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 | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 65 | elif error_name in [ErrorIf.InputZeroPointNotZero, ErrorIf.OutputZeroPointNotZero]: |
| 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 |
| 98 | input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) |
| 99 | weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) |
| 100 | qinfo.ConvQuantInfo(input_zp, weights_zp) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 101 | return qinfo |
| 102 | |
| 103 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 104 | def qgMatmul(testGen, op, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 105 | qinfo = ts.TosaSerializerQuantInfo() |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 106 | qinfo.MatMulQuantInfo( |
| 107 | TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) |
| 108 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 109 | return qinfo |
| 110 | |
| 111 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 112 | def qgPad(testGen, op, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 113 | qinfo = ts.TosaSerializerQuantInfo() |
Les Bell | 30e4680 | 2021-07-23 09:43:31 +0100 | [diff] [blame] | 114 | qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 115 | return qinfo |
| 116 | |
| 117 | @staticmethod |
| 118 | def computeMultiplierAndShift(scaleFp, scale32): |
| 119 | # Derived from computeMultiplierAndShiftTosaScale32 |
| 120 | # Provide a floating-point scaling factor and the scale32 parameter |
| 121 | # to compute the multiplier and shift |
| 122 | |
| 123 | if scale32: |
| 124 | scaleBits = 31 |
| 125 | else: |
| 126 | scaleBits = 15 |
| 127 | |
| 128 | m, shift = math.frexp(scaleFp) |
| 129 | |
| 130 | if scaleFp < 0.0: |
| 131 | m = -m |
| 132 | |
| 133 | multiplier = round(m * (1 << scaleBits)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 134 | assert multiplier <= (1 << scaleBits) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 135 | |
| 136 | if multiplier == (1 << scaleBits): |
| 137 | multiplier = multiplier // 2 |
| 138 | shift = shift + 1 |
| 139 | |
| 140 | shift = (-shift) + scaleBits |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 141 | #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) |
| 142 | |
| 143 | # Adjust multiplier such that shift is in allowed value range. |
| 144 | if shift == 0: |
| 145 | multiplier = multiplier // 4 |
| 146 | shift = shift + 2 |
| 147 | elif shift == 1: |
| 148 | multiplier = multiplier // 2 |
| 149 | shift = shift + 1 |
| 150 | elif shift == 63: |
| 151 | multiplier = multiplier * 2 |
| 152 | shift = shift - 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 153 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 154 | assert multiplier <= (1 << scaleBits) |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 155 | assert shift >= 2 and shift <= 62 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 156 | |
| 157 | return multiplier, shift |
| 158 | |
| 159 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 160 | class TosaTensorGen: |
| 161 | """Tensor generators create a shape list for the placeholder and const tensor |
| 162 | data operands for the operator. The actual random data is generated separately for each test.""" |
| 163 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 164 | def __init__(self): |
| 165 | pass |
| 166 | |
| 167 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 168 | def tgBasic(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 169 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 170 | shape = testGen.makeShape(rank) |
| 171 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 172 | # Constrict dimension size for large ranks when creating WrongRank tests |
| 173 | shape = TosaErrorIfArgGen.eiRestrictDimension(shape, error_name) |
| 174 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 175 | shape_list = [] |
| 176 | for i in range(pl + const): |
| 177 | shape_list.append(shape.copy()) |
| 178 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 179 | if error_name == ErrorIf.RankMismatch: |
| 180 | if rank == 1 and i != 1: |
| 181 | shape = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) |
| 182 | elif i != 1: |
| 183 | shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) |
| 184 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 185 | return shape_list |
| 186 | |
| 187 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 188 | def tgNHWC(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 189 | pl, const = opName["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 190 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 191 | if error_name != ErrorIf.WrongRank: |
| 192 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 193 | |
| 194 | shape = testGen.makeShape(rank) |
| 195 | |
| 196 | # Constrict the batch size? |
| 197 | if testGen.args.max_batch_size: |
| 198 | shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 199 | # Constrict dimension size for large ranks |
| 200 | if rank > 4: |
| 201 | shape[4] = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 202 | |
| 203 | shape_list = [] |
| 204 | for i in range(pl + const): |
| 205 | shape_list.append(shape.copy()) |
| 206 | |
| 207 | return shape_list |
| 208 | |
| 209 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 210 | def tgScatter(testGen, opName, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 211 | pl, const = opName["operands"] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 212 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 213 | assert pl == 2 |
| 214 | assert const == 0 |
| 215 | assert rank == 3 |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 216 | |
| 217 | values_in_shape = testGen.makeShape(rank) |
| 218 | |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 219 | # ignore max batch size if target shape is set |
| 220 | if testGen.args.max_batch_size and not testGen.args.target_shapes: |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 221 | values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 |
| 222 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 223 | W = testGen.randInt( |
| 224 | testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] |
| 225 | ) |
Matthew Haddon | 4b2881a | 2021-08-24 14:25:43 +0100 | [diff] [blame] | 226 | # Constrict W if one dimension is too large to keep tensor size reasonable |
| 227 | if max(values_in_shape) > 5000: |
| 228 | W = testGen.randInt(0, 16) |
| 229 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 230 | input_shape = [values_in_shape[0], W, values_in_shape[2]] |
| 231 | |
| 232 | shape_list = [] |
| 233 | shape_list.append(values_in_shape.copy()) |
| 234 | shape_list.append(input_shape.copy()) |
| 235 | |
| 236 | return shape_list |
| 237 | |
| 238 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 239 | def tgBroadcastFuzz(testGen, op, rank, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 240 | shape = testGen.makeShape(rank) |
| 241 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 242 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 243 | |
| 244 | shape_list = [] |
| 245 | |
| 246 | # Choose one of the inputs to broadcast |
| 247 | bcast_idx = testGen.randInt(0, pl + const) |
| 248 | for i in range(pl + const): |
| 249 | shape_bcast = shape.copy() |
| 250 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 251 | if error_name == ErrorIf.RankMismatch: |
| 252 | bcast_idx = -1 # Turn off broadcast because we are not testing it |
| 253 | if rank == 1 and i != 1: |
| 254 | shape_bcast = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) |
| 255 | elif i != 1: |
| 256 | shape_bcast = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) |
| 257 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 258 | # If the chosen input, pick a random index to broadcast |
| 259 | if i == bcast_idx: |
| 260 | fuzz_idx = testGen.randInt(0, rank) |
| 261 | shape_bcast[fuzz_idx] = 1 |
| 262 | |
| 263 | shape_list.append(shape_bcast) |
| 264 | |
| 265 | return shape_list |
| 266 | |
| 267 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 268 | def tgConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 269 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 270 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 271 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 272 | |
| 273 | # IFM dimensions are NHWC |
| 274 | ifm_shape = testGen.makeShape(rank) |
| 275 | |
| 276 | # Constrict the batch size? |
| 277 | if testGen.args.max_batch_size: |
| 278 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 279 | |
| 280 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 281 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 282 | |
| 283 | # Generate a random OFM depth |
| 284 | ofm_depth = testGen.makeShape(1)[0] |
| 285 | |
| 286 | # The filter dimensions are OHWI |
| 287 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 288 | |
| 289 | # The bias is OC |
| 290 | bias_shape = np.asarray([ofm_depth]) |
| 291 | |
| 292 | return [ifm_shape, filter_shape, bias_shape] |
| 293 | |
| 294 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 295 | def tgConv3D(testGen, op, rank, error_name=None): |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 296 | pl, const = op["operands"] |
| 297 | |
| 298 | assert rank == 5 |
| 299 | |
| 300 | # IFM dimensions are NDHWC |
| 301 | ifm_shape = testGen.makeShape(rank) |
| 302 | |
| 303 | # Constrict the batch size? |
| 304 | if testGen.args.max_batch_size: |
| 305 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 306 | |
| 307 | # Get the filter depth/height/width from the operator parameters |
| 308 | filter_dhw = op["filter"] |
| 309 | |
| 310 | # Generate a random OFM channel |
| 311 | ofm_channel = testGen.makeShape(1)[0] |
| 312 | |
| 313 | # The filter dimensions are ODHWI |
| 314 | filter_shape = np.asarray( |
| 315 | [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] |
| 316 | ) |
| 317 | |
| 318 | # The bias is OC |
| 319 | bias_shape = np.asarray([ofm_channel]) |
| 320 | |
| 321 | return [ifm_shape, filter_shape, bias_shape] |
| 322 | |
| 323 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 324 | def tgTransposeConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 325 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 326 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 327 | assert rank == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 328 | |
| 329 | # IFM dimensions are NHWC |
| 330 | ifm_shape = testGen.makeShape(rank) |
| 331 | |
| 332 | # Constrict the batch size? |
| 333 | if testGen.args.max_batch_size: |
| 334 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 335 | |
| 336 | # Get the filter height/width from the operator parameters |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 337 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 338 | |
| 339 | # Generate a random OFM depth |
| 340 | ofm_depth = testGen.makeShape(1)[0] |
| 341 | |
| 342 | # The filter dimensions are OHWI |
| 343 | filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) |
| 344 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 345 | # The bias is OC |
| 346 | bias_shape = np.asarray([ofm_depth]) |
| 347 | |
| 348 | return [ifm_shape, filter_shape, bias_shape] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 349 | |
| 350 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 351 | def tgDepthwiseConv2D(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 352 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 353 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 354 | assert rank == 4 |
| 355 | assert pl == 1 and const == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 356 | |
| 357 | # IFM dimensions are NHWC |
| 358 | ifm_shape = testGen.makeShape(rank) |
| 359 | |
| 360 | # Constrict the batch size? |
| 361 | if testGen.args.max_batch_size: |
| 362 | ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 |
| 363 | |
| 364 | # Get the filter height/width from the operator parameters |
| 365 | # Filter is KH, HW, C, M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 366 | filter_hw = op["filter"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 367 | |
| 368 | # Generate a random OFM depth, but don't let it get too big because |
| 369 | # the output depth is M * C |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 370 | filter_m = ( |
| 371 | testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) |
| 372 | ) + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 373 | |
| 374 | # The filter dimensions are HWCM |
| 375 | filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) |
| 376 | |
| 377 | # The bias is M * C |
| 378 | bias_shape = np.asarray([ifm_shape[3] * filter_m]) |
| 379 | |
| 380 | return [ifm_shape, filter_shape, bias_shape] |
| 381 | |
| 382 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 383 | def tgFullyConnected(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 384 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 385 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 386 | assert rank == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 387 | |
| 388 | input_shape = testGen.makeShape(rank) |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 389 | filter_oc = testGen.rng.integers( |
| 390 | low=testGen.args.tensor_shape_range[0], |
| 391 | high=testGen.args.tensor_shape_range[1], |
| 392 | size=1, |
| 393 | )[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 394 | filter_shape = np.asarray([filter_oc, input_shape[1]]) |
| 395 | |
| 396 | bias_shape = np.asarray([filter_oc]) |
| 397 | |
| 398 | return [input_shape, filter_shape, bias_shape] |
| 399 | |
| 400 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 401 | def tgMatmul(testGen, op, rank, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 402 | pl, const = op["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 403 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 404 | assert rank == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 405 | assert pl == 2 and const == 0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 406 | |
| 407 | a_shape = testGen.makeShape(rank) |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 408 | # Get a random number for b_oc even if target shape is defined |
| 409 | b_oc = np.int32( |
| 410 | testGen.rng.integers( |
| 411 | low=testGen.args.tensor_shape_range[0], |
| 412 | high=testGen.args.tensor_shape_range[1], |
| 413 | size=1, |
| 414 | ) |
| 415 | )[0] |
| 416 | # If N or H is large let b_oc be 1 to reduce output tensor size |
| 417 | if max(a_shape) > 1000: |
| 418 | b_oc = 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 419 | |
Matthew Haddon | 68e7aee | 2021-08-16 11:20:25 +0100 | [diff] [blame] | 420 | b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 421 | return [a_shape, b_shape] |
| 422 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 423 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 424 | def tgConcat(testGen, opName, rank, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 425 | pl, const = opName["operands"] |
| 426 | shape = testGen.makeShape(rank) |
| 427 | |
| 428 | # Create extra tensors to concat. |
| 429 | # Take into account value of pl when getting maximum number of concats |
| 430 | num_tensors = testGen.randInt(0, 4) |
| 431 | shape_list = [] |
| 432 | for i in range(pl + const + num_tensors): |
| 433 | shape_list.append(shape.copy()) |
| 434 | |
| 435 | return shape_list |
| 436 | |
| 437 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 438 | def tgConcatConstInput(testGen, shapeList, axis, error_name=None): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 439 | # Split concat shape along axis to allow for multiple const inputs |
| 440 | # without making too many large tensors |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 441 | if len(shapeList) == 2 or shapeList[0][axis] < len(shapeList): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 442 | return shapeList |
| 443 | |
Jeremy Johnson | 960985a | 2021-10-06 10:58:14 +0100 | [diff] [blame] | 444 | # Create copy of shape we are going to split (so we don't alter shapeList) |
| 445 | shape = shapeList[0].copy() |
| 446 | # Add original shape as first input |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 447 | new_shapeList = [shape.copy()] |
| 448 | length_on_axis = shape[axis] |
| 449 | remaining_length = length_on_axis |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 450 | for i in range(len(shapeList) - 2): |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 451 | # Calculate split on axis and remaining value |
| 452 | split_shape_val = int(shape[axis] / 2) |
| 453 | remaining_length = remaining_length - split_shape_val |
| 454 | |
| 455 | # Append new shape, and set remaining shape |
| 456 | shape[axis] = split_shape_val |
| 457 | new_shapeList.append(shape.copy()) |
| 458 | shape[axis] = remaining_length |
| 459 | if i == len(shapeList) - 3: |
| 460 | new_shapeList.append(shape.copy()) |
| 461 | |
| 462 | return new_shapeList |
| 463 | |
| 464 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 465 | class TosaArgGen: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 466 | """Argument generators create exhaustive or random lists of attributes for operators that take |
| 467 | attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) |
| 468 | tuples where the descriptive_name is appended to the test name and the arglist is expanded |
| 469 | as arguments to the operator build function.""" |
| 470 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 471 | def __init__(self): |
| 472 | pass |
| 473 | |
| 474 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 475 | def agNone(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 476 | """A trivial argument generator for operators that don't take any |
| 477 | non-tensor arguments""" |
| 478 | return [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 479 | |
| 480 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 481 | def agAxis(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 482 | """Build the axis argument for operators that take a single axis""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 483 | axes = [] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 484 | shape = shapeList[0] |
| 485 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 486 | if error_name == ErrorIf.AxisSmallerZero: |
| 487 | small_axis = testGen.rng.integers(-5, 0) |
| 488 | axes.append(("axis{}".format(small_axis), [small_axis])) |
| 489 | elif error_name == ErrorIf.AxisLargerRank: |
| 490 | large_axis = testGen.rng.integers(len(shape) + 1, len(shape) + 10) |
| 491 | axes.append(("axis{}".format(large_axis), [large_axis])) |
| 492 | else: |
| 493 | for a in range(0, len(shape)): |
| 494 | axes.append(("axis{}".format(a), [a])) |
| 495 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 496 | return axes |
| 497 | |
| 498 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 499 | def agConv(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 500 | arg_list = [] |
| 501 | |
| 502 | ifm_shape = shapeList[0] |
| 503 | filter_shape = shapeList[1] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 504 | # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) |
| 505 | k = [int(x) for x in opName.split("_")[-1].split("x")] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 506 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 507 | # Check the rank |
| 508 | rank = 5 if opName.startswith("conv3d") else 4 |
| 509 | assert len(ifm_shape) == rank |
| 510 | assert len(filter_shape) == rank |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 511 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 512 | # kernel rank omits batch and channels |
| 513 | k_rank = rank - 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 514 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 515 | # Generate comprehensive argument lists |
| 516 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 517 | paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} |
| 518 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 519 | strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| 520 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 521 | dilations = {x for x in itertools.product(*([d_vals] * k_rank))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 522 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 523 | # add some oversize argument values |
| 524 | if max(ifm_shape) < 64: |
| 525 | bigPadding = 9 |
| 526 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) |
| 527 | bigStride = 8 |
| 528 | strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) |
| 529 | bigDilation = 7 |
| 530 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 531 | |
| 532 | # There are too many parameter combinations, so generate them sparsely |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 533 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 534 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 535 | if sparsity < 13: |
| 536 | sparsity = 1 |
| 537 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 538 | sparsity += 1 |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 539 | n = 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 540 | for s in sorted(list(strides)): |
| 541 | for p in sorted(list(paddings)): |
| 542 | for d in sorted(list(dilations)): |
| 543 | if (n % sparsity == 0 |
| 544 | # padding must not exceed the kernel size ? |
| 545 | # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 546 | # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) |
| 547 | # the padded shape must exceed the kernel size |
| 548 | and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] |
| 549 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) |
| 550 | # the padded shape must exceed the dilation |
| 551 | and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] |
| 552 | and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) |
| 553 | ): |
Les Bell | f414b3c | 2021-09-06 11:29:46 +0100 | [diff] [blame] | 554 | arg_list.append( |
| 555 | ( |
| 556 | "st{}_pad{}_dilat{}".format( |
| 557 | "".join([str(x) for x in s]), |
| 558 | "".join([str(x) for x in p]), |
| 559 | "".join([str(x) for x in d]), |
| 560 | ), |
| 561 | [s, p, d], |
| 562 | ) |
| 563 | ) |
| 564 | n += 1 |
| 565 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 566 | return arg_list |
| 567 | |
| 568 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 569 | def agTransposeConv2D(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 570 | arg_list = [] |
| 571 | |
| 572 | ifm_shape = shapeList[0] |
| 573 | filter_shape = shapeList[1] |
| 574 | |
| 575 | # Must be rank 4 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 576 | assert len(ifm_shape) == 4 |
| 577 | assert len(filter_shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 578 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 579 | # Generate comprehensive argument lists |
| 580 | p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] |
| 581 | paddings = {x for x in itertools.product(*([p_vals] * 2))} |
| 582 | s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] |
| 583 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 584 | d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] |
| 585 | dilations = {x for x in itertools.product(*([d_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 586 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 587 | # add some oversize argument values |
| 588 | if max(ifm_shape) < 64: |
| 589 | bigPadding = 9 |
| 590 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) |
| 591 | bigStride = 8 |
| 592 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 593 | bigDilation = 7 |
| 594 | dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 595 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 596 | # There are too many parameter combinations, so generate them sparsely |
| 597 | # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 |
| 598 | sparsity = len(paddings) * len(strides) * len(dilations) // 100 + 1 |
| 599 | if sparsity < 13: |
| 600 | sparsity = 1 |
| 601 | while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: |
| 602 | sparsity += 1 |
| 603 | n = 0 |
| 604 | for s in sorted(list(strides)): |
| 605 | for p in sorted(list(paddings)): |
| 606 | for d in sorted(list(dilations)): |
| 607 | if n % sparsity == 0: |
| 608 | # Determine the output shape |
| 609 | oh = ( |
| 610 | ifm_shape[1] |
| 611 | - filter_shape[1] |
| 612 | - (filter_shape[1] - 1) * (d[0] - 1) |
| 613 | + 2 * p[0] |
| 614 | ) // s[0] + 1 |
| 615 | ow = ( |
| 616 | ifm_shape[2] |
| 617 | - filter_shape[2] |
| 618 | - (filter_shape[2] - 1) * (d[1] - 1) |
| 619 | + 2 * p[1] |
| 620 | ) // s[1] + 1 |
| 621 | os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| 622 | arg_list.append( |
| 623 | ( |
| 624 | "st{}_pad{}_dilat{}_os{}".format( |
| 625 | "".join([str(x) for x in s]), |
| 626 | "".join([str(x) for x in p]), |
| 627 | "".join([str(x) for x in d]), |
| 628 | "x".join([str(x) for x in os]), |
| 629 | ), |
| 630 | [s, p, d, os], |
| 631 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 632 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 633 | n += 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 634 | |
| 635 | return arg_list |
| 636 | |
| 637 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 638 | def agPad(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 639 | arg_list = [] |
| 640 | rank = len(shapeList[0]) |
| 641 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 642 | # Exhaustively test combinations of padding on each side of each dimension |
| 643 | # - the range of padding values is defined by pad_min and pad_max |
| 644 | # - for padding >9, the name format needs to be more distinctive |
| 645 | pad_min, pad_max = 0, 1 |
| 646 | pad_values = [x for x in range(pad_min, pad_max + 1)] |
| 647 | axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] |
| 648 | shape_pad_values = itertools.product(*([axis_pad_values] * rank)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 649 | |
Les Bell | 7ffccce | 2021-07-28 15:37:02 +0100 | [diff] [blame] | 650 | for paddings in shape_pad_values: |
| 651 | name = "pad" |
| 652 | for r in range(rank): |
| 653 | before, after = paddings[r] |
| 654 | name = f"{name}{before}{after}" |
| 655 | arg_list.append((name, [np.array(paddings)])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 656 | |
| 657 | return arg_list |
| 658 | |
| 659 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 660 | def agPooling(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 661 | arg_list = [] |
| 662 | |
| 663 | shape = shapeList[0] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 664 | if error_name != ErrorIf.WrongRank: |
| 665 | assert len(shape) == 4 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 666 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 667 | # Generate comprehensive argument lists |
| 668 | p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] |
| 669 | paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| 670 | s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] |
| 671 | strides = {x for x in itertools.product(*([s_vals] * 2))} |
| 672 | k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] |
| 673 | kernels = {x for x in itertools.product(*([k_vals] * 2))} |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 674 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 675 | # add some oversize argument values |
| 676 | bigStride = 7 |
| 677 | strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| 678 | bigKernel = 6 |
| 679 | kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) |
| 680 | if max(shape) < 64: |
| 681 | # padding must be less than the kernel size |
| 682 | bigPadding = bigKernel - 1 |
| 683 | paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 684 | |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 685 | # There are too many parameter combinations, so generate them sparsely |
| 686 | sparsity = len(paddings) * len(strides) * len(kernels) // 500 + 1 |
| 687 | n = 0 |
| 688 | for s in sorted(list(strides)): |
| 689 | for p in sorted(list(paddings)): |
| 690 | for k in sorted(list(kernels)): |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 691 | # Calculate output height to test for error_if conditions |
| 692 | oh = (shape[1] + p[0] + p[1] + s[0] - k[0]) // s[0] |
| 693 | ow = (shape[2] + p[2] + p[3] + s[1] - k[1]) // s[1] |
| 694 | y = (oh * s[0]) - p[0] - p[1] - s[0] + k[0] |
| 695 | x = (ow * s[1]) - p[2] - p[3] - s[1] + k[1] |
| 696 | |
| 697 | if error_name in [ErrorIf.StrideSmallerOne, ErrorIf.KernelSmallerOne, ErrorIf.PadSmallerZero, ErrorIf.PadLargerEqualKernel]: |
| 698 | sNew, pNew, kNew = TosaErrorIfArgGen.eiPoolingErrorIf(testGen, error_name, s, p, k) |
| 699 | if None not in [sNew, pNew, kNew] and n % sparsity == 0: |
| 700 | arg_list.append( |
| 701 | ( |
| 702 | "st{}_kern{}_pad{}".format( |
| 703 | "".join([str(x) for x in sNew]), |
| 704 | "".join([str(x) for x in kNew]), |
| 705 | "".join([str(x) for x in pNew]), |
| 706 | ), |
| 707 | [sNew, pNew, kNew], |
| 708 | ) |
| 709 | ) |
| 710 | elif (n % sparsity == 0 |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 711 | # padding must not exceed the kernel size |
| 712 | and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] |
| 713 | # the padded shape must exceed the kernel size |
| 714 | and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 715 | and y < shape[1] and x < shape[2] |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 716 | ): |
| 717 | arg_list.append( |
| 718 | ( |
| 719 | "st{}_kern{}_pad{}".format( |
| 720 | "".join([str(x) for x in s]), |
| 721 | "".join([str(x) for x in k]), |
| 722 | "".join([str(x) for x in p]), |
| 723 | ), |
| 724 | [s, p, k], |
| 725 | ) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 726 | ) |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 727 | n += 1 |
| 728 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 729 | return arg_list |
| 730 | |
| 731 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 732 | def agCast(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 733 | arg_list = [] |
| 734 | |
| 735 | # Enumerate the output types here |
| 736 | if inDtype == DType.INT8: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 737 | dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 738 | elif inDtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 739 | dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 740 | elif inDtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 741 | dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 742 | elif inDtype == DType.BOOL: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 743 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 744 | elif inDtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 745 | dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 746 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 747 | raise Exception("Unexpected input dtype: {}".format(inDtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 748 | |
| 749 | for dtype in dtypeList: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 750 | arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 751 | |
| 752 | return arg_list |
| 753 | |
| 754 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 755 | def agRescale(testGen, opName, shapeList, inDtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 756 | arg_list = [] |
| 757 | |
| 758 | # Enumerate the output types here |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 759 | for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 760 | if dtype in [DType.UINT8, DType.INT8] and error_name == ErrorIf.OutputZeroPointNotZero: |
| 761 | continue |
| 762 | 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] | 763 | # The only output dtype for UINT8 is INT8, skip all other combinations |
| 764 | continue |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 765 | 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] | 766 | # The only input dtype for UINT8 is INT8, skip all other combinations |
| 767 | continue |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 768 | if error_name == ErrorIf.WrongOutputType and not TosaErrorIfArgGen.eiRescaleWrongOutputType(inDtype, dtype): |
| 769 | continue |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 770 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 771 | for scale32 in [False, True]: |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 772 | if error_name == ErrorIf.ScaleTrue and scale32 == False: |
| 773 | continue |
| 774 | elif error_name == ErrorIf.ScaleNotTrue and scale32 == True: |
| 775 | continue |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 776 | for double_round in [False, True]: |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 777 | if error_name == ErrorIf.ScaleNotTrue and double_round == False: |
| 778 | continue |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 779 | for per_channel in [False, True]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 780 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 781 | if inDtype == DType.INT48 and scale32 and error_name != ErrorIf.ScaleTrue: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 782 | # Illegal condition. Must be scale32=False |
| 783 | continue |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 784 | if double_round and not scale32 and error_name != ErrorIf.ScaleNotTrue: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 785 | # Illegal condition. ERROR_IF(!scale32 && double_round) |
| 786 | continue |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 787 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 788 | arg_list.append( |
| 789 | ( |
| 790 | "out{}_sc{}_dr{}_pc{}".format( |
| 791 | DTypeNames[dtype], |
| 792 | int(scale32), |
| 793 | int(double_round), |
| 794 | int(per_channel), |
| 795 | ), |
| 796 | [dtype, scale32, double_round, per_channel], |
| 797 | ) |
| 798 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 799 | |
| 800 | return arg_list |
| 801 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 802 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 803 | def agMul(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 804 | arg_list = [] |
| 805 | |
| 806 | if dtype is DType.INT32: |
| 807 | for p in range(testGen.args.num_rand_permutations): |
| 808 | |
| 809 | shift = testGen.randInt(0, 32) |
| 810 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 811 | arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 812 | else: |
Matthew Haddon | 43e3719 | 2021-07-09 14:13:02 +0100 | [diff] [blame] | 813 | arg_list.append(("perm0_shift0", [0])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 814 | |
| 815 | return arg_list |
| 816 | |
| 817 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 818 | def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None): |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 819 | arg_list = [] |
| 820 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 821 | arg_list.append(("roundTrue", [True])) |
| 822 | arg_list.append(("roundFalse", [False])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 823 | |
| 824 | return arg_list |
| 825 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 826 | # Helper function for reshape. Gets some factors of a larger number. |
| 827 | @staticmethod |
| 828 | def getFactors(val, start=1): |
| 829 | factors = [] |
| 830 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 831 | for i in range(start, int(np.sqrt(val)) + 1): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 832 | if (val % i) == 0: |
| 833 | factors.append(i) |
| 834 | |
| 835 | return factors |
| 836 | |
| 837 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 838 | def agReshape(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 839 | arg_list = [] |
| 840 | |
| 841 | origShape = shapeList[0] |
| 842 | |
| 843 | totalElements = 1 |
| 844 | for s in origShape: |
| 845 | totalElements *= s |
| 846 | |
| 847 | # This code is NOT fast. Fortunately, the numbers are fairly small. |
| 848 | factors = TosaArgGen.getFactors(totalElements) |
| 849 | |
| 850 | for p in range(testGen.args.num_rand_permutations): |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 851 | newRank = testGen.randInt(1, 7) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 852 | if len(factors) < newRank: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 853 | continue |
| 854 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 855 | found = True |
| 856 | # escape_counter breaks while loop if it continues on for too long |
| 857 | escape_counter = 0 |
| 858 | while found: |
| 859 | newShape = [] |
| 860 | # Generate newShape ensuring it isn't a duplicate |
| 861 | remainingElements = totalElements |
| 862 | shuffledFactors = testGen.rng.permutation(factors) |
Matthew Haddon | 5fc4e68 | 2021-07-07 11:28:29 +0100 | [diff] [blame] | 863 | for i in range(1, newRank): |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 864 | # pick rank-1 factors |
| 865 | newShape.append(shuffledFactors[0]) |
| 866 | remainingElements = remainingElements // shuffledFactors[0] |
| 867 | shuffledFactors = testGen.rng.permutation( |
| 868 | TosaArgGen.getFactors(remainingElements) |
| 869 | ) |
| 870 | newShape.append(remainingElements) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 871 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 872 | # Toss in a -1 sometimes |
| 873 | minusOne = testGen.randInt(0, newRank * 4) |
| 874 | if minusOne < newRank: |
| 875 | newShape[minusOne] = -1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 876 | |
Matthew Haddon | 2ad047d | 2021-06-22 16:55:23 +0100 | [diff] [blame] | 877 | # Check for duplicates |
| 878 | found = False |
| 879 | for name, other_shape in arg_list: |
| 880 | if other_shape[0] == newShape: |
| 881 | found = True |
| 882 | break |
| 883 | |
| 884 | escape_counter += 1 |
| 885 | if escape_counter >= 100: |
| 886 | break |
| 887 | |
| 888 | if not found: |
| 889 | arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 890 | |
| 891 | return arg_list |
| 892 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 893 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 894 | def agTranspose(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 895 | arg_list = [] |
| 896 | |
| 897 | ifm_shape = shapeList[0] |
| 898 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 899 | # Get all permutations |
| 900 | permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 901 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 902 | # Limit to possible permutations from shape dimension or argument setting |
| 903 | limit = min(len(permutations), testGen.args.num_rand_permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 904 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 905 | # Get random permutation generator that uses all permutations |
| 906 | random_permutations = testGen.rng.permutation(permutations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 907 | |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 908 | # Create list of required amount of permutations |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 909 | arg_list = [ |
| 910 | ("perm{}".format(p), [random_permutations[p].tolist()]) |
| 911 | for p in range(limit) |
| 912 | ] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 913 | return arg_list |
| 914 | |
| 915 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 916 | def agSlice(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 | rank = len(ifm_shape) |
| 921 | |
| 922 | for p in range(testGen.args.num_rand_permutations): |
| 923 | begin = [] |
| 924 | size = [] |
| 925 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 926 | valid = True |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 927 | |
| 928 | for i in range(rank): |
| 929 | if ifm_shape[i] > 1: |
| 930 | begin.append(testGen.randInt(0, ifm_shape[i])) |
| 931 | size.append(testGen.randInt(0, ifm_shape[i] - begin[i])) |
| 932 | |
| 933 | # Invalid slice size? |
| 934 | if size[i] == 0: |
| 935 | valid = False |
| 936 | else: |
| 937 | begin.append(0) |
| 938 | size.append(1) |
| 939 | |
| 940 | if valid: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 941 | arg_list.append(("perm{}".format(p), [begin, size])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 942 | return arg_list |
| 943 | |
| 944 | @staticmethod |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 945 | def agTile(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 946 | arg_list = [] |
| 947 | |
| 948 | ifm_shape = shapeList[0] |
| 949 | rank = len(ifm_shape) |
| 950 | |
| 951 | for p in range(testGen.args.num_rand_permutations): |
| 952 | |
| 953 | # Pick a few random, but small multiple values |
| 954 | # because otherwise this has a tendency to generate |
| 955 | # enormous tensors |
| 956 | multiples = [] |
| 957 | for i in range(rank): |
Matthew Haddon | 82ad4d6 | 2021-08-20 15:02:39 +0100 | [diff] [blame] | 958 | if ifm_shape[i] > 1000: |
| 959 | # Multiple of 1 if ifm_shape dimension is large to reduce tensor size |
| 960 | multiples.append(1) |
| 961 | elif max(ifm_shape) > 1000: |
| 962 | multiples.append(2) |
| 963 | else: |
| 964 | multiples.append(testGen.randInt(1, 4)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 965 | arg_list.append(("perm{}".format(p), [multiples])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 966 | |
| 967 | return arg_list |
| 968 | |
| 969 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 970 | def agResize(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 971 | arg_list = [] |
| 972 | |
| 973 | ifm_shape = shapeList[0] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 974 | for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 975 | |
| 976 | # Exclude illegal {mode, type} configurations. Pick legal output types |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 977 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 978 | outputDTypeList = [DType.INT8] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 979 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 980 | outputDTypeList = [DType.INT16] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 981 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
Les Bell | 33d837e | 2021-08-10 08:34:43 +0100 | [diff] [blame] | 982 | outputDTypeList = [DType.INT32] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 983 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 984 | outputDTypeList = [DType.INT48] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 985 | elif dtype == DType.FLOAT: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 986 | outputDTypeList = [DType.FLOAT] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 987 | elif error_name == ErrorIf.WrongInputType: |
| 988 | # If an incorrect input type is used then we set a 'correct' |
| 989 | # output type to avoid other errors |
| 990 | outputDTypeList = [DType.INT8, DType.INT16, DType.INT32] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 991 | else: |
| 992 | continue |
| 993 | |
| 994 | for outputDType in outputDTypeList: |
| 995 | for perm in range(testGen.args.num_rand_permutations): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 996 | # Randomly generate legal output dimensions and shift |
| 997 | # and then compute the stride and offset based on them |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 998 | # A output_dim of 1 will cause offset to exceed allowed range |
| 999 | # so minimum value 2 produced below |
| 1000 | output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1] |
| 1001 | while ((float(ifm_shape[1]) / float(output_dims[0])) >= 16): |
| 1002 | output_dims[0] += 1 |
| 1003 | while ((float(ifm_shape[2]) / float(output_dims[1])) >= 16): |
| 1004 | output_dims[1] += 1 |
| 1005 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1006 | in_center_h = (ifm_shape[1] - 1) / 2.0 |
| 1007 | in_center_w = (ifm_shape[2] - 1) / 2.0 |
| 1008 | out_center_h = (output_dims[0] - 1) / 2.0 |
| 1009 | out_center_w = (output_dims[1] - 1) / 2.0 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1010 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1011 | fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) |
| 1012 | fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) |
| 1013 | fp_offset_y = in_center_h - fp_stride_y * out_center_h |
| 1014 | fp_offset_x = in_center_w - fp_stride_x * out_center_w |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1015 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1016 | if outputDType == DType.FLOAT: |
| 1017 | shift = 0 |
| 1018 | stride = [0, 0] |
| 1019 | offset = [0, 0] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1020 | stride_fp = [fp_stride_y, fp_stride_x] |
| 1021 | offset_fp = [fp_offset_y, fp_offset_x] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1022 | |
| 1023 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1024 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1025 | testGen, |
| 1026 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1027 | mode, |
| 1028 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1029 | shapeList, |
| 1030 | outputDType, |
| 1031 | shift, |
| 1032 | stride, |
| 1033 | stride_fp, |
| 1034 | offset, |
| 1035 | offset_fp |
| 1036 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1037 | else: |
| 1038 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1039 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1040 | arg_list.append( |
| 1041 | ( |
| 1042 | "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] | 1043 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1044 | output_dims[0], |
| 1045 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1046 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1047 | stride_fp[0], |
| 1048 | stride_fp[1], |
| 1049 | offset_fp[0], |
| 1050 | offset_fp[1], |
| 1051 | ), |
| 1052 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1053 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1054 | stride, |
| 1055 | offset, |
| 1056 | shift, |
| 1057 | stride_fp, |
| 1058 | offset_fp, |
| 1059 | output_dims, |
| 1060 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1061 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1062 | ], |
| 1063 | ) |
| 1064 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1065 | else: |
| 1066 | shift = 11 |
| 1067 | unit = float(1 << shift) |
| 1068 | stride_y = int(round(fp_stride_y * unit)) |
| 1069 | stride_x = int(round(fp_stride_x * unit)) |
| 1070 | offset_y = int(round(fp_offset_y * unit)) |
| 1071 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1072 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1073 | while ( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1074 | stride_y >= (16 << shift) |
| 1075 | or stride_x >= (16 << shift) |
| 1076 | or offset_y >= (16 << shift) |
| 1077 | or offset_x >= (16 << shift) |
| 1078 | or offset_y <= (-16 << shift) |
| 1079 | or offset_x <= (-16 << shift) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1080 | ): |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1081 | shift = shift - 1 |
| 1082 | unit = float(1 << shift) |
| 1083 | stride_y = int(round(fp_stride_y * unit)) |
| 1084 | stride_x = int(round(fp_stride_x * unit)) |
| 1085 | offset_y = int(round(fp_offset_y * unit)) |
| 1086 | offset_x = int(round(fp_offset_x * unit)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1087 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1088 | stride = [stride_y, stride_x] |
| 1089 | offset = [offset_y, offset_x] |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 1090 | |
| 1091 | stride_fp = [0.0, 0.0] |
| 1092 | offset_fp = [0.0, 0.0] |
| 1093 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1094 | if error_name is not None: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1095 | shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1096 | testGen, |
| 1097 | error_name, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1098 | mode, |
| 1099 | dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1100 | shapeList, |
| 1101 | outputDType, |
| 1102 | shift, |
| 1103 | stride, |
| 1104 | stride_fp, |
| 1105 | offset, |
| 1106 | offset_fp |
| 1107 | ) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1108 | else: |
| 1109 | outputDTypeNew = outputDType |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1110 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1111 | arg_list.append( |
| 1112 | ( |
| 1113 | "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1114 | "N" if mode == ResizeMode.NEAREST else "B", |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1115 | shift, |
| 1116 | output_dims[0], |
| 1117 | output_dims[1], |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1118 | testGen.typeStr(outputDTypeNew), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1119 | stride[0], |
| 1120 | stride[1], |
| 1121 | offset[0], |
| 1122 | offset[1], |
| 1123 | ), |
| 1124 | [ |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1125 | mode, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1126 | stride, |
| 1127 | offset, |
| 1128 | shift, |
| 1129 | stride_fp, |
| 1130 | offset_fp, |
| 1131 | output_dims, |
| 1132 | dtype, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1133 | outputDTypeNew, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1134 | ], |
| 1135 | ) |
| 1136 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1137 | |
| 1138 | return arg_list |
| 1139 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1140 | def agCondIf(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1141 | # CondIf generates the condition values here. |
| 1142 | # Convert to tensors in the build function, along with the |
| 1143 | # then and else blocks |
| 1144 | arg_list = [] |
| 1145 | |
| 1146 | for c in [False, True]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1147 | arg_list.append(("cond{}".format(int(c)), [c])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1148 | |
| 1149 | return arg_list |
| 1150 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1151 | def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1152 | # While loop: 0 iterations, 1, more than 1 |
| 1153 | arg_list = [] |
| 1154 | |
| 1155 | for iter in [0, 1, 4]: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 1156 | arg_list.append(("iter{}".format(iter), [iter])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 1157 | |
| 1158 | return arg_list |
| 1159 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1160 | class TosaErrorIfArgGen: |
| 1161 | |
| 1162 | @staticmethod |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1163 | 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] | 1164 | |
| 1165 | if outputDType == DType.FLOAT: |
| 1166 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1167 | stride_fp = testGen.rng.random(size=[2]) - 2 |
| 1168 | elif error_name == ErrorIf.ShiftNotZero: |
| 1169 | shift = testGen.rng.integers(1, 5) |
| 1170 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1171 | shape = shapeList[0] |
| 1172 | transform_height = testGen.rng.choice([False, True]) |
| 1173 | if transform_height: |
| 1174 | stride_fp[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1175 | else: |
| 1176 | stride_fp[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1177 | else: |
| 1178 | if error_name == ErrorIf.StrideSmallerEqualZero: |
| 1179 | stride = np.int16(testGen.rng.integers(-1, 1, size=[2])) |
| 1180 | elif error_name == ErrorIf.ShiftSmallerOne: |
| 1181 | shift = testGen.rng.integers(-3, 1) |
| 1182 | if shift <= 0: |
| 1183 | stride = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1184 | offset = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks |
| 1185 | else: |
| 1186 | stride = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1187 | offset = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks |
| 1188 | elif error_name == ErrorIf.ShiftLargerEleven: |
| 1189 | shift = np.int16(testGen.rng.integers(12, 15)) |
| 1190 | elif error_name == ErrorIf.StrideLargerDimension: |
| 1191 | shape = shapeList[0] |
| 1192 | transform_height = testGen.rng.choice([False, True]) |
| 1193 | if transform_height: |
| 1194 | stride[0] = shape[1] + testGen.rng.integers(1, 10) |
| 1195 | else: |
| 1196 | stride[1] = shape[2] + testGen.rng.integers(1, 10) |
| 1197 | elif error_name == ErrorIf.StrideLargerEqualMax: |
| 1198 | stride = [(16 << shift) + 1, (16 << shift) + 1] |
| 1199 | elif error_name == ErrorIf.OffsetLargerEqualMax: |
| 1200 | offset = [(16 << shift) + 1, (16 << shift) + 1] |
| 1201 | elif error_name == ErrorIf.OffsetSmallerEqualMin: |
| 1202 | offset = [(-16 << shift) - 1, (-16 << shift) - 1] |
| 1203 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 1204 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1205 | if error_name == ErrorIf.WrongOutputType: |
| 1206 | if mode == ResizeMode.NEAREST and dtype == DType.INT8: |
| 1207 | incorrect_types = (DType.INT4, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1208 | elif mode == ResizeMode.NEAREST and dtype == DType.INT16: |
| 1209 | incorrect_types = (DType.INT4, DType.INT8, DType.INT32, DType.INT48, DType.FLOAT) |
| 1210 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: |
| 1211 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) |
| 1212 | elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: |
| 1213 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) |
| 1214 | elif dtype == DType.FLOAT: |
| 1215 | incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) |
| 1216 | outputDType = testGen.rng.choice(a=incorrect_types) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1217 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1218 | return shift, stride, stride_fp, offset, offset_fp, outputDType |
| 1219 | |
| 1220 | @staticmethod |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1221 | def eiPoolingErrorIf(testGen, error_name, stride, pad, kernel): |
| 1222 | if (error_name == ErrorIf.StrideSmallerOne |
| 1223 | # padding must not exceed the kernel size |
| 1224 | and pad[0] < kernel[0] and pad[1] < kernel[0] and pad[2] < kernel[1] and pad[3] < kernel[1]): |
| 1225 | wrongStride = (testGen.rng.choice([0, -1, -2, -3]), testGen.rng.choice([0, -1, -2, -3])) |
| 1226 | return wrongStride, pad, kernel |
| 1227 | elif error_name == ErrorIf.PadSmallerZero: |
| 1228 | wrongPad = (testGen.rng.choice([-1, -2, -3]), |
| 1229 | testGen.rng.choice([-1, -2, -3]), |
| 1230 | testGen.rng.choice([-1, -2, -3]), |
| 1231 | testGen.rng.choice([-1, -2, -3])) |
| 1232 | return stride, wrongPad, kernel |
| 1233 | elif error_name == ErrorIf.KernelSmallerOne: |
| 1234 | wrongKernel = (testGen.rng.choice([0, -1, -2, -3]), testGen.rng.choice([0, -1, -2, -3])) |
| 1235 | return stride, pad, wrongKernel |
| 1236 | elif error_name == ErrorIf.PadLargerEqualKernel: |
| 1237 | wrongPad = (testGen.rng.choice([kernel[0], kernel[0]+1, kernel[0]+2]), |
| 1238 | testGen.rng.choice([kernel[0], kernel[0]+1, kernel[0]+2]), |
| 1239 | testGen.rng.choice([kernel[1], kernel[1]+1, kernel[1]+2]), |
| 1240 | testGen.rng.choice([kernel[1], kernel[1]+1, kernel[1]+2])) |
| 1241 | return stride, wrongPad, kernel |
| 1242 | else: |
| 1243 | return None, None, None |
| 1244 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1245 | @staticmethod |
| 1246 | def eiRescaleWrongOutputType(input_dtype, output_dtype): |
| 1247 | if input_dtype == DType.INT8: |
| 1248 | if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 1249 | return True |
| 1250 | if input_dtype in [DType.INT16, DType.INT32]: |
| 1251 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1252 | return True |
| 1253 | elif input_dtype == DType.INT48: |
| 1254 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1255 | return True |
| 1256 | elif input_dtype == DType.UINT8: |
| 1257 | if output_dtype != DType.INT8: |
| 1258 | return True |
| 1259 | return False |
| 1260 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1261 | |
| 1262 | @staticmethod |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1263 | def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list): |
| 1264 | # Mess up input/output tensors for ERROR_IF checks |
| 1265 | if error_name == "WrongInputList": |
| 1266 | add_input = testGen.rng.choice([True, False]) |
| 1267 | if add_input: |
| 1268 | input_list.append('eiDummyInput') |
| 1269 | else: |
| 1270 | input_list = input_list[:-1] |
| 1271 | if error_name == "WrongOutputList": |
| 1272 | add_output = testGen.rng.choice([True, False]) |
| 1273 | if add_output: |
| 1274 | output_list.append('eiDummyOutput') |
| 1275 | else: |
| 1276 | output_list = [] |
| 1277 | return input_list, output_list |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1278 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1279 | @staticmethod |
| 1280 | def eiRestrictDimension(shape, error_name): |
| 1281 | # Restrict dimension size if rank is large for WrongRank Error_If |
| 1282 | # This will keep the test sizes reasonably small |
| 1283 | if error_name == ErrorIf.WrongRank: |
| 1284 | if len(shape) > 4: |
| 1285 | shape[4] = 1 |
| 1286 | |
| 1287 | return shape |
| 1288 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1289 | class TosaErrorValidator: |
| 1290 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1291 | @staticmethod |
| 1292 | def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs): |
| 1293 | # Check ERROR_IF statements |
| 1294 | |
| 1295 | for val_fcn in validator_fcns: |
| 1296 | val_result = val_fcn(True, **kwargs) |
| 1297 | |
| 1298 | validator_name = val_result['error_name'] |
| 1299 | error_result = val_result['error_result'] |
| 1300 | error_reason = val_result['error_reason'] |
| 1301 | |
| 1302 | if error_result: |
| 1303 | if error_name == validator_name: |
| 1304 | serializer.setExpectedReturnCode(2, error_reason) |
| 1305 | else: |
| 1306 | print(f"Multiple ERROR_IF checks hit \nError required: {error_name}, Error_produced: {validator_name}") |
| 1307 | return None # Return None to delete test if wrong ERROR_IF is hit |
| 1308 | else: |
| 1309 | if error_name == validator_name: |
| 1310 | print(f"No ERROR_IF hit for {error_name}") |
| 1311 | return None |
| 1312 | |
| 1313 | @staticmethod |
| 1314 | def evWrongInputType(check=False, **kwargs): |
| 1315 | all_dtypes = (DType.BOOL, DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) |
| 1316 | |
| 1317 | # Find the unsupported input data types |
| 1318 | assert 'op' in kwargs |
| 1319 | op = kwargs['op'] |
| 1320 | input_dtypes = op['types'] |
| 1321 | wrong_input_dtypes = list(set(all_dtypes) - set(input_dtypes)) |
| 1322 | |
| 1323 | error_name = ErrorIf.WrongInputType |
| 1324 | param_reqs = {"rank": None, "dtype": wrong_input_dtypes, "shape": None} |
| 1325 | error_result = False |
| 1326 | error_reason = "Input data type not supported for this operator" |
| 1327 | |
| 1328 | if check: |
| 1329 | input_dtype = kwargs['input_dtype'] |
| 1330 | if input_dtype not in input_dtypes: |
| 1331 | error_result = True |
| 1332 | |
| 1333 | info_dict = { |
| 1334 | "error_name": error_name, |
| 1335 | "error_result": error_result, |
| 1336 | "error_reason": error_reason, |
| 1337 | "param_reqs": param_reqs |
| 1338 | } |
| 1339 | return info_dict |
| 1340 | |
| 1341 | @staticmethod |
| 1342 | def evWrongOutputType(check=False, **kwargs): |
| 1343 | error_name = ErrorIf.WrongOutputType |
| 1344 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1345 | error_result = False |
| 1346 | error_reason = "Output data type not supported for this configuration of operator" |
| 1347 | |
| 1348 | if check: |
| 1349 | input_dtype = kwargs['input_dtype'] |
| 1350 | output_dtype = kwargs['output_dtype'] |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1351 | op = kwargs['op'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1352 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1353 | if op['op'] == Op.RESIZE: |
| 1354 | mode = kwargs['mode'] |
| 1355 | if ( |
| 1356 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT8 and output_dtype != DType.INT8) or |
| 1357 | (mode == ResizeMode.NEAREST and input_dtype == DType.INT16 and output_dtype != DType.INT16) or |
| 1358 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT8 and output_dtype != DType.INT32) or |
| 1359 | (mode == ResizeMode.BILINEAR and input_dtype == DType.INT16 and output_dtype != DType.INT48) or |
| 1360 | (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) |
| 1361 | ): |
| 1362 | error_result = True |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1363 | elif op['op'] == Op.RESCALE: |
| 1364 | if input_dtype == DType.INT8: |
| 1365 | if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: |
| 1366 | error_result = True |
| 1367 | if input_dtype in [DType.INT16, DType.INT32]: |
| 1368 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1369 | error_result = True |
| 1370 | elif input_dtype == DType.INT48: |
| 1371 | if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: |
| 1372 | error_result = True |
| 1373 | elif input_dtype == DType.UINT8: |
| 1374 | if output_dtype != DType.INT8: |
| 1375 | error_result = True |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1376 | else: |
| 1377 | if output_dtype != input_dtype: |
| 1378 | error_result = True |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1379 | |
| 1380 | info_dict = { |
| 1381 | "error_name": error_name, |
| 1382 | "error_result": error_result, |
| 1383 | "error_reason": error_reason, |
| 1384 | "param_reqs": param_reqs |
| 1385 | } |
| 1386 | return info_dict |
| 1387 | |
| 1388 | @staticmethod |
| 1389 | def evWrongRank(check=False, **kwargs): |
| 1390 | all_ranks = (1, 2, 3, 4, 5) |
| 1391 | |
| 1392 | # Make a list of incorrect ranks |
| 1393 | assert 'op' in kwargs |
| 1394 | op = kwargs['op'] |
| 1395 | rmin, rmax = op['rank'] |
| 1396 | rank_range = range(rmin, rmax + 1) |
| 1397 | incorrect_ranks = list(set(all_ranks) - set(rank_range)) |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1398 | # Remove small incorrect ranks to avoid index errors |
| 1399 | incorrect_ranks = [rank for rank in incorrect_ranks if rank > rmin] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1400 | # Set minimum incorrect rank to 3 to avoid index error |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1401 | if op['op'] in [Op.RESIZE]: |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1402 | incorrect_ranks = [3, 5] |
| 1403 | |
| 1404 | error_name = ErrorIf.WrongRank |
| 1405 | param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None} |
| 1406 | error_result = False |
| 1407 | error_reason = "Rank not supported for this operator" |
| 1408 | |
| 1409 | if check: |
| 1410 | input_shape = kwargs['input_shape'] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1411 | 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] | 1412 | error_result = True |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1413 | else: |
| 1414 | if len(input_shape) not in rank_range: |
| 1415 | error_result = True |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1416 | |
| 1417 | info_dict = { |
| 1418 | "error_name": error_name, |
| 1419 | "error_result": error_result, |
| 1420 | "error_reason": error_reason, |
| 1421 | "param_reqs": param_reqs |
| 1422 | } |
| 1423 | return info_dict |
| 1424 | |
| 1425 | @staticmethod |
| 1426 | def evWrongInputList(check=False, **kwargs): |
| 1427 | error_name = ErrorIf.WrongInputList |
| 1428 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1429 | error_result = False |
| 1430 | error_reason = "Op input list does not match expected input" |
| 1431 | |
| 1432 | if check: |
| 1433 | op = kwargs['op'] |
| 1434 | input_list = kwargs['input_list'] |
| 1435 | num_operands = kwargs['num_operands'] |
| 1436 | if len(input_list) != num_operands: |
| 1437 | error_result = True |
| 1438 | |
| 1439 | info_dict = { |
| 1440 | "error_name": error_name, |
| 1441 | "error_result": error_result, |
| 1442 | "error_reason": error_reason, |
| 1443 | "param_reqs": param_reqs |
| 1444 | } |
| 1445 | return info_dict |
| 1446 | |
| 1447 | @staticmethod |
| 1448 | def evWrongOutputList(check=False, **kwargs): |
| 1449 | error_name = ErrorIf.WrongOutputList |
| 1450 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1451 | error_result = False |
| 1452 | error_reason = "Op output list does not match expected output" |
| 1453 | |
| 1454 | if check: |
| 1455 | output_list = kwargs['output_list'] |
| 1456 | # Note this will be incorrect if an operator returns more than one output |
| 1457 | if len(output_list) != 1: |
| 1458 | error_result = True |
| 1459 | |
| 1460 | info_dict = { |
| 1461 | "error_name": error_name, |
| 1462 | "error_result": error_result, |
| 1463 | "error_reason": error_reason, |
| 1464 | "param_reqs": param_reqs |
| 1465 | } |
| 1466 | return info_dict |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1467 | |
| 1468 | @staticmethod |
| 1469 | def evMaxDimExceeded(check=False, **kwargs): |
| 1470 | error_name = ErrorIf.MaxDimExceeded |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1471 | param_reqs = { |
| 1472 | "rank": [4,4], |
| 1473 | "dtype": [DType.INT8], |
| 1474 | "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]] |
| 1475 | } |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1476 | error_result = False |
| 1477 | error_reason = "At least one maximum dimension is larger than 16384" |
| 1478 | |
| 1479 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1480 | input_shape = kwargs['input_shape'] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1481 | output_shape = kwargs['output_shape'] # Note this is just (OH, OW) |
| 1482 | if ((input_shape[1] > 16384) or |
| 1483 | (input_shape[2] > 16384) or |
| 1484 | (output_shape[0] > 16384) or |
| 1485 | (output_shape[1] > 16384)): |
| 1486 | error_result = True |
| 1487 | |
| 1488 | info_dict = { |
| 1489 | "error_name": error_name, |
| 1490 | "error_result": error_result, |
| 1491 | "error_reason": error_reason, |
| 1492 | "param_reqs": param_reqs |
| 1493 | } |
| 1494 | return info_dict |
| 1495 | |
| 1496 | @staticmethod |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1497 | def evBatchMismatch(check=False, **kwargs): |
| 1498 | error_name = ErrorIf.BatchMismatch |
| 1499 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1500 | error_result = False |
| 1501 | error_reason = "Input batch size not equal to output batch size" |
| 1502 | |
| 1503 | assert 'op' in kwargs |
| 1504 | op = kwargs['op'] |
| 1505 | rmin, rmax = op['rank'] |
| 1506 | rank_range = range(rmin, rmax + 1) |
| 1507 | |
| 1508 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1509 | input_shape = kwargs['input_shape'] |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1510 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1511 | |
| 1512 | if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]): |
| 1513 | error_result = True |
| 1514 | |
| 1515 | info_dict = { |
| 1516 | "error_name": error_name, |
| 1517 | "error_result": error_result, |
| 1518 | "error_reason": error_reason, |
| 1519 | "param_reqs": param_reqs |
| 1520 | } |
| 1521 | return info_dict |
| 1522 | |
| 1523 | @staticmethod |
| 1524 | def evChannelMismatch(check=False, **kwargs): |
| 1525 | error_name = ErrorIf.ChannelMismatch |
| 1526 | param_reqs = {"rank": [4,4], "dtype": None, "shape": None} |
| 1527 | error_result = False |
| 1528 | error_reason = "Input channel size not equal to output channel size" |
| 1529 | |
| 1530 | assert 'op' in kwargs |
| 1531 | op = kwargs['op'] |
| 1532 | rmin, rmax = op['rank'] |
| 1533 | rank_range = range(rmin, rmax + 1) |
| 1534 | |
| 1535 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1536 | input_shape = kwargs['input_shape'] |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 1537 | output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) |
| 1538 | if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]): |
| 1539 | error_result = True |
| 1540 | |
| 1541 | info_dict = { |
| 1542 | "error_name": error_name, |
| 1543 | "error_result": error_result, |
| 1544 | "error_reason": error_reason, |
| 1545 | "param_reqs": param_reqs |
| 1546 | } |
| 1547 | return info_dict |
| 1548 | |
| 1549 | @staticmethod |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1550 | def evStrideSmallerEqualZero(check=False, **kwargs): |
| 1551 | error_name = ErrorIf.StrideSmallerEqualZero |
| 1552 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1553 | error_result = False |
| 1554 | error_reason = "Stride value smaller than or equal zero" |
| 1555 | |
| 1556 | if check: |
| 1557 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1558 | output_dtype = kwargs['output_dtype'] |
| 1559 | if input_dtype != DType.FLOAT and output_dtype == DType.FLOAT: |
| 1560 | stride = kwargs['stride'] # Work around wrong input/output type tests |
| 1561 | elif output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1562 | stride = kwargs['stride_fp'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1563 | elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT: |
| 1564 | stride = kwargs['stride_fp'] # Work around wrong input/output type tests |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1565 | else: |
| 1566 | stride = kwargs['stride'] |
| 1567 | |
| 1568 | if min(stride) <= 0: |
| 1569 | error_result = True |
| 1570 | |
| 1571 | info_dict = { |
| 1572 | "error_name": error_name, |
| 1573 | "error_result": error_result, |
| 1574 | "error_reason": error_reason, |
| 1575 | "param_reqs": param_reqs |
| 1576 | } |
| 1577 | return info_dict |
| 1578 | |
| 1579 | @staticmethod |
| 1580 | def evStrideLargerEqualMax(check=False, **kwargs): |
| 1581 | error_name = ErrorIf.StrideLargerEqualMax |
| 1582 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1583 | error_result = False |
| 1584 | error_reason = "Stride value larger than or equal to maximum value" |
| 1585 | |
| 1586 | if check: |
| 1587 | shift = kwargs['shift'] |
| 1588 | input_dtype = kwargs['input_dtype'] |
| 1589 | stride = kwargs['stride'] |
| 1590 | if input_dtype in [DType.INT8, DType.INT16]: |
| 1591 | if shift >= 0 and (stride[0] >= (16 << shift) or stride[1] >= (16 << shift)): |
| 1592 | error_result = True |
| 1593 | elif shift < 0 and (stride[0] >= (16 >> -shift) or stride[1] >= (16 >> -shift)): |
| 1594 | error_result = True |
| 1595 | |
| 1596 | info_dict = { |
| 1597 | "error_name": error_name, |
| 1598 | "error_result": error_result, |
| 1599 | "error_reason": error_reason, |
| 1600 | "param_reqs": param_reqs |
| 1601 | } |
| 1602 | return info_dict |
| 1603 | |
| 1604 | |
| 1605 | @staticmethod |
| 1606 | def evStrideLargerDimension(check=False, **kwargs): |
| 1607 | error_name = ErrorIf.StrideLargerDimension |
| 1608 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1609 | error_result = False |
| 1610 | error_reason = "Stride value larger than or equal to H/W dimension" |
| 1611 | |
| 1612 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1613 | shape = kwargs['input_shape'] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1614 | input_dtype = kwargs['input_dtype'] |
| 1615 | stride = kwargs['stride_fp'] |
| 1616 | |
| 1617 | if input_dtype == DType.FLOAT and (stride[0] > shape[1]) or (stride[1] > shape[2]): |
| 1618 | error_result = True |
| 1619 | |
| 1620 | info_dict = { |
| 1621 | "error_name": error_name, |
| 1622 | "error_result": error_result, |
| 1623 | "error_reason": error_reason, |
| 1624 | "param_reqs": param_reqs |
| 1625 | } |
| 1626 | return info_dict |
| 1627 | |
| 1628 | |
| 1629 | @staticmethod |
| 1630 | def evOffsetSmallerEqualMin(check=False, **kwargs): |
| 1631 | error_name = ErrorIf.OffsetSmallerEqualMin |
| 1632 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1633 | error_result = False |
| 1634 | error_reason = "Offset value smaller than or equal to minimum value" |
| 1635 | |
| 1636 | if check: |
| 1637 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1638 | output_dtype = kwargs['output_dtype'] |
| 1639 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1640 | offset = kwargs['offset_fp'] |
| 1641 | else: |
| 1642 | offset = kwargs['offset'] |
| 1643 | |
| 1644 | if shift >= 0 and (offset[0] <= (-16 << shift) or offset[1] <= (-16 << shift)): |
| 1645 | error_result = True |
| 1646 | elif shift < 0 and (offset[0] <= (-16 >> -shift) or offset[1] <= (-16 >> -shift)): |
| 1647 | error_result = True |
| 1648 | |
| 1649 | info_dict = { |
| 1650 | "error_name": error_name, |
| 1651 | "error_result": error_result, |
| 1652 | "error_reason": error_reason, |
| 1653 | "param_reqs": param_reqs |
| 1654 | } |
| 1655 | return info_dict |
| 1656 | |
| 1657 | @staticmethod |
| 1658 | def evOffsetLargerEqualMax(check=False, **kwargs): |
| 1659 | error_name = ErrorIf.OffsetLargerEqualMax |
| 1660 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1661 | error_result = False |
| 1662 | error_reason = "Offset value larger than or equal to maximum value" |
| 1663 | |
| 1664 | if check: |
| 1665 | shift = kwargs['shift'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1666 | output_dtype = kwargs['output_dtype'] |
| 1667 | if output_dtype == DType.FLOAT: |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 1668 | offset = kwargs['offset_fp'] |
| 1669 | else: |
| 1670 | offset = kwargs['offset'] |
| 1671 | |
| 1672 | if shift >= 0: |
| 1673 | if offset[0] >= (16 << shift) or offset[1] >= (16 << shift): |
| 1674 | error_result = True |
| 1675 | |
| 1676 | if shift >= 0 and (offset[0] >= (16 << shift) or offset[1] >= (16 << shift)): |
| 1677 | error_result = True |
| 1678 | elif shift < 0 and (offset[0] >= (16 >> -shift) or offset[1] >= (16 >> -shift)): |
| 1679 | error_result = True |
| 1680 | |
| 1681 | info_dict = { |
| 1682 | "error_name": error_name, |
| 1683 | "error_result": error_result, |
| 1684 | "error_reason": error_reason, |
| 1685 | "param_reqs": param_reqs |
| 1686 | } |
| 1687 | return info_dict |
| 1688 | |
| 1689 | @staticmethod |
| 1690 | def evShiftNotZero(check=False, **kwargs): |
| 1691 | error_name = ErrorIf.ShiftNotZero |
| 1692 | param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} |
| 1693 | error_result = False |
| 1694 | error_reason = "Shift value must be zero for float input" |
| 1695 | |
| 1696 | if check: |
| 1697 | shift = kwargs['shift'] |
| 1698 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1699 | output_dtype = kwargs['output_dtype'] |
| 1700 | 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] | 1701 | error_result = True |
| 1702 | |
| 1703 | info_dict = { |
| 1704 | "error_name": error_name, |
| 1705 | "error_result": error_result, |
| 1706 | "error_reason": error_reason, |
| 1707 | "param_reqs": param_reqs |
| 1708 | } |
| 1709 | return info_dict |
| 1710 | |
| 1711 | |
| 1712 | @staticmethod |
| 1713 | def evShiftSmallerOne(check=False, **kwargs): |
| 1714 | error_name = ErrorIf.ShiftSmallerOne |
| 1715 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1716 | error_result = False |
| 1717 | error_reason = "Shift value smaller than one" |
| 1718 | |
| 1719 | if check: |
| 1720 | shift = kwargs['shift'] |
| 1721 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 1722 | output_dtype = kwargs['output_dtype'] |
| 1723 | 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] | 1724 | error_result = True |
| 1725 | |
| 1726 | info_dict = { |
| 1727 | "error_name": error_name, |
| 1728 | "error_result": error_result, |
| 1729 | "error_reason": error_reason, |
| 1730 | "param_reqs": param_reqs |
| 1731 | } |
| 1732 | return info_dict |
| 1733 | |
| 1734 | @staticmethod |
| 1735 | def evShiftLargerEleven(check=False, **kwargs): |
| 1736 | error_name = ErrorIf.ShiftLargerEleven |
| 1737 | param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} |
| 1738 | error_result = False |
| 1739 | error_reason = "Shift value larger than eleven" |
| 1740 | |
| 1741 | if check: |
| 1742 | shift = kwargs['shift'] |
| 1743 | if shift > 11: |
| 1744 | error_result = True |
| 1745 | |
| 1746 | info_dict = { |
| 1747 | "error_name": error_name, |
| 1748 | "error_result": error_result, |
| 1749 | "error_reason": error_reason, |
| 1750 | "param_reqs": param_reqs |
| 1751 | } |
| 1752 | return info_dict |
| 1753 | |
| 1754 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1755 | @staticmethod |
| 1756 | def evRankMismatch(check=False, **kwargs): |
| 1757 | error_name = ErrorIf.RankMismatch |
| 1758 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1759 | error_result = False |
| 1760 | error_reason = "Input Rank does not match output rank" |
| 1761 | |
| 1762 | if check: |
| 1763 | input1_shape = kwargs['input1'].shape |
| 1764 | input2_shape = kwargs['input2'].shape |
| 1765 | output_shape = kwargs['result_tensor'].shape |
| 1766 | if (len(input1_shape) != len(output_shape)) or (len(input2_shape) != len(output_shape)): |
| 1767 | error_result = True |
| 1768 | |
| 1769 | info_dict = { |
| 1770 | "error_name": error_name, |
| 1771 | "error_result": error_result, |
| 1772 | "error_reason": error_reason, |
| 1773 | "param_reqs": param_reqs |
| 1774 | } |
| 1775 | return info_dict |
| 1776 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1777 | @staticmethod |
| 1778 | def evInputZeroPointNotZero(check=False, **kwargs): |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1779 | op = kwargs['op'] |
| 1780 | inputDtypes = op['types'].copy() |
| 1781 | if DType.INT8 in inputDtypes: |
| 1782 | inputDtypes.remove(DType.INT8) |
| 1783 | if DType.UINT8 in inputDtypes: |
| 1784 | inputDtypes.remove(DType.UINT8) |
| 1785 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1786 | error_name = ErrorIf.InputZeroPointNotZero |
| 1787 | param_reqs = { |
| 1788 | "rank": None, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1789 | "dtype": inputDtypes, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1790 | "shape": None |
| 1791 | } |
| 1792 | error_result = False |
| 1793 | error_reason = "Input DType not INT8 and zero point not 0" |
| 1794 | |
| 1795 | if check: |
| 1796 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1797 | if isinstance(kwargs['qinfo'], tuple): |
| 1798 | qinfo = kwargs['qinfo'] |
| 1799 | input_zero_point = qinfo[0] |
| 1800 | else: |
| 1801 | # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp |
| 1802 | qinfo = kwargs['qinfo'].ints |
| 1803 | input_zero_point = qinfo[0][1] |
| 1804 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1805 | if input_dtype not in [DType.INT8, DType.UINT8] and input_zero_point != 0: |
| 1806 | error_result = True |
| 1807 | |
| 1808 | info_dict = { |
| 1809 | "error_name": error_name, |
| 1810 | "error_result": error_result, |
| 1811 | "error_reason": error_reason, |
| 1812 | "param_reqs": param_reqs |
| 1813 | } |
| 1814 | return info_dict |
| 1815 | |
| 1816 | |
| 1817 | @staticmethod |
| 1818 | def evOutputZeroPointNotZero(check=False, **kwargs): |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1819 | op = kwargs['op'] |
| 1820 | inputDtypes = op['types'].copy() |
| 1821 | if DType.INT8 in inputDtypes: |
| 1822 | inputDtypes.remove(DType.INT8) |
| 1823 | if DType.UINT8 in inputDtypes: |
| 1824 | inputDtypes.remove(DType.UINT8) |
| 1825 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1826 | error_name = ErrorIf.OutputZeroPointNotZero |
| 1827 | param_reqs = { |
| 1828 | "rank": None, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1829 | "dtype": inputDtypes, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 1830 | "shape": None |
| 1831 | } |
| 1832 | error_result = False |
| 1833 | error_reason = "Output DType not INT8 and zero point not 0" |
| 1834 | |
| 1835 | if check: |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1836 | input_dtype = kwargs['input_dtype'] |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 1837 | output_dtype = kwargs['output_dtype'] |
| 1838 | if isinstance(kwargs['qinfo'], tuple): |
| 1839 | qinfo = kwargs['qinfo'] |
| 1840 | output_zero_point = qinfo[1] |
| 1841 | else: |
| 1842 | # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp |
| 1843 | qinfo = kwargs['qinfo'].ints |
| 1844 | output_zero_point = qinfo[1][1] |
| 1845 | if op['op'] == Op.AVG_POOL2D: |
| 1846 | if input_dtype != DType.INT8 and output_zero_point != 0: |
| 1847 | error_result = True |
| 1848 | 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] | 1849 | error_result = True |
| 1850 | |
| 1851 | info_dict = { |
| 1852 | "error_name": error_name, |
| 1853 | "error_result": error_result, |
| 1854 | "error_reason": error_reason, |
| 1855 | "param_reqs": param_reqs |
| 1856 | } |
| 1857 | return info_dict |
| 1858 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 1859 | @staticmethod |
| 1860 | def evAxisSmallerZero(check=False, **kwargs): |
| 1861 | error_name = ErrorIf.AxisSmallerZero |
| 1862 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1863 | error_result = False |
| 1864 | error_reason = "Axis smaller than zero" |
| 1865 | |
| 1866 | if check: |
| 1867 | axis = kwargs['axis'] |
| 1868 | if axis < 0: |
| 1869 | error_result = True |
| 1870 | |
| 1871 | info_dict = { |
| 1872 | "error_name": error_name, |
| 1873 | "error_result": error_result, |
| 1874 | "error_reason": error_reason, |
| 1875 | "param_reqs": param_reqs |
| 1876 | } |
| 1877 | return info_dict |
| 1878 | |
| 1879 | |
| 1880 | @staticmethod |
| 1881 | def evAxisLargerRank(check=False, **kwargs): |
| 1882 | error_name = ErrorIf.AxisLargerRank |
| 1883 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1884 | error_result = False |
| 1885 | error_reason = "Axis larger than rank" |
| 1886 | |
| 1887 | if check: |
| 1888 | axis = kwargs['axis'] |
| 1889 | shape = kwargs['input_shape'] |
| 1890 | if axis > len(shape): |
| 1891 | error_result = True |
| 1892 | |
| 1893 | info_dict = { |
| 1894 | "error_name": error_name, |
| 1895 | "error_result": error_result, |
| 1896 | "error_reason": error_reason, |
| 1897 | "param_reqs": param_reqs |
| 1898 | } |
| 1899 | return info_dict |
| 1900 | |
| 1901 | |
| 1902 | @staticmethod |
| 1903 | def evShapeOfAxisNotOne(check=False, **kwargs): |
| 1904 | error_name = ErrorIf.ShapeOfAxisNotOne |
| 1905 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1906 | error_result = False |
| 1907 | error_reason = "shape[axis] is not equal to 1" |
| 1908 | |
| 1909 | if check: |
| 1910 | axis = kwargs['axis'] |
| 1911 | shape = kwargs['output_shape'] |
| 1912 | if (0 <= axis < len(shape)) and shape[axis] != 1: |
| 1913 | error_result = True |
| 1914 | |
| 1915 | info_dict = { |
| 1916 | "error_name": error_name, |
| 1917 | "error_result": error_result, |
| 1918 | "error_reason": error_reason, |
| 1919 | "param_reqs": param_reqs |
| 1920 | } |
| 1921 | return info_dict |
| 1922 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 1923 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 1924 | @staticmethod |
| 1925 | def evPadSmallerZero(check=False, **kwargs): |
| 1926 | error_name = ErrorIf.PadSmallerZero |
| 1927 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1928 | error_result = False |
| 1929 | error_reason = "At least one pad is smaller than zero" |
| 1930 | |
| 1931 | if check: |
| 1932 | pad = kwargs['pad'] |
| 1933 | if min(pad) < 0: |
| 1934 | error_result = True |
| 1935 | |
| 1936 | info_dict = { |
| 1937 | "error_name": error_name, |
| 1938 | "error_result": error_result, |
| 1939 | "error_reason": error_reason, |
| 1940 | "param_reqs": param_reqs |
| 1941 | } |
| 1942 | return info_dict |
| 1943 | |
| 1944 | |
| 1945 | @staticmethod |
| 1946 | def evPadLargerEqualKernel(check=False, **kwargs): |
| 1947 | error_name = ErrorIf.PadLargerEqualKernel |
| 1948 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1949 | error_result = False |
| 1950 | error_reason = "At least one pad is larger than kernel dimension" |
| 1951 | |
| 1952 | if check: |
| 1953 | pad = kwargs['pad'] |
| 1954 | kernel = kwargs['kernel'] |
| 1955 | if min(pad) > 0 and min(kernel) > 1: |
| 1956 | if pad[0] >= kernel[0] or pad[1] >= kernel[0] or pad[2] >= kernel[1] or pad[3] >= kernel[1]: |
| 1957 | error_result = True |
| 1958 | |
| 1959 | info_dict = { |
| 1960 | "error_name": error_name, |
| 1961 | "error_result": error_result, |
| 1962 | "error_reason": error_reason, |
| 1963 | "param_reqs": param_reqs |
| 1964 | } |
| 1965 | return info_dict |
| 1966 | |
| 1967 | @staticmethod |
| 1968 | def evPoolingOutputShapeMismatch(check=False, **kwargs): |
| 1969 | error_name = ErrorIf.PoolingOutputShapeMismatch |
| 1970 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 1971 | error_result = False |
| 1972 | error_reason = "Mismatch between output shape provided and expected output shape" |
| 1973 | |
| 1974 | if check: |
| 1975 | pad = kwargs['pad'] |
| 1976 | pad_top, pad_bottom, pad_left, pad_right = pad[0], pad[1], pad[2], pad[3] |
| 1977 | |
| 1978 | kernel = kwargs['kernel'] |
| 1979 | kernel_y, kernel_x = kernel[0], kernel[1] |
| 1980 | |
| 1981 | input_shape = kwargs['input_shape'] |
| 1982 | IH, IW = input_shape[1], input_shape[2] |
| 1983 | |
| 1984 | output_shape = kwargs['output_shape'] |
| 1985 | OH, OW = output_shape[1], output_shape[2] |
| 1986 | |
| 1987 | stride = kwargs['stride'] |
| 1988 | stride_y, stride_x = stride[0], stride[1] |
| 1989 | |
| 1990 | # calculate correct height, width dimensions |
| 1991 | if stride_x != 0 and stride_y != 0: |
| 1992 | y_correct = (IH + pad_top + pad_bottom + stride_y - kernel_y) // stride_y |
| 1993 | x_correct = (IW + pad_left + pad_right + stride_x - kernel_x) // stride_x |
| 1994 | |
| 1995 | # ensure parameters are valid |
| 1996 | params_valid = (min(kernel) >= 1 and min(stride) >= 1 and min(pad) >= 0 |
| 1997 | and not (pad[0] >= kernel[0] or pad[1] >= kernel[0] or pad[2] >= kernel[1] or pad[3] >= kernel[1])) |
| 1998 | |
| 1999 | if params_valid and (OH != y_correct or OW != x_correct): |
| 2000 | error_result = True |
| 2001 | |
| 2002 | info_dict = { |
| 2003 | "error_name": error_name, |
| 2004 | "error_result": error_result, |
| 2005 | "error_reason": error_reason, |
| 2006 | "param_reqs": param_reqs |
| 2007 | } |
| 2008 | return info_dict |
| 2009 | |
| 2010 | |
| 2011 | @staticmethod |
| 2012 | def evKernelSmallerOne(check=False, **kwargs): |
| 2013 | error_name = ErrorIf.KernelSmallerOne |
| 2014 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2015 | error_result = False |
| 2016 | error_reason = "At least one kernel dimension is smaller than zero" |
| 2017 | |
| 2018 | if check: |
| 2019 | kernel = kwargs['kernel'] |
| 2020 | if min(kernel) < 1: |
| 2021 | error_result = True |
| 2022 | |
| 2023 | info_dict = { |
| 2024 | "error_name": error_name, |
| 2025 | "error_result": error_result, |
| 2026 | "error_reason": error_reason, |
| 2027 | "param_reqs": param_reqs |
| 2028 | } |
| 2029 | return info_dict |
| 2030 | |
| 2031 | @staticmethod |
| 2032 | def evStrideSmallerOne(check=False, **kwargs): |
| 2033 | error_name = ErrorIf.StrideSmallerOne |
| 2034 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2035 | error_result = False |
| 2036 | error_reason = "At least one stride dimension is smaller than zero" |
| 2037 | |
| 2038 | if check: |
| 2039 | stride = kwargs['stride'] |
| 2040 | if min(stride) < 1: |
| 2041 | error_result = True |
| 2042 | |
| 2043 | info_dict = { |
| 2044 | "error_name": error_name, |
| 2045 | "error_result": error_result, |
| 2046 | "error_reason": error_reason, |
| 2047 | "param_reqs": param_reqs |
| 2048 | } |
| 2049 | return info_dict |
| 2050 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 2051 | @staticmethod |
| 2052 | def evScaleTrue(check=False, **kwargs): |
| 2053 | error_name = ErrorIf.ScaleTrue |
| 2054 | param_reqs = {"rank": None, "dtype": [DType.INT48], "shape": None} |
| 2055 | error_result = False |
| 2056 | error_reason = "Scale set to true but input type is INT48" |
| 2057 | |
| 2058 | if check: |
| 2059 | input_dtype = kwargs['input_dtype'] |
| 2060 | scale32 = kwargs['scale32'] |
| 2061 | if scale32 and input_dtype == DType.INT48: |
| 2062 | error_result = True |
| 2063 | |
| 2064 | info_dict = { |
| 2065 | "error_name": error_name, |
| 2066 | "error_result": error_result, |
| 2067 | "error_reason": error_reason, |
| 2068 | "param_reqs": param_reqs |
| 2069 | } |
| 2070 | return info_dict |
| 2071 | |
| 2072 | @staticmethod |
| 2073 | def evScaleNotTrue(check=False, **kwargs): |
| 2074 | error_name = ErrorIf.ScaleNotTrue |
| 2075 | param_reqs = {"rank": None, "dtype": None, "shape": None} |
| 2076 | error_result = False |
| 2077 | error_reason = "Scale set to false but double round set to true" |
| 2078 | |
| 2079 | if check: |
| 2080 | scale32 = kwargs['scale32'] |
| 2081 | double_round = kwargs['double_round'] |
| 2082 | if not scale32 and double_round: |
| 2083 | error_result = True |
| 2084 | |
| 2085 | info_dict = { |
| 2086 | "error_name": error_name, |
| 2087 | "error_result": error_result, |
| 2088 | "error_reason": error_reason, |
| 2089 | "param_reqs": param_reqs |
| 2090 | } |
| 2091 | return info_dict |
| 2092 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2093 | |
| 2094 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2095 | class TosaInvalidValidator: |
| 2096 | |
| 2097 | @staticmethod |
| 2098 | def ivWrongDataTypeOrModeResize(**kwargs): |
| 2099 | input_dtype = kwargs["input_dtype"] |
| 2100 | args = kwargs["args"] |
| 2101 | mode = args[0] |
| 2102 | stride = args[1] |
| 2103 | stride_fp = args[4] |
| 2104 | output_dtype = args[8] |
| 2105 | |
| 2106 | if mode == ResizeMode.BILINEAR: |
| 2107 | # Invalid output data type / Invalid input datatype |
| 2108 | return ( |
| 2109 | not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or |
| 2110 | not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or |
| 2111 | not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or |
| 2112 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 2113 | ) |
| 2114 | elif mode == ResizeMode.NEAREST: |
| 2115 | # Invalid output data type / Invalid input datatype |
| 2116 | return ( |
| 2117 | (input_dtype != output_dtype) or |
| 2118 | (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) |
| 2119 | ) |
| 2120 | else: |
| 2121 | # Invalid resize mode |
| 2122 | return True |
| 2123 | |
| 2124 | @staticmethod |
| 2125 | def ivBadStride(**kwargs): |
| 2126 | input_dtype = kwargs["input_dtype"] |
| 2127 | args = kwargs["args"] |
| 2128 | stride_x = args[1][0] |
| 2129 | stride_y = args[1][1] |
| 2130 | stride_fp_x = args[4][0] |
| 2131 | stride_fp_y = args[4][1] |
| 2132 | |
| 2133 | if input_dtype == DType.FLOAT: |
| 2134 | if stride_fp_x <= 0 or stride_fp_y <= 0: |
| 2135 | # Negative or zero stride |
| 2136 | return True |
| 2137 | else: |
| 2138 | if stride_x <= 0 or stride_y <= 0: |
| 2139 | # Negative or zero stride |
| 2140 | return True |
| 2141 | return False |
| 2142 | |
| 2143 | |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2144 | @staticmethod |
| 2145 | def ivHeightWidthSmallerZero(**kwargs): |
| 2146 | opName = kwargs['opName'] |
| 2147 | |
| 2148 | inputShapes = kwargs['shapeList'] |
| 2149 | input = inputShapes[0] |
| 2150 | if not opName.endswith("pool2d"): |
| 2151 | filter = inputShapes[1] |
| 2152 | |
| 2153 | args = kwargs['args'] |
| 2154 | strides = args[0] |
| 2155 | padding = args[1] |
| 2156 | dilations = args[2] |
| 2157 | if opName.endswith("pool2d"): |
| 2158 | kernel = args[2] |
| 2159 | |
| 2160 | if opName.startswith('conv2d'): |
| 2161 | h = ( |
| 2162 | input[1] |
| 2163 | - filter[1] |
| 2164 | - (filter[1] - 1) * (dilations[0] - 1) |
| 2165 | + padding[0] |
| 2166 | + padding[1] |
| 2167 | ) // strides[0] + 1 |
| 2168 | |
| 2169 | w = ( |
| 2170 | input[2] |
| 2171 | - filter[2] |
| 2172 | - (filter[2] - 1) * (dilations[1] - 1) |
| 2173 | + padding[2] |
| 2174 | + padding[3] |
| 2175 | ) // strides[1] + 1 |
| 2176 | elif opName.startswith("depthwise_conv2d"): |
| 2177 | h = ( |
| 2178 | input[1] |
| 2179 | - filter[0] |
| 2180 | - (filter[0] - 1) * (dilations[0] - 1) |
| 2181 | + padding[0] |
| 2182 | + padding[1] |
| 2183 | ) // strides[0] + 1 |
| 2184 | |
| 2185 | w = ( |
| 2186 | input[2] |
| 2187 | - filter[1] |
| 2188 | - (filter[1] - 1) * (dilations[1] - 1) |
| 2189 | + padding[2] |
| 2190 | + padding[3] |
| 2191 | ) // strides[1] + 1 |
| 2192 | elif opName.endswith("pool2d"): |
| 2193 | h = (input[1] + padding[0] + padding[1] + strides[0] - kernel[0]) // strides[0] |
| 2194 | w = (input[2] + padding[2] + padding[3] + strides[1] - kernel[1]) // strides[1] |
| 2195 | else: |
| 2196 | assert False, "Unrecognized Op" |
| 2197 | |
| 2198 | if h <= 0 or w <= 0: |
| 2199 | # Invalid parameter combination |
| 2200 | return True |
| 2201 | return False |
| 2202 | |
| 2203 | @staticmethod |
| 2204 | def ivNonPositiveOutputShape(**kwargs): |
| 2205 | args = kwargs['args'] |
| 2206 | output_shape = args[3] |
| 2207 | if output_shape[1] <= 0 or output_shape[2] <= 0: |
| 2208 | # Negative output shape |
| 2209 | return True |
| 2210 | return False |
| 2211 | |
| 2212 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2213 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2214 | class TosaTestGen: |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 2215 | # Maximum rank of tensor supported by test generator. |
| 2216 | TOSA_TENSOR_MAX_RANK = 6 |
| 2217 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2218 | def __init__(self, args): |
| 2219 | self.args = args |
| 2220 | self.basePath = args.output_dir |
| 2221 | self.random_seed = args.random_seed |
| 2222 | self.ser = None |
| 2223 | self.rng = np.random.default_rng(self.random_seed) |
| 2224 | self.createDynamicOpLists() |
| 2225 | self.initOpListDefaults() |
| 2226 | self.quantGen = TosaQuantGen() |
| 2227 | # Force makeShape to do a specific starting shape |
| 2228 | self.targetted_shape = None |
| 2229 | |
| 2230 | def createSerializer(self, opName, testPath): |
| 2231 | self.testPath = os.path.join(opName, testPath) |
| 2232 | |
| 2233 | fullPath = os.path.join(self.basePath, self.testPath) |
| 2234 | os.makedirs(fullPath, exist_ok=True) |
| 2235 | self.ser = ts.TosaSerializer(fullPath) |
| 2236 | |
| 2237 | def getSerializer(self): |
| 2238 | return self.ser |
| 2239 | |
| 2240 | def serialize(self, testName): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2241 | with open( |
| 2242 | os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| 2243 | ) as fd: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2244 | fd.write(self.ser.serialize()) |
| 2245 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2246 | with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| 2247 | fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2248 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 2249 | def resetRNG(self, seed=None): |
| 2250 | if seed == None: |
| 2251 | seed = self.random_seed + 1 |
| 2252 | self.rng = np.random.default_rng(seed) |
| 2253 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2254 | def getRandTensor(self, shape, dtype): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2255 | if dtype == DType.BOOL: |
| 2256 | np_dt = np.bool |
| 2257 | return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2258 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2259 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2260 | return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2261 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2262 | return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| 2263 | elif dtype == DType.UINT8: |
| 2264 | return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2265 | elif dtype == DType.INT16: |
| 2266 | return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| 2267 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2268 | return np.int32( |
| 2269 | self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| 2270 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2271 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2272 | return np.int64( |
| 2273 | self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| 2274 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2275 | elif dtype == DType.FLOAT: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2276 | return np.float32(self.rng.random(size=shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2277 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2278 | raise Exception("Unrecognized Dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2279 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2280 | def buildPlaceholderTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2281 | placeholders = [] |
| 2282 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2283 | assert len(shape_list) == len(dtype_list) |
| 2284 | |
| 2285 | for idx, shape in enumerate(shape_list): |
| 2286 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 2287 | placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2288 | |
| 2289 | return placeholders |
| 2290 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2291 | def buildConstTensors(self, shape_list, dtype_list): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2292 | consts = [] |
| 2293 | |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2294 | assert len(shape_list) == len(dtype_list) |
| 2295 | |
| 2296 | for idx, shape in enumerate(shape_list): |
| 2297 | arr = self.getRandTensor(shape, dtype_list[idx]) |
| 2298 | consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2299 | |
| 2300 | return consts |
| 2301 | |
| 2302 | def makeShape(self, rank): |
| 2303 | if self.targetted_shape: |
| 2304 | return np.int32(self.targetted_shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2305 | return np.int32( |
| 2306 | self.rng.integers( |
| 2307 | low=self.args.tensor_shape_range[0], |
| 2308 | high=self.args.tensor_shape_range[1], |
| 2309 | size=rank, |
| 2310 | ) |
| 2311 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2312 | |
| 2313 | def setTargetShape(self, shape): |
| 2314 | self.targetted_shape = shape |
| 2315 | |
| 2316 | def randInt(self, low=0, high=256): |
| 2317 | return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| 2318 | |
| 2319 | def getRandNumberDType(self, dtype): |
| 2320 | if dtype == DType.FLOAT: |
| 2321 | return self.rng.random() |
| 2322 | elif dtype == DType.BOOL: |
| 2323 | return self.rng.choice([False, True]) |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2324 | # TOSA specific INT4 weight range from -7 to 7 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2325 | elif dtype == DType.INT4: |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 2326 | low, high = (-7, 8) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2327 | elif dtype == DType.INT8: |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2328 | low, high = (-128, 128) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2329 | elif dtype == DType.INT16: |
| 2330 | low, high = (-32768, 32768) |
| 2331 | elif dtype == DType.INT32: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2332 | low, high = (-(1 << 31), (1 << 31)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2333 | elif dtype == DType.INT48: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2334 | low, high = (-(1 << 47), (1 << 47)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2335 | # Special size |
| 2336 | return np.int64(self.rng.integers(low, high, size=1))[0] |
| 2337 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2338 | raise Exception("Unknown dtype: {}".format(dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2339 | |
| 2340 | return np.int32(self.rng.integers(low, high, size=1))[0] |
| 2341 | |
| 2342 | def shapeStr(self, shape): |
| 2343 | |
| 2344 | sStr = [] |
| 2345 | # Convert to strings |
| 2346 | for i in shape: |
| 2347 | sStr.append(str(i)) |
| 2348 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2349 | return "x".join(sStr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2350 | |
| 2351 | def typeStr(self, t): |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2352 | if isinstance(t, list): |
| 2353 | assert len(t) >= 2 |
| 2354 | return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2355 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2356 | if t == DType.BOOL: |
| 2357 | return "b" |
| 2358 | elif t == DType.INT4: |
| 2359 | return "i4" |
| 2360 | elif t == DType.INT8: |
| 2361 | return "i8" |
| 2362 | elif t == DType.UINT8: |
| 2363 | return "u8" |
| 2364 | elif t == DType.INT16: |
| 2365 | return "i16" |
| 2366 | elif t == DType.INT32: |
| 2367 | return "i32" |
| 2368 | elif t == DType.INT48: |
| 2369 | return "i48" |
| 2370 | elif t == DType.FLOAT: |
| 2371 | return "float" |
| 2372 | else: |
| 2373 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2374 | |
| 2375 | def typeWidth(self, t): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2376 | """ Get the datatype width for integer types""" |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2377 | if t == DType.INT4: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2378 | return 4 |
| 2379 | elif t == DType.INT8: |
| 2380 | return 8 |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2381 | elif t == DType.UINT8: |
| 2382 | return 8 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2383 | elif t == DType.INT16: |
| 2384 | return 16 |
| 2385 | elif t == DType.INT32: |
| 2386 | return 32 |
| 2387 | elif t == DType.INT48: |
| 2388 | return 48 |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 2389 | elif t == DType.FLOAT: |
| 2390 | return 32 |
| 2391 | elif t == DType.BOOL: |
| 2392 | return 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2393 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2394 | raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2395 | |
| 2396 | # Argument generators |
| 2397 | # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| 2398 | # Where the string descriptor is used to generate the test name and |
| 2399 | # The build_fcn_arg_list is expanded and passed to the operator test |
| 2400 | # build function |
| 2401 | |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2402 | def build_unary(self, op, a, validator_fcns=None, error_name=None, qinfo=None): |
| 2403 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| 2404 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2405 | # build_placeholder returns an int, ABS/other ops does not |
| 2406 | if isinstance(op, int): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2407 | self.ser.addOperator(op, a.name, result_tens.name, None, qinfo) |
| 2408 | return result_tens |
| 2409 | elif op['op'] == Op.IDENTITY: |
| 2410 | self.ser.addOperator(op['op'], a.name, result_tens.name, None, qinfo) |
| 2411 | return result_tens |
| 2412 | |
| 2413 | # Ensure new output type has correct qinfo |
| 2414 | if error_name == ErrorIf.WrongOutputType: |
| 2415 | if result_tens.dtype not in [DType.INT8, DType.UINT8]: |
| 2416 | qinfo = ts.TosaSerializerQuantInfo() |
| 2417 | qinfo.UnaryQuantInfo( |
| 2418 | TosaQuantGen.getQinfo(self, a.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) |
| 2419 | ) |
| 2420 | |
| 2421 | # Invalidate Input/Output list for error if checks. |
| 2422 | input_list = [a.name] |
| 2423 | output_list = [result_tens.name] |
| 2424 | pCount, cCount = op["operands"] |
| 2425 | num_operands = pCount + cCount |
| 2426 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2427 | |
| 2428 | TosaErrorValidator.evValidateErrorIfs( |
| 2429 | self.ser, |
| 2430 | validator_fcns, |
| 2431 | error_name, |
| 2432 | op=op, |
| 2433 | input_dtype=a.dtype, |
| 2434 | output_dtype=result_tens.dtype, |
| 2435 | qinfo = qinfo, |
| 2436 | result_tensor = result_tens, |
| 2437 | input_list=input_list, |
| 2438 | output_list=output_list, |
| 2439 | num_operands=num_operands, |
| 2440 | ) |
| 2441 | |
| 2442 | self.ser.addOperator(op['op'], input_list, output_list, None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2443 | return result_tens |
| 2444 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2445 | def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None): |
| 2446 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b, error_name) |
| 2447 | |
| 2448 | |
| 2449 | # Invalidate Input/Output list for error if checks. |
| 2450 | input_list = [a.name, b.name] |
| 2451 | output_list = [result_tens.name] |
| 2452 | pCount, cCount = op["operands"] |
| 2453 | num_operands = pCount + cCount |
| 2454 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2455 | |
| 2456 | TosaErrorValidator.evValidateErrorIfs( |
| 2457 | self.ser, |
| 2458 | validator_fcns, |
| 2459 | error_name, |
| 2460 | op=op, |
| 2461 | input1 = a, |
| 2462 | input2 = b, |
| 2463 | input_dtype = a.dtype, |
| 2464 | output_dtype = result_tens.dtype, |
| 2465 | result_tensor = result_tens, |
| 2466 | input_list=input_list, |
| 2467 | output_list=output_list, |
| 2468 | num_operands=num_operands, |
| 2469 | ) |
| 2470 | |
| 2471 | self.ser.addOperator(op['op'], input_list, output_list) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2472 | return result_tens |
| 2473 | |
| 2474 | def build_binary_nonbroadcast(self, op, a, b): |
| 2475 | result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2476 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2477 | return result_tens |
| 2478 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2479 | def build_arithmetic_right_shift(self, op, a, b, round): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2480 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2481 | |
| 2482 | attr = ts.TosaSerializerAttribute() |
| 2483 | attr.ArithmeticRightShiftAttribute(round) |
| 2484 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2485 | 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] | 2486 | return result_tens |
| 2487 | |
| 2488 | def build_mul(self, op, a, b, shift): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 2489 | result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2490 | |
| 2491 | # Special for multiply: |
| 2492 | # Force the result to INT32 for INT types |
| 2493 | if a.dtype != DType.FLOAT: |
| 2494 | result_tens.setDtype(DType.INT32) |
| 2495 | |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 2496 | attr = ts.TosaSerializerAttribute() |
| 2497 | attr.MulAttribute(shift) |
| 2498 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2499 | 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] | 2500 | return result_tens |
| 2501 | |
| 2502 | def build_table(self, op, a): |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2503 | # Constant size depending on type, random values |
| 2504 | if a.dtype == DType.INT16: |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2505 | table_dtype = DType.INT16 |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2506 | table_arr = self.getRandTensor([513], table_dtype) |
| 2507 | else: |
| 2508 | assert a.dtype == DType.INT8 |
| 2509 | table_dtype = DType.INT8 |
| 2510 | table_arr = self.getRandTensor([256], table_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2511 | |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 2512 | table_tens = self.ser.addConst(table_arr.shape, table_dtype, table_arr) |
| 2513 | result_tens = OutputShaper.tableOp(self.ser, a, table_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2514 | 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] | 2515 | |
| 2516 | return result_tens |
| 2517 | |
| 2518 | def build_select(self, op, cond, a, b): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2519 | result_tens = OutputShaper.selectOp(self.ser, cond, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2520 | 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] | 2521 | return result_tens |
| 2522 | |
| 2523 | def build_comparison(self, op, a, b): |
| 2524 | result_tens = OutputShaper.binaryComparisonOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2525 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2526 | return result_tens |
| 2527 | |
| 2528 | def build_argmax(self, op, a, axis): |
| 2529 | result_tens = OutputShaper.argmaxOp(self.ser, a, axis) |
| 2530 | |
| 2531 | attr = ts.TosaSerializerAttribute() |
| 2532 | attr.AxisAttribute(axis) |
| 2533 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2534 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2535 | return result_tens |
| 2536 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2537 | def build_pool2d(self, op, input, stride, pad, kernel, validator_fcns=None, error_name=None, qinfo=None): |
| 2538 | result_tens = OutputShaper.pool2dOp(self.ser, self.rng, input, kernel, stride, pad, error_name) |
| 2539 | |
| 2540 | # Ensure new output type has correct qinfo |
| 2541 | if error_name == ErrorIf.WrongInputType: |
| 2542 | if input.dtype not in [DType.INT8, DType.UINT8]: |
| 2543 | qinfo = ts.TosaSerializerQuantInfo() |
| 2544 | qinfo.UnaryQuantInfo( |
| 2545 | TosaQuantGen.getQinfo(self, input.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) |
| 2546 | ) |
| 2547 | |
| 2548 | # Invalidate Input/Output list for error if checks. |
| 2549 | input_list = [input.name] |
| 2550 | output_list = [result_tens.name] |
| 2551 | pCount, cCount = op["operands"] |
| 2552 | num_operands = pCount + cCount |
| 2553 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2554 | |
| 2555 | TosaErrorValidator.evValidateErrorIfs( |
| 2556 | self.ser, |
| 2557 | validator_fcns, |
| 2558 | error_name, |
| 2559 | op=op, |
| 2560 | input_shape=input.shape, |
| 2561 | input_dtype=input.dtype, |
| 2562 | output_shape=result_tens.shape, |
| 2563 | output_dtype=result_tens.dtype, |
| 2564 | kernel=kernel, |
| 2565 | stride=stride, |
| 2566 | pad=pad, |
| 2567 | qinfo = qinfo, |
| 2568 | result_tensor = result_tens, |
| 2569 | input_list=input_list, |
| 2570 | output_list=output_list, |
| 2571 | num_operands=num_operands, |
| 2572 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2573 | |
| 2574 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2575 | attr.PoolAttribute(kernel, stride, pad) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2576 | |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2577 | self.ser.addOperator(op['op'], input_list, output_list, attr, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2578 | return result_tens |
| 2579 | |
| 2580 | def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2581 | assert len(padding) == 4 |
| 2582 | result_tens = OutputShaper.conv2dOp( |
| 2583 | self.ser, ifm, filter, strides, padding, dilations |
| 2584 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2585 | |
| 2586 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2587 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2588 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2589 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2590 | 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] | 2591 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2592 | return result_tens |
| 2593 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 2594 | def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): |
| 2595 | assert len(padding) == 6 |
| 2596 | result_tens = OutputShaper.conv3dOp( |
| 2597 | self.ser, ifm, filter, strides, padding, dilations |
| 2598 | ) |
| 2599 | |
| 2600 | attr = ts.TosaSerializerAttribute() |
| 2601 | attr.ConvAttribute(padding, strides, dilations) |
| 2602 | |
| 2603 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2604 | 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] | 2605 | ) |
| 2606 | return result_tens |
| 2607 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2608 | def build_transpose_conv2d( |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 2609 | self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2610 | ): |
| 2611 | assert len(outpad) == 2 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2612 | result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) |
| 2613 | |
| 2614 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2615 | attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2616 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2617 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2618 | 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] | 2619 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2620 | return result_tens |
| 2621 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2622 | def build_depthwise_conv2d( |
| 2623 | self, op, ifm, filter, bias, strides, padding, dilations, qinfo |
| 2624 | ): |
| 2625 | result_tens = OutputShaper.depthwiseConv2dOp( |
| 2626 | self.ser, ifm, filter, strides, padding, dilations |
| 2627 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2628 | |
| 2629 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2630 | attr.ConvAttribute(padding, strides, dilations) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2631 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2632 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2633 | 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] | 2634 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2635 | return result_tens |
| 2636 | |
| 2637 | def build_fully_connected(self, op, ifm, filter, bias, qinfo): |
| 2638 | result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) |
| 2639 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2640 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2641 | op['op'], [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2642 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2643 | return result_tens |
| 2644 | |
| 2645 | def build_matmul(self, op, a, b, qinfo): |
| 2646 | result_tens = OutputShaper.matmulOp(self.ser, a, b) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2647 | self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name], None, qinfo) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2648 | return result_tens |
| 2649 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 2650 | def build_reduce(self, op, a, axis, validator_fcns, error_name=None): |
| 2651 | result_tens = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name) |
| 2652 | |
| 2653 | # Invalidate Input/Output list for error if checks. |
| 2654 | input_list = [a.name] |
| 2655 | output_list = [result_tens.name] |
| 2656 | pCount, cCount = op["operands"] |
| 2657 | num_operands = pCount + cCount |
| 2658 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2659 | |
| 2660 | TosaErrorValidator.evValidateErrorIfs( |
| 2661 | self.ser, |
| 2662 | validator_fcns, |
| 2663 | error_name, |
| 2664 | op=op, |
| 2665 | axis = axis, |
| 2666 | input_shape = a.shape, |
| 2667 | output_shape = result_tens.shape, |
| 2668 | input_dtype = a.dtype, |
| 2669 | output_dtype = result_tens.dtype, |
| 2670 | result_tensor = result_tens, |
| 2671 | input_list=input_list, |
| 2672 | output_list=output_list, |
| 2673 | num_operands=num_operands, |
| 2674 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2675 | |
| 2676 | attr = ts.TosaSerializerAttribute() |
| 2677 | attr.AxisAttribute(axis) |
| 2678 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 2679 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2680 | return result_tens |
| 2681 | |
| 2682 | def build_clamp(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2683 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2684 | |
| 2685 | attr = ts.TosaSerializerAttribute() |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 2686 | v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2687 | |
| 2688 | if a.dtype == DType.FLOAT: |
| 2689 | attr.ClampAttribute(0, 0, min(v), max(v)) |
| 2690 | else: |
| 2691 | attr.ClampAttribute(min(v), max(v), 0, 0) |
| 2692 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2693 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2694 | return result_tens |
| 2695 | |
| 2696 | def build_leaky_relu(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2697 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2698 | attr = ts.TosaSerializerAttribute() |
| 2699 | |
| 2700 | attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) |
| 2701 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2702 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2703 | return result_tens |
| 2704 | |
| 2705 | # Needs an additional type/input |
| 2706 | def build_prelu(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2707 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2708 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2709 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2710 | return result_tens |
| 2711 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2712 | def build_sigmoid(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2713 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2714 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2715 | return result_tens |
| 2716 | |
| 2717 | def build_tanh(self, op, a): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2718 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2719 | self.ser.addOperator(op['op'], [a.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2720 | return result_tens |
| 2721 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2722 | def build_concat(self, op, *a): |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 2723 | assert type(a[-1]) == int |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2724 | |
| 2725 | # To store variable length list of input tensors we need to store axis along with it |
| 2726 | axis = a[-1] |
| 2727 | a = a[:-1] |
| 2728 | |
| 2729 | result_tens = OutputShaper.concatOp(self.ser, axis, *a) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2730 | |
| 2731 | attr = ts.TosaSerializerAttribute() |
| 2732 | attr.AxisAttribute(axis) |
| 2733 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 2734 | input_tensor_names = [] |
| 2735 | for tensor in a: |
| 2736 | input_tensor_names.append(tensor.name) |
| 2737 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2738 | self.ser.addOperator(op['op'], input_tensor_names, [result_tens.name], attr) |
| 2739 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2740 | |
| 2741 | def build_pad(self, op, a, padding, qinfo): |
| 2742 | result_tens = OutputShaper.padOp(self.ser, a, padding) |
| 2743 | |
| 2744 | # Need to turn the padding array into a TOSA tensor here. |
| 2745 | # This is one of the few tensor operands that does not get |
| 2746 | # randomly generated |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2747 | padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2748 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2749 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2750 | op['op'], [a.name, padding_tens.name], [result_tens.name], None, qinfo |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2751 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2752 | return result_tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2753 | |
| 2754 | def build_reshape(self, op, a, newShape): |
| 2755 | result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) |
| 2756 | |
| 2757 | attr = ts.TosaSerializerAttribute() |
| 2758 | attr.ReshapeAttribute(newShape) |
| 2759 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2760 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2761 | return result_tens |
| 2762 | |
| 2763 | def build_reverse(self, op, a, axis): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2764 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, None) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2765 | |
| 2766 | attr = ts.TosaSerializerAttribute() |
| 2767 | attr.AxisAttribute(axis) |
| 2768 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2769 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2770 | return result_tens |
| 2771 | |
| 2772 | def build_transpose(self, op, a, perms): |
| 2773 | result_tens = OutputShaper.transposeOp(self.ser, a, perms) |
| 2774 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2775 | perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2776 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2777 | self.ser.addOperator(op['op'], [a.name, perms_tens.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2778 | return result_tens |
| 2779 | |
| 2780 | def build_slice(self, op, a, begin, size): |
| 2781 | result_tens = OutputShaper.sliceOp(self.ser, a, begin, size) |
| 2782 | |
| 2783 | attr = ts.TosaSerializerAttribute() |
| 2784 | attr.SliceAttribute(begin, size) |
| 2785 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2786 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2787 | return result_tens |
| 2788 | |
| 2789 | def build_tile(self, op, a, multiples): |
| 2790 | result_tens = OutputShaper.tileOp(self.ser, a, multiples) |
| 2791 | |
| 2792 | attr = ts.TosaSerializerAttribute() |
| 2793 | attr.TileAttribute(multiples) |
| 2794 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2795 | self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2796 | return result_tens |
| 2797 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2798 | def build_gather(self, op, values): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2799 | |
| 2800 | # Create a new indicies tensor |
| 2801 | # here with data that doesn't exceed the dimensions of the values tensor |
| 2802 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2803 | K = values.shape[1] # K |
| 2804 | W = self.randInt( |
| 2805 | self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| 2806 | ) # W |
| 2807 | indicies_arr = np.int32( |
| 2808 | self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| 2809 | ) # (N, W) |
| 2810 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2811 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2812 | result_tens = OutputShaper.gatherOp(self.ser, values, indicies) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2813 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2814 | self.ser.addOperator(op['op'], [values.name, indicies.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2815 | |
| 2816 | return result_tens |
| 2817 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2818 | def build_scatter(self, op, values_in, input): |
| 2819 | |
| 2820 | # Create a new indicies tensor |
| 2821 | # here with data that doesn't exceed the dimensions of the values_in tensor |
| 2822 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2823 | K = values_in.shape[1] # K |
| 2824 | W = input.shape[1] # W |
| 2825 | indicies_arr = np.int32( |
| 2826 | self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| 2827 | ) # (N, W) |
| 2828 | indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2829 | |
| 2830 | result_tens = OutputShaper.scatterOp(self.ser, values_in, indicies, input) |
| 2831 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2832 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2833 | op['op'], [values_in.name, indicies.name, input.name], [result_tens.name] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2834 | ) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2835 | |
| 2836 | return result_tens |
| 2837 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2838 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2839 | def build_resize( |
| 2840 | self, |
| 2841 | op, |
| 2842 | input, |
| 2843 | mode, |
| 2844 | stride, |
| 2845 | offset, |
| 2846 | shift, |
| 2847 | stride_fp, |
| 2848 | offset_fp, |
| 2849 | output_dims, |
| 2850 | input_dtype, |
| 2851 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2852 | validator_fcns, |
| 2853 | error_name = None, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2854 | ): |
| 2855 | result_tens = OutputShaper.resizeOp( |
| 2856 | self.ser, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 2857 | self.rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2858 | input, |
| 2859 | mode, |
| 2860 | stride, |
| 2861 | offset, |
| 2862 | shift, |
| 2863 | stride_fp, |
| 2864 | offset_fp, |
| 2865 | output_dims, |
| 2866 | input_dtype, |
| 2867 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2868 | error_name |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2869 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2870 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2871 | # Invalidate Input/Output list for error if checks. |
| 2872 | input_list = [input.name] |
| 2873 | output_list = [result_tens.name] |
| 2874 | pCount, cCount = op["operands"] |
| 2875 | num_operands = pCount + cCount |
| 2876 | 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] | 2877 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2878 | TosaErrorValidator.evValidateErrorIfs( |
| 2879 | self.ser, |
| 2880 | validator_fcns, |
| 2881 | error_name, |
| 2882 | op=op, |
| 2883 | mode=mode, |
| 2884 | shift=shift, |
| 2885 | input_dtype=input_dtype, |
| 2886 | output_dtype=output_dtype, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 2887 | input_shape=input.shape, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2888 | output_shape=output_dims, |
| 2889 | offset=offset, |
| 2890 | offset_fp=offset_fp, |
| 2891 | stride=stride, |
| 2892 | stride_fp=stride_fp, |
| 2893 | input_list=input_list, |
| 2894 | output_list=output_list, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 2895 | result_tensor=result_tens, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2896 | num_operands=num_operands, |
| 2897 | ) |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 2898 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2899 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 2900 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2901 | attr.ResizeAttribute( |
| 2902 | output_dims, stride, offset, shift, stride_fp, offset_fp, mode |
| 2903 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2904 | |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2905 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2906 | return result_tens |
| 2907 | |
| 2908 | def build_identityn(self, op, val, val2): |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 2909 | result_tens = OutputShaper.unaryOp(self.ser, self.rng, val, None) |
| 2910 | result_tens2 = OutputShaper.unaryOp(self.ser, self.rng, val2, None) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2911 | self.ser.addOperator( |
| 2912 | op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| 2913 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2914 | return result_tens |
| 2915 | |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 2916 | def build_const(self, op, val): |
| 2917 | self.ser.addOutputTensor(val) |
| 2918 | return val |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2919 | |
| 2920 | # Type Conversion |
| 2921 | def build_cast(self, op, val, out_dtype): |
| 2922 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 2923 | self.ser.addOperator(op['op'], [val.name], [result_tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2924 | return result_tens |
| 2925 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 2926 | 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] | 2927 | result_tens = OutputShaper.typeConversionOp(self.ser, val, out_dtype) |
| 2928 | |
| 2929 | if per_channel: |
| 2930 | nc = val.shape[-1] |
| 2931 | else: |
| 2932 | nc = 1 |
| 2933 | |
| 2934 | in_type_width = self.typeWidth(val.dtype) |
| 2935 | out_type_width = self.typeWidth(out_dtype) |
| 2936 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2937 | if val.dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2938 | input_zp = self.randInt(-128, 128) |
| 2939 | in_type_width = in_type_width + 1 |
Kevin Cheng | acb550f | 2021-06-29 15:32:19 -0700 | [diff] [blame] | 2940 | elif val.dtype == DType.UINT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2941 | input_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2942 | in_type_width = in_type_width + 1 |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 2943 | elif error_name == ErrorIf.InputZeroPointNotZero: |
| 2944 | input_zp = self.randInt(-128, 128) |
| 2945 | if input_zp == 0: |
| 2946 | input_zp = input_zp + self.rng.integers(1, 10) |
| 2947 | in_type_width = in_type_width + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2948 | else: |
| 2949 | input_zp = 0 |
| 2950 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 2951 | if out_dtype == DType.INT8: |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 2952 | output_zp = self.randInt(-128, 128) |
| 2953 | out_type_width = out_type_width + 1 |
| 2954 | elif out_dtype == DType.UINT8: |
| 2955 | output_zp = self.randInt(0, 256) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2956 | out_type_width = out_type_width + 1 |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 2957 | elif error_name == ErrorIf.OutputZeroPointNotZero: |
| 2958 | output_zp = self.randInt(-128, 128) |
| 2959 | if output_zp == 0: |
| 2960 | output_zp = output_zp + self.rng.integers(1, 10) |
| 2961 | out_type_width = out_type_width + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2962 | else: |
| 2963 | output_zp = 0 |
| 2964 | |
| 2965 | # Calculate scale based on: |
| 2966 | # scale = a *(2^output_width)/(2^input_width)) |
| 2967 | |
| 2968 | a = np.float32(self.rng.random(size=[nc])) |
| 2969 | scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| 2970 | |
| 2971 | if scale32: |
| 2972 | pass |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 2973 | # Cap the scaling at 2^31 - 1 for scale32 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2974 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| 2975 | else: |
| 2976 | # Cap the scaling at 2^15 - 1 for scale16 |
| 2977 | scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| 2978 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2979 | # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2980 | |
| 2981 | multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| 2982 | shift_arr = np.int32(np.zeros(shape=[nc])) |
| 2983 | |
| 2984 | for i in range(nc): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2985 | multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| 2986 | scale_arr[i], scale32 |
| 2987 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 2988 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 2989 | # 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] | 2990 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 2991 | # Invalidate Input/Output list for error if checks. |
| 2992 | input_list = [val.name] |
| 2993 | output_list = [result_tens.name] |
| 2994 | pCount, cCount = op["operands"] |
| 2995 | num_operands = pCount + cCount |
| 2996 | input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) |
| 2997 | |
| 2998 | qinfo = (input_zp, output_zp) |
| 2999 | TosaErrorValidator.evValidateErrorIfs( |
| 3000 | self.ser, |
| 3001 | validator_fcns, |
| 3002 | error_name, |
| 3003 | op=op, |
| 3004 | input_dtype=val.dtype, |
| 3005 | output_dtype=out_dtype, |
| 3006 | input_shape=val.shape, |
| 3007 | qinfo=qinfo, |
| 3008 | scale32 = scale32, |
| 3009 | double_round = double_round, |
| 3010 | input_list=input_list, |
| 3011 | output_list=output_list, |
| 3012 | result_tensor=result_tens, |
| 3013 | num_operands=num_operands, |
| 3014 | ) |
| 3015 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3016 | attr = ts.TosaSerializerAttribute() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3017 | attr.RescaleAttribute( |
| 3018 | input_zp, |
| 3019 | output_zp, |
| 3020 | multiplier_arr, |
| 3021 | shift_arr, |
| 3022 | scale32, |
| 3023 | double_round, |
| 3024 | per_channel, |
| 3025 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3026 | |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 3027 | self.ser.addOperator(op['op'], input_list, output_list, attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3028 | return result_tens |
| 3029 | |
| 3030 | def build_cond_if_const(self, op, then_tens, else_tens, cond): |
| 3031 | # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| 3032 | # (except for the generated shap) and the condition. Build Then/Else blocks |
| 3033 | # and fill them with const nodes for the body. |
| 3034 | |
| 3035 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3036 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3037 | |
| 3038 | # Make then/else tensors |
| 3039 | out_shape = then_tens.shape |
Jeremy Johnson | 18e2666 | 2021-07-22 16:15:29 +0100 | [diff] [blame] | 3040 | then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| 3041 | else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3042 | |
| 3043 | # And the result tensor based on any of the outputs |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3044 | result_tens = self.ser.addOutput(out_shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3045 | |
| 3046 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3047 | then_block = "THEN_BLOCK" |
| 3048 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3049 | attr = ts.TosaSerializerAttribute() |
| 3050 | attr.CondIfAttribute(then_block, else_block) |
| 3051 | |
| 3052 | # Finally, build the op and the two blocks |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3053 | self.ser.addOperator(op['op'], [cond_tens.name], [result_tens.name], attr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3054 | |
| 3055 | self.ser.startBasicBlock(then_block) |
| 3056 | # Build the actual then/else tensors inside their blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3057 | then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3058 | self.ser.addOutputTensor(then_tens) |
| 3059 | |
| 3060 | self.ser.startBasicBlock(else_block) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3061 | else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3062 | self.ser.addOutputTensor(else_tens) |
| 3063 | |
| 3064 | return result_tens |
| 3065 | |
| 3066 | def build_cond_if_binary(self, op, a, b, cond): |
| 3067 | # For cond_if with a binary op in the then/else blocks, take a and b and |
| 3068 | # alternately add or subtract them based on the condition |
| 3069 | |
| 3070 | # Condition tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3071 | cond_tens = self.ser.addConst([], DType.BOOL, [cond]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3072 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3073 | result_tens = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3074 | |
| 3075 | # Create the attribute with the names of the then/else blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3076 | then_block = "THEN_BLOCK" |
| 3077 | else_block = "ELSE_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3078 | attr = ts.TosaSerializerAttribute() |
| 3079 | attr.CondIfAttribute(then_block, else_block) |
| 3080 | |
| 3081 | # Finally, build the op and the two blocks |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3082 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3083 | 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] | 3084 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3085 | |
Les Bell | 6040b4d | 2021-10-11 12:50:31 +0100 | [diff] [blame] | 3086 | if a.dtype in (DType.FLOAT, DType.INT32): |
| 3087 | then_op, else_op = Op.ADD, Op.SUB |
| 3088 | elif a.dtype in (DType.INT8, DType.INT16): |
| 3089 | then_op, else_op = Op.LOGICAL_RIGHT_SHIFT, Op.LOGICAL_LEFT_SHIFT |
| 3090 | else: |
| 3091 | assert False, f"No tests for DType: {a.dtype}" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3092 | |
Les Bell | 6040b4d | 2021-10-11 12:50:31 +0100 | [diff] [blame] | 3093 | for block, op in ((then_block, then_op), (else_block, else_op)): |
| 3094 | self.ser.startBasicBlock(block) |
| 3095 | self.ser.addInputTensor(a) |
| 3096 | self.ser.addInputTensor(b) |
| 3097 | tens = self.ser.addOutput(a.shape, a.dtype) |
| 3098 | self.ser.addOperator(op, [a.name, b.name], [tens.name]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3099 | |
| 3100 | return result_tens |
| 3101 | |
| 3102 | def build_while_loop(self, op, a, iter_val): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3103 | iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3104 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3105 | cond_block = "COND_BLOCK" |
| 3106 | body_block = "BODY_BLOCK" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3107 | |
| 3108 | attr = ts.TosaSerializerAttribute() |
| 3109 | attr.WhileLoopAttribute(cond_block, body_block) |
| 3110 | |
| 3111 | # Accumulator tensor |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3112 | # acc = self.ser.addOutput(a.shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3113 | acc_init_val = np.int32(np.zeros(a.shape)) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3114 | acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3115 | |
| 3116 | # Intermediate/output tensors for everything going through the loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3117 | iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 3118 | a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| 3119 | acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3120 | |
| 3121 | # While_loop operator |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3122 | self.ser.addOperator( |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 3123 | op['op'], |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3124 | [iter.name, a.name, acc.name], |
| 3125 | [iter_out.name, a_out.name, acc_out.name], |
| 3126 | attr, |
| 3127 | ) |
Kevin Cheng | b227ae5 | 2021-09-02 13:43:17 -0700 | [diff] [blame] | 3128 | self.ser.addOutputTensor(acc_out) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3129 | |
| 3130 | # COND block (input: iter, output: cond_tens ) |
| 3131 | self.ser.startBasicBlock(cond_block) |
| 3132 | self.ser.addInputTensor(iter) |
| 3133 | self.ser.addInputTensor(a) |
| 3134 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3135 | zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| 3136 | cond_tens = self.ser.addOutput([], DType.BOOL) |
| 3137 | 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] | 3138 | |
| 3139 | # BODY block (input: a, acc, iter, output: a, acc, iter) |
| 3140 | # Note that local intermediate tensors need to be declared here for the outputs |
| 3141 | self.ser.startBasicBlock(body_block) |
| 3142 | self.ser.addInputTensor(iter) |
| 3143 | self.ser.addInputTensor(a) |
| 3144 | self.ser.addInputTensor(acc) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3145 | one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| 3146 | iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| 3147 | acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3148 | self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| 3149 | self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| 3150 | self.ser.addOutputTensor(iter_body_out) |
| 3151 | self.ser.addOutputTensor(a) |
| 3152 | self.ser.addOutputTensor(acc_body_out) |
| 3153 | |
| 3154 | return acc_out |
| 3155 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3156 | def create_filter_lists(self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None): |
| 3157 | # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| 3158 | default_test_rank_range = range(1, 5) |
| 3159 | if not shapeFilter: |
| 3160 | shapeFilter = [None] |
| 3161 | |
| 3162 | # Calculate the filters based on what is requested and what the operator allows |
| 3163 | rmin, rmax = op["rank"] |
| 3164 | if rankFilter is not None: |
| 3165 | cleanRankFilter = [] |
| 3166 | # Ensure rankFilter values are allowed by operator |
| 3167 | for rank in rankFilter: |
| 3168 | if rank >= rmin and rank <= rmax: |
| 3169 | cleanRankFilter.append(rank) |
| 3170 | elif rankFilter is None and shapeFilter[0] is None: |
Jeremy Johnson | 03bec73 | 2021-10-07 12:06:00 +0100 | [diff] [blame] | 3171 | # Ensure default behaviour is bounded by default range or by operator, |
| 3172 | # whichever is the smaller range of ranks. |
| 3173 | opRankRange = range(rmin, rmax + 1) |
| 3174 | 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] | 3175 | else: |
| 3176 | cleanRankFilter = range(rmin, rmax + 1) |
| 3177 | |
| 3178 | dtypes = op["types"] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 3179 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3180 | if dtypeFilter is not None: |
| 3181 | cleanDtypeFilter = [] |
Jeremy Johnson | 03bec73 | 2021-10-07 12:06:00 +0100 | [diff] [blame] | 3182 | # Create list of operator dtypes filtered by requested dtypes |
| 3183 | for dtype in dtypes: |
| 3184 | 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] | 3185 | cleanDtypeFilter.append(dtype) |
| 3186 | else: |
| 3187 | cleanDtypeFilter = dtypes |
| 3188 | |
| 3189 | if testType == 'positive': |
| 3190 | filterDict = { |
| 3191 | 'shapeFilter': shapeFilter, |
| 3192 | 'rankFilter': cleanRankFilter, |
| 3193 | 'dtypeFilter': cleanDtypeFilter |
| 3194 | } |
| 3195 | return filterDict |
| 3196 | elif testType == 'negative': |
| 3197 | validator_info = validator(check=False, op=op) |
| 3198 | error_arguments = validator_info['param_reqs'] |
| 3199 | |
| 3200 | #Set parameters as required |
| 3201 | if error_arguments['rank'] != None: |
| 3202 | rankFilter = error_arguments['rank'] |
| 3203 | else: |
| 3204 | rankFilter = cleanRankFilter |
| 3205 | |
| 3206 | if error_arguments['dtype'] != None: |
| 3207 | dtypeFilter = error_arguments['dtype'] |
| 3208 | else: |
| 3209 | dtypeFilter = cleanDtypeFilter |
| 3210 | |
| 3211 | if error_arguments['shape'] != None: |
| 3212 | shapeFilter = error_arguments['shape'] |
| 3213 | else: |
| 3214 | shapeFilter = shapeFilter[:2] # Reduce number of shapes to keep test numbers small |
| 3215 | |
| 3216 | filterDict = { |
| 3217 | 'shapeFilter': shapeFilter, |
| 3218 | 'rankFilter': rankFilter, |
| 3219 | 'dtypeFilter': dtypeFilter |
| 3220 | } |
| 3221 | return filterDict |
| 3222 | |
| 3223 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3224 | def genOpTestList( |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3225 | self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3226 | ): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3227 | |
| 3228 | try: |
| 3229 | op = self.TOSA_OP_LIST[opName] |
| 3230 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3231 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3232 | |
| 3233 | # Initialize a new random number generator |
| 3234 | self.rng = np.random.default_rng(self.random_seed) |
| 3235 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3236 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3237 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3238 | # Test list consists of a tuple of: |
| 3239 | # (opName, testNameStr, dtype, shapeList, argumentsList) |
| 3240 | testList = [] |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3241 | if testType == 'negative' and "error_if_validators" in op: |
| 3242 | error_if_validators = op["error_if_validators"] |
| 3243 | else: |
| 3244 | error_if_validators = [None] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3245 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3246 | for validator in error_if_validators: |
| 3247 | if validator is not None: |
| 3248 | error_name = validator(check=False, op=op)['error_name'] |
| 3249 | #print("error_name: ", error_name) |
| 3250 | else: |
| 3251 | error_name = None |
| 3252 | |
| 3253 | filterDict = self.create_filter_lists(op, shapeFilter, rankFilter, dtypeFilter, testType, validator) |
| 3254 | cleanRankFilter = filterDict['rankFilter'] |
| 3255 | cleanDtypeFilter = filterDict['dtypeFilter'] |
| 3256 | cleanShapeFilter = filterDict['shapeFilter'] |
| 3257 | #print(f"Filters: S {shapeFilter}, R {cleanRankFilter}, T {cleanDtypeFilter}") |
| 3258 | |
| 3259 | for r in cleanRankFilter: |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3260 | if opName.startswith("conv3d"): |
| 3261 | assert r == 5, "conv3d test must have input rank == 5" |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3262 | for t in cleanDtypeFilter: |
| 3263 | for shape in cleanShapeFilter: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3264 | # Filter out by rank |
| 3265 | if shape is not None and len(shape) != r: |
| 3266 | continue |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3267 | self.setTargetShape(shape) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3268 | shapeList = tgen_fcn(self, op, r, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3269 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3270 | shapeStr = self.shapeStr(shapeList[0]) |
| 3271 | typeStr = self.typeStr(t) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3272 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3273 | # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| 3274 | argList = [] |
| 3275 | if agen_fcn: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3276 | argList = agen_fcn(self, opName, shapeList, t, error_name) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3277 | else: |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3278 | argList = [("", [])] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3279 | |
Matthew Haddon | 7456709 | 2021-07-16 15:38:20 +0100 | [diff] [blame] | 3280 | for argStr, args in argList: |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3281 | if testType == 'positive': |
| 3282 | if argStr: |
| 3283 | testStr = "{}_{}_{}_{}".format( |
| 3284 | opName, shapeStr, typeStr, argStr |
| 3285 | ) |
| 3286 | else: |
| 3287 | testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) |
| 3288 | elif testType == 'negative': |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3289 | if argStr: |
| 3290 | testStr = "{}_ERRORIF_{}_{}_{}_{}".format( |
| 3291 | opName, error_name, shapeStr, typeStr, argStr |
| 3292 | ) |
| 3293 | else: |
| 3294 | testStr = "{}_ERRORIF_{}_{}_{}".format(opName, error_name, shapeStr, typeStr) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3295 | |
| 3296 | testList.append((opName, testStr, t, error_name, shapeList, args)) |
| 3297 | |
| 3298 | if testType == 'positive': |
| 3299 | # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| 3300 | if "invalid_test_validators" in op: |
| 3301 | invalid_test_validators = op["invalid_test_validators"] |
| 3302 | clean_testList = [] |
| 3303 | for test in testList: |
| 3304 | for validator_fcn in invalid_test_validators: |
| 3305 | remove_test = False |
| 3306 | if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[4], args=test[5]): |
| 3307 | remove_test = True |
| 3308 | if not remove_test: |
| 3309 | clean_testList.append(test) |
| 3310 | testList = clean_testList |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3311 | |
| 3312 | return testList |
| 3313 | |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3314 | |
| 3315 | def serializeTest(self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3316 | try: |
| 3317 | op = self.TOSA_OP_LIST[opName] |
| 3318 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3319 | raise Exception("Cannot find op with name {}".format(opName)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3320 | |
| 3321 | # Create a serializer |
| 3322 | self.createSerializer(opName, testStr) |
| 3323 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3324 | build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 3325 | if "error_if_validators" in op: |
| 3326 | error_if_validators = op["error_if_validators"] |
| 3327 | else: |
| 3328 | error_if_validators = None |
| 3329 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3330 | pCount, cCount = op["operands"] |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3331 | num_operands = pCount + cCount |
| 3332 | |
| 3333 | if isinstance(dtype_or_dtypeList, list): |
| 3334 | dtypeList = dtype_or_dtypeList |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3335 | elif op["op"] == Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3336 | dtypeList = [dtype_or_dtypeList] * len(shapeList) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3337 | else: |
| 3338 | dtypeList = [dtype_or_dtypeList] * (num_operands) |
| 3339 | |
Kevin Cheng | 93a1628 | 2021-08-31 16:14:03 -0700 | [diff] [blame] | 3340 | if op["op"] != Op.CONCAT: |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3341 | assert ( |
| 3342 | len(shapeList) == num_operands |
| 3343 | ), "shapeList length {} must match number of operands {}".format( |
| 3344 | len(shapeList), num_operands |
| 3345 | ) |
| 3346 | assert ( |
| 3347 | len(dtypeList) == num_operands |
| 3348 | ), "dtypeList length {} must match number of operands {}".format( |
| 3349 | len(dtypeList), num_operands |
| 3350 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3351 | |
| 3352 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3353 | qgen = op["qgen"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3354 | except KeyError: |
| 3355 | qgen = None |
| 3356 | |
| 3357 | # Build the random tensor operands and the test |
| 3358 | tens = [] |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3359 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3360 | tens = self.generate_tensors(op, dtypeList, shapeList, testArgs, error_name) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3361 | |
| 3362 | if qgen is not None: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3363 | qinfo = qgen(self, op, dtype_or_dtypeList, error_name) |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3364 | else: |
| 3365 | qinfo = None |
| 3366 | |
| 3367 | try: |
| 3368 | if error_if_validators is None: |
| 3369 | if qinfo is not None: |
| 3370 | resultName = build_fcn(self, op, *tens, *testArgs, qinfo) |
| 3371 | else: |
| 3372 | resultName = build_fcn(self, op, *tens, *testArgs) |
| 3373 | else: |
| 3374 | if qinfo is not None: |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3375 | 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] | 3376 | else: |
| 3377 | resultName = build_fcn(self, op, *tens, *testArgs, error_if_validators, error_name) |
| 3378 | except TypeError as e: |
| 3379 | print( |
| 3380 | "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( |
| 3381 | build_fcn, tens, testArgs |
| 3382 | ) |
| 3383 | ) |
| 3384 | raise e |
| 3385 | |
| 3386 | if resultName is None: |
| 3387 | print("Invalid ERROR_IF tests created") |
| 3388 | |
| 3389 | # Save the serialized test |
| 3390 | self.serialize("test") |
| 3391 | |
| 3392 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3393 | def generate_tensors(self, op, dtypeList, shapeList, testArgs, error_name=None): |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3394 | pCount, cCount = op["operands"] |
| 3395 | |
| 3396 | tens = [] |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3397 | 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] | 3398 | # Make sure the operation does not cause value saturation - where |
| 3399 | # the number wraps due to limited number of bits to store the answer |
| 3400 | assert ( |
| 3401 | pCount == 2 and cCount == 0 |
| 3402 | ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
Jeremy Johnson | ef509a4 | 2021-09-07 13:59:47 +0100 | [diff] [blame] | 3403 | placeholders = [] |
| 3404 | add = (op["op"] == Op.ADD) |
| 3405 | a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 3406 | b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 3407 | if add: |
| 3408 | res_arr = np.add(a_arr, b_arr, dtype=np.int64) |
| 3409 | else: |
| 3410 | res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) |
| 3411 | |
| 3412 | # Work out the saturation limits |
| 3413 | max_i32 = (1 << 31)-1 |
| 3414 | min_i32 = -(1 << 31) |
| 3415 | max_arr = np.full(shapeList[1], max_i32) |
| 3416 | min_arr = np.full(shapeList[1], min_i32) |
| 3417 | |
| 3418 | # Find how much values exceed the maximum/minimums |
| 3419 | sat_max_arr = np.maximum(res_arr - max_arr, 0) |
| 3420 | sat_min_arr = np.minimum(res_arr - min_arr, 0) |
| 3421 | |
| 3422 | if not add: |
| 3423 | # Swap saturation values and negate values as we need to perform opposite operations |
| 3424 | sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr |
| 3425 | |
| 3426 | # Create new array of unsaturated values by clipping values as needed |
| 3427 | b_unsat_arr = b_arr |
| 3428 | if (sat_max_arr != 0).any(): |
| 3429 | # Clip values that cause saturation |
| 3430 | b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) |
| 3431 | # Reduce axes in unsaturated tensor to match original tensor |
| 3432 | for axis, dim in enumerate(b_arr.shape): |
| 3433 | if dim != b_unsat_arr.shape[axis]: |
| 3434 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 3435 | b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) |
| 3436 | |
| 3437 | if (sat_min_arr != 0).any(): |
| 3438 | # Clip values that cause saturation |
| 3439 | b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) |
| 3440 | # Reduce axes in unsaturated tensor to match original tensor |
| 3441 | for axis, dim in enumerate(b_arr.shape): |
| 3442 | if dim != b_unsat_arr.shape[axis]: |
| 3443 | assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" |
| 3444 | b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) |
| 3445 | |
| 3446 | placeholders.append( |
| 3447 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 3448 | ) |
| 3449 | placeholders.append( |
| 3450 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| 3451 | ) |
| 3452 | |
| 3453 | tens.extend(placeholders) |
| 3454 | elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: |
| 3455 | # Force value of operand[1] to be within [0, num_bits] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3456 | assert ( |
| 3457 | pCount == 2 and cCount == 0 |
| 3458 | ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3459 | |
| 3460 | placeholders = [] |
| 3461 | for idx, shape in enumerate(shapeList[:]): |
| 3462 | if idx == 1: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3463 | if dtypeList[idx] == DType.INT8: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3464 | arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3465 | elif dtypeList[idx] == DType.INT16: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3466 | arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3467 | elif dtypeList[idx] == DType.INT32: |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3468 | arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) |
| 3469 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3470 | raise Exception("OpArithmeticRightShift: invalid input dtype") |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3471 | else: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3472 | arr = self.getRandTensor(shape, dtypeList[idx]) |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3473 | placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3474 | |
| 3475 | tens.extend(placeholders) |
Matthew Haddon | a44ac5e | 2021-07-27 16:31:16 +0100 | [diff] [blame] | 3476 | elif op["op"] == Op.SELECT: |
| 3477 | # Set datatype of condition tensor to boolean |
| 3478 | dtypeList[0] = DType.BOOL |
| 3479 | tens.extend( |
| 3480 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 3481 | ) |
| 3482 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3483 | elif op["op"] == Op.INTDIV and error_name == None: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3484 | assert ( |
| 3485 | pCount == 2 and cCount == 0 |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 3486 | ), "Op.INTDIV must have 2 placeholders, 0 consts" |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3487 | |
| 3488 | placeholders = [] |
| 3489 | |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 3490 | # Two invalid cases for Op.INTDIV: |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3491 | # 1. divisor == 0 |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 3492 | # 2. dividend == -(1<<31) and divisor == -1 |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3493 | while True: |
| 3494 | dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) |
| 3495 | divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) |
| 3496 | |
| 3497 | if (divisor_arr == 0).any(): |
| 3498 | continue |
| 3499 | |
Kevin Cheng | 47315e1 | 2021-05-13 17:41:28 -0700 | [diff] [blame] | 3500 | if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3501 | continue |
| 3502 | |
| 3503 | break |
| 3504 | |
| 3505 | placeholders.append( |
| 3506 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| 3507 | ) |
| 3508 | placeholders.append( |
| 3509 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| 3510 | ) |
| 3511 | |
| 3512 | tens.extend(placeholders) |
| 3513 | elif op["op"] == Op.MUL: |
| 3514 | assert ( |
| 3515 | pCount == 2 and cCount == 0 |
| 3516 | ), "Op.MUL must have 2 placeholders, 0 consts" |
| 3517 | |
| 3518 | if dtypeList[0] == DType.FLOAT: |
| 3519 | tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) |
| 3520 | else: |
| 3521 | placeholders = [] |
| 3522 | |
| 3523 | # Make sure multiply result in int32 range |
| 3524 | shift = testArgs[0] |
| 3525 | if dtypeList[0] == DType.INT8: |
| 3526 | num_bits = 8 |
| 3527 | elif dtypeList[0] == DType.INT16: |
| 3528 | num_bits = 16 |
| 3529 | elif dtypeList[0] == DType.INT32: |
| 3530 | num_bits = 32 |
| 3531 | else: |
| 3532 | raise Exception("OpMul: invalid input dtype") |
| 3533 | |
| 3534 | for idx, shape in enumerate(shapeList[:]): |
| 3535 | low = -(2 ** (num_bits - 1)) |
| 3536 | high = (2 ** (num_bits - 1)) - 1 |
| 3537 | |
| 3538 | a_arr = np.int32( |
| 3539 | self.rng.integers(low=low, high=high, size=shapeList[0]) |
| 3540 | ) |
| 3541 | b_arr = np.int32( |
| 3542 | self.rng.integers(low=low, high=high, size=shapeList[1]) |
| 3543 | ) |
| 3544 | |
| 3545 | i = 0 |
| 3546 | while True: |
| 3547 | |
| 3548 | a_arr_64 = a_arr.astype(np.int64) |
| 3549 | b_arr_64 = b_arr.astype(np.int64) |
| 3550 | |
| 3551 | if shift > 0: |
| 3552 | rounding = 1 << (shift - 1) |
| 3553 | result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift |
| 3554 | else: |
| 3555 | result_arr = a_arr_64 * b_arr_64 |
| 3556 | |
| 3557 | if (result_arr > -(2 ** 31)).all() and ( |
| 3558 | result_arr <= ((2 ** 31) - 1) |
| 3559 | ).all(): |
| 3560 | break |
| 3561 | |
| 3562 | i = i + 1 |
| 3563 | a_arr = a_arr // 2 |
| 3564 | b_arr = b_arr // 2 |
| 3565 | |
| 3566 | placeholders.append( |
| 3567 | self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| 3568 | ) |
| 3569 | placeholders.append( |
| 3570 | self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| 3571 | ) |
| 3572 | |
| 3573 | tens.extend(placeholders) |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 3574 | elif op["op"] == Op.CONCAT: |
| 3575 | count = len(shapeList) - self.args.num_const_inputs_concat |
| 3576 | if count < 1: |
| 3577 | count = 1 |
| 3578 | if self.args.num_const_inputs_concat == 0: |
| 3579 | count = len(shapeList) |
| 3580 | |
| 3581 | shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0]) |
| 3582 | tens.extend( |
| 3583 | self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) |
| 3584 | ) |
| 3585 | tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) |
Kevin Cheng | aee1fac | 2020-11-11 13:54:06 -0800 | [diff] [blame] | 3586 | else: |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3587 | tens.extend( |
| 3588 | self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) |
| 3589 | ) |
| 3590 | tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3591 | |
Matthew Haddon | 1c00b71 | 2021-10-01 15:51:03 +0100 | [diff] [blame] | 3592 | return tens |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3593 | |
| 3594 | def createDynamicOpLists(self): |
| 3595 | |
| 3596 | # Dynamically create op lists for convolutions with a list of kernel sizes |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3597 | 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] | 3598 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3599 | for k in KERNELS_2D: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3600 | testName = "conv2d_{}x{}".format(k[0], k[1]) |
| 3601 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| 3602 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3603 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3604 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3605 | testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| 3606 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 3607 | "depthwise_conv2d_TEMPLATE" |
| 3608 | ].copy() |
| 3609 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3610 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3611 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3612 | testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| 3613 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| 3614 | "transpose_conv2d_TEMPLATE" |
| 3615 | ].copy() |
| 3616 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3617 | self.TOSA_OP_LIST[testName]["template"] = False |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3618 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3619 | KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| 3620 | for k in KERNELS_3D: |
| 3621 | testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| 3622 | self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| 3623 | self.TOSA_OP_LIST[testName]["filter"] = k |
| 3624 | self.TOSA_OP_LIST[testName]["template"] = False |
| 3625 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3626 | # Delete any templates after having created any dynamic ops |
| 3627 | # This is a two-pass operation because it's bad practice to delete |
| 3628 | # keys from dictionaries while iterating |
| 3629 | keyList = [] |
| 3630 | for k in self.TOSA_OP_LIST: |
| 3631 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3632 | if self.TOSA_OP_LIST[k]["template"] == True: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3633 | keyList.append(k) |
| 3634 | continue |
| 3635 | except KeyError: |
| 3636 | pass |
| 3637 | |
| 3638 | for k in keyList: |
| 3639 | del self.TOSA_OP_LIST[k] |
| 3640 | |
| 3641 | def initOpListDefaults(self): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3642 | """Fill in default fields for ops if they aren't already specified. |
| 3643 | Look for missing required fields (datastructure linting).""" |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3644 | for op in self.TOSA_OP_LIST: |
| 3645 | |
| 3646 | # Required fields |
| 3647 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3648 | pl, c = self.TOSA_OP_LIST[op]["operands"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3649 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3650 | raise Exception( |
| 3651 | "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| 3652 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3653 | |
| 3654 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3655 | fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3656 | except (KeyError, ValueError, TypeError): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3657 | raise Exception( |
| 3658 | "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| 3659 | op |
| 3660 | ) |
| 3661 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3662 | |
| 3663 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3664 | types = self.TOSA_OP_LIST[op]["types"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3665 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3666 | raise Exception( |
| 3667 | "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| 3668 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3669 | |
| 3670 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3671 | opcode = self.TOSA_OP_LIST[op]["op"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3672 | except KeyError as e: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3673 | raise Exception( |
| 3674 | "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| 3675 | ) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3676 | |
| 3677 | # Put in default rank range, if missing |
| 3678 | try: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3679 | rank = self.TOSA_OP_LIST[op]["rank"] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3680 | except KeyError: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3681 | self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3682 | |
| 3683 | # Tensor operator list |
| 3684 | # 'op': op name |
| 3685 | # 'operands': tuple of (placeholder, const) operands |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 3686 | # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| 3687 | # if not specified, defaults to (1, 4) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3688 | # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| 3689 | # 'types': array of datatypes to be tested |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3690 | TYPE_FP = [DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3691 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3692 | TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| 3693 | 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] | 3694 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3695 | TYPE_BOOL = [DType.BOOL] |
| 3696 | TYPE_FI32 = [DType.FLOAT, DType.INT32] |
| 3697 | TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] |
| 3698 | TYPE_FI16 = [DType.FLOAT, DType.INT16] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3699 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3700 | TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3701 | |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3702 | TYPE_CONV = [ |
Kevin Cheng | a901740 | 2021-07-28 17:19:23 -0700 | [diff] [blame] | 3703 | [DType.INT8, DType.INT4, DType.INT32], |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3704 | [DType.INT8, DType.INT8, DType.INT32], |
| 3705 | [DType.INT16, DType.INT8, DType.INT48], |
| 3706 | DType.FLOAT, |
| 3707 | ] |
| 3708 | |
Jeremy Johnson | 97eb75f | 2021-07-08 11:58:02 +0100 | [diff] [blame] | 3709 | DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3710 | |
| 3711 | TOSA_OP_LIST = { |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3712 | # Tensor operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3713 | "argmax": { |
| 3714 | "op": Op.ARGMAX, |
| 3715 | "operands": (1, 0), |
| 3716 | "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 3717 | "types": TYPE_NARROW_INT_FP, |
| 3718 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3719 | "avg_pool2d": { |
| 3720 | "op": Op.AVG_POOL2D, |
| 3721 | "operands": (1, 0), |
| 3722 | "rank": (4, 4), |
| 3723 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 3724 | "qgen": TosaQuantGen.qgUnary, |
| 3725 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 3726 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
| 3727 | "error_if_validators": (TosaErrorValidator.evKernelSmallerOne, TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evPadSmallerZero, |
| 3728 | TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, |
| 3729 | TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, |
| 3730 | TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3731 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3732 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3733 | "conv2d_TEMPLATE": { |
| 3734 | "op": Op.CONV2D, |
| 3735 | "operands": (1, 2), |
| 3736 | "rank": (4, 4), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3737 | "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3738 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3739 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3740 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3741 | "template": True, |
| 3742 | }, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3743 | # Templated operator. Filled in by createDynamicOpLists |
| 3744 | "conv3d_TEMPLATE": { |
| 3745 | "op": Op.CONV3D, |
| 3746 | "operands": (1, 2), |
| 3747 | "rank": (5, 5), |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3748 | "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3749 | "qgen": TosaQuantGen.qgConv, |
| 3750 | "types": TYPE_CONV, |
| 3751 | "template": True, |
| 3752 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3753 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3754 | "depthwise_conv2d_TEMPLATE": { |
| 3755 | "op": Op.DEPTHWISE_CONV2D, |
| 3756 | "operands": (1, 2), |
| 3757 | "filter": [1, 1], |
| 3758 | "rank": (4, 4), |
| 3759 | "build_fcn": ( |
| 3760 | build_depthwise_conv2d, |
| 3761 | TosaTensorGen.tgDepthwiseConv2D, |
Les Bell | 7aa69f4 | 2021-09-20 10:44:07 +0100 | [diff] [blame] | 3762 | TosaArgGen.agConv, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3763 | ), |
| 3764 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3765 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3766 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3767 | "template": True, |
| 3768 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3769 | "fully_connected": { |
| 3770 | "op": Op.FULLY_CONNECTED, |
| 3771 | "operands": (1, 2), |
| 3772 | "rank": (2, 2), |
| 3773 | "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), |
| 3774 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3775 | "types": TYPE_CONV, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3776 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3777 | "matmul": { |
| 3778 | "op": Op.MATMUL, |
| 3779 | "operands": (2, 0), |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 3780 | "rank": (3, 3), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3781 | "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), |
| 3782 | "qgen": TosaQuantGen.qgMatmul, |
| 3783 | "types": TYPE_NARROW_INT_FP, |
| 3784 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3785 | "max_pool2d": { |
| 3786 | "op": Op.MAX_POOL2D, |
| 3787 | "operands": (1, 0), |
| 3788 | "rank": (4, 4), |
| 3789 | "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), |
| 3790 | "types": TYPE_NARROW_INT_FP, |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 3791 | "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthSmallerZero,), |
| 3792 | "error_if_validators": (TosaErrorValidator.evKernelSmallerOne, TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evPadSmallerZero, |
| 3793 | TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, |
| 3794 | TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3795 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3796 | # Templated operator. Filled in by createDynamicOpLists |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3797 | "transpose_conv2d_TEMPLATE": { |
| 3798 | "op": Op.TRANSPOSE_CONV2D, |
Kevin Cheng | 989cb05 | 2021-04-28 16:29:44 -0700 | [diff] [blame] | 3799 | "operands": (1, 2), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3800 | "rank": (4, 4), |
| 3801 | "build_fcn": ( |
| 3802 | build_transpose_conv2d, |
| 3803 | TosaTensorGen.tgTransposeConv2D, |
| 3804 | TosaArgGen.agTransposeConv2D, |
| 3805 | ), |
| 3806 | "qgen": TosaQuantGen.qgConv, |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 3807 | "types": TYPE_CONV, |
Matthew Haddon | b724efc | 2021-08-25 16:40:29 +0100 | [diff] [blame] | 3808 | "invalid_test_validators": (TosaInvalidValidator.ivNonPositiveOutputShape,), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3809 | "template": True, |
| 3810 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 3811 | # Activation functions |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3812 | "clamp": { |
| 3813 | "op": Op.CLAMP, |
| 3814 | "operands": (1, 0), |
| 3815 | "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), |
| 3816 | "types": TYPE_NARROW_INT_FP, |
| 3817 | }, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 3818 | "sigmoid": { |
| 3819 | "op": Op.SIGMOID, |
| 3820 | "operands": (1, 0), |
| 3821 | "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), |
| 3822 | "types": TYPE_FP, |
| 3823 | }, |
| 3824 | "tanh": { |
| 3825 | "op": Op.TANH, |
| 3826 | "operands": (1, 0), |
| 3827 | "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), |
| 3828 | "types": TYPE_FP, |
| 3829 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3830 | # Elementwise Binary Operators |
| 3831 | "add": { |
| 3832 | "op": Op.ADD, |
| 3833 | "operands": (2, 0), |
| 3834 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3835 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3836 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3837 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3838 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3839 | "arithmetic_right_shift": { |
| 3840 | "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| 3841 | "operands": (2, 0), |
| 3842 | "build_fcn": ( |
| 3843 | build_arithmetic_right_shift, |
| 3844 | TosaTensorGen.tgBroadcastFuzz, |
| 3845 | TosaArgGen.agArithmeticRightShift, |
| 3846 | ), |
| 3847 | "types": TYPE_INT, |
| 3848 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3849 | "bitwise_and": { |
| 3850 | "op": Op.BITWISE_AND, |
| 3851 | "operands": (2, 0), |
| 3852 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3853 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3854 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3855 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3856 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3857 | "bitwise_or": { |
| 3858 | "op": Op.BITWISE_OR, |
| 3859 | "operands": (2, 0), |
| 3860 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3861 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3862 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3863 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3864 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3865 | "bitwise_xor": { |
| 3866 | "op": Op.BITWISE_XOR, |
| 3867 | "operands": (2, 0), |
| 3868 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3869 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3870 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3871 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3872 | }, |
Matthew Haddon | 459443c | 2021-08-23 16:43:13 +0100 | [diff] [blame] | 3873 | "intdiv": { |
| 3874 | "op": Op.INTDIV, |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3875 | "operands": (2, 0), |
| 3876 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3877 | "types": [DType.INT32], |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3878 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3879 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 14d7f7a | 2021-05-12 10:44:49 -0700 | [diff] [blame] | 3880 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3881 | "logical_and": { |
| 3882 | "op": Op.LOGICAL_AND, |
| 3883 | "operands": (2, 0), |
| 3884 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3885 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3886 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3887 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3888 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3889 | "logical_left_shift": { |
| 3890 | "op": Op.LOGICAL_LEFT_SHIFT, |
| 3891 | "operands": (2, 0), |
| 3892 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3893 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3894 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3895 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3896 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3897 | "logical_right_shift": { |
| 3898 | "op": Op.LOGICAL_RIGHT_SHIFT, |
| 3899 | "operands": (2, 0), |
| 3900 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3901 | "types": TYPE_INT, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3902 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3903 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3904 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3905 | "logical_or": { |
| 3906 | "op": Op.LOGICAL_OR, |
| 3907 | "operands": (2, 0), |
| 3908 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3909 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3910 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3911 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3912 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3913 | "logical_xor": { |
| 3914 | "op": Op.LOGICAL_XOR, |
| 3915 | "operands": (2, 0), |
| 3916 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3917 | "types": TYPE_BOOL, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3918 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3919 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3920 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3921 | "maximum": { |
| 3922 | "op": Op.MAXIMUM, |
| 3923 | "operands": (2, 0), |
| 3924 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3925 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3926 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3927 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3928 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3929 | "minimum": { |
| 3930 | "op": Op.MINIMUM, |
| 3931 | "operands": (2, 0), |
| 3932 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3933 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3934 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3935 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3936 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3937 | "mul": { |
| 3938 | "op": Op.MUL, |
| 3939 | "operands": (2, 0), |
| 3940 | "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), |
| 3941 | "types": TYPE_INT_FP, |
| 3942 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3943 | "pow": { |
| 3944 | "op": Op.POW, |
| 3945 | "operands": (2, 0), |
| 3946 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), |
| 3947 | "types": TYPE_FP, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3948 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3949 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3950 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3951 | "sub": { |
| 3952 | "op": Op.SUB, |
| 3953 | "operands": (2, 0), |
| 3954 | "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), |
| 3955 | "types": TYPE_FI32, |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 3956 | "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3957 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3958 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3959 | "table": { |
| 3960 | "op": Op.TABLE, |
| 3961 | # Use the automatic generation functions to create the input array |
| 3962 | # but create the table tensor in the build function, as it may be |
| 3963 | # a different type from the input |
| 3964 | "operands": (1, 0), |
| 3965 | "build_fcn": (build_table, TosaTensorGen.tgBasic, None), |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 3966 | "types": [DType.INT8, DType.INT16], |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3967 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3968 | # Elementwise Unary operators |
| 3969 | "abs": { |
| 3970 | "op": Op.ABS, |
| 3971 | "operands": (1, 0), |
| 3972 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3973 | "types": TYPE_FI32, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3974 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3975 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3976 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3977 | "bitwise_not": { |
| 3978 | "op": Op.BITWISE_NOT, |
| 3979 | "operands": (1, 0), |
| 3980 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3981 | "types": TYPE_INT, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3982 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3983 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3984 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3985 | "ceil": { |
| 3986 | "op": Op.CEIL, |
| 3987 | "operands": (1, 0), |
| 3988 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3989 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3990 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3991 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3992 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 3993 | "clz": { |
| 3994 | "op": Op.CLZ, |
| 3995 | "operands": (1, 0), |
| 3996 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 3997 | "types": [DType.INT32], |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 3998 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 3999 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4000 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4001 | "exp": { |
| 4002 | "op": Op.EXP, |
| 4003 | "operands": (1, 0), |
| 4004 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4005 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4006 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4007 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4008 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4009 | "floor": { |
| 4010 | "op": Op.FLOOR, |
| 4011 | "operands": (1, 0), |
| 4012 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4013 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4014 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4015 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4016 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4017 | "log": { |
| 4018 | "op": Op.LOG, |
| 4019 | "operands": (1, 0), |
| 4020 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4021 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4022 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4023 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4024 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4025 | "logical_not": { |
| 4026 | "op": Op.LOGICAL_NOT, |
| 4027 | "operands": (1, 0), |
| 4028 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4029 | "types": TYPE_BOOL, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4030 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4031 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4032 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4033 | "negate": { |
| 4034 | "op": Op.NEGATE, |
| 4035 | "operands": (1, 0), |
| 4036 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4037 | "qgen": TosaQuantGen.qgUnary, |
| 4038 | "types": TYPE_INT_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4039 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, |
| 4040 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, |
| 4041 | TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4042 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4043 | "reciprocal": { |
| 4044 | "op": Op.RECIPROCAL, |
| 4045 | "operands": (1, 0), |
| 4046 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4047 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4048 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4049 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4050 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4051 | "rsqrt": { |
| 4052 | "op": Op.RSQRT, |
| 4053 | "operands": (1, 0), |
| 4054 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4055 | "types": TYPE_FP, |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4056 | "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, |
| 4057 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4058 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4059 | # Elementwise Ternary operators |
| 4060 | "select": { |
| 4061 | "op": Op.SELECT, |
| 4062 | "operands": (3, 0), |
| 4063 | "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), |
| 4064 | "types": TYPE_FIB, |
| 4065 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4066 | # Comparison operators |
| 4067 | "equal": { |
| 4068 | "op": Op.EQUAL, |
| 4069 | "operands": (2, 0), |
| 4070 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 4071 | "types": TYPE_FI32, |
| 4072 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4073 | "greater_equal": { |
| 4074 | "op": Op.GREATER_EQUAL, |
| 4075 | "operands": (2, 0), |
| 4076 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 4077 | "types": TYPE_FI32, |
| 4078 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4079 | "greater": { |
| 4080 | "op": Op.GREATER, |
| 4081 | "operands": (2, 0), |
| 4082 | "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), |
| 4083 | "types": TYPE_FI32, |
| 4084 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4085 | # Reduction operators |
| 4086 | "reduce_all": { |
| 4087 | "op": Op.REDUCE_ALL, |
| 4088 | "operands": (1, 0), |
| 4089 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4090 | "types": TYPE_BOOL, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4091 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4092 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4093 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4094 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4095 | "reduce_any": { |
| 4096 | "op": Op.REDUCE_ANY, |
| 4097 | "operands": (1, 0), |
| 4098 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4099 | "types": TYPE_BOOL, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4100 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4101 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4102 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4103 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4104 | "reduce_max": { |
| 4105 | "op": Op.REDUCE_MAX, |
| 4106 | "operands": (1, 0), |
| 4107 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4108 | "types": TYPE_INT_FP, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4109 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4110 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4111 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4112 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4113 | "reduce_min": { |
| 4114 | "op": Op.REDUCE_MAX, |
| 4115 | "operands": (1, 0), |
| 4116 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4117 | "types": TYPE_INT_FP, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4118 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4119 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4120 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4121 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4122 | "reduce_product": { |
| 4123 | "op": Op.REDUCE_PRODUCT, |
| 4124 | "operands": (1, 0), |
| 4125 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4126 | "types": TYPE_FP, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4127 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4128 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4129 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4130 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4131 | "reduce_sum": { |
| 4132 | "op": Op.REDUCE_SUM, |
| 4133 | "operands": (1, 0), |
| 4134 | "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4135 | "types": TYPE_FI32, |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4136 | "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, |
| 4137 | TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4138 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4139 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4140 | # Data layout operators |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4141 | "concat": { |
| 4142 | "op": Op.CONCAT, |
| 4143 | "operands": (2, 0), |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 4144 | "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4145 | "types": TYPE_FIB, |
| 4146 | }, |
| 4147 | "pad": { |
| 4148 | "op": Op.PAD, |
| 4149 | "operands": (1, 0), |
| 4150 | "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), |
| 4151 | "qgen": TosaQuantGen.qgPad, |
| 4152 | "types": TYPE_FIB, |
| 4153 | }, |
| 4154 | "reshape": { |
| 4155 | "op": Op.RESHAPE, |
| 4156 | "operands": (1, 0), |
| 4157 | "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), |
| 4158 | "types": TYPE_FIB, |
| 4159 | }, |
| 4160 | "reverse": { |
| 4161 | "op": Op.REVERSE, |
| 4162 | "operands": (1, 0), |
| 4163 | "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), |
| 4164 | "types": TYPE_FIB, |
| 4165 | }, |
| 4166 | "slice": { |
| 4167 | "op": Op.SLICE, |
| 4168 | "operands": (1, 0), |
| 4169 | "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), |
| 4170 | "types": TYPE_FIB, |
| 4171 | }, |
| 4172 | "tile": { |
| 4173 | "op": Op.TILE, |
| 4174 | "operands": (1, 0), |
| 4175 | "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), |
| 4176 | "types": TYPE_FIB, |
| 4177 | }, |
| 4178 | "transpose": { |
| 4179 | "op": Op.TRANSPOSE, |
| 4180 | "operands": (1, 0), |
Jeremy Johnson | a618557 | 2021-06-21 15:55:35 +0100 | [diff] [blame] | 4181 | "rank": (1, 4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4182 | "build_fcn": ( |
| 4183 | build_transpose, |
| 4184 | TosaTensorGen.tgBasic, |
| 4185 | TosaArgGen.agTranspose, |
| 4186 | ), |
| 4187 | "types": TYPE_FIB, |
| 4188 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4189 | # Data nodes |
| 4190 | "const": { |
| 4191 | "op": Op.CONST, |
Kevin Cheng | 17e9202 | 2021-10-01 14:33:33 -0700 | [diff] [blame] | 4192 | "operands": (0, 1), |
| 4193 | "build_fcn": (build_const, TosaTensorGen.tgBasic, None), |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4194 | "types": TYPE_FIB, |
| 4195 | }, |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4196 | "identity": { |
| 4197 | "op": Op.IDENTITY, |
| 4198 | "operands": (1, 0), |
| 4199 | "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), |
| 4200 | "types": TYPE_FIB, |
| 4201 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4202 | # Scatter/Gather |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4203 | "gather": { |
| 4204 | "op": Op.GATHER, |
| 4205 | # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| 4206 | "operands": (1, 0), |
| 4207 | "rank": (3, 3), |
| 4208 | "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), |
| 4209 | "types": TYPE_INT_FP, |
| 4210 | }, |
| 4211 | "scatter": { |
| 4212 | "op": Op.SCATTER, |
| 4213 | # Only specify 'values_in' tensor here. |
| 4214 | #'indices' and 'input' are generated in op building stage |
| 4215 | "operands": (2, 0), |
| 4216 | "rank": (3, 3), |
| 4217 | "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), |
| 4218 | "types": TYPE_INT_FP, |
| 4219 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4220 | # Image operations |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4221 | "resize": { |
| 4222 | "op": Op.RESIZE, |
| 4223 | "operands": (1, 0), |
| 4224 | "rank": (4, 4), |
| 4225 | "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), |
| 4226 | "types": [DType.INT8, DType.INT16, DType.FLOAT], |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 4227 | "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride), |
| 4228 | "error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension, |
| 4229 | TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax, |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 4230 | TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven, TosaErrorValidator.evWrongInputType, |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4231 | TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, |
| 4232 | TosaErrorValidator.evBatchMismatch, TosaErrorValidator.evChannelMismatch) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4233 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4234 | # Type conversion |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4235 | "cast": { |
| 4236 | "op": Op.CAST, |
| 4237 | "operands": (1, 0), |
| 4238 | "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), |
| 4239 | "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], |
| 4240 | }, |
| 4241 | "rescale": { |
| 4242 | "op": Op.RESCALE, |
| 4243 | "operands": (1, 0), |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 4244 | "rank": (1,4), |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4245 | "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), |
Matthew Haddon | cac4ee9 | 2021-07-22 14:30:53 +0100 | [diff] [blame] | 4246 | "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], |
Matthew Haddon | c202521 | 2021-10-08 21:21:05 +0100 | [diff] [blame^] | 4247 | "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, TosaErrorValidator.evScaleTrue, |
| 4248 | TosaErrorValidator.evScaleNotTrue, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, |
| 4249 | TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4250 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4251 | # Custom |
| 4252 | # Not implemented. |
Jared Smolens | 573ecd4 | 2021-03-04 15:24:10 -0800 | [diff] [blame] | 4253 | # Control flow operators |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4254 | # Two varients of cond_if, one that generates one of two constant tensors (no |
| 4255 | # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| 4256 | # (two inputs to the basic blocks, one output) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4257 | "cond_if_const": { |
| 4258 | "op": Op.COND_IF, |
| 4259 | "operands": (0, 2), |
| 4260 | "build_fcn": ( |
| 4261 | build_cond_if_const, |
| 4262 | TosaTensorGen.tgBasic, |
| 4263 | TosaArgGen.agCondIf, |
| 4264 | ), |
| 4265 | "types": [DType.BOOL], |
| 4266 | }, |
| 4267 | "cond_if_binary": { |
| 4268 | "op": Op.COND_IF, |
| 4269 | "operands": (2, 0), |
| 4270 | "build_fcn": ( |
| 4271 | build_cond_if_binary, |
| 4272 | TosaTensorGen.tgBasic, |
| 4273 | TosaArgGen.agCondIf, |
| 4274 | ), |
Les Bell | 6040b4d | 2021-10-11 12:50:31 +0100 | [diff] [blame] | 4275 | "types": TYPE_INT_FP, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4276 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4277 | # while_loop |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4278 | "while_loop": { |
| 4279 | "op": Op.WHILE_LOOP, |
| 4280 | "operands": (0, 1), |
| 4281 | "build_fcn": ( |
| 4282 | build_while_loop, |
| 4283 | TosaTensorGen.tgBasic, |
| 4284 | TosaArgGen.agWhileLoop, |
| 4285 | ), |
| 4286 | "types": [DType.INT32], |
| 4287 | }, |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4288 | } |
| 4289 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4290 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4291 | class OutputShaper: |
| 4292 | # Methods in this class compute the expected output shape and datatype |
| 4293 | # for common classes of operations |
| 4294 | def __init__(self): |
| 4295 | pass |
| 4296 | |
| 4297 | # These methods return arguments that can be used for |
| 4298 | # creating a new output tensor |
| 4299 | @staticmethod |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4300 | def binaryBroadcastOp(ser, rng, a, b, error_name=None): |
| 4301 | if error_name != ErrorIf.RankMismatch: |
| 4302 | assert len(a.shape) == len(b.shape) |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4303 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4304 | |
| 4305 | shape = [] |
| 4306 | for i in range(len(a.shape)): |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4307 | if a.shape[i] == 1 and error_name == None: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4308 | shape.append(b.shape[i]) |
| 4309 | else: |
| 4310 | shape.append(a.shape[i]) |
| 4311 | |
Matthew Haddon | eacff9a | 2021-09-24 14:42:13 +0100 | [diff] [blame] | 4312 | if error_name == ErrorIf.WrongOutputType: |
| 4313 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4314 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 4315 | outputDType = rng.choice(wrong_dtypes) |
| 4316 | else: |
| 4317 | outputDType = a.dtype |
| 4318 | |
| 4319 | return ser.addOutput(shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4320 | |
| 4321 | @staticmethod |
| 4322 | def binaryNonBroadcastOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4323 | assert len(a.shape) == len(b.shape) |
| 4324 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4325 | |
| 4326 | shape = [] |
| 4327 | for i in range(len(a.shape)): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4328 | assert a.shape[i] == b.shape[i] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4329 | shape.append(a.shape[i]) |
| 4330 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4331 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4332 | |
| 4333 | @staticmethod |
Matthew Haddon | e4ecdb2 | 2021-09-28 11:38:21 +0100 | [diff] [blame] | 4334 | def unaryOp(ser, rng, a, error_name=None): |
| 4335 | if error_name == ErrorIf.WrongOutputType: |
| 4336 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4337 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 4338 | outputDType = rng.choice(wrong_dtypes) |
| 4339 | else: |
| 4340 | outputDType = a.dtype |
| 4341 | |
| 4342 | return ser.addOutput(a.shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4343 | |
| 4344 | @staticmethod |
| 4345 | def selectOp(ser, cond, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4346 | assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| 4347 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4348 | |
| 4349 | shape = [] |
| 4350 | for i in range(len(a.shape)): |
| 4351 | shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| 4352 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4353 | return ser.addOutput(shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4354 | |
| 4355 | @staticmethod |
| 4356 | def binaryComparisonOp(ser, a, b): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4357 | assert len(a.shape) == len(b.shape) |
| 4358 | assert a.dtype == b.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4359 | |
| 4360 | # Do broadcast |
| 4361 | shape = [] |
| 4362 | for i in range(len(a.shape)): |
| 4363 | if a.shape[i] == 1: |
| 4364 | shape.append(b.shape[i]) |
| 4365 | else: |
| 4366 | shape.append(a.shape[i]) |
| 4367 | |
| 4368 | # Force the output type to bool |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4369 | return ser.addOutput(shape, DType.BOOL) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4370 | |
| 4371 | @staticmethod |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4372 | def reduceOp(ser, rng, a, axis, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4373 | shape = a.shape.copy() |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4374 | if error_name not in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank, ErrorIf.ShapeOfAxisNotOne]: |
| 4375 | shape[axis] = 1 |
| 4376 | if error_name == ErrorIf.ShapeOfAxisNotOne and shape[axis] == 1: |
| 4377 | shape[axis] = rng.integers(2, 10) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4378 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4379 | if error_name == ErrorIf.WrongOutputType: |
| 4380 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4381 | wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| 4382 | outputDType = rng.choice(wrong_dtypes) |
| 4383 | else: |
| 4384 | outputDType = a.dtype |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4385 | |
Matthew Haddon | d6ce725 | 2021-09-29 15:35:44 +0100 | [diff] [blame] | 4386 | return ser.addOutput(shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4387 | |
| 4388 | @staticmethod |
| 4389 | def argmaxOp(ser, a, axis): |
| 4390 | shape = a.shape.copy() |
| 4391 | del shape[axis] |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4392 | return ser.addOutput(shape, DType.INT32) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4393 | |
| 4394 | @staticmethod |
| 4395 | def conv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 4396 | |
| 4397 | # IFM: NHWC |
| 4398 | # Filter: OHWI |
| 4399 | # OFM: NHWC |
| 4400 | |
| 4401 | if len(padding) == 2: |
| 4402 | # Expand padding to 4 parameters in the case of transpose_conv2d |
| 4403 | # From H,W to T,B,L,R |
| 4404 | padding = [padding[0], padding[0], padding[1], padding[1]] |
| 4405 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4406 | h = ( |
| 4407 | ifm.shape[1] |
| 4408 | - filter.shape[1] |
| 4409 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 4410 | + padding[0] |
| 4411 | + padding[1] |
| 4412 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4413 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4414 | w = ( |
| 4415 | ifm.shape[2] |
| 4416 | - filter.shape[2] |
| 4417 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 4418 | + padding[2] |
| 4419 | + padding[3] |
| 4420 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4421 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4422 | ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| 4423 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4424 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4425 | out_dtype = DType.INT32 |
| 4426 | elif ifm.dtype == DType.INT16: |
| 4427 | out_dtype = DType.INT48 |
| 4428 | elif ifm.dtype == DType.FLOAT: |
| 4429 | out_dtype = DType.FLOAT |
| 4430 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4431 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4432 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4433 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4434 | |
| 4435 | @staticmethod |
Kevin Cheng | 1533b85 | 2021-09-01 12:51:58 -0700 | [diff] [blame] | 4436 | def conv3dOp(ser, ifm, filter, strides, padding, dilations): |
| 4437 | |
| 4438 | # IFM: NDHWC |
| 4439 | # Filter: ODHWI |
| 4440 | # OFM: NDHWC |
| 4441 | |
| 4442 | d = ( |
| 4443 | ifm.shape[1] |
| 4444 | - filter.shape[1] |
| 4445 | - (filter.shape[1] - 1) * (dilations[0] - 1) |
| 4446 | + padding[0] |
| 4447 | + padding[1] |
| 4448 | ) // strides[0] + 1 |
| 4449 | |
| 4450 | h = ( |
| 4451 | ifm.shape[2] |
| 4452 | - filter.shape[2] |
| 4453 | - (filter.shape[2] - 1) * (dilations[1] - 1) |
| 4454 | + padding[2] |
| 4455 | + padding[3] |
| 4456 | ) // strides[1] + 1 |
| 4457 | |
| 4458 | w = ( |
| 4459 | ifm.shape[3] |
| 4460 | - filter.shape[3] |
| 4461 | - (filter.shape[3] - 1) * (dilations[2] - 1) |
| 4462 | + padding[4] |
| 4463 | + padding[5] |
| 4464 | ) // strides[2] + 1 |
| 4465 | |
| 4466 | ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| 4467 | |
| 4468 | if ifm.dtype == DType.INT8: |
| 4469 | out_dtype = DType.INT32 |
| 4470 | elif ifm.dtype == DType.INT16: |
| 4471 | out_dtype = DType.INT48 |
| 4472 | elif ifm.dtype == DType.FLOAT: |
| 4473 | out_dtype = DType.FLOAT |
| 4474 | else: |
| 4475 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
| 4476 | |
| 4477 | return ser.addOutput(ofm_shape, out_dtype) |
| 4478 | |
| 4479 | @staticmethod |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4480 | def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): |
| 4481 | # IFM: NHWC |
| 4482 | # Filter: HWCM |
| 4483 | # OFM: NHW C*M |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4484 | h = ( |
| 4485 | ifm.shape[1] |
| 4486 | - filter.shape[0] |
| 4487 | - (filter.shape[0] - 1) * (dilations[0] - 1) |
| 4488 | + padding[0] |
| 4489 | + padding[1] |
| 4490 | ) // strides[0] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4491 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4492 | w = ( |
| 4493 | ifm.shape[2] |
| 4494 | - filter.shape[1] |
| 4495 | - (filter.shape[1] - 1) * (dilations[1] - 1) |
| 4496 | + padding[2] |
| 4497 | + padding[3] |
| 4498 | ) // strides[1] + 1 |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4499 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4500 | ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| 4501 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4502 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4503 | out_dtype = DType.INT32 |
| 4504 | elif ifm.dtype == DType.INT16: |
| 4505 | out_dtype = DType.INT48 |
| 4506 | elif ifm.dtype == DType.FLOAT: |
| 4507 | out_dtype = DType.FLOAT |
| 4508 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4509 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4510 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4511 | return ser.addOutput(ofm_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4512 | |
| 4513 | @staticmethod |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 4514 | def pool2dOp(ser, rng, ifm, kernel, stride, pad, error_name=None): |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4515 | # input: NHWC |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 4516 | if stride[0] <= 0 or stride[1] <= 0 or min(pad) < 0: |
| 4517 | # If an incorrect stride is used set dimensions to 0, test is invalid anyway. |
| 4518 | h = 1 |
| 4519 | w = 1 |
| 4520 | else: |
| 4521 | h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] |
| 4522 | w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] |
| 4523 | |
| 4524 | if error_name == ErrorIf.PoolingOutputShapeMismatch: |
| 4525 | choices = [1, 2, 3, 4, 5] |
| 4526 | h = h + rng.choice(choices) |
| 4527 | w = w + rng.choice(choices) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4528 | |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4529 | ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
Matthew Haddon | b6b59e3 | 2021-10-07 17:19:20 +0100 | [diff] [blame] | 4530 | |
| 4531 | if error_name == ErrorIf.WrongOutputType: |
| 4532 | all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] |
| 4533 | wrong_dtypes = list(set(all_dtypes) - set([ifm.dtype])) |
| 4534 | outputDType = rng.choice(wrong_dtypes) |
| 4535 | else: |
| 4536 | outputDType = ifm.dtype |
| 4537 | |
| 4538 | return ser.addOutput(ofm_shape, outputDType) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4539 | |
| 4540 | @staticmethod |
| 4541 | def fullyConnectedOp(ser, input, filter): |
| 4542 | # input: N, IC |
| 4543 | # filter: OC, IC |
| 4544 | # output: N, OC |
| 4545 | |
| 4546 | output_shape = [input.shape[0], filter.shape[0]] |
| 4547 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4548 | if input.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4549 | out_dtype = DType.INT32 |
| 4550 | elif input.dtype == DType.INT16: |
| 4551 | out_dtype = DType.INT48 |
| 4552 | elif input.dtype == DType.FLOAT: |
| 4553 | out_dtype = DType.FLOAT |
| 4554 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4555 | raise Exception("Unsupported input dtype: {}".format(input.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4556 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4557 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4558 | |
| 4559 | @staticmethod |
| 4560 | def matmulOp(ser, a, b): |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 4561 | # a: N, H, C |
| 4562 | # b: N, C, W |
| 4563 | # out: N, H, W |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4564 | |
Kevin Cheng | 2d60f00 | 2021-06-09 14:18:32 -0700 | [diff] [blame] | 4565 | output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4566 | |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4567 | if a.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4568 | out_dtype = DType.INT32 |
| 4569 | elif a.dtype == DType.INT16: |
| 4570 | out_dtype = DType.INT48 |
| 4571 | elif a.dtype == DType.FLOAT: |
| 4572 | out_dtype = DType.FLOAT |
| 4573 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4574 | raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4575 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4576 | return ser.addOutput(output_shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4577 | |
| 4578 | @staticmethod |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 4579 | def concatOp(ser, axis, *a): |
| 4580 | input1 = a[0] |
| 4581 | remaining_inputs = a[1:] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4582 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 4583 | output_shape = input1.shape.copy() |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4584 | |
Matthew Haddon | 818ab90 | 2021-07-27 09:12:49 +0100 | [diff] [blame] | 4585 | output_shape[axis] = input1.shape[axis] |
| 4586 | |
| 4587 | for tensor in remaining_inputs: |
| 4588 | output_shape[axis] += tensor.shape[axis] |
| 4589 | |
| 4590 | return ser.addOutput(output_shape, input1.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4591 | |
| 4592 | @staticmethod |
| 4593 | def padOp(ser, a, padding): |
| 4594 | |
| 4595 | output_shape = a.shape.copy() |
| 4596 | |
| 4597 | for i in range(len(output_shape)): |
| 4598 | output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| 4599 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4600 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4601 | |
| 4602 | @staticmethod |
| 4603 | def reshapeOp(ser, a, shape): |
| 4604 | output_shape = shape.copy() |
| 4605 | |
| 4606 | totalElements = 1 |
| 4607 | for i in a.shape: |
| 4608 | totalElements *= i |
| 4609 | |
| 4610 | # If there are any -1 elements, figure out what that dimension must be |
| 4611 | totalOutputElements = 1 |
| 4612 | for i in output_shape: |
| 4613 | if i != -1: |
| 4614 | totalOutputElements *= i |
| 4615 | |
| 4616 | # And fill it in |
| 4617 | for i in range(len(output_shape)): |
| 4618 | if output_shape[i] == -1: |
| 4619 | output_shape[i] = totalElements // totalOutputElements |
| 4620 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4621 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4622 | |
| 4623 | @staticmethod |
| 4624 | def sliceOp(ser, a, begin, size): |
| 4625 | |
| 4626 | output_shape = size.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4627 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4628 | |
| 4629 | @staticmethod |
| 4630 | def tileOp(ser, a, multiples): |
| 4631 | |
| 4632 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4633 | assert len(multiples) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4634 | |
| 4635 | for i in range(len(output_shape)): |
| 4636 | output_shape[i] = a.shape[i] * multiples[i] |
| 4637 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4638 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4639 | |
| 4640 | @staticmethod |
| 4641 | def transposeOp(ser, a, perms): |
| 4642 | output_shape = a.shape.copy() |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4643 | assert len(perms) == len(output_shape) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4644 | |
| 4645 | for i in range(len(output_shape)): |
| 4646 | output_shape[i] = a.shape[perms[i]] |
| 4647 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4648 | return ser.addOutput(output_shape, a.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4649 | |
| 4650 | @staticmethod |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 4651 | def gatherOp(ser, values, indices): |
| 4652 | assert len(values.shape) == 3 |
| 4653 | assert len(indices.shape) == 2 |
| 4654 | assert values.shape[0] == indices.shape[0] |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4655 | |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 4656 | output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| 4657 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4658 | return ser.addOutput(output_shape, values.dtype) |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 4659 | |
| 4660 | @staticmethod |
| 4661 | def scatterOp(ser, values_in, indices, input): |
| 4662 | assert len(values_in.shape) == 3 |
| 4663 | assert len(indices.shape) == 2 |
| 4664 | assert len(input.shape) == 3 |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4665 | assert values_in.shape[0] == indices.shape[0] # N |
| 4666 | assert input.shape[1] == indices.shape[1] # W |
| 4667 | assert values_in.shape[2] == input.shape[2] # C |
Kevin Cheng | 77d0f76 | 2020-11-24 10:26:32 -0800 | [diff] [blame] | 4668 | |
| 4669 | output_shape = values_in.shape |
| 4670 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4671 | return ser.addOutput(output_shape, values_in.dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4672 | |
| 4673 | @staticmethod |
Jeremy Johnson | f54d8a2 | 2021-07-20 16:01:06 +0100 | [diff] [blame] | 4674 | def tableOp(ser, input, table_dtype): |
| 4675 | # Same shape as the input, but dtype dependent on table dtype |
| 4676 | assert table_dtype == DType.INT16 or table_dtype == DType.INT8 |
| 4677 | output_dtype = DType.INT32 if table_dtype == DType.INT16 else DType.INT8 |
| 4678 | return ser.addOutput(input.shape, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4679 | |
| 4680 | @staticmethod |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4681 | def resizeOp( |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4682 | serializer, |
| 4683 | rng, |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4684 | input, |
| 4685 | mode, |
| 4686 | stride, |
| 4687 | offset, |
| 4688 | shift, |
| 4689 | stride_fp, |
| 4690 | offset_fp, |
| 4691 | output_dims, |
| 4692 | input_dtype, |
| 4693 | output_dtype, |
Matthew Haddon | e86fd34 | 2021-09-07 16:12:21 +0100 | [diff] [blame] | 4694 | error_name = None |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4695 | ): |
Matthew Haddon | 848efb4 | 2021-09-09 12:30:53 +0100 | [diff] [blame] | 4696 | if error_name == ErrorIf.WrongRank: |
| 4697 | output_dims = [input.shape[0], output_dims[0], output_dims[0], input.shape[0]] |
| 4698 | else: |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4699 | if error_name == ErrorIf.BatchMismatch: |
| 4700 | output_dims = [input.shape[0] + rng.integers(1, 10), output_dims[0], output_dims[1], input.shape[3]] |
| 4701 | elif error_name == ErrorIf.ChannelMismatch: |
| 4702 | output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3] + rng.integers(1, 10)] |
| 4703 | else: |
| 4704 | 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] | 4705 | |
Matthew Haddon | 693ba9e | 2021-09-22 11:24:37 +0100 | [diff] [blame] | 4706 | return serializer.addOutput(output_dims, output_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4707 | |
| 4708 | @staticmethod |
| 4709 | def typeConversionOp(ser, val, out_dtype): |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4710 | return ser.addOutput(val.shape, out_dtype) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4711 | |
| 4712 | @staticmethod |
| 4713 | def transposeConv2DOp(ser, ifm, output_shape): |
Kevin Cheng | 3a47857 | 2021-01-22 17:21:02 -0800 | [diff] [blame] | 4714 | if ifm.dtype == DType.INT8: |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4715 | out_dtype = DType.INT32 |
| 4716 | elif ifm.dtype == DType.INT16: |
| 4717 | out_dtype = DType.INT48 |
| 4718 | elif ifm.dtype == DType.FLOAT: |
| 4719 | out_dtype = DType.FLOAT |
| 4720 | else: |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4721 | raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) |
Eric Kunze | e5e2676 | 2020-10-13 16:11:07 -0700 | [diff] [blame] | 4722 | |
Kevin Cheng | 550ccc5 | 2021-03-03 11:21:43 -0800 | [diff] [blame] | 4723 | return ser.addOutput(output_shape, out_dtype) |