Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1 | #!/usr/bin/env python3 |
| 2 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3 | # Copyright (c) 2020-2021, ARM Limited. |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4 | # |
| 5 | # Licensed under the Apache License, Version 2.0 (the "License"); |
| 6 | # you may not use this file except in compliance with the License. |
| 7 | # You may obtain a copy of the License at |
| 8 | # |
| 9 | # http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | # |
| 11 | # Unless required by applicable law or agreed to in writing, software |
| 12 | # distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | # See the License for the specific language governing permissions and |
| 15 | # limitations under the License. |
| 16 | |
| 17 | |
| 18 | import numpy as np |
| 19 | import argparse |
| 20 | import sys |
| 21 | import re |
| 22 | import os |
| 23 | import subprocess |
| 24 | import shlex |
| 25 | import json |
| 26 | import glob |
| 27 | import math |
| 28 | import queue |
| 29 | import threading |
| 30 | import traceback |
| 31 | import math |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 32 | import itertools |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 33 | |
| 34 | from enum import IntEnum, Enum, unique |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 35 | from tosa_ref_run import TosaReturnCode |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 36 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 37 | # Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH |
| 38 | parent_dir = os.path.dirname(os.path.realpath(__file__)) |
| 39 | sys.path.append( |
| 40 | os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") |
| 41 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 42 | import tosa_serializer as ts |
| 43 | from tosa_serializer import * |
| 44 | import tosa |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 45 | from tosa_error_if import ErrorIf |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 46 | |
| 47 | # Convenience variables to the flatc-generated types that should be enums, but aren't |
| 48 | DType = tosa.DType.DType() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 49 | Op = tosa.Op.Op() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 50 | ResizeMode = tosa.ResizeMode.ResizeMode() |
| 51 | |
| 52 | class TosaQuantGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 53 | """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" |
| 54 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 55 | def __init__(self): |
| 56 | pass |
| 57 | |
| 58 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 59 | def getQinfo(testGen, dtype): |
| 60 | if dtype == DType.INT8: |
| 61 | return testGen.randInt(-128, 128) |
| 62 | if dtype == DType.UINT8: |
| 63 | return testGen.randInt(0, 256) |
| 64 | return 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 65 | |
| 66 | @staticmethod |
| 67 | def qgUnary(testGen, op, dtype): |
| 68 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 69 | qinfo.UnaryQuantInfo( |
| 70 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 71 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 72 | return qinfo |
| 73 | |
| 74 | @staticmethod |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 75 | def qgConv(testGen, op, dtype_or_dtypeList): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 76 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 77 | if isinstance(dtype_or_dtypeList, list): |
| 78 | # a list of [input, weights, accumulator] dtypes |
| 79 | dtypeList = dtype_or_dtypeList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 80 | else: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 81 | # an int, [input, weights, accumulator] dtypes are the same |
| 82 | dtypeList = [dtype_or_dtypeList] * 3 |
| 83 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 84 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 85 | qinfo.ConvQuantInfo(input_zp, weights_zp) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 86 | return qinfo |
| 87 | |
| 88 | @staticmethod |
| 89 | def qgMatmul(testGen, op, dtype): |
| 90 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 91 | qinfo.MatMulQuantInfo( |
| 92 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 93 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 94 | return qinfo |
| 95 | |
| 96 | @staticmethod |
| 97 | def qgPad(testGen, op, dtype): |
| 98 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 99 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 100 | return qinfo |
| 101 | |
| 102 | @staticmethod |
| 103 | def computeMultiplierAndShift(scaleFp, scale32): |
| 104 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 105 | # Provide a floating-point scaling factor and the scale32 parameter |
| 106 | # to compute the multiplier and shift |
| 107 | |
| 108 | if scale32: |
| 109 | scaleBits = 31 |
| 110 | else: |
| 111 | scaleBits = 15 |
| 112 | |
| 113 | m, shift = math.frexp(scaleFp) |
| 114 | |
| 115 | if scaleFp < 0.0: |
| 116 | m = -m |
| 117 | |
| 118 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 119 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 120 | |
| 121 | if multiplier == (1 << scaleBits): |
| 122 | multiplier = multiplier // 2 |
| 123 | shift = shift + 1 |
| 124 | |
| 125 | shift = (-shift) + scaleBits |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 126 | #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
| 127 | |
| 128 | # Adjust multiplier such that shift is in allowed value range. |
| 129 | if shift == 0: |
| 130 | multiplier = multiplier // 4 |
| 131 | shift = shift + 2 |
| 132 | elif shift == 1: |
| 133 | multiplier = multiplier // 2 |
| 134 | shift = shift + 1 |
| 135 | elif shift == 63: |
| 136 | multiplier = multiplier * 2 |
| 137 | shift = shift - 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 138 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 139 | assert multiplier <= (1 << scaleBits) |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 140 | assert shift >= 2 and shift <= 62 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 141 | |
| 142 | return multiplier, shift |
| 143 | |
| 144 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 145 | class TosaTensorGen: |
| 146 | """Tensor generators create a shape list for the placeholder and const tensor |
| 147 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 148 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 149 | def __init__(self): |
| 150 | pass |
| 151 | |
| 152 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 153 | def tgBasic(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 154 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 155 | shape = testGen.makeShape(rank) |
| 156 | |
| 157 | shape_list = [] |
| 158 | for i in range(pl + const): |
| 159 | shape_list.append(shape.copy()) |
| 160 | |
| 161 | return shape_list |
| 162 | |
| 163 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 164 | def tgNHWC(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 165 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 166 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 167 | if error_name != ErrorIf.WrongRank: |
| 168 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 169 | |
| 170 | shape = testGen.makeShape(rank) |
| 171 | |
| 172 | # Constrict the batch size? |
| 173 | if testGen.args.max_batch_size: |
| 174 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
| 175 | |
| 176 | shape_list = [] |
| 177 | for i in range(pl + const): |
| 178 | shape_list.append(shape.copy()) |
| 179 | |
| 180 | return shape_list |
| 181 | |
| 182 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 183 | def tgScatter(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 184 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 185 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 186 | assert pl == 2 |
| 187 | assert const == 0 |
| 188 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 189 | |
| 190 | values_in_shape = testGen.makeShape(rank) |
| 191 | |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 192 | # ignore max batch size if target shape is set |
| 193 | if testGen.args.max_batch_size and not testGen.args.target_shapes: |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 194 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 195 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 196 | W = testGen.randInt( |
| 197 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 198 | ) |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 199 | # Constrict W if one dimension is too large to keep tensor size reasonable |
| 200 | if max(values_in_shape) > 5000: |
| 201 | W = testGen.randInt(0, 16) |
| 202 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 203 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 204 | |
| 205 | shape_list = [] |
| 206 | shape_list.append(values_in_shape.copy()) |
| 207 | shape_list.append(input_shape.copy()) |
| 208 | |
| 209 | return shape_list |
| 210 | |
| 211 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 212 | def tgBroadcastFuzz(testGen, op, rank, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 213 | shape = testGen.makeShape(rank) |
| 214 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 215 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 216 | |
| 217 | shape_list = [] |
| 218 | |
| 219 | # Choose one of the inputs to broadcast |
| 220 | bcast_idx = testGen.randInt(0, pl + const) |
| 221 | for i in range(pl + const): |
| 222 | shape_bcast = shape.copy() |
| 223 | |
| 224 | # If the chosen input, pick a random index to broadcast |
| 225 | if i == bcast_idx: |
| 226 | fuzz_idx = testGen.randInt(0, rank) |
| 227 | shape_bcast[fuzz_idx] = 1 |
| 228 | |
| 229 | shape_list.append(shape_bcast) |
| 230 | |
| 231 | return shape_list |
| 232 | |
| 233 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 234 | def tgConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 235 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 236 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 237 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 238 | |
| 239 | # IFM dimensions are NHWC |
| 240 | ifm_shape = testGen.makeShape(rank) |
| 241 | |
| 242 | # Constrict the batch size? |
| 243 | if testGen.args.max_batch_size: |
| 244 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 245 | |
| 246 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 247 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 248 | |
| 249 | # Generate a random OFM depth |
| 250 | ofm_depth = testGen.makeShape(1)[0] |
| 251 | |
| 252 | # The filter dimensions are OHWI |
| 253 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 254 | |
| 255 | # The bias is OC |
| 256 | bias_shape = np.asarray([ofm_depth]) |
| 257 | |
| 258 | return [ifm_shape, filter_shape, bias_shape] |
| 259 | |
| 260 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 261 | def tgConv3D(testGen, op, rank, error_name=None): |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 262 | pl, const = op["operands"] |
| 263 | |
| 264 | assert rank == 5 |
| 265 | |
| 266 | # IFM dimensions are NDHWC |
| 267 | ifm_shape = testGen.makeShape(rank) |
| 268 | |
| 269 | # Constrict the batch size? |
| 270 | if testGen.args.max_batch_size: |
| 271 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 272 | |
| 273 | # Get the filter depth/height/width from the operator parameters |
| 274 | filter_dhw = op["filter"] |
| 275 | |
| 276 | # Generate a random OFM channel |
| 277 | ofm_channel = testGen.makeShape(1)[0] |
| 278 | |
| 279 | # The filter dimensions are ODHWI |
| 280 | filter_shape = np.asarray( |
| 281 | [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] |
| 282 | ) |
| 283 | |
| 284 | # The bias is OC |
| 285 | bias_shape = np.asarray([ofm_channel]) |
| 286 | |
| 287 | return [ifm_shape, filter_shape, bias_shape] |
| 288 | |
| 289 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 290 | def tgTransposeConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 291 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 292 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 293 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 294 | |
| 295 | # IFM dimensions are NHWC |
| 296 | ifm_shape = testGen.makeShape(rank) |
| 297 | |
| 298 | # Constrict the batch size? |
| 299 | if testGen.args.max_batch_size: |
| 300 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 301 | |
| 302 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 303 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 304 | |
| 305 | # Generate a random OFM depth |
| 306 | ofm_depth = testGen.makeShape(1)[0] |
| 307 | |
| 308 | # The filter dimensions are OHWI |
| 309 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 310 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 311 | # The bias is OC |
| 312 | bias_shape = np.asarray([ofm_depth]) |
| 313 | |
| 314 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 315 | |
| 316 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 317 | def tgDepthwiseConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 318 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 319 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 320 | assert rank == 4 |
| 321 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 322 | |
| 323 | # IFM dimensions are NHWC |
| 324 | ifm_shape = testGen.makeShape(rank) |
| 325 | |
| 326 | # Constrict the batch size? |
| 327 | if testGen.args.max_batch_size: |
| 328 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 329 | |
| 330 | # Get the filter height/width from the operator parameters |
| 331 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 332 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 333 | |
| 334 | # Generate a random OFM depth, but don't let it get too big because |
| 335 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 336 | filter_m = ( |
| 337 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 338 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 339 | |
| 340 | # The filter dimensions are HWCM |
| 341 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 342 | |
| 343 | # The bias is M * C |
| 344 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 345 | |
| 346 | return [ifm_shape, filter_shape, bias_shape] |
| 347 | |
| 348 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 349 | def tgFullyConnected(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 350 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 351 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 352 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 353 | |
| 354 | input_shape = testGen.makeShape(rank) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 355 | filter_oc = testGen.rng.integers( |
| 356 | low=testGen.args.tensor_shape_range[0], |
| 357 | high=testGen.args.tensor_shape_range[1], |
| 358 | size=1, |
| 359 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 360 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 361 | |
| 362 | bias_shape = np.asarray([filter_oc]) |
| 363 | |
| 364 | return [input_shape, filter_shape, bias_shape] |
| 365 | |
| 366 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 367 | def tgMatmul(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 368 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 369 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 370 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 371 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 372 | |
| 373 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 374 | # Get a random number for b_oc even if target shape is defined |
| 375 | b_oc = np.int32( |
| 376 | testGen.rng.integers( |
| 377 | low=testGen.args.tensor_shape_range[0], |
| 378 | high=testGen.args.tensor_shape_range[1], |
| 379 | size=1, |
| 380 | ) |
| 381 | )[0] |
| 382 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 383 | if max(a_shape) > 1000: |
| 384 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 385 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 386 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 387 | return [a_shape, b_shape] |
| 388 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 389 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 390 | def tgConcat(testGen, opName, rank, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 391 | pl, const = opName["operands"] |
| 392 | shape = testGen.makeShape(rank) |
| 393 | |
| 394 | # Create extra tensors to concat. |
| 395 | # Take into account value of pl when getting maximum number of concats |
| 396 | num_tensors = testGen.randInt(0, 4) |
| 397 | shape_list = [] |
| 398 | for i in range(pl + const + num_tensors): |
| 399 | shape_list.append(shape.copy()) |
| 400 | |
| 401 | return shape_list |
| 402 | |
| 403 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 404 | def tgConcatConstInput(testGen, shapeList, axis, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 405 | # Split concat shape along axis to allow for multiple const inputs |
| 406 | # without making too many large tensors |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 407 | if len(shapeList) == 2 or shapeList[0][axis] < len(shapeList): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 408 | return shapeList |
| 409 | |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 410 | # Create copy of shape we are going to split (so we don't alter shapeList) |
| 411 | shape = shapeList[0].copy() |
| 412 | # Add original shape as first input |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 413 | new_shapeList = [shape.copy()] |
| 414 | length_on_axis = shape[axis] |
| 415 | remaining_length = length_on_axis |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 416 | for i in range(len(shapeList) - 2): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 417 | # Calculate split on axis and remaining value |
| 418 | split_shape_val = int(shape[axis] / 2) |
| 419 | remaining_length = remaining_length - split_shape_val |
| 420 | |
| 421 | # Append new shape, and set remaining shape |
| 422 | shape[axis] = split_shape_val |
| 423 | new_shapeList.append(shape.copy()) |
| 424 | shape[axis] = remaining_length |
| 425 | if i == len(shapeList) - 3: |
| 426 | new_shapeList.append(shape.copy()) |
| 427 | |
| 428 | return new_shapeList |
| 429 | |
| 430 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 431 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 432 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 433 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 434 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 435 | as arguments to the operator build function.""" |
| 436 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 437 | def __init__(self): |
| 438 | pass |
| 439 | |
| 440 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 441 | def agNone(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 442 | """A trivial argument generator for operators that don't take any |
| 443 | non-tensor arguments""" |
| 444 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 445 | |
| 446 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 447 | def agAxis(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 448 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 449 | axes = [] |
| 450 | |
| 451 | shape = shapeList[0] |
| 452 | |
| 453 | for a in range(0, len(shape)): |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 454 | axes.append(("axis{}".format(a), [a])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 455 | return axes |
| 456 | |
| 457 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 458 | def agConv(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 459 | arg_list = [] |
| 460 | |
| 461 | ifm_shape = shapeList[0] |
| 462 | filter_shape = shapeList[1] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 463 | # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) |
| 464 | k = [int(x) for x in opName.split("_")[-1].split("x")] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 465 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 466 | # Check the rank |
| 467 | rank = 5 if opName.startswith("conv3d") else 4 |
| 468 | assert len(ifm_shape) == rank |
| 469 | assert len(filter_shape) == rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 470 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 471 | # kernel rank omits batch and channels |
| 472 | k_rank = rank - 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 473 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 474 | # Generate comprehensive argument lists |
| 475 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 476 | paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} |
| 477 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 478 | strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| 479 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 480 | dilations = {x for x in itertools.product(*([d_vals] * k_rank))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 481 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 482 | # add some oversize argument values |
| 483 | if max(ifm_shape) < 64: |
| 484 | bigPadding = 9 |
| 485 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) |
| 486 | bigStride = 8 |
| 487 | strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) |
| 488 | bigDilation = 7 |
| 489 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 490 | |
| 491 | # There are too many parameter combinations, so generate them sparsely |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 492 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 493 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 494 | if sparsity < 13: |
| 495 | sparsity = 1 |
| 496 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 497 | sparsity += 1 |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 498 | n = 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 499 | for s in sorted(list(strides)): |
| 500 | for p in sorted(list(paddings)): |
| 501 | for d in sorted(list(dilations)): |
| 502 | if (n % sparsity == 0 |
| 503 | # padding must not exceed the kernel size ? |
| 504 | # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 505 | # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) |
| 506 | # the padded shape must exceed the kernel size |
| 507 | and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] |
| 508 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) |
| 509 | # the padded shape must exceed the dilation |
| 510 | and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] |
| 511 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) |
| 512 | ): |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 513 | arg_list.append( |
| 514 | ( |
| 515 | "st{}_pad{}_dilat{}".format( |
| 516 | "".join([str(x) for x in s]), |
| 517 | "".join([str(x) for x in p]), |
| 518 | "".join([str(x) for x in d]), |
| 519 | ), |
| 520 | [s, p, d], |
| 521 | ) |
| 522 | ) |
| 523 | n += 1 |
| 524 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 525 | return arg_list |
| 526 | |
| 527 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 528 | def agTransposeConv2D(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 529 | arg_list = [] |
| 530 | |
| 531 | ifm_shape = shapeList[0] |
| 532 | filter_shape = shapeList[1] |
| 533 | |
| 534 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 535 | assert len(ifm_shape) == 4 |
| 536 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 537 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 538 | # Generate comprehensive argument lists |
| 539 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 540 | paddings = {x for x in itertools.product(*([p_vals] * 2))} |
| 541 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 542 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 543 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 544 | dilations = {x for x in itertools.product(*([d_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 545 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 546 | # add some oversize argument values |
| 547 | if max(ifm_shape) < 64: |
| 548 | bigPadding = 9 |
| 549 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) |
| 550 | bigStride = 8 |
| 551 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 552 | bigDilation = 7 |
| 553 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 554 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 555 | # There are too many parameter combinations, so generate them sparsely |
| 556 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 557 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 558 | if sparsity < 13: |
| 559 | sparsity = 1 |
| 560 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 561 | sparsity += 1 |
| 562 | n = 0 |
| 563 | for s in sorted(list(strides)): |
| 564 | for p in sorted(list(paddings)): |
| 565 | for d in sorted(list(dilations)): |
| 566 | if n % sparsity == 0: |
| 567 | # Determine the output shape |
| 568 | oh = ( |
| 569 | ifm_shape[1] |
| 570 | - filter_shape[1] |
| 571 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 572 | + 2 * p[0] |
| 573 | ) // s[0] + 1 |
| 574 | ow = ( |
| 575 | ifm_shape[2] |
| 576 | - filter_shape[2] |
| 577 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 578 | + 2 * p[1] |
| 579 | ) // s[1] + 1 |
| 580 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| 581 | arg_list.append( |
| 582 | ( |
| 583 | "st{}_pad{}_dilat{}_os{}".format( |
| 584 | "".join([str(x) for x in s]), |
| 585 | "".join([str(x) for x in p]), |
| 586 | "".join([str(x) for x in d]), |
| 587 | "x".join([str(x) for x in os]), |
| 588 | ), |
| 589 | [s, p, d, os], |
| 590 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 591 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 592 | n += 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 593 | |
| 594 | return arg_list |
| 595 | |
| 596 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 597 | def agPad(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 598 | arg_list = [] |
| 599 | rank = len(shapeList[0]) |
| 600 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 601 | # Exhaustively test combinations of padding on each side of each dimension |
| 602 | # - the range of padding values is defined by pad_min and pad_max |
| 603 | # - for padding >9, the name format needs to be more distinctive |
| 604 | pad_min, pad_max = 0, 1 |
| 605 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
| 606 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 607 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 608 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 609 | for paddings in shape_pad_values: |
| 610 | name = "pad" |
| 611 | for r in range(rank): |
| 612 | before, after = paddings[r] |
| 613 | name = f"{name}{before}{after}" |
| 614 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 615 | |
| 616 | return arg_list |
| 617 | |
| 618 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 619 | def agPooling(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 620 | arg_list = [] |
| 621 | |
| 622 | shape = shapeList[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 623 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 624 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 625 | # Generate comprehensive argument lists |
| 626 | p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] |
| 627 | paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| 628 | s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] |
| 629 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 630 | k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] |
| 631 | kernels = {x for x in itertools.product(*([k_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 632 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 633 | # add some oversize argument values |
| 634 | bigStride = 7 |
| 635 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 636 | bigKernel = 6 |
| 637 | kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) |
| 638 | if max(shape) < 64: |
| 639 | # padding must be less than the kernel size |
| 640 | bigPadding = bigKernel - 1 |
| 641 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 642 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 643 | # There are too many parameter combinations, so generate them sparsely |
| 644 | sparsity = len(paddings) * len(strides) * len(kernels) // 500 + 1 |
| 645 | n = 0 |
| 646 | for s in sorted(list(strides)): |
| 647 | for p in sorted(list(paddings)): |
| 648 | for k in sorted(list(kernels)): |
| 649 | if (n % sparsity == 0 |
| 650 | # padding must not exceed the kernel size |
| 651 | and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 652 | # the padded shape must exceed the kernel size |
| 653 | and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] |
| 654 | ): |
| 655 | arg_list.append( |
| 656 | ( |
| 657 | "st{}_kern{}_pad{}".format( |
| 658 | "".join([str(x) for x in s]), |
| 659 | "".join([str(x) for x in k]), |
| 660 | "".join([str(x) for x in p]), |
| 661 | ), |
| 662 | [s, p, k], |
| 663 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 664 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 665 | n += 1 |
| 666 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 667 | return arg_list |
| 668 | |
| 669 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 670 | def agCast(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 671 | arg_list = [] |
| 672 | |
| 673 | # Enumerate the output types here |
| 674 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 675 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 676 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 677 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 678 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 679 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 680 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 681 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 682 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 683 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 684 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 685 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 686 | |
| 687 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 688 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 689 | |
| 690 | return arg_list |
| 691 | |
| 692 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 693 | def agRescale(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 694 | arg_list = [] |
| 695 | |
| 696 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 697 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 698 | if inDtype == DType.UINT8 and dtype != DType.INT8: |
| 699 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 700 | continue |
| 701 | if inDtype != DType.INT8 and dtype == DType.UINT8: |
| 702 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 703 | continue |
| 704 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 705 | for scale32 in [False, True]: |
| 706 | for double_round in [False, True]: |
| 707 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 708 | |
| 709 | if inDtype == DType.INT48 and scale32: |
| 710 | # Illegal condition. Must be scale32=False |
| 711 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 712 | if double_round and not scale32: |
| 713 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 714 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 715 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 716 | arg_list.append( |
| 717 | ( |
| 718 | "out{}_sc{}_dr{}_pc{}".format( |
| 719 | DTypeNames[dtype], |
| 720 | int(scale32), |
| 721 | int(double_round), |
| 722 | int(per_channel), |
| 723 | ), |
| 724 | [dtype, scale32, double_round, per_channel], |
| 725 | ) |
| 726 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 727 | |
| 728 | return arg_list |
| 729 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 730 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 731 | def agMul(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 732 | arg_list = [] |
| 733 | |
| 734 | if dtype is DType.INT32: |
| 735 | for p in range(testGen.args.num_rand_permutations): |
| 736 | |
| 737 | shift = testGen.randInt(0, 32) |
| 738 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 739 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 740 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 741 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 742 | |
| 743 | return arg_list |
| 744 | |
| 745 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 746 | def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 747 | arg_list = [] |
| 748 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 749 | arg_list.append(("roundTrue", [True])) |
| 750 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 751 | |
| 752 | return arg_list |
| 753 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 754 | # Helper function for reshape. Gets some factors of a larger number. |
| 755 | @staticmethod |
| 756 | def getFactors(val, start=1): |
| 757 | factors = [] |
| 758 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 759 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 760 | if (val % i) == 0: |
| 761 | factors.append(i) |
| 762 | |
| 763 | return factors |
| 764 | |
| 765 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 766 | def agReshape(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 767 | arg_list = [] |
| 768 | |
| 769 | origShape = shapeList[0] |
| 770 | |
| 771 | totalElements = 1 |
| 772 | for s in origShape: |
| 773 | totalElements *= s |
| 774 | |
| 775 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 776 | factors = TosaArgGen.getFactors(totalElements) |
| 777 | |
| 778 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 779 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 780 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 781 | continue |
| 782 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 783 | found = True |
| 784 | # escape_counter breaks while loop if it continues on for too long |
| 785 | escape_counter = 0 |
| 786 | while found: |
| 787 | newShape = [] |
| 788 | # Generate newShape ensuring it isn't a duplicate |
| 789 | remainingElements = totalElements |
| 790 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 791 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 792 | # pick rank-1 factors |
| 793 | newShape.append(shuffledFactors[0]) |
| 794 | remainingElements = remainingElements // shuffledFactors[0] |
| 795 | shuffledFactors = testGen.rng.permutation( |
| 796 | TosaArgGen.getFactors(remainingElements) |
| 797 | ) |
| 798 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 799 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 800 | # Toss in a -1 sometimes |
| 801 | minusOne = testGen.randInt(0, newRank * 4) |
| 802 | if minusOne < newRank: |
| 803 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 804 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 805 | # Check for duplicates |
| 806 | found = False |
| 807 | for name, other_shape in arg_list: |
| 808 | if other_shape[0] == newShape: |
| 809 | found = True |
| 810 | break |
| 811 | |
| 812 | escape_counter += 1 |
| 813 | if escape_counter >= 100: |
| 814 | break |
| 815 | |
| 816 | if not found: |
| 817 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 818 | |
| 819 | return arg_list |
| 820 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 821 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 822 | def agTranspose(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 823 | arg_list = [] |
| 824 | |
| 825 | ifm_shape = shapeList[0] |
| 826 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 827 | # Get all permutations |
| 828 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 829 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 830 | # Limit to possible permutations from shape dimension or argument setting |
| 831 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 832 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 833 | # Get random permutation generator that uses all permutations |
| 834 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 835 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 836 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 837 | arg_list = [ |
| 838 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 839 | for p in range(limit) |
| 840 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 841 | return arg_list |
| 842 | |
| 843 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 844 | def agSlice(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 845 | arg_list = [] |
| 846 | |
| 847 | ifm_shape = shapeList[0] |
| 848 | rank = len(ifm_shape) |
| 849 | |
| 850 | for p in range(testGen.args.num_rand_permutations): |
| 851 | begin = [] |
| 852 | size = [] |
| 853 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 854 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 855 | |
| 856 | for i in range(rank): |
| 857 | if ifm_shape[i] > 1: |
| 858 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 859 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 860 | |
| 861 | # Invalid slice size? |
| 862 | if size[i] == 0: |
| 863 | valid = False |
| 864 | else: |
| 865 | begin.append(0) |
| 866 | size.append(1) |
| 867 | |
| 868 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 869 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 870 | return arg_list |
| 871 | |
| 872 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 873 | def agTile(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 874 | arg_list = [] |
| 875 | |
| 876 | ifm_shape = shapeList[0] |
| 877 | rank = len(ifm_shape) |
| 878 | |
| 879 | for p in range(testGen.args.num_rand_permutations): |
| 880 | |
| 881 | # Pick a few random, but small multiple values |
| 882 | # because otherwise this has a tendency to generate |
| 883 | # enormous tensors |
| 884 | multiples = [] |
| 885 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame] | 886 | if ifm_shape[i] > 1000: |
| 887 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 888 | multiples.append(1) |
| 889 | elif max(ifm_shape) > 1000: |
| 890 | multiples.append(2) |
| 891 | else: |
| 892 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 893 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 894 | |
| 895 | return arg_list |
| 896 | |
| 897 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 898 | def agResize(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 899 | arg_list = [] |
| 900 | |
| 901 | ifm_shape = shapeList[0] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 902 | for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 903 | |
| 904 | # Exclude illegal {mode, type} configurations. Pick legal output types |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 905 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 906 | outputDTypeList = [DType.INT8] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 907 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 908 | outputDTypeList = [DType.INT16] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 909 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 910 | outputDTypeList = [DType.INT32] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 911 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 912 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 913 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 914 | outputDTypeList = [DType.FLOAT] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 915 | elif error_name == ErrorIf.WrongInputType: |
| 916 | # If an incorrect input type is used then we set a 'correct' |
| 917 | # output type to avoid other errors |
| 918 | outputDTypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 919 | else: |
| 920 | continue |
| 921 | |
| 922 | for outputDType in outputDTypeList: |
| 923 | for perm in range(testGen.args.num_rand_permutations): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 924 | # Randomly generate legal output dimensions and shift |
| 925 | # and then compute the stride and offset based on them |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 926 | # A output_dim of 1 will cause offset to exceed allowed range |
| 927 | # so minimum value 2 produced below |
| 928 | output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1] |
| 929 | while ((float(ifm_shape[1]) / float(output_dims[0])) >= 16): |
| 930 | output_dims[0] += 1 |
| 931 | while ((float(ifm_shape[2]) / float(output_dims[1])) >= 16): |
| 932 | output_dims[1] += 1 |
| 933 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 934 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 935 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 936 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 937 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 938 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 939 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 940 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 941 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 942 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 943 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 944 | if outputDType == DType.FLOAT: |
| 945 | shift = 0 |
| 946 | stride = [0, 0] |
| 947 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 948 | stride_fp = [fp_stride_y, fp_stride_x] |
| 949 | offset_fp = [fp_offset_y, fp_offset_x] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 950 | |
| 951 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 952 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 953 | testGen, |
| 954 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 955 | mode, |
| 956 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 957 | shapeList, |
| 958 | outputDType, |
| 959 | shift, |
| 960 | stride, |
| 961 | stride_fp, |
| 962 | offset, |
| 963 | offset_fp |
| 964 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 965 | else: |
| 966 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 967 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 968 | arg_list.append( |
| 969 | ( |
| 970 | "mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}".format( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 971 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 972 | output_dims[0], |
| 973 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 974 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 975 | stride_fp[0], |
| 976 | stride_fp[1], |
| 977 | offset_fp[0], |
| 978 | offset_fp[1], |
| 979 | ), |
| 980 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 981 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 982 | stride, |
| 983 | offset, |
| 984 | shift, |
| 985 | stride_fp, |
| 986 | offset_fp, |
| 987 | output_dims, |
| 988 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 989 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 990 | ], |
| 991 | ) |
| 992 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 993 | else: |
| 994 | shift = 11 |
| 995 | unit = float(1 << shift) |
| 996 | stride_y = int(round(fp_stride_y * unit)) |
| 997 | stride_x = int(round(fp_stride_x * unit)) |
| 998 | offset_y = int(round(fp_offset_y * unit)) |
| 999 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1000 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1001 | while ( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1002 | stride_y >= (16 << shift) |
| 1003 | or stride_x >= (16 << shift) |
| 1004 | or offset_y >= (16 << shift) |
| 1005 | or offset_x >= (16 << shift) |
| 1006 | or offset_y <= (-16 << shift) |
| 1007 | or offset_x <= (-16 << shift) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1008 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1009 | shift = shift - 1 |
| 1010 | unit = float(1 << shift) |
| 1011 | stride_y = int(round(fp_stride_y * unit)) |
| 1012 | stride_x = int(round(fp_stride_x * unit)) |
| 1013 | offset_y = int(round(fp_offset_y * unit)) |
| 1014 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1015 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1016 | stride = [stride_y, stride_x] |
| 1017 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1018 | |
| 1019 | stride_fp = [0.0, 0.0] |
| 1020 | offset_fp = [0.0, 0.0] |
| 1021 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1022 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1023 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1024 | testGen, |
| 1025 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1026 | mode, |
| 1027 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1028 | shapeList, |
| 1029 | outputDType, |
| 1030 | shift, |
| 1031 | stride, |
| 1032 | stride_fp, |
| 1033 | offset, |
| 1034 | offset_fp |
| 1035 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1036 | else: |
| 1037 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1038 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1039 | arg_list.append( |
| 1040 | ( |
| 1041 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1042 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1043 | shift, |
| 1044 | output_dims[0], |
| 1045 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1046 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1047 | stride[0], |
| 1048 | stride[1], |
| 1049 | offset[0], |
| 1050 | offset[1], |
| 1051 | ), |
| 1052 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1053 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1054 | stride, |
| 1055 | offset, |
| 1056 | shift, |
| 1057 | stride_fp, |
| 1058 | offset_fp, |
| 1059 | output_dims, |
| 1060 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1061 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1062 | ], |
| 1063 | ) |
| 1064 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1065 | |
| 1066 | return arg_list |
| 1067 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1068 | def agCondIf(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1069 | # CondIf generates the condition values here. |
| 1070 | # Convert to tensors in the build function, along with the |
| 1071 | # then and else blocks |
| 1072 | arg_list = [] |
| 1073 | |
| 1074 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1075 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1076 | |
| 1077 | return arg_list |
| 1078 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1079 | def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1080 | # While loop: 0 iterations, 1, more than 1 |
| 1081 | arg_list = [] |
| 1082 | |
| 1083 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1084 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1085 | |
| 1086 | return arg_list |
| 1087 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1088 | class TosaErrorIfArgGen: |
| 1089 | |
| 1090 | @staticmethod |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1091 | def eiResizeErrorIf(testGen, error_name, mode, dtype, shapeList, outputDType, shift, stride, stride_fp, offset, offset_fp): |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1092 | |
| 1093 | if outputDType == DType.FLOAT: |
| 1094 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1095 | stride_fp = testGen.rng.random(size=[2]) - 2 |
| 1096 | elif error_name == ErrorIf.ShiftNotZero: |
| 1097 | shift = testGen.rng.integers(1, 5) |
| 1098 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1099 | shape = shapeList[0] |
| 1100 | transform_height = testGen.rng.choice([False, True]) |
| 1101 | if transform_height: |
| 1102 | stride_fp[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1103 | else: |
| 1104 | stride_fp[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1105 | else: |
| 1106 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1107 | stride = np.int16(testGen.rng.integers(-1, 1, size=[2])) |
| 1108 | elif error_name == ErrorIf.ShiftSmallerOne: |
| 1109 | shift = testGen.rng.integers(-3, 1) |
| 1110 | if shift <= 0: |
| 1111 | stride = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1112 | offset = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1113 | else: |
| 1114 | stride = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1115 | offset = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1116 | elif error_name == ErrorIf.ShiftLargerEleven: |
| 1117 | shift = np.int16(testGen.rng.integers(12, 15)) |
| 1118 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1119 | shape = shapeList[0] |
| 1120 | transform_height = testGen.rng.choice([False, True]) |
| 1121 | if transform_height: |
| 1122 | stride[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1123 | else: |
| 1124 | stride[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1125 | elif error_name == ErrorIf.StrideLargerEqualMax: |
| 1126 | stride = [(16 << shift) + 1, (16 << shift) + 1] |
| 1127 | elif error_name == ErrorIf.OffsetLargerEqualMax: |
| 1128 | offset = [(16 << shift) + 1, (16 << shift) + 1] |
| 1129 | elif error_name == ErrorIf.OffsetSmallerEqualMin: |
| 1130 | offset = [(-16 << shift) - 1, (-16 << shift) - 1] |
| 1131 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1132 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1133 | if error_name == ErrorIf.WrongOutputType: |
| 1134 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
| 1135 | incorrect_types = (DType.INT4, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1136 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
| 1137 | incorrect_types = (DType.INT4, DType.INT8, DType.INT32, DType.INT48, DType.FLOAT) |
| 1138 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
| 1139 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) |
| 1140 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
| 1141 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) |
| 1142 | elif dtype == DType.FLOAT: |
| 1143 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) |
| 1144 | outputDType = testGen.rng.choice(a=incorrect_types) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1145 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1146 | return shift, stride, stride_fp, offset, offset_fp, outputDType |
| 1147 | |
| 1148 | @staticmethod |
| 1149 | def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list): |
| 1150 | # Mess up input/output tensors for ERROR_IF checks |
| 1151 | if error_name == "WrongInputList": |
| 1152 | add_input = testGen.rng.choice([True, False]) |
| 1153 | if add_input: |
| 1154 | input_list.append('eiDummyInput') |
| 1155 | else: |
| 1156 | input_list = input_list[:-1] |
| 1157 | if error_name == "WrongOutputList": |
| 1158 | add_output = testGen.rng.choice([True, False]) |
| 1159 | if add_output: |
| 1160 | output_list.append('eiDummyOutput') |
| 1161 | else: |
| 1162 | output_list = [] |
| 1163 | return input_list, output_list |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1164 | |
| 1165 | class TosaErrorValidator: |
| 1166 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1167 | @staticmethod |
| 1168 | def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs): |
| 1169 | # Check ERROR_IF statements |
| 1170 | |
| 1171 | for val_fcn in validator_fcns: |
| 1172 | val_result = val_fcn(True, **kwargs) |
| 1173 | |
| 1174 | validator_name = val_result['error_name'] |
| 1175 | error_result = val_result['error_result'] |
| 1176 | error_reason = val_result['error_reason'] |
| 1177 | |
| 1178 | if error_result: |
| 1179 | if error_name == validator_name: |
| 1180 | serializer.setExpectedReturnCode(2, error_reason) |
| 1181 | else: |
| 1182 | print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}") |
| 1183 | return None # Return None to delete test if wrong ERROR_IF is hit |
| 1184 | else: |
| 1185 | if error_name == validator_name: |
| 1186 | print(f"No ERROR_IF hit for {error_name}") |
| 1187 | return None |
| 1188 | |
| 1189 | @staticmethod |
| 1190 | def evWrongInputType(check=False, **kwargs): |
| 1191 | all_dtypes = (DType.BOOL, DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1192 | |
| 1193 | # Find the unsupported input data types |
| 1194 | assert 'op' in kwargs |
| 1195 | op = kwargs['op'] |
| 1196 | input_dtypes = op['types'] |
| 1197 | wrong_input_dtypes = list(set(all_dtypes) - set(input_dtypes)) |
| 1198 | |
| 1199 | error_name = ErrorIf.WrongInputType |
| 1200 | param_reqs = {"rank": None, "dtype": wrong_input_dtypes, "shape": None} |
| 1201 | error_result = False |
| 1202 | error_reason = "Input data type not supported for this operator" |
| 1203 | |
| 1204 | if check: |
| 1205 | input_dtype = kwargs['input_dtype'] |
| 1206 | if input_dtype not in input_dtypes: |
| 1207 | error_result = True |
| 1208 | |
| 1209 | info_dict = { |
| 1210 | "error_name": error_name, |
| 1211 | "error_result": error_result, |
| 1212 | "error_reason": error_reason, |
| 1213 | "param_reqs": param_reqs |
| 1214 | } |
| 1215 | return info_dict |
| 1216 | |
| 1217 | @staticmethod |
| 1218 | def evWrongOutputType(check=False, **kwargs): |
| 1219 | error_name = ErrorIf.WrongOutputType |
| 1220 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1221 | error_result = False |
| 1222 | error_reason = "Output data type not supported for this configuration of operator" |
| 1223 | |
| 1224 | if check: |
| 1225 | input_dtype = kwargs['input_dtype'] |
| 1226 | output_dtype = kwargs['output_dtype'] |
| 1227 | mode = kwargs['mode'] |
| 1228 | |
| 1229 | if ( |
| 1230 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT8 and output_dtype != DType.INT8) or |
| 1231 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT16 and output_dtype != DType.INT16) or |
| 1232 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT8 and output_dtype != DType.INT32) or |
| 1233 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT16 and output_dtype != DType.INT48) or |
| 1234 | (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) |
| 1235 | ): |
| 1236 | error_result = True |
| 1237 | |
| 1238 | info_dict = { |
| 1239 | "error_name": error_name, |
| 1240 | "error_result": error_result, |
| 1241 | "error_reason": error_reason, |
| 1242 | "param_reqs": param_reqs |
| 1243 | } |
| 1244 | return info_dict |
| 1245 | |
| 1246 | @staticmethod |
| 1247 | def evWrongRank(check=False, **kwargs): |
| 1248 | all_ranks = (1, 2, 3, 4, 5) |
| 1249 | |
| 1250 | # Make a list of incorrect ranks |
| 1251 | assert 'op' in kwargs |
| 1252 | op = kwargs['op'] |
| 1253 | rmin, rmax = op['rank'] |
| 1254 | rank_range = range(rmin, rmax + 1) |
| 1255 | incorrect_ranks = list(set(all_ranks) - set(rank_range)) |
| 1256 | # Set minimum incorrect rank to 3 to avoid index error |
| 1257 | if op['op'] == Op.RESIZE: |
| 1258 | incorrect_ranks = [3, 5] |
| 1259 | |
| 1260 | error_name = ErrorIf.WrongRank |
| 1261 | param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None} |
| 1262 | error_result = False |
| 1263 | error_reason = "Rank not supported for this operator" |
| 1264 | |
| 1265 | if check: |
| 1266 | input_shape = kwargs['input_shape'] |
| 1267 | if op['op'] == Op.RESIZE and len(input_shape.shape) != 4: |
| 1268 | error_result = True |
| 1269 | |
| 1270 | info_dict = { |
| 1271 | "error_name": error_name, |
| 1272 | "error_result": error_result, |
| 1273 | "error_reason": error_reason, |
| 1274 | "param_reqs": param_reqs |
| 1275 | } |
| 1276 | return info_dict |
| 1277 | |
| 1278 | @staticmethod |
| 1279 | def evWrongInputList(check=False, **kwargs): |
| 1280 | error_name = ErrorIf.WrongInputList |
| 1281 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1282 | error_result = False |
| 1283 | error_reason = "Op input list does not match expected input" |
| 1284 | |
| 1285 | if check: |
| 1286 | op = kwargs['op'] |
| 1287 | input_list = kwargs['input_list'] |
| 1288 | num_operands = kwargs['num_operands'] |
| 1289 | if len(input_list) != num_operands: |
| 1290 | error_result = True |
| 1291 | |
| 1292 | info_dict = { |
| 1293 | "error_name": error_name, |
| 1294 | "error_result": error_result, |
| 1295 | "error_reason": error_reason, |
| 1296 | "param_reqs": param_reqs |
| 1297 | } |
| 1298 | return info_dict |
| 1299 | |
| 1300 | @staticmethod |
| 1301 | def evWrongOutputList(check=False, **kwargs): |
| 1302 | error_name = ErrorIf.WrongOutputList |
| 1303 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1304 | error_result = False |
| 1305 | error_reason = "Op output list does not match expected output" |
| 1306 | |
| 1307 | if check: |
| 1308 | output_list = kwargs['output_list'] |
| 1309 | # Note this will be incorrect if an operator returns more than one output |
| 1310 | if len(output_list) != 1: |
| 1311 | error_result = True |
| 1312 | |
| 1313 | info_dict = { |
| 1314 | "error_name": error_name, |
| 1315 | "error_result": error_result, |
| 1316 | "error_reason": error_reason, |
| 1317 | "param_reqs": param_reqs |
| 1318 | } |
| 1319 | return info_dict |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1320 | |
| 1321 | @staticmethod |
| 1322 | def evMaxDimExceeded(check=False, **kwargs): |
| 1323 | error_name = ErrorIf.MaxDimExceeded |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1324 | param_reqs = { |
| 1325 | "rank": [4,4], |
| 1326 | "dtype": [DType.INT8], |
| 1327 | "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]] |
| 1328 | } |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1329 | error_result = False |
| 1330 | error_reason = "At least one maximum dimension is larger than 16384" |
| 1331 | |
| 1332 | if check: |
| 1333 | input_shape = kwargs['input_shape'].shape |
| 1334 | output_shape = kwargs['output_shape'] # Note this is just (OH, OW) |
| 1335 | if ((input_shape[1] > 16384) or |
| 1336 | (input_shape[2] > 16384) or |
| 1337 | (output_shape[0] > 16384) or |
| 1338 | (output_shape[1] > 16384)): |
| 1339 | error_result = True |
| 1340 | |
| 1341 | info_dict = { |
| 1342 | "error_name": error_name, |
| 1343 | "error_result": error_result, |
| 1344 | "error_reason": error_reason, |
| 1345 | "param_reqs": param_reqs |
| 1346 | } |
| 1347 | return info_dict |
| 1348 | |
| 1349 | @staticmethod |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 1350 | def evBatchMismatch(check=False, **kwargs): |
| 1351 | error_name = ErrorIf.BatchMismatch |
| 1352 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1353 | error_result = False |
| 1354 | error_reason = "Input batch size not equal to output batch size" |
| 1355 | |
| 1356 | assert 'op' in kwargs |
| 1357 | op = kwargs['op'] |
| 1358 | rmin, rmax = op['rank'] |
| 1359 | rank_range = range(rmin, rmax + 1) |
| 1360 | |
| 1361 | if check: |
| 1362 | input_shape = kwargs['input_shape'].shape |
| 1363 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1364 | |
| 1365 | if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]): |
| 1366 | error_result = True |
| 1367 | |
| 1368 | info_dict = { |
| 1369 | "error_name": error_name, |
| 1370 | "error_result": error_result, |
| 1371 | "error_reason": error_reason, |
| 1372 | "param_reqs": param_reqs |
| 1373 | } |
| 1374 | return info_dict |
| 1375 | |
| 1376 | @staticmethod |
| 1377 | def evChannelMismatch(check=False, **kwargs): |
| 1378 | error_name = ErrorIf.ChannelMismatch |
| 1379 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1380 | error_result = False |
| 1381 | error_reason = "Input channel size not equal to output channel size" |
| 1382 | |
| 1383 | assert 'op' in kwargs |
| 1384 | op = kwargs['op'] |
| 1385 | rmin, rmax = op['rank'] |
| 1386 | rank_range = range(rmin, rmax + 1) |
| 1387 | |
| 1388 | if check: |
| 1389 | input_shape = kwargs['input_shape'].shape |
| 1390 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1391 | if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]): |
| 1392 | error_result = True |
| 1393 | |
| 1394 | info_dict = { |
| 1395 | "error_name": error_name, |
| 1396 | "error_result": error_result, |
| 1397 | "error_reason": error_reason, |
| 1398 | "param_reqs": param_reqs |
| 1399 | } |
| 1400 | return info_dict |
| 1401 | |
| 1402 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1403 | def evStrideSmallerEqualZero(check=False, **kwargs): |
| 1404 | error_name = ErrorIf.StrideSmallerEqualZero |
| 1405 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1406 | error_result = False |
| 1407 | error_reason = "Stride value smaller than or equal zero" |
| 1408 | |
| 1409 | if check: |
| 1410 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1411 | output_dtype = kwargs['output_dtype'] |
| 1412 | if input_dtype != DType.FLOAT and output_dtype == DType.FLOAT: |
| 1413 | stride = kwargs['stride'] # Work around wrong input/output type tests |
| 1414 | elif output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1415 | stride = kwargs['stride_fp'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1416 | elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT: |
| 1417 | stride = kwargs['stride_fp'] # Work around wrong input/output type tests |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1418 | else: |
| 1419 | stride = kwargs['stride'] |
| 1420 | |
| 1421 | if min(stride) <= 0: |
| 1422 | error_result = True |
| 1423 | |
| 1424 | info_dict = { |
| 1425 | "error_name": error_name, |
| 1426 | "error_result": error_result, |
| 1427 | "error_reason": error_reason, |
| 1428 | "param_reqs": param_reqs |
| 1429 | } |
| 1430 | return info_dict |
| 1431 | |
| 1432 | @staticmethod |
| 1433 | def evStrideLargerEqualMax(check=False, **kwargs): |
| 1434 | error_name = ErrorIf.StrideLargerEqualMax |
| 1435 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1436 | error_result = False |
| 1437 | error_reason = "Stride value larger than or equal to maximum value" |
| 1438 | |
| 1439 | if check: |
| 1440 | shift = kwargs['shift'] |
| 1441 | input_dtype = kwargs['input_dtype'] |
| 1442 | stride = kwargs['stride'] |
| 1443 | if input_dtype in [DType.INT8, DType.INT16]: |
| 1444 | if shift >= 0 and (stride[0] >= (16 << shift) or stride[1] >= (16 << shift)): |
| 1445 | error_result = True |
| 1446 | elif shift < 0 and (stride[0] >= (16 >> -shift) or stride[1] >= (16 >> -shift)): |
| 1447 | error_result = True |
| 1448 | |
| 1449 | info_dict = { |
| 1450 | "error_name": error_name, |
| 1451 | "error_result": error_result, |
| 1452 | "error_reason": error_reason, |
| 1453 | "param_reqs": param_reqs |
| 1454 | } |
| 1455 | return info_dict |
| 1456 | |
| 1457 | |
| 1458 | @staticmethod |
| 1459 | def evStrideLargerDimension(check=False, **kwargs): |
| 1460 | error_name = ErrorIf.StrideLargerDimension |
| 1461 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1462 | error_result = False |
| 1463 | error_reason = "Stride value larger than or equal to H/W dimension" |
| 1464 | |
| 1465 | if check: |
| 1466 | shape = kwargs['input_shape'].shape |
| 1467 | input_dtype = kwargs['input_dtype'] |
| 1468 | stride = kwargs['stride_fp'] |
| 1469 | |
| 1470 | if input_dtype == DType.FLOAT and (stride[0] > shape[1]) or (stride[1] > shape[2]): |
| 1471 | error_result = True |
| 1472 | |
| 1473 | info_dict = { |
| 1474 | "error_name": error_name, |
| 1475 | "error_result": error_result, |
| 1476 | "error_reason": error_reason, |
| 1477 | "param_reqs": param_reqs |
| 1478 | } |
| 1479 | return info_dict |
| 1480 | |
| 1481 | |
| 1482 | @staticmethod |
| 1483 | def evOffsetSmallerEqualMin(check=False, **kwargs): |
| 1484 | error_name = ErrorIf.OffsetSmallerEqualMin |
| 1485 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1486 | error_result = False |
| 1487 | error_reason = "Offset value smaller than or equal to minimum value" |
| 1488 | |
| 1489 | if check: |
| 1490 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1491 | output_dtype = kwargs['output_dtype'] |
| 1492 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1493 | offset = kwargs['offset_fp'] |
| 1494 | else: |
| 1495 | offset = kwargs['offset'] |
| 1496 | |
| 1497 | if shift >= 0 and (offset[0] <= (-16 << shift) or offset[1] <= (-16 << shift)): |
| 1498 | error_result = True |
| 1499 | elif shift < 0 and (offset[0] <= (-16 >> -shift) or offset[1] <= (-16 >> -shift)): |
| 1500 | error_result = True |
| 1501 | |
| 1502 | info_dict = { |
| 1503 | "error_name": error_name, |
| 1504 | "error_result": error_result, |
| 1505 | "error_reason": error_reason, |
| 1506 | "param_reqs": param_reqs |
| 1507 | } |
| 1508 | return info_dict |
| 1509 | |
| 1510 | @staticmethod |
| 1511 | def evOffsetLargerEqualMax(check=False, **kwargs): |
| 1512 | error_name = ErrorIf.OffsetLargerEqualMax |
| 1513 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1514 | error_result = False |
| 1515 | error_reason = "Offset value larger than or equal to maximum value" |
| 1516 | |
| 1517 | if check: |
| 1518 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1519 | output_dtype = kwargs['output_dtype'] |
| 1520 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1521 | offset = kwargs['offset_fp'] |
| 1522 | else: |
| 1523 | offset = kwargs['offset'] |
| 1524 | |
| 1525 | if shift >= 0: |
| 1526 | if offset[0] >= (16 << shift) or offset[1] >= (16 << shift): |
| 1527 | error_result = True |
| 1528 | |
| 1529 | if shift >= 0 and (offset[0] >= (16 << shift) or offset[1] >= (16 << shift)): |
| 1530 | error_result = True |
| 1531 | elif shift < 0 and (offset[0] >= (16 >> -shift) or offset[1] >= (16 >> -shift)): |
| 1532 | error_result = True |
| 1533 | |
| 1534 | info_dict = { |
| 1535 | "error_name": error_name, |
| 1536 | "error_result": error_result, |
| 1537 | "error_reason": error_reason, |
| 1538 | "param_reqs": param_reqs |
| 1539 | } |
| 1540 | return info_dict |
| 1541 | |
| 1542 | @staticmethod |
| 1543 | def evShiftNotZero(check=False, **kwargs): |
| 1544 | error_name = ErrorIf.ShiftNotZero |
| 1545 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1546 | error_result = False |
| 1547 | error_reason = "Shift value must be zero for float input" |
| 1548 | |
| 1549 | if check: |
| 1550 | shift = kwargs['shift'] |
| 1551 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1552 | output_dtype = kwargs['output_dtype'] |
| 1553 | if input_dtype == DType.FLOAT and output_dtype == DType.FLOAT and shift != 0: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1554 | error_result = True |
| 1555 | |
| 1556 | info_dict = { |
| 1557 | "error_name": error_name, |
| 1558 | "error_result": error_result, |
| 1559 | "error_reason": error_reason, |
| 1560 | "param_reqs": param_reqs |
| 1561 | } |
| 1562 | return info_dict |
| 1563 | |
| 1564 | |
| 1565 | @staticmethod |
| 1566 | def evShiftSmallerOne(check=False, **kwargs): |
| 1567 | error_name = ErrorIf.ShiftSmallerOne |
| 1568 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1569 | error_result = False |
| 1570 | error_reason = "Shift value smaller than one" |
| 1571 | |
| 1572 | if check: |
| 1573 | shift = kwargs['shift'] |
| 1574 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1575 | output_dtype = kwargs['output_dtype'] |
| 1576 | if shift < 1 and input_dtype != DType.FLOAT and output_dtype != DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1577 | error_result = True |
| 1578 | |
| 1579 | info_dict = { |
| 1580 | "error_name": error_name, |
| 1581 | "error_result": error_result, |
| 1582 | "error_reason": error_reason, |
| 1583 | "param_reqs": param_reqs |
| 1584 | } |
| 1585 | return info_dict |
| 1586 | |
| 1587 | @staticmethod |
| 1588 | def evShiftLargerEleven(check=False, **kwargs): |
| 1589 | error_name = ErrorIf.ShiftLargerEleven |
| 1590 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1591 | error_result = False |
| 1592 | error_reason = "Shift value larger than eleven" |
| 1593 | |
| 1594 | if check: |
| 1595 | shift = kwargs['shift'] |
| 1596 | if shift > 11: |
| 1597 | error_result = True |
| 1598 | |
| 1599 | info_dict = { |
| 1600 | "error_name": error_name, |
| 1601 | "error_result": error_result, |
| 1602 | "error_reason": error_reason, |
| 1603 | "param_reqs": param_reqs |
| 1604 | } |
| 1605 | return info_dict |
| 1606 | |
| 1607 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1608 | class TosaInvalidValidator: |
| 1609 | |
| 1610 | @staticmethod |
| 1611 | def ivWrongDataTypeOrModeResize(**kwargs): |
| 1612 | input_dtype = kwargs["input_dtype"] |
| 1613 | args = kwargs["args"] |
| 1614 | mode = args[0] |
| 1615 | stride = args[1] |
| 1616 | stride_fp = args[4] |
| 1617 | output_dtype = args[8] |
| 1618 | |
| 1619 | if mode == ResizeMode.BILINEAR: |
| 1620 | # Invalid output data type / Invalid input datatype |
| 1621 | return ( |
| 1622 | not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or |
| 1623 | not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or |
| 1624 | not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or |
| 1625 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1626 | ) |
| 1627 | elif mode == ResizeMode.NEAREST: |
| 1628 | # Invalid output data type / Invalid input datatype |
| 1629 | return ( |
| 1630 | (input_dtype != output_dtype) or |
| 1631 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1632 | ) |
| 1633 | else: |
| 1634 | # Invalid resize mode |
| 1635 | return True |
| 1636 | |
| 1637 | @staticmethod |
| 1638 | def ivBadStride(**kwargs): |
| 1639 | input_dtype = kwargs["input_dtype"] |
| 1640 | args = kwargs["args"] |
| 1641 | stride_x = args[1][0] |
| 1642 | stride_y = args[1][1] |
| 1643 | stride_fp_x = args[4][0] |
| 1644 | stride_fp_y = args[4][1] |
| 1645 | |
| 1646 | if input_dtype == DType.FLOAT: |
| 1647 | if stride_fp_x <= 0 or stride_fp_y <= 0: |
| 1648 | # Negative or zero stride |
| 1649 | return True |
| 1650 | else: |
| 1651 | if stride_x <= 0 or stride_y <= 0: |
| 1652 | # Negative or zero stride |
| 1653 | return True |
| 1654 | return False |
| 1655 | |
| 1656 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1657 | @staticmethod |
| 1658 | def ivHeightWidthSmallerZero(**kwargs): |
| 1659 | opName = kwargs['opName'] |
| 1660 | |
| 1661 | inputShapes = kwargs['shapeList'] |
| 1662 | input = inputShapes[0] |
| 1663 | if not opName.endswith("pool2d"): |
| 1664 | filter = inputShapes[1] |
| 1665 | |
| 1666 | args = kwargs['args'] |
| 1667 | strides = args[0] |
| 1668 | padding = args[1] |
| 1669 | dilations = args[2] |
| 1670 | if opName.endswith("pool2d"): |
| 1671 | kernel = args[2] |
| 1672 | |
| 1673 | if opName.startswith('conv2d'): |
| 1674 | h = ( |
| 1675 | input[1] |
| 1676 | - filter[1] |
| 1677 | - (filter[1] - 1) * (dilations[0] - 1) |
| 1678 | + padding[0] |
| 1679 | + padding[1] |
| 1680 | ) // strides[0] + 1 |
| 1681 | |
| 1682 | w = ( |
| 1683 | input[2] |
| 1684 | - filter[2] |
| 1685 | - (filter[2] - 1) * (dilations[1] - 1) |
| 1686 | + padding[2] |
| 1687 | + padding[3] |
| 1688 | ) // strides[1] + 1 |
| 1689 | elif opName.startswith("depthwise_conv2d"): |
| 1690 | h = ( |
| 1691 | input[1] |
| 1692 | - filter[0] |
| 1693 | - (filter[0] - 1) * (dilations[0] - 1) |
| 1694 | + padding[0] |
| 1695 | + padding[1] |
| 1696 | ) // strides[0] + 1 |
| 1697 | |
| 1698 | w = ( |
| 1699 | input[2] |
| 1700 | - filter[1] |
| 1701 | - (filter[1] - 1) * (dilations[1] - 1) |
| 1702 | + padding[2] |
| 1703 | + padding[3] |
| 1704 | ) // strides[1] + 1 |
| 1705 | elif opName.endswith("pool2d"): |
| 1706 | h = (input[1] + padding[0] + padding[1] + strides[0] - kernel[0]) // strides[0] |
| 1707 | w = (input[2] + padding[2] + padding[3] + strides[1] - kernel[1]) // strides[1] |
| 1708 | else: |
| 1709 | assert False, "Unrecognized Op" |
| 1710 | |
| 1711 | if h <= 0 or w <= 0: |
| 1712 | # Invalid parameter combination |
| 1713 | return True |
| 1714 | return False |
| 1715 | |
| 1716 | @staticmethod |
| 1717 | def ivNonPositiveOutputShape(**kwargs): |
| 1718 | args = kwargs['args'] |
| 1719 | output_shape = args[3] |
| 1720 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
| 1721 | # Negative output shape |
| 1722 | return True |
| 1723 | return False |
| 1724 | |
| 1725 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1726 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1727 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1728 | # Maximum rank of tensor supported by test generator. |
| 1729 | TOSA_TENSOR_MAX_RANK = 6 |
| 1730 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1731 | def __init__(self, args): |
| 1732 | self.args = args |
| 1733 | self.basePath = args.output_dir |
| 1734 | self.random_seed = args.random_seed |
| 1735 | self.ser = None |
| 1736 | self.rng = np.random.default_rng(self.random_seed) |
| 1737 | self.createDynamicOpLists() |
| 1738 | self.initOpListDefaults() |
| 1739 | self.quantGen = TosaQuantGen() |
| 1740 | # Force makeShape to do a specific starting shape |
| 1741 | self.targetted_shape = None |
| 1742 | |
| 1743 | def createSerializer(self, opName, testPath): |
| 1744 | self.testPath = os.path.join(opName, testPath) |
| 1745 | |
| 1746 | fullPath = os.path.join(self.basePath, self.testPath) |
| 1747 | os.makedirs(fullPath, exist_ok=True) |
| 1748 | self.ser = ts.TosaSerializer(fullPath) |
| 1749 | |
| 1750 | def getSerializer(self): |
| 1751 | return self.ser |
| 1752 | |
| 1753 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1754 | with open( |
| 1755 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 1756 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1757 | fd.write(self.ser.serialize()) |
| 1758 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1759 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 1760 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1761 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1762 | def resetRNG(self, seed=None): |
| 1763 | if seed == None: |
| 1764 | seed = self.random_seed + 1 |
| 1765 | self.rng = np.random.default_rng(seed) |
| 1766 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1767 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1768 | if dtype == DType.BOOL: |
| 1769 | np_dt = np.bool |
| 1770 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1771 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1772 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1773 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1774 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1775 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 1776 | elif dtype == DType.UINT8: |
| 1777 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1778 | elif dtype == DType.INT16: |
| 1779 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 1780 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1781 | return np.int32( |
| 1782 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 1783 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1784 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1785 | return np.int64( |
| 1786 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 1787 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1788 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1789 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1790 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1791 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1792 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1793 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1794 | placeholders = [] |
| 1795 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1796 | assert len(shape_list) == len(dtype_list) |
| 1797 | |
| 1798 | for idx, shape in enumerate(shape_list): |
| 1799 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1800 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1801 | |
| 1802 | return placeholders |
| 1803 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1804 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1805 | consts = [] |
| 1806 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1807 | assert len(shape_list) == len(dtype_list) |
| 1808 | |
| 1809 | for idx, shape in enumerate(shape_list): |
| 1810 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1811 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1812 | |
| 1813 | return consts |
| 1814 | |
| 1815 | def makeShape(self, rank): |
| 1816 | if self.targetted_shape: |
| 1817 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1818 | return np.int32( |
| 1819 | self.rng.integers( |
| 1820 | low=self.args.tensor_shape_range[0], |
| 1821 | high=self.args.tensor_shape_range[1], |
| 1822 | size=rank, |
| 1823 | ) |
| 1824 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1825 | |
| 1826 | def setTargetShape(self, shape): |
| 1827 | self.targetted_shape = shape |
| 1828 | |
| 1829 | def randInt(self, low=0, high=256): |
| 1830 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 1831 | |
| 1832 | def getRandNumberDType(self, dtype): |
| 1833 | if dtype == DType.FLOAT: |
| 1834 | return self.rng.random() |
| 1835 | elif dtype == DType.BOOL: |
| 1836 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1837 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1838 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1839 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1840 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1841 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1842 | elif dtype == DType.INT16: |
| 1843 | low, high = (-32768, 32768) |
| 1844 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1845 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1846 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1847 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1848 | # Special size |
| 1849 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 1850 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1851 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1852 | |
| 1853 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 1854 | |
| 1855 | def shapeStr(self, shape): |
| 1856 | |
| 1857 | sStr = [] |
| 1858 | # Convert to strings |
| 1859 | for i in shape: |
| 1860 | sStr.append(str(i)) |
| 1861 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1862 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1863 | |
| 1864 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1865 | if isinstance(t, list): |
| 1866 | assert len(t) >= 2 |
| 1867 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1868 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1869 | if t == DType.BOOL: |
| 1870 | return "b" |
| 1871 | elif t == DType.INT4: |
| 1872 | return "i4" |
| 1873 | elif t == DType.INT8: |
| 1874 | return "i8" |
| 1875 | elif t == DType.UINT8: |
| 1876 | return "u8" |
| 1877 | elif t == DType.INT16: |
| 1878 | return "i16" |
| 1879 | elif t == DType.INT32: |
| 1880 | return "i32" |
| 1881 | elif t == DType.INT48: |
| 1882 | return "i48" |
| 1883 | elif t == DType.FLOAT: |
| 1884 | return "float" |
| 1885 | else: |
| 1886 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1887 | |
| 1888 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1889 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1890 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1891 | return 4 |
| 1892 | elif t == DType.INT8: |
| 1893 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1894 | elif t == DType.UINT8: |
| 1895 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1896 | elif t == DType.INT16: |
| 1897 | return 16 |
| 1898 | elif t == DType.INT32: |
| 1899 | return 32 |
| 1900 | elif t == DType.INT48: |
| 1901 | return 48 |
| 1902 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1903 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1904 | |
| 1905 | # Argument generators |
| 1906 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 1907 | # Where the string descriptor is used to generate the test name and |
| 1908 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 1909 | # build function |
| 1910 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1911 | def build_unary(self, op, a, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1912 | result_tens = OutputShaper.unaryOp(self.ser, a) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1913 | # build_placeholder returns an int, ABS/other ops does not |
| 1914 | if isinstance(op, int): |
| 1915 | self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) |
| 1916 | else: |
| 1917 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1918 | return result_tens |
| 1919 | |
| 1920 | def build_binary_broadcast(self, op, a, b): |
| 1921 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1922 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1923 | return result_tens |
| 1924 | |
| 1925 | def build_binary_nonbroadcast(self, op, a, b): |
| 1926 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1927 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1928 | return result_tens |
| 1929 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1930 | def build_arithmetic_right_shift(self, op, a, b, round): |
| 1931 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1932 | |
| 1933 | attr = ts.TosaSerializerAttribute() |
| 1934 | attr.ArithmeticRightShiftAttribute(round) |
| 1935 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1936 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], attr) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1937 | return result_tens |
| 1938 | |
| 1939 | def build_mul(self, op, a, b, shift): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1940 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, a, b) |
| 1941 | |
| 1942 | # Special for multiply: |
| 1943 | # Force the result to INT32 for INT types |
| 1944 | if a.dtype != DType.FLOAT: |
| 1945 | result_tens.setDtype(DType.INT32) |
| 1946 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1947 | attr = ts.TosaSerializerAttribute() |
| 1948 | attr.MulAttribute(shift) |
| 1949 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1950 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1951 | return result_tens |
| 1952 | |
| 1953 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1954 | # Constant size depending on type, random values |
| 1955 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 1956 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1957 | table_arr = self.getRandTensor([513], table_dtype) |
| 1958 | else: |
| 1959 | assert a.dtype == DType.INT8 |
| 1960 | table_dtype = DType.INT8 |
| 1961 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1962 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 1963 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 1964 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1965 | self.ser.addOperator(op['op'], [a.name, table_tens.name], [result_tens.name], None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1966 | |
| 1967 | return result_tens |
| 1968 | |
| 1969 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1970 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1971 | self.ser.addOperator(op['op'], [cond.name, a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1972 | return result_tens |
| 1973 | |
| 1974 | def build_comparison(self, op, a, b): |
| 1975 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1976 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1977 | return result_tens |
| 1978 | |
| 1979 | def build_argmax(self, op, a, axis): |
| 1980 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 1981 | |
| 1982 | attr = ts.TosaSerializerAttribute() |
| 1983 | attr.AxisAttribute(axis) |
| 1984 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1985 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1986 | return result_tens |
| 1987 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1988 | def build_pool2d(self, op, input, stride, pad, kernel, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1989 | result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) |
| 1990 | |
| 1991 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 1992 | attr.PoolAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1993 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1994 | self.ser.addOperator(op['op'], [input.name], [result_tens.name], attr, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1995 | return result_tens |
| 1996 | |
| 1997 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1998 | assert len(padding) == 4 |
| 1999 | result_tens = OutputShaper.conv2dOp( |
| 2000 | self.ser, ifm, filter, strides, padding, dilations |
| 2001 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2002 | |
| 2003 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2004 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2005 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2006 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2007 | op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2008 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2009 | return result_tens |
| 2010 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2011 | def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
| 2012 | assert len(padding) == 6 |
| 2013 | result_tens = OutputShaper.conv3dOp( |
| 2014 | self.ser, ifm, filter, strides, padding, dilations |
| 2015 | ) |
| 2016 | |
| 2017 | attr = ts.TosaSerializerAttribute() |
| 2018 | attr.ConvAttribute(padding, strides, dilations) |
| 2019 | |
| 2020 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2021 | op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2022 | ) |
| 2023 | return result_tens |
| 2024 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2025 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2026 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2027 | ): |
| 2028 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2029 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 2030 | |
| 2031 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2032 | attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2033 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2034 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2035 | op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2036 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2037 | return result_tens |
| 2038 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2039 | def build_depthwise_conv2d( |
| 2040 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 2041 | ): |
| 2042 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 2043 | self.ser, ifm, filter, strides, padding, dilations |
| 2044 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2045 | |
| 2046 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2047 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2048 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2049 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2050 | op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2051 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2052 | return result_tens |
| 2053 | |
| 2054 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 2055 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 2056 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2057 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2058 | op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2059 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2060 | return result_tens |
| 2061 | |
| 2062 | def build_matmul(self, op, a, b, qinfo): |
| 2063 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2064 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2065 | return result_tens |
| 2066 | |
| 2067 | def build_reduce(self, op, a, axis): |
| 2068 | result_tens = OutputShaper.reduceOp(self.ser, a, axis) |
| 2069 | |
| 2070 | attr = ts.TosaSerializerAttribute() |
| 2071 | attr.AxisAttribute(axis) |
| 2072 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2073 | self.ser.addOperator(op['op'], [a.name], result_tens.name, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2074 | return result_tens |
| 2075 | |
| 2076 | def build_clamp(self, op, a): |
| 2077 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2078 | |
| 2079 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2080 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2081 | |
| 2082 | if a.dtype == DType.FLOAT: |
| 2083 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 2084 | else: |
| 2085 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 2086 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2087 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2088 | return result_tens |
| 2089 | |
| 2090 | def build_leaky_relu(self, op, a): |
| 2091 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2092 | attr = ts.TosaSerializerAttribute() |
| 2093 | |
| 2094 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 2095 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2096 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2097 | return result_tens |
| 2098 | |
| 2099 | # Needs an additional type/input |
| 2100 | def build_prelu(self, op, a): |
| 2101 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2102 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2103 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2104 | return result_tens |
| 2105 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2106 | def build_sigmoid(self, op, a): |
| 2107 | result_tens = OutputShaper.unaryOp(self.ser, a) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2108 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2109 | return result_tens |
| 2110 | |
| 2111 | def build_tanh(self, op, a): |
| 2112 | result_tens = OutputShaper.unaryOp(self.ser, a) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2113 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2114 | return result_tens |
| 2115 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2116 | def build_concat(self, op, *a): |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2117 | assert type(a[-1]) == int |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2118 | |
| 2119 | # To store variable length list of input tensors we need to store axis along with it |
| 2120 | axis = a[-1] |
| 2121 | a = a[:-1] |
| 2122 | |
| 2123 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2124 | |
| 2125 | attr = ts.TosaSerializerAttribute() |
| 2126 | attr.AxisAttribute(axis) |
| 2127 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2128 | input_tensor_names = [] |
| 2129 | for tensor in a: |
| 2130 | input_tensor_names.append(tensor.name) |
| 2131 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2132 | self.ser.addOperator(op['op'], input_tensor_names, [result_tens.name], attr) |
| 2133 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2134 | |
| 2135 | def build_pad(self, op, a, padding, qinfo): |
| 2136 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 2137 | |
| 2138 | # Need to turn the padding array into a TOSA tensor here. |
| 2139 | # This is one of the few tensor operands that does not get |
| 2140 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2141 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2142 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2143 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2144 | op['op'], [a.name, padding_tens.name], [result_tens.name], None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2145 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2146 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2147 | |
| 2148 | def build_reshape(self, op, a, newShape): |
| 2149 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 2150 | |
| 2151 | attr = ts.TosaSerializerAttribute() |
| 2152 | attr.ReshapeAttribute(newShape) |
| 2153 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2154 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2155 | return result_tens |
| 2156 | |
| 2157 | def build_reverse(self, op, a, axis): |
| 2158 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2159 | |
| 2160 | attr = ts.TosaSerializerAttribute() |
| 2161 | attr.AxisAttribute(axis) |
| 2162 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2163 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2164 | return result_tens |
| 2165 | |
| 2166 | def build_transpose(self, op, a, perms): |
| 2167 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 2168 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2169 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2170 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2171 | self.ser.addOperator(op['op'], [a.name, perms_tens.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2172 | return result_tens |
| 2173 | |
| 2174 | def build_slice(self, op, a, begin, size): |
| 2175 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 2176 | |
| 2177 | attr = ts.TosaSerializerAttribute() |
| 2178 | attr.SliceAttribute(begin, size) |
| 2179 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2180 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2181 | return result_tens |
| 2182 | |
| 2183 | def build_tile(self, op, a, multiples): |
| 2184 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 2185 | |
| 2186 | attr = ts.TosaSerializerAttribute() |
| 2187 | attr.TileAttribute(multiples) |
| 2188 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2189 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2190 | return result_tens |
| 2191 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2192 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2193 | |
| 2194 | # Create a new indicies tensor |
| 2195 | # here with data that doesn't exceed the dimensions of the values tensor |
| 2196 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2197 | K = values.shape[1] # K |
| 2198 | W = self.randInt( |
| 2199 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 2200 | ) # W |
| 2201 | indicies_arr = np.int32( |
| 2202 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 2203 | ) # (N, W) |
| 2204 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2205 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2206 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2207 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2208 | self.ser.addOperator(op['op'], [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2209 | |
| 2210 | return result_tens |
| 2211 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2212 | def build_scatter(self, op, values_in, input): |
| 2213 | |
| 2214 | # Create a new indicies tensor |
| 2215 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 2216 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2217 | K = values_in.shape[1] # K |
| 2218 | W = input.shape[1] # W |
| 2219 | indicies_arr = np.int32( |
| 2220 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 2221 | ) # (N, W) |
| 2222 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2223 | |
| 2224 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 2225 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2226 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2227 | op['op'], [values_in.name, indicies.name, input.name], [result_tens.name] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2228 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2229 | |
| 2230 | return result_tens |
| 2231 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2232 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2233 | def build_resize( |
| 2234 | self, |
| 2235 | op, |
| 2236 | input, |
| 2237 | mode, |
| 2238 | stride, |
| 2239 | offset, |
| 2240 | shift, |
| 2241 | stride_fp, |
| 2242 | offset_fp, |
| 2243 | output_dims, |
| 2244 | input_dtype, |
| 2245 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2246 | validator_fcns, |
| 2247 | error_name = None, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2248 | ): |
| 2249 | result_tens = OutputShaper.resizeOp( |
| 2250 | self.ser, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 2251 | self.rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2252 | input, |
| 2253 | mode, |
| 2254 | stride, |
| 2255 | offset, |
| 2256 | shift, |
| 2257 | stride_fp, |
| 2258 | offset_fp, |
| 2259 | output_dims, |
| 2260 | input_dtype, |
| 2261 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2262 | error_name |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2263 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2264 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2265 | # Invalidate Input/Output list for error if checks. |
| 2266 | input_list = [input.name] |
| 2267 | output_list = [result_tens.name] |
| 2268 | pCount, cCount = op["operands"] |
| 2269 | num_operands = pCount + cCount |
| 2270 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2271 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2272 | TosaErrorValidator.evValidateErrorIfs( |
| 2273 | self.ser, |
| 2274 | validator_fcns, |
| 2275 | error_name, |
| 2276 | op=op, |
| 2277 | mode=mode, |
| 2278 | shift=shift, |
| 2279 | input_dtype=input_dtype, |
| 2280 | output_dtype=output_dtype, |
| 2281 | input_shape=input, |
| 2282 | output_shape=output_dims, |
| 2283 | offset=offset, |
| 2284 | offset_fp=offset_fp, |
| 2285 | stride=stride, |
| 2286 | stride_fp=stride_fp, |
| 2287 | input_list=input_list, |
| 2288 | output_list=output_list, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 2289 | result_tensor=result_tens, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2290 | num_operands=num_operands, |
| 2291 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2292 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2293 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2294 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2295 | attr.ResizeAttribute( |
| 2296 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 2297 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2298 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2299 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2300 | return result_tens |
| 2301 | |
| 2302 | def build_identityn(self, op, val, val2): |
| 2303 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2304 | result_tens = OutputShaper.unaryOp(self.ser, val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2305 | result_tens2 = OutputShaper.unaryOp(self.ser, val2) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2306 | self.ser.addOperator( |
| 2307 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 2308 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2309 | return result_tens |
| 2310 | |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 2311 | def build_const(self, op, val): |
| 2312 | self.ser.addOutputTensor(val) |
| 2313 | return val |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2314 | |
| 2315 | # Type Conversion |
| 2316 | def build_cast(self, op, val, out_dtype): |
| 2317 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2318 | self.ser.addOperator(op['op'], [val.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2319 | return result_tens |
| 2320 | |
| 2321 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel): |
| 2322 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 2323 | |
| 2324 | if per_channel: |
| 2325 | nc = val.shape[-1] |
| 2326 | else: |
| 2327 | nc = 1 |
| 2328 | |
| 2329 | in_type_width = self.typeWidth(val.dtype) |
| 2330 | out_type_width = self.typeWidth(out_dtype) |
| 2331 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2332 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2333 | input_zp = self.randInt(-128, 128) |
| 2334 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2335 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2336 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2337 | in_type_width = in_type_width + 1 |
| 2338 | else: |
| 2339 | input_zp = 0 |
| 2340 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2341 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2342 | output_zp = self.randInt(-128, 128) |
| 2343 | out_type_width = out_type_width + 1 |
| 2344 | elif out_dtype == DType.UINT8: |
| 2345 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2346 | out_type_width = out_type_width + 1 |
| 2347 | else: |
| 2348 | output_zp = 0 |
| 2349 | |
| 2350 | # Calculate scale based on: |
| 2351 | # scale = a *(2^output_width)/(2^input_width)) |
| 2352 | |
| 2353 | a = np.float32(self.rng.random(size=[nc])) |
| 2354 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 2355 | |
| 2356 | if scale32: |
| 2357 | pass |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2358 | # Cap the scaling at 2^31 - 1 for scale32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2359 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 2360 | else: |
| 2361 | # Cap the scaling at 2^15 - 1 for scale16 |
| 2362 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 2363 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2364 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2365 | |
| 2366 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 2367 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 2368 | |
| 2369 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2370 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 2371 | scale_arr[i], scale32 |
| 2372 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2373 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2374 | # 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] | 2375 | |
| 2376 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2377 | attr.RescaleAttribute( |
| 2378 | input_zp, |
| 2379 | output_zp, |
| 2380 | multiplier_arr, |
| 2381 | shift_arr, |
| 2382 | scale32, |
| 2383 | double_round, |
| 2384 | per_channel, |
| 2385 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2386 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2387 | self.ser.addOperator(op['op'], [val.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2388 | return result_tens |
| 2389 | |
| 2390 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 2391 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 2392 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 2393 | # and fill them with const nodes for the body. |
| 2394 | |
| 2395 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2396 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2397 | |
| 2398 | # Make then/else tensors |
| 2399 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2400 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 2401 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2402 | |
| 2403 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2404 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2405 | |
| 2406 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2407 | then_block = "THEN_BLOCK" |
| 2408 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2409 | attr = ts.TosaSerializerAttribute() |
| 2410 | attr.CondIfAttribute(then_block, else_block) |
| 2411 | |
| 2412 | # Finally, build the op and the two blocks |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2413 | self.ser.addOperator(op['op'], [cond_tens.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2414 | |
| 2415 | self.ser.startBasicBlock(then_block) |
| 2416 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2417 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2418 | self.ser.addOutputTensor(then_tens) |
| 2419 | |
| 2420 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2421 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2422 | self.ser.addOutputTensor(else_tens) |
| 2423 | |
| 2424 | return result_tens |
| 2425 | |
| 2426 | def build_cond_if_binary(self, op, a, b, cond): |
| 2427 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 2428 | # alternately add or subtract them based on the condition |
| 2429 | |
| 2430 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2431 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2432 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2433 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2434 | |
| 2435 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2436 | then_block = "THEN_BLOCK" |
| 2437 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2438 | attr = ts.TosaSerializerAttribute() |
| 2439 | attr.CondIfAttribute(then_block, else_block) |
| 2440 | |
| 2441 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2442 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2443 | op['op'], [cond_tens.name, a.name, b.name], [result_tens.name], attr |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2444 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2445 | |
| 2446 | self.ser.startBasicBlock(then_block) |
| 2447 | self.ser.addInputTensor(a) |
| 2448 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2449 | then_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2450 | self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) |
| 2451 | |
| 2452 | self.ser.startBasicBlock(else_block) |
| 2453 | self.ser.addInputTensor(a) |
| 2454 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2455 | else_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2456 | self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) |
| 2457 | |
| 2458 | return result_tens |
| 2459 | |
| 2460 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2461 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2462 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2463 | cond_block = "COND_BLOCK" |
| 2464 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2465 | |
| 2466 | attr = ts.TosaSerializerAttribute() |
| 2467 | attr.WhileLoopAttribute(cond_block, body_block) |
| 2468 | |
| 2469 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2470 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2471 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2472 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2473 | |
| 2474 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2475 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 2476 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 2477 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2478 | |
| 2479 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2480 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2481 | op['op'], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2482 | [iter.name, a.name, acc.name], |
| 2483 | [iter_out.name, a_out.name, acc_out.name], |
| 2484 | attr, |
| 2485 | ) |
Kevin Cheng | b227ae5 | 2021-09-02 13:43:17 -0700 | [diff] [blame] | 2486 | self.ser.addOutputTensor(acc_out) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2487 | |
| 2488 | # COND block (input: iter, output: cond_tens ) |
| 2489 | self.ser.startBasicBlock(cond_block) |
| 2490 | self.ser.addInputTensor(iter) |
| 2491 | self.ser.addInputTensor(a) |
| 2492 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2493 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 2494 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 2495 | 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] | 2496 | |
| 2497 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 2498 | # Note that local intermediate tensors need to be declared here for the outputs |
| 2499 | self.ser.startBasicBlock(body_block) |
| 2500 | self.ser.addInputTensor(iter) |
| 2501 | self.ser.addInputTensor(a) |
| 2502 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2503 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 2504 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 2505 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2506 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 2507 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 2508 | self.ser.addOutputTensor(iter_body_out) |
| 2509 | self.ser.addOutputTensor(a) |
| 2510 | self.ser.addOutputTensor(acc_body_out) |
| 2511 | |
| 2512 | return acc_out |
| 2513 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2514 | def create_filter_lists(self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None): |
| 2515 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 2516 | default_test_rank_range = range(1, 5) |
| 2517 | if not shapeFilter: |
| 2518 | shapeFilter = [None] |
| 2519 | |
| 2520 | # Calculate the filters based on what is requested and what the operator allows |
| 2521 | rmin, rmax = op["rank"] |
| 2522 | if rankFilter is not None: |
| 2523 | cleanRankFilter = [] |
| 2524 | # Ensure rankFilter values are allowed by operator |
| 2525 | for rank in rankFilter: |
| 2526 | if rank >= rmin and rank <= rmax: |
| 2527 | cleanRankFilter.append(rank) |
| 2528 | elif rankFilter is None and shapeFilter[0] is None: |
| 2529 | cleanRankFilter = [] |
| 2530 | # Ensure default behaviour is bounded by default range or by operator, whichever is smaller. |
| 2531 | rankRange = range(rmin, rmax + 1) |
| 2532 | for rank in rankRange: |
| 2533 | if rank >= min(default_test_rank_range) and rank <= max(default_test_rank_range): |
| 2534 | cleanRankFilter.append(rank) |
| 2535 | else: |
| 2536 | cleanRankFilter = range(rmin, rmax + 1) |
| 2537 | |
| 2538 | dtypes = op["types"] |
| 2539 | if dtypeFilter is not None: |
| 2540 | cleanDtypeFilter = [] |
| 2541 | # Ensure filtered dtypes are allowed by operator |
| 2542 | for dtype in dtypeFilter: |
| 2543 | if dtype in dtypes: |
| 2544 | cleanDtypeFilter.append(dtype) |
| 2545 | else: |
| 2546 | cleanDtypeFilter = dtypes |
| 2547 | |
| 2548 | if testType == 'positive': |
| 2549 | filterDict = { |
| 2550 | 'shapeFilter': shapeFilter, |
| 2551 | 'rankFilter': cleanRankFilter, |
| 2552 | 'dtypeFilter': cleanDtypeFilter |
| 2553 | } |
| 2554 | return filterDict |
| 2555 | elif testType == 'negative': |
| 2556 | validator_info = validator(check=False, op=op) |
| 2557 | error_arguments = validator_info['param_reqs'] |
| 2558 | |
| 2559 | #Set parameters as required |
| 2560 | if error_arguments['rank'] != None: |
| 2561 | rankFilter = error_arguments['rank'] |
| 2562 | else: |
| 2563 | rankFilter = cleanRankFilter |
| 2564 | |
| 2565 | if error_arguments['dtype'] != None: |
| 2566 | dtypeFilter = error_arguments['dtype'] |
| 2567 | else: |
| 2568 | dtypeFilter = cleanDtypeFilter |
| 2569 | |
| 2570 | if error_arguments['shape'] != None: |
| 2571 | shapeFilter = error_arguments['shape'] |
| 2572 | else: |
| 2573 | shapeFilter = shapeFilter[:2] # Reduce number of shapes to keep test numbers small |
| 2574 | |
| 2575 | filterDict = { |
| 2576 | 'shapeFilter': shapeFilter, |
| 2577 | 'rankFilter': rankFilter, |
| 2578 | 'dtypeFilter': dtypeFilter |
| 2579 | } |
| 2580 | return filterDict |
| 2581 | |
| 2582 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2583 | def genOpTestList( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2584 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2585 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2586 | |
| 2587 | try: |
| 2588 | op = self.TOSA_OP_LIST[opName] |
| 2589 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2590 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2591 | |
| 2592 | # Initialize a new random number generator |
| 2593 | self.rng = np.random.default_rng(self.random_seed) |
| 2594 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2595 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2596 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2597 | # Test list consists of a tuple of: |
| 2598 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 2599 | testList = [] |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2600 | if testType == 'negative' and "error_if_validators" in op: |
| 2601 | error_if_validators = op["error_if_validators"] |
| 2602 | else: |
| 2603 | error_if_validators = [None] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2604 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2605 | for validator in error_if_validators: |
| 2606 | if validator is not None: |
| 2607 | error_name = validator(check=False, op=op)['error_name'] |
| 2608 | #print("error_name: ", error_name) |
| 2609 | else: |
| 2610 | error_name = None |
| 2611 | |
| 2612 | filterDict = self.create_filter_lists(op, shapeFilter, rankFilter, dtypeFilter, testType, validator) |
| 2613 | cleanRankFilter = filterDict['rankFilter'] |
| 2614 | cleanDtypeFilter = filterDict['dtypeFilter'] |
| 2615 | cleanShapeFilter = filterDict['shapeFilter'] |
| 2616 | #print(f"Filters: S {shapeFilter}, R {cleanRankFilter}, T {cleanDtypeFilter}") |
| 2617 | |
| 2618 | for r in cleanRankFilter: |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2619 | if opName.startswith("conv3d"): |
| 2620 | assert r == 5, "conv3d test must have input rank == 5" |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2621 | for t in cleanDtypeFilter: |
| 2622 | for shape in cleanShapeFilter: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2623 | # Filter out by rank |
| 2624 | if shape is not None and len(shape) != r: |
| 2625 | continue |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2626 | self.setTargetShape(shape) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2627 | shapeList = tgen_fcn(self, op, r, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2628 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2629 | shapeStr = self.shapeStr(shapeList[0]) |
| 2630 | typeStr = self.typeStr(t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2631 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2632 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 2633 | argList = [] |
| 2634 | if agen_fcn: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2635 | argList = agen_fcn(self, opName, shapeList, t, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2636 | else: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2637 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2638 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2639 | for argStr, args in argList: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2640 | if testType == 'positive': |
| 2641 | if argStr: |
| 2642 | testStr = "{}_{}_{}_{}".format( |
| 2643 | opName, shapeStr, typeStr, argStr |
| 2644 | ) |
| 2645 | else: |
| 2646 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
| 2647 | elif testType == 'negative': |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2648 | if argStr: |
| 2649 | testStr = "{}_ERRORIF_{}_{}_{}_{}".format( |
| 2650 | opName, error_name, shapeStr, typeStr, argStr |
| 2651 | ) |
| 2652 | else: |
| 2653 | testStr = "{}_ERRORIF_{}_{}_{}".format(opName, error_name, shapeStr, typeStr) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2654 | |
| 2655 | testList.append((opName, testStr, t, error_name, shapeList, args)) |
| 2656 | |
| 2657 | if testType == 'positive': |
| 2658 | # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| 2659 | if "invalid_test_validators" in op: |
| 2660 | invalid_test_validators = op["invalid_test_validators"] |
| 2661 | clean_testList = [] |
| 2662 | for test in testList: |
| 2663 | for validator_fcn in invalid_test_validators: |
| 2664 | remove_test = False |
| 2665 | if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[4], args=test[5]): |
| 2666 | remove_test = True |
| 2667 | if not remove_test: |
| 2668 | clean_testList.append(test) |
| 2669 | testList = clean_testList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2670 | |
| 2671 | return testList |
| 2672 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2673 | |
| 2674 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2675 | try: |
| 2676 | op = self.TOSA_OP_LIST[opName] |
| 2677 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2678 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2679 | |
| 2680 | # Create a serializer |
| 2681 | self.createSerializer(opName, testStr) |
| 2682 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2683 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2684 | if "error_if_validators" in op: |
| 2685 | error_if_validators = op["error_if_validators"] |
| 2686 | else: |
| 2687 | error_if_validators = None |
| 2688 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2689 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2690 | num_operands = pCount + cCount |
| 2691 | |
| 2692 | if isinstance(dtype_or_dtypeList, list): |
| 2693 | dtypeList = dtype_or_dtypeList |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2694 | elif op["op"] == Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2695 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2696 | else: |
| 2697 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 2698 | |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2699 | if op["op"] != Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2700 | assert ( |
| 2701 | len(shapeList) == num_operands |
| 2702 | ), "shapeList length {} must match number of operands {}".format( |
| 2703 | len(shapeList), num_operands |
| 2704 | ) |
| 2705 | assert ( |
| 2706 | len(dtypeList) == num_operands |
| 2707 | ), "dtypeList length {} must match number of operands {}".format( |
| 2708 | len(dtypeList), num_operands |
| 2709 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2710 | |
| 2711 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2712 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2713 | except KeyError: |
| 2714 | qgen = None |
| 2715 | |
| 2716 | # Build the random tensor operands and the test |
| 2717 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2718 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2719 | tens = self.generate_tensors(op, dtypeList, shapeList, testArgs) |
| 2720 | |
| 2721 | if qgen is not None: |
| 2722 | qinfo = qgen(self, op, dtype_or_dtypeList) |
| 2723 | else: |
| 2724 | qinfo = None |
| 2725 | |
| 2726 | try: |
| 2727 | if error_if_validators is None: |
| 2728 | if qinfo is not None: |
| 2729 | resultName = build_fcn(self, op, *tens, *testArgs, qinfo) |
| 2730 | else: |
| 2731 | resultName = build_fcn(self, op, *tens, *testArgs) |
| 2732 | else: |
| 2733 | if qinfo is not None: |
| 2734 | resultName = build_fcn(self, op, *tens, *testArgs, qinfo, error_if_validators, error_name) |
| 2735 | else: |
| 2736 | resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name) |
| 2737 | except TypeError as e: |
| 2738 | print( |
| 2739 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 2740 | build_fcn, tens, testArgs |
| 2741 | ) |
| 2742 | ) |
| 2743 | raise e |
| 2744 | |
| 2745 | if resultName is None: |
| 2746 | print("Invalid ERROR_IF tests created") |
| 2747 | |
| 2748 | # Save the serialized test |
| 2749 | self.serialize("test") |
| 2750 | |
| 2751 | |
| 2752 | def generate_tensors(self, op, dtypeList, shapeList, testArgs): |
| 2753 | pCount, cCount = op["operands"] |
| 2754 | |
| 2755 | tens = [] |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 2756 | if (op["op"] == Op.ADD or op["op"] == Op.SUB) and dtypeList[0] == DType.INT32: |
| 2757 | # Make sure the operation does not cause value saturation - where |
| 2758 | # the number wraps due to limited number of bits to store the answer |
| 2759 | assert ( |
| 2760 | pCount == 2 and cCount == 0 |
| 2761 | ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
| 2762 | |
| 2763 | placeholders = [] |
| 2764 | add = (op["op"] == Op.ADD) |
| 2765 | a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2766 | b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2767 | if add: |
| 2768 | res_arr = np.add(a_arr, b_arr, dtype=np.int64) |
| 2769 | else: |
| 2770 | res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) |
| 2771 | |
| 2772 | # Work out the saturation limits |
| 2773 | max_i32 = (1 << 31)-1 |
| 2774 | min_i32 = -(1 << 31) |
| 2775 | max_arr = np.full(shapeList[1], max_i32) |
| 2776 | min_arr = np.full(shapeList[1], min_i32) |
| 2777 | |
| 2778 | # Find how much values exceed the maximum/minimums |
| 2779 | sat_max_arr = np.maximum(res_arr - max_arr, 0) |
| 2780 | sat_min_arr = np.minimum(res_arr - min_arr, 0) |
| 2781 | |
| 2782 | if not add: |
| 2783 | # Swap saturation values and negate values as we need to perform opposite operations |
| 2784 | sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr |
| 2785 | |
| 2786 | # Create new array of unsaturated values by clipping values as needed |
| 2787 | b_unsat_arr = b_arr |
| 2788 | if (sat_max_arr != 0).any(): |
| 2789 | # Clip values that cause saturation |
| 2790 | b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) |
| 2791 | # Reduce axes in unsaturated tensor to match original tensor |
| 2792 | for axis, dim in enumerate(b_arr.shape): |
| 2793 | if dim != b_unsat_arr.shape[axis]: |
| 2794 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2795 | b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) |
| 2796 | |
| 2797 | if (sat_min_arr != 0).any(): |
| 2798 | # Clip values that cause saturation |
| 2799 | b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) |
| 2800 | # Reduce axes in unsaturated tensor to match original tensor |
| 2801 | for axis, dim in enumerate(b_arr.shape): |
| 2802 | if dim != b_unsat_arr.shape[axis]: |
| 2803 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2804 | b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) |
| 2805 | |
| 2806 | placeholders.append( |
| 2807 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2808 | ) |
| 2809 | placeholders.append( |
| 2810 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| 2811 | ) |
| 2812 | |
| 2813 | tens.extend(placeholders) |
| 2814 | elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 2815 | # Force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2816 | assert ( |
| 2817 | pCount == 2 and cCount == 0 |
| 2818 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2819 | |
| 2820 | placeholders = [] |
| 2821 | for idx, shape in enumerate(shapeList[:]): |
| 2822 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2823 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2824 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2825 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2826 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2827 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2828 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 2829 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2830 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2831 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2832 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2833 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2834 | |
| 2835 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 2836 | elif op["op"] == Op.SELECT: |
| 2837 | # Set datatype of condition tensor to boolean |
| 2838 | dtypeList[0] = DType.BOOL |
| 2839 | tens.extend( |
| 2840 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2841 | ) |
| 2842 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2843 | elif op["op"] == Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2844 | assert ( |
| 2845 | pCount == 2 and cCount == 0 |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2846 | ), "Op.INTDIV must have 2 placeholders, 0 consts" |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2847 | |
| 2848 | placeholders = [] |
| 2849 | |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2850 | # Two invalid cases for Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2851 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2852 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2853 | while True: |
| 2854 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2855 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2856 | |
| 2857 | if (divisor_arr == 0).any(): |
| 2858 | continue |
| 2859 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2860 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2861 | continue |
| 2862 | |
| 2863 | break |
| 2864 | |
| 2865 | placeholders.append( |
| 2866 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 2867 | ) |
| 2868 | placeholders.append( |
| 2869 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 2870 | ) |
| 2871 | |
| 2872 | tens.extend(placeholders) |
| 2873 | elif op["op"] == Op.MUL: |
| 2874 | assert ( |
| 2875 | pCount == 2 and cCount == 0 |
| 2876 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 2877 | |
| 2878 | if dtypeList[0] == DType.FLOAT: |
| 2879 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 2880 | else: |
| 2881 | placeholders = [] |
| 2882 | |
| 2883 | # Make sure multiply result in int32 range |
| 2884 | shift = testArgs[0] |
| 2885 | if dtypeList[0] == DType.INT8: |
| 2886 | num_bits = 8 |
| 2887 | elif dtypeList[0] == DType.INT16: |
| 2888 | num_bits = 16 |
| 2889 | elif dtypeList[0] == DType.INT32: |
| 2890 | num_bits = 32 |
| 2891 | else: |
| 2892 | raise Exception("OpMul: invalid input dtype") |
| 2893 | |
| 2894 | for idx, shape in enumerate(shapeList[:]): |
| 2895 | low = -(2 ** (num_bits - 1)) |
| 2896 | high = (2 ** (num_bits - 1)) - 1 |
| 2897 | |
| 2898 | a_arr = np.int32( |
| 2899 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 2900 | ) |
| 2901 | b_arr = np.int32( |
| 2902 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 2903 | ) |
| 2904 | |
| 2905 | i = 0 |
| 2906 | while True: |
| 2907 | |
| 2908 | a_arr_64 = a_arr.astype(np.int64) |
| 2909 | b_arr_64 = b_arr.astype(np.int64) |
| 2910 | |
| 2911 | if shift > 0: |
| 2912 | rounding = 1 << (shift - 1) |
| 2913 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 2914 | else: |
| 2915 | result_arr = a_arr_64 * b_arr_64 |
| 2916 | |
| 2917 | if (result_arr > -(2 ** 31)).all() and ( |
| 2918 | result_arr <= ((2 ** 31) - 1) |
| 2919 | ).all(): |
| 2920 | break |
| 2921 | |
| 2922 | i = i + 1 |
| 2923 | a_arr = a_arr // 2 |
| 2924 | b_arr = b_arr // 2 |
| 2925 | |
| 2926 | placeholders.append( |
| 2927 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2928 | ) |
| 2929 | placeholders.append( |
| 2930 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 2931 | ) |
| 2932 | |
| 2933 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2934 | elif op["op"] == Op.CONCAT: |
| 2935 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 2936 | if count < 1: |
| 2937 | count = 1 |
| 2938 | if self.args.num_const_inputs_concat == 0: |
| 2939 | count = len(shapeList) |
| 2940 | |
| 2941 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 2942 | tens.extend( |
| 2943 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 2944 | ) |
| 2945 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2946 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2947 | tens.extend( |
| 2948 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2949 | ) |
| 2950 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2951 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2952 | return tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2953 | |
| 2954 | def createDynamicOpLists(self): |
| 2955 | |
| 2956 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2957 | KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2958 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2959 | for k in KERNELS_2D: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2960 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 2961 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 2962 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2963 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2964 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2965 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 2966 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 2967 | "depthwise_conv2d_TEMPLATE" |
| 2968 | ].copy() |
| 2969 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2970 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2971 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2972 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 2973 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 2974 | "transpose_conv2d_TEMPLATE" |
| 2975 | ].copy() |
| 2976 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2977 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2978 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2979 | KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| 2980 | for k in KERNELS_3D: |
| 2981 | testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| 2982 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| 2983 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 2984 | self.TOSA_OP_LIST[testName]["template"] = False |
| 2985 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2986 | # Delete any templates after having created any dynamic ops |
| 2987 | # This is a two-pass operation because it's bad practice to delete |
| 2988 | # keys from dictionaries while iterating |
| 2989 | keyList = [] |
| 2990 | for k in self.TOSA_OP_LIST: |
| 2991 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2992 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2993 | keyList.append(k) |
| 2994 | continue |
| 2995 | except KeyError: |
| 2996 | pass |
| 2997 | |
| 2998 | for k in keyList: |
| 2999 | del self.TOSA_OP_LIST[k] |
| 3000 | |
| 3001 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3002 | """Fill in default fields for ops if they aren't already specified. |
| 3003 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3004 | for op in self.TOSA_OP_LIST: |
| 3005 | |
| 3006 | # Required fields |
| 3007 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3008 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3009 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3010 | raise Exception( |
| 3011 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 3012 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3013 | |
| 3014 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3015 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3016 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3017 | raise Exception( |
| 3018 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 3019 | op |
| 3020 | ) |
| 3021 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3022 | |
| 3023 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3024 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3025 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3026 | raise Exception( |
| 3027 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 3028 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3029 | |
| 3030 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3031 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3032 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3033 | raise Exception( |
| 3034 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 3035 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3036 | |
| 3037 | # Put in default rank range, if missing |
| 3038 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3039 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3040 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3041 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3042 | |
| 3043 | # Tensor operator list |
| 3044 | # 'op': op name |
| 3045 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3046 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 3047 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3048 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 3049 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3050 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3051 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3052 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 3053 | 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] | 3054 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3055 | TYPE_BOOL = [DType.BOOL] |
| 3056 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 3057 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 3058 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3059 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3060 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3061 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3062 | TYPE_CONV = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 3063 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3064 | [DType.INT8, DType.INT8, DType.INT32], |
| 3065 | [DType.INT16, DType.INT8, DType.INT48], |
| 3066 | DType.FLOAT, |
| 3067 | ] |
| 3068 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 3069 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3070 | |
| 3071 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3072 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3073 | "argmax": { |
| 3074 | "op": Op.ARGMAX, |
| 3075 | "operands": (1, 0), |
| 3076 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3077 | "types": TYPE_NARROW_INT_FP, |
| 3078 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3079 | "avg_pool2d": { |
| 3080 | "op": Op.AVG_POOL2D, |
| 3081 | "operands": (1, 0), |
| 3082 | "rank": (4, 4), |
| 3083 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 3084 | "qgen": TosaQuantGen.qgUnary, |
| 3085 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3086 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3087 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3088 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3089 | "conv2d_TEMPLATE": { |
| 3090 | "op": Op.CONV2D, |
| 3091 | "operands": (1, 2), |
| 3092 | "rank": (4, 4), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3093 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3094 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3095 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3096 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3097 | "template": True, |
| 3098 | }, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3099 | # Templated operator. Filled in by createDynamicOpLists |
| 3100 | "conv3d_TEMPLATE": { |
| 3101 | "op": Op.CONV3D, |
| 3102 | "operands": (1, 2), |
| 3103 | "rank": (5, 5), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3104 | "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3105 | "qgen": TosaQuantGen.qgConv, |
| 3106 | "types": TYPE_CONV, |
| 3107 | "template": True, |
| 3108 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3109 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3110 | "depthwise_conv2d_TEMPLATE": { |
| 3111 | "op": Op.DEPTHWISE_CONV2D, |
| 3112 | "operands": (1, 2), |
| 3113 | "filter": [1, 1], |
| 3114 | "rank": (4, 4), |
| 3115 | "build_fcn": ( |
| 3116 | build_depthwise_conv2d, |
| 3117 | TosaTensorGen.tgDepthwiseConv2D, |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3118 | TosaArgGen.agConv, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3119 | ), |
| 3120 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3121 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3122 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3123 | "template": True, |
| 3124 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3125 | "fully_connected": { |
| 3126 | "op": Op.FULLY_CONNECTED, |
| 3127 | "operands": (1, 2), |
| 3128 | "rank": (2, 2), |
| 3129 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 3130 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3131 | "types": TYPE_CONV, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3132 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3133 | "matmul": { |
| 3134 | "op": Op.MATMUL, |
| 3135 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3136 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3137 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 3138 | "qgen": TosaQuantGen.qgMatmul, |
| 3139 | "types": TYPE_NARROW_INT_FP, |
| 3140 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3141 | "max_pool2d": { |
| 3142 | "op": Op.MAX_POOL2D, |
| 3143 | "operands": (1, 0), |
| 3144 | "rank": (4, 4), |
| 3145 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 3146 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3147 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3148 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3149 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3150 | "transpose_conv2d_TEMPLATE": { |
| 3151 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3152 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3153 | "rank": (4, 4), |
| 3154 | "build_fcn": ( |
| 3155 | build_transpose_conv2d, |
| 3156 | TosaTensorGen.tgTransposeConv2D, |
| 3157 | TosaArgGen.agTransposeConv2D, |
| 3158 | ), |
| 3159 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3160 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3161 | "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3162 | "template": True, |
| 3163 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3164 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3165 | "clamp": { |
| 3166 | "op": Op.CLAMP, |
| 3167 | "operands": (1, 0), |
| 3168 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 3169 | "types": TYPE_NARROW_INT_FP, |
| 3170 | }, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3171 | "sigmoid": { |
| 3172 | "op": Op.SIGMOID, |
| 3173 | "operands": (1, 0), |
| 3174 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 3175 | "types": TYPE_FP, |
| 3176 | }, |
| 3177 | "tanh": { |
| 3178 | "op": Op.TANH, |
| 3179 | "operands": (1, 0), |
| 3180 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 3181 | "types": TYPE_FP, |
| 3182 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3183 | # Elementwise Binary Operators |
| 3184 | "add": { |
| 3185 | "op": Op.ADD, |
| 3186 | "operands": (2, 0), |
| 3187 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3188 | "types": TYPE_FI32, |
| 3189 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3190 | "arithmetic_right_shift": { |
| 3191 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 3192 | "operands": (2, 0), |
| 3193 | "build_fcn": ( |
| 3194 | build_arithmetic_right_shift, |
| 3195 | TosaTensorGen.tgBroadcastFuzz, |
| 3196 | TosaArgGen.agArithmeticRightShift, |
| 3197 | ), |
| 3198 | "types": TYPE_INT, |
| 3199 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3200 | "bitwise_and": { |
| 3201 | "op": Op.BITWISE_AND, |
| 3202 | "operands": (2, 0), |
| 3203 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3204 | "types": TYPE_INT, |
| 3205 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3206 | "bitwise_or": { |
| 3207 | "op": Op.BITWISE_OR, |
| 3208 | "operands": (2, 0), |
| 3209 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3210 | "types": TYPE_INT, |
| 3211 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3212 | "bitwise_xor": { |
| 3213 | "op": Op.BITWISE_XOR, |
| 3214 | "operands": (2, 0), |
| 3215 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3216 | "types": TYPE_INT, |
| 3217 | }, |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 3218 | "intdiv": { |
| 3219 | "op": Op.INTDIV, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3220 | "operands": (2, 0), |
| 3221 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3222 | "types": [DType.INT32], |
| 3223 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3224 | "logical_and": { |
| 3225 | "op": Op.LOGICAL_AND, |
| 3226 | "operands": (2, 0), |
| 3227 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3228 | "types": TYPE_BOOL, |
| 3229 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3230 | "logical_left_shift": { |
| 3231 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 3232 | "operands": (2, 0), |
| 3233 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3234 | "types": TYPE_INT, |
| 3235 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3236 | "logical_right_shift": { |
| 3237 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 3238 | "operands": (2, 0), |
| 3239 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3240 | "types": TYPE_INT, |
| 3241 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3242 | "logical_or": { |
| 3243 | "op": Op.LOGICAL_OR, |
| 3244 | "operands": (2, 0), |
| 3245 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3246 | "types": TYPE_BOOL, |
| 3247 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3248 | "logical_xor": { |
| 3249 | "op": Op.LOGICAL_XOR, |
| 3250 | "operands": (2, 0), |
| 3251 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3252 | "types": TYPE_BOOL, |
| 3253 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3254 | "maximum": { |
| 3255 | "op": Op.MAXIMUM, |
| 3256 | "operands": (2, 0), |
| 3257 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3258 | "types": TYPE_FI32, |
| 3259 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3260 | "minimum": { |
| 3261 | "op": Op.MINIMUM, |
| 3262 | "operands": (2, 0), |
| 3263 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3264 | "types": TYPE_FI32, |
| 3265 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3266 | "mul": { |
| 3267 | "op": Op.MUL, |
| 3268 | "operands": (2, 0), |
| 3269 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 3270 | "types": TYPE_INT_FP, |
| 3271 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3272 | "pow": { |
| 3273 | "op": Op.POW, |
| 3274 | "operands": (2, 0), |
| 3275 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 3276 | "types": TYPE_FP, |
| 3277 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3278 | "sub": { |
| 3279 | "op": Op.SUB, |
| 3280 | "operands": (2, 0), |
| 3281 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3282 | "types": TYPE_FI32, |
| 3283 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3284 | "table": { |
| 3285 | "op": Op.TABLE, |
| 3286 | # Use the automatic generation functions to create the input array |
| 3287 | # but create the table tensor in the build function, as it may be |
| 3288 | # a different type from the input |
| 3289 | "operands": (1, 0), |
| 3290 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3291 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3292 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3293 | # Elementwise Unary operators |
| 3294 | "abs": { |
| 3295 | "op": Op.ABS, |
| 3296 | "operands": (1, 0), |
| 3297 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3298 | "types": TYPE_FI32, |
| 3299 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3300 | "bitwise_not": { |
| 3301 | "op": Op.BITWISE_NOT, |
| 3302 | "operands": (1, 0), |
| 3303 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3304 | "types": TYPE_INT, |
| 3305 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3306 | "ceil": { |
| 3307 | "op": Op.CEIL, |
| 3308 | "operands": (1, 0), |
| 3309 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3310 | "types": TYPE_FP, |
| 3311 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3312 | "clz": { |
| 3313 | "op": Op.CLZ, |
| 3314 | "operands": (1, 0), |
| 3315 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3316 | "types": [DType.INT32], |
| 3317 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3318 | "exp": { |
| 3319 | "op": Op.EXP, |
| 3320 | "operands": (1, 0), |
| 3321 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3322 | "types": TYPE_FP, |
| 3323 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3324 | "floor": { |
| 3325 | "op": Op.FLOOR, |
| 3326 | "operands": (1, 0), |
| 3327 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3328 | "types": TYPE_FP, |
| 3329 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3330 | "log": { |
| 3331 | "op": Op.LOG, |
| 3332 | "operands": (1, 0), |
| 3333 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3334 | "types": TYPE_FP, |
| 3335 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3336 | "logical_not": { |
| 3337 | "op": Op.LOGICAL_NOT, |
| 3338 | "operands": (1, 0), |
| 3339 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3340 | "types": TYPE_BOOL, |
| 3341 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3342 | "negate": { |
| 3343 | "op": Op.NEGATE, |
| 3344 | "operands": (1, 0), |
| 3345 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3346 | "qgen": TosaQuantGen.qgUnary, |
| 3347 | "types": TYPE_INT_FP, |
| 3348 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3349 | "reciprocal": { |
| 3350 | "op": Op.RECIPROCAL, |
| 3351 | "operands": (1, 0), |
| 3352 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3353 | "types": TYPE_FP, |
| 3354 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3355 | "rsqrt": { |
| 3356 | "op": Op.RSQRT, |
| 3357 | "operands": (1, 0), |
| 3358 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3359 | "types": TYPE_FP, |
| 3360 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3361 | # Elementwise Ternary operators |
| 3362 | "select": { |
| 3363 | "op": Op.SELECT, |
| 3364 | "operands": (3, 0), |
| 3365 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 3366 | "types": TYPE_FIB, |
| 3367 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3368 | # Comparison operators |
| 3369 | "equal": { |
| 3370 | "op": Op.EQUAL, |
| 3371 | "operands": (2, 0), |
| 3372 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3373 | "types": TYPE_FI32, |
| 3374 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3375 | "greater_equal": { |
| 3376 | "op": Op.GREATER_EQUAL, |
| 3377 | "operands": (2, 0), |
| 3378 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3379 | "types": TYPE_FI32, |
| 3380 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3381 | "greater": { |
| 3382 | "op": Op.GREATER, |
| 3383 | "operands": (2, 0), |
| 3384 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3385 | "types": TYPE_FI32, |
| 3386 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3387 | # Reduction operators |
| 3388 | "reduce_all": { |
| 3389 | "op": Op.REDUCE_ALL, |
| 3390 | "operands": (1, 0), |
| 3391 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3392 | "types": TYPE_BOOL, |
| 3393 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3394 | "reduce_any": { |
| 3395 | "op": Op.REDUCE_ANY, |
| 3396 | "operands": (1, 0), |
| 3397 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3398 | "types": TYPE_BOOL, |
| 3399 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3400 | "reduce_max": { |
| 3401 | "op": Op.REDUCE_MAX, |
| 3402 | "operands": (1, 0), |
| 3403 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3404 | "types": TYPE_INT_FP, |
| 3405 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3406 | "reduce_min": { |
| 3407 | "op": Op.REDUCE_MAX, |
| 3408 | "operands": (1, 0), |
| 3409 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3410 | "types": TYPE_INT_FP, |
| 3411 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3412 | "reduce_product": { |
| 3413 | "op": Op.REDUCE_PRODUCT, |
| 3414 | "operands": (1, 0), |
| 3415 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3416 | "types": TYPE_FP, |
| 3417 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3418 | "reduce_sum": { |
| 3419 | "op": Op.REDUCE_SUM, |
| 3420 | "operands": (1, 0), |
| 3421 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3422 | "types": TYPE_FI32, |
| 3423 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3424 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3425 | "concat": { |
| 3426 | "op": Op.CONCAT, |
| 3427 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3428 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3429 | "types": TYPE_FIB, |
| 3430 | }, |
| 3431 | "pad": { |
| 3432 | "op": Op.PAD, |
| 3433 | "operands": (1, 0), |
| 3434 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 3435 | "qgen": TosaQuantGen.qgPad, |
| 3436 | "types": TYPE_FIB, |
| 3437 | }, |
| 3438 | "reshape": { |
| 3439 | "op": Op.RESHAPE, |
| 3440 | "operands": (1, 0), |
| 3441 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 3442 | "types": TYPE_FIB, |
| 3443 | }, |
| 3444 | "reverse": { |
| 3445 | "op": Op.REVERSE, |
| 3446 | "operands": (1, 0), |
| 3447 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3448 | "types": TYPE_FIB, |
| 3449 | }, |
| 3450 | "slice": { |
| 3451 | "op": Op.SLICE, |
| 3452 | "operands": (1, 0), |
| 3453 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 3454 | "types": TYPE_FIB, |
| 3455 | }, |
| 3456 | "tile": { |
| 3457 | "op": Op.TILE, |
| 3458 | "operands": (1, 0), |
| 3459 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 3460 | "types": TYPE_FIB, |
| 3461 | }, |
| 3462 | "transpose": { |
| 3463 | "op": Op.TRANSPOSE, |
| 3464 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 3465 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3466 | "build_fcn": ( |
| 3467 | build_transpose, |
| 3468 | TosaTensorGen.tgBasic, |
| 3469 | TosaArgGen.agTranspose, |
| 3470 | ), |
| 3471 | "types": TYPE_FIB, |
| 3472 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3473 | # Data nodes |
| 3474 | "const": { |
| 3475 | "op": Op.CONST, |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 3476 | "operands": (0, 1), |
| 3477 | "build_fcn": (build_const, TosaTensorGen.tgBasic, None), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3478 | "types": TYPE_FIB, |
| 3479 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3480 | "identity": { |
| 3481 | "op": Op.IDENTITY, |
| 3482 | "operands": (1, 0), |
| 3483 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3484 | "types": TYPE_FIB, |
| 3485 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3486 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3487 | "gather": { |
| 3488 | "op": Op.GATHER, |
| 3489 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 3490 | "operands": (1, 0), |
| 3491 | "rank": (3, 3), |
| 3492 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 3493 | "types": TYPE_INT_FP, |
| 3494 | }, |
| 3495 | "scatter": { |
| 3496 | "op": Op.SCATTER, |
| 3497 | # Only specify 'values_in' tensor here. |
| 3498 | #'indices' and 'input' are generated in op building stage |
| 3499 | "operands": (2, 0), |
| 3500 | "rank": (3, 3), |
| 3501 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 3502 | "types": TYPE_INT_FP, |
| 3503 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3504 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3505 | "resize": { |
| 3506 | "op": Op.RESIZE, |
| 3507 | "operands": (1, 0), |
| 3508 | "rank": (4, 4), |
| 3509 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 3510 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3511 | "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride), |
| 3512 | "error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension, |
| 3513 | TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3514 | TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven, TosaErrorValidator.evWrongInputType, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 3515 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, |
| 3516 | TosaErrorValidator.evBatchMismatch, TosaErrorValidator.evChannelMismatch) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3517 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3518 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3519 | "cast": { |
| 3520 | "op": Op.CAST, |
| 3521 | "operands": (1, 0), |
| 3522 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 3523 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 3524 | }, |
| 3525 | "rescale": { |
| 3526 | "op": Op.RESCALE, |
| 3527 | "operands": (1, 0), |
| 3528 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 3529 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3530 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3531 | # Custom |
| 3532 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3533 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3534 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 3535 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 3536 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3537 | "cond_if_const": { |
| 3538 | "op": Op.COND_IF, |
| 3539 | "operands": (0, 2), |
| 3540 | "build_fcn": ( |
| 3541 | build_cond_if_const, |
| 3542 | TosaTensorGen.tgBasic, |
| 3543 | TosaArgGen.agCondIf, |
| 3544 | ), |
| 3545 | "types": [DType.BOOL], |
| 3546 | }, |
| 3547 | "cond_if_binary": { |
| 3548 | "op": Op.COND_IF, |
| 3549 | "operands": (2, 0), |
| 3550 | "build_fcn": ( |
| 3551 | build_cond_if_binary, |
| 3552 | TosaTensorGen.tgBasic, |
| 3553 | TosaArgGen.agCondIf, |
| 3554 | ), |
| 3555 | "types": TYPE_FI32, |
| 3556 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3557 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3558 | "while_loop": { |
| 3559 | "op": Op.WHILE_LOOP, |
| 3560 | "operands": (0, 1), |
| 3561 | "build_fcn": ( |
| 3562 | build_while_loop, |
| 3563 | TosaTensorGen.tgBasic, |
| 3564 | TosaArgGen.agWhileLoop, |
| 3565 | ), |
| 3566 | "types": [DType.INT32], |
| 3567 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3568 | } |
| 3569 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3570 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3571 | class OutputShaper: |
| 3572 | # Methods in this class compute the expected output shape and datatype |
| 3573 | # for common classes of operations |
| 3574 | def __init__(self): |
| 3575 | pass |
| 3576 | |
| 3577 | # These methods return arguments that can be used for |
| 3578 | # creating a new output tensor |
| 3579 | @staticmethod |
| 3580 | def binaryBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3581 | assert len(a.shape) == len(b.shape) |
| 3582 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3583 | |
| 3584 | shape = [] |
| 3585 | for i in range(len(a.shape)): |
| 3586 | if a.shape[i] == 1: |
| 3587 | shape.append(b.shape[i]) |
| 3588 | else: |
| 3589 | shape.append(a.shape[i]) |
| 3590 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3591 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3592 | |
| 3593 | @staticmethod |
| 3594 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3595 | assert len(a.shape) == len(b.shape) |
| 3596 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3597 | |
| 3598 | shape = [] |
| 3599 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3600 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3601 | shape.append(a.shape[i]) |
| 3602 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3603 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3604 | |
| 3605 | @staticmethod |
| 3606 | def unaryOp(ser, a): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3607 | return ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3608 | |
| 3609 | @staticmethod |
| 3610 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3611 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 3612 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3613 | |
| 3614 | shape = [] |
| 3615 | for i in range(len(a.shape)): |
| 3616 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 3617 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3618 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3619 | |
| 3620 | @staticmethod |
| 3621 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3622 | assert len(a.shape) == len(b.shape) |
| 3623 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3624 | |
| 3625 | # Do broadcast |
| 3626 | shape = [] |
| 3627 | for i in range(len(a.shape)): |
| 3628 | if a.shape[i] == 1: |
| 3629 | shape.append(b.shape[i]) |
| 3630 | else: |
| 3631 | shape.append(a.shape[i]) |
| 3632 | |
| 3633 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3634 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3635 | |
| 3636 | @staticmethod |
| 3637 | def reduceOp(ser, a, axis): |
| 3638 | |
| 3639 | shape = a.shape.copy() |
| 3640 | |
| 3641 | shape[axis] = 1 |
| 3642 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3643 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3644 | |
| 3645 | @staticmethod |
| 3646 | def argmaxOp(ser, a, axis): |
| 3647 | shape = a.shape.copy() |
| 3648 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3649 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3650 | |
| 3651 | @staticmethod |
| 3652 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3653 | |
| 3654 | # IFM: NHWC |
| 3655 | # Filter: OHWI |
| 3656 | # OFM: NHWC |
| 3657 | |
| 3658 | if len(padding) == 2: |
| 3659 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 3660 | # From H,W to T,B,L,R |
| 3661 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 3662 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3663 | h = ( |
| 3664 | ifm.shape[1] |
| 3665 | - filter.shape[1] |
| 3666 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3667 | + padding[0] |
| 3668 | + padding[1] |
| 3669 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3670 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3671 | w = ( |
| 3672 | ifm.shape[2] |
| 3673 | - filter.shape[2] |
| 3674 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3675 | + padding[2] |
| 3676 | + padding[3] |
| 3677 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3678 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3679 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 3680 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3681 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3682 | out_dtype = DType.INT32 |
| 3683 | elif ifm.dtype == DType.INT16: |
| 3684 | out_dtype = DType.INT48 |
| 3685 | elif ifm.dtype == DType.FLOAT: |
| 3686 | out_dtype = DType.FLOAT |
| 3687 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3688 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3689 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3690 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3691 | |
| 3692 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3693 | def conv3dOp(ser, ifm, filter, strides, padding, dilations): |
| 3694 | |
| 3695 | # IFM: NDHWC |
| 3696 | # Filter: ODHWI |
| 3697 | # OFM: NDHWC |
| 3698 | |
| 3699 | d = ( |
| 3700 | ifm.shape[1] |
| 3701 | - filter.shape[1] |
| 3702 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3703 | + padding[0] |
| 3704 | + padding[1] |
| 3705 | ) // strides[0] + 1 |
| 3706 | |
| 3707 | h = ( |
| 3708 | ifm.shape[2] |
| 3709 | - filter.shape[2] |
| 3710 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3711 | + padding[2] |
| 3712 | + padding[3] |
| 3713 | ) // strides[1] + 1 |
| 3714 | |
| 3715 | w = ( |
| 3716 | ifm.shape[3] |
| 3717 | - filter.shape[3] |
| 3718 | - (filter.shape[3] - 1) * (dilations[2] - 1) |
| 3719 | + padding[4] |
| 3720 | + padding[5] |
| 3721 | ) // strides[2] + 1 |
| 3722 | |
| 3723 | ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| 3724 | |
| 3725 | if ifm.dtype == DType.INT8: |
| 3726 | out_dtype = DType.INT32 |
| 3727 | elif ifm.dtype == DType.INT16: |
| 3728 | out_dtype = DType.INT48 |
| 3729 | elif ifm.dtype == DType.FLOAT: |
| 3730 | out_dtype = DType.FLOAT |
| 3731 | else: |
| 3732 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
| 3733 | |
| 3734 | return ser.addOutput(ofm_shape, out_dtype) |
| 3735 | |
| 3736 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3737 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3738 | # IFM: NHWC |
| 3739 | # Filter: HWCM |
| 3740 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3741 | h = ( |
| 3742 | ifm.shape[1] |
| 3743 | - filter.shape[0] |
| 3744 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 3745 | + padding[0] |
| 3746 | + padding[1] |
| 3747 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3748 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3749 | w = ( |
| 3750 | ifm.shape[2] |
| 3751 | - filter.shape[1] |
| 3752 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 3753 | + padding[2] |
| 3754 | + padding[3] |
| 3755 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3756 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3757 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 3758 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3759 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3760 | out_dtype = DType.INT32 |
| 3761 | elif ifm.dtype == DType.INT16: |
| 3762 | out_dtype = DType.INT48 |
| 3763 | elif ifm.dtype == DType.FLOAT: |
| 3764 | out_dtype = DType.FLOAT |
| 3765 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3766 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3767 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3768 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3769 | |
| 3770 | @staticmethod |
| 3771 | def pool2dOp(ser, ifm, kernel, stride, pad): |
| 3772 | # input: NHWC |
| 3773 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 3774 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 3775 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3776 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3777 | return ser.addOutput(ofm_shape, ifm.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3778 | |
| 3779 | @staticmethod |
| 3780 | def fullyConnectedOp(ser, input, filter): |
| 3781 | # input: N, IC |
| 3782 | # filter: OC, IC |
| 3783 | # output: N, OC |
| 3784 | |
| 3785 | output_shape = [input.shape[0], filter.shape[0]] |
| 3786 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3787 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3788 | out_dtype = DType.INT32 |
| 3789 | elif input.dtype == DType.INT16: |
| 3790 | out_dtype = DType.INT48 |
| 3791 | elif input.dtype == DType.FLOAT: |
| 3792 | out_dtype = DType.FLOAT |
| 3793 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3794 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3795 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3796 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3797 | |
| 3798 | @staticmethod |
| 3799 | def matmulOp(ser, a, b): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3800 | # a: N, H, C |
| 3801 | # b: N, C, W |
| 3802 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3803 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3804 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3805 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3806 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3807 | out_dtype = DType.INT32 |
| 3808 | elif a.dtype == DType.INT16: |
| 3809 | out_dtype = DType.INT48 |
| 3810 | elif a.dtype == DType.FLOAT: |
| 3811 | out_dtype = DType.FLOAT |
| 3812 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3813 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3814 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3815 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3816 | |
| 3817 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3818 | def concatOp(ser, axis, *a): |
| 3819 | input1 = a[0] |
| 3820 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3821 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3822 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3823 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3824 | output_shape[axis] = input1.shape[axis] |
| 3825 | |
| 3826 | for tensor in remaining_inputs: |
| 3827 | output_shape[axis] += tensor.shape[axis] |
| 3828 | |
| 3829 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3830 | |
| 3831 | @staticmethod |
| 3832 | def padOp(ser, a, padding): |
| 3833 | |
| 3834 | output_shape = a.shape.copy() |
| 3835 | |
| 3836 | for i in range(len(output_shape)): |
| 3837 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 3838 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3839 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3840 | |
| 3841 | @staticmethod |
| 3842 | def reshapeOp(ser, a, shape): |
| 3843 | output_shape = shape.copy() |
| 3844 | |
| 3845 | totalElements = 1 |
| 3846 | for i in a.shape: |
| 3847 | totalElements *= i |
| 3848 | |
| 3849 | # If there are any -1 elements, figure out what that dimension must be |
| 3850 | totalOutputElements = 1 |
| 3851 | for i in output_shape: |
| 3852 | if i != -1: |
| 3853 | totalOutputElements *= i |
| 3854 | |
| 3855 | # And fill it in |
| 3856 | for i in range(len(output_shape)): |
| 3857 | if output_shape[i] == -1: |
| 3858 | output_shape[i] = totalElements // totalOutputElements |
| 3859 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3860 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3861 | |
| 3862 | @staticmethod |
| 3863 | def sliceOp(ser, a, begin, size): |
| 3864 | |
| 3865 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3866 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3867 | |
| 3868 | @staticmethod |
| 3869 | def tileOp(ser, a, multiples): |
| 3870 | |
| 3871 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3872 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3873 | |
| 3874 | for i in range(len(output_shape)): |
| 3875 | output_shape[i] = a.shape[i] * multiples[i] |
| 3876 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3877 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3878 | |
| 3879 | @staticmethod |
| 3880 | def transposeOp(ser, a, perms): |
| 3881 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3882 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3883 | |
| 3884 | for i in range(len(output_shape)): |
| 3885 | output_shape[i] = a.shape[perms[i]] |
| 3886 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3887 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3888 | |
| 3889 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3890 | def gatherOp(ser, values, indices): |
| 3891 | assert len(values.shape) == 3 |
| 3892 | assert len(indices.shape) == 2 |
| 3893 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3894 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3895 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 3896 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3897 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3898 | |
| 3899 | @staticmethod |
| 3900 | def scatterOp(ser, values_in, indices, input): |
| 3901 | assert len(values_in.shape) == 3 |
| 3902 | assert len(indices.shape) == 2 |
| 3903 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3904 | assert values_in.shape[0] == indices.shape[0] # N |
| 3905 | assert input.shape[1] == indices.shape[1] # W |
| 3906 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3907 | |
| 3908 | output_shape = values_in.shape |
| 3909 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3910 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3911 | |
| 3912 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3913 | def tableOp(ser, input, table_dtype): |
| 3914 | # Same shape as the input, but dtype dependent on table dtype |
| 3915 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 3916 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 3917 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3918 | |
| 3919 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3920 | def resizeOp( |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 3921 | serializer, |
| 3922 | rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3923 | input, |
| 3924 | mode, |
| 3925 | stride, |
| 3926 | offset, |
| 3927 | shift, |
| 3928 | stride_fp, |
| 3929 | offset_fp, |
| 3930 | output_dims, |
| 3931 | input_dtype, |
| 3932 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3933 | error_name = None |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3934 | ): |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3935 | if error_name == ErrorIf.WrongRank: |
| 3936 | output_dims = [input.shape[0], output_dims[0], output_dims[0], input.shape[0]] |
| 3937 | else: |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 3938 | if error_name == ErrorIf.BatchMismatch: |
| 3939 | output_dims = [input.shape[0] + rng.integers(1, 10), output_dims[0], output_dims[1], input.shape[3]] |
| 3940 | elif error_name == ErrorIf.ChannelMismatch: |
| 3941 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3] + rng.integers(1, 10)] |
| 3942 | else: |
| 3943 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3944 | |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame^] | 3945 | return serializer.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3946 | |
| 3947 | @staticmethod |
| 3948 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3949 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3950 | |
| 3951 | @staticmethod |
| 3952 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3953 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3954 | out_dtype = DType.INT32 |
| 3955 | elif ifm.dtype == DType.INT16: |
| 3956 | out_dtype = DType.INT48 |
| 3957 | elif ifm.dtype == DType.FLOAT: |
| 3958 | out_dtype = DType.FLOAT |
| 3959 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3960 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3961 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3962 | return ser.addOutput(output_shape, out_dtype) |