Refactor verif/generator/tosa_test_gen.py into different files
Move all error & validation into tosa_error_if.py
Move all argument and tensor generation into tosa_arg_gen.py
Move utility functions into tosa_utils.py
Create new TosaTensorValuesGen class for specialising tensor
value generation.
Change-Id: Ib9ac65e2308b14471a567c6f11d775c76585bc5b
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
new file mode 100644
index 0000000..e3492cd
--- /dev/null
+++ b/verif/generator/tosa_arg_gen.py
@@ -0,0 +1,1809 @@
+# Copyright (c) 2021-2022, ARM Limited.
+# SPDX-License-Identifier: Apache-2.0
+import itertools
+import math
+
+import numpy as np
+import serializer.tosa_serializer as ts
+from generator.tosa_error_if import ErrorIf
+from generator.tosa_error_if import TosaErrorIfArgGen
+from serializer.tosa_serializer import DTypeNames
+from tosa.DType import DType
+from tosa.Op import Op
+from tosa.ResizeMode import ResizeMode
+
+# DTypeNames, DType, Op and ResizeMode are convenience variables to the
+# flatc-generated types that should be enums, but aren't
+
+
+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 is 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 TosaTensorValuesGen:
+ """Tensor Value generators create the random data for each test."""
+
+ def __init__(self):
+ pass
+
+ @staticmethod
+ def tvgDefault(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ pCount, cCount = op["operands"]
+
+ tens = []
+ tens.extend(
+ testGen.buildPlaceholderTensors(shapeList[0:pCount], dtypeList[0:pCount])
+ )
+ tens.extend(testGen.buildConstTensors(shapeList[pCount:], dtypeList[pCount:]))
+
+ return tens
+
+ @staticmethod
+ def tvgNegate(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ if dtypeList[0] != DType.FLOAT and error_name is None:
+ pCount, cCount = op["operands"]
+ assert (
+ pCount == 1 and cCount == 0
+ ), "Op.NEGATE must have 1 placeholders, 0 consts"
+ # Must create tensors with values within negatable ranges
+ if dtypeList[0] == DType.INT8:
+ # Must be within int8, adjustable by input_zp and then negatable
+ # and be within int8
+ # For use: qinfo.ints[0][1] = input_zp, qinfo.ints[1][1] = output_zp
+ max_val = min(127, 127 + qinfo.ints[0][1])
+ min_val = max(-127, -127 + qinfo.ints[0][1])
+ elif dtypeList[0] == DType.INT16:
+ max_val = 32767
+ min_val = -max_val
+ else:
+ assert (
+ dtypeList[0] == DType.INT32
+ ), "Op.NEGATE found with unsupported input type"
+ max_val = (1 << 31) - 1
+ min_val = -max_val
+ arr = np.int32(
+ testGen.rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0])
+ )
+ placeholders = []
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], arr)
+ )
+ return placeholders
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgAddSub(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ if dtypeList[0] == DType.INT32 and error_name is None:
+ # Make sure the operation does not cause value saturation - where
+ # the number wraps due to limited number of bits to store the answer
+ pCount, cCount = op["operands"]
+ assert (
+ pCount == 2 and cCount == 0
+ ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts"
+ placeholders = []
+ add = op["op"] == Op.ADD
+ a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
+ b_arr = testGen.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(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
+ )
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr)
+ )
+
+ return placeholders
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgCondIfWhileLoop(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None
+ ):
+ if dtypeList[0] in (
+ DType.INT32,
+ DType.INT16,
+ DType.INT8,
+ ):
+ # Limit input tensors with cond_if_binary or while_loop to stop
+ # saturation of add/sub ops with int32 and keep all logical shift
+ # values between 0 to 31 for int16 or int8
+ pCount, cCount = op["operands"]
+ pRemain = pCount
+ placeholders = []
+ for idx, shape in enumerate(shapeList[:]):
+ if dtypeList[0] == DType.INT32:
+ arr = testGen.getRandTensor(shapeList[idx], DType.INT16)
+ else:
+ arr = np.int32(
+ testGen.rng.integers(low=0, high=32, size=shapeList[idx])
+ )
+ if pRemain > 0:
+ placeholders.append(
+ testGen.ser.addPlaceholder(shape, dtypeList[idx], arr)
+ )
+ pRemain -= 1
+ else:
+ placeholders.append(
+ testGen.ser.addConst(shape, dtypeList[idx], arr)
+ )
+
+ return placeholders
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgArithmeticRightShift(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None
+ ):
+ pCount, cCount = op["operands"]
+ # 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(testGen.rng.integers(low=0, high=8, size=shape))
+ elif dtypeList[idx] == DType.INT16:
+ arr = np.int32(testGen.rng.integers(low=0, high=16, size=shape))
+ elif dtypeList[idx] == DType.INT32:
+ arr = np.int32(testGen.rng.integers(low=0, high=32, size=shape))
+ elif error_name == ErrorIf.WrongInputType:
+ arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape))
+ else:
+ raise Exception("OpArithmeticRightShift: invalid input dtype")
+ else:
+ arr = testGen.getRandTensor(shape, dtypeList[idx])
+ placeholders.append(testGen.ser.addPlaceholder(shape, dtypeList[idx], arr))
+
+ return placeholders
+
+ @staticmethod
+ def tvgSelect(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ # Set datatype of condition tensor to boolean
+ dtypeList[0] = DType.BOOL
+
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgIntDiv(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ if error_name is None:
+ pCount, cCount = op["operands"]
+ 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 = testGen.getRandTensor(shapeList[0], dtypeList[0])
+ divisor_arr = testGen.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(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr)
+ )
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr)
+ )
+
+ return placeholders
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgMul(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ if error_name is None:
+ pCount, cCount = op["operands"]
+ assert (
+ pCount == 2 and cCount == 0
+ ), "Op.MUL must have 2 placeholders, 0 consts"
+
+ tens = []
+ if dtypeList[0] == DType.FLOAT:
+ tens.extend(testGen.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(
+ testGen.rng.integers(low=low, high=high, size=shapeList[0])
+ )
+ b_arr = np.int32(
+ testGen.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(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
+ )
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr)
+ )
+
+ tens.extend(placeholders)
+
+ return tens
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgConcat(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ count = len(shapeList) - testGen.args.num_const_inputs_concat
+ if count < 1:
+ count = 1
+ if testGen.args.num_const_inputs_concat == 0:
+ count = len(shapeList)
+
+ # Ensure axis is an int
+ testArgs[0] = int(testArgs[0])
+
+ shapeList = TosaTensorGen.tgConcatConstInput(
+ testGen, shapeList, testArgs[0], error_name
+ )
+
+ tens = []
+ tens.extend(
+ testGen.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count])
+ )
+ tens.extend(testGen.buildConstTensors(shapeList[count:], dtypeList[count:]))
+
+ return tens
+
+ @staticmethod
+ def tvgLogicalShift(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None
+ ):
+ pCount, cCount = op["operands"]
+ assert (
+ pCount == 2 and cCount == 0
+ ), "Op.LOGICAL_LEFT_SHIFT or Op.LOGICAL_RIGHT_SHIFT must have 2 placeholders, 0 consts"
+ values_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
+ shift_arr = np.int32(testGen.rng.integers(low=0, high=32, size=shapeList[1]))
+ placeholders = []
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
+ )
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], shift_arr)
+ )
+
+ return placeholders
+
+ @staticmethod
+ def tvgEqual(testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None):
+ if error_name is None:
+ pCount, cCount = op["operands"]
+ assert (
+ pCount == 2 and cCount == 0
+ ), "Op.EQUAL must have 2 placeholders, 0 consts"
+ a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0])
+ b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1])
+ # Using random numbers means that it will be very unlikely that
+ # there are any matching (equal) values, therefore force that
+ # there are twice the number of matching values as the tensor rank
+ for num in range(0, len(shapeList[0]) * 2):
+ a_index = []
+ b_index = []
+ # Choose an index in each axis for the whole shape
+ for axis in range(0, len(shapeList[0])):
+ # Index can be up to the largest dimension in both shapes
+ index = np.int32(
+ testGen.rng.integers(
+ 0, max(shapeList[0][axis], shapeList[1][axis])
+ )
+ )
+ # Reduce the index down to a shape's dim for broadcasting
+ a_index.append(min(shapeList[0][axis] - 1, index))
+ b_index.append(min(shapeList[1][axis] - 1, index))
+
+ a_arr[tuple(a_index)] = b_arr[tuple(b_index)]
+
+ placeholders = []
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr)
+ )
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr)
+ )
+ return placeholders
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+ @staticmethod
+ def tvgReduceSum(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name=None
+ ):
+ if dtypeList[0] == DType.INT32:
+ pCount, cCount = op["operands"]
+ assert (
+ pCount == 1 and cCount == 0
+ ), "Op.REDUCE_SUM must have 1 placeholders, 0 consts"
+ # Limit values so that the sum cannot exceed the range of an int32 during
+ # summation of any axis
+ range_val = int((1 << 31) / max(shapeList[0]))
+ values_arr = np.int32(
+ testGen.rng.integers(low=-range_val, high=range_val, size=shapeList[0])
+ )
+ placeholders = []
+ placeholders.append(
+ testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
+ )
+ return placeholders
+ else:
+ return TosaTensorValuesGen.tvgDefault(
+ testGen, op, dtypeList, shapeList, testArgs, qinfo, error_name
+ )
+
+
+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 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 and testGen.args.oversize:
+ # 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 + 1)]
+ kernels = {x for x in itertools.product(*([k_vals] * 2))}
+
+ if testGen.args.oversize:
+ # 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 not scale32:
+ continue
+ elif error_name == ErrorIf.ScaleNotTrue and scale32:
+ continue
+ for double_round in [False, True]:
+ if error_name == ErrorIf.ScaleNotTrue and not double_round:
+ 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