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author | Jeremy Johnson <jeremy.johnson@arm.com> | 2021-12-14 16:34:05 +0000 |
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committer | Jeremy Johnson <jeremy.johnson@arm.com> | 2022-01-06 11:38:19 +0000 |
commit | 2ec3494060ffdafec072fe1b2099a8177b8eca6a (patch) | |
tree | fbe9d2dfdc4abeff9ba374a90065b7ed98e97509 /verif/tosa_test_gen.py | |
parent | a1d49853082f5454144209c1d95dc9203f510746 (diff) | |
download | reference_model-2ec3494060ffdafec072fe1b2099a8177b8eca6a.tar.gz |
Reorganize verif and create packages
Split generator and runner scripts
Add package setup
Add py-dev-env.sh/.bash to allow editing source files during dev
Update README.md with installation info
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: I172fe426d99e2e9aeeacedc8b8f3b6a79c8bd39d
Diffstat (limited to 'verif/tosa_test_gen.py')
-rw-r--r-- | verif/tosa_test_gen.py | 6874 |
1 files changed, 0 insertions, 6874 deletions
diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py deleted file mode 100644 index 0d29704..0000000 --- a/verif/tosa_test_gen.py +++ /dev/null @@ -1,6874 +0,0 @@ -#!/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 -from tosa_ref_run import TosaReturnCode - -# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH -parent_dir = os.path.dirname(os.path.realpath(__file__)) -sys.path.append( - os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") -) -import tosa_serializer as ts -from tosa_serializer import * -import tosa -from 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, 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) |