors |
| 2308 | for shape in shapeFilter: |
| 2309 | # A None shape chooses a random shape of a given rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2310 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2311 | # Filter out by rank |
| 2312 | if shape is not None and len(shape) != r: |
| 2313 | continue |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2314 | self.setTargetShape(shape) |
| 2315 | shapeList = tgen_fcn(self, op, r) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2316 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2317 | shapeStr = self.shapeStr(shapeList[0]) |
| 2318 | typeStr = self.typeStr(t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2319 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2320 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 2321 | argList = [] |
| 2322 | if agen_fcn: |
| 2323 | argList = agen_fcn(self, opName, shapeList, t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2324 | else: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2325 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2326 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2327 | for argStr, args in argList: |
| 2328 | if argStr: |
| 2329 | testStr = "{}_{}_{}_{}".format( |
| 2330 | opName, shapeStr, typeStr, argStr |
| 2331 | ) |
| 2332 | else: |
| 2333 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
| 2334 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2335 | testList.append((opName, testStr, t, None, shapeList, args)) |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2336 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2337 | # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| 2338 | if "invalid_test_validators" in op: |
| 2339 | invalid_test_validators = op["invalid_test_validators"] |
| 2340 | clean_testList = [] |
| 2341 | for test in testList: |
| 2342 | for validator_fcn in invalid_test_validators: |
| 2343 | remove_test = False |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2344 | if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[4], args=test[5]): |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2345 | remove_test = True |
| 2346 | if not remove_test: |
| 2347 | clean_testList.append(test) |
| 2348 | testList = clean_testList |
| 2349 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2350 | # Store the original filters so they can be reused if required |
| 2351 | base_rankFilter = rankFilter |
| 2352 | base_dtypeFilter = dtypeFilter |
| 2353 | base_shapeFilter = shapeFilter |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2354 | # Reset RNG so both positive and negative tests are reproducible |
| 2355 | self.resetRNG() |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2356 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2357 | # Negative test loop |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2358 | if testType in ['negative', 'both'] and "error_if_validators" in op: |
| 2359 | error_if_validators = op["error_if_validators"] |
| 2360 | for validator in error_if_validators: |
| 2361 | validator_info = validator() |
| 2362 | error_name = validator_info['error_name'] |
| 2363 | error_arguments = validator_info['param_reqs'] |
| 2364 | |
| 2365 | #Set parameters as required |
| 2366 | if error_arguments['rank'] != None: |
| 2367 | rmin, rmax = error_arguments['rank'] |
| 2368 | rankFilter = range(rmin, rmax + 1) |
| 2369 | else: |
| 2370 | rankFilter = base_rankFilter |
| 2371 | if error_arguments['dtype'] != None: |
| 2372 | dtypeFilter = error_arguments['dtype'] |
| 2373 | else: |
| 2374 | dtypeFilter = base_dtypeFilter |
| 2375 | if error_arguments['shape'] != None: |
| 2376 | shapes = error_arguments['shape'] |
| 2377 | else: |
| 2378 | shapes = base_shapeFilter[:2] # Reduce number of shapes to keep test numbers small |
| 2379 | |
| 2380 | for r in range(rmin, rmax + 1): |
| 2381 | for t in dtypeFilter: |
| 2382 | # Create the placeholder and const tensors |
| 2383 | for shape in shapes: |
| 2384 | # A None shape chooses a random shape of a given rank |
| 2385 | # Filter out by rank |
| 2386 | if shape is not None and len(shape) != r: |
| 2387 | continue |
| 2388 | self.setTargetShape(shape) |
| 2389 | shapeList = tgen_fcn(self, op, r, error_name) |
| 2390 | shapeStr = self.shapeStr(shapeList[0]) |
| 2391 | typeStr = self.typeStr(t) |
| 2392 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 2393 | argList = [] |
| 2394 | if agen_fcn: |
| 2395 | argList = agen_fcn(self, opName, shapeList, t, error_name) |
| 2396 | else: |
| 2397 | argList = [("", [])] |
| 2398 | for argStr, args in argList: |
| 2399 | if argStr: |
| 2400 | testStr = "{}_ERRORIF_{}_{}_{}_{}".format( |
| 2401 | opName, error_name, shapeStr, typeStr, argStr |
| 2402 | ) |
| 2403 | else: |
| 2404 | testStr = "{}_ERRORIF_{}_{}_{}".format(opName, error_name, shapeStr, typeStr) |
| 2405 | testList.append((opName, testStr, t, error_name, shapeList, args)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2406 | |
| 2407 | return testList |
| 2408 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2409 | |
| 2410 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2411 | try: |
| 2412 | op = self.TOSA_OP_LIST[opName] |
| 2413 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2414 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2415 | |
| 2416 | # Create a serializer |
| 2417 | self.createSerializer(opName, testStr) |
| 2418 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2419 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2420 | if "error_if_validators" in op: |
| 2421 | error_if_validators = op["error_if_validators"] |
| 2422 | else: |
| 2423 | error_if_validators = None |
| 2424 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2425 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2426 | num_operands = pCount + cCount |
| 2427 | |
| 2428 | if isinstance(dtype_or_dtypeList, list): |
| 2429 | dtypeList = dtype_or_dtypeList |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2430 | elif op["op"] == Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2431 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2432 | else: |
| 2433 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 2434 | |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2435 | if op["op"] != Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2436 | assert ( |
| 2437 | len(shapeList) == num_operands |
| 2438 | ), "shapeList length {} must match number of operands {}".format( |
| 2439 | len(shapeList), num_operands |
| 2440 | ) |
| 2441 | assert ( |
| 2442 | len(dtypeList) == num_operands |
| 2443 | ), "dtypeList length {} must match number of operands {}".format( |
| 2444 | len(dtypeList), num_operands |
| 2445 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2446 | |
| 2447 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2448 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2449 | except KeyError: |
| 2450 | qgen = None |
| 2451 | |
| 2452 | # Build the random tensor operands and the test |
| 2453 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2454 | |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 2455 | if (op["op"] == Op.ADD or op["op"] == Op.SUB) and dtypeList[0] == DType.INT32: |
| 2456 | # Make sure the operation does not cause value saturation - where |
| 2457 | # the number wraps due to limited number of bits to store the answer |
| 2458 | assert ( |
| 2459 | pCount == 2 and cCount == 0 |
| 2460 | ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
| 2461 | |
| 2462 | placeholders = [] |
| 2463 | add = (op["op"] == Op.ADD) |
| 2464 | a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2465 | b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2466 | if add: |
| 2467 | res_arr = np.add(a_arr, b_arr, dtype=np.int64) |
| 2468 | else: |
| 2469 | res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) |
| 2470 | |
| 2471 | # Work out the saturation limits |
| 2472 | max_i32 = (1 << 31)-1 |
| 2473 | min_i32 = -(1 << 31) |
| 2474 | max_arr = np.full(shapeList[1], max_i32) |
| 2475 | min_arr = np.full(shapeList[1], min_i32) |
| 2476 | |
| 2477 | # Find how much values exceed the maximum/minimums |
| 2478 | sat_max_arr = np.maximum(res_arr - max_arr, 0) |
| 2479 | sat_min_arr = np.minimum(res_arr - min_arr, 0) |
| 2480 | |
| 2481 | if not add: |
| 2482 | # Swap saturation values and negate values as we need to perform opposite operations |
| 2483 | sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr |
| 2484 | |
| 2485 | # Create new array of unsaturated values by clipping values as needed |
| 2486 | b_unsat_arr = b_arr |
| 2487 | if (sat_max_arr != 0).any(): |
| 2488 | # Clip values that cause saturation |
| 2489 | b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) |
| 2490 | # Reduce axes in unsaturated tensor to match original tensor |
| 2491 | for axis, dim in enumerate(b_arr.shape): |
| 2492 | if dim != b_unsat_arr.shape[axis]: |
| 2493 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2494 | b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) |
| 2495 | |
| 2496 | if (sat_min_arr != 0).any(): |
| 2497 | # Clip values that cause saturation |
| 2498 | b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) |
| 2499 | # Reduce axes in unsaturated tensor to match original tensor |
| 2500 | for axis, dim in enumerate(b_arr.shape): |
| 2501 | if dim != b_unsat_arr.shape[axis]: |
| 2502 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2503 | b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) |
| 2504 | |
| 2505 | placeholders.append( |
| 2506 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2507 | ) |
| 2508 | placeholders.append( |
| 2509 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| 2510 | ) |
| 2511 | |
| 2512 | tens.extend(placeholders) |
| 2513 | elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 2514 | # Force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2515 | assert ( |
| 2516 | pCount == 2 and cCount == 0 |
| 2517 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2518 | |
| 2519 | placeholders = [] |
| 2520 | for idx, shape in enumerate(shapeList[:]): |
| 2521 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2522 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2523 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2524 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2525 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2526 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2527 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 2528 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2529 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2530 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2531 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2532 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2533 | |
| 2534 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 2535 | elif op["op"] == Op.SELECT: |
| 2536 | # Set datatype of condition tensor to boolean |
| 2537 | dtypeList[0] = DType.BOOL |
| 2538 | tens.extend( |
| 2539 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2540 | ) |
| 2541 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2542 | elif op["op"] == Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2543 | assert ( |
| 2544 | pCount == 2 and cCount == 0 |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2545 | ), "Op.INTDIV must have 2 placeholders, 0 consts" |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2546 | |
| 2547 | placeholders = [] |
| 2548 | |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2549 | # Two invalid cases for Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2550 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2551 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2552 | while True: |
| 2553 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2554 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2555 | |
| 2556 | if (divisor_arr == 0).any(): |
| 2557 | continue |
| 2558 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2559 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2560 | continue |
| 2561 | |
| 2562 | break |
| 2563 | |
| 2564 | placeholders.append( |
| 2565 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 2566 | ) |
| 2567 | placeholders.append( |
| 2568 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 2569 | ) |
| 2570 | |
| 2571 | tens.extend(placeholders) |
| 2572 | elif op["op"] == Op.MUL: |
| 2573 | assert ( |
| 2574 | pCount == 2 and cCount == 0 |
| 2575 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 2576 | |
| 2577 | if dtypeList[0] == DType.FLOAT: |
| 2578 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 2579 | else: |
| 2580 | placeholders = [] |
| 2581 | |
| 2582 | # Make sure multiply result in int32 range |
| 2583 | shift = testArgs[0] |
| 2584 | if dtypeList[0] == DType.INT8: |
| 2585 | num_bits = 8 |
| 2586 | elif dtypeList[0] == DType.INT16: |
| 2587 | num_bits = 16 |
| 2588 | elif dtypeList[0] == DType.INT32: |
| 2589 | num_bits = 32 |
| 2590 | else: |
| 2591 | raise Exception("OpMul: invalid input dtype") |
| 2592 | |
| 2593 | for idx, shape in enumerate(shapeList[:]): |
| 2594 | low = -(2 ** (num_bits - 1)) |
| 2595 | high = (2 ** (num_bits - 1)) - 1 |
| 2596 | |
| 2597 | a_arr = np.int32( |
| 2598 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 2599 | ) |
| 2600 | b_arr = np.int32( |
| 2601 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 2602 | ) |
| 2603 | |
| 2604 | i = 0 |
| 2605 | while True: |
| 2606 | |
| 2607 | a_arr_64 = a_arr.astype(np.int64) |
| 2608 | b_arr_64 = b_arr.astype(np.int64) |
| 2609 | |
| 2610 | if shift > 0: |
| 2611 | rounding = 1 << (shift - 1) |
| 2612 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 2613 | else: |
| 2614 | result_arr = a_arr_64 * b_arr_64 |
| 2615 | |
| 2616 | if (result_arr > -(2 ** 31)).all() and ( |
| 2617 | result_arr <= ((2 ** 31) - 1) |
| 2618 | ).all(): |
| 2619 | break |
| 2620 | |
| 2621 | i = i + 1 |
| 2622 | a_arr = a_arr // 2 |
| 2623 | b_arr = b_arr // 2 |
| 2624 | |
| 2625 | placeholders.append( |
| 2626 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2627 | ) |
| 2628 | placeholders.append( |
| 2629 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 2630 | ) |
| 2631 | |
| 2632 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2633 | elif op["op"] == Op.CONCAT: |
| 2634 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 2635 | if count < 1: |
| 2636 | count = 1 |
| 2637 | if self.args.num_const_inputs_concat == 0: |
| 2638 | count = len(shapeList) |
| 2639 | |
| 2640 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 2641 | tens.extend( |
| 2642 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 2643 | ) |
| 2644 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2645 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2646 | tens.extend( |
| 2647 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2648 | ) |
| 2649 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2650 | |
| 2651 | if qgen is not None: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 2652 | qinfo = qgen(self, op, dtype_or_dtypeList) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2653 | else: |
| 2654 | qinfo = None |
| 2655 | |
| 2656 | try: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2657 | if error_if_validators is None: |
| 2658 | if qinfo is not None: |
| 2659 | resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo) |
| 2660 | else: |
| 2661 | resultName = build_fcn(self, op["op"], *tens, *testArgs) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2662 | else: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2663 | if qinfo is not None: |
| 2664 | resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo, error_if_validators, error_name) |
| 2665 | else: |
| 2666 | resultName = build_fcn(self, op["op"], *tens, *testArgs, error_if_validators, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2667 | except TypeError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2668 | print( |
| 2669 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 2670 | build_fcn, tens, testArgs |
| 2671 | ) |
| 2672 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2673 | raise e |
| 2674 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2675 | if resultName is None: |
| 2676 | print("Invalid ERROR_IF tests created") |
| 2677 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2678 | # Save the serialized test |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2679 | self.serialize("test") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2680 | |
| 2681 | def createDynamicOpLists(self): |
| 2682 | |
| 2683 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2684 | 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] | 2685 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2686 | for k in KERNELS_2D: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2687 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 2688 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 2689 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2690 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2691 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2692 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 2693 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 2694 | "depthwise_conv2d_TEMPLATE" |
| 2695 | ].copy() |
| 2696 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2697 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2698 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2699 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 2700 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 2701 | "transpose_conv2d_TEMPLATE" |
| 2702 | ].copy() |
| 2703 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2704 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2705 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2706 | KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| 2707 | for k in KERNELS_3D: |
| 2708 | testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| 2709 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| 2710 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2711 | self.TOSA_OP_LIST[testName]["template"] = False |
| 2712 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2713 | # Delete any templates after having created any dynamic ops |
| 2714 | # This is a two-pass operation because it's bad practice to delete |
| 2715 | # keys from dictionaries while iterating |
| 2716 | keyList = [] |
| 2717 | for k in self.TOSA_OP_LIST: |
| 2718 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2719 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2720 | keyList.append(k) |
| 2721 | continue |
| 2722 | except KeyError: |
| 2723 | pass |
| 2724 | |
| 2725 | for k in keyList: |
| 2726 | del self.TOSA_OP_LIST[k] |
| 2727 | |
| 2728 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2729 | """Fill in default fields for ops if they aren't already specified. |
| 2730 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2731 | for op in self.TOSA_OP_LIST: |
| 2732 | |
| 2733 | # Required fields |
| 2734 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2735 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2736 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2737 | raise Exception( |
| 2738 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 2739 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2740 | |
| 2741 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2742 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2743 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2744 | raise Exception( |
| 2745 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 2746 | op |
| 2747 | ) |
| 2748 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2749 | |
| 2750 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2751 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2752 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2753 | raise Exception( |
| 2754 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 2755 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2756 | |
| 2757 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2758 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2759 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2760 | raise Exception( |
| 2761 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 2762 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2763 | |
| 2764 | # Put in default rank range, if missing |
| 2765 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2766 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2767 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2768 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2769 | |
| 2770 | # Tensor operator list |
| 2771 | # 'op': op name |
| 2772 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2773 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 2774 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2775 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 2776 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2777 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2778 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2779 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 2780 | 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] | 2781 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2782 | TYPE_BOOL = [DType.BOOL] |
| 2783 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 2784 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 2785 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2786 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2787 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2788 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2789 | TYPE_CONV = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2790 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2791 | [DType.INT8, DType.INT8, DType.INT32], |
| 2792 | [DType.INT16, DType.INT8, DType.INT48], |
| 2793 | DType.FLOAT, |
| 2794 | ] |
| 2795 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2796 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2797 | |
| 2798 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2799 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2800 | "argmax": { |
| 2801 | "op": Op.ARGMAX, |
| 2802 | "operands": (1, 0), |
| 2803 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2804 | "types": TYPE_NARROW_INT_FP, |
| 2805 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2806 | "avg_pool2d": { |
| 2807 | "op": Op.AVG_POOL2D, |
| 2808 | "operands": (1, 0), |
| 2809 | "rank": (4, 4), |
| 2810 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2811 | "qgen": TosaQuantGen.qgUnary, |
| 2812 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2813 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2814 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2815 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2816 | "conv2d_TEMPLATE": { |
| 2817 | "op": Op.CONV2D, |
| 2818 | "operands": (1, 2), |
| 2819 | "rank": (4, 4), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 2820 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2821 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2822 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2823 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2824 | "template": True, |
| 2825 | }, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2826 | # Templated operator. Filled in by createDynamicOpLists |
| 2827 | "conv3d_TEMPLATE": { |
| 2828 | "op": Op.CONV3D, |
| 2829 | "operands": (1, 2), |
| 2830 | "rank": (5, 5), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 2831 | "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2832 | "qgen": TosaQuantGen.qgConv, |
| 2833 | "types": TYPE_CONV, |
| 2834 | "template": True, |
| 2835 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2836 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2837 | "depthwise_conv2d_TEMPLATE": { |
| 2838 | "op": Op.DEPTHWISE_CONV2D, |
| 2839 | "operands": (1, 2), |
| 2840 | "filter": [1, 1], |
| 2841 | "rank": (4, 4), |
| 2842 | "build_fcn": ( |
| 2843 | build_depthwise_conv2d, |
| 2844 | TosaTensorGen.tgDepthwiseConv2D, |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 2845 | TosaArgGen.agConv, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2846 | ), |
| 2847 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2848 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2849 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2850 | "template": True, |
| 2851 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2852 | "fully_connected": { |
| 2853 | "op": Op.FULLY_CONNECTED, |
| 2854 | "operands": (1, 2), |
| 2855 | "rank": (2, 2), |
| 2856 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 2857 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2858 | "types": TYPE_CONV, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2859 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2860 | "matmul": { |
| 2861 | "op": Op.MATMUL, |
| 2862 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 2863 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2864 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 2865 | "qgen": TosaQuantGen.qgMatmul, |
| 2866 | "types": TYPE_NARROW_INT_FP, |
| 2867 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2868 | "max_pool2d": { |
| 2869 | "op": Op.MAX_POOL2D, |
| 2870 | "operands": (1, 0), |
| 2871 | "rank": (4, 4), |
| 2872 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2873 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2874 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2875 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2876 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2877 | "transpose_conv2d_TEMPLATE": { |
| 2878 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2879 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2880 | "rank": (4, 4), |
| 2881 | "build_fcn": ( |
| 2882 | build_transpose_conv2d, |
| 2883 | TosaTensorGen.tgTransposeConv2D, |
| 2884 | TosaArgGen.agTransposeConv2D, |
| 2885 | ), |
| 2886 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2887 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2888 | "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2889 | "template": True, |
| 2890 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2891 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2892 | "clamp": { |
| 2893 | "op": Op.CLAMP, |
| 2894 | "operands": (1, 0), |
| 2895 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 2896 | "types": TYPE_NARROW_INT_FP, |
| 2897 | }, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2898 | "sigmoid": { |
| 2899 | "op": Op.SIGMOID, |
| 2900 | "operands": (1, 0), |
| 2901 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 2902 | "types": TYPE_FP, |
| 2903 | }, |
| 2904 | "tanh": { |
| 2905 | "op": Op.TANH, |
| 2906 | "operands": (1, 0), |
| 2907 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 2908 | "types": TYPE_FP, |
| 2909 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2910 | # Elementwise Binary Operators |
| 2911 | "add": { |
| 2912 | "op": Op.ADD, |
| 2913 | "operands": (2, 0), |
| 2914 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2915 | "types": TYPE_FI32, |
| 2916 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2917 | "arithmetic_right_shift": { |
| 2918 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 2919 | "operands": (2, 0), |
| 2920 | "build_fcn": ( |
| 2921 | build_arithmetic_right_shift, |
| 2922 | TosaTensorGen.tgBroadcastFuzz, |
| 2923 | TosaArgGen.agArithmeticRightShift, |
| 2924 | ), |
| 2925 | "types": TYPE_INT, |
| 2926 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2927 | "bitwise_and": { |
| 2928 | "op": Op.BITWISE_AND, |
| 2929 | "operands": (2, 0), |
| 2930 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2931 | "types": TYPE_INT, |
| 2932 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2933 | "bitwise_or": { |
| 2934 | "op": Op.BITWISE_OR, |
| 2935 | "operands": (2, 0), |
| 2936 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2937 | "types": TYPE_INT, |
| 2938 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2939 | "bitwise_xor": { |
| 2940 | "op": Op.BITWISE_XOR, |
| 2941 | "operands": (2, 0), |
| 2942 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2943 | "types": TYPE_INT, |
| 2944 | }, |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2945 | "intdiv": { |
| 2946 | "op": Op.INTDIV, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2947 | "operands": (2, 0), |
| 2948 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2949 | "types": [DType.INT32], |
| 2950 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2951 | "logical_and": { |
| 2952 | "op": Op.LOGICAL_AND, |
| 2953 | "operands": (2, 0), |
| 2954 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2955 | "types": TYPE_BOOL, |
| 2956 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2957 | "logical_left_shift": { |
| 2958 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 2959 | "operands": (2, 0), |
| 2960 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2961 | "types": TYPE_INT, |
| 2962 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2963 | "logical_right_shift": { |
| 2964 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 2965 | "operands": (2, 0), |
| 2966 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2967 | "types": TYPE_INT, |
| 2968 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2969 | "logical_or": { |
| 2970 | "op": Op.LOGICAL_OR, |
| 2971 | "operands": (2, 0), |
| 2972 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2973 | "types": TYPE_BOOL, |
| 2974 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2975 | "logical_xor": { |
| 2976 | "op": Op.LOGICAL_XOR, |
| 2977 | "operands": (2, 0), |
| 2978 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2979 | "types": TYPE_BOOL, |
| 2980 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2981 | "maximum": { |
| 2982 | "op": Op.MAXIMUM, |
| 2983 | "operands": (2, 0), |
| 2984 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2985 | "types": TYPE_FI32, |
| 2986 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2987 | "minimum": { |
| 2988 | "op": Op.MINIMUM, |
| 2989 | "operands": (2, 0), |
| 2990 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2991 | "types": TYPE_FI32, |
| 2992 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2993 | "mul": { |
| 2994 | "op": Op.MUL, |
| 2995 | "operands": (2, 0), |
| 2996 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 2997 | "types": TYPE_INT_FP, |
| 2998 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2999 | "pow": { |
| 3000 | "op": Op.POW, |
| 3001 | "operands": (2, 0), |
| 3002 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 3003 | "types": TYPE_FP, |
| 3004 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3005 | "sub": { |
| 3006 | "op": Op.SUB, |
| 3007 | "operands": (2, 0), |
| 3008 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3009 | "types": TYPE_FI32, |
| 3010 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3011 | "table": { |
| 3012 | "op": Op.TABLE, |
| 3013 | # Use the automatic generation functions to create the input array |
| 3014 | # but create the table tensor in the build function, as it may be |
| 3015 | # a different type from the input |
| 3016 | "operands": (1, 0), |
| 3017 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3018 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3019 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3020 | # Elementwise Unary operators |
| 3021 | "abs": { |
| 3022 | "op": Op.ABS, |
| 3023 | "operands": (1, 0), |
| 3024 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3025 | "types": TYPE_FI32, |
| 3026 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3027 | "bitwise_not": { |
| 3028 | "op": Op.BITWISE_NOT, |
| 3029 | "operands": (1, 0), |
| 3030 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3031 | "types": TYPE_INT, |
| 3032 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3033 | "ceil": { |
| 3034 | "op": Op.CEIL, |
| 3035 | "operands": (1, 0), |
| 3036 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3037 | "types": TYPE_FP, |
| 3038 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3039 | "clz": { |
| 3040 | "op": Op.CLZ, |
| 3041 | "operands": (1, 0), |
| 3042 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3043 | "types": [DType.INT32], |
| 3044 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3045 | "exp": { |
| 3046 | "op": Op.EXP, |
| 3047 | "operands": (1, 0), |
| 3048 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3049 | "types": TYPE_FP, |
| 3050 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3051 | "floor": { |
| 3052 | "op": Op.FLOOR, |
| 3053 | "operands": (1, 0), |
| 3054 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3055 | "types": TYPE_FP, |
| 3056 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3057 | "log": { |
| 3058 | "op": Op.LOG, |
| 3059 | "operands": (1, 0), |
| 3060 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3061 | "types": TYPE_FP, |
| 3062 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3063 | "logical_not": { |
| 3064 | "op": Op.LOGICAL_NOT, |
| 3065 | "operands": (1, 0), |
| 3066 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3067 | "types": TYPE_BOOL, |
| 3068 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3069 | "negate": { |
| 3070 | "op": Op.NEGATE, |
| 3071 | "operands": (1, 0), |
| 3072 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3073 | "qgen": TosaQuantGen.qgUnary, |
| 3074 | "types": TYPE_INT_FP, |
| 3075 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3076 | "reciprocal": { |
| 3077 | "op": Op.RECIPROCAL, |
| 3078 | "operands": (1, 0), |
| 3079 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3080 | "types": TYPE_FP, |
| 3081 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3082 | "rsqrt": { |
| 3083 | "op": Op.RSQRT, |
| 3084 | "operands": (1, 0), |
| 3085 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3086 | "types": TYPE_FP, |
| 3087 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3088 | # Elementwise Ternary operators |
| 3089 | "select": { |
| 3090 | "op": Op.SELECT, |
| 3091 | "operands": (3, 0), |
| 3092 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 3093 | "types": TYPE_FIB, |
| 3094 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3095 | # Comparison operators |
| 3096 | "equal": { |
| 3097 | "op": Op.EQUAL, |
| 3098 | "operands": (2, 0), |
| 3099 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3100 | "types": TYPE_FI32, |
| 3101 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3102 | "greater_equal": { |
| 3103 | "op": Op.GREATER_EQUAL, |
| 3104 | "operands": (2, 0), |
| 3105 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3106 | "types": TYPE_FI32, |
| 3107 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3108 | "greater": { |
| 3109 | "op": Op.GREATER, |
| 3110 | "operands": (2, 0), |
| 3111 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3112 | "types": TYPE_FI32, |
| 3113 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3114 | # Reduction operators |
| 3115 | "reduce_all": { |
| 3116 | "op": Op.REDUCE_ALL, |
| 3117 | "operands": (1, 0), |
| 3118 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3119 | "types": TYPE_BOOL, |
| 3120 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3121 | "reduce_any": { |
| 3122 | "op": Op.REDUCE_ANY, |
| 3123 | "operands": (1, 0), |
| 3124 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3125 | "types": TYPE_BOOL, |
| 3126 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3127 | "reduce_max": { |
| 3128 | "op": Op.REDUCE_MAX, |
| 3129 | "operands": (1, 0), |
| 3130 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3131 | "types": TYPE_INT_FP, |
| 3132 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3133 | "reduce_min": { |
| 3134 | "op": Op.REDUCE_MAX, |
| 3135 | "operands": (1, 0), |
| 3136 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3137 | "types": TYPE_INT_FP, |
| 3138 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3139 | "reduce_product": { |
| 3140 | "op": Op.REDUCE_PRODUCT, |
| 3141 | "operands": (1, 0), |
| 3142 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3143 | "types": TYPE_FP, |
| 3144 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3145 | "reduce_sum": { |
| 3146 | "op": Op.REDUCE_SUM, |
| 3147 | "operands": (1, 0), |
| 3148 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3149 | "types": TYPE_FI32, |
| 3150 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3151 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3152 | "concat": { |
| 3153 | "op": Op.CONCAT, |
| 3154 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3155 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3156 | "types": TYPE_FIB, |
| 3157 | }, |
| 3158 | "pad": { |
| 3159 | "op": Op.PAD, |
| 3160 | "operands": (1, 0), |
| 3161 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 3162 | "qgen": TosaQuantGen.qgPad, |
| 3163 | "types": TYPE_FIB, |
| 3164 | }, |
| 3165 | "reshape": { |
| 3166 | "op": Op.RESHAPE, |
| 3167 | "operands": (1, 0), |
| 3168 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 3169 | "types": TYPE_FIB, |
| 3170 | }, |
| 3171 | "reverse": { |
| 3172 | "op": Op.REVERSE, |
| 3173 | "operands": (1, 0), |
| 3174 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3175 | "types": TYPE_FIB, |
| 3176 | }, |
| 3177 | "slice": { |
| 3178 | "op": Op.SLICE, |
| 3179 | "operands": (1, 0), |
| 3180 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 3181 | "types": TYPE_FIB, |
| 3182 | }, |
| 3183 | "tile": { |
| 3184 | "op": Op.TILE, |
| 3185 | "operands": (1, 0), |
| 3186 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 3187 | "types": TYPE_FIB, |
| 3188 | }, |
| 3189 | "transpose": { |
| 3190 | "op": Op.TRANSPOSE, |
| 3191 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 3192 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3193 | "build_fcn": ( |
| 3194 | build_transpose, |
| 3195 | TosaTensorGen.tgBasic, |
| 3196 | TosaArgGen.agTranspose, |
| 3197 | ), |
| 3198 | "types": TYPE_FIB, |
| 3199 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3200 | # Data nodes |
| 3201 | "const": { |
| 3202 | "op": Op.CONST, |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame^] | 3203 | "operands": (0, 1), |
| 3204 | "build_fcn": (build_const, TosaTensorGen.tgBasic, None), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3205 | "types": TYPE_FIB, |
| 3206 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3207 | "identity": { |
| 3208 | "op": Op.IDENTITY, |
| 3209 | "operands": (1, 0), |
| 3210 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3211 | "types": TYPE_FIB, |
| 3212 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3213 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3214 | "gather": { |
| 3215 | "op": Op.GATHER, |
| 3216 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 3217 | "operands": (1, 0), |
| 3218 | "rank": (3, 3), |
| 3219 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 3220 | "types": TYPE_INT_FP, |
| 3221 | }, |
| 3222 | "scatter": { |
| 3223 | "op": Op.SCATTER, |
| 3224 | # Only specify 'values_in' tensor here. |
| 3225 | #'indices' and 'input' are generated in op building stage |
| 3226 | "operands": (2, 0), |
| 3227 | "rank": (3, 3), |
| 3228 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 3229 | "types": TYPE_INT_FP, |
| 3230 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3231 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3232 | "resize": { |
| 3233 | "op": Op.RESIZE, |
| 3234 | "operands": (1, 0), |
| 3235 | "rank": (4, 4), |
| 3236 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 3237 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3238 | "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride), |
| 3239 | "error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension, |
| 3240 | TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax, |
| 3241 | TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3242 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3243 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3244 | "cast": { |
| 3245 | "op": Op.CAST, |
| 3246 | "operands": (1, 0), |
| 3247 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 3248 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 3249 | }, |
| 3250 | "rescale": { |
| 3251 | "op": Op.RESCALE, |
| 3252 | "operands": (1, 0), |
| 3253 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 3254 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3255 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3256 | # Custom |
| 3257 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3258 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3259 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 3260 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 3261 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3262 | "cond_if_const": { |
| 3263 | "op": Op.COND_IF, |
| 3264 | "operands": (0, 2), |
| 3265 | "build_fcn": ( |
| 3266 | build_cond_if_const, |
| 3267 | TosaTensorGen.tgBasic, |
| 3268 | TosaArgGen.agCondIf, |
| 3269 | ), |
| 3270 | "types": [DType.BOOL], |
| 3271 | }, |
| 3272 | "cond_if_binary": { |
| 3273 | "op": Op.COND_IF, |
| 3274 | "operands": (2, 0), |
| 3275 | "build_fcn": ( |
| 3276 | build_cond_if_binary, |
| 3277 | TosaTensorGen.tgBasic, |
| 3278 | TosaArgGen.agCondIf, |
| 3279 | ), |
| 3280 | "types": TYPE_FI32, |
| 3281 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3282 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3283 | "while_loop": { |
| 3284 | "op": Op.WHILE_LOOP, |
| 3285 | "operands": (0, 1), |
| 3286 | "build_fcn": ( |
| 3287 | build_while_loop, |
| 3288 | TosaTensorGen.tgBasic, |
| 3289 | TosaArgGen.agWhileLoop, |
| 3290 | ), |
| 3291 | "types": [DType.INT32], |
| 3292 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3293 | } |
| 3294 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3295 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3296 | class OutputShaper: |
| 3297 | # Methods in this class compute the expected output shape and datatype |
| 3298 | # for common classes of operations |
| 3299 | def __init__(self): |
| 3300 | pass |
| 3301 | |
| 3302 | # These methods return arguments that can be used for |
| 3303 | # creating a new output tensor |
| 3304 | @staticmethod |
| 3305 | def binaryBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3306 | assert len(a.shape) == len(b.shape) |
| 3307 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3308 | |
| 3309 | shape = [] |
| 3310 | for i in range(len(a.shape)): |
| 3311 | if a.shape[i] == 1: |
| 3312 | shape.append(b.shape[i]) |
| 3313 | else: |
| 3314 | shape.append(a.shape[i]) |
| 3315 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3316 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3317 | |
| 3318 | @staticmethod |
| 3319 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3320 | assert len(a.shape) == len(b.shape) |
| 3321 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3322 | |
| 3323 | shape = [] |
| 3324 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3325 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3326 | shape.append(a.shape[i]) |
| 3327 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3328 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3329 | |
| 3330 | @staticmethod |
| 3331 | def unaryOp(ser, a): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3332 | return ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3333 | |
| 3334 | @staticmethod |
| 3335 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3336 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 3337 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3338 | |
| 3339 | shape = [] |
| 3340 | for i in range(len(a.shape)): |
| 3341 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 3342 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3343 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3344 | |
| 3345 | @staticmethod |
| 3346 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3347 | assert len(a.shape) == len(b.shape) |
| 3348 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3349 | |
| 3350 | # Do broadcast |
| 3351 | shape = [] |
| 3352 | for i in range(len(a.shape)): |
| 3353 | if a.shape[i] == 1: |
| 3354 | shape.append(b.shape[i]) |
| 3355 | else: |
| 3356 | shape.append(a.shape[i]) |
| 3357 | |
| 3358 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3359 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3360 | |
| 3361 | @staticmethod |
| 3362 | def reduceOp(ser, a, axis): |
| 3363 | |
| 3364 | shape = a.shape.copy() |
| 3365 | |
| 3366 | shape[axis] = 1 |
| 3367 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3368 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3369 | |
| 3370 | @staticmethod |
| 3371 | def argmaxOp(ser, a, axis): |
| 3372 | shape = a.shape.copy() |
| 3373 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3374 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3375 | |
| 3376 | @staticmethod |
| 3377 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3378 | |
| 3379 | # IFM: NHWC |
| 3380 | # Filter: OHWI |
| 3381 | # OFM: NHWC |
| 3382 | |
| 3383 | if len(padding) == 2: |
| 3384 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 3385 | # From H,W to T,B,L,R |
| 3386 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 3387 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3388 | h = ( |
| 3389 | ifm.shape[1] |
| 3390 | - filter.shape[1] |
| 3391 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3392 | + padding[0] |
| 3393 | + padding[1] |
| 3394 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3395 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3396 | w = ( |
| 3397 | ifm.shape[2] |
| 3398 | - filter.shape[2] |
| 3399 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3400 | + padding[2] |
| 3401 | + padding[3] |
| 3402 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3403 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3404 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 3405 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3406 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3407 | out_dtype = DType.INT32 |
| 3408 | elif ifm.dtype == DType.INT16: |
| 3409 | out_dtype = DType.INT48 |
| 3410 | elif ifm.dtype == DType.FLOAT: |
| 3411 | out_dtype = DType.FLOAT |
| 3412 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3413 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3414 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3415 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3416 | |
| 3417 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3418 | def conv3dOp(ser, ifm, filter, strides, padding, dilations): |
| 3419 | |
| 3420 | # IFM: NDHWC |
| 3421 | # Filter: ODHWI |
| 3422 | # OFM: NDHWC |
| 3423 | |
| 3424 | d = ( |
| 3425 | ifm.shape[1] |
| 3426 | - filter.shape[1] |
| 3427 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3428 | + padding[0] |
| 3429 | + padding[1] |
| 3430 | ) // strides[0] + 1 |
| 3431 | |
| 3432 | h = ( |
| 3433 | ifm.shape[2] |
| 3434 | - filter.shape[2] |
| 3435 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3436 | + padding[2] |
| 3437 | + padding[3] |
| 3438 | ) // strides[1] + 1 |
| 3439 | |
| 3440 | w = ( |
| 3441 | ifm.shape[3] |
| 3442 | - filter.shape[3] |
| 3443 | - (filter.shape[3] - 1) * (dilations[2] - 1) |
| 3444 | + padding[4] |
| 3445 | + padding[5] |
| 3446 | ) // strides[2] + 1 |
| 3447 | |
| 3448 | ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| 3449 | |
| 3450 | if ifm.dtype == DType.INT8: |
| 3451 | out_dtype = DType.INT32 |
| 3452 | elif ifm.dtype == DType.INT16: |
| 3453 | out_dtype = DType.INT48 |
| 3454 | elif ifm.dtype == DType.FLOAT: |
| 3455 | out_dtype = DType.FLOAT |
| 3456 | else: |
| 3457 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
| 3458 | |
| 3459 | return ser.addOutput(ofm_shape, out_dtype) |
| 3460 | |
| 3461 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3462 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3463 | # IFM: NHWC |
| 3464 | # Filter: HWCM |
| 3465 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3466 | h = ( |
| 3467 | ifm.shape[1] |
| 3468 | - filter.shape[0] |
| 3469 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 3470 | + padding[0] |
| 3471 | + padding[1] |
| 3472 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3473 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3474 | w = ( |
| 3475 | ifm.shape[2] |
| 3476 | - filter.shape[1] |
| 3477 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 3478 | + padding[2] |
| 3479 | + padding[3] |
| 3480 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3481 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3482 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 3483 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3484 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3485 | out_dtype = DType.INT32 |
| 3486 | elif ifm.dtype == DType.INT16: |
| 3487 | out_dtype = DType.INT48 |
| 3488 | elif ifm.dtype == DType.FLOAT: |
| 3489 | out_dtype = DType.FLOAT |
| 3490 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3491 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3492 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3493 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3494 | |
| 3495 | @staticmethod |
| 3496 | def pool2dOp(ser, ifm, kernel, stride, pad): |
| 3497 | # input: NHWC |
| 3498 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 3499 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 3500 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3501 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3502 | return ser.addOutput(ofm_shape, ifm.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3503 | |
| 3504 | @staticmethod |
| 3505 | def fullyConnectedOp(ser, input, filter): |
| 3506 | # input: N, IC |
| 3507 | # filter: OC, IC |
| 3508 | # output: N, OC |
| 3509 | |
| 3510 | output_shape = [input.shape[0], filter.shape[0]] |
| 3511 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3512 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3513 | out_dtype = DType.INT32 |
| 3514 | elif input.dtype == DType.INT16: |
| 3515 | out_dtype = DType.INT48 |
| 3516 | elif input.dtype == DType.FLOAT: |
| 3517 | out_dtype = DType.FLOAT |
| 3518 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3519 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3520 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3521 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3522 | |
| 3523 | @staticmethod |
| 3524 | def matmulOp(ser, a, b): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3525 | # a: N, H, C |
| 3526 | # b: N, C, W |
| 3527 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3528 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3529 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3530 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3531 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3532 | out_dtype = DType.INT32 |
| 3533 | elif a.dtype == DType.INT16: |
| 3534 | out_dtype = DType.INT48 |
| 3535 | elif a.dtype == DType.FLOAT: |
| 3536 | out_dtype = DType.FLOAT |
| 3537 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3538 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3539 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3540 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3541 | |
| 3542 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3543 | def concatOp(ser, axis, *a): |
| 3544 | input1 = a[0] |
| 3545 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3546 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3547 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3548 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3549 | output_shape[axis] = input1.shape[axis] |
| 3550 | |
| 3551 | for tensor in remaining_inputs: |
| 3552 | output_shape[axis] += tensor.shape[axis] |
| 3553 | |
| 3554 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3555 | |
| 3556 | @staticmethod |
| 3557 | def padOp(ser, a, padding): |
| 3558 | |
| 3559 | output_shape = a.shape.copy() |
| 3560 | |
| 3561 | for i in range(len(output_shape)): |
| 3562 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 3563 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3564 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3565 | |
| 3566 | @staticmethod |
| 3567 | def reshapeOp(ser, a, shape): |
| 3568 | output_shape = shape.copy() |
| 3569 | |
| 3570 | totalElements = 1 |
| 3571 | for i in a.shape: |
| 3572 | totalElements *= i |
| 3573 | |
| 3574 | # If there are any -1 elements, figure out what that dimension must be |
| 3575 | totalOutputElements = 1 |
| 3576 | for i in output_shape: |
| 3577 | if i != -1: |
| 3578 | totalOutputElements *= i |
| 3579 | |
| 3580 | # And fill it in |
| 3581 | for i in range(len(output_shape)): |
| 3582 | if output_shape[i] == -1: |
| 3583 | output_shape[i] = totalElements // totalOutputElements |
| 3584 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3585 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3586 | |
| 3587 | @staticmethod |
| 3588 | def sliceOp(ser, a, begin, size): |
| 3589 | |
| 3590 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3591 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3592 | |
| 3593 | @staticmethod |
| 3594 | def tileOp(ser, a, multiples): |
| 3595 | |
| 3596 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3597 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3598 | |
| 3599 | for i in range(len(output_shape)): |
| 3600 | output_shape[i] = a.shape[i] * multiples[i] |
| 3601 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3602 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3603 | |
| 3604 | @staticmethod |
| 3605 | def transposeOp(ser, a, perms): |
| 3606 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3607 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3608 | |
| 3609 | for i in range(len(output_shape)): |
| 3610 | output_shape[i] = a.shape[perms[i]] |
| 3611 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3612 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3613 | |
| 3614 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3615 | def gatherOp(ser, values, indices): |
| 3616 | assert len(values.shape) == 3 |
| 3617 | assert len(indices.shape) == 2 |
| 3618 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3619 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3620 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 3621 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3622 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3623 | |
| 3624 | @staticmethod |
| 3625 | def scatterOp(ser, values_in, indices, input): |
| 3626 | assert len(values_in.shape) == 3 |
| 3627 | assert len(indices.shape) == 2 |
| 3628 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3629 | assert values_in.shape[0] == indices.shape[0] # N |
| 3630 | assert input.shape[1] == indices.shape[1] # W |
| 3631 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3632 | |
| 3633 | output_shape = values_in.shape |
| 3634 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3635 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3636 | |
| 3637 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3638 | def tableOp(ser, input, table_dtype): |
| 3639 | # Same shape as the input, but dtype dependent on table dtype |
| 3640 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 3641 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 3642 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3643 | |
| 3644 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3645 | def resizeOp( |
| 3646 | ser, |
| 3647 | input, |
| 3648 | mode, |
| 3649 | stride, |
| 3650 | offset, |
| 3651 | shift, |
| 3652 | stride_fp, |
| 3653 | offset_fp, |
| 3654 | output_dims, |
| 3655 | input_dtype, |
| 3656 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3657 | error_name = None |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3658 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3659 | |
| 3660 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] |
| 3661 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3662 | return ser.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3663 | |
| 3664 | @staticmethod |
| 3665 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3666 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3667 | |
| 3668 | @staticmethod |
| 3669 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3670 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3671 | out_dtype = DType.INT32 |
| 3672 | elif ifm.dtype == DType.INT16: |
| 3673 | out_dtype = DType.INT48 |
| 3674 | elif ifm.dtype == DType.FLOAT: |
| 3675 | out_dtype = DType.FLOAT |
| 3676 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3677 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3678 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3679 | return ser.addOutput(output_shape, out_dtype) |