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 |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 59 | def getQinfo(testGen, dtype, error_name=None): |
| 60 | |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 61 | if dtype == DType.INT8: |
| 62 | return testGen.randInt(-128, 128) |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 63 | elif dtype == DType.UINT8: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 64 | return testGen.randInt(0, 256) |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 65 | elif error_name in [ErrorIf.InputZeroPointNotZero, ErrorIf.WeightZeroPointNotZero, ErrorIf.OutputZeroPointNotZero]: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 66 | zero_point = testGen.randInt(-128, 128) |
| 67 | if zero_point == 0: |
| 68 | zero_point = 1 |
| 69 | return zero_point |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 70 | return 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 71 | |
| 72 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 73 | def qgUnary(testGen, op, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 74 | qinfo = ts.TosaSerializerQuantInfo() |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 75 | if error_name == ErrorIf.InputZeroPointNotZero: |
| 76 | qinfo.UnaryQuantInfo( |
| 77 | TosaQuantGen.getQinfo(testGen, dtype, error_name), TosaQuantGen.getQinfo(testGen, dtype) |
| 78 | ) |
| 79 | elif error_name == ErrorIf.OutputZeroPointNotZero: |
| 80 | qinfo.UnaryQuantInfo( |
| 81 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype, error_name) |
| 82 | ) |
| 83 | else: |
| 84 | qinfo.UnaryQuantInfo( |
| 85 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 86 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 87 | return qinfo |
| 88 | |
| 89 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 90 | def qgConv(testGen, op, dtype_or_dtypeList, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 91 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 92 | if isinstance(dtype_or_dtypeList, list): |
| 93 | # a list of [input, weights, accumulator] dtypes |
| 94 | dtypeList = dtype_or_dtypeList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 95 | else: |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 96 | # an int, [input, weights, accumulator] dtypes are the same |
| 97 | dtypeList = [dtype_or_dtypeList] * 3 |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 98 | |
| 99 | if error_name == ErrorIf.InputZeroPointNotZero: |
| 100 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0], error_name) |
| 101 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 102 | elif error_name == ErrorIf.WeightZeroPointNotZero: |
| 103 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 104 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1], error_name) |
| 105 | else: |
| 106 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 107 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 108 | |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 109 | qinfo.ConvQuantInfo(input_zp, weights_zp) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 110 | return qinfo |
| 111 | |
| 112 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 113 | def qgMatmul(testGen, op, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 114 | qinfo = ts.TosaSerializerQuantInfo() |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 115 | if error_name == ErrorIf.InputZeroPointNotZero: |
| 116 | qinfo.MatMulQuantInfo( |
| 117 | TosaQuantGen.getQinfo(testGen, dtype, error_name), TosaQuantGen.getQinfo(testGen, dtype, error_name) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 118 | ) |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 119 | else: |
| 120 | qinfo.MatMulQuantInfo( |
| 121 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 122 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 123 | return qinfo |
| 124 | |
| 125 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 126 | def qgPad(testGen, op, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 127 | qinfo = ts.TosaSerializerQuantInfo() |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 128 | if error_name == ErrorIf.InputZeroPointNotZero: |
| 129 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype, error_name)) |
| 130 | else: |
| 131 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 132 | return qinfo |
| 133 | |
| 134 | @staticmethod |
| 135 | def computeMultiplierAndShift(scaleFp, scale32): |
| 136 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 137 | # Provide a floating-point scaling factor and the scale32 parameter |
| 138 | # to compute the multiplier and shift |
| 139 | |
| 140 | if scale32: |
| 141 | scaleBits = 31 |
| 142 | else: |
| 143 | scaleBits = 15 |
| 144 | |
| 145 | m, shift = math.frexp(scaleFp) |
| 146 | |
| 147 | if scaleFp < 0.0: |
| 148 | m = -m |
| 149 | |
| 150 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 151 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 152 | |
| 153 | if multiplier == (1 << scaleBits): |
| 154 | multiplier = multiplier // 2 |
| 155 | shift = shift + 1 |
| 156 | |
| 157 | shift = (-shift) + scaleBits |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 158 | #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
| 159 | |
| 160 | # Adjust multiplier such that shift is in allowed value range. |
| 161 | if shift == 0: |
| 162 | multiplier = multiplier // 4 |
| 163 | shift = shift + 2 |
| 164 | elif shift == 1: |
| 165 | multiplier = multiplier // 2 |
| 166 | shift = shift + 1 |
| 167 | elif shift == 63: |
| 168 | multiplier = multiplier * 2 |
| 169 | shift = shift - 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 170 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 171 | assert multiplier <= (1 << scaleBits) |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 172 | assert shift >= 2 and shift <= 62 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 173 | |
| 174 | return multiplier, shift |
| 175 | |
| 176 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 177 | class TosaTensorGen: |
| 178 | """Tensor generators create a shape list for the placeholder and const tensor |
| 179 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 180 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 181 | def __init__(self): |
| 182 | pass |
| 183 | |
| 184 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 185 | def tgBasic(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 186 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 187 | shape = testGen.makeShape(rank) |
| 188 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 189 | # Constrict dimension size for large ranks when creating WrongRank tests |
| 190 | shape = TosaErrorIfArgGen.eiRestrictDimension(shape, error_name) |
| 191 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 192 | shape_list = [] |
| 193 | for i in range(pl + const): |
| 194 | shape_list.append(shape.copy()) |
| 195 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 196 | if error_name == ErrorIf.RankMismatch: |
| 197 | if rank == 1 and i != 1: |
| 198 | shape = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) |
| 199 | elif i != 1: |
| 200 | shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) |
| 201 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 202 | return shape_list |
| 203 | |
| 204 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 205 | def tgNHWC(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 206 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 207 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 208 | if error_name != ErrorIf.WrongRank: |
| 209 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 210 | |
| 211 | shape = testGen.makeShape(rank) |
| 212 | |
| 213 | # Constrict the batch size? |
| 214 | if testGen.args.max_batch_size: |
| 215 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 216 | |
| 217 | # Constrict dimension size for large ranks when creating WrongRank tests |
| 218 | shape = TosaErrorIfArgGen.eiRestrictDimension(shape, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 219 | |
| 220 | shape_list = [] |
| 221 | for i in range(pl + const): |
| 222 | shape_list.append(shape.copy()) |
| 223 | |
| 224 | return shape_list |
| 225 | |
| 226 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 227 | def tgScatter(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 228 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 229 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 230 | assert pl == 2 |
| 231 | assert const == 0 |
| 232 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 233 | |
| 234 | values_in_shape = testGen.makeShape(rank) |
| 235 | |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 236 | # ignore max batch size if target shape is set |
| 237 | if testGen.args.max_batch_size and not testGen.args.target_shapes: |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 238 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 239 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 240 | W = testGen.randInt( |
| 241 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 242 | ) |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 243 | # Constrict W if one dimension is too large to keep tensor size reasonable |
| 244 | if max(values_in_shape) > 5000: |
| 245 | W = testGen.randInt(0, 16) |
| 246 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 247 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 248 | |
| 249 | shape_list = [] |
| 250 | shape_list.append(values_in_shape.copy()) |
| 251 | shape_list.append(input_shape.copy()) |
| 252 | |
| 253 | return shape_list |
| 254 | |
| 255 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 256 | def tgBroadcastFuzz(testGen, op, rank, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 257 | shape = testGen.makeShape(rank) |
| 258 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 259 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 260 | |
| 261 | shape_list = [] |
| 262 | |
| 263 | # Choose one of the inputs to broadcast |
| 264 | bcast_idx = testGen.randInt(0, pl + const) |
| 265 | for i in range(pl + const): |
| 266 | shape_bcast = shape.copy() |
| 267 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 268 | if error_name == ErrorIf.RankMismatch: |
| 269 | bcast_idx = -1 # Turn off broadcast because we are not testing it |
| 270 | if rank == 1 and i != 1: |
| 271 | shape_bcast = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) |
| 272 | elif i != 1: |
| 273 | shape_bcast = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) |
| 274 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 275 | # If the chosen input, pick a random index to broadcast |
| 276 | if i == bcast_idx: |
| 277 | fuzz_idx = testGen.randInt(0, rank) |
| 278 | shape_bcast[fuzz_idx] = 1 |
| 279 | |
| 280 | shape_list.append(shape_bcast) |
| 281 | |
| 282 | return shape_list |
| 283 | |
| 284 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 285 | def tgConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 286 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 287 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 288 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 289 | |
| 290 | # IFM dimensions are NHWC |
| 291 | ifm_shape = testGen.makeShape(rank) |
| 292 | |
| 293 | # Constrict the batch size? |
| 294 | if testGen.args.max_batch_size: |
| 295 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 296 | |
| 297 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 298 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 299 | |
| 300 | # Generate a random OFM depth |
| 301 | ofm_depth = testGen.makeShape(1)[0] |
| 302 | |
| 303 | # The filter dimensions are OHWI |
| 304 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 305 | |
| 306 | # The bias is OC |
| 307 | bias_shape = np.asarray([ofm_depth]) |
| 308 | |
| 309 | return [ifm_shape, filter_shape, bias_shape] |
| 310 | |
| 311 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 312 | def tgConv3D(testGen, op, rank, error_name=None): |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 313 | pl, const = op["operands"] |
| 314 | |
| 315 | assert rank == 5 |
| 316 | |
| 317 | # IFM dimensions are NDHWC |
| 318 | ifm_shape = testGen.makeShape(rank) |
| 319 | |
| 320 | # Constrict the batch size? |
| 321 | if testGen.args.max_batch_size: |
| 322 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 323 | |
| 324 | # Get the filter depth/height/width from the operator parameters |
| 325 | filter_dhw = op["filter"] |
| 326 | |
| 327 | # Generate a random OFM channel |
| 328 | ofm_channel = testGen.makeShape(1)[0] |
| 329 | |
| 330 | # The filter dimensions are ODHWI |
| 331 | filter_shape = np.asarray( |
| 332 | [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] |
| 333 | ) |
| 334 | |
| 335 | # The bias is OC |
| 336 | bias_shape = np.asarray([ofm_channel]) |
| 337 | |
| 338 | return [ifm_shape, filter_shape, bias_shape] |
| 339 | |
| 340 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 341 | def tgTransposeConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 342 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 343 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 344 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 345 | |
| 346 | # IFM dimensions are NHWC |
| 347 | ifm_shape = testGen.makeShape(rank) |
| 348 | |
| 349 | # Constrict the batch size? |
| 350 | if testGen.args.max_batch_size: |
| 351 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 352 | |
| 353 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 354 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 355 | |
| 356 | # Generate a random OFM depth |
| 357 | ofm_depth = testGen.makeShape(1)[0] |
| 358 | |
| 359 | # The filter dimensions are OHWI |
| 360 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 361 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 362 | # The bias is OC |
| 363 | bias_shape = np.asarray([ofm_depth]) |
| 364 | |
| 365 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 366 | |
| 367 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 368 | def tgDepthwiseConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 369 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 370 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 371 | assert rank == 4 |
| 372 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 373 | |
| 374 | # IFM dimensions are NHWC |
| 375 | ifm_shape = testGen.makeShape(rank) |
| 376 | |
| 377 | # Constrict the batch size? |
| 378 | if testGen.args.max_batch_size: |
| 379 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 380 | |
| 381 | # Get the filter height/width from the operator parameters |
| 382 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 383 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 384 | |
| 385 | # Generate a random OFM depth, but don't let it get too big because |
| 386 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 387 | filter_m = ( |
| 388 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 389 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 390 | |
| 391 | # The filter dimensions are HWCM |
| 392 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 393 | |
| 394 | # The bias is M * C |
| 395 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 396 | |
| 397 | return [ifm_shape, filter_shape, bias_shape] |
| 398 | |
| 399 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 400 | def tgFullyConnected(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 401 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 402 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 403 | if error_name != ErrorIf.WrongRank: |
| 404 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 405 | |
| 406 | input_shape = testGen.makeShape(rank) |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 407 | |
| 408 | # Constrict dimension size for large ranks when creating WrongRank tests |
| 409 | shape = TosaErrorIfArgGen.eiRestrictDimension(input_shape, error_name) |
| 410 | |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 411 | filter_oc = testGen.rng.integers( |
| 412 | low=testGen.args.tensor_shape_range[0], |
| 413 | high=testGen.args.tensor_shape_range[1], |
| 414 | size=1, |
| 415 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 416 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 417 | |
| 418 | bias_shape = np.asarray([filter_oc]) |
| 419 | |
| 420 | return [input_shape, filter_shape, bias_shape] |
| 421 | |
| 422 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 423 | def tgMatmul(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 424 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 425 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 426 | if error_name != ErrorIf.WrongRank: |
| 427 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 428 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 429 | |
| 430 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 431 | |
| 432 | # Constrict dimension size for large ranks when creating WrongRank tests |
| 433 | shape = TosaErrorIfArgGen.eiRestrictDimension(a_shape, error_name) |
| 434 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 435 | # Get a random number for b_oc even if target shape is defined |
| 436 | b_oc = np.int32( |
| 437 | testGen.rng.integers( |
| 438 | low=testGen.args.tensor_shape_range[0], |
| 439 | high=testGen.args.tensor_shape_range[1], |
| 440 | size=1, |
| 441 | ) |
| 442 | )[0] |
| 443 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 444 | if max(a_shape) > 1000: |
| 445 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 446 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 447 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 448 | return [a_shape, b_shape] |
| 449 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 450 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 451 | def tgConcat(testGen, opName, rank, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 452 | pl, const = opName["operands"] |
| 453 | shape = testGen.makeShape(rank) |
| 454 | |
| 455 | # Create extra tensors to concat. |
| 456 | # Take into account value of pl when getting maximum number of concats |
| 457 | num_tensors = testGen.randInt(0, 4) |
| 458 | shape_list = [] |
| 459 | for i in range(pl + const + num_tensors): |
| 460 | shape_list.append(shape.copy()) |
| 461 | |
| 462 | return shape_list |
| 463 | |
| 464 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 465 | def tgConcatConstInput(testGen, shapeList, axis, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 466 | # Split concat shape along axis to allow for multiple const inputs |
| 467 | # without making too many large tensors |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 468 | if len(shapeList) == 2 or shapeList[0][axis] < len(shapeList): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 469 | return shapeList |
| 470 | |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 471 | # Create copy of shape we are going to split (so we don't alter shapeList) |
| 472 | shape = shapeList[0].copy() |
| 473 | # Add original shape as first input |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 474 | new_shapeList = [shape.copy()] |
| 475 | length_on_axis = shape[axis] |
| 476 | remaining_length = length_on_axis |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 477 | for i in range(len(shapeList) - 2): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 478 | # Calculate split on axis and remaining value |
| 479 | split_shape_val = int(shape[axis] / 2) |
| 480 | remaining_length = remaining_length - split_shape_val |
| 481 | |
| 482 | # Append new shape, and set remaining shape |
| 483 | shape[axis] = split_shape_val |
| 484 | new_shapeList.append(shape.copy()) |
| 485 | shape[axis] = remaining_length |
| 486 | if i == len(shapeList) - 3: |
| 487 | new_shapeList.append(shape.copy()) |
| 488 | |
| 489 | return new_shapeList |
| 490 | |
| 491 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 492 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 493 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 494 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 495 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 496 | as arguments to the operator build function.""" |
| 497 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 498 | def __init__(self): |
| 499 | pass |
| 500 | |
| 501 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 502 | def agNone(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 503 | """A trivial argument generator for operators that don't take any |
| 504 | non-tensor arguments""" |
| 505 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 506 | |
| 507 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 508 | def agAxis(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 509 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 510 | axes = [] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 511 | shape = shapeList[0] |
| 512 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 513 | if error_name == ErrorIf.AxisSmallerZero: |
| 514 | small_axis = testGen.rng.integers(-5, 0) |
| 515 | axes.append(("axis{}".format(small_axis), [small_axis])) |
| 516 | elif error_name == ErrorIf.AxisLargerRank: |
| 517 | large_axis = testGen.rng.integers(len(shape) + 1, len(shape) + 10) |
| 518 | axes.append(("axis{}".format(large_axis), [large_axis])) |
| 519 | else: |
| 520 | for a in range(0, len(shape)): |
| 521 | axes.append(("axis{}".format(a), [a])) |
| 522 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 523 | return axes |
| 524 | |
| 525 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 526 | def agConv(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 527 | arg_list = [] |
| 528 | |
| 529 | ifm_shape = shapeList[0] |
| 530 | filter_shape = shapeList[1] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 531 | # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) |
| 532 | k = [int(x) for x in opName.split("_")[-1].split("x")] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 533 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 534 | # Check the rank |
| 535 | rank = 5 if opName.startswith("conv3d") else 4 |
| 536 | assert len(ifm_shape) == rank |
| 537 | assert len(filter_shape) == rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 538 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 539 | # kernel rank omits batch and channels |
| 540 | k_rank = rank - 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 541 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 542 | # Generate comprehensive argument lists |
| 543 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 544 | paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} |
| 545 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 546 | strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| 547 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 548 | dilations = {x for x in itertools.product(*([d_vals] * k_rank))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 549 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 550 | # add some oversize argument values |
| 551 | if max(ifm_shape) < 64: |
| 552 | bigPadding = 9 |
| 553 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) |
| 554 | bigStride = 8 |
| 555 | strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) |
| 556 | bigDilation = 7 |
| 557 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 558 | |
| 559 | # There are too many parameter combinations, so generate them sparsely |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 560 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 561 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 562 | if sparsity < 13: |
| 563 | sparsity = 1 |
| 564 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 565 | sparsity += 1 |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 566 | n = 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 567 | for s in sorted(list(strides)): |
| 568 | for p in sorted(list(paddings)): |
| 569 | for d in sorted(list(dilations)): |
| 570 | if (n % sparsity == 0 |
| 571 | # padding must not exceed the kernel size ? |
| 572 | # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 573 | # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) |
| 574 | # the padded shape must exceed the kernel size |
| 575 | and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] |
| 576 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) |
| 577 | # the padded shape must exceed the dilation |
| 578 | and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] |
| 579 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) |
| 580 | ): |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 581 | arg_list.append( |
| 582 | ( |
| 583 | "st{}_pad{}_dilat{}".format( |
| 584 | "".join([str(x) for x in s]), |
| 585 | "".join([str(x) for x in p]), |
| 586 | "".join([str(x) for x in d]), |
| 587 | ), |
| 588 | [s, p, d], |
| 589 | ) |
| 590 | ) |
| 591 | n += 1 |
| 592 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 593 | return arg_list |
| 594 | |
| 595 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 596 | def agTransposeConv2D(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 597 | arg_list = [] |
| 598 | |
| 599 | ifm_shape = shapeList[0] |
| 600 | filter_shape = shapeList[1] |
| 601 | |
| 602 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 603 | assert len(ifm_shape) == 4 |
| 604 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 605 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 606 | # Generate comprehensive argument lists |
| 607 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 608 | paddings = {x for x in itertools.product(*([p_vals] * 2))} |
| 609 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 610 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 611 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 612 | dilations = {x for x in itertools.product(*([d_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 613 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 614 | # add some oversize argument values |
| 615 | if max(ifm_shape) < 64: |
| 616 | bigPadding = 9 |
| 617 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) |
| 618 | bigStride = 8 |
| 619 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 620 | bigDilation = 7 |
| 621 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 622 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 623 | # There are too many parameter combinations, so generate them sparsely |
| 624 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 625 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 626 | if sparsity < 13: |
| 627 | sparsity = 1 |
| 628 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 629 | sparsity += 1 |
| 630 | n = 0 |
| 631 | for s in sorted(list(strides)): |
| 632 | for p in sorted(list(paddings)): |
| 633 | for d in sorted(list(dilations)): |
| 634 | if n % sparsity == 0: |
| 635 | # Determine the output shape |
| 636 | oh = ( |
| 637 | ifm_shape[1] |
| 638 | - filter_shape[1] |
| 639 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 640 | + 2 * p[0] |
| 641 | ) // s[0] + 1 |
| 642 | ow = ( |
| 643 | ifm_shape[2] |
| 644 | - filter_shape[2] |
| 645 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 646 | + 2 * p[1] |
| 647 | ) // s[1] + 1 |
| 648 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| 649 | arg_list.append( |
| 650 | ( |
| 651 | "st{}_pad{}_dilat{}_os{}".format( |
| 652 | "".join([str(x) for x in s]), |
| 653 | "".join([str(x) for x in p]), |
| 654 | "".join([str(x) for x in d]), |
| 655 | "x".join([str(x) for x in os]), |
| 656 | ), |
| 657 | [s, p, d, os], |
| 658 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 659 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 660 | n += 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 661 | |
| 662 | return arg_list |
| 663 | |
| 664 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 665 | def agPad(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 666 | arg_list = [] |
| 667 | rank = len(shapeList[0]) |
| 668 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 669 | # Exhaustively test combinations of padding on each side of each dimension |
| 670 | # - the range of padding values is defined by pad_min and pad_max |
| 671 | # - for padding >9, the name format needs to be more distinctive |
| 672 | pad_min, pad_max = 0, 1 |
| 673 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 674 | if error_name == ErrorIf.PadSmallerZero: |
| 675 | pad_values = [x for x in range(-2, 0)] |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 676 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 677 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 678 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 679 | for paddings in shape_pad_values: |
| 680 | name = "pad" |
| 681 | for r in range(rank): |
| 682 | before, after = paddings[r] |
| 683 | name = f"{name}{before}{after}" |
| 684 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 685 | |
| 686 | return arg_list |
| 687 | |
| 688 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 689 | def agPooling(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 690 | arg_list = [] |
| 691 | |
| 692 | shape = shapeList[0] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 693 | if error_name != ErrorIf.WrongRank: |
| 694 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 695 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 696 | # Generate comprehensive argument lists |
| 697 | p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] |
| 698 | paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| 699 | s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] |
| 700 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 701 | k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] |
| 702 | kernels = {x for x in itertools.product(*([k_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 703 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 704 | # add some oversize argument values |
| 705 | bigStride = 7 |
| 706 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 707 | bigKernel = 6 |
| 708 | kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) |
| 709 | if max(shape) < 64: |
| 710 | # padding must be less than the kernel size |
| 711 | bigPadding = bigKernel - 1 |
| 712 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 713 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 714 | # There are too many parameter combinations, so generate them sparsely |
| 715 | sparsity = len(paddings) * len(strides) * len(kernels) // 500 + 1 |
| 716 | n = 0 |
| 717 | for s in sorted(list(strides)): |
| 718 | for p in sorted(list(paddings)): |
| 719 | for k in sorted(list(kernels)): |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 720 | if error_name in [ErrorIf.StrideSmallerOne, ErrorIf.KernelSmallerOne, ErrorIf.PadSmallerZero, ErrorIf.PadLargerEqualKernel]: |
| 721 | sNew, pNew, kNew = TosaErrorIfArgGen.eiPoolingErrorIf(testGen, error_name, s, p, k) |
| 722 | if None not in [sNew, pNew, kNew] and n % sparsity == 0: |
| 723 | arg_list.append( |
| 724 | ( |
| 725 | "st{}_kern{}_pad{}".format( |
| 726 | "".join([str(x) for x in sNew]), |
| 727 | "".join([str(x) for x in kNew]), |
| 728 | "".join([str(x) for x in pNew]), |
| 729 | ), |
| 730 | [sNew, pNew, kNew], |
| 731 | ) |
| 732 | ) |
| 733 | elif (n % sparsity == 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 734 | # padding must not exceed the kernel size |
| 735 | and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 736 | # the padded shape must exceed the kernel size |
| 737 | and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] |
| 738 | ): |
| 739 | arg_list.append( |
| 740 | ( |
| 741 | "st{}_kern{}_pad{}".format( |
| 742 | "".join([str(x) for x in s]), |
| 743 | "".join([str(x) for x in k]), |
| 744 | "".join([str(x) for x in p]), |
| 745 | ), |
| 746 | [s, p, k], |
| 747 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 748 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 749 | n += 1 |
| 750 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 751 | return arg_list |
| 752 | |
| 753 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 754 | def agCast(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 755 | arg_list = [] |
| 756 | |
| 757 | # Enumerate the output types here |
| 758 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 759 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 760 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 761 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 762 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 763 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 764 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 765 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 766 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 767 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 768 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 769 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 770 | |
| 771 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 772 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 773 | |
| 774 | return arg_list |
| 775 | |
| 776 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 777 | def agRescale(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 778 | arg_list = [] |
| 779 | |
| 780 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 781 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 782 | if dtype in [DType.UINT8, DType.INT8] and error_name == ErrorIf.OutputZeroPointNotZero: |
| 783 | continue |
| 784 | if inDtype == DType.UINT8 and dtype != DType.INT8 and error_name != ErrorIf.WrongOutputType: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 785 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 786 | continue |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 787 | if inDtype != DType.INT8 and dtype == DType.UINT8 and error_name != ErrorIf.WrongOutputType: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 788 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 789 | continue |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 790 | if error_name == ErrorIf.WrongOutputType and not TosaErrorIfArgGen.eiRescaleWrongOutputType(inDtype, dtype): |
| 791 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 792 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 793 | for scale32 in [False, True]: |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 794 | if error_name == ErrorIf.ScaleTrue and scale32 == False: |
| 795 | continue |
| 796 | elif error_name == ErrorIf.ScaleNotTrue and scale32 == True: |
| 797 | continue |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 798 | for double_round in [False, True]: |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 799 | if error_name == ErrorIf.ScaleNotTrue and double_round == False: |
| 800 | continue |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 801 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 802 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 803 | if inDtype == DType.INT48 and scale32 and error_name != ErrorIf.ScaleTrue: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 804 | # Illegal condition. Must be scale32=False |
| 805 | continue |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 806 | if double_round and not scale32 and error_name != ErrorIf.ScaleNotTrue: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 807 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 808 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 809 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 810 | arg_list.append( |
| 811 | ( |
| 812 | "out{}_sc{}_dr{}_pc{}".format( |
| 813 | DTypeNames[dtype], |
| 814 | int(scale32), |
| 815 | int(double_round), |
| 816 | int(per_channel), |
| 817 | ), |
| 818 | [dtype, scale32, double_round, per_channel], |
| 819 | ) |
| 820 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 821 | |
| 822 | return arg_list |
| 823 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 824 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 825 | def agMul(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 826 | arg_list = [] |
| 827 | |
| 828 | if dtype is DType.INT32: |
| 829 | for p in range(testGen.args.num_rand_permutations): |
| 830 | |
| 831 | shift = testGen.randInt(0, 32) |
| 832 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 833 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 834 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 835 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 836 | |
| 837 | return arg_list |
| 838 | |
| 839 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 840 | def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 841 | arg_list = [] |
| 842 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 843 | arg_list.append(("roundTrue", [True])) |
| 844 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 845 | |
| 846 | return arg_list |
| 847 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 848 | # Helper function for reshape. Gets some factors of a larger number. |
| 849 | @staticmethod |
| 850 | def getFactors(val, start=1): |
| 851 | factors = [] |
| 852 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 853 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 854 | if (val % i) == 0: |
| 855 | factors.append(i) |
| 856 | |
| 857 | return factors |
| 858 | |
| 859 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 860 | def agReshape(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 861 | arg_list = [] |
| 862 | |
| 863 | origShape = shapeList[0] |
| 864 | |
| 865 | totalElements = 1 |
| 866 | for s in origShape: |
| 867 | totalElements *= s |
| 868 | |
| 869 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 870 | factors = TosaArgGen.getFactors(totalElements) |
| 871 | |
| 872 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 873 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 874 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 875 | continue |
| 876 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 877 | found = True |
| 878 | # escape_counter breaks while loop if it continues on for too long |
| 879 | escape_counter = 0 |
| 880 | while found: |
| 881 | newShape = [] |
| 882 | # Generate newShape ensuring it isn't a duplicate |
| 883 | remainingElements = totalElements |
| 884 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 885 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 886 | # pick rank-1 factors |
| 887 | newShape.append(shuffledFactors[0]) |
| 888 | remainingElements = remainingElements // shuffledFactors[0] |
| 889 | shuffledFactors = testGen.rng.permutation( |
| 890 | TosaArgGen.getFactors(remainingElements) |
| 891 | ) |
| 892 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 893 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 894 | # Toss in a -1 sometimes |
| 895 | minusOne = testGen.randInt(0, newRank * 4) |
| 896 | if minusOne < newRank: |
| 897 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 898 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 899 | # Check for duplicates |
| 900 | found = False |
| 901 | for name, other_shape in arg_list: |
| 902 | if other_shape[0] == newShape: |
| 903 | found = True |
| 904 | break |
| 905 | |
| 906 | escape_counter += 1 |
| 907 | if escape_counter >= 100: |
| 908 | break |
| 909 | |
| 910 | if not found: |
| 911 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 912 | |
| 913 | return arg_list |
| 914 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 915 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 916 | def agTranspose(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 917 | arg_list = [] |
| 918 | |
| 919 | ifm_shape = shapeList[0] |
| 920 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 921 | |
| 922 | if error_name == ErrorIf.IndexOutsideBounds: |
| 923 | incorrect_large_index = range(len(ifm_shape)+1, 2*len(ifm_shape)+1) |
| 924 | incorrect_small_index = range(-len(ifm_shape), 0) |
| 925 | permutations = [p for p in itertools.permutations(incorrect_large_index)] |
| 926 | permutations.extend([p for p in itertools.permutations(incorrect_small_index)]) |
| 927 | elif error_name == ErrorIf.IndexUsedTwice: |
| 928 | # Create list with a duplicated index |
| 929 | perm_range = list(range(len(ifm_shape))) |
| 930 | index_choice = testGen.rng.choice(range(len(perm_range))) |
| 931 | perm_range[(index_choice + 1) % len(perm_range)] = perm_range[index_choice] |
| 932 | permutations = [p for p in itertools.permutations(perm_range)] |
| 933 | |
| 934 | |
| 935 | else: |
| 936 | # Get all permutations |
| 937 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 938 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 939 | # Limit to possible permutations from shape dimension or argument setting |
| 940 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 941 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 942 | # Get random permutation generator that uses all permutations |
| 943 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 944 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 945 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 946 | arg_list = [ |
| 947 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 948 | for p in range(limit) |
| 949 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 950 | return arg_list |
| 951 | |
| 952 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 953 | def agSlice(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 954 | arg_list = [] |
| 955 | |
| 956 | ifm_shape = shapeList[0] |
| 957 | rank = len(ifm_shape) |
| 958 | |
| 959 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 960 | start = [] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 961 | size = [] |
| 962 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 963 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 964 | |
| 965 | for i in range(rank): |
| 966 | if ifm_shape[i] > 1: |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 967 | start.append(testGen.randInt(0, ifm_shape[i])) |
| 968 | size.append(testGen.randInt(0, ifm_shape[i] - start[i])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 969 | |
| 970 | # Invalid slice size? |
| 971 | if size[i] == 0: |
| 972 | valid = False |
| 973 | else: |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 974 | start.append(0) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 975 | size.append(1) |
| 976 | |
| 977 | if valid: |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 978 | # If ERROR_IF test required then incorrect start, size will be returned |
| 979 | start, size = TosaErrorIfArgGen.eiSliceErrorIf(testGen, error_name, ifm_shape, start, size) |
| 980 | arg_list.append(("perm{}".format(p), [start, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 981 | return arg_list |
| 982 | |
| 983 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 984 | def agTile(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 985 | arg_list = [] |
| 986 | |
| 987 | ifm_shape = shapeList[0] |
| 988 | rank = len(ifm_shape) |
| 989 | |
| 990 | for p in range(testGen.args.num_rand_permutations): |
| 991 | |
| 992 | # Pick a few random, but small multiple values |
| 993 | # because otherwise this has a tendency to generate |
| 994 | # enormous tensors |
| 995 | multiples = [] |
| 996 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame] | 997 | if ifm_shape[i] > 1000: |
| 998 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 999 | multiples.append(1) |
| 1000 | elif max(ifm_shape) > 1000: |
| 1001 | multiples.append(2) |
| 1002 | else: |
| 1003 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1004 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1005 | |
| 1006 | return arg_list |
| 1007 | |
| 1008 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1009 | def agResize(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1010 | arg_list = [] |
| 1011 | |
| 1012 | ifm_shape = shapeList[0] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1013 | for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1014 | |
| 1015 | # Exclude illegal {mode, type} configurations. Pick legal output types |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1016 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 1017 | outputDTypeList = [DType.INT8] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1018 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1019 | outputDTypeList = [DType.INT16] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1020 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 1021 | outputDTypeList = [DType.INT32] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1022 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1023 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1024 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1025 | outputDTypeList = [DType.FLOAT] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1026 | elif error_name == ErrorIf.WrongInputType: |
| 1027 | # If an incorrect input type is used then we set a 'correct' |
| 1028 | # output type to avoid other errors |
| 1029 | outputDTypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1030 | else: |
| 1031 | continue |
| 1032 | |
| 1033 | for outputDType in outputDTypeList: |
| 1034 | for perm in range(testGen.args.num_rand_permutations): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1035 | # Randomly generate legal output dimensions and shift |
| 1036 | # and then compute the stride and offset based on them |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1037 | # A output_dim of 1 will cause offset to exceed allowed range |
| 1038 | # so minimum value 2 produced below |
| 1039 | output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1] |
| 1040 | while ((float(ifm_shape[1]) / float(output_dims[0])) >= 16): |
| 1041 | output_dims[0] += 1 |
| 1042 | while ((float(ifm_shape[2]) / float(output_dims[1])) >= 16): |
| 1043 | output_dims[1] += 1 |
| 1044 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1045 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 1046 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 1047 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 1048 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1049 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1050 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 1051 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 1052 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 1053 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1054 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1055 | if outputDType == DType.FLOAT: |
| 1056 | shift = 0 |
| 1057 | stride = [0, 0] |
| 1058 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1059 | stride_fp = [fp_stride_y, fp_stride_x] |
| 1060 | offset_fp = [fp_offset_y, fp_offset_x] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1061 | |
| 1062 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1063 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1064 | testGen, |
| 1065 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1066 | mode, |
| 1067 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1068 | shapeList, |
| 1069 | outputDType, |
| 1070 | shift, |
| 1071 | stride, |
| 1072 | stride_fp, |
| 1073 | offset, |
| 1074 | offset_fp |
| 1075 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1076 | else: |
| 1077 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1078 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1079 | arg_list.append( |
| 1080 | ( |
| 1081 | "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] | 1082 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1083 | output_dims[0], |
| 1084 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1085 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1086 | stride_fp[0], |
| 1087 | stride_fp[1], |
| 1088 | offset_fp[0], |
| 1089 | offset_fp[1], |
| 1090 | ), |
| 1091 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1092 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1093 | stride, |
| 1094 | offset, |
| 1095 | shift, |
| 1096 | stride_fp, |
| 1097 | offset_fp, |
| 1098 | output_dims, |
| 1099 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1100 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1101 | ], |
| 1102 | ) |
| 1103 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1104 | else: |
| 1105 | shift = 11 |
| 1106 | unit = float(1 << shift) |
| 1107 | stride_y = int(round(fp_stride_y * unit)) |
| 1108 | stride_x = int(round(fp_stride_x * unit)) |
| 1109 | offset_y = int(round(fp_offset_y * unit)) |
| 1110 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1111 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1112 | while ( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1113 | stride_y >= (16 << shift) |
| 1114 | or stride_x >= (16 << shift) |
| 1115 | or offset_y >= (16 << shift) |
| 1116 | or offset_x >= (16 << shift) |
| 1117 | or offset_y <= (-16 << shift) |
| 1118 | or offset_x <= (-16 << shift) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1119 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1120 | shift = shift - 1 |
| 1121 | unit = float(1 << shift) |
| 1122 | stride_y = int(round(fp_stride_y * unit)) |
| 1123 | stride_x = int(round(fp_stride_x * unit)) |
| 1124 | offset_y = int(round(fp_offset_y * unit)) |
| 1125 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1126 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1127 | stride = [stride_y, stride_x] |
| 1128 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1129 | |
| 1130 | stride_fp = [0.0, 0.0] |
| 1131 | offset_fp = [0.0, 0.0] |
| 1132 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1133 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1134 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1135 | testGen, |
| 1136 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1137 | mode, |
| 1138 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1139 | shapeList, |
| 1140 | outputDType, |
| 1141 | shift, |
| 1142 | stride, |
| 1143 | stride_fp, |
| 1144 | offset, |
| 1145 | offset_fp |
| 1146 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1147 | else: |
| 1148 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1149 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1150 | arg_list.append( |
| 1151 | ( |
| 1152 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1153 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1154 | shift, |
| 1155 | output_dims[0], |
| 1156 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1157 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1158 | stride[0], |
| 1159 | stride[1], |
| 1160 | offset[0], |
| 1161 | offset[1], |
| 1162 | ), |
| 1163 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1164 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1165 | stride, |
| 1166 | offset, |
| 1167 | shift, |
| 1168 | stride_fp, |
| 1169 | offset_fp, |
| 1170 | output_dims, |
| 1171 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1172 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1173 | ], |
| 1174 | ) |
| 1175 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1176 | |
| 1177 | return arg_list |
| 1178 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1179 | def agCondIf(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1180 | # CondIf generates the condition values here. |
| 1181 | # Convert to tensors in the build function, along with the |
| 1182 | # then and else blocks |
| 1183 | arg_list = [] |
| 1184 | |
| 1185 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1186 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1187 | |
| 1188 | return arg_list |
| 1189 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1190 | def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1191 | # While loop: 0 iterations, 1, more than 1 |
| 1192 | arg_list = [] |
| 1193 | |
| 1194 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1195 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1196 | |
| 1197 | return arg_list |
| 1198 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1199 | class TosaErrorIfArgGen: |
| 1200 | |
| 1201 | @staticmethod |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1202 | 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] | 1203 | |
| 1204 | if outputDType == DType.FLOAT: |
| 1205 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1206 | stride_fp = testGen.rng.random(size=[2]) - 2 |
| 1207 | elif error_name == ErrorIf.ShiftNotZero: |
| 1208 | shift = testGen.rng.integers(1, 5) |
| 1209 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1210 | shape = shapeList[0] |
| 1211 | transform_height = testGen.rng.choice([False, True]) |
| 1212 | if transform_height: |
| 1213 | stride_fp[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1214 | else: |
| 1215 | stride_fp[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1216 | else: |
| 1217 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1218 | stride = np.int16(testGen.rng.integers(-1, 1, size=[2])) |
| 1219 | elif error_name == ErrorIf.ShiftSmallerOne: |
| 1220 | shift = testGen.rng.integers(-3, 1) |
| 1221 | if shift <= 0: |
| 1222 | stride = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1223 | offset = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1224 | else: |
| 1225 | stride = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1226 | offset = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1227 | elif error_name == ErrorIf.ShiftLargerEleven: |
| 1228 | shift = np.int16(testGen.rng.integers(12, 15)) |
| 1229 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1230 | shape = shapeList[0] |
| 1231 | transform_height = testGen.rng.choice([False, True]) |
| 1232 | if transform_height: |
| 1233 | stride[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1234 | else: |
| 1235 | stride[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1236 | elif error_name == ErrorIf.StrideLargerEqualMax: |
| 1237 | stride = [(16 << shift) + 1, (16 << shift) + 1] |
| 1238 | elif error_name == ErrorIf.OffsetLargerEqualMax: |
| 1239 | offset = [(16 << shift) + 1, (16 << shift) + 1] |
| 1240 | elif error_name == ErrorIf.OffsetSmallerEqualMin: |
| 1241 | offset = [(-16 << shift) - 1, (-16 << shift) - 1] |
| 1242 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1243 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1244 | if error_name == ErrorIf.WrongOutputType: |
| 1245 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
| 1246 | incorrect_types = (DType.INT4, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1247 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
| 1248 | incorrect_types = (DType.INT4, DType.INT8, DType.INT32, DType.INT48, DType.FLOAT) |
| 1249 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
| 1250 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) |
| 1251 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
| 1252 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) |
| 1253 | elif dtype == DType.FLOAT: |
| 1254 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) |
| 1255 | outputDType = testGen.rng.choice(a=incorrect_types) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1256 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1257 | return shift, stride, stride_fp, offset, offset_fp, outputDType |
| 1258 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 1259 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1260 | @staticmethod |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1261 | def eiPoolingErrorIf(testGen, error_name, stride, pad, kernel): |
| 1262 | if (error_name == ErrorIf.StrideSmallerOne |
| 1263 | # padding must not exceed the kernel size |
| 1264 | and pad[0] < kernel[0] and pad[1] < kernel[0] and pad[2] < kernel[1] and pad[3] < kernel[1]): |
| 1265 | wrongStride = (testGen.rng.choice([0, -1, -2, -3]), testGen.rng.choice([0, -1, -2, -3])) |
| 1266 | return wrongStride, pad, kernel |
| 1267 | elif error_name == ErrorIf.PadSmallerZero: |
| 1268 | wrongPad = (testGen.rng.choice([-1, -2, -3]), |
| 1269 | testGen.rng.choice([-1, -2, -3]), |
| 1270 | testGen.rng.choice([-1, -2, -3]), |
| 1271 | testGen.rng.choice([-1, -2, -3])) |
| 1272 | return stride, wrongPad, kernel |
| 1273 | elif error_name == ErrorIf.KernelSmallerOne: |
| 1274 | wrongKernel = (testGen.rng.choice([0, -1, -2, -3]), testGen.rng.choice([0, -1, -2, -3])) |
| 1275 | return stride, pad, wrongKernel |
| 1276 | elif error_name == ErrorIf.PadLargerEqualKernel: |
| 1277 | wrongPad = (testGen.rng.choice([kernel[0], kernel[0]+1, kernel[0]+2]), |
| 1278 | testGen.rng.choice([kernel[0], kernel[0]+1, kernel[0]+2]), |
| 1279 | testGen.rng.choice([kernel[1], kernel[1]+1, kernel[1]+2]), |
| 1280 | testGen.rng.choice([kernel[1], kernel[1]+1, kernel[1]+2])) |
| 1281 | return stride, wrongPad, kernel |
| 1282 | else: |
| 1283 | return None, None, None |
| 1284 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 1285 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1286 | @staticmethod |
| 1287 | def eiRescaleWrongOutputType(input_dtype, output_dtype): |
| 1288 | if input_dtype == DType.INT8: |
| 1289 | if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 1290 | return True |
| 1291 | if input_dtype in [DType.INT16, DType.INT32]: |
| 1292 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1293 | return True |
| 1294 | elif input_dtype == DType.INT48: |
| 1295 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1296 | return True |
| 1297 | elif input_dtype == DType.UINT8: |
| 1298 | if output_dtype != DType.INT8: |
| 1299 | return True |
| 1300 | return False |
| 1301 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1302 | |
| 1303 | @staticmethod |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1304 | def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list): |
| 1305 | # Mess up input/output tensors for ERROR_IF checks |
| 1306 | if error_name == "WrongInputList": |
| 1307 | add_input = testGen.rng.choice([True, False]) |
| 1308 | if add_input: |
| 1309 | input_list.append('eiDummyInput') |
| 1310 | else: |
| 1311 | input_list = input_list[:-1] |
| 1312 | if error_name == "WrongOutputList": |
| 1313 | add_output = testGen.rng.choice([True, False]) |
| 1314 | if add_output: |
| 1315 | output_list.append('eiDummyOutput') |
| 1316 | else: |
| 1317 | output_list = [] |
| 1318 | return input_list, output_list |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1319 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 1320 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1321 | @staticmethod |
| 1322 | def eiRestrictDimension(shape, error_name): |
| 1323 | # Restrict dimension size if rank is large for WrongRank Error_If |
| 1324 | # This will keep the test sizes reasonably small |
| 1325 | if error_name == ErrorIf.WrongRank: |
| 1326 | if len(shape) > 4: |
| 1327 | shape[4] = 1 |
| 1328 | |
| 1329 | return shape |
| 1330 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 1331 | |
| 1332 | def eiSliceErrorIf(testGen, error_name, input_shape, start, size): |
| 1333 | if error_name == ErrorIf.StartSmallerZero: |
| 1334 | newStart = [] |
| 1335 | for i in range(len(input_shape)): |
| 1336 | newStart.append(testGen.rng.choice([-3, -2, -1])) |
| 1337 | return newStart, size |
| 1338 | elif error_name == ErrorIf.SizeSmallerEqualZero: |
| 1339 | newSize = [] |
| 1340 | for i in range(len(input_shape)): |
| 1341 | newSize.append(testGen.rng.choice([-3, -2, -1, 0])) |
| 1342 | return start, newSize |
| 1343 | elif error_name == ErrorIf.StartSizeOutsideBounds: |
| 1344 | newStart, newSize = [], [] |
| 1345 | for i in range(len(input_shape)): |
| 1346 | newStart.append(input_shape[i]-1) |
| 1347 | newSize.append(testGen.rng.choice([2, 3, 4])) |
| 1348 | return newStart, newSize |
| 1349 | elif error_name == ErrorIf.InputSizeStartLengthMismatch: |
| 1350 | remove = testGen.rng.choice([True, False]) |
| 1351 | if remove: |
| 1352 | newStart = start[1:] |
| 1353 | newSize = size[1:] |
| 1354 | else: |
| 1355 | newStart = start |
| 1356 | newStart.append(1) |
| 1357 | newSize = size |
| 1358 | newSize.append(1) |
| 1359 | return newStart, newSize |
| 1360 | else: |
| 1361 | return start, size |
| 1362 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1363 | class TosaErrorValidator: |
| 1364 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1365 | @staticmethod |
| 1366 | def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs): |
| 1367 | # Check ERROR_IF statements |
| 1368 | |
| 1369 | for val_fcn in validator_fcns: |
| 1370 | val_result = val_fcn(True, **kwargs) |
| 1371 | |
| 1372 | validator_name = val_result['error_name'] |
| 1373 | error_result = val_result['error_result'] |
| 1374 | error_reason = val_result['error_reason'] |
| 1375 | |
| 1376 | if error_result: |
| 1377 | if error_name == validator_name: |
| 1378 | serializer.setExpectedReturnCode(2, error_reason) |
| 1379 | else: |
| 1380 | print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}") |
| 1381 | return None # Return None to delete test if wrong ERROR_IF is hit |
| 1382 | else: |
| 1383 | if error_name == validator_name: |
| 1384 | print(f"No ERROR_IF hit for {error_name}") |
| 1385 | return None |
| 1386 | |
| 1387 | @staticmethod |
| 1388 | def evWrongInputType(check=False, **kwargs): |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1389 | all_dtypes = {DType.BOOL, DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT} |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1390 | |
| 1391 | # Find the unsupported input data types |
| 1392 | assert 'op' in kwargs |
| 1393 | op = kwargs['op'] |
| 1394 | input_dtypes = op['types'] |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1395 | |
| 1396 | allowed_input_dtypes = {t[0] if isinstance(t, list) else t for t in input_dtypes} |
| 1397 | wrong_input_dtypes = list(all_dtypes - allowed_input_dtypes) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1398 | |
| 1399 | error_name = ErrorIf.WrongInputType |
| 1400 | param_reqs = {"rank": None, "dtype": wrong_input_dtypes, "shape": None} |
| 1401 | error_result = False |
| 1402 | error_reason = "Input data type not supported for this operator" |
| 1403 | |
| 1404 | if check: |
| 1405 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1406 | if op['op'] == Op.FULLY_CONNECTED: |
| 1407 | if input_dtype not in allowed_input_dtypes: |
| 1408 | error_result = True |
| 1409 | elif input_dtype not in input_dtypes: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 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 |
| 1421 | def evWrongOutputType(check=False, **kwargs): |
| 1422 | error_name = ErrorIf.WrongOutputType |
| 1423 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1424 | error_result = False |
| 1425 | error_reason = "Output data type not supported for this configuration of operator" |
| 1426 | |
| 1427 | if check: |
| 1428 | input_dtype = kwargs['input_dtype'] |
| 1429 | output_dtype = kwargs['output_dtype'] |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1430 | op = kwargs['op'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1431 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1432 | if op['op'] == Op.RESIZE: |
| 1433 | mode = kwargs['mode'] |
| 1434 | if ( |
| 1435 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT8 and output_dtype != DType.INT8) or |
| 1436 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT16 and output_dtype != DType.INT16) or |
| 1437 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT8 and output_dtype != DType.INT32) or |
| 1438 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT16 and output_dtype != DType.INT48) or |
| 1439 | (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) |
| 1440 | ): |
| 1441 | error_result = True |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1442 | elif op['op'] == Op.RESCALE: |
| 1443 | if input_dtype == DType.INT8: |
| 1444 | if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 1445 | error_result = True |
| 1446 | if input_dtype in [DType.INT16, DType.INT32]: |
| 1447 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1448 | error_result = True |
| 1449 | elif input_dtype == DType.INT48: |
| 1450 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1451 | error_result = True |
| 1452 | elif input_dtype == DType.UINT8: |
| 1453 | if output_dtype != DType.INT8: |
| 1454 | error_result = True |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1455 | elif op['op'] in [Op.FULLY_CONNECTED, Op.MATMUL]: |
| 1456 | if ( |
| 1457 | (input_dtype == DType.INT8 and output_dtype != DType.INT32) or |
| 1458 | (input_dtype == DType.INT16 and output_dtype != DType.INT48) or |
| 1459 | (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) |
| 1460 | ): |
| 1461 | error_result = True |
| 1462 | elif op['op'] == Op.ARGMAX: |
| 1463 | if input_dtype in [DType.INT8, DType.INT16, DType.FLOAT] and output_dtype != DType.INT32: |
| 1464 | error_result = True |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1465 | else: |
| 1466 | if output_dtype != input_dtype: |
| 1467 | error_result = True |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1468 | |
| 1469 | info_dict = { |
| 1470 | "error_name": error_name, |
| 1471 | "error_result": error_result, |
| 1472 | "error_reason": error_reason, |
| 1473 | "param_reqs": param_reqs |
| 1474 | } |
| 1475 | return info_dict |
| 1476 | |
| 1477 | @staticmethod |
| 1478 | def evWrongRank(check=False, **kwargs): |
| 1479 | all_ranks = (1, 2, 3, 4, 5) |
| 1480 | |
| 1481 | # Make a list of incorrect ranks |
| 1482 | assert 'op' in kwargs |
| 1483 | op = kwargs['op'] |
| 1484 | rmin, rmax = op['rank'] |
| 1485 | rank_range = range(rmin, rmax + 1) |
| 1486 | incorrect_ranks = list(set(all_ranks) - set(rank_range)) |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1487 | # Remove small incorrect ranks to avoid index errors |
| 1488 | incorrect_ranks = [rank for rank in incorrect_ranks if rank > rmin] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1489 | # Set minimum incorrect rank to 3 to avoid index error |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1490 | if op['op'] in [Op.RESIZE]: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1491 | incorrect_ranks = [3, 5] |
| 1492 | |
| 1493 | error_name = ErrorIf.WrongRank |
| 1494 | param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None} |
| 1495 | error_result = False |
| 1496 | error_reason = "Rank not supported for this operator" |
| 1497 | |
| 1498 | if check: |
| 1499 | input_shape = kwargs['input_shape'] |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1500 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1501 | if op['op'] in [Op.RESIZE, Op.AVG_POOL2D, Op.MAX_POOL2D] and len(input_shape) != 4: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1502 | error_result = True |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1503 | elif op['op'] == Op.FULLY_CONNECTED and len(input_shape) != 2: |
| 1504 | error_result = True |
| 1505 | elif op['op'] == Op.MATMUL and len(input_shape) != 3: |
| 1506 | error_result = True |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1507 | else: |
| 1508 | if len(input_shape) not in rank_range: |
| 1509 | error_result = True |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1510 | |
| 1511 | info_dict = { |
| 1512 | "error_name": error_name, |
| 1513 | "error_result": error_result, |
| 1514 | "error_reason": error_reason, |
| 1515 | "param_reqs": param_reqs |
| 1516 | } |
| 1517 | return info_dict |
| 1518 | |
| 1519 | @staticmethod |
| 1520 | def evWrongInputList(check=False, **kwargs): |
| 1521 | error_name = ErrorIf.WrongInputList |
| 1522 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1523 | error_result = False |
| 1524 | error_reason = "Op input list does not match expected input" |
| 1525 | |
| 1526 | if check: |
| 1527 | op = kwargs['op'] |
| 1528 | input_list = kwargs['input_list'] |
| 1529 | num_operands = kwargs['num_operands'] |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 1530 | # both PAD, TRANSPOSE add an extra const layer in the build function |
| 1531 | if op['op'] in [Op.PAD, Op.TRANSPOSE]: |
| 1532 | if len(input_list) != num_operands + 1: |
| 1533 | error_result = True |
| 1534 | else: |
| 1535 | if len(input_list) != num_operands: |
| 1536 | error_result = True |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1537 | |
| 1538 | info_dict = { |
| 1539 | "error_name": error_name, |
| 1540 | "error_result": error_result, |
| 1541 | "error_reason": error_reason, |
| 1542 | "param_reqs": param_reqs |
| 1543 | } |
| 1544 | return info_dict |
| 1545 | |
| 1546 | @staticmethod |
| 1547 | def evWrongOutputList(check=False, **kwargs): |
| 1548 | error_name = ErrorIf.WrongOutputList |
| 1549 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1550 | error_result = False |
| 1551 | error_reason = "Op output list does not match expected output" |
| 1552 | |
| 1553 | if check: |
| 1554 | output_list = kwargs['output_list'] |
| 1555 | # Note this will be incorrect if an operator returns more than one output |
| 1556 | if len(output_list) != 1: |
| 1557 | error_result = True |
| 1558 | |
| 1559 | info_dict = { |
| 1560 | "error_name": error_name, |
| 1561 | "error_result": error_result, |
| 1562 | "error_reason": error_reason, |
| 1563 | "param_reqs": param_reqs |
| 1564 | } |
| 1565 | return info_dict |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1566 | |
| 1567 | @staticmethod |
| 1568 | def evMaxDimExceeded(check=False, **kwargs): |
| 1569 | error_name = ErrorIf.MaxDimExceeded |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1570 | param_reqs = { |
| 1571 | "rank": [4,4], |
| 1572 | "dtype": [DType.INT8], |
| 1573 | "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]] |
| 1574 | } |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1575 | error_result = False |
| 1576 | error_reason = "At least one maximum dimension is larger than 16384" |
| 1577 | |
| 1578 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1579 | input_shape = kwargs['input_shape'] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1580 | output_shape = kwargs['output_shape'] # Note this is just (OH, OW) |
| 1581 | if ((input_shape[1] > 16384) or |
| 1582 | (input_shape[2] > 16384) or |
| 1583 | (output_shape[0] > 16384) or |
| 1584 | (output_shape[1] > 16384)): |
| 1585 | error_result = True |
| 1586 | |
| 1587 | info_dict = { |
| 1588 | "error_name": error_name, |
| 1589 | "error_result": error_result, |
| 1590 | "error_reason": error_reason, |
| 1591 | "param_reqs": param_reqs |
| 1592 | } |
| 1593 | return info_dict |
| 1594 | |
| 1595 | @staticmethod |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1596 | def evBatchMismatch(check=False, **kwargs): |
| 1597 | error_name = ErrorIf.BatchMismatch |
| 1598 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1599 | error_result = False |
| 1600 | error_reason = "Input batch size not equal to output batch size" |
| 1601 | |
| 1602 | assert 'op' in kwargs |
| 1603 | op = kwargs['op'] |
| 1604 | rmin, rmax = op['rank'] |
| 1605 | rank_range = range(rmin, rmax + 1) |
| 1606 | |
| 1607 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1608 | input_shape = kwargs['input_shape'] |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1609 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1610 | |
| 1611 | if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]): |
| 1612 | error_result = True |
| 1613 | |
| 1614 | info_dict = { |
| 1615 | "error_name": error_name, |
| 1616 | "error_result": error_result, |
| 1617 | "error_reason": error_reason, |
| 1618 | "param_reqs": param_reqs |
| 1619 | } |
| 1620 | return info_dict |
| 1621 | |
| 1622 | @staticmethod |
| 1623 | def evChannelMismatch(check=False, **kwargs): |
| 1624 | error_name = ErrorIf.ChannelMismatch |
| 1625 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1626 | error_result = False |
| 1627 | error_reason = "Input channel size not equal to output channel size" |
| 1628 | |
| 1629 | assert 'op' in kwargs |
| 1630 | op = kwargs['op'] |
| 1631 | rmin, rmax = op['rank'] |
| 1632 | rank_range = range(rmin, rmax + 1) |
| 1633 | |
| 1634 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1635 | input_shape = kwargs['input_shape'] |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1636 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1637 | if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]): |
| 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 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1649 | def evStrideSmallerEqualZero(check=False, **kwargs): |
| 1650 | error_name = ErrorIf.StrideSmallerEqualZero |
| 1651 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1652 | error_result = False |
| 1653 | error_reason = "Stride value smaller than or equal zero" |
| 1654 | |
| 1655 | if check: |
| 1656 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1657 | output_dtype = kwargs['output_dtype'] |
| 1658 | if input_dtype != DType.FLOAT and output_dtype == DType.FLOAT: |
| 1659 | stride = kwargs['stride'] # Work around wrong input/output type tests |
| 1660 | elif output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1661 | stride = kwargs['stride_fp'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1662 | elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT: |
| 1663 | stride = kwargs['stride_fp'] # Work around wrong input/output type tests |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1664 | else: |
| 1665 | stride = kwargs['stride'] |
| 1666 | |
| 1667 | if min(stride) <= 0: |
| 1668 | error_result = True |
| 1669 | |
| 1670 | info_dict = { |
| 1671 | "error_name": error_name, |
| 1672 | "error_result": error_result, |
| 1673 | "error_reason": error_reason, |
| 1674 | "param_reqs": param_reqs |
| 1675 | } |
| 1676 | return info_dict |
| 1677 | |
| 1678 | @staticmethod |
| 1679 | def evStrideLargerEqualMax(check=False, **kwargs): |
| 1680 | error_name = ErrorIf.StrideLargerEqualMax |
| 1681 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1682 | error_result = False |
| 1683 | error_reason = "Stride value larger than or equal to maximum value" |
| 1684 | |
| 1685 | if check: |
| 1686 | shift = kwargs['shift'] |
| 1687 | input_dtype = kwargs['input_dtype'] |
| 1688 | stride = kwargs['stride'] |
| 1689 | if input_dtype in [DType.INT8, DType.INT16]: |
| 1690 | if shift >= 0 and (stride[0] >= (16 << shift) or stride[1] >= (16 << shift)): |
| 1691 | error_result = True |
| 1692 | elif shift < 0 and (stride[0] >= (16 >> -shift) or stride[1] >= (16 >> -shift)): |
| 1693 | error_result = True |
| 1694 | |
| 1695 | info_dict = { |
| 1696 | "error_name": error_name, |
| 1697 | "error_result": error_result, |
| 1698 | "error_reason": error_reason, |
| 1699 | "param_reqs": param_reqs |
| 1700 | } |
| 1701 | return info_dict |
| 1702 | |
| 1703 | |
| 1704 | @staticmethod |
| 1705 | def evStrideLargerDimension(check=False, **kwargs): |
| 1706 | error_name = ErrorIf.StrideLargerDimension |
| 1707 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1708 | error_result = False |
| 1709 | error_reason = "Stride value larger than or equal to H/W dimension" |
| 1710 | |
| 1711 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1712 | shape = kwargs['input_shape'] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1713 | input_dtype = kwargs['input_dtype'] |
| 1714 | stride = kwargs['stride_fp'] |
| 1715 | |
| 1716 | if input_dtype == DType.FLOAT and (stride[0] > shape[1]) or (stride[1] > shape[2]): |
| 1717 | error_result = True |
| 1718 | |
| 1719 | info_dict = { |
| 1720 | "error_name": error_name, |
| 1721 | "error_result": error_result, |
| 1722 | "error_reason": error_reason, |
| 1723 | "param_reqs": param_reqs |
| 1724 | } |
| 1725 | return info_dict |
| 1726 | |
| 1727 | |
| 1728 | @staticmethod |
| 1729 | def evOffsetSmallerEqualMin(check=False, **kwargs): |
| 1730 | error_name = ErrorIf.OffsetSmallerEqualMin |
| 1731 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1732 | error_result = False |
| 1733 | error_reason = "Offset value smaller than or equal to minimum value" |
| 1734 | |
| 1735 | if check: |
| 1736 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1737 | output_dtype = kwargs['output_dtype'] |
| 1738 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1739 | offset = kwargs['offset_fp'] |
| 1740 | else: |
| 1741 | offset = kwargs['offset'] |
| 1742 | |
| 1743 | if shift >= 0 and (offset[0] <= (-16 << shift) or offset[1] <= (-16 << shift)): |
| 1744 | error_result = True |
| 1745 | elif shift < 0 and (offset[0] <= (-16 >> -shift) or offset[1] <= (-16 >> -shift)): |
| 1746 | error_result = True |
| 1747 | |
| 1748 | info_dict = { |
| 1749 | "error_name": error_name, |
| 1750 | "error_result": error_result, |
| 1751 | "error_reason": error_reason, |
| 1752 | "param_reqs": param_reqs |
| 1753 | } |
| 1754 | return info_dict |
| 1755 | |
| 1756 | @staticmethod |
| 1757 | def evOffsetLargerEqualMax(check=False, **kwargs): |
| 1758 | error_name = ErrorIf.OffsetLargerEqualMax |
| 1759 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1760 | error_result = False |
| 1761 | error_reason = "Offset value larger than or equal to maximum value" |
| 1762 | |
| 1763 | if check: |
| 1764 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1765 | output_dtype = kwargs['output_dtype'] |
| 1766 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1767 | offset = kwargs['offset_fp'] |
| 1768 | else: |
| 1769 | offset = kwargs['offset'] |
| 1770 | |
| 1771 | if shift >= 0: |
| 1772 | if offset[0] >= (16 << shift) or offset[1] >= (16 << shift): |
| 1773 | error_result = True |
| 1774 | |
| 1775 | if shift >= 0 and (offset[0] >= (16 << shift) or offset[1] >= (16 << shift)): |
| 1776 | error_result = True |
| 1777 | elif shift < 0 and (offset[0] >= (16 >> -shift) or offset[1] >= (16 >> -shift)): |
| 1778 | error_result = True |
| 1779 | |
| 1780 | info_dict = { |
| 1781 | "error_name": error_name, |
| 1782 | "error_result": error_result, |
| 1783 | "error_reason": error_reason, |
| 1784 | "param_reqs": param_reqs |
| 1785 | } |
| 1786 | return info_dict |
| 1787 | |
| 1788 | @staticmethod |
| 1789 | def evShiftNotZero(check=False, **kwargs): |
| 1790 | error_name = ErrorIf.ShiftNotZero |
| 1791 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1792 | error_result = False |
| 1793 | error_reason = "Shift value must be zero for float input" |
| 1794 | |
| 1795 | if check: |
| 1796 | shift = kwargs['shift'] |
| 1797 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1798 | output_dtype = kwargs['output_dtype'] |
| 1799 | 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] | 1800 | error_result = True |
| 1801 | |
| 1802 | info_dict = { |
| 1803 | "error_name": error_name, |
| 1804 | "error_result": error_result, |
| 1805 | "error_reason": error_reason, |
| 1806 | "param_reqs": param_reqs |
| 1807 | } |
| 1808 | return info_dict |
| 1809 | |
| 1810 | |
| 1811 | @staticmethod |
| 1812 | def evShiftSmallerOne(check=False, **kwargs): |
| 1813 | error_name = ErrorIf.ShiftSmallerOne |
| 1814 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1815 | error_result = False |
| 1816 | error_reason = "Shift value smaller than one" |
| 1817 | |
| 1818 | if check: |
| 1819 | shift = kwargs['shift'] |
| 1820 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1821 | output_dtype = kwargs['output_dtype'] |
| 1822 | 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] | 1823 | error_result = True |
| 1824 | |
| 1825 | info_dict = { |
| 1826 | "error_name": error_name, |
| 1827 | "error_result": error_result, |
| 1828 | "error_reason": error_reason, |
| 1829 | "param_reqs": param_reqs |
| 1830 | } |
| 1831 | return info_dict |
| 1832 | |
| 1833 | @staticmethod |
| 1834 | def evShiftLargerEleven(check=False, **kwargs): |
| 1835 | error_name = ErrorIf.ShiftLargerEleven |
| 1836 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1837 | error_result = False |
| 1838 | error_reason = "Shift value larger than eleven" |
| 1839 | |
| 1840 | if check: |
| 1841 | shift = kwargs['shift'] |
| 1842 | if shift > 11: |
| 1843 | error_result = True |
| 1844 | |
| 1845 | info_dict = { |
| 1846 | "error_name": error_name, |
| 1847 | "error_result": error_result, |
| 1848 | "error_reason": error_reason, |
| 1849 | "param_reqs": param_reqs |
| 1850 | } |
| 1851 | return info_dict |
| 1852 | |
| 1853 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1854 | @staticmethod |
| 1855 | def evRankMismatch(check=False, **kwargs): |
| 1856 | error_name = ErrorIf.RankMismatch |
| 1857 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1858 | error_result = False |
| 1859 | error_reason = "Input Rank does not match output rank" |
| 1860 | |
| 1861 | if check: |
| 1862 | input1_shape = kwargs['input1'].shape |
| 1863 | input2_shape = kwargs['input2'].shape |
| 1864 | output_shape = kwargs['result_tensor'].shape |
| 1865 | if (len(input1_shape) != len(output_shape)) or (len(input2_shape) != len(output_shape)): |
| 1866 | error_result = True |
| 1867 | |
| 1868 | info_dict = { |
| 1869 | "error_name": error_name, |
| 1870 | "error_result": error_result, |
| 1871 | "error_reason": error_reason, |
| 1872 | "param_reqs": param_reqs |
| 1873 | } |
| 1874 | return info_dict |
| 1875 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1876 | @staticmethod |
| 1877 | def evInputZeroPointNotZero(check=False, **kwargs): |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1878 | op = kwargs['op'] |
| 1879 | inputDtypes = op['types'].copy() |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1880 | # If inputDtypes is a list then only the first two elements are INT8 inputs |
| 1881 | if isinstance(inputDtypes, list): |
| 1882 | inputDtypes = inputDtypes[2:] |
| 1883 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1884 | if DType.INT8 in inputDtypes: |
| 1885 | inputDtypes.remove(DType.INT8) |
| 1886 | if DType.UINT8 in inputDtypes: |
| 1887 | inputDtypes.remove(DType.UINT8) |
| 1888 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1889 | error_name = ErrorIf.InputZeroPointNotZero |
| 1890 | param_reqs = { |
| 1891 | "rank": None, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1892 | "dtype": inputDtypes, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1893 | "shape": None |
| 1894 | } |
| 1895 | error_result = False |
| 1896 | error_reason = "Input DType not INT8 and zero point not 0" |
| 1897 | |
| 1898 | if check: |
| 1899 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1900 | if isinstance(kwargs['qinfo'], tuple): |
| 1901 | qinfo = kwargs['qinfo'] |
| 1902 | input_zero_point = qinfo[0] |
| 1903 | else: |
| 1904 | # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp |
| 1905 | qinfo = kwargs['qinfo'].ints |
| 1906 | input_zero_point = qinfo[0][1] |
| 1907 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 1908 | if op['op'] == Op.MATMUL: |
| 1909 | input1_dtype = kwargs['input_dtype'] |
| 1910 | input2_dtype = kwargs['input2_dtype'] |
| 1911 | qinfo = kwargs['qinfo'].ints |
| 1912 | input1_zero_point = qinfo[0][1] |
| 1913 | input2_zero_point = qinfo[1][1] |
| 1914 | if (input1_dtype != DType.INT8 and input1_zero_point != 0) or (input2_dtype != DType.INT8 and input2_zero_point != 0): |
| 1915 | error_result = True |
| 1916 | else: |
| 1917 | if input_dtype not in [DType.INT8, DType.UINT8] and input_zero_point != 0: |
| 1918 | error_result = True |
| 1919 | |
| 1920 | info_dict = { |
| 1921 | "error_name": error_name, |
| 1922 | "error_result": error_result, |
| 1923 | "error_reason": error_reason, |
| 1924 | "param_reqs": param_reqs |
| 1925 | } |
| 1926 | return info_dict |
| 1927 | |
| 1928 | |
| 1929 | @staticmethod |
| 1930 | def evWeightZeroPointNotZero(check=False, **kwargs): |
| 1931 | op = kwargs['op'] |
| 1932 | |
| 1933 | # exclude inputs with INT8 weights |
| 1934 | inputDtypes = [t for t in op['types'] |
| 1935 | if not isinstance(t, list) or t[1] != DType.INT8] |
| 1936 | |
| 1937 | error_name = ErrorIf.WeightZeroPointNotZero |
| 1938 | param_reqs = { |
| 1939 | "rank": None, |
| 1940 | "dtype": inputDtypes, |
| 1941 | "shape": None |
| 1942 | } |
| 1943 | error_result = False |
| 1944 | error_reason = "Weight DType not INT8 and zero point not 0" |
| 1945 | |
| 1946 | if check: |
| 1947 | weight_dtype = kwargs['weight_dtype'] |
| 1948 | # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = weight_zp |
| 1949 | qinfo = kwargs['qinfo'].ints |
| 1950 | weight_zero_point = qinfo[1][1] |
| 1951 | if weight_dtype != DType.INT8 and weight_zero_point != 0: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1952 | error_result = True |
| 1953 | |
| 1954 | info_dict = { |
| 1955 | "error_name": error_name, |
| 1956 | "error_result": error_result, |
| 1957 | "error_reason": error_reason, |
| 1958 | "param_reqs": param_reqs |
| 1959 | } |
| 1960 | return info_dict |
| 1961 | |
| 1962 | |
| 1963 | @staticmethod |
| 1964 | def evOutputZeroPointNotZero(check=False, **kwargs): |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1965 | op = kwargs['op'] |
| 1966 | inputDtypes = op['types'].copy() |
| 1967 | if DType.INT8 in inputDtypes: |
| 1968 | inputDtypes.remove(DType.INT8) |
| 1969 | if DType.UINT8 in inputDtypes: |
| 1970 | inputDtypes.remove(DType.UINT8) |
| 1971 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1972 | error_name = ErrorIf.OutputZeroPointNotZero |
| 1973 | param_reqs = { |
| 1974 | "rank": None, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1975 | "dtype": inputDtypes, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1976 | "shape": None |
| 1977 | } |
| 1978 | error_result = False |
| 1979 | error_reason = "Output DType not INT8 and zero point not 0" |
| 1980 | |
| 1981 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1982 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 1983 | output_dtype = kwargs['output_dtype'] |
| 1984 | if isinstance(kwargs['qinfo'], tuple): |
| 1985 | qinfo = kwargs['qinfo'] |
| 1986 | output_zero_point = qinfo[1] |
| 1987 | else: |
| 1988 | # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp |
| 1989 | qinfo = kwargs['qinfo'].ints |
| 1990 | output_zero_point = qinfo[1][1] |
| 1991 | if op['op'] == Op.AVG_POOL2D: |
| 1992 | if input_dtype != DType.INT8 and output_zero_point != 0: |
| 1993 | error_result = True |
| 1994 | elif output_dtype not in [DType.INT8, DType.UINT8] and output_zero_point != 0: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1995 | error_result = True |
| 1996 | |
| 1997 | info_dict = { |
| 1998 | "error_name": error_name, |
| 1999 | "error_result": error_result, |
| 2000 | "error_reason": error_reason, |
| 2001 | "param_reqs": param_reqs |
| 2002 | } |
| 2003 | return info_dict |
| 2004 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 2005 | @staticmethod |
| 2006 | def evAxisSmallerZero(check=False, **kwargs): |
| 2007 | error_name = ErrorIf.AxisSmallerZero |
| 2008 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2009 | error_result = False |
| 2010 | error_reason = "Axis smaller than zero" |
| 2011 | |
| 2012 | if check: |
| 2013 | axis = kwargs['axis'] |
| 2014 | if axis < 0: |
| 2015 | error_result = True |
| 2016 | |
| 2017 | info_dict = { |
| 2018 | "error_name": error_name, |
| 2019 | "error_result": error_result, |
| 2020 | "error_reason": error_reason, |
| 2021 | "param_reqs": param_reqs |
| 2022 | } |
| 2023 | return info_dict |
| 2024 | |
| 2025 | |
| 2026 | @staticmethod |
| 2027 | def evAxisLargerRank(check=False, **kwargs): |
| 2028 | error_name = ErrorIf.AxisLargerRank |
| 2029 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2030 | error_result = False |
| 2031 | error_reason = "Axis larger than rank" |
| 2032 | |
| 2033 | if check: |
| 2034 | axis = kwargs['axis'] |
| 2035 | shape = kwargs['input_shape'] |
| 2036 | if axis > len(shape): |
| 2037 | error_result = True |
| 2038 | |
| 2039 | info_dict = { |
| 2040 | "error_name": error_name, |
| 2041 | "error_result": error_result, |
| 2042 | "error_reason": error_reason, |
| 2043 | "param_reqs": param_reqs |
| 2044 | } |
| 2045 | return info_dict |
| 2046 | |
| 2047 | |
| 2048 | @staticmethod |
| 2049 | def evShapeOfAxisNotOne(check=False, **kwargs): |
| 2050 | error_name = ErrorIf.ShapeOfAxisNotOne |
| 2051 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2052 | error_result = False |
| 2053 | error_reason = "shape[axis] is not equal to 1" |
| 2054 | |
| 2055 | if check: |
| 2056 | axis = kwargs['axis'] |
| 2057 | shape = kwargs['output_shape'] |
| 2058 | if (0 <= axis < len(shape)) and shape[axis] != 1: |
| 2059 | error_result = True |
| 2060 | |
| 2061 | info_dict = { |
| 2062 | "error_name": error_name, |
| 2063 | "error_result": error_result, |
| 2064 | "error_reason": error_reason, |
| 2065 | "param_reqs": param_reqs |
| 2066 | } |
| 2067 | return info_dict |
| 2068 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2069 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2070 | @staticmethod |
| 2071 | def evPadSmallerZero(check=False, **kwargs): |
| 2072 | error_name = ErrorIf.PadSmallerZero |
| 2073 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2074 | error_result = False |
| 2075 | error_reason = "At least one pad is smaller than zero" |
| 2076 | |
| 2077 | if check: |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 2078 | op = kwargs['op'] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2079 | pad = kwargs['pad'] |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 2080 | if op['op'] == Op.PAD: |
| 2081 | for padding in pad: |
| 2082 | if min(padding) < 0: |
| 2083 | error_result = True |
| 2084 | else: |
| 2085 | if min(pad) < 0: |
| 2086 | error_result = True |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2087 | |
| 2088 | info_dict = { |
| 2089 | "error_name": error_name, |
| 2090 | "error_result": error_result, |
| 2091 | "error_reason": error_reason, |
| 2092 | "param_reqs": param_reqs |
| 2093 | } |
| 2094 | return info_dict |
| 2095 | |
| 2096 | |
| 2097 | @staticmethod |
| 2098 | def evPadLargerEqualKernel(check=False, **kwargs): |
| 2099 | error_name = ErrorIf.PadLargerEqualKernel |
| 2100 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2101 | error_result = False |
| 2102 | error_reason = "At least one pad is larger than kernel dimension" |
| 2103 | |
| 2104 | if check: |
| 2105 | pad = kwargs['pad'] |
| 2106 | kernel = kwargs['kernel'] |
| 2107 | if min(pad) > 0 and min(kernel) > 1: |
| 2108 | if pad[0] >= kernel[0] or pad[1] >= kernel[0] or pad[2] >= kernel[1] or pad[3] >= kernel[1]: |
| 2109 | error_result = True |
| 2110 | |
| 2111 | info_dict = { |
| 2112 | "error_name": error_name, |
| 2113 | "error_result": error_result, |
| 2114 | "error_reason": error_reason, |
| 2115 | "param_reqs": param_reqs |
| 2116 | } |
| 2117 | return info_dict |
| 2118 | |
| 2119 | @staticmethod |
| 2120 | def evPoolingOutputShapeMismatch(check=False, **kwargs): |
| 2121 | error_name = ErrorIf.PoolingOutputShapeMismatch |
| 2122 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2123 | error_result = False |
| 2124 | error_reason = "Mismatch between output shape provided and expected output shape" |
| 2125 | |
| 2126 | if check: |
| 2127 | pad = kwargs['pad'] |
| 2128 | pad_top, pad_bottom, pad_left, pad_right = pad[0], pad[1], pad[2], pad[3] |
| 2129 | |
| 2130 | kernel = kwargs['kernel'] |
| 2131 | kernel_y, kernel_x = kernel[0], kernel[1] |
| 2132 | |
| 2133 | input_shape = kwargs['input_shape'] |
| 2134 | IH, IW = input_shape[1], input_shape[2] |
| 2135 | |
| 2136 | output_shape = kwargs['output_shape'] |
| 2137 | OH, OW = output_shape[1], output_shape[2] |
| 2138 | |
| 2139 | stride = kwargs['stride'] |
| 2140 | stride_y, stride_x = stride[0], stride[1] |
| 2141 | |
| 2142 | # calculate correct height, width dimensions |
| 2143 | if stride_x != 0 and stride_y != 0: |
| 2144 | y_correct = (IH + pad_top + pad_bottom + stride_y - kernel_y) // stride_y |
| 2145 | x_correct = (IW + pad_left + pad_right + stride_x - kernel_x) // stride_x |
| 2146 | |
| 2147 | # ensure parameters are valid |
| 2148 | params_valid = (min(kernel) >= 1 and min(stride) >= 1 and min(pad) >= 0 |
| 2149 | and not (pad[0] >= kernel[0] or pad[1] >= kernel[0] or pad[2] >= kernel[1] or pad[3] >= kernel[1])) |
| 2150 | |
| 2151 | if params_valid and (OH != y_correct or OW != x_correct): |
| 2152 | error_result = True |
| 2153 | |
| 2154 | info_dict = { |
| 2155 | "error_name": error_name, |
| 2156 | "error_result": error_result, |
| 2157 | "error_reason": error_reason, |
| 2158 | "param_reqs": param_reqs |
| 2159 | } |
| 2160 | return info_dict |
| 2161 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 2162 | @staticmethod |
| 2163 | def evArgmaxOutputShapeMismatch(check=False, **kwargs): |
| 2164 | error_name = ErrorIf.ArgmaxOutputShapeMismatch |
| 2165 | param_reqs = {"rank": [2,4], "dtype": None, "shape": None} |
| 2166 | error_result = False |
| 2167 | error_reason = "Mismatch between output shape provided and expected output shape" |
| 2168 | |
| 2169 | if check: |
| 2170 | output_shape = kwargs['output_shape'] |
| 2171 | input_shape = kwargs['input_shape'] |
| 2172 | axis = kwargs['axis'] |
| 2173 | |
| 2174 | dimension_match = True |
| 2175 | axis_shift = 0 |
| 2176 | |
| 2177 | # Check that rank is correct before trying to check dimensions |
| 2178 | if (len(input_shape) - 1) == len(output_shape): |
| 2179 | for i in range(len(input_shape)): |
| 2180 | if i == axis: |
| 2181 | axis_shift = 1 |
| 2182 | continue |
| 2183 | if input_shape[i] != output_shape[i - axis_shift]: |
| 2184 | dimension_match = False |
| 2185 | |
| 2186 | if not dimension_match: |
| 2187 | error_result = True |
| 2188 | |
| 2189 | info_dict = { |
| 2190 | "error_name": error_name, |
| 2191 | "error_result": error_result, |
| 2192 | "error_reason": error_reason, |
| 2193 | "param_reqs": param_reqs |
| 2194 | } |
| 2195 | return info_dict |
| 2196 | |
| 2197 | @staticmethod |
| 2198 | def evArgmaxOutputRankMismatch(check=False, **kwargs): |
| 2199 | error_name = ErrorIf.ArgmaxOutputRankMismatch |
| 2200 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2201 | error_result = False |
| 2202 | error_reason = "Mismatch between output shape provided and expected output shape" |
| 2203 | |
| 2204 | if check: |
| 2205 | output_shape = kwargs['output_shape'] |
| 2206 | input_shape = kwargs['input_shape'] |
| 2207 | axis = kwargs['axis'] |
| 2208 | valid_params = axis >= 0 and axis < len(input_shape) |
| 2209 | |
| 2210 | if valid_params and (len(input_shape) - 1) != len(output_shape): |
| 2211 | error_result = True |
| 2212 | |
| 2213 | info_dict = { |
| 2214 | "error_name": error_name, |
| 2215 | "error_result": error_result, |
| 2216 | "error_reason": error_reason, |
| 2217 | "param_reqs": param_reqs |
| 2218 | } |
| 2219 | return info_dict |
| 2220 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2221 | |
| 2222 | @staticmethod |
| 2223 | def evKernelSmallerOne(check=False, **kwargs): |
| 2224 | error_name = ErrorIf.KernelSmallerOne |
| 2225 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2226 | error_result = False |
| 2227 | error_reason = "At least one kernel dimension is smaller than zero" |
| 2228 | |
| 2229 | if check: |
| 2230 | kernel = kwargs['kernel'] |
| 2231 | if min(kernel) < 1: |
| 2232 | error_result = True |
| 2233 | |
| 2234 | info_dict = { |
| 2235 | "error_name": error_name, |
| 2236 | "error_result": error_result, |
| 2237 | "error_reason": error_reason, |
| 2238 | "param_reqs": param_reqs |
| 2239 | } |
| 2240 | return info_dict |
| 2241 | |
| 2242 | @staticmethod |
| 2243 | def evStrideSmallerOne(check=False, **kwargs): |
| 2244 | error_name = ErrorIf.StrideSmallerOne |
| 2245 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2246 | error_result = False |
| 2247 | error_reason = "At least one stride dimension is smaller than zero" |
| 2248 | |
| 2249 | if check: |
| 2250 | stride = kwargs['stride'] |
| 2251 | if min(stride) < 1: |
| 2252 | error_result = True |
| 2253 | |
| 2254 | info_dict = { |
| 2255 | "error_name": error_name, |
| 2256 | "error_result": error_result, |
| 2257 | "error_reason": error_reason, |
| 2258 | "param_reqs": param_reqs |
| 2259 | } |
| 2260 | return info_dict |
| 2261 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 2262 | @staticmethod |
| 2263 | def evScaleTrue(check=False, **kwargs): |
| 2264 | error_name = ErrorIf.ScaleTrue |
| 2265 | param_reqs = {"rank": None, "dtype": [DType.INT48], "shape": None} |
| 2266 | error_result = False |
| 2267 | error_reason = "Scale set to true but input type is INT48" |
| 2268 | |
| 2269 | if check: |
| 2270 | input_dtype = kwargs['input_dtype'] |
| 2271 | scale32 = kwargs['scale32'] |
| 2272 | if scale32 and input_dtype == DType.INT48: |
| 2273 | error_result = True |
| 2274 | |
| 2275 | info_dict = { |
| 2276 | "error_name": error_name, |
| 2277 | "error_result": error_result, |
| 2278 | "error_reason": error_reason, |
| 2279 | "param_reqs": param_reqs |
| 2280 | } |
| 2281 | return info_dict |
| 2282 | |
| 2283 | @staticmethod |
| 2284 | def evScaleNotTrue(check=False, **kwargs): |
| 2285 | error_name = ErrorIf.ScaleNotTrue |
| 2286 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2287 | error_result = False |
| 2288 | error_reason = "Scale set to false but double round set to true" |
| 2289 | |
| 2290 | if check: |
| 2291 | scale32 = kwargs['scale32'] |
| 2292 | double_round = kwargs['double_round'] |
| 2293 | if not scale32 and double_round: |
| 2294 | error_result = True |
| 2295 | |
| 2296 | info_dict = { |
| 2297 | "error_name": error_name, |
| 2298 | "error_result": error_result, |
| 2299 | "error_reason": error_reason, |
| 2300 | "param_reqs": param_reqs |
| 2301 | } |
| 2302 | return info_dict |
| 2303 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 2304 | @staticmethod |
| 2305 | def evTensorSizeInputOutputMismatch(check=False, **kwargs): |
| 2306 | error_name = ErrorIf.TensorSizeInputOutputMismatch |
| 2307 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2308 | error_result = False |
| 2309 | error_reason = "Input tensor size does not match output tensor size" |
| 2310 | |
| 2311 | if check: |
| 2312 | input_shape = kwargs['input_shape'] |
| 2313 | output_shape = kwargs['output_shape'] |
| 2314 | input_size = np.prod(input_shape) |
| 2315 | output_size = np.prod(output_shape) |
| 2316 | if input_size != output_size: |
| 2317 | error_result = True |
| 2318 | |
| 2319 | info_dict = { |
| 2320 | "error_name": error_name, |
| 2321 | "error_result": error_result, |
| 2322 | "error_reason": error_reason, |
| 2323 | "param_reqs": param_reqs |
| 2324 | } |
| 2325 | return info_dict |
| 2326 | |
| 2327 | @staticmethod |
| 2328 | def evStartSmallerZero(check=False, **kwargs): |
| 2329 | error_name = ErrorIf.StartSmallerZero |
| 2330 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2331 | error_result = False |
| 2332 | error_reason = "Starting point smaller than zero" |
| 2333 | |
| 2334 | if check: |
| 2335 | input_shape = kwargs['input_shape'] |
| 2336 | start = kwargs['start'] |
| 2337 | rank = len(input_shape) |
| 2338 | if len(start) == rank: |
| 2339 | for index in range(rank): |
| 2340 | if start[index] < 0: |
| 2341 | error_result = True |
| 2342 | |
| 2343 | info_dict = { |
| 2344 | "error_name": error_name, |
| 2345 | "error_result": error_result, |
| 2346 | "error_reason": error_reason, |
| 2347 | "param_reqs": param_reqs |
| 2348 | } |
| 2349 | return info_dict |
| 2350 | |
| 2351 | |
| 2352 | @staticmethod |
| 2353 | def evSizeSmallerEqualZero(check=False, **kwargs): |
| 2354 | error_name = ErrorIf.SizeSmallerEqualZero |
| 2355 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2356 | error_result = False |
| 2357 | error_reason = "Size smaller than or equal to zero" |
| 2358 | |
| 2359 | if check: |
| 2360 | input_shape = kwargs['input_shape'] |
| 2361 | size = kwargs['size'] |
| 2362 | rank = len(input_shape) |
| 2363 | if len(size) == rank: |
| 2364 | for index in range(rank): |
| 2365 | if size[index] <= 0: |
| 2366 | error_result = True |
| 2367 | |
| 2368 | info_dict = { |
| 2369 | "error_name": error_name, |
| 2370 | "error_result": error_result, |
| 2371 | "error_reason": error_reason, |
| 2372 | "param_reqs": param_reqs |
| 2373 | } |
| 2374 | return info_dict |
| 2375 | |
| 2376 | |
| 2377 | @staticmethod |
| 2378 | def evStartSizeOutsideBounds(check=False, **kwargs): |
| 2379 | error_name = ErrorIf.StartSizeOutsideBounds |
| 2380 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2381 | error_result = False |
| 2382 | error_reason = "starting point plus size larger than input dimension" |
| 2383 | |
| 2384 | if check: |
| 2385 | input_shape = kwargs['input_shape'] |
| 2386 | start = kwargs['start'] |
| 2387 | size = kwargs['size'] |
| 2388 | rank = len(input_shape) |
| 2389 | if len(start) == rank and len(size) == rank: |
| 2390 | for index in range(rank): |
| 2391 | if start[index] + size[index] > input_shape[index]: |
| 2392 | error_result = True |
| 2393 | |
| 2394 | info_dict = { |
| 2395 | "error_name": error_name, |
| 2396 | "error_result": error_result, |
| 2397 | "error_reason": error_reason, |
| 2398 | "param_reqs": param_reqs |
| 2399 | } |
| 2400 | return info_dict |
| 2401 | |
| 2402 | |
| 2403 | @staticmethod |
| 2404 | def evSizeOutputShapeMismatch(check=False, **kwargs): |
| 2405 | error_name = ErrorIf.SizeOutputShapeMismatch |
| 2406 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2407 | error_result = False |
| 2408 | error_reason = "Size does not match output dimension" |
| 2409 | |
| 2410 | if check: |
| 2411 | input_shape = kwargs['input_shape'] |
| 2412 | output_shape = kwargs['output_shape'] |
| 2413 | size = kwargs['size'] |
| 2414 | rank = len(input_shape) |
| 2415 | if len(size) == rank: |
| 2416 | for index in range(rank): |
| 2417 | if size[index] != output_shape[index]: |
| 2418 | error_result = True |
| 2419 | |
| 2420 | info_dict = { |
| 2421 | "error_name": error_name, |
| 2422 | "error_result": error_result, |
| 2423 | "error_reason": error_reason, |
| 2424 | "param_reqs": param_reqs |
| 2425 | } |
| 2426 | return info_dict |
| 2427 | |
| 2428 | @staticmethod |
| 2429 | def evInputSizeStartLengthMismatch(check=False, **kwargs): |
| 2430 | error_name = ErrorIf.InputSizeStartLengthMismatch |
| 2431 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2432 | error_result = False |
| 2433 | error_reason = "rank of input not equal to length of start or size" |
| 2434 | |
| 2435 | if check: |
| 2436 | input_shape = kwargs['input_shape'] |
| 2437 | start = kwargs['start'] |
| 2438 | size = kwargs['size'] |
| 2439 | rank = len(input_shape) |
| 2440 | if rank != len(start) or rank != len(size): |
| 2441 | error_result = True |
| 2442 | |
| 2443 | info_dict = { |
| 2444 | "error_name": error_name, |
| 2445 | "error_result": error_result, |
| 2446 | "error_reason": error_reason, |
| 2447 | "param_reqs": param_reqs |
| 2448 | } |
| 2449 | return info_dict |
| 2450 | |
| 2451 | @staticmethod |
| 2452 | def evIndexOutsideBounds(check=False, **kwargs): |
| 2453 | error_name = ErrorIf.IndexOutsideBounds |
| 2454 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2455 | error_result = False |
| 2456 | error_reason = "Index outside of allowed bounds" |
| 2457 | |
| 2458 | if check: |
| 2459 | input_shape = kwargs['input_shape'] |
| 2460 | perms = kwargs['perms'] |
| 2461 | rank = len(input_shape) |
| 2462 | |
| 2463 | for index in perms: |
| 2464 | if index < 0 or index > rank: |
| 2465 | error_result = True |
| 2466 | |
| 2467 | info_dict = { |
| 2468 | "error_name": error_name, |
| 2469 | "error_result": error_result, |
| 2470 | "error_reason": error_reason, |
| 2471 | "param_reqs": param_reqs |
| 2472 | } |
| 2473 | return info_dict |
| 2474 | |
| 2475 | @staticmethod |
| 2476 | def evIndexUsedTwice(check=False, **kwargs): |
| 2477 | error_name = ErrorIf.IndexUsedTwice |
| 2478 | param_reqs = {"rank": [2,4], "dtype": None, "shape": None} |
| 2479 | error_result = False |
| 2480 | error_reason = "Index used multiple times" |
| 2481 | |
| 2482 | if check: |
| 2483 | input_shape = kwargs['input_shape'] |
| 2484 | perms = kwargs['perms'] |
| 2485 | rank = len(input_shape) |
| 2486 | |
| 2487 | unique_indices = [] |
| 2488 | for index in perms: |
| 2489 | if index in unique_indices: |
| 2490 | error_result = True |
| 2491 | else: |
| 2492 | unique_indices.append(index) |
| 2493 | |
| 2494 | info_dict = { |
| 2495 | "error_name": error_name, |
| 2496 | "error_result": error_result, |
| 2497 | "error_reason": error_reason, |
| 2498 | "param_reqs": param_reqs |
| 2499 | } |
| 2500 | return info_dict |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2501 | |
| 2502 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2503 | class TosaInvalidValidator: |
| 2504 | |
| 2505 | @staticmethod |
| 2506 | def ivWrongDataTypeOrModeResize(**kwargs): |
| 2507 | input_dtype = kwargs["input_dtype"] |
| 2508 | args = kwargs["args"] |
| 2509 | mode = args[0] |
| 2510 | stride = args[1] |
| 2511 | stride_fp = args[4] |
| 2512 | output_dtype = args[8] |
| 2513 | |
| 2514 | if mode == ResizeMode.BILINEAR: |
| 2515 | # Invalid output data type / Invalid input datatype |
| 2516 | return ( |
| 2517 | not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or |
| 2518 | not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or |
| 2519 | not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or |
| 2520 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 2521 | ) |
| 2522 | elif mode == ResizeMode.NEAREST: |
| 2523 | # Invalid output data type / Invalid input datatype |
| 2524 | return ( |
| 2525 | (input_dtype != output_dtype) or |
| 2526 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 2527 | ) |
| 2528 | else: |
| 2529 | # Invalid resize mode |
| 2530 | return True |
| 2531 | |
| 2532 | @staticmethod |
| 2533 | def ivBadStride(**kwargs): |
| 2534 | input_dtype = kwargs["input_dtype"] |
| 2535 | args = kwargs["args"] |
| 2536 | stride_x = args[1][0] |
| 2537 | stride_y = args[1][1] |
| 2538 | stride_fp_x = args[4][0] |
| 2539 | stride_fp_y = args[4][1] |
| 2540 | |
| 2541 | if input_dtype == DType.FLOAT: |
| 2542 | if stride_fp_x <= 0 or stride_fp_y <= 0: |
| 2543 | # Negative or zero stride |
| 2544 | return True |
| 2545 | else: |
| 2546 | if stride_x <= 0 or stride_y <= 0: |
| 2547 | # Negative or zero stride |
| 2548 | return True |
| 2549 | return False |
| 2550 | |
| 2551 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2552 | @staticmethod |
| 2553 | def ivHeightWidthSmallerZero(**kwargs): |
| 2554 | opName = kwargs['opName'] |
| 2555 | |
| 2556 | inputShapes = kwargs['shapeList'] |
| 2557 | input = inputShapes[0] |
| 2558 | if not opName.endswith("pool2d"): |
| 2559 | filter = inputShapes[1] |
| 2560 | |
| 2561 | args = kwargs['args'] |
| 2562 | strides = args[0] |
| 2563 | padding = args[1] |
| 2564 | dilations = args[2] |
| 2565 | if opName.endswith("pool2d"): |
| 2566 | kernel = args[2] |
| 2567 | |
| 2568 | if opName.startswith('conv2d'): |
| 2569 | h = ( |
| 2570 | input[1] |
| 2571 | - filter[1] |
| 2572 | - (filter[1] - 1) * (dilations[0] - 1) |
| 2573 | + padding[0] |
| 2574 | + padding[1] |
| 2575 | ) // strides[0] + 1 |
| 2576 | |
| 2577 | w = ( |
| 2578 | input[2] |
| 2579 | - filter[2] |
| 2580 | - (filter[2] - 1) * (dilations[1] - 1) |
| 2581 | + padding[2] |
| 2582 | + padding[3] |
| 2583 | ) // strides[1] + 1 |
| 2584 | elif opName.startswith("depthwise_conv2d"): |
| 2585 | h = ( |
| 2586 | input[1] |
| 2587 | - filter[0] |
| 2588 | - (filter[0] - 1) * (dilations[0] - 1) |
| 2589 | + padding[0] |
| 2590 | + padding[1] |
| 2591 | ) // strides[0] + 1 |
| 2592 | |
| 2593 | w = ( |
| 2594 | input[2] |
| 2595 | - filter[1] |
| 2596 | - (filter[1] - 1) * (dilations[1] - 1) |
| 2597 | + padding[2] |
| 2598 | + padding[3] |
| 2599 | ) // strides[1] + 1 |
| 2600 | elif opName.endswith("pool2d"): |
| 2601 | h = (input[1] + padding[0] + padding[1] + strides[0] - kernel[0]) // strides[0] |
| 2602 | w = (input[2] + padding[2] + padding[3] + strides[1] - kernel[1]) // strides[1] |
| 2603 | else: |
| 2604 | assert False, "Unrecognized Op" |
| 2605 | |
| 2606 | if h <= 0 or w <= 0: |
| 2607 | # Invalid parameter combination |
| 2608 | return True |
| 2609 | return False |
| 2610 | |
| 2611 | @staticmethod |
| 2612 | def ivNonPositiveOutputShape(**kwargs): |
| 2613 | args = kwargs['args'] |
| 2614 | output_shape = args[3] |
| 2615 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
| 2616 | # Negative output shape |
| 2617 | return True |
| 2618 | return False |
| 2619 | |
| 2620 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2621 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2622 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2623 | # Maximum rank of tensor supported by test generator. |
| 2624 | TOSA_TENSOR_MAX_RANK = 6 |
| 2625 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2626 | def __init__(self, args): |
| 2627 | self.args = args |
| 2628 | self.basePath = args.output_dir |
| 2629 | self.random_seed = args.random_seed |
| 2630 | self.ser = None |
| 2631 | self.rng = np.random.default_rng(self.random_seed) |
| 2632 | self.createDynamicOpLists() |
| 2633 | self.initOpListDefaults() |
| 2634 | self.quantGen = TosaQuantGen() |
| 2635 | # Force makeShape to do a specific starting shape |
| 2636 | self.targetted_shape = None |
| 2637 | |
| 2638 | def createSerializer(self, opName, testPath): |
| 2639 | self.testPath = os.path.join(opName, testPath) |
| 2640 | |
| 2641 | fullPath = os.path.join(self.basePath, self.testPath) |
| 2642 | os.makedirs(fullPath, exist_ok=True) |
| 2643 | self.ser = ts.TosaSerializer(fullPath) |
| 2644 | |
| 2645 | def getSerializer(self): |
| 2646 | return self.ser |
| 2647 | |
| 2648 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2649 | with open( |
| 2650 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 2651 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2652 | fd.write(self.ser.serialize()) |
| 2653 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2654 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 2655 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2656 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2657 | def resetRNG(self, seed=None): |
| 2658 | if seed == None: |
| 2659 | seed = self.random_seed + 1 |
| 2660 | self.rng = np.random.default_rng(seed) |
| 2661 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2662 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2663 | if dtype == DType.BOOL: |
| 2664 | np_dt = np.bool |
| 2665 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2666 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2667 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2668 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2669 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2670 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 2671 | elif dtype == DType.UINT8: |
| 2672 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2673 | elif dtype == DType.INT16: |
| 2674 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 2675 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2676 | return np.int32( |
| 2677 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 2678 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2679 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2680 | return np.int64( |
| 2681 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 2682 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2683 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2684 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2685 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2686 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2687 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2688 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2689 | placeholders = [] |
| 2690 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2691 | assert len(shape_list) == len(dtype_list) |
| 2692 | |
| 2693 | for idx, shape in enumerate(shape_list): |
| 2694 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 2695 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2696 | |
| 2697 | return placeholders |
| 2698 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2699 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2700 | consts = [] |
| 2701 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2702 | assert len(shape_list) == len(dtype_list) |
| 2703 | |
| 2704 | for idx, shape in enumerate(shape_list): |
| 2705 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 2706 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2707 | |
| 2708 | return consts |
| 2709 | |
| 2710 | def makeShape(self, rank): |
| 2711 | if self.targetted_shape: |
| 2712 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2713 | return np.int32( |
| 2714 | self.rng.integers( |
| 2715 | low=self.args.tensor_shape_range[0], |
| 2716 | high=self.args.tensor_shape_range[1], |
| 2717 | size=rank, |
| 2718 | ) |
| 2719 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2720 | |
| 2721 | def setTargetShape(self, shape): |
| 2722 | self.targetted_shape = shape |
| 2723 | |
| 2724 | def randInt(self, low=0, high=256): |
| 2725 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 2726 | |
| 2727 | def getRandNumberDType(self, dtype): |
| 2728 | if dtype == DType.FLOAT: |
| 2729 | return self.rng.random() |
| 2730 | elif dtype == DType.BOOL: |
| 2731 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2732 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2733 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2734 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2735 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2736 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2737 | elif dtype == DType.INT16: |
| 2738 | low, high = (-32768, 32768) |
| 2739 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2740 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2741 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2742 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2743 | # Special size |
| 2744 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 2745 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2746 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2747 | |
| 2748 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 2749 | |
| 2750 | def shapeStr(self, shape): |
| 2751 | |
| 2752 | sStr = [] |
| 2753 | # Convert to strings |
| 2754 | for i in shape: |
| 2755 | sStr.append(str(i)) |
| 2756 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2757 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2758 | |
| 2759 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2760 | if isinstance(t, list): |
| 2761 | assert len(t) >= 2 |
| 2762 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2763 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2764 | if t == DType.BOOL: |
| 2765 | return "b" |
| 2766 | elif t == DType.INT4: |
| 2767 | return "i4" |
| 2768 | elif t == DType.INT8: |
| 2769 | return "i8" |
| 2770 | elif t == DType.UINT8: |
| 2771 | return "u8" |
| 2772 | elif t == DType.INT16: |
| 2773 | return "i16" |
| 2774 | elif t == DType.INT32: |
| 2775 | return "i32" |
| 2776 | elif t == DType.INT48: |
| 2777 | return "i48" |
| 2778 | elif t == DType.FLOAT: |
| 2779 | return "float" |
| 2780 | else: |
| 2781 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2782 | |
| 2783 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2784 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2785 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2786 | return 4 |
| 2787 | elif t == DType.INT8: |
| 2788 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2789 | elif t == DType.UINT8: |
| 2790 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2791 | elif t == DType.INT16: |
| 2792 | return 16 |
| 2793 | elif t == DType.INT32: |
| 2794 | return 32 |
| 2795 | elif t == DType.INT48: |
| 2796 | return 48 |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 2797 | elif t == DType.FLOAT: |
| 2798 | return 32 |
| 2799 | elif t == DType.BOOL: |
| 2800 | return 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2801 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2802 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2803 | |
| 2804 | # Argument generators |
| 2805 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 2806 | # Where the string descriptor is used to generate the test name and |
| 2807 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 2808 | # build function |
| 2809 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2810 | def build_unary(self, op, a, validator_fcns=None, error_name=None, qinfo=None): |
| 2811 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| 2812 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2813 | # build_placeholder returns an int, ABS/other ops does not |
| 2814 | if isinstance(op, int): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2815 | self.ser.addOperator(op, a.name, result_tens.name, None, qinfo) |
| 2816 | return result_tens |
| 2817 | elif op['op'] == Op.IDENTITY: |
| 2818 | self.ser.addOperator(op['op'], a.name, result_tens.name, None, qinfo) |
| 2819 | return result_tens |
| 2820 | |
| 2821 | # Ensure new output type has correct qinfo |
| 2822 | if error_name == ErrorIf.WrongOutputType: |
| 2823 | if result_tens.dtype not in [DType.INT8, DType.UINT8]: |
| 2824 | qinfo = ts.TosaSerializerQuantInfo() |
| 2825 | qinfo.UnaryQuantInfo( |
| 2826 | TosaQuantGen.getQinfo(self, a.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) |
| 2827 | ) |
| 2828 | |
| 2829 | # Invalidate Input/Output list for error if checks. |
| 2830 | input_list = [a.name] |
| 2831 | output_list = [result_tens.name] |
| 2832 | pCount, cCount = op["operands"] |
| 2833 | num_operands = pCount + cCount |
| 2834 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2835 | |
| 2836 | TosaErrorValidator.evValidateErrorIfs( |
| 2837 | self.ser, |
| 2838 | validator_fcns, |
| 2839 | error_name, |
| 2840 | op=op, |
| 2841 | input_dtype=a.dtype, |
| 2842 | output_dtype=result_tens.dtype, |
| 2843 | qinfo = qinfo, |
| 2844 | result_tensor = result_tens, |
| 2845 | input_list=input_list, |
| 2846 | output_list=output_list, |
| 2847 | num_operands=num_operands, |
| 2848 | ) |
| 2849 | |
| 2850 | self.ser.addOperator(op['op'], input_list, output_list, None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2851 | return result_tens |
| 2852 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2853 | def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None): |
| 2854 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b, error_name) |
| 2855 | |
| 2856 | |
| 2857 | # Invalidate Input/Output list for error if checks. |
| 2858 | input_list = [a.name, b.name] |
| 2859 | output_list = [result_tens.name] |
| 2860 | pCount, cCount = op["operands"] |
| 2861 | num_operands = pCount + cCount |
| 2862 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2863 | |
| 2864 | TosaErrorValidator.evValidateErrorIfs( |
| 2865 | self.ser, |
| 2866 | validator_fcns, |
| 2867 | error_name, |
| 2868 | op=op, |
| 2869 | input1 = a, |
| 2870 | input2 = b, |
| 2871 | input_dtype = a.dtype, |
| 2872 | output_dtype = result_tens.dtype, |
| 2873 | result_tensor = result_tens, |
| 2874 | input_list=input_list, |
| 2875 | output_list=output_list, |
| 2876 | num_operands=num_operands, |
| 2877 | ) |
| 2878 | |
| 2879 | self.ser.addOperator(op['op'], input_list, output_list) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2880 | return result_tens |
| 2881 | |
| 2882 | def build_binary_nonbroadcast(self, op, a, b): |
| 2883 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2884 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2885 | return result_tens |
| 2886 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2887 | def build_arithmetic_right_shift(self, op, a, b, round): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2888 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2889 | |
| 2890 | attr = ts.TosaSerializerAttribute() |
| 2891 | attr.ArithmeticRightShiftAttribute(round) |
| 2892 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2893 | 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] | 2894 | return result_tens |
| 2895 | |
| 2896 | def build_mul(self, op, a, b, shift): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2897 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2898 | |
| 2899 | # Special for multiply: |
| 2900 | # Force the result to INT32 for INT types |
| 2901 | if a.dtype != DType.FLOAT: |
| 2902 | result_tens.setDtype(DType.INT32) |
| 2903 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2904 | attr = ts.TosaSerializerAttribute() |
| 2905 | attr.MulAttribute(shift) |
| 2906 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2907 | 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] | 2908 | return result_tens |
| 2909 | |
| 2910 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2911 | # Constant size depending on type, random values |
| 2912 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2913 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2914 | table_arr = self.getRandTensor([513], table_dtype) |
| 2915 | else: |
| 2916 | assert a.dtype == DType.INT8 |
| 2917 | table_dtype = DType.INT8 |
| 2918 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2919 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2920 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 2921 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2922 | 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] | 2923 | |
| 2924 | return result_tens |
| 2925 | |
| 2926 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2927 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2928 | 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] | 2929 | return result_tens |
| 2930 | |
| 2931 | def build_comparison(self, op, a, b): |
| 2932 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2933 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2934 | return result_tens |
| 2935 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 2936 | def build_argmax(self, op, a, axis, validator_fcns, error_name): |
| 2937 | result_tens = OutputShaper.argmaxOp(self.ser, self.rng, a, axis, error_name) |
| 2938 | |
| 2939 | # Invalidate Input/Output list for error if checks. |
| 2940 | input_list = [a.name] |
| 2941 | output_list = [result_tens.name] |
| 2942 | pCount, cCount = op["operands"] |
| 2943 | num_operands = pCount + cCount |
| 2944 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2945 | |
| 2946 | TosaErrorValidator.evValidateErrorIfs( |
| 2947 | self.ser, |
| 2948 | validator_fcns, |
| 2949 | error_name, |
| 2950 | op=op, |
| 2951 | axis=axis, |
| 2952 | input_shape = a.shape, |
| 2953 | input_dtype = a.dtype, |
| 2954 | output_shape = result_tens.shape, |
| 2955 | output_dtype = result_tens.dtype, |
| 2956 | result_tensor = result_tens, |
| 2957 | input_list=input_list, |
| 2958 | output_list=output_list, |
| 2959 | num_operands=num_operands, |
| 2960 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2961 | |
| 2962 | attr = ts.TosaSerializerAttribute() |
| 2963 | attr.AxisAttribute(axis) |
| 2964 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 2965 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2966 | return result_tens |
| 2967 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2968 | def build_pool2d(self, op, input, stride, pad, kernel, validator_fcns=None, error_name=None, qinfo=None): |
| 2969 | result_tens = OutputShaper.pool2dOp(self.ser, self.rng, input, kernel, stride, pad, error_name) |
| 2970 | |
| 2971 | # Ensure new output type has correct qinfo |
| 2972 | if error_name == ErrorIf.WrongInputType: |
| 2973 | if input.dtype not in [DType.INT8, DType.UINT8]: |
| 2974 | qinfo = ts.TosaSerializerQuantInfo() |
| 2975 | qinfo.UnaryQuantInfo( |
| 2976 | TosaQuantGen.getQinfo(self, input.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) |
| 2977 | ) |
| 2978 | |
| 2979 | # Invalidate Input/Output list for error if checks. |
| 2980 | input_list = [input.name] |
| 2981 | output_list = [result_tens.name] |
| 2982 | pCount, cCount = op["operands"] |
| 2983 | num_operands = pCount + cCount |
| 2984 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2985 | |
| 2986 | TosaErrorValidator.evValidateErrorIfs( |
| 2987 | self.ser, |
| 2988 | validator_fcns, |
| 2989 | error_name, |
| 2990 | op=op, |
| 2991 | input_shape=input.shape, |
| 2992 | input_dtype=input.dtype, |
| 2993 | output_shape=result_tens.shape, |
| 2994 | output_dtype=result_tens.dtype, |
| 2995 | kernel=kernel, |
| 2996 | stride=stride, |
| 2997 | pad=pad, |
| 2998 | qinfo = qinfo, |
| 2999 | result_tensor = result_tens, |
| 3000 | input_list=input_list, |
| 3001 | output_list=output_list, |
| 3002 | num_operands=num_operands, |
| 3003 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3004 | |
| 3005 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3006 | attr.PoolAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3007 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 3008 | self.ser.addOperator(op['op'], input_list, output_list, attr, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3009 | return result_tens |
| 3010 | |
| 3011 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3012 | assert len(padding) == 4 |
| 3013 | result_tens = OutputShaper.conv2dOp( |
| 3014 | self.ser, ifm, filter, strides, padding, dilations |
| 3015 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3016 | |
| 3017 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3018 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3019 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3020 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3021 | 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] | 3022 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3023 | return result_tens |
| 3024 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3025 | def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
| 3026 | assert len(padding) == 6 |
| 3027 | result_tens = OutputShaper.conv3dOp( |
| 3028 | self.ser, ifm, filter, strides, padding, dilations |
| 3029 | ) |
| 3030 | |
| 3031 | attr = ts.TosaSerializerAttribute() |
| 3032 | attr.ConvAttribute(padding, strides, dilations) |
| 3033 | |
| 3034 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3035 | 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] | 3036 | ) |
| 3037 | return result_tens |
| 3038 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3039 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3040 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3041 | ): |
| 3042 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3043 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 3044 | |
| 3045 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3046 | attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3047 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3048 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3049 | 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] | 3050 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3051 | return result_tens |
| 3052 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3053 | def build_depthwise_conv2d( |
| 3054 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 3055 | ): |
| 3056 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 3057 | self.ser, ifm, filter, strides, padding, dilations |
| 3058 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3059 | |
| 3060 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3061 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3062 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3063 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3064 | 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] | 3065 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3066 | return result_tens |
| 3067 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 3068 | def build_fully_connected(self, op, ifm, filter, bias, validator_fcns=None, error_name=None, qinfo=None): |
| 3069 | result_tens = OutputShaper.fullyConnectedOp(self.ser, self.rng, ifm, filter, error_name) |
| 3070 | |
| 3071 | # Invalidate Input/Output list for error if checks. |
| 3072 | input_list = [ifm.name, filter.name, bias.name] |
| 3073 | output_list = [result_tens.name] |
| 3074 | pCount, cCount = op["operands"] |
| 3075 | num_operands = pCount + cCount |
| 3076 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3077 | |
| 3078 | TosaErrorValidator.evValidateErrorIfs( |
| 3079 | self.ser, |
| 3080 | validator_fcns, |
| 3081 | error_name, |
| 3082 | op=op, |
| 3083 | input_shape=ifm.shape, |
| 3084 | input_dtype=ifm.dtype, |
| 3085 | weight_dtype=filter.dtype, |
| 3086 | output_shape=result_tens.shape, |
| 3087 | output_dtype=result_tens.dtype, |
| 3088 | qinfo = qinfo, |
| 3089 | result_tensor = result_tens, |
| 3090 | input_list=input_list, |
| 3091 | output_list=output_list, |
| 3092 | num_operands=num_operands, |
| 3093 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3094 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3095 | self.ser.addOperator( |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 3096 | op['op'], input_list, output_list, None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3097 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3098 | return result_tens |
| 3099 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 3100 | def build_matmul(self, op, a, b, validator_fcns=None, error_name=None, qinfo=None): |
| 3101 | result_tens = OutputShaper.matmulOp(self.ser, self.rng, a, b, error_name) |
| 3102 | |
| 3103 | # Invalidate Input/Output list for error if checks. |
| 3104 | input_list = [a.name, b.name] |
| 3105 | output_list = [result_tens.name] |
| 3106 | pCount, cCount = op["operands"] |
| 3107 | num_operands = pCount + cCount |
| 3108 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3109 | |
| 3110 | TosaErrorValidator.evValidateErrorIfs( |
| 3111 | self.ser, |
| 3112 | validator_fcns, |
| 3113 | error_name, |
| 3114 | op=op, |
| 3115 | input_shape=a.shape, |
| 3116 | input_dtype=a.dtype, |
| 3117 | input2_shape=b.shape, |
| 3118 | input2_dtype=b.dtype, |
| 3119 | output_shape=result_tens.shape, |
| 3120 | output_dtype=result_tens.dtype, |
| 3121 | qinfo = qinfo, |
| 3122 | result_tensor = result_tens, |
| 3123 | input_list=input_list, |
| 3124 | output_list=output_list, |
| 3125 | num_operands=num_operands, |
| 3126 | ) |
| 3127 | |
| 3128 | self.ser.addOperator(op['op'], input_list, output_list, None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3129 | return result_tens |
| 3130 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 3131 | def build_reduce(self, op, a, axis, validator_fcns, error_name=None): |
| 3132 | result_tens = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name) |
| 3133 | |
| 3134 | # Invalidate Input/Output list for error if checks. |
| 3135 | input_list = [a.name] |
| 3136 | output_list = [result_tens.name] |
| 3137 | pCount, cCount = op["operands"] |
| 3138 | num_operands = pCount + cCount |
| 3139 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3140 | |
| 3141 | TosaErrorValidator.evValidateErrorIfs( |
| 3142 | self.ser, |
| 3143 | validator_fcns, |
| 3144 | error_name, |
| 3145 | op=op, |
| 3146 | axis = axis, |
| 3147 | input_shape = a.shape, |
| 3148 | output_shape = result_tens.shape, |
| 3149 | input_dtype = a.dtype, |
| 3150 | output_dtype = result_tens.dtype, |
| 3151 | result_tensor = result_tens, |
| 3152 | input_list=input_list, |
| 3153 | output_list=output_list, |
| 3154 | num_operands=num_operands, |
| 3155 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3156 | |
| 3157 | attr = ts.TosaSerializerAttribute() |
| 3158 | attr.AxisAttribute(axis) |
| 3159 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 3160 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3161 | return result_tens |
| 3162 | |
| 3163 | def build_clamp(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3164 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3165 | |
| 3166 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 3167 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3168 | |
| 3169 | if a.dtype == DType.FLOAT: |
| 3170 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 3171 | else: |
| 3172 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 3173 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3174 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3175 | return result_tens |
| 3176 | |
| 3177 | def build_leaky_relu(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3178 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3179 | attr = ts.TosaSerializerAttribute() |
| 3180 | |
| 3181 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 3182 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3183 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3184 | return result_tens |
| 3185 | |
| 3186 | # Needs an additional type/input |
| 3187 | def build_prelu(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3188 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3189 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3190 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3191 | return result_tens |
| 3192 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3193 | def build_sigmoid(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3194 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3195 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3196 | return result_tens |
| 3197 | |
| 3198 | def build_tanh(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3199 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3200 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3201 | return result_tens |
| 3202 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3203 | def build_concat(self, op, *a): |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3204 | assert type(a[-1]) == int |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3205 | |
| 3206 | # To store variable length list of input tensors we need to store axis along with it |
| 3207 | axis = a[-1] |
| 3208 | a = a[:-1] |
| 3209 | |
| 3210 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3211 | |
| 3212 | attr = ts.TosaSerializerAttribute() |
| 3213 | attr.AxisAttribute(axis) |
| 3214 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3215 | input_tensor_names = [] |
| 3216 | for tensor in a: |
| 3217 | input_tensor_names.append(tensor.name) |
| 3218 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3219 | self.ser.addOperator(op['op'], input_tensor_names, [result_tens.name], attr) |
| 3220 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3221 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3222 | def build_pad(self, op, a, padding, validator_fcns=None, error_name=None, qinfo=None): |
| 3223 | result_tens = OutputShaper.padOp(self.ser, self.rng, a, padding, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3224 | |
| 3225 | # Need to turn the padding array into a TOSA tensor here. |
| 3226 | # This is one of the few tensor operands that does not get |
| 3227 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3228 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3229 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3230 | # Invalidate Input/Output list for error if checks. |
| 3231 | input_list = [a.name, padding_tens.name] |
| 3232 | output_list = [result_tens.name] |
| 3233 | pCount, cCount = op["operands"] |
| 3234 | num_operands = pCount + cCount |
| 3235 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3236 | |
| 3237 | TosaErrorValidator.evValidateErrorIfs( |
| 3238 | self.ser, |
| 3239 | validator_fcns, |
| 3240 | error_name, |
| 3241 | op=op, |
| 3242 | input_shape = a.shape, |
| 3243 | output_shape = result_tens.shape, |
| 3244 | input_dtype = a.dtype, |
| 3245 | output_dtype = result_tens.dtype, |
| 3246 | pad=padding, |
| 3247 | qinfo=qinfo, |
| 3248 | result_tensor = result_tens, |
| 3249 | input_list=input_list, |
| 3250 | output_list=output_list, |
| 3251 | num_operands=num_operands, |
| 3252 | ) |
| 3253 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3254 | self.ser.addOperator( |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3255 | op['op'], input_list, output_list, None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3256 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3257 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3258 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3259 | def build_reshape(self, op, a, newShape, validator_fcns=None, error_name=None): |
| 3260 | result_tens = OutputShaper.reshapeOp(self.ser, self.rng, a, newShape, error_name) |
| 3261 | |
| 3262 | # Invalidate Input/Output list for error if checks. |
| 3263 | input_list = [a.name] |
| 3264 | output_list = [result_tens.name] |
| 3265 | pCount, cCount = op["operands"] |
| 3266 | num_operands = pCount + cCount |
| 3267 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3268 | |
| 3269 | TosaErrorValidator.evValidateErrorIfs( |
| 3270 | self.ser, |
| 3271 | validator_fcns, |
| 3272 | error_name, |
| 3273 | op=op, |
| 3274 | input_shape = a.shape, |
| 3275 | output_shape = result_tens.shape, |
| 3276 | input_dtype = a.dtype, |
| 3277 | output_dtype = result_tens.dtype, |
| 3278 | result_tensor = result_tens, |
| 3279 | input_list=input_list, |
| 3280 | output_list=output_list, |
| 3281 | num_operands=num_operands, |
| 3282 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3283 | |
| 3284 | attr = ts.TosaSerializerAttribute() |
| 3285 | attr.ReshapeAttribute(newShape) |
| 3286 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3287 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3288 | return result_tens |
| 3289 | |
| 3290 | def build_reverse(self, op, a, axis): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3291 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3292 | |
| 3293 | attr = ts.TosaSerializerAttribute() |
| 3294 | attr.AxisAttribute(axis) |
| 3295 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3296 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3297 | return result_tens |
| 3298 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3299 | def build_transpose(self, op, a, perms, validator_fcns=None, error_name=None): |
| 3300 | result_tens = OutputShaper.transposeOp(self.ser, self.rng, a, perms, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3301 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3302 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3303 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3304 | # Invalidate Input/Output list for error if checks. |
| 3305 | input_list = [a.name, perms_tens.name] |
| 3306 | output_list = [result_tens.name] |
| 3307 | pCount, cCount = op["operands"] |
| 3308 | num_operands = pCount + cCount |
| 3309 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3310 | |
| 3311 | TosaErrorValidator.evValidateErrorIfs( |
| 3312 | self.ser, |
| 3313 | validator_fcns, |
| 3314 | error_name, |
| 3315 | op=op, |
| 3316 | input_shape = a.shape, |
| 3317 | output_shape = result_tens.shape, |
| 3318 | perms=perms, |
| 3319 | input_dtype = a.dtype, |
| 3320 | output_dtype = result_tens.dtype, |
| 3321 | result_tensor = result_tens, |
| 3322 | input_list=input_list, |
| 3323 | output_list=output_list, |
| 3324 | num_operands=num_operands, |
| 3325 | ) |
| 3326 | |
| 3327 | |
| 3328 | self.ser.addOperator(op['op'], input_list, output_list) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3329 | return result_tens |
| 3330 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3331 | def build_slice(self, op, a, start, size, validator_fcns=None, error_name=None): |
| 3332 | result_tens = OutputShaper.sliceOp(self.ser, self.rng, a, start, size, error_name) |
| 3333 | |
| 3334 | # Invalidate Input/Output list for error if checks. |
| 3335 | input_list = [a.name] |
| 3336 | output_list = [result_tens.name] |
| 3337 | pCount, cCount = op["operands"] |
| 3338 | num_operands = pCount + cCount |
| 3339 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3340 | |
| 3341 | TosaErrorValidator.evValidateErrorIfs( |
| 3342 | self.ser, |
| 3343 | validator_fcns, |
| 3344 | error_name, |
| 3345 | op=op, |
| 3346 | input_shape = a.shape, |
| 3347 | output_shape = result_tens.shape, |
| 3348 | input_dtype = a.dtype, |
| 3349 | output_dtype = result_tens.dtype, |
| 3350 | start=start, |
| 3351 | size=size, |
| 3352 | result_tensor = result_tens, |
| 3353 | input_list=input_list, |
| 3354 | output_list=output_list, |
| 3355 | num_operands=num_operands, |
| 3356 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3357 | |
| 3358 | attr = ts.TosaSerializerAttribute() |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3359 | attr.SliceAttribute(start, size) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3360 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3361 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3362 | return result_tens |
| 3363 | |
| 3364 | def build_tile(self, op, a, multiples): |
| 3365 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 3366 | |
| 3367 | attr = ts.TosaSerializerAttribute() |
| 3368 | attr.TileAttribute(multiples) |
| 3369 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3370 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3371 | return result_tens |
| 3372 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3373 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3374 | |
| 3375 | # Create a new indicies tensor |
| 3376 | # here with data that doesn't exceed the dimensions of the values tensor |
| 3377 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3378 | K = values.shape[1] # K |
| 3379 | W = self.randInt( |
| 3380 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 3381 | ) # W |
| 3382 | indicies_arr = np.int32( |
| 3383 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 3384 | ) # (N, W) |
| 3385 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3386 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3387 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3388 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3389 | self.ser.addOperator(op['op'], [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3390 | |
| 3391 | return result_tens |
| 3392 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3393 | def build_scatter(self, op, values_in, input): |
| 3394 | |
| 3395 | # Create a new indicies tensor |
| 3396 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 3397 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3398 | K = values_in.shape[1] # K |
| 3399 | W = input.shape[1] # W |
| 3400 | indicies_arr = np.int32( |
| 3401 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 3402 | ) # (N, W) |
| 3403 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3404 | |
| 3405 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 3406 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3407 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3408 | op['op'], [values_in.name, indicies.name, input.name], [result_tens.name] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3409 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3410 | |
| 3411 | return result_tens |
| 3412 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3413 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3414 | def build_resize( |
| 3415 | self, |
| 3416 | op, |
| 3417 | input, |
| 3418 | mode, |
| 3419 | stride, |
| 3420 | offset, |
| 3421 | shift, |
| 3422 | stride_fp, |
| 3423 | offset_fp, |
| 3424 | output_dims, |
| 3425 | input_dtype, |
| 3426 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3427 | validator_fcns, |
| 3428 | error_name = None, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3429 | ): |
| 3430 | result_tens = OutputShaper.resizeOp( |
| 3431 | self.ser, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 3432 | self.rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3433 | input, |
| 3434 | mode, |
| 3435 | stride, |
| 3436 | offset, |
| 3437 | shift, |
| 3438 | stride_fp, |
| 3439 | offset_fp, |
| 3440 | output_dims, |
| 3441 | input_dtype, |
| 3442 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3443 | error_name |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3444 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3445 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3446 | # Invalidate Input/Output list for error if checks. |
| 3447 | input_list = [input.name] |
| 3448 | output_list = [result_tens.name] |
| 3449 | pCount, cCount = op["operands"] |
| 3450 | num_operands = pCount + cCount |
| 3451 | 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] | 3452 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3453 | TosaErrorValidator.evValidateErrorIfs( |
| 3454 | self.ser, |
| 3455 | validator_fcns, |
| 3456 | error_name, |
| 3457 | op=op, |
| 3458 | mode=mode, |
| 3459 | shift=shift, |
| 3460 | input_dtype=input_dtype, |
| 3461 | output_dtype=output_dtype, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 3462 | input_shape=input.shape, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3463 | output_shape=output_dims, |
| 3464 | offset=offset, |
| 3465 | offset_fp=offset_fp, |
| 3466 | stride=stride, |
| 3467 | stride_fp=stride_fp, |
| 3468 | input_list=input_list, |
| 3469 | output_list=output_list, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 3470 | result_tensor=result_tens, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3471 | num_operands=num_operands, |
| 3472 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3473 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3474 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 3475 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3476 | attr.ResizeAttribute( |
| 3477 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 3478 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3479 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3480 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3481 | return result_tens |
| 3482 | |
| 3483 | def build_identityn(self, op, val, val2): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3484 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, val, None) |
| 3485 | result_tens2 = OutputShaper.unaryOp(self.ser, self.rng, val2, None) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3486 | self.ser.addOperator( |
| 3487 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 3488 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3489 | return result_tens |
| 3490 | |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 3491 | def build_const(self, op, val): |
| 3492 | self.ser.addOutputTensor(val) |
| 3493 | return val |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3494 | |
| 3495 | # Type Conversion |
| 3496 | def build_cast(self, op, val, out_dtype): |
| 3497 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3498 | self.ser.addOperator(op['op'], [val.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3499 | return result_tens |
| 3500 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 3501 | def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel, validator_fcns, error_name): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3502 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 3503 | |
| 3504 | if per_channel: |
| 3505 | nc = val.shape[-1] |
| 3506 | else: |
| 3507 | nc = 1 |
| 3508 | |
| 3509 | in_type_width = self.typeWidth(val.dtype) |
| 3510 | out_type_width = self.typeWidth(out_dtype) |
| 3511 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3512 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 3513 | input_zp = self.randInt(-128, 128) |
| 3514 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 3515 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 3516 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3517 | in_type_width = in_type_width + 1 |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 3518 | elif error_name == ErrorIf.InputZeroPointNotZero: |
| 3519 | input_zp = self.randInt(-128, 128) |
| 3520 | if input_zp == 0: |
| 3521 | input_zp = input_zp + self.rng.integers(1, 10) |
| 3522 | in_type_width = in_type_width + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3523 | else: |
| 3524 | input_zp = 0 |
| 3525 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3526 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 3527 | output_zp = self.randInt(-128, 128) |
| 3528 | out_type_width = out_type_width + 1 |
| 3529 | elif out_dtype == DType.UINT8: |
| 3530 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3531 | out_type_width = out_type_width + 1 |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 3532 | elif error_name == ErrorIf.OutputZeroPointNotZero: |
| 3533 | output_zp = self.randInt(-128, 128) |
| 3534 | if output_zp == 0: |
| 3535 | output_zp = output_zp + self.rng.integers(1, 10) |
| 3536 | out_type_width = out_type_width + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3537 | else: |
| 3538 | output_zp = 0 |
| 3539 | |
| 3540 | # Calculate scale based on: |
| 3541 | # scale = a *(2^output_width)/(2^input_width)) |
| 3542 | |
| 3543 | a = np.float32(self.rng.random(size=[nc])) |
| 3544 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 3545 | |
| 3546 | if scale32: |
| 3547 | pass |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3548 | # Cap the scaling at 2^31 - 1 for scale32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3549 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 3550 | else: |
| 3551 | # Cap the scaling at 2^15 - 1 for scale16 |
| 3552 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 3553 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3554 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3555 | |
| 3556 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 3557 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 3558 | |
| 3559 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3560 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 3561 | scale_arr[i], scale32 |
| 3562 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3563 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3564 | # 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] | 3565 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 3566 | # Invalidate Input/Output list for error if checks. |
| 3567 | input_list = [val.name] |
| 3568 | output_list = [result_tens.name] |
| 3569 | pCount, cCount = op["operands"] |
| 3570 | num_operands = pCount + cCount |
| 3571 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 3572 | |
| 3573 | qinfo = (input_zp, output_zp) |
| 3574 | TosaErrorValidator.evValidateErrorIfs( |
| 3575 | self.ser, |
| 3576 | validator_fcns, |
| 3577 | error_name, |
| 3578 | op=op, |
| 3579 | input_dtype=val.dtype, |
| 3580 | output_dtype=out_dtype, |
| 3581 | input_shape=val.shape, |
| 3582 | qinfo=qinfo, |
| 3583 | scale32 = scale32, |
| 3584 | double_round = double_round, |
| 3585 | input_list=input_list, |
| 3586 | output_list=output_list, |
| 3587 | result_tensor=result_tens, |
| 3588 | num_operands=num_operands, |
| 3589 | ) |
| 3590 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3591 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3592 | attr.RescaleAttribute( |
| 3593 | input_zp, |
| 3594 | output_zp, |
| 3595 | multiplier_arr, |
| 3596 | shift_arr, |
| 3597 | scale32, |
| 3598 | double_round, |
| 3599 | per_channel, |
| 3600 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3601 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 3602 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3603 | return result_tens |
| 3604 | |
| 3605 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 3606 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 3607 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 3608 | # and fill them with const nodes for the body. |
| 3609 | |
| 3610 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3611 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3612 | |
| 3613 | # Make then/else tensors |
| 3614 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 3615 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 3616 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3617 | |
| 3618 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3619 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3620 | |
| 3621 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3622 | then_block = "THEN_BLOCK" |
| 3623 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3624 | attr = ts.TosaSerializerAttribute() |
| 3625 | attr.CondIfAttribute(then_block, else_block) |
| 3626 | |
| 3627 | # Finally, build the op and the two blocks |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3628 | self.ser.addOperator(op['op'], [cond_tens.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3629 | |
| 3630 | self.ser.startBasicBlock(then_block) |
| 3631 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3632 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3633 | self.ser.addOutputTensor(then_tens) |
| 3634 | |
| 3635 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3636 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3637 | self.ser.addOutputTensor(else_tens) |
| 3638 | |
| 3639 | return result_tens |
| 3640 | |
| 3641 | def build_cond_if_binary(self, op, a, b, cond): |
| 3642 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 3643 | # alternately add or subtract them based on the condition |
| 3644 | |
| 3645 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3646 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3647 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3648 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3649 | |
| 3650 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3651 | then_block = "THEN_BLOCK" |
| 3652 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3653 | attr = ts.TosaSerializerAttribute() |
| 3654 | attr.CondIfAttribute(then_block, else_block) |
| 3655 | |
| 3656 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3657 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3658 | 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] | 3659 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3660 | |
Les Bell | 6040b4d | 2021-10-11 12:50:31 +0100 | [diff] [blame] | 3661 | if a.dtype in (DType.FLOAT, DType.INT32): |
| 3662 | then_op, else_op = Op.ADD, Op.SUB |
| 3663 | elif a.dtype in (DType.INT8, DType.INT16): |
| 3664 | then_op, else_op = Op.LOGICAL_RIGHT_SHIFT, Op.LOGICAL_LEFT_SHIFT |
| 3665 | else: |
| 3666 | assert False, f"No tests for DType: {a.dtype}" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3667 | |
Les Bell | 6040b4d | 2021-10-11 12:50:31 +0100 | [diff] [blame] | 3668 | for block, op in ((then_block, then_op), (else_block, else_op)): |
| 3669 | self.ser.startBasicBlock(block) |
| 3670 | self.ser.addInputTensor(a) |
| 3671 | self.ser.addInputTensor(b) |
| 3672 | tens = self.ser.addOutput(a.shape, a.dtype) |
| 3673 | self.ser.addOperator(op, [a.name, b.name], [tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3674 | |
| 3675 | return result_tens |
| 3676 | |
| 3677 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3678 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3679 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3680 | cond_block = "COND_BLOCK" |
| 3681 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3682 | |
| 3683 | attr = ts.TosaSerializerAttribute() |
| 3684 | attr.WhileLoopAttribute(cond_block, body_block) |
| 3685 | |
| 3686 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3687 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3688 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3689 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3690 | |
| 3691 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3692 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 3693 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 3694 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3695 | |
| 3696 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3697 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3698 | op['op'], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3699 | [iter.name, a.name, acc.name], |
| 3700 | [iter_out.name, a_out.name, acc_out.name], |
| 3701 | attr, |
| 3702 | ) |
Kevin Cheng | b227ae5 | 2021-09-02 13:43:17 -0700 | [diff] [blame] | 3703 | self.ser.addOutputTensor(acc_out) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3704 | |
| 3705 | # COND block (input: iter, output: cond_tens ) |
| 3706 | self.ser.startBasicBlock(cond_block) |
| 3707 | self.ser.addInputTensor(iter) |
| 3708 | self.ser.addInputTensor(a) |
| 3709 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3710 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 3711 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 3712 | 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] | 3713 | |
| 3714 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 3715 | # Note that local intermediate tensors need to be declared here for the outputs |
| 3716 | self.ser.startBasicBlock(body_block) |
| 3717 | self.ser.addInputTensor(iter) |
| 3718 | self.ser.addInputTensor(a) |
| 3719 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3720 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 3721 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 3722 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3723 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 3724 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 3725 | self.ser.addOutputTensor(iter_body_out) |
| 3726 | self.ser.addOutputTensor(a) |
| 3727 | self.ser.addOutputTensor(acc_body_out) |
| 3728 | |
| 3729 | return acc_out |
| 3730 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3731 | def create_filter_lists(self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None): |
| 3732 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 3733 | default_test_rank_range = range(1, 5) |
| 3734 | if not shapeFilter: |
| 3735 | shapeFilter = [None] |
| 3736 | |
| 3737 | # Calculate the filters based on what is requested and what the operator allows |
| 3738 | rmin, rmax = op["rank"] |
| 3739 | if rankFilter is not None: |
| 3740 | cleanRankFilter = [] |
| 3741 | # Ensure rankFilter values are allowed by operator |
| 3742 | for rank in rankFilter: |
| 3743 | if rank >= rmin and rank <= rmax: |
| 3744 | cleanRankFilter.append(rank) |
| 3745 | elif rankFilter is None and shapeFilter[0] is None: |
Jeremy Johnson | 03bec73 | 2021-10-07 12:06:00 +0100 | [diff] [blame] | 3746 | # Ensure default behaviour is bounded by default range or by operator, |
| 3747 | # whichever is the smaller range of ranks. |
| 3748 | opRankRange = range(rmin, rmax + 1) |
| 3749 | cleanRankFilter = opRankRange if len(opRankRange) <= len(default_test_rank_range) else default_test_rank_range |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3750 | else: |
| 3751 | cleanRankFilter = range(rmin, rmax + 1) |
| 3752 | |
| 3753 | dtypes = op["types"] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 3754 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3755 | if dtypeFilter is not None: |
| 3756 | cleanDtypeFilter = [] |
Jeremy Johnson | 03bec73 | 2021-10-07 12:06:00 +0100 | [diff] [blame] | 3757 | # Create list of operator dtypes filtered by requested dtypes |
| 3758 | for dtype in dtypes: |
| 3759 | if dtype in dtypeFilter or (isinstance(dtype, list) and dtype[0] in dtypeFilter): |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3760 | cleanDtypeFilter.append(dtype) |
| 3761 | else: |
| 3762 | cleanDtypeFilter = dtypes |
| 3763 | |
| 3764 | if testType == 'positive': |
| 3765 | filterDict = { |
| 3766 | 'shapeFilter': shapeFilter, |
| 3767 | 'rankFilter': cleanRankFilter, |
| 3768 | 'dtypeFilter': cleanDtypeFilter |
| 3769 | } |
| 3770 | return filterDict |
| 3771 | elif testType == 'negative': |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3772 | if validator is not None: |
| 3773 | validator_info = validator(check=False, op=op) |
| 3774 | else: |
| 3775 | return None |
| 3776 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3777 | error_arguments = validator_info['param_reqs'] |
| 3778 | |
| 3779 | #Set parameters as required |
| 3780 | if error_arguments['rank'] != None: |
| 3781 | rankFilter = error_arguments['rank'] |
| 3782 | else: |
| 3783 | rankFilter = cleanRankFilter |
| 3784 | |
| 3785 | if error_arguments['dtype'] != None: |
| 3786 | dtypeFilter = error_arguments['dtype'] |
| 3787 | else: |
| 3788 | dtypeFilter = cleanDtypeFilter |
| 3789 | |
| 3790 | if error_arguments['shape'] != None: |
| 3791 | shapeFilter = error_arguments['shape'] |
| 3792 | else: |
| 3793 | shapeFilter = shapeFilter[:2] # Reduce number of shapes to keep test numbers small |
| 3794 | |
| 3795 | filterDict = { |
| 3796 | 'shapeFilter': shapeFilter, |
| 3797 | 'rankFilter': rankFilter, |
| 3798 | 'dtypeFilter': dtypeFilter |
| 3799 | } |
| 3800 | return filterDict |
| 3801 | |
| 3802 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3803 | def genOpTestList( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3804 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3805 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3806 | |
| 3807 | try: |
| 3808 | op = self.TOSA_OP_LIST[opName] |
| 3809 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3810 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3811 | |
| 3812 | # Initialize a new random number generator |
| 3813 | self.rng = np.random.default_rng(self.random_seed) |
| 3814 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3815 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3816 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3817 | # Test list consists of a tuple of: |
| 3818 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 3819 | testList = [] |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3820 | if testType == 'negative' and "error_if_validators" in op: |
| 3821 | error_if_validators = op["error_if_validators"] |
| 3822 | else: |
| 3823 | error_if_validators = [None] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3824 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3825 | for validator in error_if_validators: |
| 3826 | if validator is not None: |
| 3827 | error_name = validator(check=False, op=op)['error_name'] |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3828 | else: |
| 3829 | error_name = None |
| 3830 | |
| 3831 | filterDict = self.create_filter_lists(op, shapeFilter, rankFilter, dtypeFilter, testType, validator) |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 3832 | if filterDict == None: |
| 3833 | return [] |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3834 | cleanRankFilter = filterDict['rankFilter'] |
| 3835 | cleanDtypeFilter = filterDict['dtypeFilter'] |
| 3836 | cleanShapeFilter = filterDict['shapeFilter'] |
| 3837 | #print(f"Filters: S {shapeFilter}, R {cleanRankFilter}, T {cleanDtypeFilter}") |
| 3838 | |
| 3839 | for r in cleanRankFilter: |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3840 | if opName.startswith("conv3d"): |
| 3841 | assert r == 5, "conv3d test must have input rank == 5" |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3842 | for t in cleanDtypeFilter: |
| 3843 | for shape in cleanShapeFilter: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3844 | # Filter out by rank |
| 3845 | if shape is not None and len(shape) != r: |
| 3846 | continue |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3847 | self.setTargetShape(shape) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3848 | shapeList = tgen_fcn(self, op, r, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3849 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3850 | shapeStr = self.shapeStr(shapeList[0]) |
| 3851 | typeStr = self.typeStr(t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3852 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3853 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 3854 | argList = [] |
| 3855 | if agen_fcn: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3856 | argList = agen_fcn(self, opName, shapeList, t, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3857 | else: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3858 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3859 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3860 | for argStr, args in argList: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3861 | if testType == 'positive': |
| 3862 | if argStr: |
| 3863 | testStr = "{}_{}_{}_{}".format( |
| 3864 | opName, shapeStr, typeStr, argStr |
| 3865 | ) |
| 3866 | else: |
| 3867 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
| 3868 | elif testType == 'negative': |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3869 | if argStr: |
| 3870 | testStr = "{}_ERRORIF_{}_{}_{}_{}".format( |
| 3871 | opName, error_name, shapeStr, typeStr, argStr |
| 3872 | ) |
| 3873 | else: |
| 3874 | testStr = "{}_ERRORIF_{}_{}_{}".format(opName, error_name, shapeStr, typeStr) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3875 | |
| 3876 | testList.append((opName, testStr, t, error_name, shapeList, args)) |
| 3877 | |
| 3878 | if testType == 'positive': |
| 3879 | # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| 3880 | if "invalid_test_validators" in op: |
| 3881 | invalid_test_validators = op["invalid_test_validators"] |
| 3882 | clean_testList = [] |
| 3883 | for test in testList: |
| 3884 | for validator_fcn in invalid_test_validators: |
| 3885 | remove_test = False |
| 3886 | if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[4], args=test[5]): |
| 3887 | remove_test = True |
| 3888 | if not remove_test: |
| 3889 | clean_testList.append(test) |
| 3890 | testList = clean_testList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3891 | |
| 3892 | return testList |
| 3893 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3894 | |
| 3895 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3896 | try: |
| 3897 | op = self.TOSA_OP_LIST[opName] |
| 3898 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3899 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3900 | |
| 3901 | # Create a serializer |
| 3902 | self.createSerializer(opName, testStr) |
| 3903 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3904 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3905 | if "error_if_validators" in op: |
| 3906 | error_if_validators = op["error_if_validators"] |
| 3907 | else: |
| 3908 | error_if_validators = None |
| 3909 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3910 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3911 | num_operands = pCount + cCount |
| 3912 | |
| 3913 | if isinstance(dtype_or_dtypeList, list): |
| 3914 | dtypeList = dtype_or_dtypeList |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3915 | elif op["op"] == Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3916 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3917 | else: |
| 3918 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 3919 | |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3920 | if op["op"] != Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3921 | assert ( |
| 3922 | len(shapeList) == num_operands |
| 3923 | ), "shapeList length {} must match number of operands {}".format( |
| 3924 | len(shapeList), num_operands |
| 3925 | ) |
| 3926 | assert ( |
| 3927 | len(dtypeList) == num_operands |
| 3928 | ), "dtypeList length {} must match number of operands {}".format( |
| 3929 | len(dtypeList), num_operands |
| 3930 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3931 | |
| 3932 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3933 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3934 | except KeyError: |
| 3935 | qgen = None |
| 3936 | |
| 3937 | # Build the random tensor operands and the test |
| 3938 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3939 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3940 | tens = self.generate_tensors(op, dtypeList, shapeList, testArgs, error_name) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3941 | |
| 3942 | if qgen is not None: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3943 | qinfo = qgen(self, op, dtype_or_dtypeList, error_name) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3944 | else: |
| 3945 | qinfo = None |
| 3946 | |
| 3947 | try: |
| 3948 | if error_if_validators is None: |
| 3949 | if qinfo is not None: |
| 3950 | resultName = build_fcn(self, op, *tens, *testArgs, qinfo) |
| 3951 | else: |
| 3952 | resultName = build_fcn(self, op, *tens, *testArgs) |
| 3953 | else: |
| 3954 | if qinfo is not None: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3955 | resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name, qinfo) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3956 | else: |
| 3957 | resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name) |
| 3958 | except TypeError as e: |
| 3959 | print( |
| 3960 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 3961 | build_fcn, tens, testArgs |
| 3962 | ) |
| 3963 | ) |
| 3964 | raise e |
| 3965 | |
| 3966 | if resultName is None: |
| 3967 | print("Invalid ERROR_IF tests created") |
| 3968 | |
| 3969 | # Save the serialized test |
| 3970 | self.serialize("test") |
| 3971 | |
| 3972 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3973 | def generate_tensors(self, op, dtypeList, shapeList, testArgs, error_name=None): |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3974 | pCount, cCount = op["operands"] |
| 3975 | |
| 3976 | tens = [] |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3977 | 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] | 3978 | # Make sure the operation does not cause value saturation - where |
| 3979 | # the number wraps due to limited number of bits to store the answer |
| 3980 | assert ( |
| 3981 | pCount == 2 and cCount == 0 |
| 3982 | ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 3983 | placeholders = [] |
| 3984 | add = (op["op"] == Op.ADD) |
| 3985 | a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 3986 | b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 3987 | if add: |
| 3988 | res_arr = np.add(a_arr, b_arr, dtype=np.int64) |
| 3989 | else: |
| 3990 | res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) |
| 3991 | |
| 3992 | # Work out the saturation limits |
| 3993 | max_i32 = (1 << 31)-1 |
| 3994 | min_i32 = -(1 << 31) |
| 3995 | max_arr = np.full(shapeList[1], max_i32) |
| 3996 | min_arr = np.full(shapeList[1], min_i32) |
| 3997 | |
| 3998 | # Find how much values exceed the maximum/minimums |
| 3999 | sat_max_arr = np.maximum(res_arr - max_arr, 0) |
| 4000 | sat_min_arr = np.minimum(res_arr - min_arr, 0) |
| 4001 | |
| 4002 | if not add: |
| 4003 | # Swap saturation values and negate values as we need to perform opposite operations |
| 4004 | sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr |
| 4005 | |
| 4006 | # Create new array of unsaturated values by clipping values as needed |
| 4007 | b_unsat_arr = b_arr |
| 4008 | if (sat_max_arr != 0).any(): |
| 4009 | # Clip values that cause saturation |
| 4010 | b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) |
| 4011 | # Reduce axes in unsaturated tensor to match original tensor |
| 4012 | for axis, dim in enumerate(b_arr.shape): |
| 4013 | if dim != b_unsat_arr.shape[axis]: |
| 4014 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 4015 | b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) |
| 4016 | |
| 4017 | if (sat_min_arr != 0).any(): |
| 4018 | # Clip values that cause saturation |
| 4019 | b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) |
| 4020 | # Reduce axes in unsaturated tensor to match original tensor |
| 4021 | for axis, dim in enumerate(b_arr.shape): |
| 4022 | if dim != b_unsat_arr.shape[axis]: |
| 4023 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 4024 | b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) |
| 4025 | |
| 4026 | placeholders.append( |
| 4027 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 4028 | ) |
| 4029 | placeholders.append( |
| 4030 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| 4031 | ) |
| 4032 | |
| 4033 | tens.extend(placeholders) |
Jeremy Johnson | 8c06a65 | 2021-10-20 15:51:11 +0100 | [diff] [blame^] | 4034 | elif (op["op"] == Op.COND_IF or op["op"] == Op.WHILE_LOOP) and dtypeList[0] == DType.INT32: |
| 4035 | # Limit input tensors with cond_if_binary or while_loop to stop |
| 4036 | # saturation of add/sub ops |
| 4037 | pRemain = pCount |
| 4038 | placeholders = [] |
| 4039 | for idx, shape in enumerate(shapeList[:]): |
| 4040 | arr = self.getRandTensor(shapeList[idx], DType.INT16) |
| 4041 | if pRemain > 0: |
| 4042 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
| 4043 | pRemain -= 1 |
| 4044 | else: |
| 4045 | placeholders.append(self.ser.addConst(shape, dtypeList[idx], arr)) |
| 4046 | |
| 4047 | tens.extend(placeholders) |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 4048 | elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 4049 | # Force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4050 | assert ( |
| 4051 | pCount == 2 and cCount == 0 |
| 4052 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4053 | |
| 4054 | placeholders = [] |
| 4055 | for idx, shape in enumerate(shapeList[:]): |
| 4056 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4057 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4058 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4059 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4060 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4061 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4062 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 4063 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4064 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4065 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4066 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4067 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4068 | |
| 4069 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 4070 | elif op["op"] == Op.SELECT: |
| 4071 | # Set datatype of condition tensor to boolean |
| 4072 | dtypeList[0] = DType.BOOL |
| 4073 | tens.extend( |
| 4074 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 4075 | ) |
| 4076 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4077 | elif op["op"] == Op.INTDIV and error_name == None: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4078 | assert ( |
| 4079 | pCount == 2 and cCount == 0 |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 4080 | ), "Op.INTDIV must have 2 placeholders, 0 consts" |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4081 | |
| 4082 | placeholders = [] |
| 4083 | |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 4084 | # Two invalid cases for Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4085 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 4086 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4087 | while True: |
| 4088 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 4089 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 4090 | |
| 4091 | if (divisor_arr == 0).any(): |
| 4092 | continue |
| 4093 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 4094 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4095 | continue |
| 4096 | |
| 4097 | break |
| 4098 | |
| 4099 | placeholders.append( |
| 4100 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 4101 | ) |
| 4102 | placeholders.append( |
| 4103 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 4104 | ) |
| 4105 | |
| 4106 | tens.extend(placeholders) |
| 4107 | elif op["op"] == Op.MUL: |
| 4108 | assert ( |
| 4109 | pCount == 2 and cCount == 0 |
| 4110 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 4111 | |
| 4112 | if dtypeList[0] == DType.FLOAT: |
| 4113 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 4114 | else: |
| 4115 | placeholders = [] |
| 4116 | |
| 4117 | # Make sure multiply result in int32 range |
| 4118 | shift = testArgs[0] |
| 4119 | if dtypeList[0] == DType.INT8: |
| 4120 | num_bits = 8 |
| 4121 | elif dtypeList[0] == DType.INT16: |
| 4122 | num_bits = 16 |
| 4123 | elif dtypeList[0] == DType.INT32: |
| 4124 | num_bits = 32 |
| 4125 | else: |
| 4126 | raise Exception("OpMul: invalid input dtype") |
| 4127 | |
| 4128 | for idx, shape in enumerate(shapeList[:]): |
| 4129 | low = -(2 ** (num_bits - 1)) |
| 4130 | high = (2 ** (num_bits - 1)) - 1 |
| 4131 | |
| 4132 | a_arr = np.int32( |
| 4133 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 4134 | ) |
| 4135 | b_arr = np.int32( |
| 4136 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 4137 | ) |
| 4138 | |
| 4139 | i = 0 |
| 4140 | while True: |
| 4141 | |
| 4142 | a_arr_64 = a_arr.astype(np.int64) |
| 4143 | b_arr_64 = b_arr.astype(np.int64) |
| 4144 | |
| 4145 | if shift > 0: |
| 4146 | rounding = 1 << (shift - 1) |
| 4147 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 4148 | else: |
| 4149 | result_arr = a_arr_64 * b_arr_64 |
| 4150 | |
| 4151 | if (result_arr > -(2 ** 31)).all() and ( |
| 4152 | result_arr <= ((2 ** 31) - 1) |
| 4153 | ).all(): |
| 4154 | break |
| 4155 | |
| 4156 | i = i + 1 |
| 4157 | a_arr = a_arr // 2 |
| 4158 | b_arr = b_arr // 2 |
| 4159 | |
| 4160 | placeholders.append( |
| 4161 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 4162 | ) |
| 4163 | placeholders.append( |
| 4164 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 4165 | ) |
| 4166 | |
| 4167 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 4168 | elif op["op"] == Op.CONCAT: |
| 4169 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 4170 | if count < 1: |
| 4171 | count = 1 |
| 4172 | if self.args.num_const_inputs_concat == 0: |
| 4173 | count = len(shapeList) |
| 4174 | |
| 4175 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 4176 | tens.extend( |
| 4177 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 4178 | ) |
| 4179 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 4180 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4181 | tens.extend( |
| 4182 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 4183 | ) |
| 4184 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4185 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 4186 | return tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4187 | |
| 4188 | def createDynamicOpLists(self): |
| 4189 | |
| 4190 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4191 | 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] | 4192 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4193 | for k in KERNELS_2D: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4194 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 4195 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 4196 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 4197 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4198 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4199 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 4200 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 4201 | "depthwise_conv2d_TEMPLATE" |
| 4202 | ].copy() |
| 4203 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 4204 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4205 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4206 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 4207 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 4208 | "transpose_conv2d_TEMPLATE" |
| 4209 | ].copy() |
| 4210 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 4211 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4212 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4213 | KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| 4214 | for k in KERNELS_3D: |
| 4215 | testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| 4216 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| 4217 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 4218 | self.TOSA_OP_LIST[testName]["template"] = False |
| 4219 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4220 | # Delete any templates after having created any dynamic ops |
| 4221 | # This is a two-pass operation because it's bad practice to delete |
| 4222 | # keys from dictionaries while iterating |
| 4223 | keyList = [] |
| 4224 | for k in self.TOSA_OP_LIST: |
| 4225 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4226 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4227 | keyList.append(k) |
| 4228 | continue |
| 4229 | except KeyError: |
| 4230 | pass |
| 4231 | |
| 4232 | for k in keyList: |
| 4233 | del self.TOSA_OP_LIST[k] |
| 4234 | |
| 4235 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4236 | """Fill in default fields for ops if they aren't already specified. |
| 4237 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4238 | for op in self.TOSA_OP_LIST: |
| 4239 | |
| 4240 | # Required fields |
| 4241 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4242 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4243 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4244 | raise Exception( |
| 4245 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 4246 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4247 | |
| 4248 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4249 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4250 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4251 | raise Exception( |
| 4252 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 4253 | op |
| 4254 | ) |
| 4255 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4256 | |
| 4257 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4258 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4259 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4260 | raise Exception( |
| 4261 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 4262 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4263 | |
| 4264 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4265 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4266 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4267 | raise Exception( |
| 4268 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 4269 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4270 | |
| 4271 | # Put in default rank range, if missing |
| 4272 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4273 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4274 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4275 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4276 | |
| 4277 | # Tensor operator list |
| 4278 | # 'op': op name |
| 4279 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4280 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 4281 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4282 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 4283 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4284 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4285 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4286 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 4287 | 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] | 4288 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4289 | TYPE_BOOL = [DType.BOOL] |
| 4290 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 4291 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 4292 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4293 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4294 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4295 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4296 | TYPE_CONV = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 4297 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4298 | [DType.INT8, DType.INT8, DType.INT32], |
| 4299 | [DType.INT16, DType.INT8, DType.INT48], |
| 4300 | DType.FLOAT, |
| 4301 | ] |
| 4302 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 4303 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4304 | |
| 4305 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4306 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4307 | "argmax": { |
| 4308 | "op": Op.ARGMAX, |
| 4309 | "operands": (1, 0), |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 4310 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4311 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4312 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 4313 | "error_if_validators": (TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evArgmaxOutputRankMismatch, |
| 4314 | TosaErrorValidator.evArgmaxOutputShapeMismatch, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, |
| 4315 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4316 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4317 | "avg_pool2d": { |
| 4318 | "op": Op.AVG_POOL2D, |
| 4319 | "operands": (1, 0), |
| 4320 | "rank": (4, 4), |
| 4321 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 4322 | "qgen": TosaQuantGen.qgUnary, |
| 4323 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 4324 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
| 4325 | "error_if_validators": (TosaErrorValidator.evKernelSmallerOne, TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evPadSmallerZero, |
| 4326 | TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, |
| 4327 | TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, |
| 4328 | TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4329 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4330 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4331 | "conv2d_TEMPLATE": { |
| 4332 | "op": Op.CONV2D, |
| 4333 | "operands": (1, 2), |
| 4334 | "rank": (4, 4), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 4335 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4336 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4337 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 4338 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4339 | "template": True, |
| 4340 | }, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4341 | # Templated operator. Filled in by createDynamicOpLists |
| 4342 | "conv3d_TEMPLATE": { |
| 4343 | "op": Op.CONV3D, |
| 4344 | "operands": (1, 2), |
| 4345 | "rank": (5, 5), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 4346 | "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4347 | "qgen": TosaQuantGen.qgConv, |
| 4348 | "types": TYPE_CONV, |
| 4349 | "template": True, |
| 4350 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4351 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4352 | "depthwise_conv2d_TEMPLATE": { |
| 4353 | "op": Op.DEPTHWISE_CONV2D, |
| 4354 | "operands": (1, 2), |
| 4355 | "filter": [1, 1], |
| 4356 | "rank": (4, 4), |
| 4357 | "build_fcn": ( |
| 4358 | build_depthwise_conv2d, |
| 4359 | TosaTensorGen.tgDepthwiseConv2D, |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 4360 | TosaArgGen.agConv, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4361 | ), |
| 4362 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4363 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 4364 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4365 | "template": True, |
| 4366 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4367 | "fully_connected": { |
| 4368 | "op": Op.FULLY_CONNECTED, |
| 4369 | "operands": (1, 2), |
| 4370 | "rank": (2, 2), |
| 4371 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 4372 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4373 | "types": TYPE_CONV, |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 4374 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evWeightZeroPointNotZero, TosaErrorValidator.evWrongRank, |
| 4375 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4376 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4377 | "matmul": { |
| 4378 | "op": Op.MATMUL, |
| 4379 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 4380 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4381 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 4382 | "qgen": TosaQuantGen.qgMatmul, |
| 4383 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 4384 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, |
| 4385 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4386 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4387 | "max_pool2d": { |
| 4388 | "op": Op.MAX_POOL2D, |
| 4389 | "operands": (1, 0), |
| 4390 | "rank": (4, 4), |
| 4391 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 4392 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 4393 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
| 4394 | "error_if_validators": (TosaErrorValidator.evKernelSmallerOne, TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evPadSmallerZero, |
| 4395 | TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, |
| 4396 | TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4397 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4398 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4399 | "transpose_conv2d_TEMPLATE": { |
| 4400 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 4401 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4402 | "rank": (4, 4), |
| 4403 | "build_fcn": ( |
| 4404 | build_transpose_conv2d, |
| 4405 | TosaTensorGen.tgTransposeConv2D, |
| 4406 | TosaArgGen.agTransposeConv2D, |
| 4407 | ), |
| 4408 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4409 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 4410 | "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4411 | "template": True, |
| 4412 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4413 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4414 | "clamp": { |
| 4415 | "op": Op.CLAMP, |
| 4416 | "operands": (1, 0), |
| 4417 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 4418 | "types": TYPE_NARROW_INT_FP, |
| 4419 | }, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4420 | "sigmoid": { |
| 4421 | "op": Op.SIGMOID, |
| 4422 | "operands": (1, 0), |
| 4423 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 4424 | "types": TYPE_FP, |
| 4425 | }, |
| 4426 | "tanh": { |
| 4427 | "op": Op.TANH, |
| 4428 | "operands": (1, 0), |
| 4429 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 4430 | "types": TYPE_FP, |
| 4431 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4432 | # Elementwise Binary Operators |
| 4433 | "add": { |
| 4434 | "op": Op.ADD, |
| 4435 | "operands": (2, 0), |
| 4436 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4437 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4438 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4439 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4440 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4441 | "arithmetic_right_shift": { |
| 4442 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 4443 | "operands": (2, 0), |
| 4444 | "build_fcn": ( |
| 4445 | build_arithmetic_right_shift, |
| 4446 | TosaTensorGen.tgBroadcastFuzz, |
| 4447 | TosaArgGen.agArithmeticRightShift, |
| 4448 | ), |
| 4449 | "types": TYPE_INT, |
| 4450 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4451 | "bitwise_and": { |
| 4452 | "op": Op.BITWISE_AND, |
| 4453 | "operands": (2, 0), |
| 4454 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4455 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4456 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4457 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4458 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4459 | "bitwise_or": { |
| 4460 | "op": Op.BITWISE_OR, |
| 4461 | "operands": (2, 0), |
| 4462 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4463 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4464 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4465 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4466 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4467 | "bitwise_xor": { |
| 4468 | "op": Op.BITWISE_XOR, |
| 4469 | "operands": (2, 0), |
| 4470 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4471 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4472 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4473 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4474 | }, |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 4475 | "intdiv": { |
| 4476 | "op": Op.INTDIV, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4477 | "operands": (2, 0), |
| 4478 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4479 | "types": [DType.INT32], |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4480 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4481 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 4482 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4483 | "logical_and": { |
| 4484 | "op": Op.LOGICAL_AND, |
| 4485 | "operands": (2, 0), |
| 4486 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4487 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4488 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4489 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4490 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4491 | "logical_left_shift": { |
| 4492 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 4493 | "operands": (2, 0), |
| 4494 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4495 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4496 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4497 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4498 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4499 | "logical_right_shift": { |
| 4500 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 4501 | "operands": (2, 0), |
| 4502 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4503 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4504 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4505 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4506 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4507 | "logical_or": { |
| 4508 | "op": Op.LOGICAL_OR, |
| 4509 | "operands": (2, 0), |
| 4510 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4511 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4512 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4513 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4514 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4515 | "logical_xor": { |
| 4516 | "op": Op.LOGICAL_XOR, |
| 4517 | "operands": (2, 0), |
| 4518 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4519 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4520 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4521 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4522 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4523 | "maximum": { |
| 4524 | "op": Op.MAXIMUM, |
| 4525 | "operands": (2, 0), |
| 4526 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4527 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4528 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4529 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4530 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4531 | "minimum": { |
| 4532 | "op": Op.MINIMUM, |
| 4533 | "operands": (2, 0), |
| 4534 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4535 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4536 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4537 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4538 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4539 | "mul": { |
| 4540 | "op": Op.MUL, |
| 4541 | "operands": (2, 0), |
| 4542 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 4543 | "types": TYPE_INT_FP, |
| 4544 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4545 | "pow": { |
| 4546 | "op": Op.POW, |
| 4547 | "operands": (2, 0), |
| 4548 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 4549 | "types": TYPE_FP, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4550 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4551 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4552 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4553 | "sub": { |
| 4554 | "op": Op.SUB, |
| 4555 | "operands": (2, 0), |
| 4556 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 4557 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4558 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4559 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4560 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4561 | "table": { |
| 4562 | "op": Op.TABLE, |
| 4563 | # Use the automatic generation functions to create the input array |
| 4564 | # but create the table tensor in the build function, as it may be |
| 4565 | # a different type from the input |
| 4566 | "operands": (1, 0), |
| 4567 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 4568 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4569 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4570 | # Elementwise Unary operators |
| 4571 | "abs": { |
| 4572 | "op": Op.ABS, |
| 4573 | "operands": (1, 0), |
| 4574 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4575 | "types": TYPE_FI32, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4576 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4577 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4578 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4579 | "bitwise_not": { |
| 4580 | "op": Op.BITWISE_NOT, |
| 4581 | "operands": (1, 0), |
| 4582 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4583 | "types": TYPE_INT, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4584 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4585 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4586 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4587 | "ceil": { |
| 4588 | "op": Op.CEIL, |
| 4589 | "operands": (1, 0), |
| 4590 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4591 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4592 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4593 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4594 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4595 | "clz": { |
| 4596 | "op": Op.CLZ, |
| 4597 | "operands": (1, 0), |
| 4598 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4599 | "types": [DType.INT32], |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4600 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4601 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4602 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4603 | "exp": { |
| 4604 | "op": Op.EXP, |
| 4605 | "operands": (1, 0), |
| 4606 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4607 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4608 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4609 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4610 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4611 | "floor": { |
| 4612 | "op": Op.FLOOR, |
| 4613 | "operands": (1, 0), |
| 4614 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4615 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4616 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4617 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4618 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4619 | "log": { |
| 4620 | "op": Op.LOG, |
| 4621 | "operands": (1, 0), |
| 4622 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4623 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4624 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4625 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4626 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4627 | "logical_not": { |
| 4628 | "op": Op.LOGICAL_NOT, |
| 4629 | "operands": (1, 0), |
| 4630 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4631 | "types": TYPE_BOOL, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4632 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4633 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4634 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4635 | "negate": { |
| 4636 | "op": Op.NEGATE, |
| 4637 | "operands": (1, 0), |
| 4638 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4639 | "qgen": TosaQuantGen.qgUnary, |
| 4640 | "types": TYPE_INT_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4641 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, |
| 4642 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, |
| 4643 | TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4644 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4645 | "reciprocal": { |
| 4646 | "op": Op.RECIPROCAL, |
| 4647 | "operands": (1, 0), |
| 4648 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4649 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4650 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4651 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4652 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4653 | "rsqrt": { |
| 4654 | "op": Op.RSQRT, |
| 4655 | "operands": (1, 0), |
| 4656 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4657 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4658 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4659 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4660 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4661 | # Elementwise Ternary operators |
| 4662 | "select": { |
| 4663 | "op": Op.SELECT, |
| 4664 | "operands": (3, 0), |
| 4665 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 4666 | "types": TYPE_FIB, |
| 4667 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4668 | # Comparison operators |
| 4669 | "equal": { |
| 4670 | "op": Op.EQUAL, |
| 4671 | "operands": (2, 0), |
| 4672 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 4673 | "types": TYPE_FI32, |
| 4674 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4675 | "greater_equal": { |
| 4676 | "op": Op.GREATER_EQUAL, |
| 4677 | "operands": (2, 0), |
| 4678 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 4679 | "types": TYPE_FI32, |
| 4680 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4681 | "greater": { |
| 4682 | "op": Op.GREATER, |
| 4683 | "operands": (2, 0), |
| 4684 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 4685 | "types": TYPE_FI32, |
| 4686 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4687 | # Reduction operators |
| 4688 | "reduce_all": { |
| 4689 | "op": Op.REDUCE_ALL, |
| 4690 | "operands": (1, 0), |
| 4691 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4692 | "types": TYPE_BOOL, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4693 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4694 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4695 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4696 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4697 | "reduce_any": { |
| 4698 | "op": Op.REDUCE_ANY, |
| 4699 | "operands": (1, 0), |
| 4700 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4701 | "types": TYPE_BOOL, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4702 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4703 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4704 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4705 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4706 | "reduce_max": { |
| 4707 | "op": Op.REDUCE_MAX, |
| 4708 | "operands": (1, 0), |
| 4709 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4710 | "types": TYPE_INT_FP, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4711 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4712 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4713 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4714 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4715 | "reduce_min": { |
| 4716 | "op": Op.REDUCE_MAX, |
| 4717 | "operands": (1, 0), |
| 4718 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4719 | "types": TYPE_INT_FP, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4720 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4721 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4722 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4723 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4724 | "reduce_product": { |
| 4725 | "op": Op.REDUCE_PRODUCT, |
| 4726 | "operands": (1, 0), |
| 4727 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4728 | "types": TYPE_FP, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4729 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4730 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4731 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4732 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4733 | "reduce_sum": { |
| 4734 | "op": Op.REDUCE_SUM, |
| 4735 | "operands": (1, 0), |
| 4736 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4737 | "types": TYPE_FI32, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4738 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4739 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4740 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4741 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4742 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4743 | "concat": { |
| 4744 | "op": Op.CONCAT, |
| 4745 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 4746 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4747 | "types": TYPE_FIB, |
| 4748 | }, |
| 4749 | "pad": { |
| 4750 | "op": Op.PAD, |
| 4751 | "operands": (1, 0), |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 4752 | "rank": (1, 5), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4753 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 4754 | "qgen": TosaQuantGen.qgPad, |
| 4755 | "types": TYPE_FIB, |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 4756 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evPadSmallerZero, |
| 4757 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4758 | }, |
| 4759 | "reshape": { |
| 4760 | "op": Op.RESHAPE, |
| 4761 | "operands": (1, 0), |
| 4762 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 4763 | "types": TYPE_FIB, |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 4764 | "error_if_validators": (TosaErrorValidator.evTensorSizeInputOutputMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4765 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4766 | }, |
| 4767 | "reverse": { |
| 4768 | "op": Op.REVERSE, |
| 4769 | "operands": (1, 0), |
| 4770 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4771 | "types": TYPE_FIB, |
| 4772 | }, |
| 4773 | "slice": { |
| 4774 | "op": Op.SLICE, |
| 4775 | "operands": (1, 0), |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 4776 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4777 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 4778 | "types": TYPE_FIB, |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 4779 | "error_if_validators": (TosaErrorValidator.evStartSmallerZero, TosaErrorValidator.evSizeSmallerEqualZero, TosaErrorValidator.evStartSizeOutsideBounds, |
| 4780 | TosaErrorValidator.evSizeOutputShapeMismatch, TosaErrorValidator.evInputSizeStartLengthMismatch, TosaErrorValidator.evWrongRank, |
| 4781 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4782 | }, |
| 4783 | "tile": { |
| 4784 | "op": Op.TILE, |
| 4785 | "operands": (1, 0), |
| 4786 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 4787 | "types": TYPE_FIB, |
| 4788 | }, |
| 4789 | "transpose": { |
| 4790 | "op": Op.TRANSPOSE, |
| 4791 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 4792 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4793 | "build_fcn": ( |
| 4794 | build_transpose, |
| 4795 | TosaTensorGen.tgBasic, |
| 4796 | TosaArgGen.agTranspose, |
| 4797 | ), |
| 4798 | "types": TYPE_FIB, |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 4799 | "error_if_validators": (TosaErrorValidator.evIndexOutsideBounds, TosaErrorValidator.evIndexUsedTwice, TosaErrorValidator.evWrongRank, |
| 4800 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4801 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4802 | # Data nodes |
| 4803 | "const": { |
| 4804 | "op": Op.CONST, |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 4805 | "operands": (0, 1), |
| 4806 | "build_fcn": (build_const, TosaTensorGen.tgBasic, None), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4807 | "types": TYPE_FIB, |
| 4808 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4809 | "identity": { |
| 4810 | "op": Op.IDENTITY, |
| 4811 | "operands": (1, 0), |
| 4812 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4813 | "types": TYPE_FIB, |
| 4814 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4815 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4816 | "gather": { |
| 4817 | "op": Op.GATHER, |
| 4818 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 4819 | "operands": (1, 0), |
| 4820 | "rank": (3, 3), |
| 4821 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 4822 | "types": TYPE_INT_FP, |
| 4823 | }, |
| 4824 | "scatter": { |
| 4825 | "op": Op.SCATTER, |
| 4826 | # Only specify 'values_in' tensor here. |
| 4827 | #'indices' and 'input' are generated in op building stage |
| 4828 | "operands": (2, 0), |
| 4829 | "rank": (3, 3), |
| 4830 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 4831 | "types": TYPE_INT_FP, |
| 4832 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4833 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4834 | "resize": { |
| 4835 | "op": Op.RESIZE, |
| 4836 | "operands": (1, 0), |
| 4837 | "rank": (4, 4), |
| 4838 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 4839 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 4840 | "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride), |
| 4841 | "error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension, |
| 4842 | TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 4843 | TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven, TosaErrorValidator.evWrongInputType, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4844 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, |
| 4845 | TosaErrorValidator.evBatchMismatch, TosaErrorValidator.evChannelMismatch) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4846 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4847 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4848 | "cast": { |
| 4849 | "op": Op.CAST, |
| 4850 | "operands": (1, 0), |
| 4851 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 4852 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 4853 | }, |
| 4854 | "rescale": { |
| 4855 | "op": Op.RESCALE, |
| 4856 | "operands": (1, 0), |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 4857 | "rank": (1,4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4858 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 4859 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame] | 4860 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, TosaErrorValidator.evScaleTrue, |
| 4861 | TosaErrorValidator.evScaleNotTrue, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4862 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4863 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4864 | # Custom |
| 4865 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4866 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4867 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 4868 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 4869 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4870 | "cond_if_const": { |
| 4871 | "op": Op.COND_IF, |
| 4872 | "operands": (0, 2), |
| 4873 | "build_fcn": ( |
| 4874 | build_cond_if_const, |
| 4875 | TosaTensorGen.tgBasic, |
| 4876 | TosaArgGen.agCondIf, |
| 4877 | ), |
| 4878 | "types": [DType.BOOL], |
| 4879 | }, |
| 4880 | "cond_if_binary": { |
| 4881 | "op": Op.COND_IF, |
| 4882 | "operands": (2, 0), |
| 4883 | "build_fcn": ( |
| 4884 | build_cond_if_binary, |
| 4885 | TosaTensorGen.tgBasic, |
| 4886 | TosaArgGen.agCondIf, |
| 4887 | ), |
Les Bell | 6040b4d | 2021-10-11 12:50:31 +0100 | [diff] [blame] | 4888 | "types": TYPE_INT_FP, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4889 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4890 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4891 | "while_loop": { |
| 4892 | "op": Op.WHILE_LOOP, |
| 4893 | "operands": (0, 1), |
| 4894 | "build_fcn": ( |
| 4895 | build_while_loop, |
| 4896 | TosaTensorGen.tgBasic, |
| 4897 | TosaArgGen.agWhileLoop, |
| 4898 | ), |
| 4899 | "types": [DType.INT32], |
| 4900 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4901 | } |
| 4902 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4903 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4904 | class OutputShaper: |
| 4905 | # Methods in this class compute the expected output shape and datatype |
| 4906 | # for common classes of operations |
| 4907 | def __init__(self): |
| 4908 | pass |
| 4909 | |
| 4910 | # These methods return arguments that can be used for |
| 4911 | # creating a new output tensor |
| 4912 | @staticmethod |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4913 | def binaryBroadcastOp(ser, rng, a, b, error_name=None): |
| 4914 | if error_name != ErrorIf.RankMismatch: |
| 4915 | assert len(a.shape) == len(b.shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4916 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4917 | |
| 4918 | shape = [] |
| 4919 | for i in range(len(a.shape)): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4920 | if a.shape[i] == 1 and error_name == None: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4921 | shape.append(b.shape[i]) |
| 4922 | else: |
| 4923 | shape.append(a.shape[i]) |
| 4924 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4925 | if error_name == ErrorIf.WrongOutputType: |
| 4926 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4927 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 4928 | outputDType = rng.choice(wrong_dtypes) |
| 4929 | else: |
| 4930 | outputDType = a.dtype |
| 4931 | |
| 4932 | return ser.addOutput(shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4933 | |
| 4934 | @staticmethod |
| 4935 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4936 | assert len(a.shape) == len(b.shape) |
| 4937 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4938 | |
| 4939 | shape = [] |
| 4940 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4941 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4942 | shape.append(a.shape[i]) |
| 4943 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4944 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4945 | |
| 4946 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4947 | def unaryOp(ser, rng, a, error_name=None): |
| 4948 | if error_name == ErrorIf.WrongOutputType: |
| 4949 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4950 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 4951 | outputDType = rng.choice(wrong_dtypes) |
| 4952 | else: |
| 4953 | outputDType = a.dtype |
| 4954 | |
| 4955 | return ser.addOutput(a.shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4956 | |
| 4957 | @staticmethod |
| 4958 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4959 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 4960 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4961 | |
| 4962 | shape = [] |
| 4963 | for i in range(len(a.shape)): |
| 4964 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 4965 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4966 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4967 | |
| 4968 | @staticmethod |
| 4969 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4970 | assert len(a.shape) == len(b.shape) |
| 4971 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4972 | |
| 4973 | # Do broadcast |
| 4974 | shape = [] |
| 4975 | for i in range(len(a.shape)): |
| 4976 | if a.shape[i] == 1: |
| 4977 | shape.append(b.shape[i]) |
| 4978 | else: |
| 4979 | shape.append(a.shape[i]) |
| 4980 | |
| 4981 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4982 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4983 | |
| 4984 | @staticmethod |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4985 | def reduceOp(ser, rng, a, axis, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4986 | shape = a.shape.copy() |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4987 | if error_name not in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank, ErrorIf.ShapeOfAxisNotOne]: |
| 4988 | shape[axis] = 1 |
| 4989 | if error_name == ErrorIf.ShapeOfAxisNotOne and shape[axis] == 1: |
| 4990 | shape[axis] = rng.integers(2, 10) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4991 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4992 | if error_name == ErrorIf.WrongOutputType: |
| 4993 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4994 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 4995 | outputDType = rng.choice(wrong_dtypes) |
| 4996 | else: |
| 4997 | outputDType = a.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4998 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4999 | return ser.addOutput(shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5000 | |
| 5001 | @staticmethod |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5002 | def argmaxOp(ser, rng, a, axis, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5003 | shape = a.shape.copy() |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5004 | |
| 5005 | if error_name not in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank]: |
| 5006 | del shape[axis] |
| 5007 | |
| 5008 | if error_name == ErrorIf.ArgmaxOutputRankMismatch: |
| 5009 | remove = rng.choice([True, False]) |
| 5010 | if remove and len(shape) > 1: |
| 5011 | del shape[0] |
| 5012 | else: |
| 5013 | shape.append(1) |
| 5014 | elif error_name == ErrorIf.ArgmaxOutputShapeMismatch: |
| 5015 | for i in range(len(shape)): |
| 5016 | shape[i] = shape[i] + rng.integers(1, 10) |
| 5017 | |
| 5018 | if error_name == ErrorIf.WrongOutputType: |
| 5019 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 5020 | wrong_dtypes = list(set(all_dtypes) - set([DType.INT32])) |
| 5021 | outputDType = rng.choice(wrong_dtypes) |
| 5022 | else: |
| 5023 | outputDType = DType.INT32 |
| 5024 | |
| 5025 | return ser.addOutput(shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5026 | |
| 5027 | @staticmethod |
| 5028 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 5029 | |
| 5030 | # IFM: NHWC |
| 5031 | # Filter: OHWI |
| 5032 | # OFM: NHWC |
| 5033 | |
| 5034 | if len(padding) == 2: |
| 5035 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 5036 | # From H,W to T,B,L,R |
| 5037 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 5038 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5039 | h = ( |
| 5040 | ifm.shape[1] |
| 5041 | - filter.shape[1] |
| 5042 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 5043 | + padding[0] |
| 5044 | + padding[1] |
| 5045 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5046 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5047 | w = ( |
| 5048 | ifm.shape[2] |
| 5049 | - filter.shape[2] |
| 5050 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 5051 | + padding[2] |
| 5052 | + padding[3] |
| 5053 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5054 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5055 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 5056 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 5057 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5058 | out_dtype = DType.INT32 |
| 5059 | elif ifm.dtype == DType.INT16: |
| 5060 | out_dtype = DType.INT48 |
| 5061 | elif ifm.dtype == DType.FLOAT: |
| 5062 | out_dtype = DType.FLOAT |
| 5063 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5064 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5065 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5066 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5067 | |
| 5068 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 5069 | def conv3dOp(ser, ifm, filter, strides, padding, dilations): |
| 5070 | |
| 5071 | # IFM: NDHWC |
| 5072 | # Filter: ODHWI |
| 5073 | # OFM: NDHWC |
| 5074 | |
| 5075 | d = ( |
| 5076 | ifm.shape[1] |
| 5077 | - filter.shape[1] |
| 5078 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 5079 | + padding[0] |
| 5080 | + padding[1] |
| 5081 | ) // strides[0] + 1 |
| 5082 | |
| 5083 | h = ( |
| 5084 | ifm.shape[2] |
| 5085 | - filter.shape[2] |
| 5086 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 5087 | + padding[2] |
| 5088 | + padding[3] |
| 5089 | ) // strides[1] + 1 |
| 5090 | |
| 5091 | w = ( |
| 5092 | ifm.shape[3] |
| 5093 | - filter.shape[3] |
| 5094 | - (filter.shape[3] - 1) * (dilations[2] - 1) |
| 5095 | + padding[4] |
| 5096 | + padding[5] |
| 5097 | ) // strides[2] + 1 |
| 5098 | |
| 5099 | ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| 5100 | |
| 5101 | if ifm.dtype == DType.INT8: |
| 5102 | out_dtype = DType.INT32 |
| 5103 | elif ifm.dtype == DType.INT16: |
| 5104 | out_dtype = DType.INT48 |
| 5105 | elif ifm.dtype == DType.FLOAT: |
| 5106 | out_dtype = DType.FLOAT |
| 5107 | else: |
| 5108 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
| 5109 | |
| 5110 | return ser.addOutput(ofm_shape, out_dtype) |
| 5111 | |
| 5112 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5113 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 5114 | # IFM: NHWC |
| 5115 | # Filter: HWCM |
| 5116 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5117 | h = ( |
| 5118 | ifm.shape[1] |
| 5119 | - filter.shape[0] |
| 5120 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 5121 | + padding[0] |
| 5122 | + padding[1] |
| 5123 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5124 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5125 | w = ( |
| 5126 | ifm.shape[2] |
| 5127 | - filter.shape[1] |
| 5128 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 5129 | + padding[2] |
| 5130 | + padding[3] |
| 5131 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5132 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5133 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 5134 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 5135 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5136 | out_dtype = DType.INT32 |
| 5137 | elif ifm.dtype == DType.INT16: |
| 5138 | out_dtype = DType.INT48 |
| 5139 | elif ifm.dtype == DType.FLOAT: |
| 5140 | out_dtype = DType.FLOAT |
| 5141 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5142 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5143 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5144 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5145 | |
| 5146 | @staticmethod |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 5147 | def pool2dOp(ser, rng, ifm, kernel, stride, pad, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5148 | # input: NHWC |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 5149 | if stride[0] <= 0 or stride[1] <= 0 or min(pad) < 0: |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5150 | # If an incorrect stride is used set dimensions to 1, test is invalid anyway. |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 5151 | h = 1 |
| 5152 | w = 1 |
| 5153 | else: |
| 5154 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 5155 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 5156 | |
| 5157 | if error_name == ErrorIf.PoolingOutputShapeMismatch: |
| 5158 | choices = [1, 2, 3, 4, 5] |
| 5159 | h = h + rng.choice(choices) |
| 5160 | w = w + rng.choice(choices) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5161 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5162 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 5163 | |
| 5164 | if error_name == ErrorIf.WrongOutputType: |
| 5165 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 5166 | wrong_dtypes = list(set(all_dtypes) - set([ifm.dtype])) |
| 5167 | outputDType = rng.choice(wrong_dtypes) |
| 5168 | else: |
| 5169 | outputDType = ifm.dtype |
| 5170 | |
| 5171 | return ser.addOutput(ofm_shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5172 | |
| 5173 | @staticmethod |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5174 | def fullyConnectedOp(ser, rng, input, filter, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5175 | # input: N, IC |
| 5176 | # filter: OC, IC |
| 5177 | # output: N, OC |
| 5178 | |
| 5179 | output_shape = [input.shape[0], filter.shape[0]] |
| 5180 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5181 | if error_name == ErrorIf.WrongOutputType: |
| 5182 | if input.dtype == DType.INT8: |
| 5183 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) |
| 5184 | elif input.dtype == DType.INT16: |
| 5185 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) |
| 5186 | elif input.dtype == DType.FLOAT: |
| 5187 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) |
| 5188 | out_dtype = rng.choice(a=incorrect_types) |
| 5189 | elif input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5190 | out_dtype = DType.INT32 |
| 5191 | elif input.dtype == DType.INT16: |
| 5192 | out_dtype = DType.INT48 |
| 5193 | elif input.dtype == DType.FLOAT: |
| 5194 | out_dtype = DType.FLOAT |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5195 | elif error_name == ErrorIf.WrongInputType: |
| 5196 | # Pick some potentially correct output dtype if input type is incorrect |
| 5197 | out_dtype = DType.INT32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5198 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5199 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5200 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5201 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5202 | |
| 5203 | @staticmethod |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5204 | def matmulOp(ser, rng, a, b, error_name=None): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 5205 | # a: N, H, C |
| 5206 | # b: N, C, W |
| 5207 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5208 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 5209 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5210 | |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5211 | if error_name == ErrorIf.WrongOutputType: |
| 5212 | if a.dtype == DType.INT8: |
| 5213 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) |
| 5214 | elif a.dtype == DType.INT16: |
| 5215 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) |
| 5216 | elif a.dtype == DType.FLOAT: |
| 5217 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) |
| 5218 | out_dtype = rng.choice(a=incorrect_types) |
| 5219 | elif a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5220 | out_dtype = DType.INT32 |
| 5221 | elif a.dtype == DType.INT16: |
| 5222 | out_dtype = DType.INT48 |
| 5223 | elif a.dtype == DType.FLOAT: |
| 5224 | out_dtype = DType.FLOAT |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5225 | elif error_name == ErrorIf.WrongInputType: |
| 5226 | # Pick some potentially correct output dtype if input type is incorrect |
| 5227 | out_dtype = DType.INT32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5228 | else: |
Matthew Haddon | c4cf037 | 2021-10-11 09:38:10 +0100 | [diff] [blame] | 5229 | raise Exception("Unsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5230 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5231 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5232 | |
| 5233 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 5234 | def concatOp(ser, axis, *a): |
| 5235 | input1 = a[0] |
| 5236 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5237 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 5238 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5239 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 5240 | output_shape[axis] = input1.shape[axis] |
| 5241 | |
| 5242 | for tensor in remaining_inputs: |
| 5243 | output_shape[axis] += tensor.shape[axis] |
| 5244 | |
| 5245 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5246 | |
| 5247 | @staticmethod |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5248 | def padOp(ser, rng, a, padding, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5249 | |
| 5250 | output_shape = a.shape.copy() |
| 5251 | |
| 5252 | for i in range(len(output_shape)): |
| 5253 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 5254 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5255 | # Fix negative output shape if error_if test causes it |
| 5256 | if error_name == ErrorIf.PadSmallerZero and min(output_shape) < 1: |
| 5257 | output_shape = [i if i >= 1 else 1 for i in output_shape] |
| 5258 | |
| 5259 | if error_name == ErrorIf.WrongOutputType: |
| 5260 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 5261 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 5262 | outputDType = rng.choice(wrong_dtypes) |
| 5263 | else: |
| 5264 | outputDType = a.dtype |
| 5265 | |
| 5266 | return ser.addOutput(output_shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5267 | |
| 5268 | @staticmethod |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5269 | def reshapeOp(ser, rng, a, shape, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5270 | output_shape = shape.copy() |
| 5271 | |
| 5272 | totalElements = 1 |
| 5273 | for i in a.shape: |
| 5274 | totalElements *= i |
| 5275 | |
| 5276 | # If there are any -1 elements, figure out what that dimension must be |
| 5277 | totalOutputElements = 1 |
| 5278 | for i in output_shape: |
| 5279 | if i != -1: |
| 5280 | totalOutputElements *= i |
| 5281 | |
| 5282 | # And fill it in |
| 5283 | for i in range(len(output_shape)): |
| 5284 | if output_shape[i] == -1: |
| 5285 | output_shape[i] = totalElements // totalOutputElements |
| 5286 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5287 | if error_name == ErrorIf.TensorSizeInputOutputMismatch: |
| 5288 | for i in range(len(output_shape)): |
| 5289 | output_shape[i] = output_shape[i] + rng.integers(1, 10) |
| 5290 | |
| 5291 | if error_name == ErrorIf.WrongOutputType: |
| 5292 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 5293 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 5294 | outputDType = rng.choice(wrong_dtypes) |
| 5295 | else: |
| 5296 | outputDType = a.dtype |
| 5297 | |
| 5298 | return ser.addOutput(output_shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5299 | |
| 5300 | @staticmethod |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5301 | def sliceOp(ser, rng, a, start, size, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5302 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5303 | if error_name == ErrorIf.WrongOutputType: |
| 5304 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 5305 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 5306 | outputDType = rng.choice(wrong_dtypes) |
| 5307 | else: |
| 5308 | outputDType = a.dtype |
| 5309 | |
| 5310 | if error_name == ErrorIf.SizeOutputShapeMismatch: |
| 5311 | output_shape = size.copy() |
| 5312 | for index in range(len(output_shape)): |
| 5313 | if output_shape[index] <= 2: |
| 5314 | output_shape[index] = output_shape[index] + rng.choice([1, 2]) |
| 5315 | else: |
| 5316 | output_shape[index] = output_shape[index] + rng.choice([-2, -1, 1, 2]) |
| 5317 | else: |
| 5318 | output_shape = size.copy() |
| 5319 | |
| 5320 | return ser.addOutput(output_shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5321 | |
| 5322 | @staticmethod |
| 5323 | def tileOp(ser, a, multiples): |
| 5324 | |
| 5325 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5326 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5327 | |
| 5328 | for i in range(len(output_shape)): |
| 5329 | output_shape[i] = a.shape[i] * multiples[i] |
| 5330 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5331 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5332 | |
| 5333 | @staticmethod |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5334 | def transposeOp(ser, rng, a, perms, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5335 | output_shape = a.shape.copy() |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5336 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5337 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5338 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5339 | if error_name == ErrorIf.IndexOutsideBounds: |
| 5340 | for i in range(len(output_shape)): |
| 5341 | output_shape[i] = a.shape[0] |
| 5342 | else: |
| 5343 | for i in range(len(output_shape)): |
| 5344 | output_shape[i] = a.shape[perms[i]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5345 | |
Matthew Haddon | e807aae | 2021-10-11 18:12:58 +0100 | [diff] [blame] | 5346 | if error_name == ErrorIf.WrongOutputType: |
| 5347 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 5348 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 5349 | outputDType = rng.choice(wrong_dtypes) |
| 5350 | else: |
| 5351 | outputDType = a.dtype |
| 5352 | |
| 5353 | return ser.addOutput(output_shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5354 | |
| 5355 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 5356 | def gatherOp(ser, values, indices): |
| 5357 | assert len(values.shape) == 3 |
| 5358 | assert len(indices.shape) == 2 |
| 5359 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5360 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 5361 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 5362 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5363 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 5364 | |
| 5365 | @staticmethod |
| 5366 | def scatterOp(ser, values_in, indices, input): |
| 5367 | assert len(values_in.shape) == 3 |
| 5368 | assert len(indices.shape) == 2 |
| 5369 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5370 | assert values_in.shape[0] == indices.shape[0] # N |
| 5371 | assert input.shape[1] == indices.shape[1] # W |
| 5372 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 5373 | |
| 5374 | output_shape = values_in.shape |
| 5375 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5376 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5377 | |
| 5378 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 5379 | def tableOp(ser, input, table_dtype): |
| 5380 | # Same shape as the input, but dtype dependent on table dtype |
| 5381 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 5382 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 5383 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5384 | |
| 5385 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5386 | def resizeOp( |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 5387 | serializer, |
| 5388 | rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5389 | input, |
| 5390 | mode, |
| 5391 | stride, |
| 5392 | offset, |
| 5393 | shift, |
| 5394 | stride_fp, |
| 5395 | offset_fp, |
| 5396 | output_dims, |
| 5397 | input_dtype, |
| 5398 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 5399 | error_name = None |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5400 | ): |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 5401 | if error_name == ErrorIf.WrongRank: |
| 5402 | output_dims = [input.shape[0], output_dims[0], output_dims[0], input.shape[0]] |
| 5403 | else: |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 5404 | if error_name == ErrorIf.BatchMismatch: |
| 5405 | output_dims = [input.shape[0] + rng.integers(1, 10), output_dims[0], output_dims[1], input.shape[3]] |
| 5406 | elif error_name == ErrorIf.ChannelMismatch: |
| 5407 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3] + rng.integers(1, 10)] |
| 5408 | else: |
| 5409 | 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] | 5410 | |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 5411 | return serializer.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5412 | |
| 5413 | @staticmethod |
| 5414 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5415 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5416 | |
| 5417 | @staticmethod |
| 5418 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 5419 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5420 | out_dtype = DType.INT32 |
| 5421 | elif ifm.dtype == DType.INT16: |
| 5422 | out_dtype = DType.INT48 |
| 5423 | elif ifm.dtype == DType.FLOAT: |
| 5424 | out_dtype = DType.FLOAT |
| 5425 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5426 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 5427 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 5428 | return ser.addOutput(output_shape, out_dtype) |