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 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 161 | if error_name == ErrorIf.RankMismatch: |
| 162 | if rank == 1 and i != 1: |
| 163 | shape = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) |
| 164 | elif i != 1: |
| 165 | shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) |
| 166 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 167 | return shape_list |
| 168 | |
| 169 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 170 | def tgNHWC(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 171 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 172 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 173 | if error_name != ErrorIf.WrongRank: |
| 174 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 175 | |
| 176 | shape = testGen.makeShape(rank) |
| 177 | |
| 178 | # Constrict the batch size? |
| 179 | if testGen.args.max_batch_size: |
| 180 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
| 181 | |
| 182 | shape_list = [] |
| 183 | for i in range(pl + const): |
| 184 | shape_list.append(shape.copy()) |
| 185 | |
| 186 | return shape_list |
| 187 | |
| 188 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 189 | def tgScatter(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 190 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 191 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 192 | assert pl == 2 |
| 193 | assert const == 0 |
| 194 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 195 | |
| 196 | values_in_shape = testGen.makeShape(rank) |
| 197 | |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 198 | # ignore max batch size if target shape is set |
| 199 | if testGen.args.max_batch_size and not testGen.args.target_shapes: |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 200 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 201 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 202 | W = testGen.randInt( |
| 203 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 204 | ) |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 205 | # Constrict W if one dimension is too large to keep tensor size reasonable |
| 206 | if max(values_in_shape) > 5000: |
| 207 | W = testGen.randInt(0, 16) |
| 208 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 209 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 210 | |
| 211 | shape_list = [] |
| 212 | shape_list.append(values_in_shape.copy()) |
| 213 | shape_list.append(input_shape.copy()) |
| 214 | |
| 215 | return shape_list |
| 216 | |
| 217 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 218 | def tgBroadcastFuzz(testGen, op, rank, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 219 | shape = testGen.makeShape(rank) |
| 220 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 221 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 222 | |
| 223 | shape_list = [] |
| 224 | |
| 225 | # Choose one of the inputs to broadcast |
| 226 | bcast_idx = testGen.randInt(0, pl + const) |
| 227 | for i in range(pl + const): |
| 228 | shape_bcast = shape.copy() |
| 229 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 230 | if error_name == ErrorIf.RankMismatch: |
| 231 | bcast_idx = -1 # Turn off broadcast because we are not testing it |
| 232 | if rank == 1 and i != 1: |
| 233 | shape_bcast = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) |
| 234 | elif i != 1: |
| 235 | shape_bcast = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) |
| 236 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 237 | # If the chosen input, pick a random index to broadcast |
| 238 | if i == bcast_idx: |
| 239 | fuzz_idx = testGen.randInt(0, rank) |
| 240 | shape_bcast[fuzz_idx] = 1 |
| 241 | |
| 242 | shape_list.append(shape_bcast) |
| 243 | |
| 244 | return shape_list |
| 245 | |
| 246 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 247 | def tgConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 248 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 249 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 250 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 251 | |
| 252 | # IFM dimensions are NHWC |
| 253 | ifm_shape = testGen.makeShape(rank) |
| 254 | |
| 255 | # Constrict the batch size? |
| 256 | if testGen.args.max_batch_size: |
| 257 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 258 | |
| 259 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 260 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 261 | |
| 262 | # Generate a random OFM depth |
| 263 | ofm_depth = testGen.makeShape(1)[0] |
| 264 | |
| 265 | # The filter dimensions are OHWI |
| 266 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 267 | |
| 268 | # The bias is OC |
| 269 | bias_shape = np.asarray([ofm_depth]) |
| 270 | |
| 271 | return [ifm_shape, filter_shape, bias_shape] |
| 272 | |
| 273 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 274 | def tgConv3D(testGen, op, rank, error_name=None): |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 275 | pl, const = op["operands"] |
| 276 | |
| 277 | assert rank == 5 |
| 278 | |
| 279 | # IFM dimensions are NDHWC |
| 280 | ifm_shape = testGen.makeShape(rank) |
| 281 | |
| 282 | # Constrict the batch size? |
| 283 | if testGen.args.max_batch_size: |
| 284 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 285 | |
| 286 | # Get the filter depth/height/width from the operator parameters |
| 287 | filter_dhw = op["filter"] |
| 288 | |
| 289 | # Generate a random OFM channel |
| 290 | ofm_channel = testGen.makeShape(1)[0] |
| 291 | |
| 292 | # The filter dimensions are ODHWI |
| 293 | filter_shape = np.asarray( |
| 294 | [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] |
| 295 | ) |
| 296 | |
| 297 | # The bias is OC |
| 298 | bias_shape = np.asarray([ofm_channel]) |
| 299 | |
| 300 | return [ifm_shape, filter_shape, bias_shape] |
| 301 | |
| 302 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 303 | def tgTransposeConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 304 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 305 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 306 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 307 | |
| 308 | # IFM dimensions are NHWC |
| 309 | ifm_shape = testGen.makeShape(rank) |
| 310 | |
| 311 | # Constrict the batch size? |
| 312 | if testGen.args.max_batch_size: |
| 313 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 314 | |
| 315 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 316 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 317 | |
| 318 | # Generate a random OFM depth |
| 319 | ofm_depth = testGen.makeShape(1)[0] |
| 320 | |
| 321 | # The filter dimensions are OHWI |
| 322 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 323 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 324 | # The bias is OC |
| 325 | bias_shape = np.asarray([ofm_depth]) |
| 326 | |
| 327 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 328 | |
| 329 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 330 | def tgDepthwiseConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 331 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 332 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 333 | assert rank == 4 |
| 334 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 335 | |
| 336 | # IFM dimensions are NHWC |
| 337 | ifm_shape = testGen.makeShape(rank) |
| 338 | |
| 339 | # Constrict the batch size? |
| 340 | if testGen.args.max_batch_size: |
| 341 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 342 | |
| 343 | # Get the filter height/width from the operator parameters |
| 344 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 345 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 346 | |
| 347 | # Generate a random OFM depth, but don't let it get too big because |
| 348 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 349 | filter_m = ( |
| 350 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 351 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 352 | |
| 353 | # The filter dimensions are HWCM |
| 354 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 355 | |
| 356 | # The bias is M * C |
| 357 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 358 | |
| 359 | return [ifm_shape, filter_shape, bias_shape] |
| 360 | |
| 361 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 362 | def tgFullyConnected(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 363 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 364 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 365 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 366 | |
| 367 | input_shape = testGen.makeShape(rank) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 368 | filter_oc = testGen.rng.integers( |
| 369 | low=testGen.args.tensor_shape_range[0], |
| 370 | high=testGen.args.tensor_shape_range[1], |
| 371 | size=1, |
| 372 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 373 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 374 | |
| 375 | bias_shape = np.asarray([filter_oc]) |
| 376 | |
| 377 | return [input_shape, filter_shape, bias_shape] |
| 378 | |
| 379 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 380 | def tgMatmul(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 381 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 382 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 383 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 384 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 385 | |
| 386 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 387 | # Get a random number for b_oc even if target shape is defined |
| 388 | b_oc = np.int32( |
| 389 | testGen.rng.integers( |
| 390 | low=testGen.args.tensor_shape_range[0], |
| 391 | high=testGen.args.tensor_shape_range[1], |
| 392 | size=1, |
| 393 | ) |
| 394 | )[0] |
| 395 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 396 | if max(a_shape) > 1000: |
| 397 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 398 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 399 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 400 | return [a_shape, b_shape] |
| 401 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 402 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 403 | def tgConcat(testGen, opName, rank, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 404 | pl, const = opName["operands"] |
| 405 | shape = testGen.makeShape(rank) |
| 406 | |
| 407 | # Create extra tensors to concat. |
| 408 | # Take into account value of pl when getting maximum number of concats |
| 409 | num_tensors = testGen.randInt(0, 4) |
| 410 | shape_list = [] |
| 411 | for i in range(pl + const + num_tensors): |
| 412 | shape_list.append(shape.copy()) |
| 413 | |
| 414 | return shape_list |
| 415 | |
| 416 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 417 | def tgConcatConstInput(testGen, shapeList, axis, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 418 | # Split concat shape along axis to allow for multiple const inputs |
| 419 | # without making too many large tensors |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 420 | if len(shapeList) == 2 or shapeList[0][axis] < len(shapeList): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 421 | return shapeList |
| 422 | |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 423 | # Create copy of shape we are going to split (so we don't alter shapeList) |
| 424 | shape = shapeList[0].copy() |
| 425 | # Add original shape as first input |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 426 | new_shapeList = [shape.copy()] |
| 427 | length_on_axis = shape[axis] |
| 428 | remaining_length = length_on_axis |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 429 | for i in range(len(shapeList) - 2): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 430 | # Calculate split on axis and remaining value |
| 431 | split_shape_val = int(shape[axis] / 2) |
| 432 | remaining_length = remaining_length - split_shape_val |
| 433 | |
| 434 | # Append new shape, and set remaining shape |
| 435 | shape[axis] = split_shape_val |
| 436 | new_shapeList.append(shape.copy()) |
| 437 | shape[axis] = remaining_length |
| 438 | if i == len(shapeList) - 3: |
| 439 | new_shapeList.append(shape.copy()) |
| 440 | |
| 441 | return new_shapeList |
| 442 | |
| 443 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 444 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 445 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 446 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 447 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 448 | as arguments to the operator build function.""" |
| 449 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 450 | def __init__(self): |
| 451 | pass |
| 452 | |
| 453 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 454 | def agNone(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 455 | """A trivial argument generator for operators that don't take any |
| 456 | non-tensor arguments""" |
| 457 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 458 | |
| 459 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 460 | def agAxis(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 461 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 462 | axes = [] |
| 463 | |
| 464 | shape = shapeList[0] |
| 465 | |
| 466 | for a in range(0, len(shape)): |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 467 | axes.append(("axis{}".format(a), [a])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 468 | return axes |
| 469 | |
| 470 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 471 | def agConv(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 472 | arg_list = [] |
| 473 | |
| 474 | ifm_shape = shapeList[0] |
| 475 | filter_shape = shapeList[1] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 476 | # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) |
| 477 | k = [int(x) for x in opName.split("_")[-1].split("x")] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 478 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 479 | # Check the rank |
| 480 | rank = 5 if opName.startswith("conv3d") else 4 |
| 481 | assert len(ifm_shape) == rank |
| 482 | assert len(filter_shape) == rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 483 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 484 | # kernel rank omits batch and channels |
| 485 | k_rank = rank - 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 486 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 487 | # Generate comprehensive argument lists |
| 488 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 489 | paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} |
| 490 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 491 | strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| 492 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 493 | dilations = {x for x in itertools.product(*([d_vals] * k_rank))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 494 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 495 | # add some oversize argument values |
| 496 | if max(ifm_shape) < 64: |
| 497 | bigPadding = 9 |
| 498 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) |
| 499 | bigStride = 8 |
| 500 | strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) |
| 501 | bigDilation = 7 |
| 502 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 503 | |
| 504 | # There are too many parameter combinations, so generate them sparsely |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 505 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 506 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 507 | if sparsity < 13: |
| 508 | sparsity = 1 |
| 509 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 510 | sparsity += 1 |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 511 | n = 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 512 | for s in sorted(list(strides)): |
| 513 | for p in sorted(list(paddings)): |
| 514 | for d in sorted(list(dilations)): |
| 515 | if (n % sparsity == 0 |
| 516 | # padding must not exceed the kernel size ? |
| 517 | # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 518 | # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) |
| 519 | # the padded shape must exceed the kernel size |
| 520 | and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] |
| 521 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) |
| 522 | # the padded shape must exceed the dilation |
| 523 | and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] |
| 524 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) |
| 525 | ): |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 526 | arg_list.append( |
| 527 | ( |
| 528 | "st{}_pad{}_dilat{}".format( |
| 529 | "".join([str(x) for x in s]), |
| 530 | "".join([str(x) for x in p]), |
| 531 | "".join([str(x) for x in d]), |
| 532 | ), |
| 533 | [s, p, d], |
| 534 | ) |
| 535 | ) |
| 536 | n += 1 |
| 537 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 538 | return arg_list |
| 539 | |
| 540 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 541 | def agTransposeConv2D(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 542 | arg_list = [] |
| 543 | |
| 544 | ifm_shape = shapeList[0] |
| 545 | filter_shape = shapeList[1] |
| 546 | |
| 547 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 548 | assert len(ifm_shape) == 4 |
| 549 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 550 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 551 | # Generate comprehensive argument lists |
| 552 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 553 | paddings = {x for x in itertools.product(*([p_vals] * 2))} |
| 554 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 555 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 556 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 557 | dilations = {x for x in itertools.product(*([d_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 558 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 559 | # add some oversize argument values |
| 560 | if max(ifm_shape) < 64: |
| 561 | bigPadding = 9 |
| 562 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) |
| 563 | bigStride = 8 |
| 564 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 565 | bigDilation = 7 |
| 566 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 567 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 568 | # There are too many parameter combinations, so generate them sparsely |
| 569 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 570 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 571 | if sparsity < 13: |
| 572 | sparsity = 1 |
| 573 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 574 | sparsity += 1 |
| 575 | n = 0 |
| 576 | for s in sorted(list(strides)): |
| 577 | for p in sorted(list(paddings)): |
| 578 | for d in sorted(list(dilations)): |
| 579 | if n % sparsity == 0: |
| 580 | # Determine the output shape |
| 581 | oh = ( |
| 582 | ifm_shape[1] |
| 583 | - filter_shape[1] |
| 584 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 585 | + 2 * p[0] |
| 586 | ) // s[0] + 1 |
| 587 | ow = ( |
| 588 | ifm_shape[2] |
| 589 | - filter_shape[2] |
| 590 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 591 | + 2 * p[1] |
| 592 | ) // s[1] + 1 |
| 593 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| 594 | arg_list.append( |
| 595 | ( |
| 596 | "st{}_pad{}_dilat{}_os{}".format( |
| 597 | "".join([str(x) for x in s]), |
| 598 | "".join([str(x) for x in p]), |
| 599 | "".join([str(x) for x in d]), |
| 600 | "x".join([str(x) for x in os]), |
| 601 | ), |
| 602 | [s, p, d, os], |
| 603 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 604 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 605 | n += 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 606 | |
| 607 | return arg_list |
| 608 | |
| 609 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 610 | def agPad(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 611 | arg_list = [] |
| 612 | rank = len(shapeList[0]) |
| 613 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 614 | # Exhaustively test combinations of padding on each side of each dimension |
| 615 | # - the range of padding values is defined by pad_min and pad_max |
| 616 | # - for padding >9, the name format needs to be more distinctive |
| 617 | pad_min, pad_max = 0, 1 |
| 618 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
| 619 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 620 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 621 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 622 | for paddings in shape_pad_values: |
| 623 | name = "pad" |
| 624 | for r in range(rank): |
| 625 | before, after = paddings[r] |
| 626 | name = f"{name}{before}{after}" |
| 627 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 628 | |
| 629 | return arg_list |
| 630 | |
| 631 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 632 | def agPooling(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 633 | arg_list = [] |
| 634 | |
| 635 | shape = shapeList[0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 636 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 637 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 638 | # Generate comprehensive argument lists |
| 639 | p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] |
| 640 | paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| 641 | s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] |
| 642 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 643 | k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] |
| 644 | kernels = {x for x in itertools.product(*([k_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 645 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 646 | # add some oversize argument values |
| 647 | bigStride = 7 |
| 648 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 649 | bigKernel = 6 |
| 650 | kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) |
| 651 | if max(shape) < 64: |
| 652 | # padding must be less than the kernel size |
| 653 | bigPadding = bigKernel - 1 |
| 654 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 655 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 656 | # There are too many parameter combinations, so generate them sparsely |
| 657 | sparsity = len(paddings) * len(strides) * len(kernels) // 500 + 1 |
| 658 | n = 0 |
| 659 | for s in sorted(list(strides)): |
| 660 | for p in sorted(list(paddings)): |
| 661 | for k in sorted(list(kernels)): |
| 662 | if (n % sparsity == 0 |
| 663 | # padding must not exceed the kernel size |
| 664 | and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 665 | # the padded shape must exceed the kernel size |
| 666 | and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] |
| 667 | ): |
| 668 | arg_list.append( |
| 669 | ( |
| 670 | "st{}_kern{}_pad{}".format( |
| 671 | "".join([str(x) for x in s]), |
| 672 | "".join([str(x) for x in k]), |
| 673 | "".join([str(x) for x in p]), |
| 674 | ), |
| 675 | [s, p, k], |
| 676 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 677 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 678 | n += 1 |
| 679 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 680 | return arg_list |
| 681 | |
| 682 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 683 | def agCast(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 684 | arg_list = [] |
| 685 | |
| 686 | # Enumerate the output types here |
| 687 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 688 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 689 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 690 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 691 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 692 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 693 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 694 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 695 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 696 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 697 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 698 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 699 | |
| 700 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 701 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 702 | |
| 703 | return arg_list |
| 704 | |
| 705 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 706 | def agRescale(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 707 | arg_list = [] |
| 708 | |
| 709 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 710 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 711 | if inDtype == DType.UINT8 and dtype != DType.INT8: |
| 712 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 713 | continue |
| 714 | if inDtype != DType.INT8 and dtype == DType.UINT8: |
| 715 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 716 | continue |
| 717 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 718 | for scale32 in [False, True]: |
| 719 | for double_round in [False, True]: |
| 720 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 721 | |
| 722 | if inDtype == DType.INT48 and scale32: |
| 723 | # Illegal condition. Must be scale32=False |
| 724 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 725 | if double_round and not scale32: |
| 726 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 727 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 728 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 729 | arg_list.append( |
| 730 | ( |
| 731 | "out{}_sc{}_dr{}_pc{}".format( |
| 732 | DTypeNames[dtype], |
| 733 | int(scale32), |
| 734 | int(double_round), |
| 735 | int(per_channel), |
| 736 | ), |
| 737 | [dtype, scale32, double_round, per_channel], |
| 738 | ) |
| 739 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 740 | |
| 741 | return arg_list |
| 742 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 743 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 744 | def agMul(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 745 | arg_list = [] |
| 746 | |
| 747 | if dtype is DType.INT32: |
| 748 | for p in range(testGen.args.num_rand_permutations): |
| 749 | |
| 750 | shift = testGen.randInt(0, 32) |
| 751 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 752 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 753 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 754 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 755 | |
| 756 | return arg_list |
| 757 | |
| 758 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 759 | def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 760 | arg_list = [] |
| 761 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 762 | arg_list.append(("roundTrue", [True])) |
| 763 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 764 | |
| 765 | return arg_list |
| 766 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 767 | # Helper function for reshape. Gets some factors of a larger number. |
| 768 | @staticmethod |
| 769 | def getFactors(val, start=1): |
| 770 | factors = [] |
| 771 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 772 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 773 | if (val % i) == 0: |
| 774 | factors.append(i) |
| 775 | |
| 776 | return factors |
| 777 | |
| 778 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 779 | def agReshape(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 780 | arg_list = [] |
| 781 | |
| 782 | origShape = shapeList[0] |
| 783 | |
| 784 | totalElements = 1 |
| 785 | for s in origShape: |
| 786 | totalElements *= s |
| 787 | |
| 788 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 789 | factors = TosaArgGen.getFactors(totalElements) |
| 790 | |
| 791 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 792 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 793 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 794 | continue |
| 795 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 796 | found = True |
| 797 | # escape_counter breaks while loop if it continues on for too long |
| 798 | escape_counter = 0 |
| 799 | while found: |
| 800 | newShape = [] |
| 801 | # Generate newShape ensuring it isn't a duplicate |
| 802 | remainingElements = totalElements |
| 803 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 804 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 805 | # pick rank-1 factors |
| 806 | newShape.append(shuffledFactors[0]) |
| 807 | remainingElements = remainingElements // shuffledFactors[0] |
| 808 | shuffledFactors = testGen.rng.permutation( |
| 809 | TosaArgGen.getFactors(remainingElements) |
| 810 | ) |
| 811 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 812 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 813 | # Toss in a -1 sometimes |
| 814 | minusOne = testGen.randInt(0, newRank * 4) |
| 815 | if minusOne < newRank: |
| 816 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 817 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 818 | # Check for duplicates |
| 819 | found = False |
| 820 | for name, other_shape in arg_list: |
| 821 | if other_shape[0] == newShape: |
| 822 | found = True |
| 823 | break |
| 824 | |
| 825 | escape_counter += 1 |
| 826 | if escape_counter >= 100: |
| 827 | break |
| 828 | |
| 829 | if not found: |
| 830 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 831 | |
| 832 | return arg_list |
| 833 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 834 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 835 | def agTranspose(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 836 | arg_list = [] |
| 837 | |
| 838 | ifm_shape = shapeList[0] |
| 839 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 840 | # Get all permutations |
| 841 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 842 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 843 | # Limit to possible permutations from shape dimension or argument setting |
| 844 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 845 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 846 | # Get random permutation generator that uses all permutations |
| 847 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 848 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 849 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 850 | arg_list = [ |
| 851 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 852 | for p in range(limit) |
| 853 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 854 | return arg_list |
| 855 | |
| 856 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 857 | def agSlice(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 858 | arg_list = [] |
| 859 | |
| 860 | ifm_shape = shapeList[0] |
| 861 | rank = len(ifm_shape) |
| 862 | |
| 863 | for p in range(testGen.args.num_rand_permutations): |
| 864 | begin = [] |
| 865 | size = [] |
| 866 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 867 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 868 | |
| 869 | for i in range(rank): |
| 870 | if ifm_shape[i] > 1: |
| 871 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 872 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 873 | |
| 874 | # Invalid slice size? |
| 875 | if size[i] == 0: |
| 876 | valid = False |
| 877 | else: |
| 878 | begin.append(0) |
| 879 | size.append(1) |
| 880 | |
| 881 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 882 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 883 | return arg_list |
| 884 | |
| 885 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 886 | def agTile(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 887 | arg_list = [] |
| 888 | |
| 889 | ifm_shape = shapeList[0] |
| 890 | rank = len(ifm_shape) |
| 891 | |
| 892 | for p in range(testGen.args.num_rand_permutations): |
| 893 | |
| 894 | # Pick a few random, but small multiple values |
| 895 | # because otherwise this has a tendency to generate |
| 896 | # enormous tensors |
| 897 | multiples = [] |
| 898 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame] | 899 | if ifm_shape[i] > 1000: |
| 900 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 901 | multiples.append(1) |
| 902 | elif max(ifm_shape) > 1000: |
| 903 | multiples.append(2) |
| 904 | else: |
| 905 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 906 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 907 | |
| 908 | return arg_list |
| 909 | |
| 910 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 911 | def agResize(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 912 | arg_list = [] |
| 913 | |
| 914 | ifm_shape = shapeList[0] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 915 | for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 916 | |
| 917 | # Exclude illegal {mode, type} configurations. Pick legal output types |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 918 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 919 | outputDTypeList = [DType.INT8] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 920 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 921 | outputDTypeList = [DType.INT16] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 922 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 923 | outputDTypeList = [DType.INT32] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 924 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 925 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 926 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 927 | outputDTypeList = [DType.FLOAT] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 928 | elif error_name == ErrorIf.WrongInputType: |
| 929 | # If an incorrect input type is used then we set a 'correct' |
| 930 | # output type to avoid other errors |
| 931 | outputDTypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 932 | else: |
| 933 | continue |
| 934 | |
| 935 | for outputDType in outputDTypeList: |
| 936 | for perm in range(testGen.args.num_rand_permutations): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 937 | # Randomly generate legal output dimensions and shift |
| 938 | # and then compute the stride and offset based on them |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 939 | # A output_dim of 1 will cause offset to exceed allowed range |
| 940 | # so minimum value 2 produced below |
| 941 | output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1] |
| 942 | while ((float(ifm_shape[1]) / float(output_dims[0])) >= 16): |
| 943 | output_dims[0] += 1 |
| 944 | while ((float(ifm_shape[2]) / float(output_dims[1])) >= 16): |
| 945 | output_dims[1] += 1 |
| 946 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 947 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 948 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 949 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 950 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 951 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 952 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 953 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 954 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 955 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 956 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 957 | if outputDType == DType.FLOAT: |
| 958 | shift = 0 |
| 959 | stride = [0, 0] |
| 960 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 961 | stride_fp = [fp_stride_y, fp_stride_x] |
| 962 | offset_fp = [fp_offset_y, fp_offset_x] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 963 | |
| 964 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 965 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 966 | testGen, |
| 967 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 968 | mode, |
| 969 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 970 | shapeList, |
| 971 | outputDType, |
| 972 | shift, |
| 973 | stride, |
| 974 | stride_fp, |
| 975 | offset, |
| 976 | offset_fp |
| 977 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 978 | else: |
| 979 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 980 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 981 | arg_list.append( |
| 982 | ( |
| 983 | "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] | 984 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 985 | output_dims[0], |
| 986 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 987 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 988 | stride_fp[0], |
| 989 | stride_fp[1], |
| 990 | offset_fp[0], |
| 991 | offset_fp[1], |
| 992 | ), |
| 993 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 994 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 995 | stride, |
| 996 | offset, |
| 997 | shift, |
| 998 | stride_fp, |
| 999 | offset_fp, |
| 1000 | output_dims, |
| 1001 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1002 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1003 | ], |
| 1004 | ) |
| 1005 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1006 | else: |
| 1007 | shift = 11 |
| 1008 | unit = float(1 << shift) |
| 1009 | stride_y = int(round(fp_stride_y * unit)) |
| 1010 | stride_x = int(round(fp_stride_x * unit)) |
| 1011 | offset_y = int(round(fp_offset_y * unit)) |
| 1012 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1013 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1014 | while ( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1015 | stride_y >= (16 << shift) |
| 1016 | or stride_x >= (16 << shift) |
| 1017 | or offset_y >= (16 << shift) |
| 1018 | or offset_x >= (16 << shift) |
| 1019 | or offset_y <= (-16 << shift) |
| 1020 | or offset_x <= (-16 << shift) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1021 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1022 | shift = shift - 1 |
| 1023 | unit = float(1 << shift) |
| 1024 | stride_y = int(round(fp_stride_y * unit)) |
| 1025 | stride_x = int(round(fp_stride_x * unit)) |
| 1026 | offset_y = int(round(fp_offset_y * unit)) |
| 1027 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1028 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1029 | stride = [stride_y, stride_x] |
| 1030 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1031 | |
| 1032 | stride_fp = [0.0, 0.0] |
| 1033 | offset_fp = [0.0, 0.0] |
| 1034 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1035 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1036 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1037 | testGen, |
| 1038 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1039 | mode, |
| 1040 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1041 | shapeList, |
| 1042 | outputDType, |
| 1043 | shift, |
| 1044 | stride, |
| 1045 | stride_fp, |
| 1046 | offset, |
| 1047 | offset_fp |
| 1048 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1049 | else: |
| 1050 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1051 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1052 | arg_list.append( |
| 1053 | ( |
| 1054 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1055 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1056 | shift, |
| 1057 | output_dims[0], |
| 1058 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1059 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1060 | stride[0], |
| 1061 | stride[1], |
| 1062 | offset[0], |
| 1063 | offset[1], |
| 1064 | ), |
| 1065 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1066 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1067 | stride, |
| 1068 | offset, |
| 1069 | shift, |
| 1070 | stride_fp, |
| 1071 | offset_fp, |
| 1072 | output_dims, |
| 1073 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1074 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1075 | ], |
| 1076 | ) |
| 1077 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1078 | |
| 1079 | return arg_list |
| 1080 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1081 | def agCondIf(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1082 | # CondIf generates the condition values here. |
| 1083 | # Convert to tensors in the build function, along with the |
| 1084 | # then and else blocks |
| 1085 | arg_list = [] |
| 1086 | |
| 1087 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1088 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1089 | |
| 1090 | return arg_list |
| 1091 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1092 | def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1093 | # While loop: 0 iterations, 1, more than 1 |
| 1094 | arg_list = [] |
| 1095 | |
| 1096 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1097 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1098 | |
| 1099 | return arg_list |
| 1100 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1101 | class TosaErrorIfArgGen: |
| 1102 | |
| 1103 | @staticmethod |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1104 | 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] | 1105 | |
| 1106 | if outputDType == DType.FLOAT: |
| 1107 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1108 | stride_fp = testGen.rng.random(size=[2]) - 2 |
| 1109 | elif error_name == ErrorIf.ShiftNotZero: |
| 1110 | shift = testGen.rng.integers(1, 5) |
| 1111 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1112 | shape = shapeList[0] |
| 1113 | transform_height = testGen.rng.choice([False, True]) |
| 1114 | if transform_height: |
| 1115 | stride_fp[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1116 | else: |
| 1117 | stride_fp[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1118 | else: |
| 1119 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1120 | stride = np.int16(testGen.rng.integers(-1, 1, size=[2])) |
| 1121 | elif error_name == ErrorIf.ShiftSmallerOne: |
| 1122 | shift = testGen.rng.integers(-3, 1) |
| 1123 | if shift <= 0: |
| 1124 | stride = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1125 | offset = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1126 | else: |
| 1127 | stride = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1128 | offset = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1129 | elif error_name == ErrorIf.ShiftLargerEleven: |
| 1130 | shift = np.int16(testGen.rng.integers(12, 15)) |
| 1131 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1132 | shape = shapeList[0] |
| 1133 | transform_height = testGen.rng.choice([False, True]) |
| 1134 | if transform_height: |
| 1135 | stride[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1136 | else: |
| 1137 | stride[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1138 | elif error_name == ErrorIf.StrideLargerEqualMax: |
| 1139 | stride = [(16 << shift) + 1, (16 << shift) + 1] |
| 1140 | elif error_name == ErrorIf.OffsetLargerEqualMax: |
| 1141 | offset = [(16 << shift) + 1, (16 << shift) + 1] |
| 1142 | elif error_name == ErrorIf.OffsetSmallerEqualMin: |
| 1143 | offset = [(-16 << shift) - 1, (-16 << shift) - 1] |
| 1144 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1145 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1146 | if error_name == ErrorIf.WrongOutputType: |
| 1147 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
| 1148 | incorrect_types = (DType.INT4, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1149 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
| 1150 | incorrect_types = (DType.INT4, DType.INT8, DType.INT32, DType.INT48, DType.FLOAT) |
| 1151 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
| 1152 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) |
| 1153 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
| 1154 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) |
| 1155 | elif dtype == DType.FLOAT: |
| 1156 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) |
| 1157 | outputDType = testGen.rng.choice(a=incorrect_types) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1158 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1159 | return shift, stride, stride_fp, offset, offset_fp, outputDType |
| 1160 | |
| 1161 | @staticmethod |
| 1162 | def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list): |
| 1163 | # Mess up input/output tensors for ERROR_IF checks |
| 1164 | if error_name == "WrongInputList": |
| 1165 | add_input = testGen.rng.choice([True, False]) |
| 1166 | if add_input: |
| 1167 | input_list.append('eiDummyInput') |
| 1168 | else: |
| 1169 | input_list = input_list[:-1] |
| 1170 | if error_name == "WrongOutputList": |
| 1171 | add_output = testGen.rng.choice([True, False]) |
| 1172 | if add_output: |
| 1173 | output_list.append('eiDummyOutput') |
| 1174 | else: |
| 1175 | output_list = [] |
| 1176 | return input_list, output_list |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1177 | |
| 1178 | class TosaErrorValidator: |
| 1179 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1180 | @staticmethod |
| 1181 | def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs): |
| 1182 | # Check ERROR_IF statements |
| 1183 | |
| 1184 | for val_fcn in validator_fcns: |
| 1185 | val_result = val_fcn(True, **kwargs) |
| 1186 | |
| 1187 | validator_name = val_result['error_name'] |
| 1188 | error_result = val_result['error_result'] |
| 1189 | error_reason = val_result['error_reason'] |
| 1190 | |
| 1191 | if error_result: |
| 1192 | if error_name == validator_name: |
| 1193 | serializer.setExpectedReturnCode(2, error_reason) |
| 1194 | else: |
| 1195 | print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}") |
| 1196 | return None # Return None to delete test if wrong ERROR_IF is hit |
| 1197 | else: |
| 1198 | if error_name == validator_name: |
| 1199 | print(f"No ERROR_IF hit for {error_name}") |
| 1200 | return None |
| 1201 | |
| 1202 | @staticmethod |
| 1203 | def evWrongInputType(check=False, **kwargs): |
| 1204 | all_dtypes = (DType.BOOL, DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1205 | |
| 1206 | # Find the unsupported input data types |
| 1207 | assert 'op' in kwargs |
| 1208 | op = kwargs['op'] |
| 1209 | input_dtypes = op['types'] |
| 1210 | wrong_input_dtypes = list(set(all_dtypes) - set(input_dtypes)) |
| 1211 | |
| 1212 | error_name = ErrorIf.WrongInputType |
| 1213 | param_reqs = {"rank": None, "dtype": wrong_input_dtypes, "shape": None} |
| 1214 | error_result = False |
| 1215 | error_reason = "Input data type not supported for this operator" |
| 1216 | |
| 1217 | if check: |
| 1218 | input_dtype = kwargs['input_dtype'] |
| 1219 | if input_dtype not in input_dtypes: |
| 1220 | error_result = True |
| 1221 | |
| 1222 | info_dict = { |
| 1223 | "error_name": error_name, |
| 1224 | "error_result": error_result, |
| 1225 | "error_reason": error_reason, |
| 1226 | "param_reqs": param_reqs |
| 1227 | } |
| 1228 | return info_dict |
| 1229 | |
| 1230 | @staticmethod |
| 1231 | def evWrongOutputType(check=False, **kwargs): |
| 1232 | error_name = ErrorIf.WrongOutputType |
| 1233 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1234 | error_result = False |
| 1235 | error_reason = "Output data type not supported for this configuration of operator" |
| 1236 | |
| 1237 | if check: |
| 1238 | input_dtype = kwargs['input_dtype'] |
| 1239 | output_dtype = kwargs['output_dtype'] |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 1240 | op = kwargs['op'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1241 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 1242 | if op['op'] == Op.RESIZE: |
| 1243 | mode = kwargs['mode'] |
| 1244 | if ( |
| 1245 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT8 and output_dtype != DType.INT8) or |
| 1246 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT16 and output_dtype != DType.INT16) or |
| 1247 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT8 and output_dtype != DType.INT32) or |
| 1248 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT16 and output_dtype != DType.INT48) or |
| 1249 | (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) |
| 1250 | ): |
| 1251 | error_result = True |
| 1252 | else: |
| 1253 | if output_dtype != input_dtype: |
| 1254 | error_result = True |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1255 | |
| 1256 | info_dict = { |
| 1257 | "error_name": error_name, |
| 1258 | "error_result": error_result, |
| 1259 | "error_reason": error_reason, |
| 1260 | "param_reqs": param_reqs |
| 1261 | } |
| 1262 | return info_dict |
| 1263 | |
| 1264 | @staticmethod |
| 1265 | def evWrongRank(check=False, **kwargs): |
| 1266 | all_ranks = (1, 2, 3, 4, 5) |
| 1267 | |
| 1268 | # Make a list of incorrect ranks |
| 1269 | assert 'op' in kwargs |
| 1270 | op = kwargs['op'] |
| 1271 | rmin, rmax = op['rank'] |
| 1272 | rank_range = range(rmin, rmax + 1) |
| 1273 | incorrect_ranks = list(set(all_ranks) - set(rank_range)) |
| 1274 | # Set minimum incorrect rank to 3 to avoid index error |
| 1275 | if op['op'] == Op.RESIZE: |
| 1276 | incorrect_ranks = [3, 5] |
| 1277 | |
| 1278 | error_name = ErrorIf.WrongRank |
| 1279 | param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None} |
| 1280 | error_result = False |
| 1281 | error_reason = "Rank not supported for this operator" |
| 1282 | |
| 1283 | if check: |
| 1284 | input_shape = kwargs['input_shape'] |
| 1285 | if op['op'] == Op.RESIZE and len(input_shape.shape) != 4: |
| 1286 | error_result = True |
| 1287 | |
| 1288 | info_dict = { |
| 1289 | "error_name": error_name, |
| 1290 | "error_result": error_result, |
| 1291 | "error_reason": error_reason, |
| 1292 | "param_reqs": param_reqs |
| 1293 | } |
| 1294 | return info_dict |
| 1295 | |
| 1296 | @staticmethod |
| 1297 | def evWrongInputList(check=False, **kwargs): |
| 1298 | error_name = ErrorIf.WrongInputList |
| 1299 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1300 | error_result = False |
| 1301 | error_reason = "Op input list does not match expected input" |
| 1302 | |
| 1303 | if check: |
| 1304 | op = kwargs['op'] |
| 1305 | input_list = kwargs['input_list'] |
| 1306 | num_operands = kwargs['num_operands'] |
| 1307 | if len(input_list) != num_operands: |
| 1308 | error_result = True |
| 1309 | |
| 1310 | info_dict = { |
| 1311 | "error_name": error_name, |
| 1312 | "error_result": error_result, |
| 1313 | "error_reason": error_reason, |
| 1314 | "param_reqs": param_reqs |
| 1315 | } |
| 1316 | return info_dict |
| 1317 | |
| 1318 | @staticmethod |
| 1319 | def evWrongOutputList(check=False, **kwargs): |
| 1320 | error_name = ErrorIf.WrongOutputList |
| 1321 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1322 | error_result = False |
| 1323 | error_reason = "Op output list does not match expected output" |
| 1324 | |
| 1325 | if check: |
| 1326 | output_list = kwargs['output_list'] |
| 1327 | # Note this will be incorrect if an operator returns more than one output |
| 1328 | if len(output_list) != 1: |
| 1329 | error_result = True |
| 1330 | |
| 1331 | info_dict = { |
| 1332 | "error_name": error_name, |
| 1333 | "error_result": error_result, |
| 1334 | "error_reason": error_reason, |
| 1335 | "param_reqs": param_reqs |
| 1336 | } |
| 1337 | return info_dict |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1338 | |
| 1339 | @staticmethod |
| 1340 | def evMaxDimExceeded(check=False, **kwargs): |
| 1341 | error_name = ErrorIf.MaxDimExceeded |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1342 | param_reqs = { |
| 1343 | "rank": [4,4], |
| 1344 | "dtype": [DType.INT8], |
| 1345 | "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]] |
| 1346 | } |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1347 | error_result = False |
| 1348 | error_reason = "At least one maximum dimension is larger than 16384" |
| 1349 | |
| 1350 | if check: |
| 1351 | input_shape = kwargs['input_shape'].shape |
| 1352 | output_shape = kwargs['output_shape'] # Note this is just (OH, OW) |
| 1353 | if ((input_shape[1] > 16384) or |
| 1354 | (input_shape[2] > 16384) or |
| 1355 | (output_shape[0] > 16384) or |
| 1356 | (output_shape[1] > 16384)): |
| 1357 | error_result = True |
| 1358 | |
| 1359 | info_dict = { |
| 1360 | "error_name": error_name, |
| 1361 | "error_result": error_result, |
| 1362 | "error_reason": error_reason, |
| 1363 | "param_reqs": param_reqs |
| 1364 | } |
| 1365 | return info_dict |
| 1366 | |
| 1367 | @staticmethod |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1368 | def evBatchMismatch(check=False, **kwargs): |
| 1369 | error_name = ErrorIf.BatchMismatch |
| 1370 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1371 | error_result = False |
| 1372 | error_reason = "Input batch size not equal to output batch size" |
| 1373 | |
| 1374 | assert 'op' in kwargs |
| 1375 | op = kwargs['op'] |
| 1376 | rmin, rmax = op['rank'] |
| 1377 | rank_range = range(rmin, rmax + 1) |
| 1378 | |
| 1379 | if check: |
| 1380 | input_shape = kwargs['input_shape'].shape |
| 1381 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1382 | |
| 1383 | if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]): |
| 1384 | error_result = True |
| 1385 | |
| 1386 | info_dict = { |
| 1387 | "error_name": error_name, |
| 1388 | "error_result": error_result, |
| 1389 | "error_reason": error_reason, |
| 1390 | "param_reqs": param_reqs |
| 1391 | } |
| 1392 | return info_dict |
| 1393 | |
| 1394 | @staticmethod |
| 1395 | def evChannelMismatch(check=False, **kwargs): |
| 1396 | error_name = ErrorIf.ChannelMismatch |
| 1397 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1398 | error_result = False |
| 1399 | error_reason = "Input channel size not equal to output channel size" |
| 1400 | |
| 1401 | assert 'op' in kwargs |
| 1402 | op = kwargs['op'] |
| 1403 | rmin, rmax = op['rank'] |
| 1404 | rank_range = range(rmin, rmax + 1) |
| 1405 | |
| 1406 | if check: |
| 1407 | input_shape = kwargs['input_shape'].shape |
| 1408 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1409 | if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]): |
| 1410 | error_result = True |
| 1411 | |
| 1412 | info_dict = { |
| 1413 | "error_name": error_name, |
| 1414 | "error_result": error_result, |
| 1415 | "error_reason": error_reason, |
| 1416 | "param_reqs": param_reqs |
| 1417 | } |
| 1418 | return info_dict |
| 1419 | |
| 1420 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1421 | def evStrideSmallerEqualZero(check=False, **kwargs): |
| 1422 | error_name = ErrorIf.StrideSmallerEqualZero |
| 1423 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1424 | error_result = False |
| 1425 | error_reason = "Stride value smaller than or equal zero" |
| 1426 | |
| 1427 | if check: |
| 1428 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1429 | output_dtype = kwargs['output_dtype'] |
| 1430 | if input_dtype != DType.FLOAT and output_dtype == DType.FLOAT: |
| 1431 | stride = kwargs['stride'] # Work around wrong input/output type tests |
| 1432 | elif output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1433 | stride = kwargs['stride_fp'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1434 | elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT: |
| 1435 | stride = kwargs['stride_fp'] # Work around wrong input/output type tests |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1436 | else: |
| 1437 | stride = kwargs['stride'] |
| 1438 | |
| 1439 | if min(stride) <= 0: |
| 1440 | error_result = True |
| 1441 | |
| 1442 | info_dict = { |
| 1443 | "error_name": error_name, |
| 1444 | "error_result": error_result, |
| 1445 | "error_reason": error_reason, |
| 1446 | "param_reqs": param_reqs |
| 1447 | } |
| 1448 | return info_dict |
| 1449 | |
| 1450 | @staticmethod |
| 1451 | def evStrideLargerEqualMax(check=False, **kwargs): |
| 1452 | error_name = ErrorIf.StrideLargerEqualMax |
| 1453 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1454 | error_result = False |
| 1455 | error_reason = "Stride value larger than or equal to maximum value" |
| 1456 | |
| 1457 | if check: |
| 1458 | shift = kwargs['shift'] |
| 1459 | input_dtype = kwargs['input_dtype'] |
| 1460 | stride = kwargs['stride'] |
| 1461 | if input_dtype in [DType.INT8, DType.INT16]: |
| 1462 | if shift >= 0 and (stride[0] >= (16 << shift) or stride[1] >= (16 << shift)): |
| 1463 | error_result = True |
| 1464 | elif shift < 0 and (stride[0] >= (16 >> -shift) or stride[1] >= (16 >> -shift)): |
| 1465 | error_result = True |
| 1466 | |
| 1467 | info_dict = { |
| 1468 | "error_name": error_name, |
| 1469 | "error_result": error_result, |
| 1470 | "error_reason": error_reason, |
| 1471 | "param_reqs": param_reqs |
| 1472 | } |
| 1473 | return info_dict |
| 1474 | |
| 1475 | |
| 1476 | @staticmethod |
| 1477 | def evStrideLargerDimension(check=False, **kwargs): |
| 1478 | error_name = ErrorIf.StrideLargerDimension |
| 1479 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1480 | error_result = False |
| 1481 | error_reason = "Stride value larger than or equal to H/W dimension" |
| 1482 | |
| 1483 | if check: |
| 1484 | shape = kwargs['input_shape'].shape |
| 1485 | input_dtype = kwargs['input_dtype'] |
| 1486 | stride = kwargs['stride_fp'] |
| 1487 | |
| 1488 | if input_dtype == DType.FLOAT and (stride[0] > shape[1]) or (stride[1] > shape[2]): |
| 1489 | error_result = True |
| 1490 | |
| 1491 | info_dict = { |
| 1492 | "error_name": error_name, |
| 1493 | "error_result": error_result, |
| 1494 | "error_reason": error_reason, |
| 1495 | "param_reqs": param_reqs |
| 1496 | } |
| 1497 | return info_dict |
| 1498 | |
| 1499 | |
| 1500 | @staticmethod |
| 1501 | def evOffsetSmallerEqualMin(check=False, **kwargs): |
| 1502 | error_name = ErrorIf.OffsetSmallerEqualMin |
| 1503 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1504 | error_result = False |
| 1505 | error_reason = "Offset value smaller than or equal to minimum value" |
| 1506 | |
| 1507 | if check: |
| 1508 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1509 | output_dtype = kwargs['output_dtype'] |
| 1510 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1511 | offset = kwargs['offset_fp'] |
| 1512 | else: |
| 1513 | offset = kwargs['offset'] |
| 1514 | |
| 1515 | if shift >= 0 and (offset[0] <= (-16 << shift) or offset[1] <= (-16 << shift)): |
| 1516 | error_result = True |
| 1517 | elif shift < 0 and (offset[0] <= (-16 >> -shift) or offset[1] <= (-16 >> -shift)): |
| 1518 | error_result = True |
| 1519 | |
| 1520 | info_dict = { |
| 1521 | "error_name": error_name, |
| 1522 | "error_result": error_result, |
| 1523 | "error_reason": error_reason, |
| 1524 | "param_reqs": param_reqs |
| 1525 | } |
| 1526 | return info_dict |
| 1527 | |
| 1528 | @staticmethod |
| 1529 | def evOffsetLargerEqualMax(check=False, **kwargs): |
| 1530 | error_name = ErrorIf.OffsetLargerEqualMax |
| 1531 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1532 | error_result = False |
| 1533 | error_reason = "Offset value larger than or equal to maximum value" |
| 1534 | |
| 1535 | if check: |
| 1536 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1537 | output_dtype = kwargs['output_dtype'] |
| 1538 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1539 | offset = kwargs['offset_fp'] |
| 1540 | else: |
| 1541 | offset = kwargs['offset'] |
| 1542 | |
| 1543 | if shift >= 0: |
| 1544 | if offset[0] >= (16 << shift) or offset[1] >= (16 << shift): |
| 1545 | error_result = True |
| 1546 | |
| 1547 | if shift >= 0 and (offset[0] >= (16 << shift) or offset[1] >= (16 << shift)): |
| 1548 | error_result = True |
| 1549 | elif shift < 0 and (offset[0] >= (16 >> -shift) or offset[1] >= (16 >> -shift)): |
| 1550 | error_result = True |
| 1551 | |
| 1552 | info_dict = { |
| 1553 | "error_name": error_name, |
| 1554 | "error_result": error_result, |
| 1555 | "error_reason": error_reason, |
| 1556 | "param_reqs": param_reqs |
| 1557 | } |
| 1558 | return info_dict |
| 1559 | |
| 1560 | @staticmethod |
| 1561 | def evShiftNotZero(check=False, **kwargs): |
| 1562 | error_name = ErrorIf.ShiftNotZero |
| 1563 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1564 | error_result = False |
| 1565 | error_reason = "Shift value must be zero for float input" |
| 1566 | |
| 1567 | if check: |
| 1568 | shift = kwargs['shift'] |
| 1569 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1570 | output_dtype = kwargs['output_dtype'] |
| 1571 | 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] | 1572 | error_result = True |
| 1573 | |
| 1574 | info_dict = { |
| 1575 | "error_name": error_name, |
| 1576 | "error_result": error_result, |
| 1577 | "error_reason": error_reason, |
| 1578 | "param_reqs": param_reqs |
| 1579 | } |
| 1580 | return info_dict |
| 1581 | |
| 1582 | |
| 1583 | @staticmethod |
| 1584 | def evShiftSmallerOne(check=False, **kwargs): |
| 1585 | error_name = ErrorIf.ShiftSmallerOne |
| 1586 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1587 | error_result = False |
| 1588 | error_reason = "Shift value smaller than one" |
| 1589 | |
| 1590 | if check: |
| 1591 | shift = kwargs['shift'] |
| 1592 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1593 | output_dtype = kwargs['output_dtype'] |
| 1594 | 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] | 1595 | error_result = True |
| 1596 | |
| 1597 | info_dict = { |
| 1598 | "error_name": error_name, |
| 1599 | "error_result": error_result, |
| 1600 | "error_reason": error_reason, |
| 1601 | "param_reqs": param_reqs |
| 1602 | } |
| 1603 | return info_dict |
| 1604 | |
| 1605 | @staticmethod |
| 1606 | def evShiftLargerEleven(check=False, **kwargs): |
| 1607 | error_name = ErrorIf.ShiftLargerEleven |
| 1608 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1609 | error_result = False |
| 1610 | error_reason = "Shift value larger than eleven" |
| 1611 | |
| 1612 | if check: |
| 1613 | shift = kwargs['shift'] |
| 1614 | if shift > 11: |
| 1615 | error_result = True |
| 1616 | |
| 1617 | info_dict = { |
| 1618 | "error_name": error_name, |
| 1619 | "error_result": error_result, |
| 1620 | "error_reason": error_reason, |
| 1621 | "param_reqs": param_reqs |
| 1622 | } |
| 1623 | return info_dict |
| 1624 | |
| 1625 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 1626 | @staticmethod |
| 1627 | def evRankMismatch(check=False, **kwargs): |
| 1628 | error_name = ErrorIf.RankMismatch |
| 1629 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1630 | error_result = False |
| 1631 | error_reason = "Input Rank does not match output rank" |
| 1632 | |
| 1633 | if check: |
| 1634 | input1_shape = kwargs['input1'].shape |
| 1635 | input2_shape = kwargs['input2'].shape |
| 1636 | output_shape = kwargs['result_tensor'].shape |
| 1637 | if (len(input1_shape) != len(output_shape)) or (len(input2_shape) != len(output_shape)): |
| 1638 | error_result = True |
| 1639 | |
| 1640 | info_dict = { |
| 1641 | "error_name": error_name, |
| 1642 | "error_result": error_result, |
| 1643 | "error_reason": error_reason, |
| 1644 | "param_reqs": param_reqs |
| 1645 | } |
| 1646 | return info_dict |
| 1647 | |
| 1648 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1649 | class TosaInvalidValidator: |
| 1650 | |
| 1651 | @staticmethod |
| 1652 | def ivWrongDataTypeOrModeResize(**kwargs): |
| 1653 | input_dtype = kwargs["input_dtype"] |
| 1654 | args = kwargs["args"] |
| 1655 | mode = args[0] |
| 1656 | stride = args[1] |
| 1657 | stride_fp = args[4] |
| 1658 | output_dtype = args[8] |
| 1659 | |
| 1660 | if mode == ResizeMode.BILINEAR: |
| 1661 | # Invalid output data type / Invalid input datatype |
| 1662 | return ( |
| 1663 | not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or |
| 1664 | not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or |
| 1665 | not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or |
| 1666 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1667 | ) |
| 1668 | elif mode == ResizeMode.NEAREST: |
| 1669 | # Invalid output data type / Invalid input datatype |
| 1670 | return ( |
| 1671 | (input_dtype != output_dtype) or |
| 1672 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 1673 | ) |
| 1674 | else: |
| 1675 | # Invalid resize mode |
| 1676 | return True |
| 1677 | |
| 1678 | @staticmethod |
| 1679 | def ivBadStride(**kwargs): |
| 1680 | input_dtype = kwargs["input_dtype"] |
| 1681 | args = kwargs["args"] |
| 1682 | stride_x = args[1][0] |
| 1683 | stride_y = args[1][1] |
| 1684 | stride_fp_x = args[4][0] |
| 1685 | stride_fp_y = args[4][1] |
| 1686 | |
| 1687 | if input_dtype == DType.FLOAT: |
| 1688 | if stride_fp_x <= 0 or stride_fp_y <= 0: |
| 1689 | # Negative or zero stride |
| 1690 | return True |
| 1691 | else: |
| 1692 | if stride_x <= 0 or stride_y <= 0: |
| 1693 | # Negative or zero stride |
| 1694 | return True |
| 1695 | return False |
| 1696 | |
| 1697 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 1698 | @staticmethod |
| 1699 | def ivHeightWidthSmallerZero(**kwargs): |
| 1700 | opName = kwargs['opName'] |
| 1701 | |
| 1702 | inputShapes = kwargs['shapeList'] |
| 1703 | input = inputShapes[0] |
| 1704 | if not opName.endswith("pool2d"): |
| 1705 | filter = inputShapes[1] |
| 1706 | |
| 1707 | args = kwargs['args'] |
| 1708 | strides = args[0] |
| 1709 | padding = args[1] |
| 1710 | dilations = args[2] |
| 1711 | if opName.endswith("pool2d"): |
| 1712 | kernel = args[2] |
| 1713 | |
| 1714 | if opName.startswith('conv2d'): |
| 1715 | h = ( |
| 1716 | input[1] |
| 1717 | - filter[1] |
| 1718 | - (filter[1] - 1) * (dilations[0] - 1) |
| 1719 | + padding[0] |
| 1720 | + padding[1] |
| 1721 | ) // strides[0] + 1 |
| 1722 | |
| 1723 | w = ( |
| 1724 | input[2] |
| 1725 | - filter[2] |
| 1726 | - (filter[2] - 1) * (dilations[1] - 1) |
| 1727 | + padding[2] |
| 1728 | + padding[3] |
| 1729 | ) // strides[1] + 1 |
| 1730 | elif opName.startswith("depthwise_conv2d"): |
| 1731 | h = ( |
| 1732 | input[1] |
| 1733 | - filter[0] |
| 1734 | - (filter[0] - 1) * (dilations[0] - 1) |
| 1735 | + padding[0] |
| 1736 | + padding[1] |
| 1737 | ) // strides[0] + 1 |
| 1738 | |
| 1739 | w = ( |
| 1740 | input[2] |
| 1741 | - filter[1] |
| 1742 | - (filter[1] - 1) * (dilations[1] - 1) |
| 1743 | + padding[2] |
| 1744 | + padding[3] |
| 1745 | ) // strides[1] + 1 |
| 1746 | elif opName.endswith("pool2d"): |
| 1747 | h = (input[1] + padding[0] + padding[1] + strides[0] - kernel[0]) // strides[0] |
| 1748 | w = (input[2] + padding[2] + padding[3] + strides[1] - kernel[1]) // strides[1] |
| 1749 | else: |
| 1750 | assert False, "Unrecognized Op" |
| 1751 | |
| 1752 | if h <= 0 or w <= 0: |
| 1753 | # Invalid parameter combination |
| 1754 | return True |
| 1755 | return False |
| 1756 | |
| 1757 | @staticmethod |
| 1758 | def ivNonPositiveOutputShape(**kwargs): |
| 1759 | args = kwargs['args'] |
| 1760 | output_shape = args[3] |
| 1761 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
| 1762 | # Negative output shape |
| 1763 | return True |
| 1764 | return False |
| 1765 | |
| 1766 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1767 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1768 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 1769 | # Maximum rank of tensor supported by test generator. |
| 1770 | TOSA_TENSOR_MAX_RANK = 6 |
| 1771 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1772 | def __init__(self, args): |
| 1773 | self.args = args |
| 1774 | self.basePath = args.output_dir |
| 1775 | self.random_seed = args.random_seed |
| 1776 | self.ser = None |
| 1777 | self.rng = np.random.default_rng(self.random_seed) |
| 1778 | self.createDynamicOpLists() |
| 1779 | self.initOpListDefaults() |
| 1780 | self.quantGen = TosaQuantGen() |
| 1781 | # Force makeShape to do a specific starting shape |
| 1782 | self.targetted_shape = None |
| 1783 | |
| 1784 | def createSerializer(self, opName, testPath): |
| 1785 | self.testPath = os.path.join(opName, testPath) |
| 1786 | |
| 1787 | fullPath = os.path.join(self.basePath, self.testPath) |
| 1788 | os.makedirs(fullPath, exist_ok=True) |
| 1789 | self.ser = ts.TosaSerializer(fullPath) |
| 1790 | |
| 1791 | def getSerializer(self): |
| 1792 | return self.ser |
| 1793 | |
| 1794 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1795 | with open( |
| 1796 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 1797 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1798 | fd.write(self.ser.serialize()) |
| 1799 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1800 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 1801 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1802 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 1803 | def resetRNG(self, seed=None): |
| 1804 | if seed == None: |
| 1805 | seed = self.random_seed + 1 |
| 1806 | self.rng = np.random.default_rng(seed) |
| 1807 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1808 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1809 | if dtype == DType.BOOL: |
| 1810 | np_dt = np.bool |
| 1811 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1812 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1813 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1814 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1815 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1816 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 1817 | elif dtype == DType.UINT8: |
| 1818 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1819 | elif dtype == DType.INT16: |
| 1820 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 1821 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1822 | return np.int32( |
| 1823 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 1824 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1825 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1826 | return np.int64( |
| 1827 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 1828 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1829 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1830 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1831 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1832 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1833 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1834 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1835 | placeholders = [] |
| 1836 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1837 | assert len(shape_list) == len(dtype_list) |
| 1838 | |
| 1839 | for idx, shape in enumerate(shape_list): |
| 1840 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1841 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1842 | |
| 1843 | return placeholders |
| 1844 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1845 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1846 | consts = [] |
| 1847 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1848 | assert len(shape_list) == len(dtype_list) |
| 1849 | |
| 1850 | for idx, shape in enumerate(shape_list): |
| 1851 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 1852 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1853 | |
| 1854 | return consts |
| 1855 | |
| 1856 | def makeShape(self, rank): |
| 1857 | if self.targetted_shape: |
| 1858 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1859 | return np.int32( |
| 1860 | self.rng.integers( |
| 1861 | low=self.args.tensor_shape_range[0], |
| 1862 | high=self.args.tensor_shape_range[1], |
| 1863 | size=rank, |
| 1864 | ) |
| 1865 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1866 | |
| 1867 | def setTargetShape(self, shape): |
| 1868 | self.targetted_shape = shape |
| 1869 | |
| 1870 | def randInt(self, low=0, high=256): |
| 1871 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 1872 | |
| 1873 | def getRandNumberDType(self, dtype): |
| 1874 | if dtype == DType.FLOAT: |
| 1875 | return self.rng.random() |
| 1876 | elif dtype == DType.BOOL: |
| 1877 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1878 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1879 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 1880 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1881 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 1882 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1883 | elif dtype == DType.INT16: |
| 1884 | low, high = (-32768, 32768) |
| 1885 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1886 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1887 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1888 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1889 | # Special size |
| 1890 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 1891 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1892 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1893 | |
| 1894 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 1895 | |
| 1896 | def shapeStr(self, shape): |
| 1897 | |
| 1898 | sStr = [] |
| 1899 | # Convert to strings |
| 1900 | for i in shape: |
| 1901 | sStr.append(str(i)) |
| 1902 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1903 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1904 | |
| 1905 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1906 | if isinstance(t, list): |
| 1907 | assert len(t) >= 2 |
| 1908 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1909 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 1910 | if t == DType.BOOL: |
| 1911 | return "b" |
| 1912 | elif t == DType.INT4: |
| 1913 | return "i4" |
| 1914 | elif t == DType.INT8: |
| 1915 | return "i8" |
| 1916 | elif t == DType.UINT8: |
| 1917 | return "u8" |
| 1918 | elif t == DType.INT16: |
| 1919 | return "i16" |
| 1920 | elif t == DType.INT32: |
| 1921 | return "i32" |
| 1922 | elif t == DType.INT48: |
| 1923 | return "i48" |
| 1924 | elif t == DType.FLOAT: |
| 1925 | return "float" |
| 1926 | else: |
| 1927 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1928 | |
| 1929 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1930 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1931 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1932 | return 4 |
| 1933 | elif t == DType.INT8: |
| 1934 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 1935 | elif t == DType.UINT8: |
| 1936 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1937 | elif t == DType.INT16: |
| 1938 | return 16 |
| 1939 | elif t == DType.INT32: |
| 1940 | return 32 |
| 1941 | elif t == DType.INT48: |
| 1942 | return 48 |
| 1943 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1944 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1945 | |
| 1946 | # Argument generators |
| 1947 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 1948 | # Where the string descriptor is used to generate the test name and |
| 1949 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 1950 | # build function |
| 1951 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1952 | def build_unary(self, op, a, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1953 | result_tens = OutputShaper.unaryOp(self.ser, a) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1954 | # build_placeholder returns an int, ABS/other ops does not |
| 1955 | if isinstance(op, int): |
| 1956 | self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) |
| 1957 | else: |
| 1958 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1959 | return result_tens |
| 1960 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 1961 | def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None): |
| 1962 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b, error_name) |
| 1963 | |
| 1964 | |
| 1965 | # Invalidate Input/Output list for error if checks. |
| 1966 | input_list = [a.name, b.name] |
| 1967 | output_list = [result_tens.name] |
| 1968 | pCount, cCount = op["operands"] |
| 1969 | num_operands = pCount + cCount |
| 1970 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 1971 | |
| 1972 | TosaErrorValidator.evValidateErrorIfs( |
| 1973 | self.ser, |
| 1974 | validator_fcns, |
| 1975 | error_name, |
| 1976 | op=op, |
| 1977 | input1 = a, |
| 1978 | input2 = b, |
| 1979 | input_dtype = a.dtype, |
| 1980 | output_dtype = result_tens.dtype, |
| 1981 | result_tensor = result_tens, |
| 1982 | input_list=input_list, |
| 1983 | output_list=output_list, |
| 1984 | num_operands=num_operands, |
| 1985 | ) |
| 1986 | |
| 1987 | self.ser.addOperator(op['op'], input_list, output_list) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1988 | return result_tens |
| 1989 | |
| 1990 | def build_binary_nonbroadcast(self, op, a, b): |
| 1991 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1992 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1993 | return result_tens |
| 1994 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1995 | def build_arithmetic_right_shift(self, op, a, b, round): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 1996 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 1997 | |
| 1998 | attr = ts.TosaSerializerAttribute() |
| 1999 | attr.ArithmeticRightShiftAttribute(round) |
| 2000 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2001 | 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] | 2002 | return result_tens |
| 2003 | |
| 2004 | def build_mul(self, op, a, b, shift): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 2005 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2006 | |
| 2007 | # Special for multiply: |
| 2008 | # Force the result to INT32 for INT types |
| 2009 | if a.dtype != DType.FLOAT: |
| 2010 | result_tens.setDtype(DType.INT32) |
| 2011 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2012 | attr = ts.TosaSerializerAttribute() |
| 2013 | attr.MulAttribute(shift) |
| 2014 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2015 | 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] | 2016 | return result_tens |
| 2017 | |
| 2018 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2019 | # Constant size depending on type, random values |
| 2020 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2021 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2022 | table_arr = self.getRandTensor([513], table_dtype) |
| 2023 | else: |
| 2024 | assert a.dtype == DType.INT8 |
| 2025 | table_dtype = DType.INT8 |
| 2026 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2027 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2028 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 2029 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2030 | 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] | 2031 | |
| 2032 | return result_tens |
| 2033 | |
| 2034 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2035 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2036 | 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] | 2037 | return result_tens |
| 2038 | |
| 2039 | def build_comparison(self, op, a, b): |
| 2040 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2041 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2042 | return result_tens |
| 2043 | |
| 2044 | def build_argmax(self, op, a, axis): |
| 2045 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 2046 | |
| 2047 | attr = ts.TosaSerializerAttribute() |
| 2048 | attr.AxisAttribute(axis) |
| 2049 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2050 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2051 | return result_tens |
| 2052 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2053 | def build_pool2d(self, op, input, stride, pad, kernel, qinfo=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2054 | result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) |
| 2055 | |
| 2056 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2057 | attr.PoolAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2058 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2059 | self.ser.addOperator(op['op'], [input.name], [result_tens.name], attr, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2060 | return result_tens |
| 2061 | |
| 2062 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2063 | assert len(padding) == 4 |
| 2064 | result_tens = OutputShaper.conv2dOp( |
| 2065 | self.ser, ifm, filter, strides, padding, dilations |
| 2066 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2067 | |
| 2068 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2069 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2070 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2071 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2072 | 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] | 2073 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2074 | return result_tens |
| 2075 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2076 | def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
| 2077 | assert len(padding) == 6 |
| 2078 | result_tens = OutputShaper.conv3dOp( |
| 2079 | self.ser, ifm, filter, strides, padding, dilations |
| 2080 | ) |
| 2081 | |
| 2082 | attr = ts.TosaSerializerAttribute() |
| 2083 | attr.ConvAttribute(padding, strides, dilations) |
| 2084 | |
| 2085 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2086 | 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] | 2087 | ) |
| 2088 | return result_tens |
| 2089 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2090 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2091 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2092 | ): |
| 2093 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2094 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 2095 | |
| 2096 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2097 | attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2098 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2099 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2100 | 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] | 2101 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2102 | return result_tens |
| 2103 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2104 | def build_depthwise_conv2d( |
| 2105 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 2106 | ): |
| 2107 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 2108 | self.ser, ifm, filter, strides, padding, dilations |
| 2109 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2110 | |
| 2111 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2112 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2113 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2114 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2115 | 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] | 2116 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2117 | return result_tens |
| 2118 | |
| 2119 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 2120 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 2121 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2122 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2123 | 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] | 2124 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2125 | return result_tens |
| 2126 | |
| 2127 | def build_matmul(self, op, a, b, qinfo): |
| 2128 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2129 | 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] | 2130 | return result_tens |
| 2131 | |
| 2132 | def build_reduce(self, op, a, axis): |
| 2133 | result_tens = OutputShaper.reduceOp(self.ser, a, axis) |
| 2134 | |
| 2135 | attr = ts.TosaSerializerAttribute() |
| 2136 | attr.AxisAttribute(axis) |
| 2137 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2138 | self.ser.addOperator(op['op'], [a.name], result_tens.name, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2139 | return result_tens |
| 2140 | |
| 2141 | def build_clamp(self, op, a): |
| 2142 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2143 | |
| 2144 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2145 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2146 | |
| 2147 | if a.dtype == DType.FLOAT: |
| 2148 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 2149 | else: |
| 2150 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 2151 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2152 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2153 | return result_tens |
| 2154 | |
| 2155 | def build_leaky_relu(self, op, a): |
| 2156 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2157 | attr = ts.TosaSerializerAttribute() |
| 2158 | |
| 2159 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 2160 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2161 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2162 | return result_tens |
| 2163 | |
| 2164 | # Needs an additional type/input |
| 2165 | def build_prelu(self, op, a): |
| 2166 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2167 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2168 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2169 | return result_tens |
| 2170 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2171 | def build_sigmoid(self, op, a): |
| 2172 | result_tens = OutputShaper.unaryOp(self.ser, a) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2173 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2174 | return result_tens |
| 2175 | |
| 2176 | def build_tanh(self, op, a): |
| 2177 | result_tens = OutputShaper.unaryOp(self.ser, a) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2178 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2179 | return result_tens |
| 2180 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2181 | def build_concat(self, op, *a): |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2182 | assert type(a[-1]) == int |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2183 | |
| 2184 | # To store variable length list of input tensors we need to store axis along with it |
| 2185 | axis = a[-1] |
| 2186 | a = a[:-1] |
| 2187 | |
| 2188 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2189 | |
| 2190 | attr = ts.TosaSerializerAttribute() |
| 2191 | attr.AxisAttribute(axis) |
| 2192 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2193 | input_tensor_names = [] |
| 2194 | for tensor in a: |
| 2195 | input_tensor_names.append(tensor.name) |
| 2196 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2197 | self.ser.addOperator(op['op'], input_tensor_names, [result_tens.name], attr) |
| 2198 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2199 | |
| 2200 | def build_pad(self, op, a, padding, qinfo): |
| 2201 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 2202 | |
| 2203 | # Need to turn the padding array into a TOSA tensor here. |
| 2204 | # This is one of the few tensor operands that does not get |
| 2205 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2206 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2207 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2208 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2209 | op['op'], [a.name, padding_tens.name], [result_tens.name], None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2210 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2211 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2212 | |
| 2213 | def build_reshape(self, op, a, newShape): |
| 2214 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 2215 | |
| 2216 | attr = ts.TosaSerializerAttribute() |
| 2217 | attr.ReshapeAttribute(newShape) |
| 2218 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2219 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2220 | return result_tens |
| 2221 | |
| 2222 | def build_reverse(self, op, a, axis): |
| 2223 | result_tens = OutputShaper.unaryOp(self.ser, a) |
| 2224 | |
| 2225 | attr = ts.TosaSerializerAttribute() |
| 2226 | attr.AxisAttribute(axis) |
| 2227 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2228 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2229 | return result_tens |
| 2230 | |
| 2231 | def build_transpose(self, op, a, perms): |
| 2232 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 2233 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2234 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2235 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2236 | 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] | 2237 | return result_tens |
| 2238 | |
| 2239 | def build_slice(self, op, a, begin, size): |
| 2240 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 2241 | |
| 2242 | attr = ts.TosaSerializerAttribute() |
| 2243 | attr.SliceAttribute(begin, size) |
| 2244 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2245 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2246 | return result_tens |
| 2247 | |
| 2248 | def build_tile(self, op, a, multiples): |
| 2249 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 2250 | |
| 2251 | attr = ts.TosaSerializerAttribute() |
| 2252 | attr.TileAttribute(multiples) |
| 2253 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2254 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2255 | return result_tens |
| 2256 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2257 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2258 | |
| 2259 | # Create a new indicies tensor |
| 2260 | # here with data that doesn't exceed the dimensions of the values tensor |
| 2261 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2262 | K = values.shape[1] # K |
| 2263 | W = self.randInt( |
| 2264 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 2265 | ) # W |
| 2266 | indicies_arr = np.int32( |
| 2267 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 2268 | ) # (N, W) |
| 2269 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2270 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2271 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2272 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2273 | self.ser.addOperator(op['op'], [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2274 | |
| 2275 | return result_tens |
| 2276 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2277 | def build_scatter(self, op, values_in, input): |
| 2278 | |
| 2279 | # Create a new indicies tensor |
| 2280 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 2281 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2282 | K = values_in.shape[1] # K |
| 2283 | W = input.shape[1] # W |
| 2284 | indicies_arr = np.int32( |
| 2285 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 2286 | ) # (N, W) |
| 2287 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2288 | |
| 2289 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 2290 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2291 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2292 | op['op'], [values_in.name, indicies.name, input.name], [result_tens.name] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2293 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2294 | |
| 2295 | return result_tens |
| 2296 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2297 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2298 | def build_resize( |
| 2299 | self, |
| 2300 | op, |
| 2301 | input, |
| 2302 | mode, |
| 2303 | stride, |
| 2304 | offset, |
| 2305 | shift, |
| 2306 | stride_fp, |
| 2307 | offset_fp, |
| 2308 | output_dims, |
| 2309 | input_dtype, |
| 2310 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2311 | validator_fcns, |
| 2312 | error_name = None, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2313 | ): |
| 2314 | result_tens = OutputShaper.resizeOp( |
| 2315 | self.ser, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 2316 | self.rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2317 | input, |
| 2318 | mode, |
| 2319 | stride, |
| 2320 | offset, |
| 2321 | shift, |
| 2322 | stride_fp, |
| 2323 | offset_fp, |
| 2324 | output_dims, |
| 2325 | input_dtype, |
| 2326 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2327 | error_name |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2328 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2329 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2330 | # Invalidate Input/Output list for error if checks. |
| 2331 | input_list = [input.name] |
| 2332 | output_list = [result_tens.name] |
| 2333 | pCount, cCount = op["operands"] |
| 2334 | num_operands = pCount + cCount |
| 2335 | 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] | 2336 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2337 | TosaErrorValidator.evValidateErrorIfs( |
| 2338 | self.ser, |
| 2339 | validator_fcns, |
| 2340 | error_name, |
| 2341 | op=op, |
| 2342 | mode=mode, |
| 2343 | shift=shift, |
| 2344 | input_dtype=input_dtype, |
| 2345 | output_dtype=output_dtype, |
| 2346 | input_shape=input, |
| 2347 | output_shape=output_dims, |
| 2348 | offset=offset, |
| 2349 | offset_fp=offset_fp, |
| 2350 | stride=stride, |
| 2351 | stride_fp=stride_fp, |
| 2352 | input_list=input_list, |
| 2353 | output_list=output_list, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 2354 | result_tensor=result_tens, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2355 | num_operands=num_operands, |
| 2356 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2357 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2358 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2359 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2360 | attr.ResizeAttribute( |
| 2361 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 2362 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2363 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2364 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2365 | return result_tens |
| 2366 | |
| 2367 | def build_identityn(self, op, val, val2): |
| 2368 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2369 | result_tens = OutputShaper.unaryOp(self.ser, val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2370 | result_tens2 = OutputShaper.unaryOp(self.ser, val2) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2371 | self.ser.addOperator( |
| 2372 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 2373 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2374 | return result_tens |
| 2375 | |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 2376 | def build_const(self, op, val): |
| 2377 | self.ser.addOutputTensor(val) |
| 2378 | return val |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2379 | |
| 2380 | # Type Conversion |
| 2381 | def build_cast(self, op, val, out_dtype): |
| 2382 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2383 | self.ser.addOperator(op['op'], [val.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2384 | return result_tens |
| 2385 | |
| 2386 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel): |
| 2387 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 2388 | |
| 2389 | if per_channel: |
| 2390 | nc = val.shape[-1] |
| 2391 | else: |
| 2392 | nc = 1 |
| 2393 | |
| 2394 | in_type_width = self.typeWidth(val.dtype) |
| 2395 | out_type_width = self.typeWidth(out_dtype) |
| 2396 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2397 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2398 | input_zp = self.randInt(-128, 128) |
| 2399 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2400 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2401 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2402 | in_type_width = in_type_width + 1 |
| 2403 | else: |
| 2404 | input_zp = 0 |
| 2405 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2406 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2407 | output_zp = self.randInt(-128, 128) |
| 2408 | out_type_width = out_type_width + 1 |
| 2409 | elif out_dtype == DType.UINT8: |
| 2410 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2411 | out_type_width = out_type_width + 1 |
| 2412 | else: |
| 2413 | output_zp = 0 |
| 2414 | |
| 2415 | # Calculate scale based on: |
| 2416 | # scale = a *(2^output_width)/(2^input_width)) |
| 2417 | |
| 2418 | a = np.float32(self.rng.random(size=[nc])) |
| 2419 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 2420 | |
| 2421 | if scale32: |
| 2422 | pass |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2423 | # Cap the scaling at 2^31 - 1 for scale32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2424 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 2425 | else: |
| 2426 | # Cap the scaling at 2^15 - 1 for scale16 |
| 2427 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 2428 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2429 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2430 | |
| 2431 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 2432 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 2433 | |
| 2434 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2435 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 2436 | scale_arr[i], scale32 |
| 2437 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2438 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2439 | # 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] | 2440 | |
| 2441 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2442 | attr.RescaleAttribute( |
| 2443 | input_zp, |
| 2444 | output_zp, |
| 2445 | multiplier_arr, |
| 2446 | shift_arr, |
| 2447 | scale32, |
| 2448 | double_round, |
| 2449 | per_channel, |
| 2450 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2451 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2452 | self.ser.addOperator(op['op'], [val.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2453 | return result_tens |
| 2454 | |
| 2455 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 2456 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 2457 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 2458 | # and fill them with const nodes for the body. |
| 2459 | |
| 2460 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2461 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2462 | |
| 2463 | # Make then/else tensors |
| 2464 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2465 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 2466 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2467 | |
| 2468 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2469 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2470 | |
| 2471 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2472 | then_block = "THEN_BLOCK" |
| 2473 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2474 | attr = ts.TosaSerializerAttribute() |
| 2475 | attr.CondIfAttribute(then_block, else_block) |
| 2476 | |
| 2477 | # Finally, build the op and the two blocks |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2478 | self.ser.addOperator(op['op'], [cond_tens.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2479 | |
| 2480 | self.ser.startBasicBlock(then_block) |
| 2481 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2482 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2483 | self.ser.addOutputTensor(then_tens) |
| 2484 | |
| 2485 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2486 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2487 | self.ser.addOutputTensor(else_tens) |
| 2488 | |
| 2489 | return result_tens |
| 2490 | |
| 2491 | def build_cond_if_binary(self, op, a, b, cond): |
| 2492 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 2493 | # alternately add or subtract them based on the condition |
| 2494 | |
| 2495 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2496 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2497 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2498 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2499 | |
| 2500 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2501 | then_block = "THEN_BLOCK" |
| 2502 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2503 | attr = ts.TosaSerializerAttribute() |
| 2504 | attr.CondIfAttribute(then_block, else_block) |
| 2505 | |
| 2506 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2507 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2508 | 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] | 2509 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2510 | |
| 2511 | self.ser.startBasicBlock(then_block) |
| 2512 | self.ser.addInputTensor(a) |
| 2513 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2514 | then_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2515 | self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) |
| 2516 | |
| 2517 | self.ser.startBasicBlock(else_block) |
| 2518 | self.ser.addInputTensor(a) |
| 2519 | self.ser.addInputTensor(b) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2520 | else_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2521 | self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) |
| 2522 | |
| 2523 | return result_tens |
| 2524 | |
| 2525 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2526 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2527 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2528 | cond_block = "COND_BLOCK" |
| 2529 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2530 | |
| 2531 | attr = ts.TosaSerializerAttribute() |
| 2532 | attr.WhileLoopAttribute(cond_block, body_block) |
| 2533 | |
| 2534 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2535 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2536 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2537 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2538 | |
| 2539 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2540 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 2541 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 2542 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2543 | |
| 2544 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2545 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2546 | op['op'], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2547 | [iter.name, a.name, acc.name], |
| 2548 | [iter_out.name, a_out.name, acc_out.name], |
| 2549 | attr, |
| 2550 | ) |
Kevin Cheng | b227ae5 | 2021-09-02 13:43:17 -0700 | [diff] [blame] | 2551 | self.ser.addOutputTensor(acc_out) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2552 | |
| 2553 | # COND block (input: iter, output: cond_tens ) |
| 2554 | self.ser.startBasicBlock(cond_block) |
| 2555 | self.ser.addInputTensor(iter) |
| 2556 | self.ser.addInputTensor(a) |
| 2557 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2558 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 2559 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 2560 | 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] | 2561 | |
| 2562 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 2563 | # Note that local intermediate tensors need to be declared here for the outputs |
| 2564 | self.ser.startBasicBlock(body_block) |
| 2565 | self.ser.addInputTensor(iter) |
| 2566 | self.ser.addInputTensor(a) |
| 2567 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2568 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 2569 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 2570 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2571 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 2572 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 2573 | self.ser.addOutputTensor(iter_body_out) |
| 2574 | self.ser.addOutputTensor(a) |
| 2575 | self.ser.addOutputTensor(acc_body_out) |
| 2576 | |
| 2577 | return acc_out |
| 2578 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2579 | def create_filter_lists(self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None): |
| 2580 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 2581 | default_test_rank_range = range(1, 5) |
| 2582 | if not shapeFilter: |
| 2583 | shapeFilter = [None] |
| 2584 | |
| 2585 | # Calculate the filters based on what is requested and what the operator allows |
| 2586 | rmin, rmax = op["rank"] |
| 2587 | if rankFilter is not None: |
| 2588 | cleanRankFilter = [] |
| 2589 | # Ensure rankFilter values are allowed by operator |
| 2590 | for rank in rankFilter: |
| 2591 | if rank >= rmin and rank <= rmax: |
| 2592 | cleanRankFilter.append(rank) |
| 2593 | elif rankFilter is None and shapeFilter[0] is None: |
| 2594 | cleanRankFilter = [] |
| 2595 | # Ensure default behaviour is bounded by default range or by operator, whichever is smaller. |
| 2596 | rankRange = range(rmin, rmax + 1) |
| 2597 | for rank in rankRange: |
| 2598 | if rank >= min(default_test_rank_range) and rank <= max(default_test_rank_range): |
| 2599 | cleanRankFilter.append(rank) |
| 2600 | else: |
| 2601 | cleanRankFilter = range(rmin, rmax + 1) |
| 2602 | |
| 2603 | dtypes = op["types"] |
| 2604 | if dtypeFilter is not None: |
| 2605 | cleanDtypeFilter = [] |
| 2606 | # Ensure filtered dtypes are allowed by operator |
| 2607 | for dtype in dtypeFilter: |
| 2608 | if dtype in dtypes: |
| 2609 | cleanDtypeFilter.append(dtype) |
| 2610 | else: |
| 2611 | cleanDtypeFilter = dtypes |
| 2612 | |
| 2613 | if testType == 'positive': |
| 2614 | filterDict = { |
| 2615 | 'shapeFilter': shapeFilter, |
| 2616 | 'rankFilter': cleanRankFilter, |
| 2617 | 'dtypeFilter': cleanDtypeFilter |
| 2618 | } |
| 2619 | return filterDict |
| 2620 | elif testType == 'negative': |
| 2621 | validator_info = validator(check=False, op=op) |
| 2622 | error_arguments = validator_info['param_reqs'] |
| 2623 | |
| 2624 | #Set parameters as required |
| 2625 | if error_arguments['rank'] != None: |
| 2626 | rankFilter = error_arguments['rank'] |
| 2627 | else: |
| 2628 | rankFilter = cleanRankFilter |
| 2629 | |
| 2630 | if error_arguments['dtype'] != None: |
| 2631 | dtypeFilter = error_arguments['dtype'] |
| 2632 | else: |
| 2633 | dtypeFilter = cleanDtypeFilter |
| 2634 | |
| 2635 | if error_arguments['shape'] != None: |
| 2636 | shapeFilter = error_arguments['shape'] |
| 2637 | else: |
| 2638 | shapeFilter = shapeFilter[:2] # Reduce number of shapes to keep test numbers small |
| 2639 | |
| 2640 | filterDict = { |
| 2641 | 'shapeFilter': shapeFilter, |
| 2642 | 'rankFilter': rankFilter, |
| 2643 | 'dtypeFilter': dtypeFilter |
| 2644 | } |
| 2645 | return filterDict |
| 2646 | |
| 2647 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2648 | def genOpTestList( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2649 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2650 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2651 | |
| 2652 | try: |
| 2653 | op = self.TOSA_OP_LIST[opName] |
| 2654 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2655 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2656 | |
| 2657 | # Initialize a new random number generator |
| 2658 | self.rng = np.random.default_rng(self.random_seed) |
| 2659 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2660 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2661 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2662 | # Test list consists of a tuple of: |
| 2663 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 2664 | testList = [] |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2665 | if testType == 'negative' and "error_if_validators" in op: |
| 2666 | error_if_validators = op["error_if_validators"] |
| 2667 | else: |
| 2668 | error_if_validators = [None] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2669 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2670 | for validator in error_if_validators: |
| 2671 | if validator is not None: |
| 2672 | error_name = validator(check=False, op=op)['error_name'] |
| 2673 | #print("error_name: ", error_name) |
| 2674 | else: |
| 2675 | error_name = None |
| 2676 | |
| 2677 | filterDict = self.create_filter_lists(op, shapeFilter, rankFilter, dtypeFilter, testType, validator) |
| 2678 | cleanRankFilter = filterDict['rankFilter'] |
| 2679 | cleanDtypeFilter = filterDict['dtypeFilter'] |
| 2680 | cleanShapeFilter = filterDict['shapeFilter'] |
| 2681 | #print(f"Filters: S {shapeFilter}, R {cleanRankFilter}, T {cleanDtypeFilter}") |
| 2682 | |
| 2683 | for r in cleanRankFilter: |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2684 | if opName.startswith("conv3d"): |
| 2685 | assert r == 5, "conv3d test must have input rank == 5" |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2686 | for t in cleanDtypeFilter: |
| 2687 | for shape in cleanShapeFilter: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2688 | # Filter out by rank |
| 2689 | if shape is not None and len(shape) != r: |
| 2690 | continue |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2691 | self.setTargetShape(shape) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2692 | shapeList = tgen_fcn(self, op, r, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2693 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2694 | shapeStr = self.shapeStr(shapeList[0]) |
| 2695 | typeStr = self.typeStr(t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2696 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2697 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 2698 | argList = [] |
| 2699 | if agen_fcn: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2700 | argList = agen_fcn(self, opName, shapeList, t, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2701 | else: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2702 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2703 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2704 | for argStr, args in argList: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2705 | if testType == 'positive': |
| 2706 | if argStr: |
| 2707 | testStr = "{}_{}_{}_{}".format( |
| 2708 | opName, shapeStr, typeStr, argStr |
| 2709 | ) |
| 2710 | else: |
| 2711 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
| 2712 | elif testType == 'negative': |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2713 | if argStr: |
| 2714 | testStr = "{}_ERRORIF_{}_{}_{}_{}".format( |
| 2715 | opName, error_name, shapeStr, typeStr, argStr |
| 2716 | ) |
| 2717 | else: |
| 2718 | testStr = "{}_ERRORIF_{}_{}_{}".format(opName, error_name, shapeStr, typeStr) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2719 | |
| 2720 | testList.append((opName, testStr, t, error_name, shapeList, args)) |
| 2721 | |
| 2722 | if testType == 'positive': |
| 2723 | # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| 2724 | if "invalid_test_validators" in op: |
| 2725 | invalid_test_validators = op["invalid_test_validators"] |
| 2726 | clean_testList = [] |
| 2727 | for test in testList: |
| 2728 | for validator_fcn in invalid_test_validators: |
| 2729 | remove_test = False |
| 2730 | if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[4], args=test[5]): |
| 2731 | remove_test = True |
| 2732 | if not remove_test: |
| 2733 | clean_testList.append(test) |
| 2734 | testList = clean_testList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2735 | |
| 2736 | return testList |
| 2737 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2738 | |
| 2739 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2740 | try: |
| 2741 | op = self.TOSA_OP_LIST[opName] |
| 2742 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2743 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2744 | |
| 2745 | # Create a serializer |
| 2746 | self.createSerializer(opName, testStr) |
| 2747 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2748 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2749 | if "error_if_validators" in op: |
| 2750 | error_if_validators = op["error_if_validators"] |
| 2751 | else: |
| 2752 | error_if_validators = None |
| 2753 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2754 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2755 | num_operands = pCount + cCount |
| 2756 | |
| 2757 | if isinstance(dtype_or_dtypeList, list): |
| 2758 | dtypeList = dtype_or_dtypeList |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2759 | elif op["op"] == Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2760 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2761 | else: |
| 2762 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 2763 | |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2764 | if op["op"] != Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2765 | assert ( |
| 2766 | len(shapeList) == num_operands |
| 2767 | ), "shapeList length {} must match number of operands {}".format( |
| 2768 | len(shapeList), num_operands |
| 2769 | ) |
| 2770 | assert ( |
| 2771 | len(dtypeList) == num_operands |
| 2772 | ), "dtypeList length {} must match number of operands {}".format( |
| 2773 | len(dtypeList), num_operands |
| 2774 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2775 | |
| 2776 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2777 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2778 | except KeyError: |
| 2779 | qgen = None |
| 2780 | |
| 2781 | # Build the random tensor operands and the test |
| 2782 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2783 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 2784 | tens = self.generate_tensors(op, dtypeList, shapeList, testArgs, error_name) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2785 | |
| 2786 | if qgen is not None: |
| 2787 | qinfo = qgen(self, op, dtype_or_dtypeList) |
| 2788 | else: |
| 2789 | qinfo = None |
| 2790 | |
| 2791 | try: |
| 2792 | if error_if_validators is None: |
| 2793 | if qinfo is not None: |
| 2794 | resultName = build_fcn(self, op, *tens, *testArgs, qinfo) |
| 2795 | else: |
| 2796 | resultName = build_fcn(self, op, *tens, *testArgs) |
| 2797 | else: |
| 2798 | if qinfo is not None: |
| 2799 | resultName = build_fcn(self, op, *tens, *testArgs, qinfo, error_if_validators, error_name) |
| 2800 | else: |
| 2801 | resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name) |
| 2802 | except TypeError as e: |
| 2803 | print( |
| 2804 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 2805 | build_fcn, tens, testArgs |
| 2806 | ) |
| 2807 | ) |
| 2808 | raise e |
| 2809 | |
| 2810 | if resultName is None: |
| 2811 | print("Invalid ERROR_IF tests created") |
| 2812 | |
| 2813 | # Save the serialized test |
| 2814 | self.serialize("test") |
| 2815 | |
| 2816 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 2817 | def generate_tensors(self, op, dtypeList, shapeList, testArgs, error_name=None): |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 2818 | pCount, cCount = op["operands"] |
| 2819 | |
| 2820 | tens = [] |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 2821 | if (op["op"] == Op.ADD or op["op"] == Op.SUB) and dtypeList[0] == DType.INT32 and error_name == None: |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 2822 | # Make sure the operation does not cause value saturation - where |
| 2823 | # the number wraps due to limited number of bits to store the answer |
| 2824 | assert ( |
| 2825 | pCount == 2 and cCount == 0 |
| 2826 | ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 2827 | placeholders = [] |
| 2828 | add = (op["op"] == Op.ADD) |
| 2829 | a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2830 | b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2831 | if add: |
| 2832 | res_arr = np.add(a_arr, b_arr, dtype=np.int64) |
| 2833 | else: |
| 2834 | res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) |
| 2835 | |
| 2836 | # Work out the saturation limits |
| 2837 | max_i32 = (1 << 31)-1 |
| 2838 | min_i32 = -(1 << 31) |
| 2839 | max_arr = np.full(shapeList[1], max_i32) |
| 2840 | min_arr = np.full(shapeList[1], min_i32) |
| 2841 | |
| 2842 | # Find how much values exceed the maximum/minimums |
| 2843 | sat_max_arr = np.maximum(res_arr - max_arr, 0) |
| 2844 | sat_min_arr = np.minimum(res_arr - min_arr, 0) |
| 2845 | |
| 2846 | if not add: |
| 2847 | # Swap saturation values and negate values as we need to perform opposite operations |
| 2848 | sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr |
| 2849 | |
| 2850 | # Create new array of unsaturated values by clipping values as needed |
| 2851 | b_unsat_arr = b_arr |
| 2852 | if (sat_max_arr != 0).any(): |
| 2853 | # Clip values that cause saturation |
| 2854 | b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) |
| 2855 | # Reduce axes in unsaturated tensor to match original tensor |
| 2856 | for axis, dim in enumerate(b_arr.shape): |
| 2857 | if dim != b_unsat_arr.shape[axis]: |
| 2858 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2859 | b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) |
| 2860 | |
| 2861 | if (sat_min_arr != 0).any(): |
| 2862 | # Clip values that cause saturation |
| 2863 | b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) |
| 2864 | # Reduce axes in unsaturated tensor to match original tensor |
| 2865 | for axis, dim in enumerate(b_arr.shape): |
| 2866 | if dim != b_unsat_arr.shape[axis]: |
| 2867 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 2868 | b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) |
| 2869 | |
| 2870 | placeholders.append( |
| 2871 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2872 | ) |
| 2873 | placeholders.append( |
| 2874 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| 2875 | ) |
| 2876 | |
| 2877 | tens.extend(placeholders) |
| 2878 | elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 2879 | # Force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2880 | assert ( |
| 2881 | pCount == 2 and cCount == 0 |
| 2882 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2883 | |
| 2884 | placeholders = [] |
| 2885 | for idx, shape in enumerate(shapeList[:]): |
| 2886 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2887 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2888 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2889 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2890 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2891 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2892 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 2893 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2894 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2895 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2896 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2897 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2898 | |
| 2899 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 2900 | elif op["op"] == Op.SELECT: |
| 2901 | # Set datatype of condition tensor to boolean |
| 2902 | dtypeList[0] = DType.BOOL |
| 2903 | tens.extend( |
| 2904 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 2905 | ) |
| 2906 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 2907 | elif op["op"] == Op.INTDIV and error_name == None: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2908 | assert ( |
| 2909 | pCount == 2 and cCount == 0 |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2910 | ), "Op.INTDIV must have 2 placeholders, 0 consts" |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2911 | |
| 2912 | placeholders = [] |
| 2913 | |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 2914 | # Two invalid cases for Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2915 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2916 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2917 | while True: |
| 2918 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 2919 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 2920 | |
| 2921 | if (divisor_arr == 0).any(): |
| 2922 | continue |
| 2923 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 2924 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 2925 | continue |
| 2926 | |
| 2927 | break |
| 2928 | |
| 2929 | placeholders.append( |
| 2930 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 2931 | ) |
| 2932 | placeholders.append( |
| 2933 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 2934 | ) |
| 2935 | |
| 2936 | tens.extend(placeholders) |
| 2937 | elif op["op"] == Op.MUL: |
| 2938 | assert ( |
| 2939 | pCount == 2 and cCount == 0 |
| 2940 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 2941 | |
| 2942 | if dtypeList[0] == DType.FLOAT: |
| 2943 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 2944 | else: |
| 2945 | placeholders = [] |
| 2946 | |
| 2947 | # Make sure multiply result in int32 range |
| 2948 | shift = testArgs[0] |
| 2949 | if dtypeList[0] == DType.INT8: |
| 2950 | num_bits = 8 |
| 2951 | elif dtypeList[0] == DType.INT16: |
| 2952 | num_bits = 16 |
| 2953 | elif dtypeList[0] == DType.INT32: |
| 2954 | num_bits = 32 |
| 2955 | else: |
| 2956 | raise Exception("OpMul: invalid input dtype") |
| 2957 | |
| 2958 | for idx, shape in enumerate(shapeList[:]): |
| 2959 | low = -(2 ** (num_bits - 1)) |
| 2960 | high = (2 ** (num_bits - 1)) - 1 |
| 2961 | |
| 2962 | a_arr = np.int32( |
| 2963 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 2964 | ) |
| 2965 | b_arr = np.int32( |
| 2966 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 2967 | ) |
| 2968 | |
| 2969 | i = 0 |
| 2970 | while True: |
| 2971 | |
| 2972 | a_arr_64 = a_arr.astype(np.int64) |
| 2973 | b_arr_64 = b_arr.astype(np.int64) |
| 2974 | |
| 2975 | if shift > 0: |
| 2976 | rounding = 1 << (shift - 1) |
| 2977 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 2978 | else: |
| 2979 | result_arr = a_arr_64 * b_arr_64 |
| 2980 | |
| 2981 | if (result_arr > -(2 ** 31)).all() and ( |
| 2982 | result_arr <= ((2 ** 31) - 1) |
| 2983 | ).all(): |
| 2984 | break |
| 2985 | |
| 2986 | i = i + 1 |
| 2987 | a_arr = a_arr // 2 |
| 2988 | b_arr = b_arr // 2 |
| 2989 | |
| 2990 | placeholders.append( |
| 2991 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 2992 | ) |
| 2993 | placeholders.append( |
| 2994 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 2995 | ) |
| 2996 | |
| 2997 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2998 | elif op["op"] == Op.CONCAT: |
| 2999 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 3000 | if count < 1: |
| 3001 | count = 1 |
| 3002 | if self.args.num_const_inputs_concat == 0: |
| 3003 | count = len(shapeList) |
| 3004 | |
| 3005 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 3006 | tens.extend( |
| 3007 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 3008 | ) |
| 3009 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3010 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3011 | tens.extend( |
| 3012 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 3013 | ) |
| 3014 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3015 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3016 | return tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3017 | |
| 3018 | def createDynamicOpLists(self): |
| 3019 | |
| 3020 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3021 | 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] | 3022 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3023 | for k in KERNELS_2D: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3024 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 3025 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 3026 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3027 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3028 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3029 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 3030 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 3031 | "depthwise_conv2d_TEMPLATE" |
| 3032 | ].copy() |
| 3033 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3034 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3035 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3036 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 3037 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 3038 | "transpose_conv2d_TEMPLATE" |
| 3039 | ].copy() |
| 3040 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3041 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3042 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3043 | KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| 3044 | for k in KERNELS_3D: |
| 3045 | testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| 3046 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| 3047 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3048 | self.TOSA_OP_LIST[testName]["template"] = False |
| 3049 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3050 | # Delete any templates after having created any dynamic ops |
| 3051 | # This is a two-pass operation because it's bad practice to delete |
| 3052 | # keys from dictionaries while iterating |
| 3053 | keyList = [] |
| 3054 | for k in self.TOSA_OP_LIST: |
| 3055 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3056 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3057 | keyList.append(k) |
| 3058 | continue |
| 3059 | except KeyError: |
| 3060 | pass |
| 3061 | |
| 3062 | for k in keyList: |
| 3063 | del self.TOSA_OP_LIST[k] |
| 3064 | |
| 3065 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3066 | """Fill in default fields for ops if they aren't already specified. |
| 3067 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3068 | for op in self.TOSA_OP_LIST: |
| 3069 | |
| 3070 | # Required fields |
| 3071 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3072 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3073 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3074 | raise Exception( |
| 3075 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 3076 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3077 | |
| 3078 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3079 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3080 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3081 | raise Exception( |
| 3082 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 3083 | op |
| 3084 | ) |
| 3085 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3086 | |
| 3087 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3088 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3089 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3090 | raise Exception( |
| 3091 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 3092 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3093 | |
| 3094 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3095 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3096 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3097 | raise Exception( |
| 3098 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 3099 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3100 | |
| 3101 | # Put in default rank range, if missing |
| 3102 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3103 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3104 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3105 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3106 | |
| 3107 | # Tensor operator list |
| 3108 | # 'op': op name |
| 3109 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3110 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 3111 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3112 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 3113 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3114 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3115 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3116 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 3117 | 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] | 3118 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3119 | TYPE_BOOL = [DType.BOOL] |
| 3120 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 3121 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 3122 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3123 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3124 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3125 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3126 | TYPE_CONV = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 3127 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3128 | [DType.INT8, DType.INT8, DType.INT32], |
| 3129 | [DType.INT16, DType.INT8, DType.INT48], |
| 3130 | DType.FLOAT, |
| 3131 | ] |
| 3132 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 3133 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3134 | |
| 3135 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3136 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3137 | "argmax": { |
| 3138 | "op": Op.ARGMAX, |
| 3139 | "operands": (1, 0), |
| 3140 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3141 | "types": TYPE_NARROW_INT_FP, |
| 3142 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3143 | "avg_pool2d": { |
| 3144 | "op": Op.AVG_POOL2D, |
| 3145 | "operands": (1, 0), |
| 3146 | "rank": (4, 4), |
| 3147 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 3148 | "qgen": TosaQuantGen.qgUnary, |
| 3149 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3150 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3151 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3152 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3153 | "conv2d_TEMPLATE": { |
| 3154 | "op": Op.CONV2D, |
| 3155 | "operands": (1, 2), |
| 3156 | "rank": (4, 4), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3157 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3158 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3159 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3160 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3161 | "template": True, |
| 3162 | }, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3163 | # Templated operator. Filled in by createDynamicOpLists |
| 3164 | "conv3d_TEMPLATE": { |
| 3165 | "op": Op.CONV3D, |
| 3166 | "operands": (1, 2), |
| 3167 | "rank": (5, 5), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3168 | "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3169 | "qgen": TosaQuantGen.qgConv, |
| 3170 | "types": TYPE_CONV, |
| 3171 | "template": True, |
| 3172 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3173 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3174 | "depthwise_conv2d_TEMPLATE": { |
| 3175 | "op": Op.DEPTHWISE_CONV2D, |
| 3176 | "operands": (1, 2), |
| 3177 | "filter": [1, 1], |
| 3178 | "rank": (4, 4), |
| 3179 | "build_fcn": ( |
| 3180 | build_depthwise_conv2d, |
| 3181 | TosaTensorGen.tgDepthwiseConv2D, |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3182 | TosaArgGen.agConv, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3183 | ), |
| 3184 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3185 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3186 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3187 | "template": True, |
| 3188 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3189 | "fully_connected": { |
| 3190 | "op": Op.FULLY_CONNECTED, |
| 3191 | "operands": (1, 2), |
| 3192 | "rank": (2, 2), |
| 3193 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 3194 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3195 | "types": TYPE_CONV, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3196 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3197 | "matmul": { |
| 3198 | "op": Op.MATMUL, |
| 3199 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3200 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3201 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 3202 | "qgen": TosaQuantGen.qgMatmul, |
| 3203 | "types": TYPE_NARROW_INT_FP, |
| 3204 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3205 | "max_pool2d": { |
| 3206 | "op": Op.MAX_POOL2D, |
| 3207 | "operands": (1, 0), |
| 3208 | "rank": (4, 4), |
| 3209 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 3210 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3211 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3212 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3213 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3214 | "transpose_conv2d_TEMPLATE": { |
| 3215 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3216 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3217 | "rank": (4, 4), |
| 3218 | "build_fcn": ( |
| 3219 | build_transpose_conv2d, |
| 3220 | TosaTensorGen.tgTransposeConv2D, |
| 3221 | TosaArgGen.agTransposeConv2D, |
| 3222 | ), |
| 3223 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3224 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3225 | "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3226 | "template": True, |
| 3227 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3228 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3229 | "clamp": { |
| 3230 | "op": Op.CLAMP, |
| 3231 | "operands": (1, 0), |
| 3232 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 3233 | "types": TYPE_NARROW_INT_FP, |
| 3234 | }, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3235 | "sigmoid": { |
| 3236 | "op": Op.SIGMOID, |
| 3237 | "operands": (1, 0), |
| 3238 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 3239 | "types": TYPE_FP, |
| 3240 | }, |
| 3241 | "tanh": { |
| 3242 | "op": Op.TANH, |
| 3243 | "operands": (1, 0), |
| 3244 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 3245 | "types": TYPE_FP, |
| 3246 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3247 | # Elementwise Binary Operators |
| 3248 | "add": { |
| 3249 | "op": Op.ADD, |
| 3250 | "operands": (2, 0), |
| 3251 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3252 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3253 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3254 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3255 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3256 | "arithmetic_right_shift": { |
| 3257 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 3258 | "operands": (2, 0), |
| 3259 | "build_fcn": ( |
| 3260 | build_arithmetic_right_shift, |
| 3261 | TosaTensorGen.tgBroadcastFuzz, |
| 3262 | TosaArgGen.agArithmeticRightShift, |
| 3263 | ), |
| 3264 | "types": TYPE_INT, |
| 3265 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3266 | "bitwise_and": { |
| 3267 | "op": Op.BITWISE_AND, |
| 3268 | "operands": (2, 0), |
| 3269 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3270 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3271 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3272 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3273 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3274 | "bitwise_or": { |
| 3275 | "op": Op.BITWISE_OR, |
| 3276 | "operands": (2, 0), |
| 3277 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3278 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3279 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3280 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3281 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3282 | "bitwise_xor": { |
| 3283 | "op": Op.BITWISE_XOR, |
| 3284 | "operands": (2, 0), |
| 3285 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3286 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3287 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3288 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3289 | }, |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 3290 | "intdiv": { |
| 3291 | "op": Op.INTDIV, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3292 | "operands": (2, 0), |
| 3293 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3294 | "types": [DType.INT32], |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3295 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3296 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3297 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3298 | "logical_and": { |
| 3299 | "op": Op.LOGICAL_AND, |
| 3300 | "operands": (2, 0), |
| 3301 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3302 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3303 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3304 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3305 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3306 | "logical_left_shift": { |
| 3307 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 3308 | "operands": (2, 0), |
| 3309 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3310 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3311 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3312 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3313 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3314 | "logical_right_shift": { |
| 3315 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 3316 | "operands": (2, 0), |
| 3317 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3318 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3319 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3320 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3321 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3322 | "logical_or": { |
| 3323 | "op": Op.LOGICAL_OR, |
| 3324 | "operands": (2, 0), |
| 3325 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3326 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3327 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3328 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3329 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3330 | "logical_xor": { |
| 3331 | "op": Op.LOGICAL_XOR, |
| 3332 | "operands": (2, 0), |
| 3333 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3334 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3335 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3336 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3337 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3338 | "maximum": { |
| 3339 | "op": Op.MAXIMUM, |
| 3340 | "operands": (2, 0), |
| 3341 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3342 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3343 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3344 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3345 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3346 | "minimum": { |
| 3347 | "op": Op.MINIMUM, |
| 3348 | "operands": (2, 0), |
| 3349 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3350 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3351 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3352 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3353 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3354 | "mul": { |
| 3355 | "op": Op.MUL, |
| 3356 | "operands": (2, 0), |
| 3357 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 3358 | "types": TYPE_INT_FP, |
| 3359 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3360 | "pow": { |
| 3361 | "op": Op.POW, |
| 3362 | "operands": (2, 0), |
| 3363 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 3364 | "types": TYPE_FP, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3365 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3366 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3367 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3368 | "sub": { |
| 3369 | "op": Op.SUB, |
| 3370 | "operands": (2, 0), |
| 3371 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3372 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3373 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3374 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3375 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3376 | "table": { |
| 3377 | "op": Op.TABLE, |
| 3378 | # Use the automatic generation functions to create the input array |
| 3379 | # but create the table tensor in the build function, as it may be |
| 3380 | # a different type from the input |
| 3381 | "operands": (1, 0), |
| 3382 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3383 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3384 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3385 | # Elementwise Unary operators |
| 3386 | "abs": { |
| 3387 | "op": Op.ABS, |
| 3388 | "operands": (1, 0), |
| 3389 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3390 | "types": TYPE_FI32, |
| 3391 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3392 | "bitwise_not": { |
| 3393 | "op": Op.BITWISE_NOT, |
| 3394 | "operands": (1, 0), |
| 3395 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3396 | "types": TYPE_INT, |
| 3397 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3398 | "ceil": { |
| 3399 | "op": Op.CEIL, |
| 3400 | "operands": (1, 0), |
| 3401 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3402 | "types": TYPE_FP, |
| 3403 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3404 | "clz": { |
| 3405 | "op": Op.CLZ, |
| 3406 | "operands": (1, 0), |
| 3407 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3408 | "types": [DType.INT32], |
| 3409 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3410 | "exp": { |
| 3411 | "op": Op.EXP, |
| 3412 | "operands": (1, 0), |
| 3413 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3414 | "types": TYPE_FP, |
| 3415 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3416 | "floor": { |
| 3417 | "op": Op.FLOOR, |
| 3418 | "operands": (1, 0), |
| 3419 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3420 | "types": TYPE_FP, |
| 3421 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3422 | "log": { |
| 3423 | "op": Op.LOG, |
| 3424 | "operands": (1, 0), |
| 3425 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3426 | "types": TYPE_FP, |
| 3427 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3428 | "logical_not": { |
| 3429 | "op": Op.LOGICAL_NOT, |
| 3430 | "operands": (1, 0), |
| 3431 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3432 | "types": TYPE_BOOL, |
| 3433 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3434 | "negate": { |
| 3435 | "op": Op.NEGATE, |
| 3436 | "operands": (1, 0), |
| 3437 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3438 | "qgen": TosaQuantGen.qgUnary, |
| 3439 | "types": TYPE_INT_FP, |
| 3440 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3441 | "reciprocal": { |
| 3442 | "op": Op.RECIPROCAL, |
| 3443 | "operands": (1, 0), |
| 3444 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3445 | "types": TYPE_FP, |
| 3446 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3447 | "rsqrt": { |
| 3448 | "op": Op.RSQRT, |
| 3449 | "operands": (1, 0), |
| 3450 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3451 | "types": TYPE_FP, |
| 3452 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3453 | # Elementwise Ternary operators |
| 3454 | "select": { |
| 3455 | "op": Op.SELECT, |
| 3456 | "operands": (3, 0), |
| 3457 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 3458 | "types": TYPE_FIB, |
| 3459 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3460 | # Comparison operators |
| 3461 | "equal": { |
| 3462 | "op": Op.EQUAL, |
| 3463 | "operands": (2, 0), |
| 3464 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3465 | "types": TYPE_FI32, |
| 3466 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3467 | "greater_equal": { |
| 3468 | "op": Op.GREATER_EQUAL, |
| 3469 | "operands": (2, 0), |
| 3470 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3471 | "types": TYPE_FI32, |
| 3472 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3473 | "greater": { |
| 3474 | "op": Op.GREATER, |
| 3475 | "operands": (2, 0), |
| 3476 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 3477 | "types": TYPE_FI32, |
| 3478 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3479 | # Reduction operators |
| 3480 | "reduce_all": { |
| 3481 | "op": Op.REDUCE_ALL, |
| 3482 | "operands": (1, 0), |
| 3483 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3484 | "types": TYPE_BOOL, |
| 3485 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3486 | "reduce_any": { |
| 3487 | "op": Op.REDUCE_ANY, |
| 3488 | "operands": (1, 0), |
| 3489 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3490 | "types": TYPE_BOOL, |
| 3491 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3492 | "reduce_max": { |
| 3493 | "op": Op.REDUCE_MAX, |
| 3494 | "operands": (1, 0), |
| 3495 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3496 | "types": TYPE_INT_FP, |
| 3497 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3498 | "reduce_min": { |
| 3499 | "op": Op.REDUCE_MAX, |
| 3500 | "operands": (1, 0), |
| 3501 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3502 | "types": TYPE_INT_FP, |
| 3503 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3504 | "reduce_product": { |
| 3505 | "op": Op.REDUCE_PRODUCT, |
| 3506 | "operands": (1, 0), |
| 3507 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3508 | "types": TYPE_FP, |
| 3509 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3510 | "reduce_sum": { |
| 3511 | "op": Op.REDUCE_SUM, |
| 3512 | "operands": (1, 0), |
| 3513 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3514 | "types": TYPE_FI32, |
| 3515 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3516 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3517 | "concat": { |
| 3518 | "op": Op.CONCAT, |
| 3519 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3520 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3521 | "types": TYPE_FIB, |
| 3522 | }, |
| 3523 | "pad": { |
| 3524 | "op": Op.PAD, |
| 3525 | "operands": (1, 0), |
| 3526 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 3527 | "qgen": TosaQuantGen.qgPad, |
| 3528 | "types": TYPE_FIB, |
| 3529 | }, |
| 3530 | "reshape": { |
| 3531 | "op": Op.RESHAPE, |
| 3532 | "operands": (1, 0), |
| 3533 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 3534 | "types": TYPE_FIB, |
| 3535 | }, |
| 3536 | "reverse": { |
| 3537 | "op": Op.REVERSE, |
| 3538 | "operands": (1, 0), |
| 3539 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3540 | "types": TYPE_FIB, |
| 3541 | }, |
| 3542 | "slice": { |
| 3543 | "op": Op.SLICE, |
| 3544 | "operands": (1, 0), |
| 3545 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 3546 | "types": TYPE_FIB, |
| 3547 | }, |
| 3548 | "tile": { |
| 3549 | "op": Op.TILE, |
| 3550 | "operands": (1, 0), |
| 3551 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 3552 | "types": TYPE_FIB, |
| 3553 | }, |
| 3554 | "transpose": { |
| 3555 | "op": Op.TRANSPOSE, |
| 3556 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 3557 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3558 | "build_fcn": ( |
| 3559 | build_transpose, |
| 3560 | TosaTensorGen.tgBasic, |
| 3561 | TosaArgGen.agTranspose, |
| 3562 | ), |
| 3563 | "types": TYPE_FIB, |
| 3564 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3565 | # Data nodes |
| 3566 | "const": { |
| 3567 | "op": Op.CONST, |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 3568 | "operands": (0, 1), |
| 3569 | "build_fcn": (build_const, TosaTensorGen.tgBasic, None), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3570 | "types": TYPE_FIB, |
| 3571 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3572 | "identity": { |
| 3573 | "op": Op.IDENTITY, |
| 3574 | "operands": (1, 0), |
| 3575 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3576 | "types": TYPE_FIB, |
| 3577 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3578 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3579 | "gather": { |
| 3580 | "op": Op.GATHER, |
| 3581 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 3582 | "operands": (1, 0), |
| 3583 | "rank": (3, 3), |
| 3584 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 3585 | "types": TYPE_INT_FP, |
| 3586 | }, |
| 3587 | "scatter": { |
| 3588 | "op": Op.SCATTER, |
| 3589 | # Only specify 'values_in' tensor here. |
| 3590 | #'indices' and 'input' are generated in op building stage |
| 3591 | "operands": (2, 0), |
| 3592 | "rank": (3, 3), |
| 3593 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 3594 | "types": TYPE_INT_FP, |
| 3595 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3596 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3597 | "resize": { |
| 3598 | "op": Op.RESIZE, |
| 3599 | "operands": (1, 0), |
| 3600 | "rank": (4, 4), |
| 3601 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 3602 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3603 | "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride), |
| 3604 | "error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension, |
| 3605 | TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3606 | TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven, TosaErrorValidator.evWrongInputType, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 3607 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, |
| 3608 | TosaErrorValidator.evBatchMismatch, TosaErrorValidator.evChannelMismatch) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3609 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3610 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3611 | "cast": { |
| 3612 | "op": Op.CAST, |
| 3613 | "operands": (1, 0), |
| 3614 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 3615 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 3616 | }, |
| 3617 | "rescale": { |
| 3618 | "op": Op.RESCALE, |
| 3619 | "operands": (1, 0), |
| 3620 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 3621 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3622 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3623 | # Custom |
| 3624 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3625 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3626 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 3627 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 3628 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3629 | "cond_if_const": { |
| 3630 | "op": Op.COND_IF, |
| 3631 | "operands": (0, 2), |
| 3632 | "build_fcn": ( |
| 3633 | build_cond_if_const, |
| 3634 | TosaTensorGen.tgBasic, |
| 3635 | TosaArgGen.agCondIf, |
| 3636 | ), |
| 3637 | "types": [DType.BOOL], |
| 3638 | }, |
| 3639 | "cond_if_binary": { |
| 3640 | "op": Op.COND_IF, |
| 3641 | "operands": (2, 0), |
| 3642 | "build_fcn": ( |
| 3643 | build_cond_if_binary, |
| 3644 | TosaTensorGen.tgBasic, |
| 3645 | TosaArgGen.agCondIf, |
| 3646 | ), |
| 3647 | "types": TYPE_FI32, |
| 3648 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3649 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3650 | "while_loop": { |
| 3651 | "op": Op.WHILE_LOOP, |
| 3652 | "operands": (0, 1), |
| 3653 | "build_fcn": ( |
| 3654 | build_while_loop, |
| 3655 | TosaTensorGen.tgBasic, |
| 3656 | TosaArgGen.agWhileLoop, |
| 3657 | ), |
| 3658 | "types": [DType.INT32], |
| 3659 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3660 | } |
| 3661 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3662 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3663 | class OutputShaper: |
| 3664 | # Methods in this class compute the expected output shape and datatype |
| 3665 | # for common classes of operations |
| 3666 | def __init__(self): |
| 3667 | pass |
| 3668 | |
| 3669 | # These methods return arguments that can be used for |
| 3670 | # creating a new output tensor |
| 3671 | @staticmethod |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3672 | def binaryBroadcastOp(ser, rng, a, b, error_name=None): |
| 3673 | if error_name != ErrorIf.RankMismatch: |
| 3674 | assert len(a.shape) == len(b.shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3675 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3676 | |
| 3677 | shape = [] |
| 3678 | for i in range(len(a.shape)): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3679 | if a.shape[i] == 1 and error_name == None: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3680 | shape.append(b.shape[i]) |
| 3681 | else: |
| 3682 | shape.append(a.shape[i]) |
| 3683 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame^] | 3684 | if error_name == ErrorIf.WrongOutputType: |
| 3685 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 3686 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 3687 | outputDType = rng.choice(wrong_dtypes) |
| 3688 | else: |
| 3689 | outputDType = a.dtype |
| 3690 | |
| 3691 | return ser.addOutput(shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3692 | |
| 3693 | @staticmethod |
| 3694 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3695 | assert len(a.shape) == len(b.shape) |
| 3696 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3697 | |
| 3698 | shape = [] |
| 3699 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3700 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3701 | shape.append(a.shape[i]) |
| 3702 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3703 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3704 | |
| 3705 | @staticmethod |
| 3706 | def unaryOp(ser, a): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3707 | return ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3708 | |
| 3709 | @staticmethod |
| 3710 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3711 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 3712 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3713 | |
| 3714 | shape = [] |
| 3715 | for i in range(len(a.shape)): |
| 3716 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 3717 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3718 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3719 | |
| 3720 | @staticmethod |
| 3721 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3722 | assert len(a.shape) == len(b.shape) |
| 3723 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3724 | |
| 3725 | # Do broadcast |
| 3726 | shape = [] |
| 3727 | for i in range(len(a.shape)): |
| 3728 | if a.shape[i] == 1: |
| 3729 | shape.append(b.shape[i]) |
| 3730 | else: |
| 3731 | shape.append(a.shape[i]) |
| 3732 | |
| 3733 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3734 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3735 | |
| 3736 | @staticmethod |
| 3737 | def reduceOp(ser, a, axis): |
| 3738 | |
| 3739 | shape = a.shape.copy() |
| 3740 | |
| 3741 | shape[axis] = 1 |
| 3742 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3743 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3744 | |
| 3745 | @staticmethod |
| 3746 | def argmaxOp(ser, a, axis): |
| 3747 | shape = a.shape.copy() |
| 3748 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3749 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3750 | |
| 3751 | @staticmethod |
| 3752 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3753 | |
| 3754 | # IFM: NHWC |
| 3755 | # Filter: OHWI |
| 3756 | # OFM: NHWC |
| 3757 | |
| 3758 | if len(padding) == 2: |
| 3759 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 3760 | # From H,W to T,B,L,R |
| 3761 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 3762 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3763 | h = ( |
| 3764 | ifm.shape[1] |
| 3765 | - filter.shape[1] |
| 3766 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3767 | + padding[0] |
| 3768 | + padding[1] |
| 3769 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3770 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3771 | w = ( |
| 3772 | ifm.shape[2] |
| 3773 | - filter.shape[2] |
| 3774 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3775 | + padding[2] |
| 3776 | + padding[3] |
| 3777 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3778 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3779 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 3780 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3781 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3782 | out_dtype = DType.INT32 |
| 3783 | elif ifm.dtype == DType.INT16: |
| 3784 | out_dtype = DType.INT48 |
| 3785 | elif ifm.dtype == DType.FLOAT: |
| 3786 | out_dtype = DType.FLOAT |
| 3787 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3788 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3789 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3790 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3791 | |
| 3792 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3793 | def conv3dOp(ser, ifm, filter, strides, padding, dilations): |
| 3794 | |
| 3795 | # IFM: NDHWC |
| 3796 | # Filter: ODHWI |
| 3797 | # OFM: NDHWC |
| 3798 | |
| 3799 | d = ( |
| 3800 | ifm.shape[1] |
| 3801 | - filter.shape[1] |
| 3802 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 3803 | + padding[0] |
| 3804 | + padding[1] |
| 3805 | ) // strides[0] + 1 |
| 3806 | |
| 3807 | h = ( |
| 3808 | ifm.shape[2] |
| 3809 | - filter.shape[2] |
| 3810 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 3811 | + padding[2] |
| 3812 | + padding[3] |
| 3813 | ) // strides[1] + 1 |
| 3814 | |
| 3815 | w = ( |
| 3816 | ifm.shape[3] |
| 3817 | - filter.shape[3] |
| 3818 | - (filter.shape[3] - 1) * (dilations[2] - 1) |
| 3819 | + padding[4] |
| 3820 | + padding[5] |
| 3821 | ) // strides[2] + 1 |
| 3822 | |
| 3823 | ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| 3824 | |
| 3825 | if ifm.dtype == DType.INT8: |
| 3826 | out_dtype = DType.INT32 |
| 3827 | elif ifm.dtype == DType.INT16: |
| 3828 | out_dtype = DType.INT48 |
| 3829 | elif ifm.dtype == DType.FLOAT: |
| 3830 | out_dtype = DType.FLOAT |
| 3831 | else: |
| 3832 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
| 3833 | |
| 3834 | return ser.addOutput(ofm_shape, out_dtype) |
| 3835 | |
| 3836 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3837 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 3838 | # IFM: NHWC |
| 3839 | # Filter: HWCM |
| 3840 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3841 | h = ( |
| 3842 | ifm.shape[1] |
| 3843 | - filter.shape[0] |
| 3844 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 3845 | + padding[0] |
| 3846 | + padding[1] |
| 3847 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3848 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3849 | w = ( |
| 3850 | ifm.shape[2] |
| 3851 | - filter.shape[1] |
| 3852 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 3853 | + padding[2] |
| 3854 | + padding[3] |
| 3855 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3856 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3857 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 3858 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3859 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3860 | out_dtype = DType.INT32 |
| 3861 | elif ifm.dtype == DType.INT16: |
| 3862 | out_dtype = DType.INT48 |
| 3863 | elif ifm.dtype == DType.FLOAT: |
| 3864 | out_dtype = DType.FLOAT |
| 3865 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3866 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3867 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3868 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3869 | |
| 3870 | @staticmethod |
| 3871 | def pool2dOp(ser, ifm, kernel, stride, pad): |
| 3872 | # input: NHWC |
| 3873 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 3874 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 3875 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3876 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3877 | return ser.addOutput(ofm_shape, ifm.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3878 | |
| 3879 | @staticmethod |
| 3880 | def fullyConnectedOp(ser, input, filter): |
| 3881 | # input: N, IC |
| 3882 | # filter: OC, IC |
| 3883 | # output: N, OC |
| 3884 | |
| 3885 | output_shape = [input.shape[0], filter.shape[0]] |
| 3886 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3887 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3888 | out_dtype = DType.INT32 |
| 3889 | elif input.dtype == DType.INT16: |
| 3890 | out_dtype = DType.INT48 |
| 3891 | elif input.dtype == DType.FLOAT: |
| 3892 | out_dtype = DType.FLOAT |
| 3893 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3894 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3895 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3896 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3897 | |
| 3898 | @staticmethod |
| 3899 | def matmulOp(ser, a, b): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3900 | # a: N, H, C |
| 3901 | # b: N, C, W |
| 3902 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3903 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3904 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3905 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3906 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3907 | out_dtype = DType.INT32 |
| 3908 | elif a.dtype == DType.INT16: |
| 3909 | out_dtype = DType.INT48 |
| 3910 | elif a.dtype == DType.FLOAT: |
| 3911 | out_dtype = DType.FLOAT |
| 3912 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3913 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3914 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3915 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3916 | |
| 3917 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3918 | def concatOp(ser, axis, *a): |
| 3919 | input1 = a[0] |
| 3920 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3921 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3922 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3923 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3924 | output_shape[axis] = input1.shape[axis] |
| 3925 | |
| 3926 | for tensor in remaining_inputs: |
| 3927 | output_shape[axis] += tensor.shape[axis] |
| 3928 | |
| 3929 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3930 | |
| 3931 | @staticmethod |
| 3932 | def padOp(ser, a, padding): |
| 3933 | |
| 3934 | output_shape = a.shape.copy() |
| 3935 | |
| 3936 | for i in range(len(output_shape)): |
| 3937 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 3938 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3939 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3940 | |
| 3941 | @staticmethod |
| 3942 | def reshapeOp(ser, a, shape): |
| 3943 | output_shape = shape.copy() |
| 3944 | |
| 3945 | totalElements = 1 |
| 3946 | for i in a.shape: |
| 3947 | totalElements *= i |
| 3948 | |
| 3949 | # If there are any -1 elements, figure out what that dimension must be |
| 3950 | totalOutputElements = 1 |
| 3951 | for i in output_shape: |
| 3952 | if i != -1: |
| 3953 | totalOutputElements *= i |
| 3954 | |
| 3955 | # And fill it in |
| 3956 | for i in range(len(output_shape)): |
| 3957 | if output_shape[i] == -1: |
| 3958 | output_shape[i] = totalElements // totalOutputElements |
| 3959 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3960 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3961 | |
| 3962 | @staticmethod |
| 3963 | def sliceOp(ser, a, begin, size): |
| 3964 | |
| 3965 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3966 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3967 | |
| 3968 | @staticmethod |
| 3969 | def tileOp(ser, a, multiples): |
| 3970 | |
| 3971 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3972 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3973 | |
| 3974 | for i in range(len(output_shape)): |
| 3975 | output_shape[i] = a.shape[i] * multiples[i] |
| 3976 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3977 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3978 | |
| 3979 | @staticmethod |
| 3980 | def transposeOp(ser, a, perms): |
| 3981 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3982 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3983 | |
| 3984 | for i in range(len(output_shape)): |
| 3985 | output_shape[i] = a.shape[perms[i]] |
| 3986 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3987 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3988 | |
| 3989 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3990 | def gatherOp(ser, values, indices): |
| 3991 | assert len(values.shape) == 3 |
| 3992 | assert len(indices.shape) == 2 |
| 3993 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3994 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3995 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 3996 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3997 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3998 | |
| 3999 | @staticmethod |
| 4000 | def scatterOp(ser, values_in, indices, input): |
| 4001 | assert len(values_in.shape) == 3 |
| 4002 | assert len(indices.shape) == 2 |
| 4003 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4004 | assert values_in.shape[0] == indices.shape[0] # N |
| 4005 | assert input.shape[1] == indices.shape[1] # W |
| 4006 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 4007 | |
| 4008 | output_shape = values_in.shape |
| 4009 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4010 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4011 | |
| 4012 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 4013 | def tableOp(ser, input, table_dtype): |
| 4014 | # Same shape as the input, but dtype dependent on table dtype |
| 4015 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 4016 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 4017 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4018 | |
| 4019 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4020 | def resizeOp( |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4021 | serializer, |
| 4022 | rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4023 | input, |
| 4024 | mode, |
| 4025 | stride, |
| 4026 | offset, |
| 4027 | shift, |
| 4028 | stride_fp, |
| 4029 | offset_fp, |
| 4030 | output_dims, |
| 4031 | input_dtype, |
| 4032 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 4033 | error_name = None |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4034 | ): |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 4035 | if error_name == ErrorIf.WrongRank: |
| 4036 | output_dims = [input.shape[0], output_dims[0], output_dims[0], input.shape[0]] |
| 4037 | else: |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4038 | if error_name == ErrorIf.BatchMismatch: |
| 4039 | output_dims = [input.shape[0] + rng.integers(1, 10), output_dims[0], output_dims[1], input.shape[3]] |
| 4040 | elif error_name == ErrorIf.ChannelMismatch: |
| 4041 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3] + rng.integers(1, 10)] |
| 4042 | else: |
| 4043 | 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] | 4044 | |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4045 | return serializer.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4046 | |
| 4047 | @staticmethod |
| 4048 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4049 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4050 | |
| 4051 | @staticmethod |
| 4052 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4053 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4054 | out_dtype = DType.INT32 |
| 4055 | elif ifm.dtype == DType.INT16: |
| 4056 | out_dtype = DType.INT48 |
| 4057 | elif ifm.dtype == DType.FLOAT: |
| 4058 | out_dtype = DType.FLOAT |
| 4059 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4060 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4061 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4062 | return ser.addOutput(output_shape, out_dtype) |