Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1 | #!/usr/bin/env python3 |
| 2 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3 | # Copyright (c) 2020-2021, ARM Limited. |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | # you may not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
| 16 | |
| 17 | |
| 18 | import numpy as np |
| 19 | import argparse |
| 20 | import sys |
| 21 | import re |
| 22 | import os |
| 23 | import subprocess |
| 24 | import shlex |
| 25 | import json |
| 26 | import glob |
| 27 | import math |
| 28 | import queue |
| 29 | import threading |
| 30 | import traceback |
| 31 | import math |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 32 | import itertools |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 33 | |
| 34 | from enum import IntEnum, Enum, unique |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 35 | from tosa_ref_run import TosaReturnCode |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 36 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 37 | # Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH |
| 38 | parent_dir = os.path.dirname(os.path.realpath(__file__)) |
| 39 | sys.path.append( |
| 40 | os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") |
| 41 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 42 | import tosa_serializer as ts |
| 43 | from tosa_serializer import * |
| 44 | import tosa |
| 45 | |
| 46 | # Convenience variables to the flatc-generated types that should be enums, but aren't |
| 47 | DType = tosa.DType.DType() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 48 | Op = tosa.Op.Op() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 49 | ResizeMode = tosa.ResizeMode.ResizeMode() |
| 50 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 51 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 52 | class TosaQuantGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 53 | """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" |
| 54 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 55 | def __init__(self): |
| 56 | pass |
| 57 | |
| 58 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 59 | def getQinfo(testGen, dtype): |
| 60 | if dtype == DType.INT8: |
| 61 | return testGen.randInt(-128, 128) |
| 62 | if dtype == DType.UINT8: |
| 63 | return testGen.randInt(0, 256) |
| 64 | return 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 65 | |
| 66 | @staticmethod |
| 67 | def qgUnary(testGen, op, dtype): |
| 68 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 69 | qinfo.UnaryQuantInfo( |
| 70 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 71 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 72 | return qinfo |
| 73 | |
| 74 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 75 | def qgConv(testGen, op, dtype_or_dtypeList): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 76 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 77 | if isinstance(dtype_or_dtypeList, list): |
| 78 | # a list of [input, weights, accumulator] dtypes |
| 79 | dtypeList = dtype_or_dtypeList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 80 | else: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 81 | # an int, [input, weights, accumulator] dtypes are the same |
| 82 | dtypeList = [dtype_or_dtypeList] * 3 |
| 83 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 84 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 85 | qinfo.ConvQuantInfo(input_zp, weights_zp) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 86 | return qinfo |
| 87 | |
| 88 | @staticmethod |
| 89 | def qgMatmul(testGen, op, dtype): |
| 90 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 91 | qinfo.MatMulQuantInfo( |
| 92 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 93 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 94 | return qinfo |
| 95 | |
| 96 | @staticmethod |
| 97 | def qgPad(testGen, op, dtype): |
| 98 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 99 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 100 | return qinfo |
| 101 | |
| 102 | @staticmethod |
| 103 | def computeMultiplierAndShift(scaleFp, scale32): |
| 104 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 105 | # Provide a floating-point scaling factor and the scale32 parameter |
| 106 | # to compute the multiplier and shift |
| 107 | |
| 108 | if scale32: |
| 109 | scaleBits = 31 |
| 110 | else: |
| 111 | scaleBits = 15 |
| 112 | |
| 113 | m, shift = math.frexp(scaleFp) |
| 114 | |
| 115 | if scaleFp < 0.0: |
| 116 | m = -m |
| 117 | |
| 118 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 119 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 120 | |
| 121 | if multiplier == (1 << scaleBits): |
| 122 | multiplier = multiplier // 2 |
| 123 | shift = shift + 1 |
| 124 | |
| 125 | shift = (-shift) + scaleBits |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 126 | # print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 127 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 128 | assert multiplier <= (1 << scaleBits) |
| 129 | assert shift >= 0 and shift <= 63 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 130 | |
| 131 | return multiplier, shift |
| 132 | |
| 133 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 134 | class TosaTensorGen: |
| 135 | """Tensor generators create a shape list for the placeholder and const tensor |
| 136 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 137 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 138 | def __init__(self): |
| 139 | pass |
| 140 | |
| 141 | @staticmethod |
| 142 | def tgBasic(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 143 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 144 | shape = testGen.makeShape(rank) |
| 145 | |
| 146 | shape_list = [] |
| 147 | for i in range(pl + const): |
| 148 | shape_list.append(shape.copy()) |
| 149 | |
| 150 | return shape_list |
| 151 | |
| 152 | @staticmethod |
| 153 | def tgNHWC(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 154 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 155 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 156 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 157 | |
| 158 | shape = testGen.makeShape(rank) |
| 159 | |
| 160 | # Constrict the batch size? |
| 161 | if testGen.args.max_batch_size: |
| 162 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
| 163 | |
| 164 | shape_list = [] |
| 165 | for i in range(pl + const): |
| 166 | shape_list.append(shape.copy()) |
| 167 | |
| 168 | return shape_list |
| 169 | |
| 170 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 171 | def tgScatter(testGen, opName, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 172 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 173 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 174 | assert pl == 2 |
| 175 | assert const == 0 |
| 176 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 177 | |
| 178 | values_in_shape = testGen.makeShape(rank) |
| 179 | |
| 180 | # Constrict the batch size? |
| 181 | if testGen.args.max_batch_size: |
| 182 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 183 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 184 | W = testGen.randInt( |
| 185 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 186 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 187 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 188 | |
| 189 | shape_list = [] |
| 190 | shape_list.append(values_in_shape.copy()) |
| 191 | shape_list.append(input_shape.copy()) |
| 192 | |
| 193 | return shape_list |
| 194 | |
| 195 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 196 | def tgBroadcastFuzz(testGen, op, rank): |
| 197 | shape = testGen.makeShape(rank) |
| 198 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 199 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 200 | |
| 201 | shape_list = [] |
| 202 | |
| 203 | # Choose one of the inputs to broadcast |
| 204 | bcast_idx = testGen.randInt(0, pl + const) |
| 205 | for i in range(pl + const): |
| 206 | shape_bcast = shape.copy() |
| 207 | |
| 208 | # If the chosen input, pick a random index to broadcast |
| 209 | if i == bcast_idx: |
| 210 | fuzz_idx = testGen.randInt(0, rank) |
| 211 | shape_bcast[fuzz_idx] = 1 |
| 212 | |
| 213 | shape_list.append(shape_bcast) |
| 214 | |
| 215 | return shape_list |
| 216 | |
| 217 | @staticmethod |
| 218 | def tgConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 219 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 220 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 221 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 222 | |
| 223 | # IFM dimensions are NHWC |
| 224 | ifm_shape = testGen.makeShape(rank) |
| 225 | |
| 226 | # Constrict the batch size? |
| 227 | if testGen.args.max_batch_size: |
| 228 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 229 | |
| 230 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 231 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 232 | |
| 233 | # Generate a random OFM depth |
| 234 | ofm_depth = testGen.makeShape(1)[0] |
| 235 | |
| 236 | # The filter dimensions are OHWI |
| 237 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 238 | |
| 239 | # The bias is OC |
| 240 | bias_shape = np.asarray([ofm_depth]) |
| 241 | |
| 242 | return [ifm_shape, filter_shape, bias_shape] |
| 243 | |
| 244 | @staticmethod |
| 245 | def tgTransposeConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 246 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 247 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 248 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 249 | |
| 250 | # IFM dimensions are NHWC |
| 251 | ifm_shape = testGen.makeShape(rank) |
| 252 | |
| 253 | # Constrict the batch size? |
| 254 | if testGen.args.max_batch_size: |
| 255 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 256 | |
| 257 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 258 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 259 | |
| 260 | # Generate a random OFM depth |
| 261 | ofm_depth = testGen.makeShape(1)[0] |
| 262 | |
| 263 | # The filter dimensions are OHWI |
| 264 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 265 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 266 | # The bias is OC |
| 267 | bias_shape = np.asarray([ofm_depth]) |
| 268 | |
| 269 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 270 | |
| 271 | @staticmethod |
| 272 | def tgDepthwiseConv2D(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 273 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 274 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 275 | assert rank == 4 |
| 276 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 277 | |
| 278 | # IFM dimensions are NHWC |
| 279 | ifm_shape = testGen.makeShape(rank) |
| 280 | |
| 281 | # Constrict the batch size? |
| 282 | if testGen.args.max_batch_size: |
| 283 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 284 | |
| 285 | # Get the filter height/width from the operator parameters |
| 286 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 287 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 288 | |
| 289 | # Generate a random OFM depth, but don't let it get too big because |
| 290 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 291 | filter_m = ( |
| 292 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 293 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 294 | |
| 295 | # The filter dimensions are HWCM |
| 296 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 297 | |
| 298 | # The bias is M * C |
| 299 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 300 | |
| 301 | return [ifm_shape, filter_shape, bias_shape] |
| 302 | |
| 303 | @staticmethod |
| 304 | def tgFullyConnected(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 305 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 306 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 307 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 308 | |
| 309 | input_shape = testGen.makeShape(rank) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 310 | filter_oc = testGen.rng.integers( |
| 311 | low=testGen.args.tensor_shape_range[0], |
| 312 | high=testGen.args.tensor_shape_range[1], |
| 313 | size=1, |
| 314 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 315 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 316 | |
| 317 | bias_shape = np.asarray([filter_oc]) |
| 318 | |
| 319 | return [input_shape, filter_shape, bias_shape] |
| 320 | |
| 321 | @staticmethod |
| 322 | def tgMatmul(testGen, op, rank): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 323 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 324 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 325 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 326 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 327 | |
| 328 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 329 | # Get a random number for b_oc even if target shape is defined |
| 330 | b_oc = np.int32( |
| 331 | testGen.rng.integers( |
| 332 | low=testGen.args.tensor_shape_range[0], |
| 333 | high=testGen.args.tensor_shape_range[1], |
| 334 | size=1, |
| 335 | ) |
| 336 | )[0] |
| 337 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 338 | if max(a_shape) > 1000: |
| 339 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 340 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 341 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 342 | return [a_shape, b_shape] |
| 343 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 344 | @staticmethod |
| 345 | def tgConcat(testGen, opName, rank): |
| 346 | pl, const = opName["operands"] |
| 347 | shape = testGen.makeShape(rank) |
| 348 | |
| 349 | # Create extra tensors to concat. |
| 350 | # Take into account value of pl when getting maximum number of concats |
| 351 | num_tensors = testGen.randInt(0, 4) |
| 352 | shape_list = [] |
| 353 | for i in range(pl + const + num_tensors): |
| 354 | shape_list.append(shape.copy()) |
| 355 | |
| 356 | return shape_list |
| 357 | |
| 358 | @staticmethod |
| 359 | def tgConcatConstInput(testGen, shapeList, axis): |
| 360 | # Split concat shape along axis to allow for multiple const inputs |
| 361 | # without making too many large tensors |
| 362 | shape = shapeList[0] |
| 363 | if len(shapeList) == 2 or shape[axis] < len(shapeList): |
| 364 | return shapeList |
| 365 | |
| 366 | new_shapeList = [shape.copy()] |
| 367 | length_on_axis = shape[axis] |
| 368 | remaining_length = length_on_axis |
| 369 | for i in range(len(shapeList)-2): |
| 370 | # Calculate split on axis and remaining value |
| 371 | split_shape_val = int(shape[axis] / 2) |
| 372 | remaining_length = remaining_length - split_shape_val |
| 373 | |
| 374 | # Append new shape, and set remaining shape |
| 375 | shape[axis] = split_shape_val |
| 376 | new_shapeList.append(shape.copy()) |
| 377 | shape[axis] = remaining_length |
| 378 | if i == len(shapeList) - 3: |
| 379 | new_shapeList.append(shape.copy()) |
| 380 | |
| 381 | return new_shapeList |
| 382 | |
| 383 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 384 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 385 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 386 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 387 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 388 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 389 | as arguments to the operator build function.""" |
| 390 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 391 | def __init__(self): |
| 392 | pass |
| 393 | |
| 394 | @staticmethod |
| 395 | def agNone(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 396 | """A trivial argument generator for operators that don't take any |
| 397 | non-tensor arguments""" |
| 398 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 399 | |
| 400 | @staticmethod |
| 401 | def agAxis(testGen, opName, shapeList, dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 402 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 403 | axes = [] |
| 404 | |
| 405 | shape = shapeList[0] |
| 406 | |
| 407 | for a in range(0, len(shape)): |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 408 | axes.append(("axis{}".format(a), [a])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 409 | return axes |
| 410 | |
| 411 | @staticmethod |
| 412 | def agConv2D(testGen, opName, shapeList, dtype): |
| 413 | arg_list = [] |
| 414 | |
| 415 | ifm_shape = shapeList[0] |
| 416 | filter_shape = shapeList[1] |
| 417 | |
| 418 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 419 | assert len(ifm_shape) == 4 |
| 420 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 421 | |
| 422 | maxStride = testGen.args.max_conv_stride |
| 423 | maxPadding = testGen.args.max_conv_padding + 1 |
| 424 | maxDilation = testGen.args.max_conv_dilation |
| 425 | |
| 426 | # Strides, padding, dilations |
| 427 | for stride in range(0, maxStride ** 2): |
| 428 | for padding in range(0, (maxPadding) ** 4): |
| 429 | for dilation in range(0, maxDilation ** 2): |
| 430 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 431 | s = [stride // maxStride + 1, stride % maxStride + 1] |
| 432 | p = [ |
| 433 | (padding // (maxPadding * 4)) % maxPadding, |
| 434 | (padding // (maxPadding * 2)) % maxPadding, |
| 435 | (padding // (maxPadding * 1)) % maxPadding, |
| 436 | padding % maxPadding, |
| 437 | ] |
| 438 | d = [dilation // maxDilation + 1, dilation % maxDilation + 1] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 439 | |
| 440 | # 4 padding parameters for regular conv2d |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 441 | arg_list.append( |
| 442 | ( |
| 443 | "st{}{}_pad{}{}{}{}_dilat{}{}".format( |
| 444 | s[0], s[1], p[0], p[1], p[2], p[3], d[0], d[1] |
| 445 | ), |
| 446 | [s, p, d], |
| 447 | ) |
| 448 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 449 | return arg_list |
| 450 | |
| 451 | @staticmethod |
| 452 | def agTransposeConv2D(testGen, opName, shapeList, dtype): |
| 453 | arg_list = [] |
| 454 | |
| 455 | ifm_shape = shapeList[0] |
| 456 | filter_shape = shapeList[1] |
| 457 | |
| 458 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 459 | assert len(ifm_shape) == 4 |
| 460 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 461 | |
| 462 | maxStride = testGen.args.max_conv_stride |
| 463 | maxPadding = testGen.args.max_conv_padding + 1 |
| 464 | maxDilation = testGen.args.max_conv_dilation |
| 465 | |
| 466 | # Strides, padding, dilations |
| 467 | for stride in range(0, maxStride ** 2): |
| 468 | for out_padding in range(0, (maxPadding) ** 2): |
| 469 | for dilation in range(0, maxDilation ** 2): |
| 470 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 471 | s = [stride // maxStride + 1, stride % maxStride + 1] |
| 472 | p = [ |
| 473 | (out_padding // (maxPadding * 1)) % maxPadding, |
| 474 | out_padding % maxPadding, |
| 475 | ] |
| 476 | d = [dilation // maxDilation + 1, dilation % maxDilation + 1] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 477 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 478 | oh = ( |
| 479 | ifm_shape[1] |
| 480 | - filter_shape[1] |
| 481 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 482 | + 2 * p[0] |
| 483 | ) // s[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 484 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 485 | ow = ( |
| 486 | ifm_shape[2] |
| 487 | - filter_shape[2] |
| 488 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 489 | + 2 * p[1] |
| 490 | ) // s[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 491 | |
| 492 | # Output shape |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 493 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 494 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 495 | arg_list.append( |
| 496 | ( |
| 497 | "st{}{}_outpad{}{}_dilat{}{}_os{}x{}x{}x{}".format( |
| 498 | s[0], |
| 499 | s[1], |
| 500 | p[0], |
| 501 | p[1], |
| 502 | d[0], |
| 503 | d[1], |
| 504 | os[0], |
| 505 | os[1], |
| 506 | os[2], |
| 507 | os[3], |
| 508 | ), |
| 509 | [s, p, d, os], |
| 510 | ) |
| 511 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 512 | |
| 513 | return arg_list |
| 514 | |
| 515 | @staticmethod |
| 516 | def agPad(testGen, opName, shapeList, dtype): |
| 517 | arg_list = [] |
| 518 | rank = len(shapeList[0]) |
| 519 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 520 | # Exhaustively test combinations of padding on each side of each dimension |
| 521 | # - the range of padding values is defined by pad_min and pad_max |
| 522 | # - for padding >9, the name format needs to be more distinctive |
| 523 | pad_min, pad_max = 0, 1 |
| 524 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
| 525 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 526 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 527 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 528 | for paddings in shape_pad_values: |
| 529 | name = "pad" |
| 530 | for r in range(rank): |
| 531 | before, after = paddings[r] |
| 532 | name = f"{name}{before}{after}" |
| 533 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 534 | |
| 535 | return arg_list |
| 536 | |
| 537 | @staticmethod |
| 538 | def agPooling(testGen, opName, shapeList, dtype): |
| 539 | arg_list = [] |
| 540 | |
| 541 | shape = shapeList[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 542 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 543 | |
| 544 | maxStride = testGen.args.max_pooling_stride |
| 545 | maxKernel = testGen.args.max_pooling_kernel |
| 546 | maxPadding = testGen.args.max_pooling_padding + 1 |
| 547 | |
| 548 | for kernel in range(0, maxKernel ** 2): |
| 549 | for stride in range(0, maxStride ** 2): |
| 550 | for padding in range(0, maxPadding ** 4): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 551 | s = [stride // maxStride + 1, stride % maxStride + 1] |
| 552 | k = [(kernel // maxKernel) + 2, (kernel % maxKernel) + 2] |
| 553 | p = [ |
| 554 | (padding // (maxPadding * 4)) % maxPadding, |
| 555 | (padding // (maxPadding * 2)) % maxPadding, |
| 556 | (padding // (maxPadding * 1)) % maxPadding, |
| 557 | padding % maxPadding, |
| 558 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 559 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 560 | arg_list.append( |
| 561 | ( |
| 562 | "st{}{}_kern{}{}_pad{}{}{}{}".format( |
| 563 | s[0], s[1], k[0], k[1], p[0], p[1], p[2], p[3] |
| 564 | ), |
| 565 | [k, s, p], |
| 566 | ) |
| 567 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 568 | return arg_list |
| 569 | |
| 570 | @staticmethod |
| 571 | def agCast(testGen, opName, shapeList, inDtype): |
| 572 | arg_list = [] |
| 573 | |
| 574 | # Enumerate the output types here |
| 575 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 576 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 577 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 578 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 579 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 580 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 581 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 582 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 583 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 584 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 585 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 586 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 587 | |
| 588 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 589 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 590 | |
| 591 | return arg_list |
| 592 | |
| 593 | @staticmethod |
| 594 | def agRescale(testGen, opName, shapeList, inDtype): |
| 595 | arg_list = [] |
| 596 | |
| 597 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 598 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 599 | if inDtype == DType.UINT8 and dtype != DType.INT8: |
| 600 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 601 | continue |
| 602 | if inDtype != DType.INT8 and dtype == DType.UINT8: |
| 603 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 604 | continue |
| 605 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 606 | for scale32 in [False, True]: |
| 607 | for double_round in [False, True]: |
| 608 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 609 | |
| 610 | if inDtype == DType.INT48 and scale32: |
| 611 | # Illegal condition. Must be scale32=False |
| 612 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 613 | if double_round and not scale32: |
| 614 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 615 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 616 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 617 | arg_list.append( |
| 618 | ( |
| 619 | "out{}_sc{}_dr{}_pc{}".format( |
| 620 | DTypeNames[dtype], |
| 621 | int(scale32), |
| 622 | int(double_round), |
| 623 | int(per_channel), |
| 624 | ), |
| 625 | [dtype, scale32, double_round, per_channel], |
| 626 | ) |
| 627 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 628 | |
| 629 | return arg_list |
| 630 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 631 | @staticmethod |
| 632 | def agMul(testGen, opName, shapeList, dtype): |
| 633 | arg_list = [] |
| 634 | |
| 635 | if dtype is DType.INT32: |
| 636 | for p in range(testGen.args.num_rand_permutations): |
| 637 | |
| 638 | shift = testGen.randInt(0, 32) |
| 639 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 640 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 641 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 642 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 643 | |
| 644 | return arg_list |
| 645 | |
| 646 | @staticmethod |
| 647 | def agArithmeticRightShift(testGen, opName, shapeList, dtype): |
| 648 | arg_list = [] |
| 649 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 650 | arg_list.append(("roundTrue", [True])) |
| 651 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 652 | |
| 653 | return arg_list |
| 654 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 655 | # Helper function for reshape. Gets some factors of a larger number. |
| 656 | @staticmethod |
| 657 | def getFactors(val, start=1): |
| 658 | factors = [] |
| 659 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 660 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 661 | if (val % i) == 0: |
| 662 | factors.append(i) |
| 663 | |
| 664 | return factors |
| 665 | |
| 666 | @staticmethod |
| 667 | def agReshape(testGen, opName, shapeList, dtype): |
| 668 | arg_list = [] |
| 669 | |
| 670 | origShape = shapeList[0] |
| 671 | |
| 672 | totalElements = 1 |
| 673 | for s in origShape: |
| 674 | totalElements *= s |
| 675 | |
| 676 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 677 | factors = TosaArgGen.getFactors(totalElements) |
| 678 | |
| 679 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 680 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 681 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 682 | continue |
| 683 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 684 | found = True |
| 685 | # escape_counter breaks while loop if it continues on for too long |
| 686 | escape_counter = 0 |
| 687 | while found: |
| 688 | newShape = [] |
| 689 | # Generate newShape ensuring it isn't a duplicate |
| 690 | remainingElements = totalElements |
| 691 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 692 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 693 | # pick rank-1 factors |
| 694 | newShape.append(shuffledFactors[0]) |
| 695 | remainingElements = remainingElements // shuffledFactors[0] |
| 696 | shuffledFactors = testGen.rng.permutation( |
| 697 | TosaArgGen.getFactors(remainingElements) |
| 698 | ) |
| 699 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 700 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 701 | # Toss in a -1 sometimes |
| 702 | minusOne = testGen.randInt(0, newRank * 4) |
| 703 | if minusOne < newRank: |
| 704 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 705 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 706 | # Check for duplicates |
| 707 | found = False |
| 708 | for name, other_shape in arg_list: |
| 709 | if other_shape[0] == newShape: |
| 710 | found = True |
| 711 | break |
| 712 | |
| 713 | escape_counter += 1 |
| 714 | if escape_counter >= 100: |
| 715 | break |
| 716 | |
| 717 | if not found: |
| 718 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 719 | |
| 720 | return arg_list |
| 721 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 722 | @staticmethod |
| 723 | def agTranspose(testGen, opName, shapeList, dtype): |
| 724 | arg_list = [] |
| 725 | |
| 726 | ifm_shape = shapeList[0] |
| 727 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 728 | # Get all permutations |
| 729 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 730 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 731 | # Limit to possible permutations from shape dimension or argument setting |
| 732 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 733 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 734 | # Get random permutation generator that uses all permutations |
| 735 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 736 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 737 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 738 | arg_list = [ |
| 739 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 740 | for p in range(limit) |
| 741 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 742 | return arg_list |
| 743 | |
| 744 | @staticmethod |
| 745 | def agSlice(testGen, opName, shapeList, dtype): |
| 746 | arg_list = [] |
| 747 | |
| 748 | ifm_shape = shapeList[0] |
| 749 | rank = len(ifm_shape) |
| 750 | |
| 751 | for p in range(testGen.args.num_rand_permutations): |
| 752 | begin = [] |
| 753 | size = [] |
| 754 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 755 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 756 | |
| 757 | for i in range(rank): |
| 758 | if ifm_shape[i] > 1: |
| 759 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 760 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 761 | |
| 762 | # Invalid slice size? |
| 763 | if size[i] == 0: |
| 764 | valid = False |
| 765 | else: |
| 766 | begin.append(0) |
| 767 | size.append(1) |
| 768 | |
| 769 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 770 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 771 | return arg_list |
| 772 | |
| 773 | @staticmethod |
| 774 | def agTile(testGen, opName, shapeList, dtype): |
| 775 | arg_list = [] |
| 776 | |
| 777 | ifm_shape = shapeList[0] |
| 778 | rank = len(ifm_shape) |
| 779 | |
| 780 | for p in range(testGen.args.num_rand_permutations): |
| 781 | |
| 782 | # Pick a few random, but small multiple values |
| 783 | # because otherwise this has a tendency to generate |
| 784 | # enormous tensors |
| 785 | multiples = [] |
| 786 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame^] | 787 | if ifm_shape[i] > 1000: |
| 788 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 789 | multiples.append(1) |
| 790 | elif max(ifm_shape) > 1000: |
| 791 | multiples.append(2) |
| 792 | else: |
| 793 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 794 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 795 | |
| 796 | return arg_list |
| 797 | |
| 798 | @staticmethod |
| 799 | def agResize(testGen, opName, shapeList, dtype): |
| 800 | arg_list = [] |
| 801 | |
| 802 | ifm_shape = shapeList[0] |
| 803 | |
| 804 | for m in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
| 805 | |
| 806 | # Exclude illegal {mode, type} configurations. Pick legal output types |
| 807 | if m == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 808 | outputDTypeList = [DType.INT8] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 809 | elif m == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 810 | outputDTypeList = [DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 811 | elif m == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 812 | outputDTypeList = [DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 813 | elif m == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 814 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 815 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 816 | outputDTypeList = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 817 | else: |
| 818 | continue |
| 819 | |
| 820 | for outputDType in outputDTypeList: |
| 821 | for perm in range(testGen.args.num_rand_permutations): |
| 822 | |
| 823 | # Randomly generate legal output dimensions and shift |
| 824 | # and then compute the stride and offset based on them |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 825 | output_dims = [testGen.randInt(1), testGen.randInt(1)] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 826 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 827 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 828 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 829 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 830 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 831 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 832 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 833 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 834 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 835 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 836 | if outputDType == DType.FLOAT: |
| 837 | shift = 0 |
| 838 | stride = [0, 0] |
| 839 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 840 | stride_fp = [fp_stride_y, fp_stride_x] |
| 841 | offset_fp = [fp_offset_y, fp_offset_x] |
| 842 | arg_list.append( |
| 843 | ( |
| 844 | "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] | 845 | "N" if m == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 846 | output_dims[0], |
| 847 | output_dims[1], |
| 848 | testGen.typeStr(outputDType), |
| 849 | stride_fp[0], |
| 850 | stride_fp[1], |
| 851 | offset_fp[0], |
| 852 | offset_fp[1], |
| 853 | ), |
| 854 | [ |
| 855 | m, |
| 856 | stride, |
| 857 | offset, |
| 858 | shift, |
| 859 | stride_fp, |
| 860 | offset_fp, |
| 861 | output_dims, |
| 862 | dtype, |
| 863 | outputDType, |
| 864 | ], |
| 865 | ) |
| 866 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 867 | else: |
| 868 | shift = 11 |
| 869 | unit = float(1 << shift) |
| 870 | stride_y = int(round(fp_stride_y * unit)) |
| 871 | stride_x = int(round(fp_stride_x * unit)) |
| 872 | offset_y = int(round(fp_offset_y * unit)) |
| 873 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 874 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 875 | while ( |
| 876 | stride_y >= 32768 |
| 877 | or stride_x >= 32768 |
| 878 | or offset_y >= 32768 |
| 879 | or offset_x >= 32768 |
| 880 | or offset_y < -32768 |
| 881 | or offset_x < -32768 |
| 882 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 883 | shift = shift - 1 |
| 884 | unit = float(1 << shift) |
| 885 | stride_y = int(round(fp_stride_y * unit)) |
| 886 | stride_x = int(round(fp_stride_x * unit)) |
| 887 | offset_y = int(round(fp_offset_y * unit)) |
| 888 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 889 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 890 | stride = [stride_y, stride_x] |
| 891 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 892 | |
| 893 | stride_fp = [0.0, 0.0] |
| 894 | offset_fp = [0.0, 0.0] |
| 895 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 896 | arg_list.append( |
| 897 | ( |
| 898 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 899 | "N" if m == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 900 | shift, |
| 901 | output_dims[0], |
| 902 | output_dims[1], |
| 903 | testGen.typeStr(outputDType), |
| 904 | stride[0], |
| 905 | stride[1], |
| 906 | offset[0], |
| 907 | offset[1], |
| 908 | ), |
| 909 | [ |
| 910 | m, |
| 911 | stride, |
| 912 | offset, |
| 913 | shift, |
| 914 | stride_fp, |
| 915 | offset_fp, |
| 916 | output_dims, |
| 917 | dtype, |
| 918 | outputDType, |
| 919 | ], |
| 920 | ) |
| 921 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 922 | |
| 923 | return arg_list |
| 924 | |
| 925 | def agCondIf(testGen, opName, shapeList, dtype): |
| 926 | # CondIf generates the condition values here. |
| 927 | # Convert to tensors in the build function, along with the |
| 928 | # then and else blocks |
| 929 | arg_list = [] |
| 930 | |
| 931 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 932 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 933 | |
| 934 | return arg_list |
| 935 | |
| 936 | def agWhileLoop(testGen, opName, shapeList, dtype): |
| 937 | # While loop: 0 iterations, 1, more than 1 |
| 938 | arg_list = [] |
| 939 | |
| 940 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 941 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 942 | |
| 943 | return arg_list |
| 944 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 945 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 946 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 947 | # Maximum rank of tensor supported by test generator. |
| 948 | TOSA_TENSOR_MAX_RANK = 6 |
| 949 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 950 | def __init__(self, args): |
| 951 | self.args = args |
| 952 | self.basePath = args.output_dir |
| 953 | self.random_seed = args.random_seed |
| 954 | self.ser = None |
| 955 | self.rng = np.random.default_rng(self.random_seed) |
| 956 | self.createDynamicOpLists() |
| 957 | self.initOpListDefaults() |
| 958 | self.quantGen = TosaQuantGen() |
| 959 | # Force makeShape to do a specific starting shape |
| 960 | self.targetted_shape = None |
| 961 | |
| 962 | def createSerializer(self, opName, testPath): |
| 963 | self.testPath = os.path.join(opName, testPath) |
| 964 | |
| 965 | fullPath = os.path.join(self.basePath, self.testPath) |
| 966 | os.makedirs(fullPath, exist_ok=True) |
| 967 | self.ser = ts.TosaSerializer(fullPath) |
| 968 | |
| 969 | def getSerializer(self): |
| 970 | return self.ser |
| 971 | |
| 972 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 973 | with open( |
| 974 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 975 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 976 | fd.write(self.ser.serialize()) |
| 977 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 978 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 979 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 980 | |
| 981 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 982 | if dtype == DType.BOOL: |
| 983 | np_dt = np.bool |
| 984 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 985 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 986 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 987 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 988 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 989 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 990 | elif dtype == DType.UINT8: |
| 991 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 992 | elif dtype == DType.INT16: |
| 993 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 994 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 995 | return np.int32( |
| 996 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 997 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 998 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 999 | return np.int64( |
| 1000 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 1001 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1002 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1003 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1004 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1005 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1006 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1007 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1008 | placeholders = [] |
| 1009 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1010 | assert len(shape_list) == len(dtype_list) |
| 1011 | |
| 1012 | for idx, shape in enumerate(shape_list): |
| 1013 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1014 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1015 | |
| 1016 | return placeholders |
| 1017 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1018 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1019 | consts = [] |
| 1020 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1021 | assert len(shape_list) == len(dtype_list) |
| 1022 | |
| 1023 | for idx, shape in enumerate(shape_list): |
| 1024 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1025 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1026 | |
| 1027 | return consts |
| 1028 | |
| 1029 | def makeShape(self, rank): |
| 1030 | if self.targetted_shape: |
| 1031 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1032 | return np.int32( |
| 1033 | self.rng.integers( |
| 1034 | low=self.args.tensor_shape_range[0], |
| 1035 | high=self.args.tensor_shape_range[1], |
| 1036 | size=rank, |
| 1037 | ) |
| 1038 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1039 | |
| 1040 | def setTargetShape(self, shape): |
| 1041 | self.targetted_shape = shape |
| 1042 | |
| 1043 | def randInt(self, low=0, high=256): |
| 1044 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 1045 | |
| 1046 | def getRandNumberDType(self, dtype): |
| 1047 | if dtype == DType.FLOAT: |
| 1048 | return self.rng.random() |
| 1049 | elif dtype == DType.BOOL: |
| 1050 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1051 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1052 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1053 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1054 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1055 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1056 | elif dtype == DType.INT16: |
| 1057 | low, high = (-32768, 32768) |
| 1058 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1059 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1060 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1061 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1062 | # Special size |
| 1063 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 1064 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1065 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1066 | |
| 1067 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 1068 | |
| 1069 | def shapeStr(self, shape): |
| 1070 | |
| 1071 | sStr = [] |
| 1072 | # Convert to strings |
| 1073 | for i in shape: |
| 1074 | sStr.append(str(i)) |
| 1075 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1076 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1077 | |
| 1078 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1079 | if isinstance(t, list): |
| 1080 | assert len(t) >= 2 |
| 1081 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1082 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1083 | if t == DType.BOOL: |
| 1084 | return "b" |
| 1085 | elif t == DType.INT4: |
| 1086 | return "i4" |
| 1087 | elif t == DType.INT8: |
| 1088 | return "i8" |
| 1089 | elif t == DType.UINT8: |
| 1090 | return "u8" |
| 1091 | elif t == DType.INT16: |
| 1092 | return "i16" |
| 1093 | elif t == DType.INT32: |
| 1094 | return "i32" |
| 1095 | elif t == DType.INT48: |
| 1096 | return "i48" |
| 1097 | elif t == DType.FLOAT: |
| 1098 | return "float" |
| 1099 | else: |
| 1100 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1101 | |
| 1102 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1103 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1104 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1105 | return 4 |
| 1106 | elif t == DType.INT8: |
| 1107 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1108 | elif t == DType.UINT8: |
| 1109 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1110 | elif t == DType.INT16: |
| 1111 | return 16 |
| 1112 | elif t == DType.INT32: |
| 1113 | return 32 |
| 1114 | elif t == DType.INT48: |
| 1115 | return 48 |
| 1116 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1117 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1118 | |
| 1119 | # Argument generators |
| 1120 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 1121 | # Where the string descriptor is used to generate the test name and |
| 1122 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 1123 | # build function |
| 1124 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1125 | def build_unary(self, op, a, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1126 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1127 | self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) |
| 1128 | return result_tens |
| 1129 | |
| 1130 | def build_binary_broadcast(self, op, a, b): |
| 1131 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1132 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1133 | return result_tens |
| 1134 | |
| 1135 | def build_binary_nonbroadcast(self, op, a, b): |
| 1136 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
| 1137 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1138 | return result_tens |
| 1139 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1140 | def build_arithmetic_right_shift(self, op, a, b, round): |
| 1141 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1142 | |
| 1143 | attr = ts.TosaSerializerAttribute() |
| 1144 | attr.ArithmeticRightShiftAttribute(round) |
| 1145 | |
| 1146 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
| 1147 | return result_tens |
| 1148 | |
| 1149 | def build_mul(self, op, a, b, shift): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1150 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1151 | |
| 1152 | # Special for multiply: |
| 1153 | # Force the result to INT32 for INT types |
| 1154 | if a.dtype != DType.FLOAT: |
| 1155 | result_tens.setDtype(DType.INT32) |
| 1156 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1157 | attr = ts.TosaSerializerAttribute() |
| 1158 | attr.MulAttribute(shift) |
| 1159 | |
| 1160 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1161 | return result_tens |
| 1162 | |
| 1163 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1164 | # Constant size depending on type, random values |
| 1165 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1166 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1167 | table_arr = self.getRandTensor([513], table_dtype) |
| 1168 | else: |
| 1169 | assert a.dtype == DType.INT8 |
| 1170 | table_dtype = DType.INT8 |
| 1171 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1172 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1173 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 1174 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1175 | self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None) |
| 1176 | |
| 1177 | return result_tens |
| 1178 | |
| 1179 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1180 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
| 1181 | 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] | 1182 | return result_tens |
| 1183 | |
| 1184 | def build_comparison(self, op, a, b): |
| 1185 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
| 1186 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name]) |
| 1187 | return result_tens |
| 1188 | |
| 1189 | def build_argmax(self, op, a, axis): |
| 1190 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 1191 | |
| 1192 | attr = ts.TosaSerializerAttribute() |
| 1193 | attr.AxisAttribute(axis) |
| 1194 | |
| 1195 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1196 | return result_tens |
| 1197 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1198 | def build_pool2d(self, op, input, kernel, stride, pad, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1199 | result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) |
| 1200 | |
| 1201 | attr = ts.TosaSerializerAttribute() |
| 1202 | attr.Pool2dAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1203 | |
| 1204 | self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo) |
| 1205 | return result_tens |
| 1206 | |
| 1207 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1208 | assert len(padding) == 4 |
| 1209 | result_tens = OutputShaper.conv2dOp( |
| 1210 | self.ser, ifm, filter, strides, padding, dilations |
| 1211 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1212 | |
| 1213 | attr = ts.TosaSerializerAttribute() |
| 1214 | attr.Conv2dAttribute(padding, strides, dilations) |
| 1215 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1216 | self.ser.addOperator( |
| 1217 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1218 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1219 | return result_tens |
| 1220 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1221 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1222 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1223 | ): |
| 1224 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1225 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 1226 | |
| 1227 | attr = ts.TosaSerializerAttribute() |
| 1228 | attr.TransposeConv2DAttribute(outpad, stride, dilation, output_shape) |
| 1229 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1230 | self.ser.addOperator( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1231 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1232 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1233 | return result_tens |
| 1234 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1235 | def build_depthwise_conv2d( |
| 1236 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 1237 | ): |
| 1238 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 1239 | self.ser, ifm, filter, strides, padding, dilations |
| 1240 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1241 | |
| 1242 | attr = ts.TosaSerializerAttribute() |
| 1243 | attr.Conv2dAttribute(padding, strides, dilations) |
| 1244 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1245 | self.ser.addOperator( |
| 1246 | op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
| 1247 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1248 | return result_tens |
| 1249 | |
| 1250 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 1251 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 1252 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1253 | self.ser.addOperator( |
| 1254 | op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo |
| 1255 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1256 | return result_tens |
| 1257 | |
| 1258 | def build_matmul(self, op, a, b, qinfo): |
| 1259 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
| 1260 | self.ser.addOperator(op, [a.name, b.name], [result_tens.name], None, qinfo) |
| 1261 | return result_tens |
| 1262 | |
| 1263 | def build_reduce(self, op, a, axis): |
| 1264 | result_tens = OutputShaper.reduceOp(self.ser, a, axis) |
| 1265 | |
| 1266 | attr = ts.TosaSerializerAttribute() |
| 1267 | attr.AxisAttribute(axis) |
| 1268 | |
| 1269 | self.ser.addOperator(op, [a.name], result_tens.name, attr) |
| 1270 | return result_tens |
| 1271 | |
| 1272 | def build_clamp(self, op, a): |
| 1273 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1274 | |
| 1275 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1276 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1277 | |
| 1278 | if a.dtype == DType.FLOAT: |
| 1279 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 1280 | else: |
| 1281 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 1282 | |
| 1283 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1284 | return result_tens |
| 1285 | |
| 1286 | def build_leaky_relu(self, op, a): |
| 1287 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1288 | attr = ts.TosaSerializerAttribute() |
| 1289 | |
| 1290 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 1291 | |
| 1292 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1293 | return result_tens |
| 1294 | |
| 1295 | # Needs an additional type/input |
| 1296 | def build_prelu(self, op, a): |
| 1297 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1298 | |
| 1299 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1300 | return result_tens |
| 1301 | |
| 1302 | def build_relun(self, op, a): |
| 1303 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1304 | |
| 1305 | attr = ts.TosaSerializerAttribute() |
| 1306 | |
| 1307 | if a.dtype == DType.FLOAT: |
| 1308 | attr.ReluNAttribute(0, self.getRandNumberDType(a.dtype)) |
| 1309 | else: |
| 1310 | attr.ReluNAttribute(self.getRandNumberDType(a.dtype), 0) |
| 1311 | |
| 1312 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1313 | return result_tens |
| 1314 | |
| 1315 | def build_sigmoid(self, op, a): |
| 1316 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1317 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1318 | return result_tens |
| 1319 | |
| 1320 | def build_tanh(self, op, a): |
| 1321 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1322 | self.ser.addOperator(op, [a.name], [result_tens.name]) |
| 1323 | return result_tens |
| 1324 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1325 | def build_concat(self, op, *a): |
| 1326 | assert (type(a[-1]) == int) |
| 1327 | |
| 1328 | # To store variable length list of input tensors we need to store axis along with it |
| 1329 | axis = a[-1] |
| 1330 | a = a[:-1] |
| 1331 | |
| 1332 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1333 | |
| 1334 | attr = ts.TosaSerializerAttribute() |
| 1335 | attr.AxisAttribute(axis) |
| 1336 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1337 | input_tensor_names = [] |
| 1338 | for tensor in a: |
| 1339 | input_tensor_names.append(tensor.name) |
| 1340 | |
| 1341 | self.ser.addOperator(op, input_tensor_names, [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1342 | |
| 1343 | def build_pad(self, op, a, padding, qinfo): |
| 1344 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 1345 | |
| 1346 | # Need to turn the padding array into a TOSA tensor here. |
| 1347 | # This is one of the few tensor operands that does not get |
| 1348 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1349 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1350 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1351 | self.ser.addOperator( |
| 1352 | op, [a.name, padding_tens.name], [result_tens.name], None, qinfo |
| 1353 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1354 | |
| 1355 | def build_reshape(self, op, a, newShape): |
| 1356 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 1357 | |
| 1358 | attr = ts.TosaSerializerAttribute() |
| 1359 | attr.ReshapeAttribute(newShape) |
| 1360 | |
| 1361 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1362 | return result_tens |
| 1363 | |
| 1364 | def build_reverse(self, op, a, axis): |
| 1365 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 1366 | |
| 1367 | attr = ts.TosaSerializerAttribute() |
| 1368 | attr.AxisAttribute(axis) |
| 1369 | |
| 1370 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1371 | return result_tens |
| 1372 | |
| 1373 | def build_transpose(self, op, a, perms): |
| 1374 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 1375 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1376 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1377 | |
| 1378 | self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name]) |
| 1379 | return result_tens |
| 1380 | |
| 1381 | def build_slice(self, op, a, begin, size): |
| 1382 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 1383 | |
| 1384 | attr = ts.TosaSerializerAttribute() |
| 1385 | attr.SliceAttribute(begin, size) |
| 1386 | |
| 1387 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1388 | return result_tens |
| 1389 | |
| 1390 | def build_tile(self, op, a, multiples): |
| 1391 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 1392 | |
| 1393 | attr = ts.TosaSerializerAttribute() |
| 1394 | attr.TileAttribute(multiples) |
| 1395 | |
| 1396 | self.ser.addOperator(op, [a.name], [result_tens.name], attr) |
| 1397 | return result_tens |
| 1398 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1399 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1400 | |
| 1401 | # Create a new indicies tensor |
| 1402 | # here with data that doesn't exceed the dimensions of the values tensor |
| 1403 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1404 | K = values.shape[1] # K |
| 1405 | W = self.randInt( |
| 1406 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 1407 | ) # W |
| 1408 | indicies_arr = np.int32( |
| 1409 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 1410 | ) # (N, W) |
| 1411 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1412 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1413 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1414 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1415 | self.ser.addOperator(op, [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1416 | |
| 1417 | return result_tens |
| 1418 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1419 | def build_scatter(self, op, values_in, input): |
| 1420 | |
| 1421 | # Create a new indicies tensor |
| 1422 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 1423 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1424 | K = values_in.shape[1] # K |
| 1425 | W = input.shape[1] # W |
| 1426 | indicies_arr = np.int32( |
| 1427 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 1428 | ) # (N, W) |
| 1429 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1430 | |
| 1431 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 1432 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1433 | self.ser.addOperator( |
| 1434 | op, [values_in.name, indicies.name, input.name], [result_tens.name] |
| 1435 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1436 | |
| 1437 | return result_tens |
| 1438 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1439 | def build_resize( |
| 1440 | self, |
| 1441 | op, |
| 1442 | input, |
| 1443 | mode, |
| 1444 | stride, |
| 1445 | offset, |
| 1446 | shift, |
| 1447 | stride_fp, |
| 1448 | offset_fp, |
| 1449 | output_dims, |
| 1450 | input_dtype, |
| 1451 | output_dtype, |
| 1452 | ): |
| 1453 | result_tens = OutputShaper.resizeOp( |
| 1454 | self.ser, |
| 1455 | input, |
| 1456 | mode, |
| 1457 | stride, |
| 1458 | offset, |
| 1459 | shift, |
| 1460 | stride_fp, |
| 1461 | offset_fp, |
| 1462 | output_dims, |
| 1463 | input_dtype, |
| 1464 | output_dtype, |
| 1465 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1466 | |
| 1467 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1468 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1469 | attr.ResizeAttribute( |
| 1470 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 1471 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1472 | |
| 1473 | self.ser.addOperator(op, [input.name], [result_tens.name], attr) |
| 1474 | return result_tens |
| 1475 | |
| 1476 | def build_identityn(self, op, val, val2): |
| 1477 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1478 | result_tens = OutputShaper.unaryOp(self.ser, val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1479 | result_tens2 = OutputShaper.unaryOp(self.ser, val2) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1480 | self.ser.addOperator( |
| 1481 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 1482 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1483 | return result_tens |
| 1484 | |
| 1485 | def build_placeholder(self, op, val): |
| 1486 | # Add an identity op to avoid warning in the reference model |
| 1487 | return self.build_unary(Op.IDENTITY, val) |
| 1488 | |
| 1489 | # Type Conversion |
| 1490 | def build_cast(self, op, val, out_dtype): |
| 1491 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 1492 | self.ser.addOperator(op, [val.name], [result_tens.name]) |
| 1493 | return result_tens |
| 1494 | |
| 1495 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel): |
| 1496 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 1497 | |
| 1498 | if per_channel: |
| 1499 | nc = val.shape[-1] |
| 1500 | else: |
| 1501 | nc = 1 |
| 1502 | |
| 1503 | in_type_width = self.typeWidth(val.dtype) |
| 1504 | out_type_width = self.typeWidth(out_dtype) |
| 1505 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1506 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 1507 | input_zp = self.randInt(-128, 128) |
| 1508 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1509 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 1510 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1511 | in_type_width = in_type_width + 1 |
| 1512 | else: |
| 1513 | input_zp = 0 |
| 1514 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1515 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 1516 | output_zp = self.randInt(-128, 128) |
| 1517 | out_type_width = out_type_width + 1 |
| 1518 | elif out_dtype == DType.UINT8: |
| 1519 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1520 | out_type_width = out_type_width + 1 |
| 1521 | else: |
| 1522 | output_zp = 0 |
| 1523 | |
| 1524 | # Calculate scale based on: |
| 1525 | # scale = a *(2^output_width)/(2^input_width)) |
| 1526 | |
| 1527 | a = np.float32(self.rng.random(size=[nc])) |
| 1528 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 1529 | |
| 1530 | if scale32: |
| 1531 | pass |
| 1532 | # Cap the scaling at 2^15 - 1 for scale16 |
| 1533 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 1534 | else: |
| 1535 | # Cap the scaling at 2^15 - 1 for scale16 |
| 1536 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 1537 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1538 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1539 | |
| 1540 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 1541 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 1542 | |
| 1543 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1544 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 1545 | scale_arr[i], scale32 |
| 1546 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1547 | if shift_arr[i] < 2 or shift_arr[i] > 62: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1548 | self.ser.setExpectedReturnCode( |
| 1549 | TosaReturnCode.UNPREDICTABLE, "OpRescale: invalid shift value" |
| 1550 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1551 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1552 | # 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] | 1553 | |
| 1554 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1555 | attr.RescaleAttribute( |
| 1556 | input_zp, |
| 1557 | output_zp, |
| 1558 | multiplier_arr, |
| 1559 | shift_arr, |
| 1560 | scale32, |
| 1561 | double_round, |
| 1562 | per_channel, |
| 1563 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1564 | |
| 1565 | self.ser.addOperator(op, [val.name], [result_tens.name], attr) |
| 1566 | return result_tens |
| 1567 | |
| 1568 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 1569 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 1570 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 1571 | # and fill them with const nodes for the body. |
| 1572 | |
| 1573 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1574 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1575 | |
| 1576 | # Make then/else tensors |
| 1577 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1578 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 1579 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1580 | |
| 1581 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1582 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1583 | |
| 1584 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1585 | then_block = "THEN_BLOCK" |
| 1586 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1587 | attr = ts.TosaSerializerAttribute() |
| 1588 | attr.CondIfAttribute(then_block, else_block) |
| 1589 | |
| 1590 | # Finally, build the op and the two blocks |
| 1591 | self.ser.addOperator(op, [cond_tens.name], [result_tens.name], attr) |
| 1592 | |
| 1593 | self.ser.startBasicBlock(then_block) |
| 1594 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1595 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1596 | self.ser.addOutputTensor(then_tens) |
| 1597 | |
| 1598 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1599 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1600 | self.ser.addOutputTensor(else_tens) |
| 1601 | |
| 1602 | return result_tens |
| 1603 | |
| 1604 | def build_cond_if_binary(self, op, a, b, cond): |
| 1605 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 1606 | # alternately add or subtract them based on the condition |
| 1607 | |
| 1608 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1609 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1610 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1611 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1612 | self.ser.currBasicBlock.addOutput(result_tens.name) |
| 1613 | |
| 1614 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1615 | then_block = "THEN_BLOCK" |
| 1616 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1617 | attr = ts.TosaSerializerAttribute() |
| 1618 | attr.CondIfAttribute(then_block, else_block) |
| 1619 | |
| 1620 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1621 | self.ser.addOperator( |
| 1622 | op, [cond_tens.name, a.name, b.name], [result_tens.name], attr |
| 1623 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1624 | |
| 1625 | self.ser.startBasicBlock(then_block) |
| 1626 | self.ser.addInputTensor(a) |
| 1627 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1628 | then_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1629 | self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) |
| 1630 | |
| 1631 | self.ser.startBasicBlock(else_block) |
| 1632 | self.ser.addInputTensor(a) |
| 1633 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1634 | else_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1635 | self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) |
| 1636 | |
| 1637 | return result_tens |
| 1638 | |
| 1639 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1640 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1641 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1642 | cond_block = "COND_BLOCK" |
| 1643 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1644 | |
| 1645 | attr = ts.TosaSerializerAttribute() |
| 1646 | attr.WhileLoopAttribute(cond_block, body_block) |
| 1647 | |
| 1648 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1649 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1650 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1651 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1652 | |
| 1653 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1654 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 1655 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 1656 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1657 | |
| 1658 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1659 | self.ser.addOperator( |
| 1660 | op, |
| 1661 | [iter.name, a.name, acc.name], |
| 1662 | [iter_out.name, a_out.name, acc_out.name], |
| 1663 | attr, |
| 1664 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1665 | |
| 1666 | # COND block (input: iter, output: cond_tens ) |
| 1667 | self.ser.startBasicBlock(cond_block) |
| 1668 | self.ser.addInputTensor(iter) |
| 1669 | self.ser.addInputTensor(a) |
| 1670 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1671 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 1672 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 1673 | 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] | 1674 | |
| 1675 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 1676 | # Note that local intermediate tensors need to be declared here for the outputs |
| 1677 | self.ser.startBasicBlock(body_block) |
| 1678 | self.ser.addInputTensor(iter) |
| 1679 | self.ser.addInputTensor(a) |
| 1680 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1681 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 1682 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 1683 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1684 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 1685 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 1686 | self.ser.addOutputTensor(iter_body_out) |
| 1687 | self.ser.addOutputTensor(a) |
| 1688 | self.ser.addOutputTensor(acc_body_out) |
| 1689 | |
| 1690 | return acc_out |
| 1691 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1692 | def genOpTestList( |
| 1693 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None |
| 1694 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1695 | |
| 1696 | try: |
| 1697 | op = self.TOSA_OP_LIST[opName] |
| 1698 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1699 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1700 | |
| 1701 | # Initialize a new random number generator |
| 1702 | self.rng = np.random.default_rng(self.random_seed) |
| 1703 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1704 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1705 | |
| 1706 | # Generate the lists of arguments |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1707 | rmin, rmax = op["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1708 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1709 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 1710 | default_test_rank_range = range(1, 5) |
| 1711 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1712 | # Test list consists of a tuple of: |
| 1713 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 1714 | testList = [] |
| 1715 | |
| 1716 | if not shapeFilter: |
| 1717 | shapeFilter = [None] |
| 1718 | |
| 1719 | for r in range(rmin, rmax + 1): |
| 1720 | |
| 1721 | # Filter out the rank? |
| 1722 | if rankFilter is not None and r not in rankFilter: |
| 1723 | continue |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1724 | if ( |
| 1725 | rankFilter is None |
| 1726 | and shapeFilter[0] is None |
| 1727 | and r not in default_test_rank_range |
| 1728 | ): |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1729 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1730 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1731 | for t in op["types"]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1732 | |
| 1733 | # Filter tests based on dtype? |
| 1734 | if dtypeFilter is not None: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1735 | if not ( |
| 1736 | t in dtypeFilter |
| 1737 | or (isinstance(t, list) and t[0] in dtypeFilter) |
| 1738 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1739 | continue |
| 1740 | |
| 1741 | # Create the placeholder and const tensors |
| 1742 | for shape in shapeFilter: |
| 1743 | # A None shape chooses a random shape of a given rank |
| 1744 | |
| 1745 | # Filter out by rank |
| 1746 | if shape is not None and len(shape) != r: |
| 1747 | continue |
| 1748 | |
| 1749 | self.setTargetShape(shape) |
| 1750 | shapeList = tgen_fcn(self, op, r) |
| 1751 | |
| 1752 | shapeStr = self.shapeStr(shapeList[0]) |
| 1753 | typeStr = self.typeStr(t) |
| 1754 | |
| 1755 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 1756 | argList = [] |
| 1757 | if agen_fcn: |
| 1758 | argList = agen_fcn(self, opName, shapeList, t) |
| 1759 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1760 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1761 | |
| 1762 | for argStr, args in argList: |
| 1763 | if argStr: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1764 | testStr = "{}_{}_{}_{}".format( |
| 1765 | opName, shapeStr, typeStr, argStr |
| 1766 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1767 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1768 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1769 | |
| 1770 | testList.append((opName, testStr, t, shapeList, args)) |
| 1771 | |
| 1772 | return testList |
| 1773 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1774 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1775 | try: |
| 1776 | op = self.TOSA_OP_LIST[opName] |
| 1777 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1778 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1779 | |
| 1780 | # Create a serializer |
| 1781 | self.createSerializer(opName, testStr) |
| 1782 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1783 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
| 1784 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1785 | num_operands = pCount + cCount |
| 1786 | |
| 1787 | if isinstance(dtype_or_dtypeList, list): |
| 1788 | dtypeList = dtype_or_dtypeList |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1789 | elif op['op'] == Op.CONCAT: |
| 1790 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1791 | else: |
| 1792 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 1793 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1794 | if op['op'] != Op.CONCAT: |
| 1795 | assert ( |
| 1796 | len(shapeList) == num_operands |
| 1797 | ), "shapeList length {} must match number of operands {}".format( |
| 1798 | len(shapeList), num_operands |
| 1799 | ) |
| 1800 | assert ( |
| 1801 | len(dtypeList) == num_operands |
| 1802 | ), "dtypeList length {} must match number of operands {}".format( |
| 1803 | len(dtypeList), num_operands |
| 1804 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1805 | |
| 1806 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1807 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1808 | except KeyError: |
| 1809 | qgen = None |
| 1810 | |
| 1811 | # Build the random tensor operands and the test |
| 1812 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1813 | |
| 1814 | # If test is ArithmeticRightShift, force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1815 | if op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 1816 | assert ( |
| 1817 | pCount == 2 and cCount == 0 |
| 1818 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1819 | |
| 1820 | placeholders = [] |
| 1821 | for idx, shape in enumerate(shapeList[:]): |
| 1822 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1823 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1824 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1825 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1826 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1827 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1828 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 1829 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1830 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1831 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 1832 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1833 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1834 | |
| 1835 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 1836 | elif op["op"] == Op.SELECT: |
| 1837 | # Set datatype of condition tensor to boolean |
| 1838 | dtypeList[0] = DType.BOOL |
| 1839 | tens.extend( |
| 1840 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 1841 | ) |
| 1842 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 1843 | elif op["op"] == Op.DIV: |
| 1844 | assert ( |
| 1845 | pCount == 2 and cCount == 0 |
| 1846 | ), "Op.Div must have 2 placeholders, 0 consts" |
| 1847 | |
| 1848 | placeholders = [] |
| 1849 | |
| 1850 | # Two invalid cases for Op.DIV: |
| 1851 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 1852 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 1853 | while True: |
| 1854 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 1855 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 1856 | |
| 1857 | if (divisor_arr == 0).any(): |
| 1858 | continue |
| 1859 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 1860 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 1861 | continue |
| 1862 | |
| 1863 | break |
| 1864 | |
| 1865 | placeholders.append( |
| 1866 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 1867 | ) |
| 1868 | placeholders.append( |
| 1869 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 1870 | ) |
| 1871 | |
| 1872 | tens.extend(placeholders) |
| 1873 | elif op["op"] == Op.MUL: |
| 1874 | assert ( |
| 1875 | pCount == 2 and cCount == 0 |
| 1876 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 1877 | |
| 1878 | if dtypeList[0] == DType.FLOAT: |
| 1879 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 1880 | else: |
| 1881 | placeholders = [] |
| 1882 | |
| 1883 | # Make sure multiply result in int32 range |
| 1884 | shift = testArgs[0] |
| 1885 | if dtypeList[0] == DType.INT8: |
| 1886 | num_bits = 8 |
| 1887 | elif dtypeList[0] == DType.INT16: |
| 1888 | num_bits = 16 |
| 1889 | elif dtypeList[0] == DType.INT32: |
| 1890 | num_bits = 32 |
| 1891 | else: |
| 1892 | raise Exception("OpMul: invalid input dtype") |
| 1893 | |
| 1894 | for idx, shape in enumerate(shapeList[:]): |
| 1895 | low = -(2 ** (num_bits - 1)) |
| 1896 | high = (2 ** (num_bits - 1)) - 1 |
| 1897 | |
| 1898 | a_arr = np.int32( |
| 1899 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 1900 | ) |
| 1901 | b_arr = np.int32( |
| 1902 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 1903 | ) |
| 1904 | |
| 1905 | i = 0 |
| 1906 | while True: |
| 1907 | |
| 1908 | a_arr_64 = a_arr.astype(np.int64) |
| 1909 | b_arr_64 = b_arr.astype(np.int64) |
| 1910 | |
| 1911 | if shift > 0: |
| 1912 | rounding = 1 << (shift - 1) |
| 1913 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 1914 | else: |
| 1915 | result_arr = a_arr_64 * b_arr_64 |
| 1916 | |
| 1917 | if (result_arr > -(2 ** 31)).all() and ( |
| 1918 | result_arr <= ((2 ** 31) - 1) |
| 1919 | ).all(): |
| 1920 | break |
| 1921 | |
| 1922 | i = i + 1 |
| 1923 | a_arr = a_arr // 2 |
| 1924 | b_arr = b_arr // 2 |
| 1925 | |
| 1926 | placeholders.append( |
| 1927 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 1928 | ) |
| 1929 | placeholders.append( |
| 1930 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 1931 | ) |
| 1932 | |
| 1933 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 1934 | elif op["op"] == Op.CONCAT: |
| 1935 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 1936 | if count < 1: |
| 1937 | count = 1 |
| 1938 | if self.args.num_const_inputs_concat == 0: |
| 1939 | count = len(shapeList) |
| 1940 | |
| 1941 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 1942 | tens.extend( |
| 1943 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 1944 | ) |
| 1945 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1946 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1947 | tens.extend( |
| 1948 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 1949 | ) |
| 1950 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1951 | |
| 1952 | if qgen is not None: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 1953 | qinfo = qgen(self, op, dtype_or_dtypeList) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1954 | else: |
| 1955 | qinfo = None |
| 1956 | |
| 1957 | try: |
| 1958 | if qinfo is not None: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1959 | resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1960 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1961 | resultName = build_fcn(self, op["op"], *tens, *testArgs) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1962 | except TypeError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1963 | print( |
| 1964 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 1965 | build_fcn, tens, testArgs |
| 1966 | ) |
| 1967 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1968 | raise e |
| 1969 | |
| 1970 | # Save the serialized test |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1971 | self.serialize("test") |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1972 | |
| 1973 | def createDynamicOpLists(self): |
| 1974 | |
| 1975 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1976 | KERNELS = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1977 | |
| 1978 | for k in KERNELS: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1979 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 1980 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 1981 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 1982 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1983 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1984 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 1985 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 1986 | "depthwise_conv2d_TEMPLATE" |
| 1987 | ].copy() |
| 1988 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 1989 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1990 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1991 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 1992 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 1993 | "transpose_conv2d_TEMPLATE" |
| 1994 | ].copy() |
| 1995 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 1996 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1997 | |
| 1998 | # Delete any templates after having created any dynamic ops |
| 1999 | # This is a two-pass operation because it's bad practice to delete |
| 2000 | # keys from dictionaries while iterating |
| 2001 | keyList = [] |
| 2002 | for k in self.TOSA_OP_LIST: |
| 2003 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2004 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2005 | keyList.append(k) |
| 2006 | continue |
| 2007 | except KeyError: |
| 2008 | pass |
| 2009 | |
| 2010 | for k in keyList: |
| 2011 | del self.TOSA_OP_LIST[k] |
| 2012 | |
| 2013 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2014 | """Fill in default fields for ops if they aren't already specified. |
| 2015 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2016 | for op in self.TOSA_OP_LIST: |
| 2017 | |
| 2018 | # Required fields |
| 2019 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2020 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2021 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2022 | raise Exception( |
| 2023 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 2024 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2025 | |
| 2026 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2027 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2028 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2029 | raise Exception( |
| 2030 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 2031 | op |
| 2032 | ) |
| 2033 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2034 | |
| 2035 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2036 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2037 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2038 | raise Exception( |
| 2039 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 2040 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2041 | |
| 2042 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2043 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2044 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2045 | raise Exception( |
| 2046 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 2047 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2048 | |
| 2049 | # Put in default rank range, if missing |
| 2050 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2051 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2052 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2053 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2054 | |
| 2055 | # Tensor operator list |
| 2056 | # 'op': op name |
| 2057 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2058 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 2059 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2060 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 2061 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2062 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2063 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2064 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 2065 | 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] | 2066 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2067 | TYPE_BOOL = [DType.BOOL] |
| 2068 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 2069 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 2070 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2071 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2072 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2073 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2074 | TYPE_CONV2D = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2075 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2076 | [DType.INT8, DType.INT8, DType.INT32], |
| 2077 | [DType.INT16, DType.INT8, DType.INT48], |
| 2078 | DType.FLOAT, |
| 2079 | ] |
| 2080 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2081 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2082 | |
| 2083 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2084 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2085 | "argmax": { |
| 2086 | "op": Op.ARGMAX, |
| 2087 | "operands": (1, 0), |
| 2088 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2089 | "types": TYPE_NARROW_INT_FP, |
| 2090 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2091 | "avg_pool2d": { |
| 2092 | "op": Op.AVG_POOL2D, |
| 2093 | "operands": (1, 0), |
| 2094 | "rank": (4, 4), |
| 2095 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2096 | "qgen": TosaQuantGen.qgUnary, |
| 2097 | "types": TYPE_NARROW_INT_FP, |
| 2098 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2099 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2100 | "conv2d_TEMPLATE": { |
| 2101 | "op": Op.CONV2D, |
| 2102 | "operands": (1, 2), |
| 2103 | "rank": (4, 4), |
| 2104 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), |
| 2105 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2106 | "types": TYPE_CONV2D, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2107 | "template": True, |
| 2108 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2109 | # Conv3d TBD |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2110 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2111 | "depthwise_conv2d_TEMPLATE": { |
| 2112 | "op": Op.DEPTHWISE_CONV2D, |
| 2113 | "operands": (1, 2), |
| 2114 | "filter": [1, 1], |
| 2115 | "rank": (4, 4), |
| 2116 | "build_fcn": ( |
| 2117 | build_depthwise_conv2d, |
| 2118 | TosaTensorGen.tgDepthwiseConv2D, |
| 2119 | TosaArgGen.agConv2D, |
| 2120 | ), |
| 2121 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2122 | "types": TYPE_CONV2D, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2123 | "template": True, |
| 2124 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2125 | "fully_connected": { |
| 2126 | "op": Op.FULLY_CONNECTED, |
| 2127 | "operands": (1, 2), |
| 2128 | "rank": (2, 2), |
| 2129 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 2130 | "qgen": TosaQuantGen.qgConv, |
| 2131 | "types": TYPE_CONV2D, |
| 2132 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2133 | "matmul": { |
| 2134 | "op": Op.MATMUL, |
| 2135 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 2136 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2137 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 2138 | "qgen": TosaQuantGen.qgMatmul, |
| 2139 | "types": TYPE_NARROW_INT_FP, |
| 2140 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2141 | "max_pool2d": { |
| 2142 | "op": Op.MAX_POOL2D, |
| 2143 | "operands": (1, 0), |
| 2144 | "rank": (4, 4), |
| 2145 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 2146 | "types": TYPE_NARROW_INT_FP, |
| 2147 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2148 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2149 | "transpose_conv2d_TEMPLATE": { |
| 2150 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2151 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2152 | "rank": (4, 4), |
| 2153 | "build_fcn": ( |
| 2154 | build_transpose_conv2d, |
| 2155 | TosaTensorGen.tgTransposeConv2D, |
| 2156 | TosaArgGen.agTransposeConv2D, |
| 2157 | ), |
| 2158 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2159 | "types": TYPE_CONV2D, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2160 | "template": True, |
| 2161 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2162 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2163 | "clamp": { |
| 2164 | "op": Op.CLAMP, |
| 2165 | "operands": (1, 0), |
| 2166 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 2167 | "types": TYPE_NARROW_INT_FP, |
| 2168 | }, |
| 2169 | "relun": { |
| 2170 | "op": Op.RELUN, |
| 2171 | "operands": (1, 0), |
| 2172 | "build_fcn": (build_relun, TosaTensorGen.tgBasic, None), |
| 2173 | "types": TYPE_FI32, |
| 2174 | }, |
| 2175 | "sigmoid": { |
| 2176 | "op": Op.SIGMOID, |
| 2177 | "operands": (1, 0), |
| 2178 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 2179 | "types": TYPE_FP, |
| 2180 | }, |
| 2181 | "tanh": { |
| 2182 | "op": Op.TANH, |
| 2183 | "operands": (1, 0), |
| 2184 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 2185 | "types": TYPE_FP, |
| 2186 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2187 | # Elementwise Binary Operators |
| 2188 | "add": { |
| 2189 | "op": Op.ADD, |
| 2190 | "operands": (2, 0), |
| 2191 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2192 | "types": TYPE_FI32, |
| 2193 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2194 | "arithmetic_right_shift": { |
| 2195 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 2196 | "operands": (2, 0), |
| 2197 | "build_fcn": ( |
| 2198 | build_arithmetic_right_shift, |
| 2199 | TosaTensorGen.tgBroadcastFuzz, |
| 2200 | TosaArgGen.agArithmeticRightShift, |
| 2201 | ), |
| 2202 | "types": TYPE_INT, |
| 2203 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2204 | "bitwise_and": { |
| 2205 | "op": Op.BITWISE_AND, |
| 2206 | "operands": (2, 0), |
| 2207 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2208 | "types": TYPE_INT, |
| 2209 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2210 | "bitwise_or": { |
| 2211 | "op": Op.BITWISE_OR, |
| 2212 | "operands": (2, 0), |
| 2213 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2214 | "types": TYPE_INT, |
| 2215 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2216 | "bitwise_xor": { |
| 2217 | "op": Op.BITWISE_XOR, |
| 2218 | "operands": (2, 0), |
| 2219 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2220 | "types": TYPE_INT, |
| 2221 | }, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2222 | "div": { |
| 2223 | "op": Op.DIV, |
| 2224 | "operands": (2, 0), |
| 2225 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2226 | "types": [DType.INT32], |
| 2227 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2228 | "logical_and": { |
| 2229 | "op": Op.LOGICAL_AND, |
| 2230 | "operands": (2, 0), |
| 2231 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2232 | "types": TYPE_BOOL, |
| 2233 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2234 | "logical_left_shift": { |
| 2235 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 2236 | "operands": (2, 0), |
| 2237 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2238 | "types": TYPE_INT, |
| 2239 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2240 | "logical_right_shift": { |
| 2241 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 2242 | "operands": (2, 0), |
| 2243 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2244 | "types": TYPE_INT, |
| 2245 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2246 | "logical_or": { |
| 2247 | "op": Op.LOGICAL_OR, |
| 2248 | "operands": (2, 0), |
| 2249 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2250 | "types": TYPE_BOOL, |
| 2251 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2252 | "logical_xor": { |
| 2253 | "op": Op.LOGICAL_XOR, |
| 2254 | "operands": (2, 0), |
| 2255 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2256 | "types": TYPE_BOOL, |
| 2257 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2258 | "maximum": { |
| 2259 | "op": Op.MAXIMUM, |
| 2260 | "operands": (2, 0), |
| 2261 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2262 | "types": TYPE_FI32, |
| 2263 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2264 | "minimum": { |
| 2265 | "op": Op.MINIMUM, |
| 2266 | "operands": (2, 0), |
| 2267 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2268 | "types": TYPE_FI32, |
| 2269 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2270 | "mul": { |
| 2271 | "op": Op.MUL, |
| 2272 | "operands": (2, 0), |
| 2273 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 2274 | "types": TYPE_INT_FP, |
| 2275 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2276 | "pow": { |
| 2277 | "op": Op.POW, |
| 2278 | "operands": (2, 0), |
| 2279 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 2280 | "types": TYPE_FP, |
| 2281 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2282 | "sub": { |
| 2283 | "op": Op.SUB, |
| 2284 | "operands": (2, 0), |
| 2285 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 2286 | "types": TYPE_FI32, |
| 2287 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2288 | "table": { |
| 2289 | "op": Op.TABLE, |
| 2290 | # Use the automatic generation functions to create the input array |
| 2291 | # but create the table tensor in the build function, as it may be |
| 2292 | # a different type from the input |
| 2293 | "operands": (1, 0), |
| 2294 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2295 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2296 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2297 | # Elementwise Unary operators |
| 2298 | "abs": { |
| 2299 | "op": Op.ABS, |
| 2300 | "operands": (1, 0), |
| 2301 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2302 | "types": TYPE_FI32, |
| 2303 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2304 | "bitwise_not": { |
| 2305 | "op": Op.BITWISE_NOT, |
| 2306 | "operands": (1, 0), |
| 2307 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2308 | "types": TYPE_INT, |
| 2309 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2310 | "ceil": { |
| 2311 | "op": Op.CEIL, |
| 2312 | "operands": (1, 0), |
| 2313 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2314 | "types": TYPE_FP, |
| 2315 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2316 | "clz": { |
| 2317 | "op": Op.CLZ, |
| 2318 | "operands": (1, 0), |
| 2319 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2320 | "types": [DType.INT32], |
| 2321 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2322 | "exp": { |
| 2323 | "op": Op.EXP, |
| 2324 | "operands": (1, 0), |
| 2325 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2326 | "types": TYPE_FP, |
| 2327 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2328 | "floor": { |
| 2329 | "op": Op.FLOOR, |
| 2330 | "operands": (1, 0), |
| 2331 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2332 | "types": TYPE_FP, |
| 2333 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2334 | "log": { |
| 2335 | "op": Op.LOG, |
| 2336 | "operands": (1, 0), |
| 2337 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2338 | "types": TYPE_FP, |
| 2339 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2340 | "logical_not": { |
| 2341 | "op": Op.LOGICAL_NOT, |
| 2342 | "operands": (1, 0), |
| 2343 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2344 | "types": TYPE_BOOL, |
| 2345 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2346 | "negate": { |
| 2347 | "op": Op.NEGATE, |
| 2348 | "operands": (1, 0), |
| 2349 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2350 | "qgen": TosaQuantGen.qgUnary, |
| 2351 | "types": TYPE_INT_FP, |
| 2352 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2353 | "reciprocal": { |
| 2354 | "op": Op.RECIPROCAL, |
| 2355 | "operands": (1, 0), |
| 2356 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2357 | "types": TYPE_FP, |
| 2358 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2359 | "rsqrt": { |
| 2360 | "op": Op.RSQRT, |
| 2361 | "operands": (1, 0), |
| 2362 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2363 | "types": TYPE_FP, |
| 2364 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2365 | # Elementwise Ternary operators |
| 2366 | "select": { |
| 2367 | "op": Op.SELECT, |
| 2368 | "operands": (3, 0), |
| 2369 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 2370 | "types": TYPE_FIB, |
| 2371 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2372 | # Comparison operators |
| 2373 | "equal": { |
| 2374 | "op": Op.EQUAL, |
| 2375 | "operands": (2, 0), |
| 2376 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2377 | "types": TYPE_FI32, |
| 2378 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2379 | "greater_equal": { |
| 2380 | "op": Op.GREATER_EQUAL, |
| 2381 | "operands": (2, 0), |
| 2382 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2383 | "types": TYPE_FI32, |
| 2384 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2385 | "greater": { |
| 2386 | "op": Op.GREATER, |
| 2387 | "operands": (2, 0), |
| 2388 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 2389 | "types": TYPE_FI32, |
| 2390 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2391 | # Reduction operators |
| 2392 | "reduce_all": { |
| 2393 | "op": Op.REDUCE_ALL, |
| 2394 | "operands": (1, 0), |
| 2395 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2396 | "types": TYPE_BOOL, |
| 2397 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2398 | "reduce_any": { |
| 2399 | "op": Op.REDUCE_ANY, |
| 2400 | "operands": (1, 0), |
| 2401 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2402 | "types": TYPE_BOOL, |
| 2403 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2404 | "reduce_max": { |
| 2405 | "op": Op.REDUCE_MAX, |
| 2406 | "operands": (1, 0), |
| 2407 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2408 | "types": TYPE_INT_FP, |
| 2409 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2410 | "reduce_min": { |
| 2411 | "op": Op.REDUCE_MAX, |
| 2412 | "operands": (1, 0), |
| 2413 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2414 | "types": TYPE_INT_FP, |
| 2415 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2416 | "reduce_product": { |
| 2417 | "op": Op.REDUCE_PRODUCT, |
| 2418 | "operands": (1, 0), |
| 2419 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2420 | "types": TYPE_FP, |
| 2421 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2422 | "reduce_sum": { |
| 2423 | "op": Op.REDUCE_SUM, |
| 2424 | "operands": (1, 0), |
| 2425 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2426 | "types": TYPE_FI32, |
| 2427 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2428 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2429 | "concat": { |
| 2430 | "op": Op.CONCAT, |
| 2431 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2432 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2433 | "types": TYPE_FIB, |
| 2434 | }, |
| 2435 | "pad": { |
| 2436 | "op": Op.PAD, |
| 2437 | "operands": (1, 0), |
| 2438 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 2439 | "qgen": TosaQuantGen.qgPad, |
| 2440 | "types": TYPE_FIB, |
| 2441 | }, |
| 2442 | "reshape": { |
| 2443 | "op": Op.RESHAPE, |
| 2444 | "operands": (1, 0), |
| 2445 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 2446 | "types": TYPE_FIB, |
| 2447 | }, |
| 2448 | "reverse": { |
| 2449 | "op": Op.REVERSE, |
| 2450 | "operands": (1, 0), |
| 2451 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 2452 | "types": TYPE_FIB, |
| 2453 | }, |
| 2454 | "slice": { |
| 2455 | "op": Op.SLICE, |
| 2456 | "operands": (1, 0), |
| 2457 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 2458 | "types": TYPE_FIB, |
| 2459 | }, |
| 2460 | "tile": { |
| 2461 | "op": Op.TILE, |
| 2462 | "operands": (1, 0), |
| 2463 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 2464 | "types": TYPE_FIB, |
| 2465 | }, |
| 2466 | "transpose": { |
| 2467 | "op": Op.TRANSPOSE, |
| 2468 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 2469 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2470 | "build_fcn": ( |
| 2471 | build_transpose, |
| 2472 | TosaTensorGen.tgBasic, |
| 2473 | TosaArgGen.agTranspose, |
| 2474 | ), |
| 2475 | "types": TYPE_FIB, |
| 2476 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2477 | # Data nodes |
| 2478 | "const": { |
| 2479 | "op": Op.CONST, |
| 2480 | "operands": (1, 0), |
| 2481 | "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), |
| 2482 | "types": TYPE_FIB, |
| 2483 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2484 | "identity": { |
| 2485 | "op": Op.IDENTITY, |
| 2486 | "operands": (1, 0), |
| 2487 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 2488 | "types": TYPE_FIB, |
| 2489 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2490 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2491 | "gather": { |
| 2492 | "op": Op.GATHER, |
| 2493 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 2494 | "operands": (1, 0), |
| 2495 | "rank": (3, 3), |
| 2496 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 2497 | "types": TYPE_INT_FP, |
| 2498 | }, |
| 2499 | "scatter": { |
| 2500 | "op": Op.SCATTER, |
| 2501 | # Only specify 'values_in' tensor here. |
| 2502 | #'indices' and 'input' are generated in op building stage |
| 2503 | "operands": (2, 0), |
| 2504 | "rank": (3, 3), |
| 2505 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 2506 | "types": TYPE_INT_FP, |
| 2507 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2508 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2509 | "resize": { |
| 2510 | "op": Op.RESIZE, |
| 2511 | "operands": (1, 0), |
| 2512 | "rank": (4, 4), |
| 2513 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 2514 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
| 2515 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2516 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2517 | "cast": { |
| 2518 | "op": Op.CAST, |
| 2519 | "operands": (1, 0), |
| 2520 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 2521 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 2522 | }, |
| 2523 | "rescale": { |
| 2524 | "op": Op.RESCALE, |
| 2525 | "operands": (1, 0), |
| 2526 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2527 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2528 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2529 | # Custom |
| 2530 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 2531 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2532 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 2533 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 2534 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2535 | "cond_if_const": { |
| 2536 | "op": Op.COND_IF, |
| 2537 | "operands": (0, 2), |
| 2538 | "build_fcn": ( |
| 2539 | build_cond_if_const, |
| 2540 | TosaTensorGen.tgBasic, |
| 2541 | TosaArgGen.agCondIf, |
| 2542 | ), |
| 2543 | "types": [DType.BOOL], |
| 2544 | }, |
| 2545 | "cond_if_binary": { |
| 2546 | "op": Op.COND_IF, |
| 2547 | "operands": (2, 0), |
| 2548 | "build_fcn": ( |
| 2549 | build_cond_if_binary, |
| 2550 | TosaTensorGen.tgBasic, |
| 2551 | TosaArgGen.agCondIf, |
| 2552 | ), |
| 2553 | "types": TYPE_FI32, |
| 2554 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2555 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2556 | "while_loop": { |
| 2557 | "op": Op.WHILE_LOOP, |
| 2558 | "operands": (0, 1), |
| 2559 | "build_fcn": ( |
| 2560 | build_while_loop, |
| 2561 | TosaTensorGen.tgBasic, |
| 2562 | TosaArgGen.agWhileLoop, |
| 2563 | ), |
| 2564 | "types": [DType.INT32], |
| 2565 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2566 | } |
| 2567 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2568 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2569 | class OutputShaper: |
| 2570 | # Methods in this class compute the expected output shape and datatype |
| 2571 | # for common classes of operations |
| 2572 | def __init__(self): |
| 2573 | pass |
| 2574 | |
| 2575 | # These methods return arguments that can be used for |
| 2576 | # creating a new output tensor |
| 2577 | @staticmethod |
| 2578 | def binaryBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2579 | assert len(a.shape) == len(b.shape) |
| 2580 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2581 | |
| 2582 | shape = [] |
| 2583 | for i in range(len(a.shape)): |
| 2584 | if a.shape[i] == 1: |
| 2585 | shape.append(b.shape[i]) |
| 2586 | else: |
| 2587 | shape.append(a.shape[i]) |
| 2588 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2589 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2590 | |
| 2591 | @staticmethod |
| 2592 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2593 | assert len(a.shape) == len(b.shape) |
| 2594 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2595 | |
| 2596 | shape = [] |
| 2597 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2598 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2599 | shape.append(a.shape[i]) |
| 2600 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2601 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2602 | |
| 2603 | @staticmethod |
| 2604 | def unaryOp(ser, a): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2605 | return ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2606 | |
| 2607 | @staticmethod |
| 2608 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2609 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 2610 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2611 | |
| 2612 | shape = [] |
| 2613 | for i in range(len(a.shape)): |
| 2614 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 2615 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2616 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2617 | |
| 2618 | @staticmethod |
| 2619 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2620 | assert len(a.shape) == len(b.shape) |
| 2621 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2622 | |
| 2623 | # Do broadcast |
| 2624 | shape = [] |
| 2625 | for i in range(len(a.shape)): |
| 2626 | if a.shape[i] == 1: |
| 2627 | shape.append(b.shape[i]) |
| 2628 | else: |
| 2629 | shape.append(a.shape[i]) |
| 2630 | |
| 2631 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2632 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2633 | |
| 2634 | @staticmethod |
| 2635 | def reduceOp(ser, a, axis): |
| 2636 | |
| 2637 | shape = a.shape.copy() |
| 2638 | |
| 2639 | shape[axis] = 1 |
| 2640 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2641 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2642 | |
| 2643 | @staticmethod |
| 2644 | def argmaxOp(ser, a, axis): |
| 2645 | shape = a.shape.copy() |
| 2646 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2647 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2648 | |
| 2649 | @staticmethod |
| 2650 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 2651 | |
| 2652 | # IFM: NHWC |
| 2653 | # Filter: OHWI |
| 2654 | # OFM: NHWC |
| 2655 | |
| 2656 | if len(padding) == 2: |
| 2657 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 2658 | # From H,W to T,B,L,R |
| 2659 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 2660 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2661 | h = ( |
| 2662 | ifm.shape[1] |
| 2663 | - filter.shape[1] |
| 2664 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 2665 | + padding[0] |
| 2666 | + padding[1] |
| 2667 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2668 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2669 | w = ( |
| 2670 | ifm.shape[2] |
| 2671 | - filter.shape[2] |
| 2672 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 2673 | + padding[2] |
| 2674 | + padding[3] |
| 2675 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2676 | |
| 2677 | if h <= 0 or w <= 0: |
| 2678 | # Invalid test parameters? |
| 2679 | h = 0 |
| 2680 | w = 0 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2681 | ser.setExpectedReturnCode( |
| 2682 | TosaReturnCode.UNPREDICTABLE, "Invalid combination of conv2d parameters" |
| 2683 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2684 | |
| 2685 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 2686 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2687 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2688 | out_dtype = DType.INT32 |
| 2689 | elif ifm.dtype == DType.INT16: |
| 2690 | out_dtype = DType.INT48 |
| 2691 | elif ifm.dtype == DType.FLOAT: |
| 2692 | out_dtype = DType.FLOAT |
| 2693 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2694 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2695 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2696 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2697 | |
| 2698 | @staticmethod |
| 2699 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 2700 | # IFM: NHWC |
| 2701 | # Filter: HWCM |
| 2702 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2703 | h = ( |
| 2704 | ifm.shape[1] |
| 2705 | - filter.shape[0] |
| 2706 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 2707 | + padding[0] |
| 2708 | + padding[1] |
| 2709 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2710 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2711 | w = ( |
| 2712 | ifm.shape[2] |
| 2713 | - filter.shape[1] |
| 2714 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 2715 | + padding[2] |
| 2716 | + padding[3] |
| 2717 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2718 | |
| 2719 | if h <= 0 or w <= 0: |
| 2720 | # Invalid test parameters? |
| 2721 | h = 0 |
| 2722 | w = 0 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2723 | ser.setExpectedReturnCode( |
| 2724 | TosaReturnCode.UNPREDICTABLE, "Invalid combination of conv2d parameters" |
| 2725 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2726 | |
| 2727 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 2728 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2729 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2730 | out_dtype = DType.INT32 |
| 2731 | elif ifm.dtype == DType.INT16: |
| 2732 | out_dtype = DType.INT48 |
| 2733 | elif ifm.dtype == DType.FLOAT: |
| 2734 | out_dtype = DType.FLOAT |
| 2735 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2736 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2737 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2738 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2739 | |
| 2740 | @staticmethod |
| 2741 | def pool2dOp(ser, ifm, kernel, stride, pad): |
| 2742 | # input: NHWC |
| 2743 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 2744 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 2745 | |
| 2746 | if h <= 0 or w <= 0: |
| 2747 | # Invalid test parameters? |
| 2748 | h = 0 |
| 2749 | w = 0 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2750 | ser.setExpectedReturnCode( |
| 2751 | TosaReturnCode.UNPREDICTABLE, "Invalid combination of pool2d parameters" |
| 2752 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2753 | |
| 2754 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2755 | return ser.addOutput(ofm_shape, ifm.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2756 | |
| 2757 | @staticmethod |
| 2758 | def fullyConnectedOp(ser, input, filter): |
| 2759 | # input: N, IC |
| 2760 | # filter: OC, IC |
| 2761 | # output: N, OC |
| 2762 | |
| 2763 | output_shape = [input.shape[0], filter.shape[0]] |
| 2764 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2765 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2766 | out_dtype = DType.INT32 |
| 2767 | elif input.dtype == DType.INT16: |
| 2768 | out_dtype = DType.INT48 |
| 2769 | elif input.dtype == DType.FLOAT: |
| 2770 | out_dtype = DType.FLOAT |
| 2771 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2772 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2773 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2774 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2775 | |
| 2776 | @staticmethod |
| 2777 | def matmulOp(ser, a, b): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 2778 | # a: N, H, C |
| 2779 | # b: N, C, W |
| 2780 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2781 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 2782 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2783 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2784 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2785 | out_dtype = DType.INT32 |
| 2786 | elif a.dtype == DType.INT16: |
| 2787 | out_dtype = DType.INT48 |
| 2788 | elif a.dtype == DType.FLOAT: |
| 2789 | out_dtype = DType.FLOAT |
| 2790 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2791 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2792 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2793 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2794 | |
| 2795 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2796 | def concatOp(ser, axis, *a): |
| 2797 | input1 = a[0] |
| 2798 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2799 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2800 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2801 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2802 | output_shape[axis] = input1.shape[axis] |
| 2803 | |
| 2804 | for tensor in remaining_inputs: |
| 2805 | output_shape[axis] += tensor.shape[axis] |
| 2806 | |
| 2807 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2808 | |
| 2809 | @staticmethod |
| 2810 | def padOp(ser, a, padding): |
| 2811 | |
| 2812 | output_shape = a.shape.copy() |
| 2813 | |
| 2814 | for i in range(len(output_shape)): |
| 2815 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 2816 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2817 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2818 | |
| 2819 | @staticmethod |
| 2820 | def reshapeOp(ser, a, shape): |
| 2821 | output_shape = shape.copy() |
| 2822 | |
| 2823 | totalElements = 1 |
| 2824 | for i in a.shape: |
| 2825 | totalElements *= i |
| 2826 | |
| 2827 | # If there are any -1 elements, figure out what that dimension must be |
| 2828 | totalOutputElements = 1 |
| 2829 | for i in output_shape: |
| 2830 | if i != -1: |
| 2831 | totalOutputElements *= i |
| 2832 | |
| 2833 | # And fill it in |
| 2834 | for i in range(len(output_shape)): |
| 2835 | if output_shape[i] == -1: |
| 2836 | output_shape[i] = totalElements // totalOutputElements |
| 2837 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2838 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2839 | |
| 2840 | @staticmethod |
| 2841 | def sliceOp(ser, a, begin, size): |
| 2842 | |
| 2843 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2844 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2845 | |
| 2846 | @staticmethod |
| 2847 | def tileOp(ser, a, multiples): |
| 2848 | |
| 2849 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2850 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2851 | |
| 2852 | for i in range(len(output_shape)): |
| 2853 | output_shape[i] = a.shape[i] * multiples[i] |
| 2854 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2855 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2856 | |
| 2857 | @staticmethod |
| 2858 | def transposeOp(ser, a, perms): |
| 2859 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2860 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2861 | |
| 2862 | for i in range(len(output_shape)): |
| 2863 | output_shape[i] = a.shape[perms[i]] |
| 2864 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2865 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2866 | |
| 2867 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2868 | def gatherOp(ser, values, indices): |
| 2869 | assert len(values.shape) == 3 |
| 2870 | assert len(indices.shape) == 2 |
| 2871 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2872 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2873 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 2874 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2875 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2876 | |
| 2877 | @staticmethod |
| 2878 | def scatterOp(ser, values_in, indices, input): |
| 2879 | assert len(values_in.shape) == 3 |
| 2880 | assert len(indices.shape) == 2 |
| 2881 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2882 | assert values_in.shape[0] == indices.shape[0] # N |
| 2883 | assert input.shape[1] == indices.shape[1] # W |
| 2884 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2885 | |
| 2886 | output_shape = values_in.shape |
| 2887 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2888 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2889 | |
| 2890 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2891 | def tableOp(ser, input, table_dtype): |
| 2892 | # Same shape as the input, but dtype dependent on table dtype |
| 2893 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 2894 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 2895 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2896 | |
| 2897 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2898 | def resizeOp( |
| 2899 | ser, |
| 2900 | input, |
| 2901 | mode, |
| 2902 | stride, |
| 2903 | offset, |
| 2904 | shift, |
| 2905 | stride_fp, |
| 2906 | offset_fp, |
| 2907 | output_dims, |
| 2908 | input_dtype, |
| 2909 | output_dtype, |
| 2910 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2911 | |
| 2912 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] |
| 2913 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2914 | if input_dtype == DType.FLOAT: |
| 2915 | if stride_fp[0] <= 0 or stride_fp[1] <= 0: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2916 | ser.setExpectedReturnCode( |
| 2917 | TosaReturnCode.ERROR, "Negative or zero stride" |
| 2918 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2919 | else: |
| 2920 | if stride[0] <= 0 or stride[1] <= 0: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2921 | ser.setExpectedReturnCode( |
| 2922 | TosaReturnCode.ERROR, "Negative or zero stride" |
| 2923 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2924 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2925 | if mode == ResizeMode.BILINEAR: |
| 2926 | if input_dtype == DType.INT8: |
| 2927 | if output_dtype != DType.INT32: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2928 | ser.setExpectedReturnCode( |
| 2929 | TosaReturnCode.ERROR, "Invalid output data type" |
| 2930 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2931 | elif input_dtype == DType.INT16: |
| 2932 | if output_dtype != DType.INT48: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2933 | ser.setExpectedReturnCode( |
| 2934 | TosaReturnCode.ERROR, "Invalid output data type" |
| 2935 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2936 | elif input_dtype == DType.FLOAT: |
| 2937 | if output_dtype != DType.FLOAT: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2938 | ser.setExpectedReturnCode( |
| 2939 | TosaReturnCode.ERROR, "Invalid output data type" |
| 2940 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2941 | else: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2942 | ser.setExpectedReturnCode( |
| 2943 | TosaReturnCode.ERROR, "Invalid input data type" |
| 2944 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2945 | |
| 2946 | elif mode == ResizeMode.NEAREST: |
| 2947 | if input_dtype == DType.INT8: |
| 2948 | if output_dtype != DType.INT8: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2949 | ser.setExpectedReturnCode( |
| 2950 | TosaReturnCode.ERROR, "Invalid output data type" |
| 2951 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2952 | elif input_dtype == DType.INT16: |
| 2953 | if output_dtype != DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2954 | ser.setExpectedReturnCode( |
| 2955 | TosaReturnCode.ERROR, "Invalid output data type" |
| 2956 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2957 | elif input_dtype == DType.FLOAT: |
| 2958 | if output_dtype != DType.FLOAT: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2959 | ser.setExpectedReturnCode( |
| 2960 | TosaReturnCode.ERROR, "Invalid output data type" |
| 2961 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2962 | else: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2963 | ser.setExpectedReturnCode( |
| 2964 | TosaReturnCode.ERROR, "Invalid input data type" |
| 2965 | ) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2966 | |
| 2967 | else: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2968 | ser.setExpectedReturnCode(TosaReturnCode.ERROR, "Invalid resize mode") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2969 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2970 | return ser.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2971 | |
| 2972 | @staticmethod |
| 2973 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2974 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2975 | |
| 2976 | @staticmethod |
| 2977 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2978 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2979 | out_dtype = DType.INT32 |
| 2980 | elif ifm.dtype == DType.INT16: |
| 2981 | out_dtype = DType.INT48 |
| 2982 | elif ifm.dtype == DType.FLOAT: |
| 2983 | out_dtype = DType.FLOAT |
| 2984 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2985 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2986 | |
| 2987 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2988 | ser.setExpectedReturnCode( |
| 2989 | TosaReturnCode.UNPREDICTABLE, "Negative output shape" |
| 2990 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2991 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2992 | return ser.addOutput(output_shape, out_dtype) |