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Diffstat (limited to 'verif/generator/tosa_test_gen.py')
| -rw-r--r-- | verif/generator/tosa_test_gen.py | 6868 |
1 files changed, 6868 insertions, 0 deletions
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py new file mode 100644 index 0000000..0d5a881 --- /dev/null +++ b/verif/generator/tosa_test_gen.py @@ -0,0 +1,6868 @@ +#!/usr/bin/env python3 + +# Copyright (c) 2020-2022, ARM Limited. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +import argparse +import sys +import re +import os +import subprocess +import shlex +import json +import glob +import math +import queue +import threading +import traceback +import math +import itertools +from copy import deepcopy + +from enum import IntEnum, Enum, unique + +import serializer.tosa_serializer as ts +from serializer.tosa_serializer import * +import tosa +from generator.tosa_error_if import ErrorIf + +# Convenience variables to the flatc-generated types that should be enums, but aren't +from tosa.DType import DType +from tosa.Op import Op +from tosa.ResizeMode import ResizeMode + + +def valueToName(item, value): + """Get the name of an attribute with the given value. + + This convenience function is needed to print meaningful names for + the values of the tosa.Op.Op and tosa.DType.DType classes. + This would not be necessary if they were subclasses of Enum, or + IntEnum, which, sadly, they are not. + + Args: + item: The class, or object, to find the value in + value: The value to find + + Example, to get the name of a DType value: + + name = valueToName(DType, DType.INT8) # returns 'INT8' + name = valueToName(DType, 4) # returns 'INT8' + + Returns: + The name of the first attribute found with a matching value, + + Raises: + ValueError if the value is not found + """ + for attr in dir(item): + if getattr(item, attr) == value: + return attr + raise ValueError(f'value ({value}) not found') + +def allDTypes(*, excludes=None): + """Get a set of all DType values, optionally excluding some values. + + This convenience function is needed to provide a sequence of DType values. + This would be much easier if DType was a subclass of Enum, or IntEnum, + as we could then iterate over the values directly, instead of using + dir() to find the attributes and then check if they are what we want. + + Args: + excludes: iterable of DTYPE values (e.g. [DType.INT8, DType.BOOL]) + + Returns: + A set of DType values + """ + excludes = () if not excludes else excludes + return {getattr(DType, t) for t in dir(DType) + if not callable(getattr(DType, t)) and not t.startswith('__') + and getattr(DType, t) not in excludes} + +def usableDTypes(*, excludes=None): + """Get a set of usable DType values, optionally excluding some values. + + Excludes (DType.UNKNOWN, DType.UINT8) in addition to the excludes + specified by the caller, as the serializer lib does not support them. + If you wish to include 'UNKNOWN' or 'UINT8' use allDTypes instead. + + Args: + excludes: iterable of DType values (e.g. [DType.INT8, DType.BOOL]) + + Returns: + A set of DType values + """ + omit = {DType.UNKNOWN, DType.UINT8} + omit.update(excludes if excludes else ()) + return allDTypes(excludes=omit) + +def product(shape): + value = 1 + for n in shape: + value *= n + return value + + +class TosaQuantGen: + """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" + + def __init__(self): + pass + + @staticmethod + def getQinfo(testGen, dtype, error_name=None): + + if dtype == DType.INT8: + return testGen.randInt(-128, 128) + elif dtype == DType.UINT8: + return testGen.randInt(0, 256) + elif error_name in [ErrorIf.InputZeroPointNotZero, ErrorIf.WeightZeroPointNotZero, ErrorIf.OutputZeroPointNotZero]: + zero_point = testGen.randInt(-128, 128) + if zero_point == 0: + zero_point = 1 + return zero_point + return 0 + + @staticmethod + def qgUnary(testGen, op, dtype, error_name=None): + qinfo = ts.TosaSerializerQuantInfo() + if error_name == ErrorIf.InputZeroPointNotZero: + qinfo.UnaryQuantInfo( + TosaQuantGen.getQinfo(testGen, dtype, error_name), TosaQuantGen.getQinfo(testGen, dtype) + ) + elif error_name == ErrorIf.OutputZeroPointNotZero: + qinfo.UnaryQuantInfo( + TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype, error_name) + ) + else: + qinfo.UnaryQuantInfo( + TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) + ) + return qinfo + + @staticmethod + def qgConv(testGen, op, dtype_or_dtypeList, error_name=None): + qinfo = ts.TosaSerializerQuantInfo() + if isinstance(dtype_or_dtypeList, list): + # a list of [input, weights, accumulator] dtypes + dtypeList = dtype_or_dtypeList + else: + # an int, [input, weights, accumulator] dtypes are the same + dtypeList = [dtype_or_dtypeList] * 3 + + if error_name == ErrorIf.InputZeroPointNotZero: + input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0], error_name) + weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) + elif error_name == ErrorIf.WeightZeroPointNotZero: + input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) + weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1], error_name) + else: + input_zp = TosaQuantGen.getQinfo(testGen, dtypeList[0]) + weights_zp = TosaQuantGen.getQinfo(testGen, dtypeList[1]) + + qinfo.ConvQuantInfo(input_zp, weights_zp) + return qinfo + + @staticmethod + def qgMatmul(testGen, op, dtype, error_name=None): + qinfo = ts.TosaSerializerQuantInfo() + if error_name == ErrorIf.InputZeroPointNotZero: + qinfo.MatMulQuantInfo( + TosaQuantGen.getQinfo(testGen, dtype, error_name), TosaQuantGen.getQinfo(testGen, dtype, error_name) + ) + else: + qinfo.MatMulQuantInfo( + TosaQuantGen.getQinfo(testGen, dtype), TosaQuantGen.getQinfo(testGen, dtype) + ) + return qinfo + + @staticmethod + def qgPad(testGen, op, dtype, error_name=None): + qinfo = ts.TosaSerializerQuantInfo() + if error_name == ErrorIf.InputZeroPointNotZero: + qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype, error_name)) + else: + qinfo.PadQuantInfo(TosaQuantGen.getQinfo(testGen, dtype)) + return qinfo + + @staticmethod + def computeMultiplierAndShift(scaleFp, scale32): + # Derived from computeMultiplierAndShiftTosaScale32 + # Provide a floating-point scaling factor and the scale32 parameter + # to compute the multiplier and shift + + if scale32: + scaleBits = 31 + else: + scaleBits = 15 + + m, shift = math.frexp(scaleFp) + + if scaleFp < 0.0: + m = -m + + multiplier = round(m * (1 << scaleBits)) + assert multiplier <= (1 << scaleBits) + + if multiplier == (1 << scaleBits): + multiplier = multiplier // 2 + shift = shift + 1 + + shift = (-shift) + scaleBits + #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) + + # Adjust multiplier such that shift is in allowed value range. + if shift == 0: + multiplier = multiplier // 4 + shift = shift + 2 + elif shift == 1: + multiplier = multiplier // 2 + shift = shift + 1 + elif shift == 63: + multiplier = multiplier * 2 + shift = shift - 1 + + assert multiplier <= (1 << scaleBits) + assert shift >= 2 and shift <= 62 + + return multiplier, shift + + +class TosaTensorGen: + """Tensor generators create a shape list for the placeholder and const tensor + data operands for the operator. The actual random data is generated separately for each test.""" + + def __init__(self): + pass + + @staticmethod + def tgBasic(testGen, opName, rank, error_name=None): + pl, const = opName["operands"] + shape = testGen.makeShape(rank) + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + shape = TosaErrorIfArgGen.eiRestrictDimensions(shape) + + shape_list = [] + for i in range(pl + const): + shape_list.append(shape.copy()) + + if error_name == ErrorIf.RankMismatch: + if rank == 1 and i != 1: + shape = testGen.makeShape(rank + testGen.rng.choice([1, 2, 3])) + elif i != 1: + shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) + + return shape_list + + @staticmethod + def tgNHWC(testGen, opName, rank, error_name=None): + pl, const = opName["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 4 + + shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + shape[0] = (shape[0] % testGen.args.max_batch_size) + 1 + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name and error_name != ErrorIf.MaxDimExceeded: + shape = TosaErrorIfArgGen.eiRestrictDimensions(shape) + + shape_list = [] + for i in range(pl + const): + shape_list.append(shape.copy()) + + return shape_list + + @staticmethod + def tgScatter(testGen, opName, rank, error_name=None): + pl, const = opName["operands"] + + assert pl == 2 + assert const == 0 + if error_name != ErrorIf.WrongRank: + assert rank == 3 + + values_in_shape = testGen.makeShape(rank) + + # ignore max batch size if target shape is set + if testGen.args.max_batch_size and not testGen.args.target_shapes: + values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 + + W = testGen.randInt( + testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] + ) + # Constrict W if one dimension is too large to keep tensor size reasonable + if max(values_in_shape) > 5000: + W = testGen.randInt(0, 16) + + input_shape = [values_in_shape[0], W, values_in_shape[2]] + + shape_list = [] + shape_list.append(values_in_shape.copy()) + shape_list.append(input_shape.copy()) + + return shape_list + + @staticmethod + def tgBroadcastFuzz(testGen, op, rank, error_name=None): + shape = testGen.makeShape(rank) + + pl, const = op["operands"] + + shape_list = [] + + # Choose one of the inputs to broadcast + # Note: Simplifies OutputShaper code if we don't change first shape for errors + bcast_idx = testGen.randInt(0 if error_name == None else 1, pl + const) + for i in range(pl + const): + shape_bcast = shape.copy() + + # If the chosen input, pick a random index to broadcast + if i == bcast_idx: + fuzz_idx = testGen.randInt(0, rank) + if error_name == ErrorIf.DimensionMismatch: + shape_bcast[fuzz_idx] += 1 + elif error_name == ErrorIf.RankMismatch: + # Add one rank to the shape (or more for rank of 1) + extra_ranks = testGen.rng.choice([1, 2, 3]) if rank == 1 else 1 + shape_bcast = np.concatenate((shape_bcast, testGen.makeShape(extra_ranks))) + if rank != 1: + # Either keep the extra rank, or remove it + new_len = testGen.rng.choice([-2, len(shape_bcast)]) + shape_bcast = shape_bcast[:new_len] + else: + shape_bcast[fuzz_idx] = 1 + + shape_list.append(shape_bcast) + + return shape_list + + @staticmethod + def tgConv2D(testGen, op, rank, error_name=None): + pl, const = op["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 4 + + # IFM dimensions are NHWC + ifm_shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape, max_dim=24, max_items=10000) + + # Get the filter height/width from the operator parameters + filter_hw = op["filter"] + + # Generate a random OFM depth + ofm_depth = testGen.makeShape(1)[0] + + # The filter dimensions are OHWI + filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) + + # The bias is OC + bias_shape = np.asarray([ofm_depth]) + + return [ifm_shape, filter_shape, bias_shape] + + @staticmethod + def tgConv3D(testGen, op, rank, error_name=None): + pl, const = op["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 5 + + # IFM dimensions are NDHWC + ifm_shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape, max_dim=24, max_items=10000) + + # Get the filter depth/height/width from the operator parameters + filter_dhw = op["filter"] + + # Generate a random OFM channel + ofm_channel = testGen.makeShape(1)[0] + + # The filter dimensions are ODHWI + filter_shape = np.asarray( + [ofm_channel, filter_dhw[0], filter_dhw[1], filter_dhw[2], ifm_shape[4]] + ) + + # The bias is OC + bias_shape = np.asarray([ofm_channel]) + + return [ifm_shape, filter_shape, bias_shape] + + @staticmethod + def tgTransposeConv2D(testGen, op, rank, error_name=None): + pl, const = op["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 4 + + # IFM dimensions are NHWC + ifm_shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape, max_dim=24, max_items=10000) + + # Get the filter height/width from the operator parameters + filter_hw = op["filter"] + + # Generate a random OFM depth + ofm_depth = testGen.makeShape(1)[0] + + # The filter dimensions are OHWI + filter_shape = np.asarray([ofm_depth, filter_hw[0], filter_hw[1], ifm_shape[3]]) + + # The bias is OC + bias_shape = np.asarray([ofm_depth]) + + return [ifm_shape, filter_shape, bias_shape] + + @staticmethod + def tgDepthwiseConv2D(testGen, op, rank, error_name=None): + pl, const = op["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 4 + assert pl == 1 and const == 2 + + # IFM dimensions are NHWC + ifm_shape = testGen.makeShape(rank) + + # Constrict the batch size? + if testGen.args.max_batch_size: + ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape, max_dim=24, max_items=10000) + + # Get the filter height/width from the operator parameters + # Filter is KH, HW, C, M + filter_hw = op["filter"] + + # Generate a random OFM depth, but don't let it get too big because + # the output depth is M * C + filter_m = ( + testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) + ) + 1 + + # The filter dimensions are HWCM + filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) + + # The bias is M * C + bias_shape = np.asarray([ifm_shape[3] * filter_m]) + + return [ifm_shape, filter_shape, bias_shape] + + @staticmethod + def tgFullyConnected(testGen, op, rank, error_name=None): + pl, const = op["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 2 + + input_shape = testGen.makeShape(rank) + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + input_shape = TosaErrorIfArgGen.eiRestrictDimensions(input_shape) + + filter_oc = testGen.rng.integers( + low=testGen.args.tensor_shape_range[0], + high=testGen.args.tensor_shape_range[1], + size=1, + )[0] + filter_shape = np.asarray([filter_oc, input_shape[1]]) + + bias_shape = np.asarray([filter_oc]) + + return [input_shape, filter_shape, bias_shape] + + @staticmethod + def tgMatmul(testGen, op, rank, error_name=None): + pl, const = op["operands"] + + if error_name != ErrorIf.WrongRank: + assert rank == 3 + assert pl == 2 and const == 0 + + a_shape = testGen.makeShape(rank) + + # Constrict the overall size of the shape when creating ERROR_IF tests + if error_name: + a_shape = TosaErrorIfArgGen.eiRestrictDimensions(a_shape) + + # Get a random number for b_oc even if target shape is defined + b_oc = np.int32( + testGen.rng.integers( + low=testGen.args.tensor_shape_range[0], + high=testGen.args.tensor_shape_range[1], + size=1, + ) + )[0] + # If N or H is large let b_oc be 1 to reduce output tensor size + if max(a_shape) > 1000: + b_oc = 1 + + b_shape = np.asarray([a_shape[0], a_shape[2], b_oc]) + return [a_shape, b_shape] + + @staticmethod + def tgConcat(testGen, opName, rank, error_name=None): + pl, const = opName["operands"] + shape = testGen.makeShape(rank) + + # Create extra tensors to concat. + # Take into account value of pl when getting maximum number of concats + num_tensors = testGen.randInt(0, 4) + shape_list = [] + for i in range(pl + const + num_tensors): + if error_name == ErrorIf.ConcatInputRankMismatch and i != 0: + remove = testGen.rng.choice([True, False]) + wrongShape = shape.copy() + + if remove and len(shape) > 1: + wrongShape = wrongShape[1:] + else: + wrongShape = list(wrongShape) + wrongShape.append(testGen.rng.integers(1, 10)) + + shape_list.append(wrongShape) + else: + shape_list.append(shape.copy()) + + return shape_list + + @staticmethod + def tgConcatConstInput(testGen, shapeList, axis, error_name=None): + if error_name in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank, ErrorIf.ConcatInputRankMismatch]: + return shapeList + + # Split concat shape along axis to allow for multiple const inputs + # without making too many large tensors + if len(shapeList) == 2 or shapeList[0][axis] < len(shapeList): + # If axis can't be split we still need to invalidate other dimensions + if error_name == ErrorIf.ConcatInputDimMismatch: + for shape in shapeList[1:]: + # Negative test shapeLists are created individually for each test, + # so no need to copy the shape before altering it. + shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10) + return shapeList + + # Create copy of shape we are going to split (so we don't alter shapeList) + shape = shapeList[0].copy() + # Add original shape as first input + new_shapeList = [shape.copy()] + length_on_axis = shape[axis] + remaining_length = length_on_axis + for i in range(len(shapeList) - 2): + # Calculate split on axis and remaining value + split_shape_val = int(shape[axis] / 2) + remaining_length = remaining_length - split_shape_val + + # Append new shape, and set remaining shape + shape[axis] = split_shape_val + new_shapeList.append(shape.copy()) + + # invalidate dimensions + if error_name == ErrorIf.ConcatInputDimMismatch: + shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10) + else: + shape[axis] = remaining_length + + if i == len(shapeList) - 3: + new_shapeList.append(shape.copy()) + + return new_shapeList + + +class TosaArgGen: + """Argument generators create exhaustive or random lists of attributes for operators that take + attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) + tuples where the descriptive_name is appended to the test name and the arglist is expanded + as arguments to the operator build function.""" + + def __init__(self): + pass + + @staticmethod + def agNone(testGen, opName, shapeList, dtype, error_name=None): + """A trivial argument generator for operators that don't take any + non-tensor arguments""" + return [("", [])] + + @staticmethod + def agAxis(testGen, opName, shapeList, dtype, error_name=None): + """Build the axis argument for operators that take a single axis""" + axes = [] + shape = shapeList[0] + + if error_name == ErrorIf.AxisSmallerZero: + small_axis = testGen.rng.integers(-5, 0) + axes.append(("axis{}".format(small_axis), [small_axis])) + elif error_name == ErrorIf.AxisLargerRank: + large_axis = testGen.rng.integers(len(shape) + 1, len(shape) + 10) + axes.append(("axis{}".format(large_axis), [large_axis])) + else: + for a in range(0, len(shape)): + axes.append(("axis{}".format(a), [a])) + + return axes + + @staticmethod + def agConv(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + ifm_shape = shapeList[0] + filter_shape = shapeList[1] + # determine the kernel shape from the operator name (e.g. "conv2d_3x3" => [3,3]) + k = [int(x) for x in opName.split("_")[-1].split("x")] + + # Check the rank + rank = 5 if opName.startswith("conv3d") else 4 + if error_name != ErrorIf.WrongRank: + assert len(ifm_shape) == rank + assert len(filter_shape) == rank + + # kernel rank omits batch and channels + k_rank = rank - 2 + assert len(k) == k_rank + + # Generate comprehensive argument lists + # - except for named errors, which use specific invalid value(s) + if error_name == ErrorIf.PadSmallerZero: + p_vals = [testGen.rng.choice(range(-5, 0))] + else: + p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] + paddings = {x for x in itertools.product(*([p_vals] * k_rank * 2))} + if error_name == ErrorIf.StrideSmallerOne: + # Can't use stride=0, as it is used to derive output shape, as a divisor + s_vals = [testGen.rng.choice(range(-5, 0))] + else: + s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] + strides = {x for x in itertools.product(*([s_vals] * k_rank))} + if error_name == ErrorIf.DilationSmallerOne: + d_vals = [testGen.rng.choice(range(-5, 1))] + else: + d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] + dilations = {x for x in itertools.product(*([d_vals] * k_rank))} + + if not error_name: + # add some oversize argument values + if max(ifm_shape) < 64: + bigPadding = 9 + paddings.update({x for x in itertools.product(*([[0, bigPadding]] * (k_rank * 2)))}) + bigStride = 8 + strides.update({x for x in itertools.product(*([[1, bigStride]] * k_rank))}) + bigDilation = 7 + dilations.update({x for x in itertools.product(*([[1, bigDilation]] * k_rank))}) + + # There are too many parameter combinations, so generate them sparsely, + # very sparse for negative tests + sparsity_factor = 2 if error_name else 100 + sparsity = len(paddings) * len(strides) * len(dilations) // sparsity_factor + 1 + # If there are only a small number of tests, just select them all + if sparsity < 13: + sparsity = 1 + # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 + while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: + sparsity += 1 + + n = 0 + for s in sorted(list(strides)): + for p in sorted(list(paddings)): + for d in sorted(list(dilations)): + if (n % sparsity == 0 + # padding must not exceed the kernel size ? + # and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] + # and (k_rank < 3 or (p[4] < k[2] and p[5] < k[2])) + # the padded shape must exceed the kernel size + and (ifm_shape[1] + p[0] + p[1]) > k[0] and (ifm_shape[2] + p[2] + p[3]) > k[1] + and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > k[2])) + # the padded shape must exceed the dilation + and (ifm_shape[1] + p[0] + p[1]) > d[0] and (ifm_shape[2] + p[2] + p[3]) > d[1] + and (k_rank < 3 or ((ifm_shape[3] + p[4] + p[5]) > d[2])) + ): + arg_list.append( + ( + "st{}_pad{}_dilat{}".format( + "".join([str(x) for x in s]), + "".join([str(x) for x in p]), + "".join([str(x) for x in d]), + ), + [s, p, d], + ) + ) + n += 1 + + return arg_list + + @staticmethod + def agTransposeConv2D(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + ifm_shape = shapeList[0] + filter_shape = shapeList[1] + + # Must be rank 4 + if error_name != ErrorIf.WrongRank: + assert len(ifm_shape) == 4 + assert len(filter_shape) == 4 + + # Generate comprehensive argument lists + # - except for named errors, which use specific invalid value(s) + if error_name == ErrorIf.PadSmallerZero: + p_vals = [testGen.rng.choice(range(-5, 0))] + else: + p_vals = [x for x in range(0, testGen.args.max_conv_padding + 1)] + paddings = {x for x in itertools.product(*([p_vals] * 2))} + if error_name == ErrorIf.StrideSmallerOne: + # Can't use stride=0, as it is used to derive output shape, as a divisor + s_vals = [testGen.rng.choice(range(-5, 0))] + else: + s_vals = [x for x in range(1, testGen.args.max_conv_stride + 1)] + strides = {x for x in itertools.product(*([s_vals] * 2))} + if error_name == ErrorIf.DilationSmallerOne: + d_vals = [testGen.rng.choice(range(-5, 1))] + else: + d_vals = [x for x in range(1, testGen.args.max_conv_dilation + 1)] + dilations = {x for x in itertools.product(*([d_vals] * 2))} + + if not error_name: + # add some oversize argument values + if max(ifm_shape) < 64: + bigPadding = 9 + paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 2))}) + bigStride = 8 + strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) + bigDilation = 7 + dilations.update({x for x in itertools.product(*([[1, bigDilation]] * 2))}) + + # There are too many parameter combinations, so generate them sparsely, + # very sparse for negative tests + sparsity_factor = 2 if error_name else 100 + sparsity = len(paddings) * len(strides) * len(dilations) // sparsity_factor + 1 + # If there are only a small number of tests, just select them all + if sparsity < 13: + sparsity = 1 + # To get a variety of parameter combinations sparsity should not be a multiple of 2, 3 or 5 + while sparsity % 2 == 0 or sparsity % 3 == 0 or sparsity % 5 == 0: + sparsity += 1 + + n = 0 + for s in sorted(list(strides)): + for p in sorted(list(paddings)): + for d in sorted(list(dilations)): + if n % sparsity == 0: + # Determine the output shape + oh = ( + ifm_shape[1] + - filter_shape[1] + - (filter_shape[1] - 1) * (d[0] - 1) + + 2 * p[0] + ) // s[0] + 1 + ow = ( + ifm_shape[2] + - filter_shape[2] + - (filter_shape[2] - 1) * (d[1] - 1) + + 2 * p[1] + ) // s[1] + 1 + os = [ifm_shape[0], oh, ow, filter_shape[0]] + arg_list.append( + ( + "st{}_pad{}_dilat{}_os{}".format( + "".join([str(x) for x in s]), + "".join([str(x) for x in p]), + "".join([str(x) for x in d]), + "x".join([str(x) for x in os]), + ), + [s, p, d, os], + ) + ) + n += 1 + + return arg_list + + @staticmethod + def agPad(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + rank = len(shapeList[0]) + + # Exhaustively test combinations of padding on each side of each dimension + # - the range of padding values is defined by pad_min and pad_max + # - for padding >9, the name format needs to be more distinctive + pad_min, pad_max = 0, 1 + pad_values = [x for x in range(pad_min, pad_max + 1)] + if error_name == ErrorIf.PadSmallerZero: + pad_values = [x for x in range(-2, 0)] + axis_pad_values = [x for x in itertools.product(pad_values, pad_values)] + shape_pad_values = itertools.product(*([axis_pad_values] * rank)) + + if dtype in [DType.BOOL, DType.INT8, DType.INT16, DType.INT32]: + pad_const_int = testGen.getRandNumberDType(dtype) + pad_const_fp = 0 + elif dtype == DType.FLOAT: + pad_const_int = 0 + pad_const_fp = testGen.getRandNumberDType(dtype) + else: + return [] + + for paddings in shape_pad_values: + name = "pad" + for r in range(rank): + before, after = paddings[r] + name = f"{name}{before}{after}" + arg_list.append((name, [np.array(paddings), pad_const_int, pad_const_fp])) + + return arg_list + + @staticmethod + def agPooling(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + shape = shapeList[0] + if error_name != ErrorIf.WrongRank: + assert len(shape) == 4 + + # Generate comprehensive argument lists + p_vals = [x for x in range(0, testGen.args.max_pooling_padding + 1)] + paddings = {x for x in itertools.product(*([p_vals] * 4))} + s_vals = [x for x in range(1, testGen.args.max_pooling_stride + 1)] + strides = {x for x in itertools.product(*([s_vals] * 2))} + k_vals = [x for x in range(2, testGen.args.max_pooling_kernel + 2)] + kernels = {x for x in itertools.product(*([k_vals] * 2))} + + # add some oversize argument values + bigStride = 7 + strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) + bigKernel = 6 + kernels.update({x for x in itertools.product(*([[2, bigKernel]] * 2))}) + if max(shape) < 64: + # padding must be less than the kernel size + bigPadding = bigKernel - 1 + paddings.update({x for x in itertools.product(*([[0, bigPadding]] * 4))}) + + # There are too many parameter combinations, so generate them sparsely, + # very sparse for negative tests + sparsity_factor = 2 if error_name else 500 + sparsity = len(paddings) * len(strides) * len(kernels) // sparsity_factor + 1 + + n = 0 + for s in sorted(list(strides)): + for p in sorted(list(paddings)): + for k in sorted(list(kernels)): + if error_name in [ErrorIf.StrideSmallerOne, ErrorIf.KernelSmallerOne, ErrorIf.PadSmallerZero, ErrorIf.PadLargerEqualKernel]: + sNew, pNew, kNew = TosaErrorIfArgGen.eiPoolingErrorIf(testGen, error_name, s, p, k) + if None not in [sNew, pNew, kNew] and n % sparsity == 0: + arg_list.append( + ( + "st{}_kern{}_pad{}".format( + "".join([str(x) for x in sNew]), + "".join([str(x) for x in kNew]), + "".join([str(x) for x in pNew]), + ), + [sNew, pNew, kNew], + ) + ) + elif (n % sparsity == 0 + # padding must not exceed the kernel size + and p[0] < k[0] and p[1] < k[0] and p[2] < k[1] and p[3] < k[1] + # the padded shape must exceed the kernel size + and (shape[1] + p[0] + p[1]) > k[0] and (shape[2] + p[2] + p[3]) > k[1] + ): + arg_list.append( + ( + "st{}_kern{}_pad{}".format( + "".join([str(x) for x in s]), + "".join([str(x) for x in k]), + "".join([str(x) for x in p]), + ), + [s, p, k], + ) + ) + n += 1 + + return arg_list + + @staticmethod + def agCast(testGen, opName, shapeList, inDtype, error_name=None): + arg_list = [] + + # Enumerate the output types here + if error_name == ErrorIf.WrongOutputType: + dtypeList = TosaErrorIfArgGen.eiCastErrorIf(testGen, inDtype) + elif inDtype == DType.INT8: + dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] + elif inDtype == DType.INT16: + dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] + elif inDtype == DType.INT32: + dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] + elif inDtype == DType.BOOL: + dtypeList = [DType.INT8, DType.INT16, DType.INT32] + elif inDtype == DType.FLOAT: + dtypeList = [DType.INT8, DType.INT16, DType.INT32] + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output type for incorrect input type + dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] + else: + raise Exception("Unexpected input dtype: {}".format(inDtype)) + + for dtype in dtypeList: + arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) + + return arg_list + + @staticmethod + def agRescale(testGen, opName, shapeList, inDtype, error_name=None): + arg_list = [] + + # Enumerate the output types here + for dtype in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: + if dtype in [DType.UINT8, DType.INT8] and error_name == ErrorIf.OutputZeroPointNotZero: + continue + if inDtype == DType.UINT8 and dtype != DType.INT8 and error_name != ErrorIf.WrongOutputType: + # The only output dtype for UINT8 is INT8, skip all other combinations + continue + if inDtype != DType.INT8 and dtype == DType.UINT8 and error_name != ErrorIf.WrongOutputType: + # The only input dtype for UINT8 is INT8, skip all other combinations + continue + if error_name == ErrorIf.WrongOutputType and not TosaErrorIfArgGen.eiRescaleWrongOutputType(inDtype, dtype): + continue + + for scale32 in [False, True]: + if error_name == ErrorIf.ScaleTrue and scale32 == False: + continue + elif error_name == ErrorIf.ScaleNotTrue and scale32 == True: + continue + for double_round in [False, True]: + if error_name == ErrorIf.ScaleNotTrue and double_round == False: + continue + for per_channel in [False, True]: + + if inDtype == DType.INT48 and scale32 and error_name != ErrorIf.ScaleTrue: + # Illegal condition. Must be scale32=False + continue + if double_round and not scale32 and error_name != ErrorIf.ScaleNotTrue: + # Illegal condition. ERROR_IF(!scale32 && double_round) + continue + + arg_list.append( + ( + "out{}_sc{}_dr{}_pc{}".format( + DTypeNames[dtype], + int(scale32), + int(double_round), + int(per_channel), + ), + [dtype, scale32, double_round, per_channel], + ) + ) + + return arg_list + + @staticmethod + def agMul(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + if dtype is DType.INT32: + for p in range(testGen.args.num_rand_permutations): + + shift = testGen.randInt(0, 32) + + arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) + else: + arg_list.append(("perm0_shift0", [0])) + + return arg_list + + @staticmethod + def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + arg_list.append(("roundTrue", [True])) + arg_list.append(("roundFalse", [False])) + + return arg_list + + # Helper function for reshape. Gets some factors of a larger number. + @staticmethod + def getFactors(val, start=1): + factors = [] + + for i in range(start, int(np.sqrt(val)) + 1): + if (val % i) == 0: + factors.append(i) + + return factors + + @staticmethod + def agReshape(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + origShape = shapeList[0] + + totalElements = 1 + for s in origShape: + totalElements *= s + + # This code is NOT fast. Fortunately, the numbers are fairly small. + factors = TosaArgGen.getFactors(totalElements) + + for p in range(testGen.args.num_rand_permutations): + newRank = testGen.randInt(1, 7) + if len(factors) < newRank: + continue + + found = True + # escape_counter breaks while loop if it continues on for too long + escape_counter = 0 + while found: + newShape = [] + # Generate newShape ensuring it isn't a duplicate + remainingElements = totalElements + shuffledFactors = testGen.rng.permutation(factors) + for i in range(1, newRank): + # pick rank-1 factors + newShape.append(shuffledFactors[0]) + remainingElements = remainingElements // shuffledFactors[0] + shuffledFactors = testGen.rng.permutation( + TosaArgGen.getFactors(remainingElements) + ) + newShape.append(remainingElements) + + # Toss in a -1 sometimes + minusOne = testGen.randInt(0, newRank * 4) + if minusOne < newRank: + newShape[minusOne] = -1 + + # Check for duplicates + found = False + for name, other_shape in arg_list: + if other_shape[0] == newShape: + found = True + break + + escape_counter += 1 + if escape_counter >= 100: + break + + if not found: + arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) + + return arg_list + + @staticmethod + def agTranspose(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + ifm_shape = shapeList[0] + + + if error_name == ErrorIf.IndexOutsideBounds: + incorrect_large_index = range(len(ifm_shape)+1, 2*len(ifm_shape)+1) + incorrect_small_index = range(-len(ifm_shape), 0) + permutations = [p for p in itertools.permutations(incorrect_large_index)] + permutations.extend([p for p in itertools.permutations(incorrect_small_index)]) + elif error_name == ErrorIf.IndexUsedTwice: + # Create list with a duplicated index + perm_range = list(range(len(ifm_shape))) + index_choice = testGen.rng.choice(range(len(perm_range))) + perm_range[(index_choice + 1) % len(perm_range)] = perm_range[index_choice] + permutations = [p for p in itertools.permutations(perm_range)] + + + else: + # Get all permutations + permutations = [p for p in itertools.permutations(range(len(ifm_shape)))] + + # Limit to possible permutations from shape dimension or argument setting + limit = min(len(permutations), testGen.args.num_rand_permutations) + + # Get random permutation generator that uses all permutations + random_permutations = testGen.rng.permutation(permutations) + + # Create list of required amount of permutations + arg_list = [ + ("perm{}".format(p), [random_permutations[p].tolist()]) + for p in range(limit) + ] + return arg_list + + @staticmethod + def agSlice(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + ifm_shape = shapeList[0] + rank = len(ifm_shape) + + for p in range(testGen.args.num_rand_permutations): + start = [] + size = [] + + valid = True + + for i in range(rank): + if ifm_shape[i] > 1: + start.append(testGen.randInt(0, ifm_shape[i])) + size.append(testGen.randInt(0, ifm_shape[i] - start[i])) + + # Invalid slice size? + if size[i] == 0: + valid = False + else: + start.append(0) + size.append(1) + + if valid: + # If ERROR_IF test required then incorrect start, size will be returned + start, size = TosaErrorIfArgGen.eiSliceErrorIf(testGen, error_name, ifm_shape, start, size) + arg_list.append(("perm{}".format(p), [start, size])) + return arg_list + + @staticmethod + def agTile(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + ifm_shape = shapeList[0] + rank = len(ifm_shape) + + for p in range(testGen.args.num_rand_permutations): + + # Pick a few random, but small multiple values + # because otherwise this has a tendency to generate + # enormous tensors + multiples = [] + for i in range(rank): + if ifm_shape[i] > 1000: + # Multiple of 1 if ifm_shape dimension is large to reduce tensor size + multiples.append(1) + elif max(ifm_shape) > 1000: + multiples.append(2) + else: + multiples.append(testGen.randInt(1, 4)) + arg_list.append(("perm{}".format(p), [multiples])) + + return arg_list + + @staticmethod + def agResize(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + ifm_shape = shapeList[0] + for mode in [ResizeMode.NEAREST, ResizeMode.BILINEAR]: + + # Exclude illegal {mode, type} configurations. Pick legal output types + if mode == ResizeMode.NEAREST and dtype == DType.INT8: + outputDTypeList = [DType.INT8] + elif mode == ResizeMode.NEAREST and dtype == DType.INT16: + outputDTypeList = [DType.INT16] + elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: + outputDTypeList = [DType.INT32] + elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: + outputDTypeList = [DType.INT48] + elif dtype == DType.FLOAT: + outputDTypeList = [DType.FLOAT] + elif error_name == ErrorIf.WrongInputType: + # If an incorrect input type is used then we set a 'correct' + # output type to avoid other errors + outputDTypeList = [DType.INT8, DType.INT16, DType.INT32] + else: + continue + + for outputDType in outputDTypeList: + for perm in range(testGen.args.num_rand_permutations): + # Randomly generate legal output dimensions and shift + # and then compute the stride and offset based on them + # A output_dim of 1 will cause offset to exceed allowed range + # so minimum value 2 produced below + output_dims = [testGen.randInt(1) + 1, testGen.randInt(1) + 1] + while ((float(ifm_shape[1]) / float(output_dims[0])) >= 16): + output_dims[0] += 1 + while ((float(ifm_shape[2]) / float(output_dims[1])) >= 16): + output_dims[1] += 1 + + in_center_h = (ifm_shape[1] - 1) / 2.0 + in_center_w = (ifm_shape[2] - 1) / 2.0 + out_center_h = (output_dims[0] - 1) / 2.0 + out_center_w = (output_dims[1] - 1) / 2.0 + + fp_stride_y = float(ifm_shape[1]) / float(output_dims[0]) + fp_stride_x = float(ifm_shape[2]) / float(output_dims[1]) + fp_offset_y = in_center_h - fp_stride_y * out_center_h + fp_offset_x = in_center_w - fp_stride_x * out_center_w + + if outputDType == DType.FLOAT: + float_op = True + arg_str = "mode{}_shift{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}" + shift = 0 + stride = [0, 0] + offset = [0, 0] + stride_fp = [fp_stride_y, fp_stride_x] + offset_fp = [fp_offset_y, fp_offset_x] + + else: + float_op = False + arg_str = "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}" + shift = testGen.randInt(1,12) + # Now search for a shift value (1 to 11) that will produce + # a valid and predictable resize operation + count = 0 + while (count < 12): + unit = float(1 << shift) + stride_y = int(round(fp_stride_y * unit)) + stride_x = int(round(fp_stride_x * unit)) + offset_y = int(round(fp_offset_y * unit)) + offset_x = int(round(fp_offset_x * unit)) + + if ( + stride_y <= 0 + or stride_x <= 0 + or stride_y >= (16 << shift) + or stride_x >= (16 << shift) + or offset_y >= (16 << shift) + or offset_x >= (16 << shift) + or offset_y <= (-16 << shift) + or offset_x <= (-16 << shift) + ): + # Change the shift value and check again + count += 1 + shift = (shift % 11) + 1 + continue + + def RESIZE_REQUIRE_CALC(length_in, length_out, stride, offset, shift): + # Perform the pseudo loop to look for out of bounds + for pos in range(0,length_out): + a = pos * stride + offset + ia = a >> shift + ia0 = max(ia, 0) + ia1 = min(ia+1, length_in-1) + if ia0 > ia1: + # Found a problem value + break + return ia0, ia1 + + iy0, iy1 = RESIZE_REQUIRE_CALC(ifm_shape[1], output_dims[0], stride_y, offset_y, shift) + ix0, ix1 = RESIZE_REQUIRE_CALC(ifm_shape[2], output_dims[1], stride_x, offset_x, shift) + if ix0 > ix1 or iy0 > iy1: + # Change the shift value and check again + count += 1 + shift = (shift % 11) + 1 + continue + break + + if count >= 12: + # Couldn't find a good set of values for this test, skip it + continue + + stride = [stride_y, stride_x] + offset = [offset_y, offset_x] + + stride_fp = [0.0, 0.0] + offset_fp = [0.0, 0.0] + + # Common for all data types + if error_name is not None: + shift, stride, stride_fp, offset, offset_fp, outputDTypeNew = TosaErrorIfArgGen.eiResizeErrorIf( + testGen, + error_name, + mode, + dtype, + shapeList, + outputDType, + shift, + stride, + stride_fp, + offset, + offset_fp + ) + else: + outputDTypeNew = outputDType + + arg_list.append( + ( + arg_str.format( + "N" if mode == ResizeMode.NEAREST else "B", + shift, + output_dims[0], + output_dims[1], + testGen.typeStr(outputDTypeNew), + stride_fp[0] if float_op else stride[0], + stride_fp[1] if float_op else stride[1], + offset_fp[0] if float_op else offset[0], + offset_fp[1] if float_op else offset[1] + ), + [ + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + dtype, + outputDTypeNew, + ], + ) + ) + + return arg_list + + @staticmethod + def agTable(testGen, opName, shapeList, dtype, error_name=None): + arg_list = [] + + if dtype == DType.INT8: + table = np.int32( + testGen.rng.integers(low=-128, high=128, size=[256]) + ).tolist() + else: # INT16 + table = np.int32( + testGen.rng.integers(low=-32768, high=32768, size=[513]) + ).tolist() + + arg_list.append( + ( + "", + [table], + ) + ) + return arg_list + + def agCondIf(testGen, opName, shapeList, dtype, error_name=None): + # CondIf generates the condition values here. + # Convert to tensors in the build function, along with the + # then and else blocks + arg_list = [] + + for c in [False, True]: + arg_list.append(("cond{}".format(int(c)), [c])) + + return arg_list + + def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None): + # While loop: 0 iterations, 1, more than 1 + arg_list = [] + + for iter in [0, 1, 4]: + arg_list.append(("iter{}".format(iter), [iter])) + + return arg_list + +class TosaErrorIfArgGen: + + @staticmethod + def eiResizeErrorIf(testGen, error_name, mode, dtype, shapeList, outputDType, shift, stride, stride_fp, offset, offset_fp): + + if outputDType == DType.FLOAT: + if error_name == ErrorIf.StrideSmallerEqualZero: + stride_fp = testGen.rng.random(size=[2]) - 2 + elif error_name == ErrorIf.ShiftNotZero: + shift = testGen.rng.integers(1, 5) + elif error_name == ErrorIf.StrideLargerDimension: + shape = shapeList[0] + transform_height = testGen.rng.choice([False, True]) + if transform_height: + stride_fp[0] = shape[1] + testGen.rng.integers(1, 10) + else: + stride_fp[1] = shape[2] + testGen.rng.integers(1, 10) + else: + if error_name == ErrorIf.StrideSmallerEqualZero: + stride = np.int16(testGen.rng.integers(-1, 1, size=[2])) + elif error_name == ErrorIf.ShiftSmallerOne: + shift = testGen.rng.integers(-3, 1) + if shift <= 0: + stride = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks + offset = [(16 >> -shift) - 1, (16 >> -shift) - 1] # avoids other ERROR_IF checks + else: + stride = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks + offset = [(16 << shift) - 1, (16 << shift) - 1] # avoids other ERROR_IF checks + elif error_name == ErrorIf.ShiftLargerEleven: + shift = np.int16(testGen.rng.integers(12, 15)) + elif error_name == ErrorIf.StrideLargerDimension: + shape = shapeList[0] + transform_height = testGen.rng.choice([False, True]) + if transform_height: + stride[0] = shape[1] + testGen.rng.integers(1, 10) + else: + stride[1] = shape[2] + testGen.rng.integers(1, 10) + elif error_name == ErrorIf.StrideLargerEqualMax: + stride = [(16 << shift) + 1, (16 << shift) + 1] + elif error_name == ErrorIf.OffsetLargerEqualMax: + offset = [(16 << shift) + 1, (16 << shift) + 1] + elif error_name == ErrorIf.OffsetSmallerEqualMin: + offset = [(-16 << shift) - 1, (-16 << shift) - 1] + + + if error_name == ErrorIf.WrongOutputType: + if mode == ResizeMode.NEAREST and dtype == DType.INT8: + incorrect_types = (DType.INT4, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT) + elif mode == ResizeMode.NEAREST and dtype == DType.INT16: + incorrect_types = (DType.INT4, DType.INT8, DType.INT32, DType.INT48, DType.FLOAT) + elif mode == ResizeMode.BILINEAR and dtype == DType.INT8: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) + elif mode == ResizeMode.BILINEAR and dtype == DType.INT16: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) + elif dtype == DType.FLOAT: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) + outputDType = testGen.rng.choice(a=incorrect_types) + + return shift, stride, stride_fp, offset, offset_fp, outputDType + + + @staticmethod + def eiPoolingErrorIf(testGen, error_name, stride, pad, kernel): + if (error_name == ErrorIf.StrideSmallerOne + # padding must not exceed the kernel size + and pad[0] < kernel[0] and pad[1] < kernel[0] and pad[2] < kernel[1] and pad[3] < kernel[1]): + wrongStride = (testGen.rng.choice([0, -1, -2, -3]), testGen.rng.choice([0, -1, -2, -3])) + return wrongStride, pad, kernel + elif error_name == ErrorIf.PadSmallerZero: + wrongPad = (testGen.rng.choice([-1, -2, -3]), + testGen.rng.choice([-1, -2, -3]), + testGen.rng.choice([-1, -2, -3]), + testGen.rng.choice([-1, -2, -3])) + return stride, wrongPad, kernel + elif error_name == ErrorIf.KernelSmallerOne: + wrongKernel = (testGen.rng.choice([0, -1, -2, -3]), testGen.rng.choice([0, -1, -2, -3])) + return stride, pad, wrongKernel + elif error_name == ErrorIf.PadLargerEqualKernel: + wrongPad = (testGen.rng.choice([kernel[0], kernel[0]+1, kernel[0]+2]), + testGen.rng.choice([kernel[0], kernel[0]+1, kernel[0]+2]), + testGen.rng.choice([kernel[1], kernel[1]+1, kernel[1]+2]), + testGen.rng.choice([kernel[1], kernel[1]+1, kernel[1]+2])) + return stride, wrongPad, kernel + else: + return None, None, None + + + @staticmethod + def eiRescaleWrongOutputType(input_dtype, output_dtype): + if input_dtype == DType.INT8: + if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: + return True + if input_dtype in [DType.INT16, DType.INT32]: + if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: + return True + elif input_dtype == DType.INT48: + if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: + return True + elif input_dtype == DType.UINT8: + if output_dtype != DType.INT8: + return True + return False + + + @staticmethod + def eiInvalidateInputOutputList(testGen, error_name, input_list, output_list): + # Mess up input/output tensors for ERROR_IF checks + if error_name == "WrongInputList": + add_input = testGen.rng.choice([True, False]) + if add_input: + input_list.append('eiDummyInput') + else: + input_list = input_list[:-1] + elif error_name == "WrongOutputList": + add_output = testGen.rng.choice([True, False]) + if add_output: + output_list.append('eiDummyOutput') + else: + output_list = [] + return input_list, output_list + + @staticmethod + def eiRestrictDimensions(shape, max_dim=32, max_items=100000): + """Restrict the dimensions and overall size of a shape to max_dim and max_items.""" + new_shape = [min(d, max_dim) for d in shape] if max(shape) > max_dim else shape + while product(new_shape) > max_items: + new_shape = [max(d - 1, 1) for d in new_shape] + return new_shape + + def eiSliceErrorIf(testGen, error_name, input_shape, start, size): + if error_name == ErrorIf.StartSmallerZero: + newStart = [] + for i in range(len(input_shape)): + newStart.append(testGen.rng.choice([-3, -2, -1])) + return newStart, size + elif error_name == ErrorIf.SizeSmallerEqualZero: + newSize = [] + for i in range(len(input_shape)): + newSize.append(testGen.rng.choice([-3, -2, -1, 0])) + return start, newSize + elif error_name == ErrorIf.StartSizeOutsideBounds: + newStart, newSize = [], [] + for i in range(len(input_shape)): + newStart.append(input_shape[i]-1) + newSize.append(testGen.rng.choice([2, 3, 4])) + return newStart, newSize + elif error_name == ErrorIf.InputSizeStartLengthMismatch: + remove = testGen.rng.choice([True, False]) + if remove: + newStart = start[1:] + newSize = size[1:] + else: + newStart = start + newStart.append(1) + newSize = size + newSize.append(1) + return newStart, newSize + else: + return start, size + + @staticmethod + def eiCastErrorIf(testGen, input_dtype): + if input_dtype in [DType.BOOL, DType.FLOAT]: + outputDType = [DType.BOOL, DType.INT48, DType.FLOAT] + elif input_dtype in [DType.INT8, DType.INT16, DType.INT32]: + outputDType = [DType.INT48] + else: + assert True, f"input_dtype ({input_dtype}) not supported" + return outputDType + + +class TosaErrorValidator: + + @staticmethod + def evValidateErrorIfs(serializer, validator_fcns, error_name, **kwargs): + """Check ERROR_IF statements are caught and set the expected result. + + Args: + serializer: the serializer to set the expected result in + validator_fcns: a sequence of validator functions to verify the result + error_name: the name of the ERROR_IF condition to check for + kwargs: keyword arguments for the validator functions + Returns: + True if the result matches the expected result; otherwise False + """ + overall_result = True + for val_fcn in validator_fcns: + val_result = val_fcn(True, **kwargs) + validator_name = val_result['error_name'] + error_result = val_result['error_result'] + error_reason = val_result['error_reason'] + + # expect an error IFF the error_name and validator_name match + expected_result = error_result == (error_name == validator_name) + overall_result &= expected_result + + if expected_result and error_result: + serializer.setExpectedReturnCode(2, True, desc=error_reason) + elif error_result: # and not expected_result + print(f"Unexpected ERROR_IF: Op: {valueToName(Op, kwargs['op']['op'])}" + f" Expected: {error_name}, Got: {validator_name}") + elif not expected_result: # and not error_result + print(f"Missed ERROR_IF: Op: {valueToName(Op, kwargs['op']['op'])}" + f" Expected: {error_name}") + + if not expected_result: + for k, v in sorted(kwargs.items()): + if k != 'op': + if k.endswith('dtype'): + v = valueToName(DType, v) + print(f' {k} = {v}') + + return overall_result + + @staticmethod + def evWrongInputType(check=False, **kwargs): + error_result = False + + # Find the unsupported input data types + op = kwargs['op'] + input_dtypes = op['types'] + allowed_input_dtypes = {t[0] if isinstance(t, list) else t for t in input_dtypes} + wrong_input_dtypes = list(usableDTypes(excludes=allowed_input_dtypes)) + + if op['op'] == Op.CLAMP: + wrong_input_dtypes.remove(DType.INT48) + + if check: + input_dtype = kwargs['input_dtype'] + if input_dtype not in allowed_input_dtypes: + error_result = True + + info_dict = { + "error_name": ErrorIf.WrongInputType, + "error_result": error_result, + "error_reason": f"Input data type not supported for this operator", + "param_reqs": {"rank": None, "dtype": wrong_input_dtypes, "shape": None} + } + return info_dict + + @staticmethod + def evWrongOutputType(check=False, **kwargs): + error_result = False + + if check: + input_dtype = kwargs['input_dtype'] + output_dtype = kwargs['output_dtype'] + op = kwargs['op'] + + if op['op'] == Op.RESIZE: + mode = kwargs['mode'] + if ( + (mode == ResizeMode.NEAREST and input_dtype == DType.INT8 and output_dtype != DType.INT8) or + (mode == ResizeMode.NEAREST and input_dtype == DType.INT16 and output_dtype != DType.INT16) or + (mode == ResizeMode.BILINEAR and input_dtype == DType.INT8 and output_dtype != DType.INT32) or + (mode == ResizeMode.BILINEAR and input_dtype == DType.INT16 and output_dtype != DType.INT48) or + (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) + ): + error_result = True + + elif op['op'] == Op.RESCALE: + if input_dtype == DType.INT8: + if output_dtype not in [DType.UINT8, DType.INT8, DType.INT16, DType.INT32]: + error_result = True + if input_dtype in [DType.INT16, DType.INT32]: + if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: + error_result = True + elif input_dtype == DType.INT48: + if output_dtype not in [DType.INT8, DType.INT16, DType.INT32]: + error_result = True + elif input_dtype == DType.UINT8: + if output_dtype != DType.INT8: + error_result = True + + elif op['op'] in [Op.FULLY_CONNECTED, Op.MATMUL]: + if ( + (input_dtype == DType.INT8 and output_dtype != DType.INT32) or + (input_dtype == DType.INT16 and output_dtype != DType.INT48) or + (input_dtype == DType.FLOAT and output_dtype != DType.FLOAT) + ): + error_result = True + + elif op['op'] == Op.ARGMAX: + if input_dtype in [DType.INT8, DType.INT16, DType.FLOAT] and output_dtype != DType.INT32: + error_result = True + + elif op['op'] == Op.MUL: + if input_dtype != DType.FLOAT and output_dtype != DType.INT32: + error_result = True + elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT: + error_result = True + + elif op['op'] == Op.TABLE: + if input_dtype == DType.INT8 and output_dtype != DType.INT8: + error_result = True + elif input_dtype == DType.INT16 and output_dtype != DType.INT32: + error_result = True + + elif op['op'] in [Op.EQUAL, Op.GREATER_EQUAL, Op.GREATER]: + if output_dtype != DType.BOOL: + error_result = True + + elif op['op'] == Op.CAST: + if ( + (input_dtype == DType.BOOL and output_dtype not in [DType.INT8, DType.INT16, DType.INT32]) + or (input_dtype == DType.INT8 and output_dtype not in [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT]) + or (input_dtype == DType.INT16 and output_dtype not in [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT]) + or (input_dtype == DType.INT32 and output_dtype not in [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT]) + or (input_dtype == DType.FLOAT and output_dtype not in [DType.INT8, DType.INT16, DType.INT32]) + ): + error_result = True + + elif op['op'] in {Op.CONV2D, Op.CONV3D, Op.DEPTHWISE_CONV2D, Op.TRANSPOSE_CONV2D}: + if ( + input_dtype == DType.INT8 and output_dtype != DType.INT32 + or input_dtype == DType.INT16 and output_dtype != DType.INT48 + or input_dtype == DType.FLOAT and output_dtype != DType.FLOAT + ): + error_result = True + # invalid input types are ignored, to avoid reporting multiple errors + + else: + if output_dtype != input_dtype: + error_result = True + + info_dict = { + "error_name": ErrorIf.WrongOutputType, + "error_result": error_result, + "error_reason": "Output data type not supported for this configuration of operator", + "param_reqs": {"rank": None, "dtype": None, "shape": None} + } + return info_dict + + @staticmethod + def evWrongRank(check=False, **kwargs): + all_ranks = (1, 2, 3, 4, 5) + + # Make a list of incorrect ranks + assert 'op' in kwargs + op = kwargs['op'] + rmin, rmax = op['rank'] + rank_range = range(rmin, rmax + 1) + incorrect_ranks = list(set(all_ranks) - set(rank_range)) + # Remove small incorrect ranks to avoid index errors + incorrect_ranks = [rank for rank in incorrect_ranks if rank > rmin] + # Set minimum incorrect rank to 3 to avoid index error + if op['op'] in [Op.RESIZE]: + incorrect_ranks = [3, 5] + elif op['op'] in [Op.TRANSPOSE]: + incorrect_ranks = [7, 8] + elif op['op'] in [Op.CONV3D]: + incorrect_ranks = [6, 7] + + error_name = ErrorIf.WrongRank + param_reqs = {"rank": incorrect_ranks, "dtype": None, "shape": None} + error_result = False + error_reason = "Rank not supported for this operator" + + if check: + input_shape = kwargs['input_shape'] + + if op['op'] in [Op.RESIZE, Op.AVG_POOL2D, Op.MAX_POOL2D] and len(input_shape) != 4: + error_result = True + elif op['op'] == Op.FULLY_CONNECTED and len(input_shape) != 2: + error_result = True + elif op['op'] == Op.MATMUL and len(input_shape) != 3: + error_result = True + else: + if len(input_shape) not in rank_range: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evWrongInputList(check=False, **kwargs): + error_name = ErrorIf.WrongInputList + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Op input list does not match expected input" + + if check: + op = kwargs['op'] + input_list = kwargs['input_list'] + num_operands = kwargs['num_operands'] + if op['op'] in [Op.SCATTER, Op.GATHER]: + # SCATTER/GATHER add an indices input tensor in their build functions + num_operands += 1 + if len(input_list) != num_operands: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evWrongOutputList(check=False, **kwargs): + error_name = ErrorIf.WrongOutputList + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Op output list does not match expected output" + + if check: + output_list = kwargs['output_list'] + # Note this will be incorrect if an operator returns more than one output + if len(output_list) != 1: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evMaxDimExceeded(check=False, **kwargs): + error_name = ErrorIf.MaxDimExceeded + param_reqs = { + "rank": [4,4], + "dtype": [DType.INT8], + "shape": [[1, 16584, 5, 1], [1, 2, 16499, 4]] + } + error_result = False + error_reason = "At least one maximum dimension is greater than or equal to 16384" + + if check: + input_shape = kwargs['input_shape'] + output_shape = kwargs['output_shape'] # Note this is just (OH, OW) + if ((input_shape[1] >= 16384) or + (input_shape[2] >= 16384) or + (output_shape[0] >= 16384) or + (output_shape[1] >= 16384)): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evBatchMismatch(check=False, **kwargs): + error_name = ErrorIf.BatchMismatch + param_reqs = {"rank": [4,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Input batch size not equal to output batch size" + + assert 'op' in kwargs + op = kwargs['op'] + rmin, rmax = op['rank'] + rank_range = range(rmin, rmax + 1) + + if check: + input_shape = kwargs['input_shape'] + output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) + + if (len(input_shape) in rank_range) and (input_shape[0] != output_shape[0]): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evChannelMismatch(check=False, **kwargs): + error_name = ErrorIf.ChannelMismatch + param_reqs = {"rank": [4,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Input channel size not equal to output channel size" + + assert 'op' in kwargs + op = kwargs['op'] + rmin, rmax = op['rank'] + rank_range = range(rmin, rmax + 1) + + if check: + input_shape = kwargs['input_shape'] + output_shape = kwargs['result_tensor'].shape # Note this is just (N, OH, OW, C) + if (len(input_shape) in rank_range) and (input_shape[3] != output_shape[3]): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evStrideSmallerEqualZero(check=False, **kwargs): + error_name = ErrorIf.StrideSmallerEqualZero + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Stride value smaller than or equal zero" + + if check: + input_dtype = kwargs['input_dtype'] + output_dtype = kwargs['output_dtype'] + if input_dtype != DType.FLOAT and output_dtype == DType.FLOAT: + stride = kwargs['stride'] # Work around wrong input/output type tests + elif output_dtype == DType.FLOAT: + stride = kwargs['stride_fp'] + elif input_dtype == DType.FLOAT and output_dtype != DType.FLOAT: + stride = kwargs['stride_fp'] # Work around wrong input/output type tests + else: + stride = kwargs['stride'] + + if min(stride) <= 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evStrideLargerEqualMax(check=False, **kwargs): + error_name = ErrorIf.StrideLargerEqualMax + param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} + error_result = False + error_reason = "Stride value larger than or equal to maximum value" + + if check: + shift = kwargs['shift'] + input_dtype = kwargs['input_dtype'] + stride = kwargs['stride'] + if input_dtype in [DType.INT8, DType.INT16]: + if shift >= 0 and (stride[0] >= (16 << shift) or stride[1] >= (16 << shift)): + error_result = True + elif shift < 0 and (stride[0] >= (16 >> -shift) or stride[1] >= (16 >> -shift)): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evStrideLargerDimension(check=False, **kwargs): + error_name = ErrorIf.StrideLargerDimension + param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} + error_result = False + error_reason = "Stride value larger than or equal to H/W dimension" + + if check: + shape = kwargs['input_shape'] + input_dtype = kwargs['input_dtype'] + stride = kwargs['stride_fp'] + + if input_dtype == DType.FLOAT and (stride[0] > shape[1]) or (stride[1] > shape[2]): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evOffsetSmallerEqualMin(check=False, **kwargs): + error_name = ErrorIf.OffsetSmallerEqualMin + param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} + error_result = False + error_reason = "Offset value smaller than or equal to minimum value" + + if check: + shift = kwargs['shift'] + output_dtype = kwargs['output_dtype'] + if output_dtype == DType.FLOAT: + offset = kwargs['offset_fp'] + else: + offset = kwargs['offset'] + + if shift >= 0 and (offset[0] <= (-16 << shift) or offset[1] <= (-16 << shift)): + error_result = True + elif shift < 0 and (offset[0] <= (-16 >> -shift) or offset[1] <= (-16 >> -shift)): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evOffsetLargerEqualMax(check=False, **kwargs): + error_name = ErrorIf.OffsetLargerEqualMax + param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} + error_result = False + error_reason = "Offset value larger than or equal to maximum value" + + if check: + shift = kwargs['shift'] + output_dtype = kwargs['output_dtype'] + if output_dtype == DType.FLOAT: + offset = kwargs['offset_fp'] + else: + offset = kwargs['offset'] + + if shift >= 0: + if offset[0] >= (16 << shift) or offset[1] >= (16 << shift): + error_result = True + + if shift >= 0 and (offset[0] >= (16 << shift) or offset[1] >= (16 << shift)): + error_result = True + elif shift < 0 and (offset[0] >= (16 >> -shift) or offset[1] >= (16 >> -shift)): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evShiftNotZero(check=False, **kwargs): + error_name = ErrorIf.ShiftNotZero + param_reqs = {"rank": None, "dtype": [DType.FLOAT], "shape": None} + error_result = False + error_reason = "Shift value must be zero for float input" + + if check: + shift = kwargs['shift'] + input_dtype = kwargs['input_dtype'] + output_dtype = kwargs['output_dtype'] + if input_dtype == DType.FLOAT and output_dtype == DType.FLOAT and shift != 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evShiftSmallerOne(check=False, **kwargs): + error_name = ErrorIf.ShiftSmallerOne + param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} + error_result = False + error_reason = "Shift value smaller than one" + + if check: + shift = kwargs['shift'] + input_dtype = kwargs['input_dtype'] + output_dtype = kwargs['output_dtype'] + if shift < 1 and input_dtype != DType.FLOAT and output_dtype != DType.FLOAT: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evShiftLargerEleven(check=False, **kwargs): + error_name = ErrorIf.ShiftLargerEleven + param_reqs = {"rank": None, "dtype": [DType.INT8, DType.INT16], "shape": None} + error_result = False + error_reason = "Shift value larger than eleven" + + if check: + shift = kwargs['shift'] + if shift > 11: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evRankMismatch(check=False, **kwargs): + error_name = ErrorIf.RankMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input Rank does not match output rank" + + if check: + input1_shape = kwargs['input1'].shape + input2_shape = kwargs['input2'].shape + # In case of SELECT op + input3_shape = kwargs['input3'].shape if 'input3' in kwargs else input2_shape + output_shape = kwargs['result_tensor'].shape + if ( + (len(input1_shape) != len(output_shape)) or + (len(input2_shape) != len(output_shape)) or + (len(input3_shape) != len(output_shape)) + ): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evDimensionMismatch(check=False, **kwargs): + error_name = ErrorIf.DimensionMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input Dimensions do not match output" + + if check: + input1_shape = kwargs['input1'].shape + input2_shape = kwargs['input2'].shape + # In case of SELECT op + input3_shape = kwargs['input3'].shape if 'input3' in kwargs else input2_shape + output_shape = kwargs['result_tensor'].shape + for i in range(min(len(input1_shape), len(input2_shape), len(input3_shape))): + if ( + (input1_shape[i] != 1 and input1_shape[i] != output_shape[i]) or + (input2_shape[i] != 1 and input2_shape[i] != output_shape[i]) or + (input3_shape[i] != 1 and input3_shape[i] != output_shape[i]) + ): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evInputZeroPointNotZero(check=False, **kwargs): + op = kwargs['op'] + error_result = False + + # Quantizable types + qTypes = (DType.INT8, DType.UINT8) + + # This does not apply to quantizable types + inputDtypes = [ + dtype for dtype in op['types'] + if (isinstance(dtype, list) and dtype[0] not in qTypes) or + (not isinstance(dtype, list) and dtype not in qTypes) + ] + + if check: + input_dtype = kwargs['input_dtype'] + if isinstance(kwargs['qinfo'], tuple): + qinfo = kwargs['qinfo'] + input_zero_point = qinfo[0] + else: + # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp + qinfo = kwargs['qinfo'].ints + input_zero_point = qinfo[0][1] + + if op['op'] == Op.MATMUL: + qinfo = kwargs['qinfo'].ints + for dtype, zp in ( + (kwargs['input_dtype'], qinfo[0][1]), + (kwargs['input2_dtype'], qinfo[1][1]), + ): + if dtype not in qTypes and zp != 0: + error_result = True + break + else: + error_result = input_dtype not in qTypes and input_zero_point != 0 + + info_dict = { + "error_name": ErrorIf.InputZeroPointNotZero, + "error_result": error_result, + "error_reason": "Input DType not INT8 and zero point not 0", + "param_reqs": {"rank": None, "dtype": inputDtypes, "shape": None} + } + return info_dict + + + @staticmethod + def evWeightZeroPointNotZero(check=False, **kwargs): + op = kwargs['op'] + + # exclude inputs with INT8 weights + inputDtypes = [t for t in op['types'] + if not isinstance(t, list) or t[1] != DType.INT8] + + error_name = ErrorIf.WeightZeroPointNotZero + param_reqs = { + "rank": None, + "dtype": inputDtypes, + "shape": None + } + error_result = False + error_reason = "Weight DType not INT8 and zero point not 0" + + if check: + weight_dtype = kwargs['weight_dtype'] + # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = weight_zp + qinfo = kwargs['qinfo'].ints + weight_zero_point = qinfo[1][1] + if weight_dtype != DType.INT8 and weight_zero_point != 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evOutputZeroPointNotZero(check=False, **kwargs): + op = kwargs['op'] + inputDtypes = op['types'].copy() + if DType.INT8 in inputDtypes: + inputDtypes.remove(DType.INT8) + if DType.UINT8 in inputDtypes: + inputDtypes.remove(DType.UINT8) + + error_name = ErrorIf.OutputZeroPointNotZero + param_reqs = { + "rank": None, + "dtype": inputDtypes, + "shape": None + } + error_result = False + error_reason = "Output DType not INT8 and zero point not 0" + + if check: + input_dtype = kwargs['input_dtype'] + output_dtype = kwargs['output_dtype'] + if isinstance(kwargs['qinfo'], tuple): + qinfo = kwargs['qinfo'] + output_zero_point = qinfo[1] + else: + # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp + qinfo = kwargs['qinfo'].ints + output_zero_point = qinfo[1][1] + if op['op'] == Op.AVG_POOL2D: + if input_dtype != DType.INT8 and output_zero_point != 0: + error_result = True + elif output_dtype not in [DType.INT8, DType.UINT8] and output_zero_point != 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evAxisSmallerZero(check=False, **kwargs): + error_name = ErrorIf.AxisSmallerZero + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Axis smaller than zero" + + if check: + axis = kwargs['axis'] + if axis < 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evAxisLargerRank(check=False, **kwargs): + error_name = ErrorIf.AxisLargerRank + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Axis larger than rank" + + if check: + axis = kwargs['axis'] + shape = kwargs['input_shape'] + if axis > len(shape): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evShapeOfAxisNotOne(check=False, **kwargs): + error_name = ErrorIf.ShapeOfAxisNotOne + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "shape[axis] is not equal to 1" + + if check: + axis = kwargs['axis'] + shape = kwargs['output_shape'] + if (0 <= axis < len(shape)) and shape[axis] != 1: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evPadSmallerZero(check=False, **kwargs): + error_name = ErrorIf.PadSmallerZero + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "At least one pad is smaller than zero" + + if check: + op = kwargs['op'] + pad = kwargs['pad'] + if op['op'] == Op.PAD: + for padding in pad: + if min(padding) < 0: + error_result = True + else: + if min(pad) < 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evPadLargerEqualKernel(check=False, **kwargs): + error_name = ErrorIf.PadLargerEqualKernel + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "At least one pad is larger than kernel dimension" + + if check: + pad = kwargs['pad'] + kernel = kwargs['kernel'] + if min(pad) > 0 and min(kernel) > 1: + if pad[0] >= kernel[0] or pad[1] >= kernel[0] or pad[2] >= kernel[1] or pad[3] >= kernel[1]: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evPoolingOutputShapeMismatch(check=False, **kwargs): + error_name = ErrorIf.PoolingOutputShapeMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Mismatch between output shape provided and expected output shape" + + if check: + pad = kwargs['pad'] + pad_top, pad_bottom, pad_left, pad_right = pad[0], pad[1], pad[2], pad[3] + + kernel = kwargs['kernel'] + kernel_y, kernel_x = kernel[0], kernel[1] + + input_shape = kwargs['input_shape'] + IH, IW = input_shape[1], input_shape[2] + + output_shape = kwargs['output_shape'] + OH, OW = output_shape[1], output_shape[2] + + stride = kwargs['stride'] + stride_y, stride_x = stride[0], stride[1] + + # calculate correct height, width dimensions + if stride_x != 0 and stride_y != 0: + y_correct = (IH + pad_top + pad_bottom + stride_y - kernel_y) // stride_y + x_correct = (IW + pad_left + pad_right + stride_x - kernel_x) // stride_x + + # ensure parameters are valid + params_valid = (min(kernel) >= 1 and min(stride) >= 1 and min(pad) >= 0 + and not (pad[0] >= kernel[0] or pad[1] >= kernel[0] or pad[2] >= kernel[1] or pad[3] >= kernel[1])) + + if params_valid and (OH != y_correct or OW != x_correct): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evArgmaxOutputShapeMismatch(check=False, **kwargs): + error_name = ErrorIf.ArgmaxOutputShapeMismatch + param_reqs = {"rank": [2,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Mismatch between output shape provided and expected output shape" + + if check: + output_shape = kwargs['output_shape'] + input_shape = kwargs['input_shape'] + axis = kwargs['axis'] + + dimension_match = True + axis_shift = 0 + + # Check that rank is correct before trying to check dimensions + if (len(input_shape) - 1) == len(output_shape): + for i in range(len(input_shape)): + if i == axis: + axis_shift = 1 + continue + if input_shape[i] != output_shape[i - axis_shift]: + dimension_match = False + + if not dimension_match: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evArgmaxOutputRankMismatch(check=False, **kwargs): + error_name = ErrorIf.ArgmaxOutputRankMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Mismatch between output shape provided and expected output shape" + + if check: + output_shape = kwargs['output_shape'] + input_shape = kwargs['input_shape'] + axis = kwargs['axis'] + valid_params = axis >= 0 and axis < len(input_shape) + + if valid_params and (len(input_shape) - 1) != len(output_shape): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evKernelSmallerOne(check=False, **kwargs): + error_name = ErrorIf.KernelSmallerOne + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "At least one kernel dimension is smaller than zero" + + if check: + kernel = kwargs['kernel'] + if min(kernel) < 1: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evStrideSmallerOne(check=False, **kwargs): + error_name = ErrorIf.StrideSmallerOne + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "At least one stride dimension is smaller than zero" + + if check: + stride = kwargs['stride'] + if min(stride) < 1: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evDilationSmallerOne(check=False, **kwargs): + error_result = check and min(kwargs['dilation']) < 1 + return { + "error_name": ErrorIf.DilationSmallerOne, + "error_reason": "At least one dilation is smaller than one", + "param_reqs": {"rank": None, "dtype": None, "shape": None}, + "error_result": error_result + } + + @staticmethod + def evScaleTrue(check=False, **kwargs): + error_name = ErrorIf.ScaleTrue + param_reqs = {"rank": None, "dtype": [DType.INT48], "shape": None} + error_result = False + error_reason = "Scale set to true but input type is INT48" + + if check: + input_dtype = kwargs['input_dtype'] + scale32 = kwargs['scale32'] + if scale32 and input_dtype == DType.INT48: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evScaleNotTrue(check=False, **kwargs): + error_name = ErrorIf.ScaleNotTrue + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Scale set to false but double round set to true" + + if check: + scale32 = kwargs['scale32'] + double_round = kwargs['double_round'] + if not scale32 and double_round: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evTensorSizeInputOutputMismatch(check=False, **kwargs): + error_name = ErrorIf.TensorSizeInputOutputMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input tensor size does not match output tensor size" + + if check: + input_shape = kwargs['input_shape'] + output_shape = kwargs['output_shape'] + input_size = np.prod(input_shape) + output_size = np.prod(output_shape) + if input_size != output_size: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evStartSmallerZero(check=False, **kwargs): + error_name = ErrorIf.StartSmallerZero + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Starting point smaller than zero" + + if check: + input_shape = kwargs['input_shape'] + start = kwargs['start'] + rank = len(input_shape) + if len(start) == rank: + for index in range(rank): + if start[index] < 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evSizeSmallerEqualZero(check=False, **kwargs): + error_name = ErrorIf.SizeSmallerEqualZero + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Size smaller than or equal to zero" + + if check: + input_shape = kwargs['input_shape'] + size = kwargs['size'] + rank = len(input_shape) + if len(size) == rank: + for index in range(rank): + if size[index] <= 0: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evStartSizeOutsideBounds(check=False, **kwargs): + error_name = ErrorIf.StartSizeOutsideBounds + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "starting point plus size larger than input dimension" + + if check: + input_shape = kwargs['input_shape'] + start = kwargs['start'] + size = kwargs['size'] + rank = len(input_shape) + if len(start) == rank and len(size) == rank: + for index in range(rank): + if start[index] + size[index] > input_shape[index]: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evSizeOutputShapeMismatch(check=False, **kwargs): + error_name = ErrorIf.SizeOutputShapeMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Size does not match output dimension" + + if check: + input_shape = kwargs['input_shape'] + output_shape = kwargs['output_shape'] + size = kwargs['size'] + rank = len(input_shape) + if len(size) == rank: + for index in range(rank): + if size[index] != output_shape[index]: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evInputSizeStartLengthMismatch(check=False, **kwargs): + error_name = ErrorIf.InputSizeStartLengthMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "rank of input not equal to length of start or size" + + if check: + input_shape = kwargs['input_shape'] + start = kwargs['start'] + size = kwargs['size'] + rank = len(input_shape) + if rank != len(start) or rank != len(size): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evIndexOutsideBounds(check=False, **kwargs): + error_name = ErrorIf.IndexOutsideBounds + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Index outside of allowed bounds" + + if check: + input_shape = kwargs['input_shape'] + perms = kwargs['perms'] + rank = len(input_shape) + + for index in perms: + if index < 0 or index > rank: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evIndexUsedTwice(check=False, **kwargs): + error_name = ErrorIf.IndexUsedTwice + param_reqs = {"rank": [2,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Index used multiple times" + + if check: + input_shape = kwargs['input_shape'] + perms = kwargs['perms'] + rank = len(input_shape) + + unique_indices = [] + for index in perms: + if index in unique_indices: + error_result = True + else: + unique_indices.append(index) + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evMaxSmallerMin(check=False, **kwargs): + error_name = ErrorIf.MaxSmallerMin + param_reqs = {"rank": [2,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Max value smaller than min value" + + if check: + max_val = kwargs['max_val'] + min_val = kwargs['min_val'] + if max_val < min_val: + error_result = True + + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evConcatInputRankMismatch(check=False, **kwargs): + error_name = ErrorIf.ConcatInputRankMismatch + param_reqs = {"rank": [2,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Input ranks are not identical" + + if check: + inputs = kwargs['inputs'] + input_shape = kwargs['input_shape'] + for input in inputs: + if len(input.shape) != len(input_shape): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evConcatInputDimMismatch(check=False, **kwargs): + error_name = ErrorIf.ConcatInputDimMismatch + param_reqs = {"rank": [2,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Input dimensions differ on too many axes" + + if check: + inputs = kwargs['inputs'] + input_shape = kwargs['input_shape'] + axis = kwargs['axis'] + + # Ensure rank is valid before checking dims. + valid_rank = True + for input in inputs: + if len(input.shape) != len(input_shape): + valid_rank = False + + if valid_rank: + for input in inputs: + for i, dim in enumerate(input.shape): + if dim != input_shape[i] and axis != i: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evConcatShapeSumMismatch(check=False, **kwargs): + error_name = ErrorIf.ConcatShapeSumMismatch + param_reqs = {"rank": [2,4], "dtype": None, "shape": None} + error_result = False + error_reason = "Sum of dimensions on axis not equal to output dimension" + + if check: + inputs = kwargs['inputs'] + input_shape = kwargs['input_shape'] + output_shape = kwargs['output_shape'] + axis = kwargs['axis'] + + # Ensure rank is valid before checking dims. + valid_params = True + for input in inputs: + if len(input.shape) != len(input_shape): + valid_params = False + if axis < 0 or axis > len(input_shape): + valid_params = False + + if valid_params: + axis_dim_sum = 0 + for input in inputs: + axis_dim_sum += input.shape[axis] + + if axis_dim_sum != output_shape[axis]: + error_result = True + + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + @staticmethod + def evInputListThenGraphMismatch(check=False, **kwargs): + error_name = ErrorIf.CondIfInputListThenGraphMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input list shape does not match then-graph shape" + + if check: + a = kwargs['a'] + b = kwargs['b'] + basicBlocks = kwargs['basicBlocks'] + then_block = basicBlocks[1] + then_inputs = then_block.inputs + then_tens = then_block.tensors + if (a.shape != then_tens[then_inputs[0]].shape) or (b.shape != then_tens[then_inputs[1]].shape): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evInputListElseGraphMismatch(check=False, **kwargs): + error_name = ErrorIf.CondIfInputListElseGraphMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input list shape does not match else-graph shape" + + if check: + a = kwargs['a'] + b = kwargs['b'] + basicBlocks = kwargs['basicBlocks'] + else_block = basicBlocks[2] + else_inputs = else_block.inputs + else_tens = else_block.tensors + if (a.shape != else_tens[else_inputs[0]].shape) or (b.shape != else_tens[else_inputs[1]].shape): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evOutputListThenGraphMismatch(check=False, **kwargs): + error_name = ErrorIf.CondIfOutputListThenGraphMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Output list shape does not match then-graph shape" + + if check: + basicBlocks = kwargs['basicBlocks'] + cond_block = basicBlocks[0] + cond_outputs = cond_block.outputs + cond_tens = cond_block.tensors + then_block = basicBlocks[1] + then_outputs = then_block.outputs + then_tens = then_block.tensors + if then_tens[then_outputs[0]].shape != cond_tens[cond_outputs[0]].shape: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evOutputListElseGraphMismatch(check=False, **kwargs): + error_name = ErrorIf.CondIfOutputListElseGraphMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Output list shape does not match else-graph shape" + + if check: + basicBlocks = kwargs['basicBlocks'] + cond_block = basicBlocks[0] + cond_outputs = cond_block.outputs + cond_tens = cond_block.tensors + else_block = basicBlocks[2] + else_outputs = else_block.outputs + else_tens = else_block.tensors + if else_tens[else_outputs[0]].shape != cond_tens[cond_outputs[0]].shape: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evInputListOutputListMismatch(check=False, **kwargs): + error_name = ErrorIf.InputListOutputListMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input list does not match output list" + + if check: + basicBlocks = kwargs['basicBlocks'] + while_block = basicBlocks[0] + while_inputs = while_block.inputs + while_outputs = while_block.outputs + while_tens = while_block.tensors + if while_tens[while_inputs[1]].shape != while_tens[while_outputs[0]].shape: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evInputListCondGraphMismatch(check=False, **kwargs): + error_name = ErrorIf.InputListCondGraphMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input list does not match cond graph" + + if check: + basicBlocks = kwargs['basicBlocks'] + while_block = basicBlocks[0] + while_inputs = while_block.inputs + while_tens = while_block.tensors + cond_block = basicBlocks[1] + cond_inputs = cond_block.inputs + cond_tens = cond_block.tensors + if ((while_tens[while_inputs[0]].shape != cond_tens[cond_inputs[0]].shape) or + (while_tens[while_inputs[1]].shape != cond_tens[cond_inputs[2]].shape)): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evInputListBodyGraphInputMismatch(check=False, **kwargs): + error_name = ErrorIf.InputListBodyGraphInputMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input list does not match body graph input" + + if check: + basicBlocks = kwargs['basicBlocks'] + while_block = basicBlocks[0] + while_inputs = while_block.inputs + while_tens = while_block.tensors + body_block = basicBlocks[2] + body_outputs = body_block.inputs + body_tens = body_block.tensors + if ((while_tens[while_inputs[0]].shape != body_tens[body_outputs[0]].shape) or + (while_tens[while_inputs[1]].shape != body_tens[body_outputs[2]].shape)): + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evInputListBodyGraphOutputMismatch(check=False, **kwargs): + error_name = ErrorIf.InputListBodyGraphOutputMismatch + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Input list does not match body graph output" + + if check: + basicBlocks = kwargs['basicBlocks'] + while_block = basicBlocks[0] + while_inputs = while_block.inputs + while_tens = while_block.tensors + body_block = basicBlocks[2] + body_outputs = body_block.outputs + body_tens = body_block.tensors + if ((while_tens[while_inputs[0]].shape != body_tens[body_outputs[0]].shape) or + (while_tens[while_inputs[1]].shape != body_tens[body_outputs[2]].shape)): + error_result = True + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + + @staticmethod + def evCondGraphOutputNotMatchingBool(check=False, **kwargs): + error_name = ErrorIf.CondGraphOutputNotMatchingBool + param_reqs = {"rank": None, "dtype": None, "shape": None} + error_result = False + error_reason = "Cond graph output is not a match list of booleans" + + if check: + basicBlocks = kwargs['basicBlocks'] + cond_block = basicBlocks[1] + cond_outputs = cond_block.outputs + cond_tens = cond_block.tensors + if cond_tens[cond_outputs[0]].dtype != DType.BOOL: + error_result = True + + info_dict = { + "error_name": error_name, + "error_result": error_result, + "error_reason": error_reason, + "param_reqs": param_reqs + } + return info_dict + + +class TosaInvalidValidator: + + @staticmethod + def ivWrongDataTypeOrModeResize(**kwargs): + input_dtype = kwargs["input_dtype"] + args = kwargs["args"] + mode = args[0] + stride = args[1] + stride_fp = args[4] + output_dtype = args[8] + + if mode == ResizeMode.BILINEAR: + # Invalid output data type / Invalid input datatype + return ( + not (input_dtype == DType.INT8 and output_dtype == DType.INT32) or + not (input_dtype == DType.INT16 and output_dtype == DType.INT48) or + not (input_dtype == DType.FLOAT and output_dtype == DType.FLOAT) or + (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) + ) + elif mode == ResizeMode.NEAREST: + # Invalid output data type / Invalid input datatype + return ( + (input_dtype != output_dtype) or + (input_dtype not in [DType.INT8, DType.INT32, DType.FLOAT]) + ) + else: + # Invalid resize mode + return True + + @staticmethod + def ivBadStride(**kwargs): + input_dtype = kwargs["input_dtype"] + args = kwargs["args"] + stride_x = args[1][0] + stride_y = args[1][1] + stride_fp_x = args[4][0] + stride_fp_y = args[4][1] + + if input_dtype == DType.FLOAT: + if stride_fp_x <= 0 or stride_fp_y <= 0: + # Negative or zero stride + return True + else: + if stride_x <= 0 or stride_y <= 0: + # Negative or zero stride + return True + return False + + @staticmethod + def ivHeightWidthInvalid(**kwargs): + opName = kwargs['opName'] + + inputShapes = kwargs['shapeList'] + input_shape = inputShapes[0] + + args = kwargs['args'] + strides = args[0] + padding = args[1] + + if opName.endswith("pool2d"): + # avg_pool2d, max_pool2d + kernel_shape = args[2] + h = (input_shape[1] + padding[0] + padding[1] + strides[0] - kernel_shape[0]) // strides[0] + w = (input_shape[2] + padding[2] + padding[3] + strides[1] - kernel_shape[1]) // strides[1] + # return True if any dimension is < 1 + return h < 1 or w < 1 + + if opName.startswith("transpose_conv2d"): + # transpose_conv2d + dilations = args[2] + output_shape = args[3] + filter_shape = inputShapes[1] + kernel_shape = filter_shape[1:-1] + + def get_out_size(in_size, stride, kernel_size, dilation, out_pad, in_pad): + """Calculate the transpose_conv2d output size for a dimension. + + Based on the keras function deconv_output_length, in + https://github.com/keras-team/keras/blob/master/keras/utils/conv_utils.py + + Args: + in_size: the input size - int + stride: the stride - int + kernel_size: the kernel size - int + dilation: the kernel dilation - int + out_pad: the output padding - int + in_pad: the input padding - int + + Returns: + the output size + """ + dilated_kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) + return (in_size - 1) * stride + dilated_kernel_size - 2 * in_pad + out_pad + + for pad_h, pad_w in ( + (kernel_shape[0] - 1, kernel_shape[1] - 1), # FULL padding + (kernel_shape[0] // 2, kernel_shape[1] // 2), # SAME padding + (0, 0) # VALID padding + ): + h = get_out_size(input_shape[1], strides[0], kernel_shape[0], dilations[0], + padding[0], pad_h) + w = get_out_size(input_shape[2], strides[1], kernel_shape[1], dilations[1], + padding[1], pad_w) + if output_shape[1] == h and output_shape[2] == w: + return False + + # output shape does not match the expected shape for any padding option + return True + + if "conv2d" in opName or "conv3d" in opName: + # conv2d, conv3d, depthwise_conv2d + dilations = args[2] + filter_shape = inputShapes[1] + kernel_shape = filter_shape[0:2] if opName.startswith("depthwise_conv2d") else filter_shape[1:-1] + + for i in range(len(kernel_shape)): + dim = ( + input_shape[i + 1] + - kernel_shape[i] + - (kernel_shape[i] - 1) * (dilations[i] - 1) + + padding[i * 2 + 0] + + padding[i * 2 + 1] + ) // strides[i] + 1 + # return True if any dimension is < 1 + if dim < 1: + return True + return False + + assert False, f"Unrecognized Op: {opName}" + + @staticmethod + def ivNonPositiveOutputShape(**kwargs): + args = kwargs['args'] + output_shape = args[3] + if output_shape[1] <= 0 or output_shape[2] <= 0: + # Negative output shape + return True + return False + + +class TosaTestGen: + # Maximum rank of tensor supported by test generator. + TOSA_TENSOR_MAX_RANK = 6 + + def __init__(self, args): + self.args = args + self.basePath = args.output_dir + self.random_seed = args.random_seed + self.ser = None + self.rng = np.random.default_rng(self.random_seed) + self.createDynamicOpLists() + self.initOpListDefaults() + self.quantGen = TosaQuantGen() + # Force makeShape to do a specific starting shape + self.targetted_shape = None + + def createSerializer(self, opName, testPath): + self.testPath = os.path.join(opName, testPath) + + fullPath = os.path.join(self.basePath, self.testPath) + os.makedirs(fullPath, exist_ok=True) + self.ser = ts.TosaSerializer(fullPath) + + def getSerializer(self): + return self.ser + + def serialize(self, testName): + with open( + os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" + ) as fd: + fd.write(self.ser.serialize()) + + with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: + fd.write(self.ser.writeJson("{}.tosa".format(testName))) + + def resetRNG(self, seed=None): + if seed == None: + seed = self.random_seed + 1 + self.rng = np.random.default_rng(seed) + + def getRandTensor(self, shape, dtype): + if dtype == DType.BOOL: + np_dt = np.bool + return np.bool_(self.rng.choice(a=[False, True], size=shape)) + # TOSA specific INT4 weight range from -7 to 7 + elif dtype == DType.INT4: + return np.int32(self.rng.integers(low=-7, high=8, size=shape)) + elif dtype == DType.INT8: + return np.int32(self.rng.integers(low=-128, high=128, size=shape)) + elif dtype == DType.UINT8: + return np.int32(self.rng.integers(low=0, high=256, size=shape)) + elif dtype == DType.INT16: + return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) + elif dtype == DType.INT32: + return np.int32( + self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) + ) + elif dtype == DType.INT48: + return np.int64( + self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) + ) + elif dtype == DType.FLOAT: + return np.float32(self.rng.random(size=shape)) + else: + raise Exception("Unrecognized Dtype: {}".format(dtype)) + + def buildPlaceholderTensors(self, shape_list, dtype_list): + placeholders = [] + + assert len(shape_list) == len(dtype_list) + + for idx, shape in enumerate(shape_list): + arr = self.getRandTensor(shape, dtype_list[idx]) + placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) + + return placeholders + + def buildConstTensors(self, shape_list, dtype_list): + consts = [] + + assert len(shape_list) == len(dtype_list) + + for idx, shape in enumerate(shape_list): + arr = self.getRandTensor(shape, dtype_list[idx]) + consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) + + return consts + + def makeShape(self, rank): + if self.targetted_shape: + return np.int32(self.targetted_shape) + return np.int32( + self.rng.integers( + low=self.args.tensor_shape_range[0], + high=self.args.tensor_shape_range[1], + size=rank, + ) + ) + + def setTargetShape(self, shape): + self.targetted_shape = shape + + def randInt(self, low=0, high=256): + return np.int32(self.rng.integers(low=low, high=high, size=1))[0] + + def getRandNumberDType(self, dtype): + if dtype == DType.FLOAT: + return self.rng.random() + elif dtype == DType.BOOL: + return self.rng.choice([False, True]) + # TOSA specific INT4 weight range from -7 to 7 + elif dtype == DType.INT4: + low, high = (-7, 8) + elif dtype == DType.INT8: + low, high = (-128, 128) + elif dtype == DType.INT16: + low, high = (-32768, 32768) + elif dtype == DType.INT32: + low, high = (-(1 << 31), (1 << 31)) + elif dtype == DType.INT48: + low, high = (-(1 << 47), (1 << 47)) + # Special size + return np.int64(self.rng.integers(low, high, size=1))[0] + else: + raise Exception("Unknown dtype: {}".format(dtype)) + + return np.int32(self.rng.integers(low, high, size=1))[0] + + def shapeStr(self, shape): + + sStr = [] + # Convert to strings + for i in shape: + sStr.append(str(i)) + + return "x".join(sStr) + + def typeStr(self, t): + if isinstance(t, list): + assert len(t) >= 2 + return "{}x{}".format(self.typeStr(t[0]), self.typeStr(t[1])) + else: + if t == DType.BOOL: + return "b" + elif t == DType.INT4: + return "i4" + elif t == DType.INT8: + return "i8" + elif t == DType.UINT8: + return "u8" + elif t == DType.INT16: + return "i16" + elif t == DType.INT32: + return "i32" + elif t == DType.INT48: + return "i48" + elif t == DType.FLOAT: + return "float" + else: + raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) + + def typeWidth(self, t): + """ Get the datatype width for integer types""" + if t == DType.INT4: + return 4 + elif t == DType.INT8: + return 8 + elif t == DType.UINT8: + return 8 + elif t == DType.INT16: + return 16 + elif t == DType.INT32: + return 32 + elif t == DType.INT48: + return 48 + elif t == DType.FLOAT: + return 32 + elif t == DType.BOOL: + return 1 + else: + raise Exception(f"Unknown dtype, cannot determine width: {t}") + + # Argument generators + # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) + # Where the string descriptor is used to generate the test name and + # The build_fcn_arg_list is expanded and passed to the operator test + # build function + + def build_unary(self, op, a, validator_fcns=None, error_name=None, qinfo=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + + # build_placeholder returns an int, ABS/other ops does not + if isinstance(op, int): + self.ser.addOperator(op, a.name, result_tens.name, None, qinfo) + return result_tens + elif op['op'] == Op.IDENTITY: + self.ser.addOperator(op['op'], a.name, result_tens.name, None, qinfo) + return result_tens + + # Ensure new output type has correct qinfo + if error_name == ErrorIf.WrongOutputType: + if result_tens.dtype not in [DType.INT8, DType.UINT8]: + qinfo = ts.TosaSerializerQuantInfo() + qinfo.UnaryQuantInfo( + TosaQuantGen.getQinfo(self, a.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) + ) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_dtype=a.dtype, + output_dtype=result_tens.dtype, + qinfo = qinfo, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list, None, qinfo) + return result_tens + + def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None): + result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b, error_name) + + + # Invalidate Input/Output list for error if checks. + input_list = [a.name, b.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input1 = a, + input2 = b, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list) + return result_tens + + def build_binary_nonbroadcast(self, op, a, b, validator_fcns=None, error_name=None): + result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) + self.ser.addOperator(op['op'], [a.name, b.name], [result_tens.name]) + return result_tens + + def build_arithmetic_right_shift(self, op, a, b, round, validator_fcns=None, error_name=None): + result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name, b.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input1 = a, + input2 = b, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.ArithmeticRightShiftAttribute(round) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_mul(self, op, a, b, shift, validator_fcns=None, error_name=None): + result_tens = OutputShaper.binaryBroadcastOp(self.ser, self.rng, a, b, error_name) + + # Special for multiply: + # Force the result to INT32 for INT types + if a.dtype != DType.FLOAT: + result_tens.setDtype(DType.INT32) + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT48] + outputDType = self.rng.choice(all_dtypes) + result_tens.setDtype(outputDType) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name, b.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input1 = a, + input2 = b, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.MulAttribute(shift) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_table(self, op, a, table, validator_fcns=None, error_name=None): + result_tens = OutputShaper.tableOp(self.ser, self.rng, a, error_name) + + attr = ts.TosaSerializerAttribute() + attr.TableAttribute(table) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list, attr) + + return result_tens + + def build_select(self, op, cond, a, b, validator_fcns=None, error_name=None): + result_tens = OutputShaper.selectOp(self.ser, self.rng, cond, a, b, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [cond.name, a.name, b.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input1 = cond, + input2 = a, + input3 = b, + input_shape = a.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list,) + return result_tens + + def build_comparison(self, op, a, b, validator_fcns=None, error_name=None): + result_tens = OutputShaper.binaryComparisonOp(self.ser, self.rng, a, b, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name, b.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input1 = a, + input2 = b, + input_shape = a.shape, + input_dtype = a.dtype, + output_shape = result_tens.shape, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list,) + return result_tens + + def build_argmax(self, op, a, axis, validator_fcns, error_name): + result_tens = OutputShaper.argmaxOp(self.ser, self.rng, a, axis, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + axis=axis, + input_shape = a.shape, + input_dtype = a.dtype, + output_shape = result_tens.shape, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.AxisAttribute(axis) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_pool2d(self, op, input, stride, pad, kernel, validator_fcns=None, error_name=None, qinfo=None): + result_tens = OutputShaper.pool2dOp(self.ser, self.rng, input, kernel, stride, pad, error_name) + + # Ensure new output type has correct qinfo + if error_name == ErrorIf.WrongInputType: + if input.dtype not in [DType.INT8, DType.UINT8]: + qinfo = ts.TosaSerializerQuantInfo() + qinfo.UnaryQuantInfo( + TosaQuantGen.getQinfo(self, input.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) + ) + + # Invalidate Input/Output list for error if checks. + input_list = [input.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape=input.shape, + input_dtype=input.dtype, + output_shape=result_tens.shape, + output_dtype=result_tens.dtype, + kernel=kernel, + stride=stride, + pad=pad, + qinfo = qinfo, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.PoolAttribute(kernel, stride, pad) + + self.ser.addOperator(op['op'], input_list, output_list, attr, qinfo) + return result_tens + + def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, validator_fcns=None, error_name=None, qinfo=None): + assert len(padding) == 4 + result_tens = OutputShaper.conv2dOp( + self.ser, self.rng, ifm, filter, strides, padding, dilations, error_name + ) + + # Ensure new output type has correct qinfo + if error_name == ErrorIf.WrongInputType and ifm.dtype not in (DType.INT8, DType.UINT8): + qinfo = ts.TosaSerializerQuantInfo() + qinfo.ConvQuantInfo( + TosaQuantGen.getQinfo(self, ifm.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) + ) + + # Invalidate Input/Output list for error_if checks. + input_list = [ifm.name, filter.name, bias.name] + output_list = [result_tens.name] + num_operands = sum(op["operands"]) + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_dtype=ifm.dtype, + weight_dtype=filter.dtype, + output_dtype=result_tens.dtype, + qinfo=qinfo, + input_list=input_list, + num_operands=num_operands, + output_list=output_list, + pad=padding, + stride=strides, + dilation=dilations, + input_shape=ifm.shape, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.ConvAttribute(padding, strides, dilations) + + self.ser.addOperator( + op['op'], input_list, output_list, attr, qinfo + ) + return result_tens + + def build_conv3d(self, op, ifm, filter, bias, strides, padding, dilations, validator_fcns=None, error_name=None, qinfo=None): + assert len(padding) == 6 + result_tens = OutputShaper.conv3dOp( + self.ser, self.rng, ifm, filter, strides, padding, dilations, error_name + ) + + # Ensure new output type has correct qinfo + if error_name == ErrorIf.WrongInputType and ifm.dtype not in (DType.INT8, DType.UINT8): + qinfo = ts.TosaSerializerQuantInfo() + qinfo.ConvQuantInfo( + TosaQuantGen.getQinfo(self, ifm.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) + ) + + # Invalidate Input/Output list for error_if checks. + input_list = [ifm.name, filter.name, bias.name] + output_list = [result_tens.name] + num_operands = sum(op["operands"]) + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_dtype=ifm.dtype, + weight_dtype=filter.dtype, + output_dtype=result_tens.dtype, + qinfo=qinfo, + input_list=input_list, + num_operands=num_operands, + output_list=output_list, + pad=padding, + stride=strides, + dilation=dilations, + input_shape=ifm.shape, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.ConvAttribute(padding, strides, dilations) + + self.ser.addOperator( + op['op'], input_list, output_list, attr, qinfo + ) + return result_tens + + def build_transpose_conv2d( + self, op, ifm, filter, bias, stride, outpad, dilation, output_shape, validator_fcns=None, error_name=None, qinfo=None + ): + assert len(outpad) == 2 + result_tens = OutputShaper.transposeConv2DOp(self.ser, self.rng, ifm, output_shape, error_name) + + # Ensure new output type has correct qinfo + if error_name == ErrorIf.WrongInputType and ifm.dtype not in (DType.INT8, DType.UINT8): + qinfo = ts.TosaSerializerQuantInfo() + qinfo.ConvQuantInfo( + TosaQuantGen.getQinfo(self, ifm.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) + ) + + # Invalidate Input/Output list for error_if checks. + input_list = [ifm.name, filter.name, bias.name] + output_list = [result_tens.name] + num_operands = sum(op["operands"]) + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_dtype=ifm.dtype, + weight_dtype=filter.dtype, + output_dtype=result_tens.dtype, + qinfo=qinfo, + input_list=input_list, + num_operands=num_operands, + output_list=output_list, + pad=outpad, + stride=stride, + dilation=dilation, + input_shape=ifm.shape, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.TransposeConvAttribute(outpad, stride, dilation, output_shape) + + self.ser.addOperator( + op['op'], input_list, output_list, attr, qinfo + ) + return result_tens + + def build_depthwise_conv2d( + self, op, ifm, filter, bias, strides, padding, dilations, validator_fcns=None, error_name=None, qinfo=None + ): + result_tens = OutputShaper.depthwiseConv2dOp( + self.ser, self.rng, ifm, filter, strides, padding, dilations, error_name + ) + + # Ensure new output type has correct qinfo + if error_name == ErrorIf.WrongInputType and ifm.dtype not in (DType.INT8, DType.UINT8): + qinfo = ts.TosaSerializerQuantInfo() + qinfo.ConvQuantInfo( + TosaQuantGen.getQinfo(self, ifm.dtype), TosaQuantGen.getQinfo(self, result_tens.dtype) + ) + + # Invalidate Input/Output list for error_if checks. + input_list = [ifm.name, filter.name, bias.name] + output_list = [result_tens.name] + num_operands = sum(op["operands"]) + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_dtype=ifm.dtype, + weight_dtype=filter.dtype, + output_dtype=result_tens.dtype, + qinfo=qinfo, + input_list=input_list, + num_operands=num_operands, + output_list=output_list, + pad=padding, + stride=strides, + dilation=dilations, + input_shape=ifm.shape, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.ConvAttribute(padding, strides, dilations) + + self.ser.addOperator( + op['op'], input_list, output_list, attr, qinfo + ) + return result_tens + + def build_fully_connected(self, op, ifm, filter, bias, validator_fcns=None, error_name=None, qinfo=None): + result_tens = OutputShaper.fullyConnectedOp(self.ser, self.rng, ifm, filter, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [ifm.name, filter.name, bias.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape=ifm.shape, + input_dtype=ifm.dtype, + weight_dtype=filter.dtype, + output_shape=result_tens.shape, + output_dtype=result_tens.dtype, + qinfo = qinfo, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator( + op['op'], input_list, output_list, None, qinfo + ) + return result_tens + + def build_matmul(self, op, a, b, validator_fcns=None, error_name=None, qinfo=None): + result_tens = OutputShaper.matmulOp(self.ser, self.rng, a, b, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name, b.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape=a.shape, + input_dtype=a.dtype, + input2_shape=b.shape, + input2_dtype=b.dtype, + output_shape=result_tens.shape, + output_dtype=result_tens.dtype, + qinfo = qinfo, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list, None, qinfo) + return result_tens + + def build_reduce(self, op, a, axis, validator_fcns, error_name=None): + result_tens = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + axis = axis, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.AxisAttribute(axis) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_clamp(self, op, a, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + + v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] + + if error_name == ErrorIf.MaxSmallerMin: + # Make sure the numbers are different to invoke this error + while v[0] == v[1]: + v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] + max_val = min(v) + min_val = max(v) + else: + max_val = max(v) + min_val = min(v) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + max_val=max_val, + min_val=min_val, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + if a.dtype == DType.FLOAT: + attr.ClampAttribute(0, 0, min_val, max_val) + else: + attr.ClampAttribute(min_val, max_val, 0, 0) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_leaky_relu(self, op, a, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + attr = ts.TosaSerializerAttribute() + + attr.LeakyReluAttribute(self.getRandNumberDType(DType.FLOAT)) + + self.ser.addOperator(op['op'], [a.name], [result_tens.name], attr) + return result_tens + + # Needs an additional type/input + def build_prelu(self, op, a, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + + self.ser.addOperator(op['op'], [a.name], [result_tens.name]) + return result_tens + + def build_sigmoid(self, op, a, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list) + return result_tens + + def build_tanh(self, op, a, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list) + return result_tens + + def build_concat(self, op, *a, validator_fcns=None, error_name=None): + if error_name != ErrorIf.WrongInputType: + assert type(a[-1]) == int + + # To store variable length list of input tensors we need to store axis along with it + axis = a[-1] + a = a[:-1] + + result_tens = OutputShaper.concatOp(self.ser, self.rng, axis, *a, error_name=error_name) + + input_tensor_names = [] + for tensor in a: + input_tensor_names.append(tensor.name) + + # Invalidate Input/Output list for error if checks. + input_list = input_tensor_names + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + axis=axis, + input_shape = a[0].shape, + output_shape = result_tens.shape, + input_dtype = a[0].dtype, + output_dtype = result_tens.dtype, + inputs=a, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.AxisAttribute(axis) + + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_pad(self, op, a, padding, pad_const_int, pad_const_float, validator_fcns=None, error_name=None, qinfo=None): + result_tens = OutputShaper.padOp(self.ser, self.rng, a, padding, error_name) + + attr = ts.TosaSerializerAttribute() + attr.PadAttribute(padding.flatten(), pad_const_int, pad_const_float) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + pad=padding, + qinfo=qinfo, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator( + op['op'], input_list, output_list, attr, qinfo + ) + return result_tens + + def build_reshape(self, op, a, newShape, validator_fcns=None, error_name=None): + result_tens = OutputShaper.reshapeOp(self.ser, self.rng, a, newShape, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.ReshapeAttribute(newShape) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_reverse(self, op, a, axis, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + axis=axis, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.AxisAttribute(axis) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_transpose(self, op, a, perms, validator_fcns=None, error_name=None): + result_tens = OutputShaper.transposeOp(self.ser, self.rng, a, perms, error_name) + + attr = ts.TosaSerializerAttribute() + attr.TransposeAttribute(perms) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + perms=perms, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_slice(self, op, a, start, size, validator_fcns=None, error_name=None): + result_tens = OutputShaper.sliceOp(self.ser, self.rng, a, start, size, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + start=start, + size=size, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.SliceAttribute(start, size) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_tile(self, op, a, multiples, validator_fcns=None, error_name=None): + result_tens = OutputShaper.tileOp(self.ser, self.rng, a, multiples, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [a.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = a.shape, + output_shape = result_tens.shape, + input_dtype = a.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.TileAttribute(multiples) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_gather(self, op, values, validator_fcns=None, error_name=None): + + # Create a new indicies tensor + # here with data that doesn't exceed the dimensions of the values tensor + + K = values.shape[1] # K + W = self.randInt( + self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] + ) # W + indicies_arr = np.int32( + self.rng.integers(low=0, high=K, size=[values.shape[0], W]) + ) # (N, W) + indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) + + result_tens = OutputShaper.gatherOp(self.ser, self.rng, values, indicies, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [values.name, indicies.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = values.shape, + output_shape = result_tens.shape, + input_dtype = values.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list) + + return result_tens + + def build_scatter(self, op, values_in, input, validator_fcns=None, error_name=None): + + # Create a new indicies tensor + # here with data that doesn't exceed the dimensions of the values_in tensor + + K = values_in.shape[1] # K + W = input.shape[1] # W + indicies_arr = np.int32( + self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) + ) # (N, W) + indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) + + result_tens = OutputShaper.scatterOp(self.ser, self.rng, values_in, indicies, input, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [values_in.name, indicies.name, input.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = values_in.shape, + output_shape = result_tens.shape, + input_dtype = values_in.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list) + + return result_tens + + + def build_resize( + self, + op, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + validator_fcns, + error_name = None, + ): + result_tens = OutputShaper.resizeOp( + self.ser, + self.rng, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + error_name + ) + + # Invalidate Input/Output list for error if checks. + input_list = [input.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + mode=mode, + shift=shift, + input_dtype=input_dtype, + output_dtype=output_dtype, + input_shape=input.shape, + output_shape=output_dims, + offset=offset, + offset_fp=offset_fp, + stride=stride, + stride_fp=stride_fp, + input_list=input_list, + output_list=output_list, + result_tensor=result_tens, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + + attr.ResizeAttribute( + output_dims, stride, offset, shift, stride_fp, offset_fp, mode + ) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_identityn(self, op, val, val2, validator_fcns=None, error_name=None): + result_tens = OutputShaper.unaryOp(self.ser, self.rng, val, error_name) + result_tens2 = OutputShaper.unaryOp(self.ser, self.rng, val2, error_name) + self.ser.addOperator( + op, [val.name, val2.name], [result_tens.name, result_tens2.name] + ) + return result_tens + + def build_const(self, op, val, validator_fcns=None, error_name=None): + self.ser.addOutputTensor(val) + return val + + # Type Conversion + def build_cast(self, op, val, out_dtype, validator_fcns=None, error_name=None): + result_tens = OutputShaper.typeConversionOp(self.ser, self.rng, val, out_dtype, error_name) + + # Invalidate Input/Output list for error if checks. + input_list = [val.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_shape = val.shape, + output_shape = result_tens.shape, + input_dtype = val.dtype, + output_dtype = result_tens.dtype, + result_tensor = result_tens, + input_list=input_list, + output_list=output_list, + num_operands=num_operands, + ): + return None + + self.ser.addOperator(op['op'], input_list, output_list) + return result_tens + + def build_rescale(self, op, val, out_dtype, scale32, double_round, per_channel, validator_fcns, error_name): + result_tens = OutputShaper.typeConversionOp(self.ser, self.rng, val, out_dtype, error_name) + + if per_channel: + nc = val.shape[-1] + else: + nc = 1 + + in_type_width = self.typeWidth(val.dtype) + out_type_width = self.typeWidth(out_dtype) + + if val.dtype == DType.INT8: + input_zp = self.randInt(-128, 128) + in_type_width = in_type_width + 1 + elif val.dtype == DType.UINT8: + input_zp = self.randInt(0, 256) + in_type_width = in_type_width + 1 + elif error_name == ErrorIf.InputZeroPointNotZero: + input_zp = self.randInt(-128, 128) + if input_zp == 0: + input_zp = input_zp + self.rng.integers(1, 10) + in_type_width = in_type_width + 1 + else: + input_zp = 0 + + if out_dtype == DType.INT8: + output_zp = self.randInt(-128, 128) + out_type_width = out_type_width + 1 + elif out_dtype == DType.UINT8: + output_zp = self.randInt(0, 256) + out_type_width = out_type_width + 1 + elif error_name == ErrorIf.OutputZeroPointNotZero: + output_zp = self.randInt(-128, 128) + if output_zp == 0: + output_zp = output_zp + self.rng.integers(1, 10) + out_type_width = out_type_width + 1 + else: + output_zp = 0 + + # Calculate scale based on: + # scale = a *(2^output_width)/(2^input_width)) + + a = np.float32(self.rng.random(size=[nc])) + scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) + + if scale32: + pass + # Cap the scaling at 2^31 - 1 for scale32 + scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) + else: + # Cap the scaling at 2^15 - 1 for scale16 + scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) + + # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) + + multiplier_arr = np.int32(np.zeros(shape=[nc])) + shift_arr = np.int32(np.zeros(shape=[nc])) + + for i in range(nc): + multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( + scale_arr[i], scale32 + ) + + # print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) + + # Invalidate Input/Output list for error if checks. + input_list = [val.name] + output_list = [result_tens.name] + pCount, cCount = op["operands"] + num_operands = pCount + cCount + input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(self, error_name, input_list, output_list) + + qinfo = (input_zp, output_zp) + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + input_dtype=val.dtype, + output_dtype=out_dtype, + input_shape=val.shape, + qinfo=qinfo, + scale32 = scale32, + double_round = double_round, + input_list=input_list, + output_list=output_list, + result_tensor=result_tens, + num_operands=num_operands, + ): + return None + + attr = ts.TosaSerializerAttribute() + attr.RescaleAttribute( + input_zp, + output_zp, + multiplier_arr, + shift_arr, + scale32, + double_round, + per_channel, + ) + + self.ser.addOperator(op['op'], input_list, output_list, attr) + return result_tens + + def build_cond_if_const(self, op, then_tens, else_tens, cond, validator_fcns=None, error_name=None): + # For cond_if with constants, we're supplied with then/else tensors that we ignore + # (except for the generated shap) and the condition. Build Then/Else blocks + # and fill them with const nodes for the body. + + # Condition tensor + cond_tens = self.ser.addConst([], DType.BOOL, [cond]) + + # Make then/else tensors + out_shape = then_tens.shape + + # Create an incorrect output shape for error_if tests + if error_name in [ErrorIf.CondIfOutputListThenGraphMismatch, ErrorIf.CondIfOutputListElseGraphMismatch]: + incorrect_shape = deepcopy(then_tens.shape) + for i in range(len(incorrect_shape)): + incorrect_shape[i] += self.rng.choice([-3, -2, 2, 3]) if incorrect_shape[i] > 3 else self.rng.choice([1, 2, 4]) + incorrect_arr = np.int32(self.rng.integers(0, 256, size=incorrect_shape)) + + then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) + else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) + + # And the result tensor based on any of the outputs + result_tens = self.ser.addOutput(out_shape, DType.INT32) + + # Create the attribute with the names of the then/else blocks + then_block = "THEN_BLOCK" + else_block = "ELSE_BLOCK" + attr = ts.TosaSerializerAttribute() + attr.CondIfAttribute(then_block, else_block) + + # Finally, build the op and the two blocks + self.ser.addOperator(op['op'], [cond_tens.name], [result_tens.name], attr) + + self.ser.startBasicBlock(then_block) + # Build the actual then/else tensors inside their blocks + if error_name == ErrorIf.CondIfOutputListThenGraphMismatch: + then_tens = self.ser.addConst(incorrect_shape, DType.INT32, incorrect_arr) + else: + then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) + self.ser.addOutputTensor(then_tens) + + self.ser.startBasicBlock(else_block) + if error_name == ErrorIf.CondIfOutputListElseGraphMismatch: + else_tens = self.ser.addConst(incorrect_shape, DType.INT32, incorrect_arr) + else: + else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) + self.ser.addOutputTensor(else_tens) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + basicBlocks=self.ser.basicBlocks + ): + return None + + return result_tens + + def build_cond_if_binary(self, op, a, b, cond, validator_fcns=None, error_name=None): + # For cond_if with a binary op in the then/else blocks, take a and b and + # alternately add or subtract them based on the condition + + # Condition tensor + cond_tens = self.ser.addConst([], DType.BOOL, [cond]) + + result_tens = self.ser.addOutput(a.shape, a.dtype) + + # Create the attribute with the names of the then/else blocks + then_block = "THEN_BLOCK" + else_block = "ELSE_BLOCK" + attr = ts.TosaSerializerAttribute() + attr.CondIfAttribute(then_block, else_block) + + if error_name in [ErrorIf.CondIfInputListThenGraphMismatch, ErrorIf.CondIfInputListElseGraphMismatch, + ErrorIf.CondIfOutputListElseGraphMismatch, ErrorIf.CondIfOutputListThenGraphMismatch]: + incorrect_shape = a.shape.copy() + for i in range(len(incorrect_shape)): + incorrect_shape[i] += self.rng.choice([-3, -2, 2, 3]) + incorrect_block_input = deepcopy(a) + incorrect_block_input.shape = incorrect_shape + + + # Finally, build the op and the two blocks + self.ser.addOperator( + op['op'], [cond_tens.name, a.name, b.name], [result_tens.name], attr + ) + + if a.dtype in (DType.FLOAT, DType.INT32): + then_op, else_op = Op.ADD, Op.SUB + elif a.dtype in (DType.INT8, DType.INT16): + then_op, else_op = Op.LOGICAL_RIGHT_SHIFT, Op.LOGICAL_LEFT_SHIFT + else: + assert False, f"No tests for DType: {a.dtype}" + + for block, op in ((then_block, then_op), (else_block, else_op)): + self.ser.startBasicBlock(block) + if ((error_name == ErrorIf.CondIfInputListThenGraphMismatch and block == then_block) or + (error_name == ErrorIf.CondIfInputListElseGraphMismatch and block == else_block)): + self.ser.addInputTensor(incorrect_block_input) + self.ser.addInputTensor(b) + tens = self.ser.addOutput(a.shape, a.dtype) + elif ((error_name == ErrorIf.CondIfOutputListThenGraphMismatch and block == then_block) or + (error_name == ErrorIf.CondIfOutputListElseGraphMismatch and block == else_block)): + self.ser.addInputTensor(a) + self.ser.addInputTensor(b) + tens = self.ser.addOutput(incorrect_block_input.shape, a.dtype) + else: + self.ser.addInputTensor(a) + self.ser.addInputTensor(b) + tens = self.ser.addOutput(a.shape, a.dtype) + self.ser.addOperator(op, [a.name, b.name], [tens.name]) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + a=a, + b=b, + basicBlocks=self.ser.basicBlocks + ): + return None + + return result_tens + + def build_while_loop(self, op, a, iter_val, validator_fcns=None, error_name=None): + iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) + + cond_block = "COND_BLOCK" + body_block = "BODY_BLOCK" + + attr = ts.TosaSerializerAttribute() + attr.WhileLoopAttribute(cond_block, body_block) + + # Accumulator tensor + # acc = self.ser.addOutput(a.shape, a.dtype) + acc_init_val = np.int32(np.zeros(a.shape)) + acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) + + # Intermediate/output tensors for everything going through the loop + iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) + a_out = self.ser.addIntermediate(a.shape, a.dtype) + if error_name == ErrorIf.InputListOutputListMismatch: + incorrect_acc = deepcopy(acc) + for i in range(len(incorrect_acc.shape)): + incorrect_acc.shape[i] += self.rng.choice([-3, -2, 2, 3]) + acc_out = self.ser.addIntermediate(incorrect_acc.shape, acc.dtype) + else: + acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) + + # While_loop operator + self.ser.addOperator( + op['op'], + [iter.name, a.name, acc.name], + [iter_out.name, a_out.name, acc_out.name], + attr, + ) + self.ser.addOutputTensor(acc_out) + + if error_name in [ErrorIf.InputListCondGraphMismatch, ErrorIf.InputListBodyGraphInputMismatch, ErrorIf.InputListBodyGraphOutputMismatch]: + incorrect_iter = deepcopy(iter) + for i in range(len(incorrect_iter.shape)): + incorrect_iter.shape[i] += self.rng.choice([-3, -2, 2, 3]) + if len(incorrect_iter.shape) == 0: + incorrect_iter.shape.append(self.rng.choice([-3, -2, 2, 3])) + + incorrect_acc = deepcopy(acc) + for i in range(len(incorrect_acc.shape)): + incorrect_acc.shape[i] += self.rng.choice([-3, -2, 2, 3]) + + # COND block (input: iter, output: cond_tens ) + self.ser.startBasicBlock(cond_block) + if error_name == ErrorIf.InputListCondGraphMismatch: + self.ser.addInputTensor(incorrect_iter) + self.ser.addInputTensor(a) + self.ser.addInputTensor(incorrect_acc) + else: + self.ser.addInputTensor(iter) + self.ser.addInputTensor(a) + self.ser.addInputTensor(acc) + zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) + + if error_name == ErrorIf.CondGraphOutputNotMatchingBool: + cond_tens = self.ser.addOutput([], self.rng.choice([DType.INT8, DType.INT32, DType.FLOAT])) + else: + cond_tens = self.ser.addOutput([], DType.BOOL) + + self.ser.addOperator(Op.GREATER, [iter.name, zero_tens.name], [cond_tens.name]) + + # BODY block (input: a, acc, iter, output: a, acc, iter) + # Note that local intermediate tensors need to be declared here for the outputs + self.ser.startBasicBlock(body_block) + if error_name == ErrorIf.InputListBodyGraphInputMismatch: + self.ser.addInputTensor(incorrect_iter) + self.ser.addInputTensor(a) + self.ser.addInputTensor(incorrect_acc) + else: + self.ser.addInputTensor(iter) + self.ser.addInputTensor(a) + self.ser.addInputTensor(acc) + + one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) + + if error_name == ErrorIf.InputListBodyGraphOutputMismatch: + iter_body_out = self.ser.addIntermediate(incorrect_iter.shape, incorrect_iter.dtype) + acc_body_out = self.ser.addIntermediate(incorrect_acc.shape, incorrect_acc.dtype) + else: + iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) + acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) + + self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) + self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) + self.ser.addOutputTensor(iter_body_out) + self.ser.addOutputTensor(a) + self.ser.addOutputTensor(acc_body_out) + + if not TosaErrorValidator.evValidateErrorIfs( + self.ser, + validator_fcns, + error_name, + op=op, + basicBlocks=self.ser.basicBlocks + ): + return None + + return acc_out + + def create_filter_lists(self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None): + # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. + default_test_rank_range = range(1, 5) + if not shapeFilter: + shapeFilter = [None] + + # Calculate the filters based on what is requested and what the operator allows + rmin, rmax = op["rank"] + if rankFilter is not None: + cleanRankFilter = [] + # Ensure rankFilter values are allowed by operator + for rank in rankFilter: + if rank >= rmin and rank <= rmax: + cleanRankFilter.append(rank) + elif rankFilter is None and shapeFilter[0] is None: + # Ensure default behaviour is bounded by default range or by operator, + # whichever is the smaller range of ranks. + opRankRange = range(rmin, rmax + 1) + cleanRankFilter = opRankRange if len(opRankRange) <= len(default_test_rank_range) else default_test_rank_range + else: + cleanRankFilter = range(rmin, rmax + 1) + + dtypes = op["types"] + + if dtypeFilter is not None: + cleanDtypeFilter = [] + # Create list of operator dtypes filtered by requested dtypes + for dtype in dtypes: + if dtype in dtypeFilter or (isinstance(dtype, list) and dtype[0] in dtypeFilter): + cleanDtypeFilter.append(dtype) + else: + cleanDtypeFilter = dtypes + + if testType == 'positive': + filterDict = { + 'shapeFilter': shapeFilter, + 'rankFilter': cleanRankFilter, + 'dtypeFilter': cleanDtypeFilter + } + return filterDict + elif testType == 'negative': + if validator is not None: + validator_info = validator(check=False, op=op) + else: + return None + + error_arguments = validator_info['param_reqs'] + + #Set parameters as required + if error_arguments['rank'] != None: + rankFilter = error_arguments['rank'] + else: + rankFilter = cleanRankFilter + + if error_arguments['dtype'] != None: + dtypeFilter = error_arguments['dtype'] + else: + dtypeFilter = cleanDtypeFilter + + if error_arguments['shape'] != None: + shapeFilter = error_arguments['shape'] + else: + shapeFilter = shapeFilter[:2] # Reduce number of shapes to keep test numbers small + + filterDict = { + 'shapeFilter': shapeFilter, + 'rankFilter': rankFilter, + 'dtypeFilter': dtypeFilter + } + return filterDict + + + def genOpTestList( + self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None, testType='positive' + ): + + try: + op = self.TOSA_OP_LIST[opName] + except KeyError as e: + raise Exception("Cannot find op with name {}".format(opName)) + + # Initialize a new random number generator + self.rng = np.random.default_rng(self.random_seed) + + build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] + + # Test list consists of a tuple of: + # (opName, testNameStr, dtype, shapeList, argumentsList) + testList = [] + if testType == 'negative' and "error_if_validators" in op: + error_if_validators = op["error_if_validators"] + else: + error_if_validators = [None] + + for validator in error_if_validators: + if validator is not None: + error_name = validator(check=False, op=op)['error_name'] + else: + error_name = None + + filterDict = self.create_filter_lists(op, shapeFilter, rankFilter, dtypeFilter, testType, validator) + if filterDict == None: + return [] + cleanRankFilter = filterDict['rankFilter'] + cleanDtypeFilter = filterDict['dtypeFilter'] + cleanShapeFilter = filterDict['shapeFilter'] + #print(f"Error: {error_name}, Filters: S {cleanShapeFilter}, R {cleanRankFilter}, T {cleanDtypeFilter}") + + for r in cleanRankFilter: + for t in cleanDtypeFilter: + for shape in cleanShapeFilter: + # Filter out by rank + if shape is not None and len(shape) != r: + continue + self.setTargetShape(shape) + shapeList = tgen_fcn(self, op, r, error_name) + + shapeStr = self.shapeStr(shapeList[0]) + typeStr = self.typeStr(t) + + # Argument lists consists of tuples of the (str, []) string representation and the build function argument list + argList = [] + if agen_fcn: + argList = agen_fcn(self, opName, shapeList, t, error_name) + else: + argList = [("", [])] + + for argStr, args in argList: + if testType == 'positive': + if argStr: + testStr = "{}_{}_{}_{}".format( + opName, shapeStr, typeStr, argStr + ) + else: + testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) + elif testType == 'negative': + if argStr: + testStr = "{}_ERRORIF_{}_{}_{}_{}".format( + opName, error_name, shapeStr, typeStr, argStr + ) + else: + testStr = "{}_ERRORIF_{}_{}_{}".format(opName, error_name, shapeStr, typeStr) + + testList.append((opName, testStr, t, error_name, shapeList, args)) + + if testType == 'positive': + # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement + if "invalid_test_validators" in op: + invalid_test_validators = op["invalid_test_validators"] + clean_testList = [] + for test in testList: + for validator_fcn in invalid_test_validators: + remove_test = False + if validator_fcn(opName=test[0], input_dtype=test[2], shapeList=test[4], args=test[5]): + remove_test = True + if not remove_test: + clean_testList.append(test) + testList = clean_testList + + return testList + + + def serializeTest(self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs): + try: + op = self.TOSA_OP_LIST[opName] + except KeyError as e: + raise Exception("Cannot find op with name {}".format(opName)) + + # Create a serializer + self.createSerializer(opName, testStr) + + build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] + if "error_if_validators" in op: + error_if_validators = op["error_if_validators"] + else: + error_if_validators = None + + pCount, cCount = op["operands"] + num_operands = pCount + cCount + + if isinstance(dtype_or_dtypeList, list): + dtypeList = dtype_or_dtypeList + elif op["op"] == Op.CONCAT: + dtypeList = [dtype_or_dtypeList] * len(shapeList) + else: + dtypeList = [dtype_or_dtypeList] * (num_operands) + + if op["op"] != Op.CONCAT: + assert ( + len(shapeList) == num_operands + ), "shapeList length {} must match number of operands {}".format( + len(shapeList), num_operands + ) + assert ( + len(dtypeList) == num_operands + ), "dtypeList length {} must match number of operands {}".format( + len(dtypeList), num_operands + ) + + try: + qgen = op["qgen"] + except KeyError: + qgen = None + + # Build the random tensor operands and the test + tens = [] + + tens = self.generate_tensors(op, dtypeList, shapeList, testArgs, error_name) + + if qgen is not None: + qinfo = qgen(self, op, dtype_or_dtypeList, error_name) + else: + qinfo = None + + try: + if error_if_validators is None: + if qinfo is not None: + resultName = build_fcn(self, op, *tens, *testArgs, qinfo) + else: + resultName = build_fcn(self, op, *tens, *testArgs) + else: + if qinfo is not None: + resultName = build_fcn(self, op, *tens, *testArgs, validator_fcns=error_if_validators, error_name=error_name, qinfo=qinfo) + else: + resultName = build_fcn(self, op, *tens, *testArgs, validator_fcns=error_if_validators, error_name=error_name) + except TypeError as e: + print(f"build_fcn: {build_fcn}\nTensors: {tens}\nArgs: {testArgs}\n") + raise e + + if resultName: + # The test is valid, serialize it + self.serialize("test") + else: + # The test is not valid + print(f"Invalid ERROR_IF test created: {opName} {testStr}") + + + def generate_tensors(self, op, dtypeList, shapeList, testArgs, error_name=None): + pCount, cCount = op["operands"] + + tens = [] + if (op["op"] == Op.ADD or op["op"] == Op.SUB) and dtypeList[0] == DType.INT32 and error_name == None: + # Make sure the operation does not cause value saturation - where + # the number wraps due to limited number of bits to store the answer + assert ( + pCount == 2 and cCount == 0 + ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" + placeholders = [] + add = (op["op"] == Op.ADD) + a_arr = self.getRandTensor(shapeList[0], dtypeList[0]) + b_arr = self.getRandTensor(shapeList[1], dtypeList[1]) + if add: + res_arr = np.add(a_arr, b_arr, dtype=np.int64) + else: + res_arr = np.subtract(a_arr, b_arr, dtype=np.int64) + + # Work out the saturation limits + max_i32 = (1 << 31)-1 + min_i32 = -(1 << 31) + max_arr = np.full(shapeList[1], max_i32) + min_arr = np.full(shapeList[1], min_i32) + + # Find how much values exceed the maximum/minimums + sat_max_arr = np.maximum(res_arr - max_arr, 0) + sat_min_arr = np.minimum(res_arr - min_arr, 0) + + if not add: + # Swap saturation values and negate values as we need to perform opposite operations + sat_max_arr, sat_min_arr = -sat_min_arr, -sat_max_arr + + # Create new array of unsaturated values by clipping values as needed + b_unsat_arr = b_arr + if (sat_max_arr != 0).any(): + # Clip values that cause saturation + b_unsat_arr = np.subtract(b_unsat_arr, sat_max_arr, dtype=np.int32) + # Reduce axes in unsaturated tensor to match original tensor + for axis, dim in enumerate(b_arr.shape): + if dim != b_unsat_arr.shape[axis]: + assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" + b_unsat_arr = np.amin(b_unsat_arr, axis=axis, keepdims=True) + + if (sat_min_arr != 0).any(): + # Clip values that cause saturation + b_unsat_arr = np.subtract(b_unsat_arr, sat_min_arr, dtype=np.int32) + # Reduce axes in unsaturated tensor to match original tensor + for axis, dim in enumerate(b_arr.shape): + if dim != b_unsat_arr.shape[axis]: + assert ( dim == 1 ), "Op.ADD / SUB dimension must be 1 or matching to be broadcastable" + b_unsat_arr = np.amax(b_unsat_arr, axis=axis, keepdims=True) + + placeholders.append( + self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) + ) + placeholders.append( + self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) + ) + + tens.extend(placeholders) + elif (op["op"] == Op.COND_IF or op["op"] == Op.WHILE_LOOP) and dtypeList[0] == DType.INT32: + # Limit input tensors with cond_if_binary or while_loop to stop + # saturation of add/sub ops + pRemain = pCount + placeholders = [] + for idx, shape in enumerate(shapeList[:]): + arr = self.getRandTensor(shapeList[idx], DType.INT16) + if pRemain > 0: + placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) + pRemain -= 1 + else: + placeholders.append(self.ser.addConst(shape, dtypeList[idx], arr)) + + tens.extend(placeholders) + elif op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: + # Force value of operand[1] to be within [0, num_bits] + assert ( + pCount == 2 and cCount == 0 + ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" + + placeholders = [] + for idx, shape in enumerate(shapeList[:]): + if idx == 1: + if dtypeList[idx] == DType.INT8: + arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) + elif dtypeList[idx] == DType.INT16: + arr = np.int32(self.rng.integers(low=0, high=16, size=shape)) + elif dtypeList[idx] == DType.INT32: + arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) + elif error_name == ErrorIf.WrongInputType: + arr = np.int32(self.rng.integers(low=0, high=8, size=shape)) + else: + raise Exception("OpArithmeticRightShift: invalid input dtype") + else: + arr = self.getRandTensor(shape, dtypeList[idx]) + placeholders.append(self.ser.addPlaceholder(shape, dtypeList[idx], arr)) + + tens.extend(placeholders) + elif op["op"] == Op.SELECT: + # Set datatype of condition tensor to boolean + dtypeList[0] = DType.BOOL + tens.extend( + self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) + ) + tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) + elif op["op"] == Op.INTDIV and error_name == None: + assert ( + pCount == 2 and cCount == 0 + ), "Op.INTDIV must have 2 placeholders, 0 consts" + + placeholders = [] + + # Two invalid cases for Op.INTDIV: + # 1. divisor == 0 + # 2. dividend == -(1<<31) and divisor == -1 + while True: + dividend_arr = self.getRandTensor(shapeList[0], dtypeList[0]) + divisor_arr = self.getRandTensor(shapeList[1], dtypeList[1]) + + if (divisor_arr == 0).any(): + continue + + if (dividend_arr == -(2 ** 31)).any() and (divisor_arr == -1).any(): + continue + + break + + placeholders.append( + self.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) + ) + placeholders.append( + self.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) + ) + + tens.extend(placeholders) + elif op["op"] == Op.MUL and error_name == None: + assert ( + pCount == 2 and cCount == 0 + ), "Op.MUL must have 2 placeholders, 0 consts" + + if dtypeList[0] == DType.FLOAT: + tens.extend(self.buildPlaceholderTensors(shapeList[:], dtypeList[:])) + else: + placeholders = [] + + # Make sure multiply result in int32 range + shift = testArgs[0] + if dtypeList[0] == DType.INT8: + num_bits = 8 + elif dtypeList[0] == DType.INT16: + num_bits = 16 + elif dtypeList[0] == DType.INT32: + num_bits = 32 + elif error_name == ErrorIf.WrongInputType: + num_bits = 8 + else: + raise Exception("OpMul: invalid input dtype") + + for idx, shape in enumerate(shapeList[:]): + low = -(2 ** (num_bits - 1)) + high = (2 ** (num_bits - 1)) - 1 + + a_arr = np.int32( + self.rng.integers(low=low, high=high, size=shapeList[0]) + ) + b_arr = np.int32( + self.rng.integers(low=low, high=high, size=shapeList[1]) + ) + + i = 0 + while True: + + a_arr_64 = a_arr.astype(np.int64) + b_arr_64 = b_arr.astype(np.int64) + + if shift > 0: + rounding = 1 << (shift - 1) + result_arr = ((a_arr_64 * b_arr_64) + rounding) >> shift + else: + result_arr = a_arr_64 * b_arr_64 + + if (result_arr > -(2 ** 31)).all() and ( + result_arr <= ((2 ** 31) - 1) + ).all(): + break + + i = i + 1 + a_arr = a_arr // 2 + b_arr = b_arr // 2 + + placeholders.append( + self.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) + ) + placeholders.append( + self.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) + ) + + tens.extend(placeholders) + elif op["op"] == Op.CONCAT: + count = len(shapeList) - self.args.num_const_inputs_concat + if count < 1: + count = 1 + if self.args.num_const_inputs_concat == 0: + count = len(shapeList) + + # Ensure axis is an int + testArgs[0] = int(testArgs[0]) + + shapeList = TosaTensorGen.tgConcatConstInput(self, shapeList, testArgs[0], error_name) + + tens.extend( + self.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count]) + ) + tens.extend(self.buildConstTensors(shapeList[count:], dtypeList[count:])) + else: + tens.extend( + self.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount]) + ) + tens.extend(self.buildConstTensors(shapeList[pCount:], dtypeList[pCount:])) + + return tens + + def createDynamicOpLists(self): + + # Dynamically create op lists for convolutions with a list of kernel sizes + KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] + + for k in KERNELS_2D: + testName = "conv2d_{}x{}".format(k[0], k[1]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False + + testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ + "depthwise_conv2d_TEMPLATE" + ].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False + + testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ + "transpose_conv2d_TEMPLATE" + ].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False + + KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] + for k in KERNELS_3D: + testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) + self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() + self.TOSA_OP_LIST[testName]["filter"] = k + self.TOSA_OP_LIST[testName]["template"] = False + + # Delete any templates after having created any dynamic ops + # This is a two-pass operation because it's bad practice to delete + # keys from dictionaries while iterating + keyList = [] + for k in self.TOSA_OP_LIST: + try: + if self.TOSA_OP_LIST[k]["template"] == True: + keyList.append(k) + continue + except KeyError: + pass + + for k in keyList: + del self.TOSA_OP_LIST[k] + + def initOpListDefaults(self): + """Fill in default fields for ops if they aren't already specified. + Look for missing required fields (datastructure linting).""" + for op in self.TOSA_OP_LIST: + + # Required fields + try: + pl, c = self.TOSA_OP_LIST[op]["operands"] + except (KeyError, ValueError, TypeError): + raise Exception( + "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) + ) + + try: + fcn, tgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] + except (KeyError, ValueError, TypeError): + raise Exception( + "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( + op + ) + ) + + try: + types = self.TOSA_OP_LIST[op]["types"] + except KeyError as e: + raise Exception( + "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) + ) + + try: + opcode = self.TOSA_OP_LIST[op]["op"] + except KeyError as e: + raise Exception( + "Op {} is missing the Op field in TOSA_OP_LIST".format(op) + ) + + # Put in default rank range, if missing + try: + rank = self.TOSA_OP_LIST[op]["rank"] + except KeyError: + self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE + + # Tensor operator list + # 'op': op name + # 'operands': tuple of (placeholder, const) operands + # 'rank': optional, restricts rank to tuple inclusive of (min, max), + # if not specified, defaults to (1, 4) + # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) + # 'types': array of datatypes to be tested + TYPE_FP = [DType.FLOAT] + + TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 + TYPE_INT_FP = [DType.INT8, DType.INT16, DType.INT32, DType.FLOAT] # Excludes INT4 + + TYPE_BOOL = [DType.BOOL] + TYPE_FI32 = [DType.FLOAT, DType.INT32] + TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] + TYPE_FI16 = [DType.FLOAT, DType.INT16] + + TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] + + TYPE_CONV = [ + [DType.INT8, DType.INT4, DType.INT32], + [DType.INT8, DType.INT8, DType.INT32], + [DType.INT16, DType.INT8, DType.INT48], + DType.FLOAT, + ] + + DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) + + TOSA_OP_LIST = { + # Tensor operators + "argmax": { + "op": Op.ARGMAX, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_NARROW_INT_FP, + "error_if_validators": (TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evArgmaxOutputRankMismatch, + TosaErrorValidator.evArgmaxOutputShapeMismatch, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "avg_pool2d": { + "op": Op.AVG_POOL2D, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), + "qgen": TosaQuantGen.qgUnary, + "types": TYPE_NARROW_INT_FP, + "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), + "error_if_validators": (TosaErrorValidator.evKernelSmallerOne, TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, + TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch) + }, + # Templated operator. Filled in by createDynamicOpLists + "conv2d_TEMPLATE": { + "op": Op.CONV2D, + "operands": (1, 2), + "rank": (4, 4), + "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_CONV, + "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), + "error_if_validators": ( + TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, + TosaErrorValidator.evInputZeroPointNotZero, + TosaErrorValidator.evWeightZeroPointNotZero, + TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evStrideSmallerOne, + TosaErrorValidator.evDilationSmallerOne, + TosaErrorValidator.evWrongRank, + ), + "template": True, + }, + # Templated operator. Filled in by createDynamicOpLists + "conv3d_TEMPLATE": { + "op": Op.CONV3D, + "operands": (1, 2), + "rank": (5, 5), + "build_fcn": (build_conv3d, TosaTensorGen.tgConv3D, TosaArgGen.agConv), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_CONV, + "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), + "error_if_validators": ( + TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, + TosaErrorValidator.evInputZeroPointNotZero, + TosaErrorValidator.evWeightZeroPointNotZero, + TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evStrideSmallerOne, + TosaErrorValidator.evDilationSmallerOne, + TosaErrorValidator.evWrongRank, + ), + "template": True, + }, + # Templated operator. Filled in by createDynamicOpLists + "depthwise_conv2d_TEMPLATE": { + "op": Op.DEPTHWISE_CONV2D, + "operands": (1, 2), + "filter": [1, 1], + "rank": (4, 4), + "build_fcn": ( + build_depthwise_conv2d, + TosaTensorGen.tgDepthwiseConv2D, + TosaArgGen.agConv, + ), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_CONV, + "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), + "error_if_validators": ( + TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, + TosaErrorValidator.evInputZeroPointNotZero, + TosaErrorValidator.evWeightZeroPointNotZero, + TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evStrideSmallerOne, + TosaErrorValidator.evDilationSmallerOne, + TosaErrorValidator.evWrongRank, + ), + "template": True, + }, + "fully_connected": { + "op": Op.FULLY_CONNECTED, + "operands": (1, 2), + "rank": (2, 2), + "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_CONV, + "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evWeightZeroPointNotZero, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "matmul": { + "op": Op.MATMUL, + "operands": (2, 0), + "rank": (3, 3), + "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), + "qgen": TosaQuantGen.qgMatmul, + "types": TYPE_NARROW_INT_FP, + "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "max_pool2d": { + "op": Op.MAX_POOL2D, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), + "types": TYPE_NARROW_INT_FP, + "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), + "error_if_validators": (TosaErrorValidator.evKernelSmallerOne, TosaErrorValidator.evStrideSmallerOne, TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evPadLargerEqualKernel, TosaErrorValidator.evPoolingOutputShapeMismatch) + }, + # Templated operator. Filled in by createDynamicOpLists + "transpose_conv2d_TEMPLATE": { + "op": Op.TRANSPOSE_CONV2D, + "operands": (1, 2), + "rank": (4, 4), + "build_fcn": ( + build_transpose_conv2d, + TosaTensorGen.tgTransposeConv2D, + TosaArgGen.agTransposeConv2D, + ), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_CONV, + "invalid_test_validators": ( + TosaInvalidValidator.ivHeightWidthInvalid, + TosaInvalidValidator.ivNonPositiveOutputShape, + ), + "error_if_validators": ( + TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, + TosaErrorValidator.evInputZeroPointNotZero, + TosaErrorValidator.evWeightZeroPointNotZero, + TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evStrideSmallerOne, + TosaErrorValidator.evDilationSmallerOne, + TosaErrorValidator.evWrongRank, + ), + "template": True, + }, + # Activation functions + "clamp": { + "op": Op.CLAMP, + "operands": (1, 0), + "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), + "types": TYPE_NARROW_INT_FP, + "error_if_validators": (TosaErrorValidator.evMaxSmallerMin, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "sigmoid": { + "op": Op.SIGMOID, + "operands": (1, 0), + "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList) + }, + "tanh": { + "op": Op.TANH, + "operands": (1, 0), + "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList) + }, + # Elementwise Binary Operators + "add": { + "op": Op.ADD, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "arithmetic_right_shift": { + "op": Op.ARITHMETIC_RIGHT_SHIFT, + "operands": (2, 0), + "build_fcn": ( + build_arithmetic_right_shift, + TosaTensorGen.tgBroadcastFuzz, + TosaArgGen.agArithmeticRightShift, + ), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "bitwise_and": { + "op": Op.BITWISE_AND, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "bitwise_or": { + "op": Op.BITWISE_OR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "bitwise_xor": { + "op": Op.BITWISE_XOR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "intdiv": { + "op": Op.INTDIV, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": [DType.INT32], + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "logical_and": { + "op": Op.LOGICAL_AND, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "logical_left_shift": { + "op": Op.LOGICAL_LEFT_SHIFT, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "logical_right_shift": { + "op": Op.LOGICAL_RIGHT_SHIFT, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "logical_or": { + "op": Op.LOGICAL_OR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "logical_xor": { + "op": Op.LOGICAL_XOR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "maximum": { + "op": Op.MAXIMUM, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "minimum": { + "op": Op.MINIMUM, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "mul": { + "op": Op.MUL, + "operands": (2, 0), + "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evRankMismatch, TosaErrorValidator.evDimensionMismatch) + }, + "pow": { + "op": Op.POW, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "sub": { + "op": Op.SUB, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "table": { + "op": Op.TABLE, + # Use the automatic generation functions to create the input array + # but create the table tensor in the build function, as it may be + # a different type from the input + "operands": (1, 0), + "build_fcn": (build_table, TosaTensorGen.tgBasic, TosaArgGen.agTable), + "types": [DType.INT8, DType.INT16], + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList) + }, + # Elementwise Unary operators + "abs": { + "op": Op.ABS, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "bitwise_not": { + "op": Op.BITWISE_NOT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_INT, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "ceil": { + "op": Op.CEIL, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "clz": { + "op": Op.CLZ, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": [DType.INT32], + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "exp": { + "op": Op.EXP, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "floor": { + "op": Op.FLOOR, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "log": { + "op": Op.LOG, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "logical_not": { + "op": Op.LOGICAL_NOT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_BOOL, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "negate": { + "op": Op.NEGATE, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "qgen": TosaQuantGen.qgUnary, + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, + TosaErrorValidator.evWrongOutputList) + }, + "reciprocal": { + "op": Op.RECIPROCAL, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "rsqrt": { + "op": Op.RSQRT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + # Elementwise Ternary operators + "select": { + "op": Op.SELECT, + "operands": (3, 0), + "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + # Comparison operators + "equal": { + "op": Op.EQUAL, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "greater_equal": { + "op": Op.GREATER_EQUAL, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + "greater": { + "op": Op.GREATER, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evRankMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evDimensionMismatch) + }, + # Reduction operators + "reduce_all": { + "op": Op.REDUCE_ALL, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_BOOL, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reduce_any": { + "op": Op.REDUCE_ANY, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_BOOL, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reduce_max": { + "op": Op.REDUCE_MAX, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reduce_min": { + "op": Op.REDUCE_MAX, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reduce_product": { + "op": Op.REDUCE_PRODUCT, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FP, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reduce_sum": { + "op": Op.REDUCE_SUM, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FI32, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evShapeOfAxisNotOne, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + # Data layout operators + "concat": { + "op": Op.CONCAT, + "operands": (2, 0), + "build_fcn": (build_concat, TosaTensorGen.tgConcat, TosaArgGen.agAxis), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evConcatInputRankMismatch, + TosaErrorValidator.evConcatShapeSumMismatch, TosaErrorValidator.evConcatInputDimMismatch, TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongOutputList) + }, + "pad": { + "op": Op.PAD, + "operands": (1, 0), + "rank": (1, 5), + "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), + "qgen": TosaQuantGen.qgPad, + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evPadSmallerZero, + TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reshape": { + "op": Op.RESHAPE, + "operands": (1, 0), + "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evTensorSizeInputOutputMismatch, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "reverse": { + "op": Op.REVERSE, + "operands": (1, 0), + "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evAxisSmallerZero, TosaErrorValidator.evAxisLargerRank, TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "slice": { + "op": Op.SLICE, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evStartSmallerZero, TosaErrorValidator.evSizeSmallerEqualZero, TosaErrorValidator.evStartSizeOutsideBounds, + TosaErrorValidator.evSizeOutputShapeMismatch, TosaErrorValidator.evInputSizeStartLengthMismatch, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "tile": { + "op": Op.TILE, + "operands": (1, 0), + "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "transpose": { + "op": Op.TRANSPOSE, + "operands": (1, 0), + "rank": (1, 4), + "build_fcn": ( + build_transpose, + TosaTensorGen.tgBasic, + TosaArgGen.agTranspose, + ), + "types": TYPE_FIB, + "error_if_validators": (TosaErrorValidator.evIndexOutsideBounds, TosaErrorValidator.evIndexUsedTwice, + TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + # Data nodes + "const": { + "op": Op.CONST, + "operands": (0, 1), + "build_fcn": (build_const, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "identity": { + "op": Op.IDENTITY, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + # Scatter/Gather + "gather": { + "op": Op.GATHER, + # Only specify 'values' tensor here. 'indices' is generated in op building stage + "operands": (1, 0), + "rank": (3, 3), + "build_fcn": (build_gather, TosaTensorGen.tgBasic, None), + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evWrongRank) + }, + "scatter": { + "op": Op.SCATTER, + # Only specify 'values_in' tensor here. + #'indices' and 'input' are generated in op building stage + "operands": (2, 0), + "rank": (3, 3), + "build_fcn": (build_scatter, TosaTensorGen.tgScatter, None), + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, TosaErrorValidator.evWrongRank) + }, + # Image operations + "resize": { + "op": Op.RESIZE, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), + "types": [DType.INT8, DType.INT16, DType.FLOAT], + "invalid_test_validators": (TosaInvalidValidator.ivWrongDataTypeOrModeResize, TosaInvalidValidator.ivBadStride), + "error_if_validators": (TosaErrorValidator.evMaxDimExceeded, TosaErrorValidator.evStrideSmallerEqualZero, TosaErrorValidator.evStrideLargerDimension, + TosaErrorValidator.evStrideLargerEqualMax, TosaErrorValidator.evOffsetSmallerEqualMin, TosaErrorValidator.evOffsetLargerEqualMax, + TosaErrorValidator.evShiftNotZero, TosaErrorValidator.evShiftSmallerOne, TosaErrorValidator.evShiftLargerEleven, TosaErrorValidator.evWrongInputType, + TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList, + TosaErrorValidator.evBatchMismatch, TosaErrorValidator.evChannelMismatch) + }, + # Type conversion + "cast": { + "op": Op.CAST, + "operands": (1, 0), + "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), + "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], + "error_if_validators": (TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + "rescale": { + "op": Op.RESCALE, + "operands": (1, 0), + "rank": (1,4), + "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), + "types": [DType.UINT8, DType.INT8, DType.INT16, DType.INT32, DType.INT48], + "error_if_validators": (TosaErrorValidator.evInputZeroPointNotZero, TosaErrorValidator.evOutputZeroPointNotZero, TosaErrorValidator.evScaleTrue, + TosaErrorValidator.evScaleNotTrue, TosaErrorValidator.evWrongInputType, TosaErrorValidator.evWrongOutputType, TosaErrorValidator.evWrongRank, + TosaErrorValidator.evWrongInputList, TosaErrorValidator.evWrongOutputList) + }, + # Custom + # Not implemented. + # Control flow operators + # Two varients of cond_if, one that generates one of two constant tensors (no + # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors + # (two inputs to the basic blocks, one output) + "cond_if_const": { + "op": Op.COND_IF, + "operands": (0, 2), + "build_fcn": ( + build_cond_if_const, + TosaTensorGen.tgBasic, + TosaArgGen.agCondIf, + ), + "types": [DType.BOOL], + "error_if_validators": (TosaErrorValidator.evOutputListThenGraphMismatch, TosaErrorValidator.evOutputListElseGraphMismatch) + }, + "cond_if_binary": { + "op": Op.COND_IF, + "operands": (2, 0), + "build_fcn": ( + build_cond_if_binary, + TosaTensorGen.tgBasic, + TosaArgGen.agCondIf, + ), + "types": TYPE_INT_FP, + "error_if_validators": (TosaErrorValidator.evInputListThenGraphMismatch, TosaErrorValidator.evInputListElseGraphMismatch, + TosaErrorValidator.evOutputListThenGraphMismatch, TosaErrorValidator.evOutputListElseGraphMismatch) + }, + # while_loop + "while_loop": { + "op": Op.WHILE_LOOP, + "operands": (0, 1), + "build_fcn": ( + build_while_loop, + TosaTensorGen.tgBasic, + TosaArgGen.agWhileLoop, + ), + "types": [DType.INT32], + "error_if_validators": (TosaErrorValidator.evInputListOutputListMismatch, TosaErrorValidator.evInputListCondGraphMismatch, + TosaErrorValidator.evInputListBodyGraphInputMismatch, TosaErrorValidator.evInputListBodyGraphOutputMismatch, + TosaErrorValidator.evCondGraphOutputNotMatchingBool) + }, + } + + +class OutputShaper: + # Methods in this class compute the expected output shape and datatype + # for common classes of operations + def __init__(self): + pass + + # These methods return arguments that can be used for + # creating a new output tensor + @staticmethod + def binaryBroadcastOp(ser, rng, a, b, error_name=None): + if error_name != ErrorIf.RankMismatch: + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype + + shape = [] + for i in range(len(a.shape)): + if a.shape[i] == 1 and error_name == None: + shape.append(b.shape[i]) + else: + shape.append(a.shape[i]) + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(shape, outputDType) + + @staticmethod + def binaryNonBroadcastOp(ser, a, b): + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype + + shape = [] + for i in range(len(a.shape)): + assert a.shape[i] == b.shape[i] + shape.append(a.shape[i]) + + return ser.addOutput(shape, a.dtype) + + @staticmethod + def unaryOp(ser, rng, a, error_name=None): + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(a.shape, outputDType) + + @staticmethod + def selectOp(ser, rng, cond, a, b, error_name=None): + if error_name != ErrorIf.RankMismatch: + assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) + assert a.dtype == b.dtype + + shape = [] + for i in range(len(cond.shape)): + if cond.shape[i] == 1 and error_name == None: + shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) + else: + shape.append(cond.shape[i]) + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(shape, outputDType) + + @staticmethod + def binaryComparisonOp(ser, rng, a, b , error_name=None): + if error_name != ErrorIf.RankMismatch: + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype + + # Do broadcast + shape = [] + for i in range(len(a.shape)): + if a.shape[i] == 1 and len(b.shape) > i: + shape.append(b.shape[i]) + else: + shape.append(a.shape[i]) + + if error_name == ErrorIf.WrongOutputType: + wrong_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = DType.BOOL + + return ser.addOutput(shape, outputDType) + + @staticmethod + def reduceOp(ser, rng, a, axis, error_name=None): + shape = a.shape.copy() + if error_name not in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank, ErrorIf.ShapeOfAxisNotOne]: + shape[axis] = 1 + if error_name == ErrorIf.ShapeOfAxisNotOne and shape[axis] == 1: + shape[axis] = rng.integers(2, 10) + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(shape, outputDType) + + @staticmethod + def argmaxOp(ser, rng, a, axis, error_name=None): + shape = a.shape.copy() + + if error_name not in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank]: + del shape[axis] + + if error_name == ErrorIf.ArgmaxOutputRankMismatch: + remove = rng.choice([True, False]) + if remove and len(shape) > 1: + del shape[0] + else: + shape.append(1) + elif error_name == ErrorIf.ArgmaxOutputShapeMismatch: + for i in range(len(shape)): + shape[i] = shape[i] + rng.integers(1, 10) + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([DType.INT32])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = DType.INT32 + + return ser.addOutput(shape, outputDType) + + @staticmethod + def conv2dOp(ser, rng, ifm, filter, strides, padding, dilations, error_name=None): + + # IFM: NHWC + # Filter: OHWI + # OFM: NHWC + + if len(padding) == 2: + # Expand padding to 4 parameters in the case of transpose_conv2d + # From H,W to T,B,L,R + padding = [padding[0], padding[0], padding[1], padding[1]] + + h = ( + ifm.shape[1] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + w = ( + ifm.shape[2] + - filter.shape[2] + - (filter.shape[2] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 + + # Avoid illegal dimensions, which can be generated in error_if tests + h = max(h, 1) + w = max(w, 1) + + ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] + + if ifm.dtype == DType.INT8: + out_dtype = DType.INT32 + elif ifm.dtype == DType.INT16: + out_dtype = DType.INT48 + elif ifm.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output dtype if input type is incorrect + out_dtype = DType.INT32 + else: + raise Exception(f"Unsupported input dtype: {ifm.dtype}") + + if error_name == ErrorIf.WrongOutputType: + wrong_dtypes = list(usableDTypes(excludes=[out_dtype])) + out_dtype = rng.choice(wrong_dtypes) + + return ser.addOutput(ofm_shape, out_dtype) + + @staticmethod + def conv3dOp(ser, rng, ifm, filter, strides, padding, dilations, error_name=None): + + # IFM: NDHWC + # Filter: ODHWI + # OFM: NDHWC + + d = ( + ifm.shape[1] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + h = ( + ifm.shape[2] + - filter.shape[2] + - (filter.shape[2] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 + + w = ( + ifm.shape[3] + - filter.shape[3] + - (filter.shape[3] - 1) * (dilations[2] - 1) + + padding[4] + + padding[5] + ) // strides[2] + 1 + + # Avoid illegal dimensions, which can be generated in error_if tests + d = max(d, 1) + h = max(h, 1) + w = max(w, 1) + + ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] + + if ifm.dtype == DType.INT8: + out_dtype = DType.INT32 + elif ifm.dtype == DType.INT16: + out_dtype = DType.INT48 + elif ifm.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output dtype if input type is incorrect + out_dtype = DType.INT32 + else: + raise Exception(f"Unsupported input dtype: {ifm.dtype}") + + if error_name == ErrorIf.WrongOutputType: + wrong_dtypes = list(usableDTypes(excludes=[out_dtype])) + out_dtype = rng.choice(wrong_dtypes) + + return ser.addOutput(ofm_shape, out_dtype) + + @staticmethod + def depthwiseConv2dOp(ser, rng, ifm, filter, strides, padding, dilations, error_name=None): + # IFM: NHWC + # Filter: HWCM + # OFM: NHW C*M + h = ( + ifm.shape[1] + - filter.shape[0] + - (filter.shape[0] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + w = ( + ifm.shape[2] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 + + # Avoid illegal dimensions, which can be generated in error_if tests + h = max(h, 1) + w = max(w, 1) + + ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] + + if ifm.dtype == DType.INT8: + out_dtype = DType.INT32 + elif ifm.dtype == DType.INT16: + out_dtype = DType.INT48 + elif ifm.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output dtype if input type is incorrect + out_dtype = DType.INT32 + else: + raise Exception(f"Unsupported input dtype: {ifm.dtype}") + + if error_name == ErrorIf.WrongOutputType: + wrong_dtypes = list(usableDTypes(excludes=[out_dtype])) + out_dtype = rng.choice(wrong_dtypes) + + return ser.addOutput(ofm_shape, out_dtype) + + @staticmethod + def pool2dOp(ser, rng, ifm, kernel, stride, pad, error_name=None): + # input: NHWC + if stride[0] <= 0 or stride[1] <= 0 or min(pad) < 0: + # If an incorrect stride is used set dimensions to 1, test is invalid anyway. + h = 1 + w = 1 + else: + h = (ifm.shape[1] + pad[0] + pad[1] + stride[0] - kernel[0]) // stride[0] + w = (ifm.shape[2] + pad[2] + pad[3] + stride[1] - kernel[1]) // stride[1] + + if error_name == ErrorIf.PoolingOutputShapeMismatch: + choices = [1, 2, 3, 4, 5] + h = h + rng.choice(choices) + w = w + rng.choice(choices) + + ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([ifm.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = ifm.dtype + + return ser.addOutput(ofm_shape, outputDType) + + @staticmethod + def fullyConnectedOp(ser, rng, input, filter, error_name=None): + # input: N, IC + # filter: OC, IC + # output: N, OC + + output_shape = [input.shape[0], filter.shape[0]] + + if error_name == ErrorIf.WrongOutputType: + if input.dtype == DType.INT8: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) + elif input.dtype == DType.INT16: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) + elif input.dtype == DType.FLOAT: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) + out_dtype = rng.choice(a=incorrect_types) + elif input.dtype == DType.INT8: + out_dtype = DType.INT32 + elif input.dtype == DType.INT16: + out_dtype = DType.INT48 + elif input.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output dtype if input type is incorrect + out_dtype = DType.INT32 + else: + raise Exception("Unsupported input dtype: {}".format(input.dtype)) + + return ser.addOutput(output_shape, out_dtype) + + @staticmethod + def matmulOp(ser, rng, a, b, error_name=None): + # a: N, H, C + # b: N, C, W + # out: N, H, W + + output_shape = [a.shape[0], a.shape[1], b.shape[2]] + + if error_name == ErrorIf.WrongOutputType: + if a.dtype == DType.INT8: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT48, DType.FLOAT) + elif a.dtype == DType.INT16: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.FLOAT) + elif a.dtype == DType.FLOAT: + incorrect_types = (DType.INT4, DType.INT8, DType.INT16, DType.INT32, DType.INT48) + out_dtype = rng.choice(a=incorrect_types) + elif a.dtype == DType.INT8: + out_dtype = DType.INT32 + elif a.dtype == DType.INT16: + out_dtype = DType.INT48 + elif a.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output dtype if input type is incorrect + out_dtype = DType.INT32 + else: + raise Exception("Unsupported input dtype for matmul: {}".format(a.dtype)) + + return ser.addOutput(output_shape, out_dtype) + + @staticmethod + def concatOp(ser, rng, axis, *a, error_name=None): + input1 = a[0] + remaining_inputs = a[1:] + + # calculate the output shape, if possible, otherwise just use the first input shape + output_shape = input1.shape.copy() + if not ( + # unable to concat tensors of different ranks + error_name == ErrorIf.ConcatInputRankMismatch + # unable to concat tensors along an invalid axis + or error_name in [ErrorIf.AxisLargerRank, ErrorIf.AxisSmallerZero] + ): + for tensor in remaining_inputs: + output_shape[axis] += tensor.shape[axis] + + if error_name == ErrorIf.ConcatShapeSumMismatch: + output_shape[axis] += rng.integers(5, 10) + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = {DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT} + wrong_dtypes = list(all_dtypes - set([input1.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = input1.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def padOp(ser, rng, a, padding, error_name=None): + + output_shape = a.shape.copy() + + for i in range(len(output_shape)): + output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] + + # Fix negative output shape if error_if test causes it + if error_name == ErrorIf.PadSmallerZero and min(output_shape) < 1: + output_shape = [i if i >= 1 else 1 for i in output_shape] + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def reshapeOp(ser, rng, a, shape, error_name=None): + output_shape = shape.copy() + + totalElements = 1 + for i in a.shape: + totalElements *= i + + # If there are any -1 elements, figure out what that dimension must be + totalOutputElements = 1 + for i in output_shape: + if i != -1: + totalOutputElements *= i + + # And fill it in + for i in range(len(output_shape)): + if output_shape[i] == -1: + output_shape[i] = totalElements // totalOutputElements + + if error_name == ErrorIf.TensorSizeInputOutputMismatch: + for i in range(len(output_shape)): + output_shape[i] = output_shape[i] + rng.integers(1, 10) + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def sliceOp(ser, rng, a, start, size, error_name=None): + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + if error_name == ErrorIf.SizeOutputShapeMismatch: + output_shape = size.copy() + for index in range(len(output_shape)): + if output_shape[index] <= 2: + output_shape[index] = output_shape[index] + rng.choice([1, 2]) + else: + output_shape[index] = output_shape[index] + rng.choice([-2, -1, 1, 2]) + else: + output_shape = size.copy() + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def tileOp(ser, rng, a, multiples, error_name=None): + + output_shape = a.shape.copy() + assert len(multiples) == len(output_shape) + + for i in range(len(output_shape)): + output_shape[i] = a.shape[i] * multiples[i] + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def transposeOp(ser, rng, a, perms, error_name=None): + output_shape = a.shape.copy() + + assert len(perms) == len(output_shape) + + if error_name == ErrorIf.IndexOutsideBounds: + for i in range(len(output_shape)): + output_shape[i] = a.shape[0] + else: + for i in range(len(output_shape)): + output_shape[i] = a.shape[perms[i]] + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = a.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def gatherOp(ser, rng, values, indices, error_name=None): + if error_name != ErrorIf.WrongRank: + assert len(values.shape) == 3 + assert len(indices.shape) == 2 + assert values.shape[0] == indices.shape[0] + + output_shape = [values.shape[0], indices.shape[1], values.shape[2]] + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([values.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = values.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def scatterOp(ser, rng, values_in, indices, input, error_name=None): + if error_name != ErrorIf.WrongRank: + assert len(values_in.shape) == 3 + assert len(indices.shape) == 2 + assert len(input.shape) == 3 + assert values_in.shape[0] == indices.shape[0] # N + assert input.shape[1] == indices.shape[1] # W + assert values_in.shape[2] == input.shape[2] # C + + output_shape = values_in.shape + + if error_name == ErrorIf.WrongOutputType: + all_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes = list(set(all_dtypes) - set([values_in.dtype])) + outputDType = rng.choice(wrong_dtypes) + else: + outputDType = values_in.dtype + + return ser.addOutput(output_shape, outputDType) + + @staticmethod + def tableOp(ser, rng, input, error_name=None): + # Same shape as the input, dtype dependent on input dtype + if error_name != ErrorIf.WrongInputType: + assert input.dtype == DType.INT16 or input.dtype == DType.INT8 + output_dtype = DType.INT32 if input.dtype == DType.INT16 else DType.INT8 + if error_name == ErrorIf.WrongOutputType: + wrong_dtypes = [DType.INT8, DType.INT16, DType.INT32, DType.INT48, DType.FLOAT] + wrong_dtypes.remove(output_dtype) + output_dtype = rng.choice(wrong_dtypes) + return ser.addOutput(input.shape, output_dtype) + + @staticmethod + def resizeOp( + serializer, + rng, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + error_name = None + ): + if error_name == ErrorIf.WrongRank: + output_dims = [input.shape[0], output_dims[0], output_dims[0], input.shape[0]] + else: + if error_name == ErrorIf.BatchMismatch: + output_dims = [input.shape[0] + rng.integers(1, 10), output_dims[0], output_dims[1], input.shape[3]] + elif error_name == ErrorIf.ChannelMismatch: + output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3] + rng.integers(1, 10)] + else: + output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] + + return serializer.addOutput(output_dims, output_dtype) + + @staticmethod + def typeConversionOp(ser, rng, val, out_dtype, error_name=None): + return ser.addOutput(val.shape, out_dtype) + + @staticmethod + def transposeConv2DOp(ser, rng, ifm, output_shape, error_name=None): + if ifm.dtype == DType.INT8: + out_dtype = DType.INT32 + elif ifm.dtype == DType.INT16: + out_dtype = DType.INT48 + elif ifm.dtype == DType.FLOAT: + out_dtype = DType.FLOAT + elif error_name == ErrorIf.WrongInputType: + # Pick some potentially correct output dtype if input type is incorrect + out_dtype = DType.INT32 + else: + raise Exception(f"Unsupported input dtype: {ifm.dtype}") + + if error_name == ErrorIf.WrongOutputType: + wrong_dtypes = list(usableDTypes(excludes=[out_dtype])) + out_dtype = rng.choice(wrong_dtypes) + + return ser.addOutput(output_shape, out_dtype) |