| # Copyright (c) 2021-2024, ARM Limited. |
| # SPDX-License-Identifier: Apache-2.0 |
| import itertools |
| import logging |
| import math |
| |
| import generator.tosa_utils as gtu |
| import numpy as np |
| 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 |
| |
| logging.basicConfig() |
| logger = logging.getLogger("tosa_verif_build_tests") |
| |
| |
| class TosaQuantGen: |
| """QuantizedInfo random generator helper functions. |
| |
| Specify with 'qgen': in the operator defintion. |
| """ |
| |
| def __init__(self): |
| pass |
| |
| @staticmethod |
| def getZeroPoint(rng, zeropoint, dtype, error_name=None): |
| |
| if dtype == DType.INT8: |
| if zeropoint is not None: |
| return min(127, max(-128, zeropoint)) |
| return rng.randInt(-128, 128) |
| elif dtype == DType.UINT8: |
| if zeropoint is not None: |
| return min(255, max(0, zeropoint)) |
| return rng.randInt(0, 256) |
| elif error_name in [ |
| ErrorIf.InputZeroPointNotZero, |
| ErrorIf.WeightZeroPointNotZero, |
| ErrorIf.OutputZeroPointNotZero, |
| ]: |
| zero_point = rng.randInt(-128, 128) |
| if zero_point == 0: |
| zero_point = 1 |
| return zero_point |
| return 0 |
| |
| @staticmethod |
| def qgUnary(rng, zeropoint, op, dtype, error_name=None): |
| if error_name == ErrorIf.InputZeroPointNotZero: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), |
| ] |
| elif error_name == ErrorIf.OutputZeroPointNotZero: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), |
| ] |
| else: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), |
| ] |
| return qinfo |
| |
| @staticmethod |
| def qgConv(rng, zeropoint, op, dtype_or_dtypeList, error_name=None): |
| 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: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0], error_name), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1]), |
| ] |
| elif error_name == ErrorIf.WeightZeroPointNotZero: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0]), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1], error_name), |
| ] |
| else: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0]), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1]), |
| ] |
| return qinfo |
| |
| @staticmethod |
| def qgMatmul(rng, zeropoint, op, dtype, error_name=None): |
| if error_name == ErrorIf.InputZeroPointNotZero: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), |
| ] |
| else: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), |
| TosaQuantGen.getZeroPoint(rng, zeropoint, 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 |
| logger.debug( |
| f"computeMultiplierAndShift: scalefp={scaleFp} scaleBits={scaleBits} m={m} mult={multiplier} shift={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, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| shape = testGen.makeShape(rng, 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()) |
| |
| # Generates an input rank mismatch for operators with more than one input |
| if error_name == ErrorIf.RankMismatch: |
| if rank == 1 and i != 1: |
| shape = testGen.makeShape(rng, rank + rng.choice([1, 2, 3])) |
| elif i != 1: |
| shape = testGen.makeShape(rng, rank + rng.choice([-1, 1])) |
| |
| return shape_list |
| |
| @staticmethod |
| def tgNHWC(testGen, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| |
| if error_name != ErrorIf.WrongRank: |
| assert rank == 4 |
| |
| shape = testGen.makeShape(rng, rank) |
| shape = testGen.constrictBatchSize(shape) |
| |
| # 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 tgGather(testGen, rng, opName, rank, error_name=None): |
| pl, const = opName["operands"] |
| |
| assert pl == 2 |
| assert const == 0 |
| if error_name != ErrorIf.WrongRank: |
| assert rank == 3 |
| |
| values_shape = testGen.makeShape(rng, rank) |
| values_shape = testGen.constrictBatchSize(values_shape) |
| |
| N = values_shape[0] |
| W = testGen.makeDimension(rng) |
| indices_shape = [N, W] |
| |
| shape_list = [values_shape, indices_shape] |
| return shape_list |
| |
| @staticmethod |
| def tgScatter(testGen, rng, opName, rank, error_name=None): |
| pl, const = opName["operands"] |
| |
| assert pl == 3 |
| assert const == 0 |
| if error_name != ErrorIf.WrongRank: |
| assert rank == 3 |
| |
| values_in_shape = testGen.makeShape(rng, rank) |
| values_in_shape = testGen.constrictBatchSize(values_in_shape) |
| |
| N = values_in_shape[0] |
| K = values_in_shape[1] |
| C = values_in_shape[2] |
| |
| # Make sure W is not greater than K, as we can only write each output index |
| # once (having a W greater than K means that you have to repeat a K index) |
| W_min = min(testGen.args.tensor_shape_range[0], K) |
| W_max = min(testGen.args.tensor_shape_range[1], K) |
| W = rng.randInt(W_min, W_max) if W_min < W_max else W_min |
| |
| input_shape = [N, W, C] |
| |
| shape_list = [] |
| shape_list.append(values_in_shape) |
| shape_list.append([N, W]) # indices |
| shape_list.append(input_shape) |
| |
| return shape_list |
| |
| @staticmethod |
| def _get_broadcast_shapes(testGen, rng, num_shapes, rank, error_name=None): |
| shape = testGen.makeShape(rng, rank) |
| shape_list = [] |
| |
| # Choose one of the inputs to broadcast |
| # Note: Simplifies OutputShaper code if we don't change first shape for errors |
| bcast_idx = rng.randInt(0 if error_name is None else 1, num_shapes) |
| fuzz_idx = rng.randInt(0, rank) |
| |
| for i in range(num_shapes): |
| shape_bcast = shape.copy() |
| |
| # To test broadcasting, the chosen fuzz index dimension should not be 1 |
| if shape_bcast[fuzz_idx] == 1: |
| shape_bcast[fuzz_idx] += 1 |
| |
| # If the chosen input, pick a random index to broadcast |
| if i == bcast_idx: |
| if error_name == ErrorIf.RankMismatch: |
| # Add one rank to the shape (or more for rank of 1) |
| extra_ranks = rng.choice([1, 2, 3]) if rank == 1 else 1 |
| shape_bcast = np.concatenate( |
| (shape_bcast, testGen.makeShape(rng, extra_ranks)) |
| ) |
| if rank != 1: |
| # Either keep the extra rank, or remove it |
| new_len = rng.choice([-2, len(shape_bcast)]) |
| shape_bcast = shape_bcast[:new_len] |
| elif error_name == ErrorIf.BroadcastShapesMismatch: |
| shape_bcast[fuzz_idx] += 2 |
| else: |
| shape_bcast[fuzz_idx] = 1 |
| |
| shape_list.append(shape_bcast) |
| |
| return shape_list |
| |
| @staticmethod |
| def tgBroadcastFuzz(testGen, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| num_shapes = pl + const |
| return TosaTensorGen._get_broadcast_shapes( |
| testGen, rng, num_shapes, rank, error_name |
| ) |
| |
| @staticmethod |
| def tgMul(testGen, rng, op, rank, error_name=None): |
| # Get broadcast shapes for the first 2 inputs as the 3rd is shift |
| shape_list = TosaTensorGen._get_broadcast_shapes( |
| testGen, rng, 2, rank, error_name |
| ) |
| # Add a single dimension tensor for shift |
| shape_list.append([1]) |
| return shape_list |
| |
| @staticmethod |
| def tgConv2D(testGen, rng, 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(rng, rank) |
| ifm_shape = testGen.constrictBatchSize(ifm_shape) |
| |
| # 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.makeDimension(rng) |
| |
| # 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, rng, 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(rng, rank) |
| ifm_shape = testGen.constrictBatchSize(ifm_shape) |
| |
| # 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.makeDimension(rng) |
| |
| # 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, rng, 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(rng, rank) |
| ifm_shape = testGen.constrictBatchSize(ifm_shape) |
| |
| # 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.makeDimension(rng) |
| |
| # 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, rng, 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(rng, rank) |
| ifm_shape = testGen.constrictBatchSize(ifm_shape) |
| |
| # 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.makeDimension(rng) % (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 tgFFT2d(testGen, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| |
| if error_name != ErrorIf.WrongRank: |
| assert rank == 3 |
| assert pl == 2 and const == 0 |
| |
| # IFM dimensions are NHW |
| ifm_shape = testGen.makeShape(rng, rank) |
| |
| # Select nearest lower power of two from input height and width |
| ifm_shape[1] = 2 ** int(math.log(ifm_shape[1], 2)) |
| ifm_shape[2] = 2 ** int(math.log(ifm_shape[2], 2)) |
| |
| # Constrict the overall size of the shape when creating ERROR_IF tests |
| if error_name: |
| ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape) |
| |
| # Generate an invalid kernel that is not a power of two |
| if error_name == ErrorIf.KernelNotPowerOfTwo: |
| inc_h = 2 if ifm_shape[1] == 1 else 1 |
| inc_w = 2 if ifm_shape[2] == 1 else 1 |
| inc_choices = [(inc_h, 0), (0, inc_w), (inc_h, inc_w)] |
| selected_inc = rng.choice(inc_choices) |
| ifm_shape[1] += selected_inc[0] |
| ifm_shape[2] += selected_inc[1] |
| |
| ifm_shape = testGen.constrictBatchSize(ifm_shape) |
| |
| ifm_shapes = [ifm_shape.copy(), ifm_shape.copy()] |
| if error_name == ErrorIf.FFTInputShapeMismatch: |
| modify_shape = rng.choice([0, 1]) |
| # Only modify kernel (H, W) |
| modify_dim = rng.choice([1, 2]) |
| ifm_shapes[modify_shape][modify_dim] *= 2 |
| |
| return [ifm_shapes[0], ifm_shapes[1]] |
| |
| @staticmethod |
| def tgRFFT2d(testGen, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| |
| if error_name != ErrorIf.WrongRank: |
| assert rank == 3 |
| assert pl == 1 and const == 0 |
| |
| # IFM dimensions are NHW |
| ifm_shape = testGen.makeShape(rng, rank) |
| |
| # Select nearest lower power of two from input height and width |
| ifm_shape[1] = 2 ** int(math.log(ifm_shape[1], 2)) |
| ifm_shape[2] = 2 ** int(math.log(ifm_shape[2], 2)) |
| |
| # Constrict the overall size of the shape when creating ERROR_IF tests |
| if error_name: |
| ifm_shape = TosaErrorIfArgGen.eiRestrictDimensions(ifm_shape) |
| |
| # Generate an invalid kernel that is not a power of two |
| if error_name == ErrorIf.KernelNotPowerOfTwo: |
| # We must increment by 2 if current size is 1 |
| inc_h = 2 if ifm_shape[1] == 1 else 1 |
| inc_w = 2 if ifm_shape[2] == 1 else 1 |
| inc_choices = [(inc_h, 0), (0, inc_w), (inc_h, inc_w)] |
| selected_inc = rng.choice(inc_choices) |
| ifm_shape[1] += selected_inc[0] |
| ifm_shape[2] += selected_inc[1] |
| |
| ifm_shape = testGen.constrictBatchSize(ifm_shape) |
| |
| return [ifm_shape] |
| |
| @staticmethod |
| def tgFullyConnected(testGen, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| |
| if error_name != ErrorIf.WrongRank: |
| assert rank == 2 |
| |
| input_shape = testGen.makeShape(rng, rank) |
| |
| # Constrict the overall size of the shape when creating ERROR_IF tests |
| if error_name: |
| input_shape = TosaErrorIfArgGen.eiRestrictDimensions(input_shape) |
| |
| filter_oc = 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, rng, 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(rng, 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( |
| 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, rng, op, rank, error_name=None): |
| pl, const = op["operands"] |
| shape = testGen.makeShape(rng, rank) |
| |
| # Create extra tensors to concat. |
| # Take into account value of pl when getting maximum number of concats |
| num_tensors = rng.randInt(0, 4) |
| shape_list = [] |
| for i in range(pl + const + num_tensors): |
| if error_name == ErrorIf.ConcatInputRankMismatch and i != 0: |
| remove = rng.choice([True, False]) |
| wrongShape = shape.copy() |
| |
| if remove and len(shape) > 1: |
| wrongShape = wrongShape[1:] |
| else: |
| wrongShape = list(wrongShape) |
| wrongShape.append(rng.integers(1, 10)) |
| |
| shape_list.append(wrongShape) |
| else: |
| shape_list.append(shape.copy()) |
| |
| return shape_list |
| |
| @staticmethod |
| def tgConcatConstInput(rng, 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)] += 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)] += 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 tensor in each test.""" |
| |
| def __init__(self): |
| pass |
| |
| class TVGInfo: |
| """Enhanced tensor values information including data gen dict.""" |
| |
| def __init__(self, tensorList, dataGenDict): |
| self.tensorList = tensorList |
| self.dataGenDict = dataGenDict |
| |
| # Default high value for random numbers |
| TVG_FLOAT_HIGH_VALUE = { |
| DType.FP32: (1 << 128) - (1 << (127 - 23)), |
| DType.FP16: (1 << 16) - (1 << (15 - 10)), |
| DType.BF16: (1 << 128) - (1 << (127 - 7)), |
| DType.FP8E4M3: 448, |
| DType.FP8E5M2: 57344, |
| } |
| |
| # Default lowest normal values for random numbers |
| TVG_FLOAT_LOW_VALUE = { |
| DType.FP32: np.exp2(-126), |
| DType.FP16: np.exp2(-14), |
| DType.BF16: np.exp2(-126), |
| DType.FP8E4M3: np.exp2(-9), |
| DType.FP8E5M2: np.exp2(-16), |
| } |
| |
| @staticmethod |
| def _get_data_range(rng, dtype, highValueLookup, lowValueLookup=None): |
| # Return a tuple of (low,high) data range values for the given data |
| # type using a combination of per operator table limits, data limits |
| # and user supplied ranges for FP numbers |
| if dtype in highValueLookup: |
| type_range = rng.dTypeRange(dtype, high_inclusive=True) |
| high_val = highValueLookup[dtype] |
| if lowValueLookup is not None and dtype in lowValueLookup: |
| low_val = lowValueLookup[dtype] |
| else: |
| low_val = -high_val |
| # Set the values to something that won't produce infinity whilst |
| # respecting the default ranges if more/less than the low/high |
| # values |
| data_range = ( |
| max(low_val, type_range[0]), |
| min(high_val, type_range[1]), |
| ) |
| if data_range[0] > data_range[1]: |
| # Invalid data range from low to high created due to user |
| # constraints revert to using internal ranges as they are |
| # known to work |
| logger.info( |
| f"Using safe data range ({low_val} to {high_val}) instead of supplied ({type_range[0]} to {type_range[1]})" |
| ) |
| data_range = (low_val, high_val) |
| return data_range |
| return None |
| |
| @staticmethod |
| def tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| # Variable inputs versus constants |
| pCount, cCount = testGen.TOSA_OP_LIST[opName]["operands"] |
| if "p_count" in argsDict: |
| # Override for operators like CONCAT |
| pCount = argsDict["p_count"] |
| cCount = argsDict["c_count"] |
| assert pCount + cCount == len( |
| shapeList |
| ), "Placeholders & Constant tensors must match shapes list" |
| |
| tens_ser_list = [] |
| |
| if ( |
| error_name is not None |
| or not gtu.dtypeIsSupportedByCompliance(dtypeList[0]) |
| or "data_gen" not in testGen.TOSA_OP_LIST[opName] |
| ): |
| # Fall back to internal data gen when dealing with unsupported types or ops |
| data_range = argsDict["data_range"] if "data_range" in argsDict else None |
| for idx, info in enumerate(zip(shapeList, dtypeList)): |
| roundMode = False |
| shape, dtype = info |
| if "data_range_list" in argsDict: |
| data_range = argsDict["data_range_list"][idx]["range"] |
| roundMode = ( |
| "round" in argsDict["data_range_list"][idx] |
| and argsDict["data_range_list"][idx]["round"] is True |
| ) |
| if data_range is not None and dtype not in ( |
| DType.FP16, |
| DType.FP32, |
| DType.BF16, |
| DType.FP8E4M3, |
| DType.FP8E5M2, |
| ): |
| # Change from inclusive to exclusive range |
| data_range = (data_range[0], data_range[1] + 1) |
| |
| # Ignore lazy data gen option and create data array using any range limits |
| if "fixed_data" in argsDict and argsDict["fixed_data"][idx] is not None: |
| if dtype == DType.SHAPE: |
| arr = np.int64(argsDict["fixed_data"][idx]) |
| elif dtype == DType.INT8: |
| arr = np.int8(argsDict["fixed_data"][idx]) |
| elif dtype == DType.INT16: |
| arr = np.int16(argsDict["fixed_data"][idx]) |
| elif dtype == DType.INT32: |
| arr = np.int32(argsDict["fixed_data"][idx]) |
| else: |
| assert False, "Unsupported fixed_data type" |
| else: |
| arr = rng.randTensor(shape, dtype, data_range) |
| if roundMode: |
| arr = np.round(arr) |
| if idx < pCount: |
| tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr)) |
| else: |
| tens_ser_list.append(testGen.ser.addConst(shape, dtype, arr)) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| |
| # Create data generator meta-data |
| dg_type = argsDict["dg_type"] |
| tens_data = { |
| "version": "0.1", |
| "tensors": {}, |
| } |
| dg_tens_meta = tens_data["tensors"] |
| for idx, shape in enumerate(shapeList): |
| |
| tens_meta = {} |
| if "fixed_data" in argsDict and argsDict["fixed_data"][idx] is not None: |
| tens_meta["generator"] = gtu.DataGenType( |
| gtu.DataGenType.FIXED_DATA |
| ).name |
| else: |
| tens_meta["generator"] = gtu.DataGenType(dg_type).name |
| |
| tens_meta["data_type"] = gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["json"] |
| tens_meta["shape"] = [int(i) for i in shape] |
| tens_meta["input_pos"] = idx |
| tens_meta["op"] = gtu.getOpNameFromOpListName(opName).upper() |
| |
| if idx < pCount: |
| tens_meta["input_type"] = "VARIABLE" |
| else: |
| tens_meta["input_type"] = "CONSTANT" |
| |
| if dg_type == gtu.DataGenType.PSEUDO_RANDOM: |
| info = {} |
| if ( |
| tens_meta["generator"] |
| == gtu.DataGenType(gtu.DataGenType.FIXED_DATA).name |
| ): |
| info["data"] = [int(i) for i in argsDict["fixed_data"][idx]] |
| tens_meta["fixed_data_info"] = info |
| else: |
| info["rng_seed"] = rng.seed |
| |
| data_range = None |
| if "data_range_list" in argsDict: |
| data_range = argsDict["data_range_list"][idx]["range"] |
| if "round" in argsDict["data_range_list"][idx]: |
| info["round"] = argsDict["data_range_list"][idx]["round"] |
| elif "data_range" in argsDict: |
| data_range = argsDict["data_range"] |
| |
| if data_range is None: |
| data_range = rng.dTypeRange(dtypeList[idx], high_inclusive=True) |
| info["range"] = [str(v) for v in data_range] |
| tens_meta["pseudo_random_info"] = info |
| elif dg_type == gtu.DataGenType.DOT_PRODUCT: |
| info = {} |
| info["s"] = argsDict["s"] |
| info["ks"] = int(argsDict["ks"]) |
| if "acc_type" in argsDict: |
| # Convert type number into JSON name |
| info["acc_type"] = gtu.DTYPE_ATTRIBUTES[argsDict["acc_type"]][ |
| "json" |
| ] |
| if "kernel" in argsDict: |
| info["kernel"] = [int(k) for k in argsDict["kernel"]] |
| if "axis" in argsDict: |
| info["axis"] = int(argsDict["axis"]) |
| tens_meta["dot_product_info"] = info |
| elif dg_type == gtu.DataGenType.FULL_RANGE: |
| info = {} |
| info["start_val"] = int( |
| rng.randInt(0, gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["fullset"]) |
| ) |
| tens_meta["full_range_info"] = info |
| else: |
| # TODO - other data gen type |
| assert False, "TODO: support other data gen types" |
| |
| # Using the finished generate config meta data - generate the data if |
| # needed and assign a tensor name from the serializer |
| |
| # Need to generate data when not lazy or for the bias tensor as we need |
| # to work out if the bias data is non-zero for compliance |
| if not testGen.args.lazy_data_gen or ( |
| idx == 2 and dg_type == gtu.DataGenType.DOT_PRODUCT |
| ): |
| # Give this tensor a temporary name until we get one from the serializer |
| temp_name = f"placeholder_{idx}" |
| dg_tens_meta[temp_name] = tens_meta |
| # Create data now using the temporary name to access meta details |
| data = testGen.dgl.get_tensor_data(temp_name, tens_data) |
| if tens_meta["data_type"] == "SHAPE": |
| # Tensor type SHAPE and Numpy file type must be the same |
| data = np.int64(data) |
| # Remove the item as we will give it the correct name later |
| del dg_tens_meta[temp_name] |
| |
| if idx == 2 and dg_type == gtu.DataGenType.DOT_PRODUCT: |
| # The KS value used by compliance verification is altered when the |
| # bias data is non-zero |
| if max(abs(data)) > 0.0: |
| argsDict["ksb"] = argsDict["ks"] + 1 |
| |
| if testGen.args.lazy_data_gen: |
| data = None |
| |
| if tens_meta["input_type"] == "VARIABLE": |
| tens = testGen.ser.addPlaceholder(shape, dtypeList[idx], data) |
| else: |
| tens = testGen.ser.addConst(shape, dtypeList[idx], data) |
| |
| tens_ser_list.append(tens) |
| # Add the meta data to the list using the serializer tensor name |
| dg_tens_meta[tens.name] = tens_meta |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, tens_data) |
| |
| @staticmethod |
| def tvgNegate( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| if dtypeList[0] == DType.INT32 and error_name is None: |
| # Integer test |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| assert ( |
| pCount == 1 and cCount == 0 |
| ), "Op.NEGATE must have 1 placeholders, 0 consts" |
| # Must create tensors with values within accumulator (int32) negatable |
| # range |
| max_val = (1 << 31) - 1 |
| min_val = -max_val |
| arr = np.int32( |
| rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0]) |
| ) |
| tens_ser_list = [] |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], arr) |
| ) |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| else: |
| # ERROR_IF or floating point test |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| # Set the ADD/SUB data range to half the largest value to avoid infinities |
| TVG_FLOAT_HIGH_VALUE_ADDSUB = { |
| DType.FP32: (TVG_FLOAT_HIGH_VALUE[DType.FP32] / 2), |
| DType.FP16: (TVG_FLOAT_HIGH_VALUE[DType.FP16] / 2), |
| DType.BF16: (TVG_FLOAT_HIGH_VALUE[DType.BF16] / 2), |
| } |
| |
| @staticmethod |
| def tvgAddSub( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| if dtypeList[0] in (DType.INT32, DType.SHAPE) and error_name is None: |
| # Make sure the integer operation does not cause value saturation - where |
| # the number wraps due to limited number of bits to store the answer |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| assert ( |
| pCount == 2 and cCount == 0 |
| ), "Op.ADD / Op.SUB must have 2 placeholders, 0 consts" |
| tens_ser_list = [] |
| add = op["op"] in (Op.ADD, Op.ADD_SHAPE) |
| data_range = testGen.args.tensor_shape_range |
| a_arr = rng.randTensor(shapeList[0], dtypeList[0], data_range) |
| b_arr = rng.randTensor(shapeList[1], dtypeList[1], data_range) |
| 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) |
| |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_unsat_arr) |
| ) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| else: |
| # ERROR_IF or floating point test |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_ADDSUB |
| ) |
| if data_range: |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgCondIfWhileLoop( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, 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 |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| pRemain = pCount |
| tens_ser_list = [] |
| for idx, shape in enumerate(shapeList[:]): |
| if dtypeList[0] == DType.INT32: |
| arr = rng.randTensor(shapeList[idx], DType.INT16) |
| else: |
| arr = np.int32(rng.integers(low=0, high=32, size=shapeList[idx])) |
| if pRemain > 0: |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shape, dtypeList[idx], arr) |
| ) |
| pRemain -= 1 |
| else: |
| tens_ser_list.append( |
| testGen.ser.addConst(shape, dtypeList[idx], arr) |
| ) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| else: |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgArithmeticRightShift( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| op = testGen.TOSA_OP_LIST[opName] |
| 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" |
| |
| tens_ser_list = [] |
| for idx, shape in enumerate(shapeList[:]): |
| if idx == 1: |
| if dtypeList[idx] == DType.INT8: |
| arr = np.int32(rng.integers(low=0, high=8, size=shape)) |
| elif dtypeList[idx] == DType.INT16: |
| arr = np.int32(rng.integers(low=0, high=16, size=shape)) |
| elif dtypeList[idx] == DType.INT32: |
| arr = np.int32(rng.integers(low=0, high=32, size=shape)) |
| elif error_name == ErrorIf.WrongInputType: |
| arr = np.int32(rng.integers(low=0, high=8, size=shape)) |
| else: |
| raise Exception("OpArithmeticRightShift: invalid input dtype") |
| else: |
| arr = rng.randTensor(shape, dtypeList[idx]) |
| tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtypeList[idx], arr)) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| |
| @staticmethod |
| def tvgReshape( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| dtypeList[1] = DType.SHAPE |
| shapeList[1] = [len(argsDict["new_shape"])] |
| # Create a new list for the pre-generated data in argsDict["fixed_data"] |
| argsDict["fixed_data"] = [None, argsDict["new_shape"]] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgRescale( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| scale32 = argsDict["scale"] |
| multiplier_arr = argsDict["multiplier"] |
| shift_arr = argsDict["shift"] |
| |
| if scale32: |
| dtypeList[1] = DType.INT32 |
| else: |
| dtypeList[1] = DType.INT16 |
| shapeList[1] = [len(multiplier_arr)] |
| dtypeList[2] = DType.INT8 |
| shapeList[2] = [len(shift_arr)] |
| # Create a new list for the pre-generated data in argsDict["fixed_data"] |
| argsDict["fixed_data"] = [None, multiplier_arr, shift_arr] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgPad(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| # argsDict["pad"] is 2D array, need to flatten it to get list of values |
| pad_values = argsDict["pad"].flatten() |
| dtypeList[1] = DType.SHAPE |
| shapeList[1] = [len(pad_values)] |
| # Create a new list for the pre-generated data in argsDict["fixed_data"] |
| argsDict["fixed_data"] = [None, pad_values] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgSlice(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| dtypeList[1] = DType.SHAPE |
| shapeList[1] = [len(argsDict["start"])] |
| dtypeList[2] = DType.SHAPE |
| shapeList[2] = [len(argsDict["size"])] |
| # Create a new list for the pre-generated data in argsDict["fixed_data"] |
| argsDict["fixed_data"] = [None, argsDict["start"], argsDict["size"]] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgTile(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| dtypeList[1] = DType.SHAPE |
| shapeList[1] = [len(argsDict["multiples"])] |
| argsDict["fixed_data"] = [None, argsDict["multiples"]] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgSelect( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| # Set datatype of condition tensor to boolean |
| dtypeList[0] = DType.BOOL |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgIntDiv( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| if error_name is None: |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| assert ( |
| pCount == 2 and cCount == 0 |
| ), "Op.INTDIV must have 2 placeholders, 0 consts" |
| |
| tens_ser_list = [] |
| |
| # Two invalid cases for Op.INTDIV: |
| # 1. divisor == 0 |
| # 2. dividend == -(1<<31) and divisor == -1 |
| while True: |
| dividend_arr = rng.randTensor(shapeList[0], dtypeList[0]) |
| divisor_arr = rng.randTensor(shapeList[1], dtypeList[1]) |
| |
| if (divisor_arr == 0).any(): |
| continue |
| |
| if (dividend_arr == -(2**31)).any() and (divisor_arr == -1).any(): |
| continue |
| |
| break |
| |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], dividend_arr) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], divisor_arr) |
| ) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| else: |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| # Set the MUL data range to the square root of the largest value |
| # to avoid infinities |
| TVG_FLOAT_HIGH_VALUE_MUL = { |
| DType.FP32: math.sqrt(TVG_FLOAT_HIGH_VALUE[DType.FP32]), |
| DType.FP16: math.sqrt(TVG_FLOAT_HIGH_VALUE[DType.FP16]), |
| DType.BF16: math.sqrt(TVG_FLOAT_HIGH_VALUE[DType.BF16]), |
| } |
| |
| @staticmethod |
| def tvgMul(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| if error_name is not None or dtypeList[0] in ( |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ): |
| # ERROR_IF or floating point test |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL |
| ) |
| if data_range: |
| argsDict["data_range"] = data_range |
| |
| if dtypeList[0] != DType.SHAPE: |
| # Need to supply shift tensor for MUL (not needed for MUL_SHAPE) |
| dtypeList[2] = DType.INT8 |
| shapeList[2] = [1] |
| # Create a new list for the pre-generated data in argsDict["fixed_data"] |
| argsDict["fixed_data"] = [None, None, [argsDict["shift"]]] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| else: |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| |
| tens_ser_list = [] |
| |
| # Make sure multiply result in int32 range |
| if dtypeList[0] == DType.SHAPE: |
| shift = 0 |
| else: |
| shift = argsDict["shift"] |
| if dtypeList[0] == DType.INT8: |
| num_bits = 8 |
| elif dtypeList[0] == DType.INT16: |
| num_bits = 16 |
| elif dtypeList[0] in (DType.INT32, DType.SHAPE): |
| num_bits = 32 |
| elif error_name == ErrorIf.WrongInputType: |
| num_bits = 8 |
| else: |
| raise Exception( |
| f"OpMul: invalid input dtype {gtu.DTYPE_ATTRIBUTES[dtypeList[0]]['str']}" |
| ) |
| |
| for idx, shape in enumerate(shapeList[:]): |
| if dtypeList[idx] == DType.SHAPE: |
| low = testGen.args.tensor_shape_range[0] |
| high = testGen.args.tensor_shape_range[1] |
| else: |
| low = -(2 ** (num_bits - 1)) |
| high = (2 ** (num_bits - 1)) - 1 |
| |
| a_arr = np.int32(rng.integers(low=low, high=high, size=shapeList[0])) |
| b_arr = np.int32(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 |
| |
| if dtypeList[0] == DType.SHAPE: |
| # MUL_SHAPE with 2 inputs |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr_64) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr_64) |
| ) |
| else: |
| # MUL with 3 inputs (3rd is shift) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder([1], DType.INT8, np.int8([shift])) |
| ) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| |
| @staticmethod |
| def tvgConcat( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, 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) |
| |
| op = testGen.TOSA_OP_LIST[opName] |
| if op["op"] == Op.CONCAT_SHAPE: |
| # Set the axis to 0 |
| shapeList = TosaTensorGen.tgConcatConstInput(rng, shapeList, 0, error_name) |
| else: |
| shapeList = TosaTensorGen.tgConcatConstInput( |
| rng, shapeList, argsDict["axis"], error_name |
| ) |
| |
| # Override default pCount/cCount for operator |
| argsDict["p_count"] = count |
| argsDict["c_count"] = len(shapeList) - count |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgLogicalShift( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| op = testGen.TOSA_OP_LIST[opName] |
| 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 = rng.randTensor(shapeList[0], dtypeList[0]) |
| shift_arr = np.int32(rng.integers(low=0, high=32, size=shapeList[1])) |
| tens_ser_list = [] |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], shift_arr) |
| ) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| |
| @staticmethod |
| def tvgEqual(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| if error_name is None and not gtu.dtypeIsSupportedByCompliance(dtypeList[0]): |
| # Integer |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| assert ( |
| pCount == 2 and cCount == 0 |
| ), "Op.EQUAL must have 2 placeholders, 0 consts" |
| |
| a_arr = rng.randTensor(shapeList[0], dtypeList[0]) |
| b_arr = rng.randTensor(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( |
| 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)] |
| |
| tens_ser_list = [] |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], a_arr) |
| ) |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[1], dtypeList[1], b_arr) |
| ) |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| else: |
| # ERROR_IF or floating point test |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgReduceSum( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| dtype = dtypeList[0] |
| if dtype == DType.INT32: |
| op = testGen.TOSA_OP_LIST[opName] |
| 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( |
| rng.integers(low=-range_val, high=range_val, size=shapeList[0]) |
| ) |
| tens_ser_list = [] |
| tens_ser_list.append( |
| testGen.ser.addPlaceholder(shapeList[0], dtype, values_arr) |
| ) |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| else: |
| # ERROR_IF or dot product floating point test |
| if ( |
| error_name is None |
| and argsDict["dg_type"] != gtu.ComplianceMode.DOT_PRODUCT |
| ): |
| # Limit ranges for (non error & non compliance) tests by using |
| # values that can be summed on any axis to not hit infinity |
| highval_lookup = { |
| dtype: TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype] |
| / max(shapeList[0]) |
| } |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, dtype, highval_lookup |
| ) |
| assert data_range is not None |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgReduceProduct( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| dtype = dtypeList[0] |
| if error_name is None: |
| # Limit ranges for (non error) tests by using |
| # values that can be multiplied on any axis to not hit infinity |
| highval_lookup = { |
| dtype: math.pow( |
| TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype], |
| 1 / max(shapeList[0]), |
| ) |
| } |
| data_range = TosaTensorValuesGen._get_data_range(rng, dtype, highval_lookup) |
| assert data_range is not None |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgResize( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| dtypeList[0], |
| TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE, |
| ) |
| if data_range: |
| argsDict["data_range"] = data_range |
| # Needed for compliance |
| argsDict["max_abs_value"] = data_range[1] |
| |
| scale_values = argsDict["scale"] |
| offset_values = argsDict["offset"] |
| border_values = argsDict["border"] |
| dtypeList[1] = DType.SHAPE |
| dtypeList[2] = DType.SHAPE |
| dtypeList[3] = DType.SHAPE |
| shapeList[1] = [len(scale_values)] |
| shapeList[2] = [len(offset_values)] |
| shapeList[3] = [len(border_values)] |
| argsDict["fixed_data"] = [None, scale_values, offset_values, border_values] |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| # Set the POW exponent high data range |
| TVG_FLOAT_HIGH_VALUE_POW_EXP = { |
| DType.FP32: 10.0, |
| DType.FP16: 10.0, |
| DType.BF16: 10.0, |
| } |
| # POW highest base value (within a safe margin of error) that can be raised |
| # to +ve exponent that doesn't become Infinity |
| TVG_FLOAT_HIGH_VALUE_POW_BASE = { |
| DType.FP32: math.floor( |
| math.pow( |
| TVG_FLOAT_HIGH_VALUE[DType.FP32], |
| 1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP32], |
| ) |
| ), |
| DType.FP16: math.floor( |
| math.pow( |
| TVG_FLOAT_HIGH_VALUE[DType.FP16], |
| 1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP16], |
| ) |
| ), |
| DType.BF16: math.floor( |
| math.pow( |
| TVG_FLOAT_HIGH_VALUE[DType.BF16], |
| 1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.BF16], |
| ) |
| ), |
| } |
| # POW lowest base value (within a safe margin of error) that can be raised |
| # to -ve exponent that doesn't become Infinity |
| TVG_FLOAT_LOW_VALUE_POW_BASE = { |
| DType.FP32: math.ceil( |
| math.pow( |
| 1.0 / TVG_FLOAT_HIGH_VALUE[DType.FP32], |
| 1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP32], |
| ) |
| * 1000 |
| ) |
| / 1000, |
| DType.FP16: math.ceil( |
| math.pow( |
| 1.0 / TVG_FLOAT_HIGH_VALUE[DType.FP16], |
| 1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.FP16], |
| ) |
| * 1000 |
| ) |
| / 1000, |
| DType.BF16: math.ceil( |
| math.pow( |
| 1.0 / TVG_FLOAT_HIGH_VALUE[DType.BF16], |
| 1.0 / TVG_FLOAT_HIGH_VALUE_POW_EXP[DType.BF16], |
| ) |
| * 1000 |
| ) |
| / 1000, |
| } |
| |
| @staticmethod |
| def tvgPow(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| if error_name is not None: |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| dtype = dtypeList[0] |
| # Different ranges for POW |
| test_set = argsDict["s"] |
| if test_set == 0: |
| # Positive base with fractional exponent |
| base_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| dtype, |
| TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE, |
| TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE, |
| ) |
| exp_range = TosaTensorValuesGen._get_data_range( |
| rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP |
| ) |
| exp_round = False |
| else: |
| # Integer exponent |
| exp_range = TosaTensorValuesGen._get_data_range( |
| rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP |
| ) |
| exp_round = True |
| if test_set == 1: |
| # Positive base |
| base_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| dtype, |
| TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE, |
| TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE, |
| ) |
| else: |
| assert test_set == 2 |
| # Negative base |
| # Supply new look up tables with negative values |
| base_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| dtype, |
| {dtype: -TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE[dtype]}, |
| {dtype: -TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE[dtype]}, |
| ) |
| |
| data_range_list = ( |
| { |
| "range": base_range, |
| }, |
| { |
| "range": exp_range, |
| "round": exp_round, |
| }, |
| ) |
| argsDict["data_range_list"] = data_range_list |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgLogRsqrt( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| # LOG & RSQRT data range from lowest expressible positive number to |
| # largest to avoid NaNs |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| dtypeList[0], |
| TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE, |
| TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE, |
| ) |
| if data_range: |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| # Set the EXP data range to the log of the largest to smallest values |
| # to avoid infinities or making the result zero |
| TVG_FLOAT_HIGH_VALUE_EXP = { |
| DType.FP32: math.log(TVG_FLOAT_HIGH_VALUE[DType.FP32]), |
| DType.FP16: math.log(TVG_FLOAT_HIGH_VALUE[DType.FP16]), |
| DType.BF16: math.log(TVG_FLOAT_HIGH_VALUE[DType.BF16]), |
| } |
| TVG_FLOAT_LOW_VALUE_EXP = { |
| DType.FP32: math.log(TVG_FLOAT_LOW_VALUE[DType.FP32]), |
| DType.FP16: math.log(TVG_FLOAT_LOW_VALUE[DType.FP16]), |
| DType.BF16: math.log(TVG_FLOAT_LOW_VALUE[DType.BF16]), |
| } |
| |
| @staticmethod |
| def tvgExp(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| dtypeList[0], |
| TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_EXP, |
| TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_EXP, |
| ) |
| if data_range: |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgFullyConnected( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| dtype = dtypeList[0] |
| if ( |
| error_name is None |
| and argsDict["dg_type"] != gtu.ComplianceMode.DOT_PRODUCT |
| and dtype in (DType.BF16,) |
| ): |
| # TODO - Remove once BF16 enabled for DOT_PRODUCT compliance |
| # Limit ranges for (non error & non compliance) FP tests by using |
| # values that can be multiplied on any axis to not hit infinity/NaN |
| IC = shapeList[0][1] |
| highval_lookup = { |
| dtype: math.pow(TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype], 1 / IC) |
| } |
| data_range = TosaTensorValuesGen._get_data_range(rng, dtype, highval_lookup) |
| assert data_range is not None |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgCast(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): |
| in_dtype = dtypeList[0] |
| out_dtype = argsDict["out_type"] |
| # Create look up to limit input tensor to output type maximums to avoid |
| # FP infinities and saturation of integers |
| out_range = rng.dTypeRange(out_dtype, high_inclusive=True) |
| highval_lookup = {in_dtype: out_range[1]} |
| data_range = TosaTensorValuesGen._get_data_range( |
| rng, |
| in_dtype, |
| highval_lookup, |
| ) |
| |
| assert data_range is not None |
| argsDict["data_range"] = data_range |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgGather( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| K = shapeList[0][1] |
| |
| # Fix the type of the indices tensor |
| dtypeList[1] = DType.INT32 |
| |
| dtype = dtypeList[0] |
| if not gtu.dtypeIsSupportedByCompliance(dtype): |
| # Test unsupported by data generator |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| assert ( |
| pCount == 2 and cCount == 0 |
| ), "Op.GATHER must have 2 placeholders, 0 consts" |
| |
| tens_ser_list = [] |
| for idx, shape in enumerate(shapeList): |
| dtype = dtypeList[idx] |
| if idx != 1: |
| arr = rng.randTensor(shape, dtype) |
| tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr)) |
| else: |
| # Limit data range of indices tensor upto K (exclusive) |
| arr = rng.randTensor(shape, dtype, (0, K)) |
| # To match old functionality - create indices as CONST |
| tens_ser_list.append(testGen.ser.addConst(shape, dtype, arr)) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| |
| else: |
| # ERROR_IF or floating point test |
| # Use inclusive values upto index K for indices tensor |
| data_range_list = ( |
| {"range": None}, |
| {"range": (0, K - 1)}, |
| ) |
| argsDict["data_range_list"] = data_range_list |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name |
| ) |
| |
| @staticmethod |
| def tvgScatter( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None |
| ): |
| K = shapeList[0][1] |
| W = shapeList[2][1] |
| |
| # Work out an indices tensor here with data that doesn't exceed the |
| # dimension K of the values_in tensor and does NOT repeat the same K |
| # location as needed by the spec: |
| # "It is not permitted to repeat the same output index within a single |
| # SCATTER operation and so each output index occurs at most once." |
| assert K >= W, "Op.SCATTER W must be smaller or equal to K" |
| |
| # Fix the type of the indices tensor |
| dtypeList[1] = DType.INT32 |
| |
| dtype = dtypeList[0] |
| if not gtu.dtypeIsSupportedByCompliance(dtype): |
| # Test unsupported by data generator |
| op = testGen.TOSA_OP_LIST[opName] |
| pCount, cCount = op["operands"] |
| assert ( |
| pCount == 3 and cCount == 0 |
| ), "Op.SCATTER must have 3 placeholders, 0 consts" |
| |
| tens_ser_list = [] |
| for idx, shape in enumerate(shapeList): |
| dtype = dtypeList[idx] |
| if idx != 1: |
| arr = rng.randTensor(shape, dtype) |
| tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr)) |
| else: |
| # Create the indices array |
| assert dtype == DType.INT32, "Op.SCATTER unexpected indices type" |
| arr = [] |
| for n in range(shape[0]): |
| # Get a shuffled list of output indices (0 to K-1) and |
| # limit length to W |
| arr.append(rng.permutation(K)[:W]) |
| indices_arr = np.array(arr, dtype=np.int32) # (N, W) |
| # To match old functionality - create indices as CONST |
| tens_ser_list.append( |
| testGen.ser.addConst(shape, dtype, indices_arr) |
| ) |
| |
| return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) |
| |
| else: |
| # ERROR_IF or floating point test |
| # Use inclusive values upto index K for indices tensor |
| data_range_list = ( |
| {"range": None}, |
| {"range": (0, K - 1)}, |
| {"range": None}, |
| ) |
| argsDict["data_range_list"] = data_range_list |
| |
| return TosaTensorValuesGen.tvgLazyGenDefault( |
| testGen, rng, opName, dtypeList, shapeList, argsDict, 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 _add_data_generators(testGen, opName, shapeList, dtype, arg_list, error_name): |
| """Add extra tests for each type of data generator for this op.""" |
| if ( |
| error_name is None |
| and "data_gen" in testGen.TOSA_OP_LIST[opName] |
| and gtu.dtypeIsSupportedByCompliance(dtype) |
| ): |
| if gtu.dtypeIsFloat(dtype): |
| dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"]["fp"] |
| else: |
| dataGenTypesList = testGen.TOSA_OP_LIST[opName]["data_gen"]["int"] |
| else: |
| # Error test or No data generator types listed - assume random |
| dataGenTypesList = (gtu.DataGenType.PSEUDO_RANDOM,) |
| |
| # Expand arg list with other data generator types |
| new_arg_list = [] |
| for dg_type in dataGenTypesList: |
| for arg_str, args_dict in arg_list: |
| |
| if dg_type == gtu.DataGenType.FULL_RANGE: |
| tensor_size = gtu.product(shapeList[0]) |
| if tensor_size >= gtu.DTYPE_ATTRIBUTES[dtype]["fullset"]: |
| # Large enough tensor data size for full range, add a single test |
| num_test_sets = 0 |
| else: |
| # Not enough data size for full range of values, revert to random numbers |
| dg_type = gtu.DataGenType.PSEUDO_RANDOM |
| |
| if dg_type == gtu.DataGenType.PSEUDO_RANDOM: |
| if error_name is None: |
| num_test_sets = ( |
| args_dict["num_test_sets"] |
| if "num_test_sets" in args_dict |
| else 0 |
| ) |
| else: |
| # Add single test for pseudo random |
| num_test_sets = 0 |
| |
| elif dg_type == gtu.DataGenType.DOT_PRODUCT: |
| # Extra tests for each dot product test set |
| dot_products = args_dict["dot_products"] |
| if dot_products < testGen.TOSA_MI_DOT_PRODUCT_MIN: |
| shape_info = ( |
| " ({})".format(testGen.shapeStr(args_dict["shape"])) |
| if "shape" in args_dict |
| else "" |
| ) |
| logger.info( |
| f"Skipping {opName}{shape_info} dot product test as too few calculations {dot_products} < {testGen.TOSA_MI_DOT_PRODUCT_MIN}" |
| ) |
| continue |
| # KS and acc_type is required by all dot product generators |
| assert "ks" in args_dict |
| assert "acc_type" in args_dict |
| |
| num_test_sets = testGen.TOSA_MI_DOT_PRODUCT_TEST_SETS |
| |
| if num_test_sets > 0: |
| for s in range(0, num_test_sets): |
| set_arg_str = f"{arg_str}_s{s}" if arg_str else f"s{s}" |
| set_args_dict = args_dict.copy() |
| set_args_dict["s"] = s |
| set_args_dict["dg_type"] = dg_type |
| new_arg_list.append((set_arg_str, set_args_dict)) |
| else: |
| # Default is a single test |
| new_args_dict = args_dict.copy() |
| new_args_dict["dg_type"] = dg_type |
| new_arg_list.append((arg_str, new_args_dict)) |
| |
| return new_arg_list |
| |
| @staticmethod |
| def agNone(testGen, rng, opName, shapeList, dtype, error_name=None): |
| """A trivial argument generator for operators that don't take any |
| non-tensor arguments""" |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| [("", {})], |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agPow(testGen, rng, opName, shapeList, dtype, error_name=None): |
| """Pow operator needs different test sets to cover random numbers |
| without creating NaNs or Infs""" |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| [("", {"num_test_sets": 3})], |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agAxis(testGen, rng, opName, shapeList, dtype, error_name=None): |
| """Build the axis argument for operators that take a single axis""" |
| arg_list = [] |
| shape = shapeList[0] |
| |
| if error_name == ErrorIf.AxisSmallerZero: |
| # Set too small axis |
| axes = [rng.integers(-5, 0)] |
| elif error_name == ErrorIf.AxisLargerRank: |
| # Set too large axis |
| axes = [rng.integers(len(shape) + 1, len(shape) + 10)] |
| else: |
| # Create tests for each dimension |
| axes = range(0, len(shape)) |
| |
| opid = testGen.TOSA_OP_LIST[opName]["op"] |
| |
| for a in axes: |
| args_dict = {"axis": int(a)} |
| if opid == Op.REDUCE_SUM: |
| output_shape = shape.copy() |
| if error_name is None: |
| # It only matters that we calculate the dot_products correctly |
| # for non error_if tests as they should never be run |
| output_shape[a] = 1 |
| args_dict["dot_products"] = gtu.product(output_shape) |
| args_dict["shape"] = shape |
| args_dict["ks"] = int(shape[a]) if a >= 0 and a < len(shape) else 1 |
| args_dict["acc_type"] = dtype if dtype != DType.BF16 else DType.FP32 |
| |
| arg_list.append(("axis{}".format(a), args_dict)) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def _calculate_sparsity(num_tests, sparsity_factor): |
| sparsity = num_tests // 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 |
| return sparsity |
| |
| @staticmethod |
| def agConv(testGen, rng, opName, shapeList, dtypes, error_name=None): |
| # Used by CONV2D, CONV3D and DEPTHWISE_CONV2D |
| arg_list = [] |
| |
| if testGen.args.level8k and error_name is not None: |
| # Don't produce negative large tests |
| return arg_list |
| |
| # Shape: Batches, (Depth), Height, Width, Channels |
| ifm_shape = shapeList[0] |
| # Shape: (OFM channels), (KD), KH, KW, IFM channels |
| filter_shape = shapeList[1] |
| |
| accum_dtype = gtu.get_accum_dtype_from_tgTypes(dtypes) |
| |
| # Op type checks |
| conv3d = opName.startswith("conv3d") |
| depthwise = opName.startswith("depthwise") |
| |
| # Check the rank |
| rank = 5 if conv3d else 4 |
| if error_name != ErrorIf.WrongRank: |
| assert len(ifm_shape) == rank |
| assert len(filter_shape) == rank |
| |
| # kernel rank omits channels |
| k_rank = rank - 2 |
| k_pos = 0 if depthwise else 1 |
| k_shape = tuple(filter_shape[k_pos : (k_pos + k_rank)]) |
| # compliance size - KS |
| k_size = gtu.product(k_shape) |
| if not depthwise: |
| k_size *= ifm_shape[-1] |
| |
| if not testGen.args.level8k: |
| # Generate comprehensive argument lists |
| # - except for named errors, which use specific invalid value(s) |
| if error_name == ErrorIf.PadSmallerZero: |
| p_vals = [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 = [rng.choice(range(-5, 0))] |
| else: |
| # Stride must be greater than 1 to force non-integer error |
| startStride = ( |
| 1 if error_name != ErrorIf.ConvOutputShapeNonInteger else 2 |
| ) |
| s_vals = [ |
| x for x in range(startStride, testGen.args.max_conv_stride + 1) |
| ] |
| strides = {x for x in itertools.product(*([s_vals] * k_rank))} |
| if error_name == ErrorIf.DilationSmallerOne: |
| d_vals = [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))} |
| ) |
| max_dim_size = None |
| |
| # There are too many parameter combinations, so generate them sparsely, |
| # very sparse for negative tests |
| sparsity_factor = 2 if error_name else 120 |
| sparsity = TosaArgGen._calculate_sparsity( |
| len(paddings) * len(strides) * len(dilations), sparsity_factor |
| ) |
| else: |
| # Only test 8k levels boundaries |
| bigStride = testGen.TOSA_8K_LEVEL_MAX_STRIDE |
| bigKernel = testGen.TOSA_8K_LEVEL_MAX_KERNEL |
| bigPadding = bigKernel |
| |
| dilation_shape = [1] * k_rank |
| pad_shape = [0] * k_rank * 2 |
| if conv3d: |
| # Small stride apart from for big kernel (see below) to keep |
| # tensor size/calculation small |
| stride_shape = [1] * k_rank |
| for idx in range(k_rank): |
| pad_offset = idx * 2 |
| if k_shape[idx] == bigKernel: |
| # Padding shape needs to account for tensor shape |
| pad_shape[pad_offset] = bigPadding - ifm_shape[idx + 1] |
| pad_shape[pad_offset + 1] = bigPadding - dilation_shape[idx] + 1 |
| # Big stride to reduce output size |
| stride_shape[idx] = bigKernel |
| else: |
| # Account for kernel size |
| pad_shape[pad_offset] = k_shape[idx] - 1 |
| else: |
| # Always have a large stride with extra padding and dilation to keep |
| # tensor calculation reasonable |
| stride_shape = [bigKernel] * k_rank |
| for idx in range(k_rank): |
| # Dilation shape must account for kernel size |
| dilation_shape[idx] = bigKernel // k_shape[idx] |
| # Padding shape needs to accommodate tensor/kernel & dilation |
| pad_offset = idx * 2 |
| pad_shape[pad_offset] = bigPadding - ifm_shape[idx + 1] |
| pad_shape[pad_offset + 1] = bigPadding - dilation_shape[idx] + 1 |
| |
| strides = {tuple(stride_shape)} |
| dilations = {tuple(dilation_shape)} |
| paddings = {tuple(pad_shape)} |
| # Create a limit for the output dimensions size |
| max_dim_size = testGen.TOSA_8K_LEVEL_MAX_KERNEL |
| |
| # Currently allow all combinations that are reasonable size |
| 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 |
| # the padded shape must exceed the dilation * kernel to get a positive |
| # sized output shape |
| and (ifm_shape[1] - 1 + p[0] + p[1]) > d[0] * (k_shape[0] - 1) |
| and (ifm_shape[2] - 1 + p[2] + p[3]) > d[1] * (k_shape[1] - 1) |
| and ( |
| k_rank < 3 |
| or ( |
| (ifm_shape[3] - 1 + p[4] + p[5]) |
| > d[2] * (k_shape[2] - 1) |
| ) |
| ) |
| ): |
| remainders = [] |
| outputs = [] |
| for index in range(k_rank): |
| pad_offset = index * 2 |
| partial = ( |
| ifm_shape[index + 1] |
| - 1 |
| + p[pad_offset] |
| + p[pad_offset + 1] |
| - (k_shape[index] - 1) * d[index] |
| ) |
| remainders.append(partial % s[index]) |
| outputs.append((partial // s[index]) + 1) |
| |
| if ( |
| # the parameters must produce integer exact output |
| error_name != ErrorIf.ConvOutputShapeNonInteger |
| and max(remainders) == 0 |
| ) or ( |
| error_name == ErrorIf.ConvOutputShapeNonInteger |
| and max(remainders) > 0 |
| ): |
| if ( |
| max_dim_size is not None |
| and max(outputs) >= max_dim_size |
| ): |
| # Test will consume too much memory - skip it |
| continue |
| |
| # Compliance - number of dot product calculations |
| if depthwise: |
| # N*OH*OW*C*M |
| dots = gtu.product( |
| (ifm_shape[0], *outputs, *filter_shape[2:]) |
| ) |
| else: |
| # N*OH*OW*OC or N*OD*OH*OW*OC |
| dots = gtu.product( |
| (ifm_shape[0], *outputs, filter_shape[0]) |
| ) |
| args_dict = { |
| "acc_type": accum_dtype, |
| "stride": s, |
| "pad": p, |
| "dilation": d, |
| "kernel": k_shape, |
| "ks": k_size, |
| "dot_products": dots, |
| "shape": ifm_shape, |
| } |
| |
| # Support for larger values than 9 needs different delimiter |
| delim = "" if max(s + p + d) <= 9 else "x" |
| arg_list.append( |
| ( |
| "acc{}_st{}_pad{}_dilat{}".format( |
| testGen.typeStr(accum_dtype), |
| delim.join([str(x) for x in s]), |
| delim.join([str(x) for x in p]), |
| delim.join([str(x) for x in d]), |
| ), |
| args_dict, |
| ) |
| ) |
| n += 1 |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtypes[0], |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agFullyConnected(testGen, rng, opName, shapeList, dtypes, error_name=None): |
| |
| assert isinstance(dtypes, (list, tuple)), f"{dtypes} unexpected" |
| input_dtype = dtypes[0] |
| |
| if error_name == ErrorIf.WrongOutputType: |
| accum_dtype = gtu.get_wrong_output_type(opName, rng, input_dtype) |
| elif error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| accum_dtype = DType.INT32 |
| else: |
| accum_dtype = gtu.get_accum_dtype_from_tgTypes(dtypes) |
| |
| # Set up compliance info |
| args_dict = { |
| "acc_type": accum_dtype, |
| "ks": int(shapeList[0][1]), # Set KS = IC, from input A (N,IC) |
| "dot_products": gtu.product((shapeList[0][0], shapeList[1][0])), |
| "shape": shapeList[0], |
| } |
| |
| arg_list = [(f"acc{testGen.typeStr(accum_dtype)}", args_dict)] |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| input_dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agMatMul(testGen, rng, opName, shapeList, dtype, error_name=None): |
| # Get valid accumulate type(s) |
| if dtype == DType.INT8: |
| accum_dtypes = [DType.INT32] |
| elif dtype == DType.INT16: |
| accum_dtypes = [DType.INT48] |
| elif dtype == DType.FP16: |
| accum_dtypes = [DType.FP16, DType.FP32] |
| elif dtype == DType.BF16: |
| accum_dtypes = [DType.FP32] |
| elif dtype == DType.FP32: |
| accum_dtypes = [DType.FP32] |
| elif dtype == DType.FP8E4M3 or dtype == DType.FP8E5M2: |
| accum_dtypes = [DType.FP16] |
| elif error_name is None: |
| assert False, f"Invalid I/O DType for MatMul: {DTypeNames[dtype]}" |
| |
| if error_name == ErrorIf.WrongOutputType: |
| # Get incorrect output dtype for ErrorIf case |
| accum_dtypes = [gtu.get_wrong_output_type(opName, rng, dtype)] |
| elif error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| accum_dtypes = [DType.INT32] |
| |
| # Set up compliance info |
| args_dict = { |
| "ks": int(shapeList[0][2]), # Set KS = C, from input A (N,H,C) |
| # Set dot_products = N*H*W |
| "dot_products": gtu.product( |
| (shapeList[0][0], shapeList[0][1], shapeList[1][2]) |
| ), |
| "shape": shapeList[0], |
| } |
| |
| # Create arg tuple of string and dict |
| arg_list = [] |
| for a in accum_dtypes: |
| d = args_dict.copy() |
| d["acc_type"] = a |
| arg_list.append((f"acc{testGen.typeStr(a)}", d)) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agTransposeConv2D(testGen, rng, opName, shapeList, dtypes, error_name=None): |
| arg_list = [] |
| |
| if testGen.args.level8k and error_name is not None: |
| # Don't produce negative large tests |
| return arg_list |
| |
| ifm_shape = shapeList[0] |
| filter_shape = shapeList[1] |
| |
| accum_dtype = gtu.get_accum_dtype_from_tgTypes(dtypes) |
| |
| # Must be rank 4 |
| if error_name != ErrorIf.WrongRank: |
| assert len(ifm_shape) == 4 |
| assert len(filter_shape) == 4 |
| |
| k_shape = tuple(filter_shape[1:3]) |
| # compliance size - KS |
| k_size = gtu.product((*k_shape, ifm_shape[3])) |
| |
| if not testGen.args.level8k: |
| # Generate comprehensive argument lists |
| # - except for named errors, which use specific invalid value(s) |
| smallest_padding_size = -min(k_shape[0], k_shape[1]) + 1 |
| if error_name == ErrorIf.PadLargerEqualKernel: |
| max_filter_size = -max(k_shape[0], k_shape[1]) |
| p_vals = [rng.choice(range(max_filter_size - 10, max_filter_size))] |
| else: |
| p_vals = [ |
| x |
| for x in range( |
| smallest_padding_size, testGen.args.max_conv_padding + 1 |
| ) |
| ] |
| paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| if error_name == ErrorIf.StrideSmallerOne: |
| # Can't use stride=0, as it is used to derive output shape, as a divisor |
| s_vals = [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 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( |
| *([[smallest_padding_size, bigPadding]] * 4) |
| ) |
| } |
| ) |
| bigStride = 8 |
| strides.update({x for x in itertools.product(*([[1, bigStride]] * 2))}) |
| |
| # There are too many parameter combinations, so generate them sparsely, |
| # very sparse for negative tests |
| sparsity_factor = 2 if error_name else 10 |
| sparsity = len(paddings) * len(strides) // 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 |
| else: |
| # Only test 8k levels boundaries |
| bigStride = testGen.TOSA_8K_LEVEL_MAX_STRIDE |
| bigKernel = testGen.TOSA_8K_LEVEL_MAX_KERNEL |
| bigPadding = bigKernel |
| |
| pad_shape = [0] * (len(k_shape) * 2) |
| stride_shape = [1] * len(k_shape) |
| # The point at which input dimension combined with the stride will |
| # create large output sizes! |
| LARGE_SIZE = 2 |
| for idx in range(len(k_shape)): |
| pad_offset = idx * 2 |
| if k_shape[idx] == bigKernel: |
| # Set large stride |
| stride_shape[idx] = bigKernel |
| # Use negative output padding to reduce shape size |
| pad_shape[pad_offset] = -(bigPadding - 1) |
| if ifm_shape[idx + 1] > LARGE_SIZE: |
| pad_shape[pad_offset + 1] = -(bigPadding - 1) |
| else: |
| # The other dimension should be the bigKernel |
| alt_idx = 1 - idx |
| if ( |
| k_shape[alt_idx] == bigKernel |
| and ifm_shape[alt_idx + 1] < LARGE_SIZE |
| ): |
| # As the input is small, the large stride won't |
| # affect the output so we can add some padding |
| pad_shape[pad_offset + 1] = bigPadding |
| |
| strides = {tuple(stride_shape)} |
| paddings = {tuple(pad_shape)} |
| |
| # Currently allow all combinations that are reasonable size |
| sparsity = 1 |
| |
| n = 0 |
| for s in sorted(list(strides)): |
| for p in sorted(list(paddings)): |
| if n % sparsity == 0: |
| # Determine the output shape |
| oh = (ifm_shape[1] - 1) * s[0] + p[0] + p[1] + k_shape[0] |
| ow = (ifm_shape[2] - 1) * s[1] + p[2] + p[3] + k_shape[1] |
| os = [ifm_shape[0], oh, ow, filter_shape[0]] |
| |
| # N*OH*OW*OC |
| dots = gtu.product((ifm_shape[0], oh, ow, filter_shape[0])) |
| args_dict = { |
| "acc_type": accum_dtype, |
| "stride": s, |
| "pad": p, |
| "kernel": k_shape, |
| "ks": k_size, |
| "dot_products": dots, |
| "shape": ifm_shape, |
| "out_shape": os, |
| } |
| |
| # Support for larger values than 9 needs different delimiter |
| delim = "" if max(s + p) <= 9 else "x" |
| arg_list.append( |
| ( |
| "acc{}_st{}_pad{}_os{}".format( |
| testGen.typeStr(accum_dtype), |
| delim.join([str(x) for x in s]), |
| delim.join([str(x) for x in p]), |
| "x".join([str(x) for x in os]), |
| ), |
| args_dict, |
| ) |
| ) |
| n += 1 |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtypes[0], |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agPad(testGen, rng, opName, shapeList, dtype, error_name=None): |
| 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 = rng.randNumberDType(dtype) |
| pad_const_fp = 0 |
| elif gtu.dtypeIsFloat(dtype): |
| pad_const_int = 0 |
| pad_const_fp = rng.randNumberDType(dtype) |
| else: |
| return [] |
| |
| list_shape_pad_values = list(shape_pad_values) |
| # If we are producing tests for rank 6 or greater use sparsity |
| if len(list_shape_pad_values) > 1024: |
| sparsity_factor = 2 if error_name else 120 |
| sparsity = TosaArgGen._calculate_sparsity( |
| len(list_shape_pad_values), sparsity_factor |
| ) |
| else: |
| sparsity = 1 |
| |
| # Build arg list |
| arg_list = [] |
| for n, paddings in enumerate(list_shape_pad_values): |
| paddings = list(paddings) |
| args_valid = True |
| |
| if error_name == ErrorIf.PadSmallerZero: |
| # Prevent negative output shapes while ensuring still testing for negative padding |
| for i in range(rank): |
| dim_after_padding = ( |
| paddings[i][0] + paddings[i][1] + shapeList[0][i] |
| ) |
| if dim_after_padding < 1: |
| paddings[i] = (0, 0) |
| if all([p > -1 for p in paddings[i]]): |
| args_valid = False |
| if args_valid and n % sparsity == 0: |
| name = "pad" |
| for r in range(rank): |
| before, after = paddings[r] |
| name = f"{name}{before}{after}" |
| args_dict = { |
| "pad": np.array(paddings), |
| "pad_const_int": pad_const_int, |
| "pad_const_fp": pad_const_fp, |
| } |
| arg_list.append((name, args_dict)) |
| |
| if error_name == ErrorIf.PadSmallerZero and len(arg_list) == 0: |
| logger.info(f"No ErrorIf test created for input shape: {shapeList[0]}") |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agPooling(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| shape = shapeList[0] |
| if error_name != ErrorIf.WrongRank: |
| assert len(shape) == 4 |
| |
| test_level8k = testGen.args.level8k and error_name is None |
| |
| startStride = 1 if error_name != ErrorIf.PoolingOutputShapeNonInteger else 2 |
| startKernel = 2 |
| startPad = 0 |
| if not test_level8k: |
| # Generate comprehensive argument lists |
| p_vals = [x for x in range(startPad, testGen.args.max_pooling_padding + 1)] |
| paddings = {x for x in itertools.product(*([p_vals] * 4))} |
| # Stride must be greater than 1 to force non-integer error |
| s_vals = [ |
| x for x in range(startStride, testGen.args.max_pooling_stride + 1) |
| ] |
| strides = {x for x in itertools.product(*([s_vals] * 2))} |
| k_vals = [ |
| x for x in range(startKernel, testGen.args.max_pooling_kernel + 1) |
| ] |
| kernels = {x for x in itertools.product(*([k_vals] * 2))} |
| max_dim_size = None |
| else: |
| # Only test 8k levels |
| bigStride = testGen.TOSA_8K_LEVEL_MAX_STRIDE |
| bigKernel = testGen.TOSA_8K_LEVEL_MAX_KERNEL |
| strides = {(1, bigStride), (bigStride, 4)} |
| kernels = {(1, bigKernel), (bigKernel, 3)} |
| paddings = set() |
| for s in sorted(list(strides)): |
| for k in sorted(list(kernels)): |
| padding = [] |
| for idx in range(len(k)): |
| total_padding = s[idx] - shape[idx + 1] + k[idx] |
| while total_padding < 0: |
| # Must meet: shape + padding > kernel |
| total_padding += s[idx] |
| if total_padding < k[idx]: |
| padding.extend([0, total_padding]) |
| else: |
| # Note this may produce padding >= k[idx] which is not |
| # allowed - but will be ignored in the creation loop below |
| padding.extend([k[idx] - 1, total_padding - (k[idx] - 1)]) |
| paddings.add(tuple(padding)) |
| # Create a limit for the output dimensions size |
| max_dim_size = testGen.TOSA_8K_LEVEL_MAX_KERNEL |
| |
| if opName == "max_pool2d": |
| accum_dtypes = [None] # max_pool has no accumulate dtype |
| elif dtype == DType.INT8 or dtype == DType.INT16: |
| accum_dtypes = [DType.INT32] |
| elif dtype == DType.FP16: |
| accum_dtypes = [DType.FP16, DType.FP32] |
| elif dtype == DType.BF16 or dtype == DType.FP32: |
| accum_dtypes = [DType.FP32] |
| elif dtype == DType.FP8E4M3 or dtype == DType.FP8E5M2: |
| accum_dtypes = [DType.FP16] |
| elif error_name is None: |
| assert False, f"Invalid I/O DType for pooling: {DTypeNames[dtype]}" |
| else: |
| # Set to something for the ErrorIf case which has |
| # incorrect input data-type |
| accum_dtypes = [DType.INT32] |
| |
| if error_name == ErrorIf.WrongAccumulatorType: |
| accum_dtypes = list(gtu.usableDTypes(excludes=accum_dtypes)) |
| |
| if not test_level8k: |
| if testGen.args.oversize: |
| # add some oversize argument values |
| bigStride = 7 |
| bigKernel = 9 |
| strides.update( |
| {x for x in itertools.product(*([[startStride, bigStride]] * 2))} |
| ) |
| kernels.update( |
| {x for x in itertools.product(*([[startKernel, 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(*([[startPad, 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 |
| ) |
| else: |
| # We have already limited test output combinations for 8k tests |
| sparsity = 1 |
| |
| arg_str = ( |
| "acc{}_st{}_kern{}_pad{}" |
| if accum_dtypes[0] is not None |
| else "st{}_kern{}_pad{}" |
| ) |
| |
| def get_arg_list_element(accum, stride, pad, kern, dot_products=0, shape=[]): |
| # Return tuple containing the formatted argument string and |
| # the corresponding argument values in a dictionary |
| |
| # Support for larger values than 9 needs different delimiter |
| delim = "" if max(stride + kern + pad) <= 9 else "x" |
| arg_str_elems = [ |
| delim.join([str(x) for x in stride]), |
| delim.join([str(x) for x in kern]), |
| delim.join([str(x) for x in pad]), |
| ] |
| args_dict = { |
| "stride": stride, |
| "pad": pad, |
| "kernel": kern, |
| "dot_products": dot_products, # Ignored for error tests |
| "shape": shape, |
| "ks": gtu.product(kern), # avg_pool2d: KS = KX*KY |
| } |
| |
| if accum is not None: |
| arg_str_elems.insert(0, testGen.typeStr(accum)) |
| args_dict["acc_type"] = accum |
| return (arg_str.format(*arg_str_elems), args_dict) |
| |
| n = 0 |
| for a in accum_dtypes: |
| 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( |
| rng, error_name, s, p, k |
| ) |
| if None not in [sNew, pNew, kNew] and n % sparsity == 0: |
| arg_list.append( |
| get_arg_list_element(a, sNew, pNew, kNew, shape) |
| ) |
| 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] |
| ): |
| partial_h = shape[1] + p[0] + p[1] - k[0] |
| partial_w = shape[2] + p[2] + p[3] - k[1] |
| remainder_h = partial_h % s[0] |
| remainder_w = partial_w % s[1] |
| output_h = partial_h // s[0] + 1 |
| output_w = partial_w // s[1] + 1 |
| logger.debug( |
| f"agPooling: {shape} remainder=({remainder_h}, {remainder_w}) output=({output_h}, {output_w})" |
| ) |
| if ( |
| # the parameters must produce integer exact output |
| error_name != ErrorIf.PoolingOutputShapeNonInteger |
| and remainder_h == 0 |
| and remainder_w == 0 |
| ) or ( |
| error_name == ErrorIf.PoolingOutputShapeNonInteger |
| and (remainder_h != 0 or remainder_w != 0) |
| ): |
| if ( |
| max_dim_size is not None |
| and max(output_h, output_w) > max_dim_size |
| ): |
| # Test will consume too much memory - skip it |
| continue |
| # Dot products = N*OH*OW*C |
| dp = gtu.product( |
| (shape[0], output_h, output_w, shape[3]) |
| ) |
| arg_list.append( |
| get_arg_list_element(a, s, p, k, dp, shape) |
| ) |
| n += 1 |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agCast(testGen, rng, opName, shapeList, inDtype, error_name=None): |
| arg_list = [] |
| |
| # Enumerate the output types here |
| if error_name == ErrorIf.WrongOutputType: |
| dtypeList = TosaErrorIfArgGen.eiCastErrorIf(inDtype) |
| elif inDtype == DType.INT8: |
| dtypeList = [ |
| DType.BOOL, |
| DType.INT16, |
| DType.INT32, |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ] |
| elif inDtype == DType.INT16: |
| dtypeList = [ |
| DType.BOOL, |
| DType.INT8, |
| DType.INT32, |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ] |
| elif inDtype == DType.INT32: |
| dtypeList = [ |
| DType.BOOL, |
| DType.INT8, |
| DType.INT16, |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ] |
| elif inDtype == DType.BOOL: |
| dtypeList = [DType.INT8, DType.INT16, DType.INT32] |
| elif inDtype == DType.FP16: |
| dtypeList = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.FP32, |
| DType.FP8E4M3, |
| DType.FP8E5M2, |
| ] |
| elif inDtype == DType.BF16: |
| dtypeList = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.FP32, |
| DType.FP8E4M3, |
| DType.FP8E5M2, |
| ] |
| elif inDtype == DType.FP32: |
| dtypeList = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.FP16, |
| DType.BF16, |
| DType.FP8E4M3, |
| DType.FP8E5M2, |
| ] |
| elif inDtype in [DType.FP8E4M3, DType.FP8E5M2]: |
| dtypeList = [DType.FP16, DType.BF16, DType.FP32] |
| elif error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output type for incorrect input type |
| dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FP32] |
| else: |
| raise Exception("Unexpected input dtype: {}".format(inDtype)) |
| |
| for dtype in dtypeList: |
| arg_list.append( |
| ("out{}".format(testGen.typeStr(dtype)), {"out_type": dtype}) |
| ) |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| |
| return arg_list |
| |
| @staticmethod |
| def agRescale(testGen, rng, opName, shapeList, inDtype, error_name=None): |
| arg_list = [] |
| |
| # Enumerate the output types here |
| for outDtype in [ |
| DType.UINT8, |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.UINT16, |
| ]: |
| if ( |
| outDtype in [DType.UINT8, DType.INT8, DType.UINT16] |
| and error_name == ErrorIf.OutputZeroPointNotZero |
| ): |
| continue |
| if ( |
| outDtype != DType.UINT16 |
| and error_name == ErrorIf.U16OutputZeroPointNotValid |
| ) or ( |
| inDtype != DType.UINT16 |
| and error_name == ErrorIf.U16InputZeroPointNotValid |
| ): |
| # ErrorIfs only valid with UINT16 |
| continue |
| if ( |
| inDtype == DType.UINT8 |
| and outDtype not in [DType.INT8, DType.INT16] |
| and error_name != ErrorIf.WrongOutputType |
| ): |
| # The only output dtypes for UINT8 are INT8/INT16, skip all others |
| continue |
| if ( |
| inDtype not in [DType.INT8, DType.INT16] |
| and outDtype == DType.UINT8 |
| and error_name != ErrorIf.WrongOutputType |
| ): |
| # The only input dtypes for UINT8 are INT8/INT16, skip all others |
| continue |
| if ( |
| inDtype == DType.UINT16 |
| and outDtype != DType.INT16 |
| and error_name != ErrorIf.WrongOutputType |
| ): |
| # The only output dtype for UINT16 is INT16, skip all others |
| continue |
| if ( |
| inDtype != DType.INT16 |
| and outDtype == DType.UINT16 |
| and error_name != ErrorIf.WrongOutputType |
| ): |
| # The only input dtype for UINT16 is INT16, skip all others |
| continue |
| if ( |
| error_name == ErrorIf.WrongOutputType |
| and not TosaErrorIfArgGen.eiRescaleWrongOutputType(inDtype, outDtype) |
| ): |
| 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 |
| |
| if per_channel: |
| nc = shapeList[0][-1] |
| else: |
| nc = 1 |
| |
| in_type_width = gtu.dtypeWidth(inDtype) |
| out_type_width = gtu.dtypeWidth(outDtype) |
| |
| # Calculate scale based on: |
| # scale = a *(2^output_width)/(2^input_width)) |
| |
| a = np.float32(rng.random(size=[nc])) |
| scale_arr = a * np.float32( |
| (1 << out_type_width) / (1 << in_type_width) |
| ) |
| |
| if scale32: |
| # 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) |
| |
| logger.debug( |
| f"agRescale: {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 |
| ) |
| |
| arg_list.append( |
| ( |
| "out{}_sc{}_dr{}_pc{}".format( |
| testGen.typeStr(outDtype), |
| int(scale32), |
| int(double_round), |
| int(per_channel), |
| ), |
| { |
| "output_dtype": outDtype, |
| "scale": scale32, |
| "double_round": double_round, |
| "per_channel": per_channel, |
| "multiplier": multiplier_arr, |
| "shift": shift_arr, |
| }, |
| ) |
| ) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| inDtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agMul(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| if dtype is DType.INT32: |
| for p in range(testGen.args.num_rand_permutations): |
| |
| shift = rng.randInt(0, 32) |
| arg_list.append(("perm{}_shift{}".format(p, shift), {"shift": shift})) |
| else: |
| arg_list.append(("perm0_shift0", {"shift": 0})) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agArithmeticRightShift(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| for round in (True, False): |
| args_dict = { |
| "round": round, |
| } |
| arg_list.append((f"round{round}", args_dict)) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agFFT2d(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| shape = shapeList[0] |
| dot_products = gtu.product(shape) |
| ks = 2 * shape[1] * shape[2] # 2*H*W |
| for inverse in (True, False): |
| args_dict = { |
| "dot_products": dot_products, |
| "shape": shape, |
| "ks": ks, |
| "acc_type": dtype, |
| "inverse": inverse, |
| } |
| arg_list.append((f"inverse{inverse}", args_dict)) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agRFFT2d(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| shape = shapeList[0] |
| dot_products = gtu.product(shape) |
| ks = shape[1] * shape[2] # H*W |
| args_dict = { |
| "dot_products": dot_products, |
| "shape": shape, |
| "ks": ks, |
| "acc_type": dtype, |
| } |
| arg_list.append(("", args_dict)) |
| |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| 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, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| origShape = shapeList[0] |
| totalElements = gtu.product(origShape) |
| factors = TosaArgGen.getFactors(totalElements) |
| |
| # Find new shapes up to the number of permutations asked for |
| # This code is NOT fast. Fortunately, the numbers are fairly small. |
| for p in range(testGen.args.num_rand_permutations): |
| # Rank from 1 to TOSA_TENSOR_MAX_RANK |
| newRank = rng.randInt(1, (testGen.TOSA_TENSOR_MAX_RANK + 1)) |
| if len(factors) < newRank: |
| continue |
| |
| # escape_counter limits the generation of new shapes to a reasonable time |
| for escape_counter in range(100): |
| |
| # Generate the new shape of the chosen new rank |
| newShape = [] |
| remainingElements = totalElements |
| shuffledFactors = rng.permutation(factors) |
| for i in range(1, newRank): |
| # pick rank-1 factors |
| newShape.append(shuffledFactors[0]) |
| remainingElements = remainingElements // shuffledFactors[0] |
| shuffledFactors = rng.permutation( |
| TosaArgGen.getFactors(remainingElements) |
| ) |
| newShape.append(remainingElements) |
| |
| # Check for duplicates |
| duplicate = False |
| for name, args_dict in arg_list: |
| if args_dict["new_shape"] == newShape: |
| duplicate = True |
| break |
| |
| if not duplicate: |
| outShape = "x".join([str(x) for x in newShape]) |
| arg_list.append( |
| ( |
| "perm{}_rank{}_out{}".format(p, newRank, outShape), |
| {"new_shape": newShape}, |
| ) |
| ) |
| # Found an output shape for this permutation |
| break |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| |
| return arg_list |
| |
| @staticmethod |
| def agTranspose(testGen, rng, 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 = 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 = rng.permutation(permutations) |
| |
| # Create list of required amount of permutations |
| arg_list = [ |
| ("perm{}".format(p), {"perms": random_permutations[p].tolist()}) |
| for p in range(limit) |
| ] |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agSlice(testGen, rng, 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(rng.randInt(0, ifm_shape[i])) |
| size.append(rng.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( |
| rng, error_name, ifm_shape, start, size |
| ) |
| arg_list.append(("perm{}".format(p), {"start": start, "size": size})) |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agTile(testGen, rng, 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(rng.randInt(1, 4)) |
| arg_list.append(("perm{}".format(p), {"multiples": multiples})) |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agResize(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| ifm_shape = shapeList[0] |
| |
| def get_aspect_ratio_resize_params(): |
| common_aspect_ratios = ((3, 2), (16, 9), (4, 3)) |
| aspect_ratio = rng.choice(common_aspect_ratios) |
| invert = rng.choice((False, True)) |
| letterbox = rng.choice((False, True)) |
| |
| scale_y_n = aspect_ratio[0] if invert else aspect_ratio[1] |
| scale_x_n = aspect_ratio[1] if invert else aspect_ratio[0] |
| scale_y_d = scale_x_d = 1 |
| offset_x = offset_y = 0 |
| |
| if letterbox: |
| max_border = scale_y_n |
| border_y = rng.randInt(low=0, high=max_border) |
| border_x = 0 |
| else: |
| # Pillarboxing |
| border_y = 0 |
| max_border = scale_x_n |
| border_x = rng.randInt(low=0, high=max_border) |
| |
| scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d) |
| offset = (offset_y, offset_x) |
| border = (border_y, border_x) |
| |
| return scale, offset, border |
| |
| def get_upscale_downscale_params(): |
| valid_params = False |
| while not valid_params: |
| upscale = rng.choice((False, True)) |
| |
| # True if sampling begins from (0,0). Otherwise (-0.5,-0.5) |
| origin_sampling = rng.choice((False, True)) |
| |
| if upscale: |
| shift = rng.randInt(low=1, high=4) |
| scale_x_d = scale_y_d = 1 |
| scale_x_n = scale_y_n = ( |
| 1 << shift if origin_sampling else 2 << shift |
| ) |
| border_x = border_y = 0 if origin_sampling else (1 << shift) - 1 |
| offset_x = offset_y = 0 if origin_sampling else -(1 << shift) + 1 |
| else: |
| scale_x_n = 1 |
| scale_y_n = 1 |
| |
| # Return list of valid scale_*_d values (max value 4) given input dim shape |
| def get_valid_denom(ifm_dim): |
| return [x for x in range(1, 5) if ifm_dim % x == 1] |
| |
| # Generate list of valid downscale values and choose one randomly |
| valid_scale_y_ds = get_valid_denom(ifm_shape[1]) |
| valid_scale_x_ds = get_valid_denom(ifm_shape[2]) |
| |
| if not valid_scale_y_ds and not valid_scale_x_ds: |
| # Bad parameters, skip |
| continue |
| |
| if not valid_scale_y_ds: |
| scale_y_d = 1 |
| else: |
| scale_y_d = rng.choice(valid_scale_y_ds) |
| |
| if not valid_scale_x_ds: |
| scale_x_d = 1 |
| else: |
| scale_x_d = rng.choice(valid_scale_x_ds) |
| |
| border_x = border_y = 0 |
| offset_y = rng.randInt(0, 16 * scale_y_n) |
| offset_x = rng.randInt(0, 16 * scale_x_n) |
| valid_params = True |
| |
| scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d) |
| offset = (offset_y, offset_x) |
| border = (border_y, border_x) |
| return scale, offset, border |
| |
| def get_rand_params(): |
| def fix_scale_to_max_scale(scale_n, scale_d, max_scale): |
| scale = scale_n / scale_d |
| if scale > max_scale: |
| factor = scale / max_scale |
| new_scale_d = math.ceil(scale_d * factor) |
| assert scale_n / new_scale_d <= max_scale |
| scale_d = new_scale_d |
| return scale_d |
| |
| # Scale |
| scale_y_n = rng.randInt(low=1, high=(1 << 11)) |
| scale_x_n = rng.randInt(low=1, high=(1 << 11)) |
| |
| scale_y_d = rng.randInt(low=1, high=(16 * scale_y_n)) |
| scale_x_d = rng.randInt(low=1, high=(16 * scale_x_n)) |
| |
| scale_y_d = fix_scale_to_max_scale( |
| scale_y_n, scale_y_d, testGen.TOSA_8K_LEVEL_MAX_SCALE |
| ) |
| scale_x_d = fix_scale_to_max_scale( |
| scale_x_n, scale_x_d, testGen.TOSA_8K_LEVEL_MAX_SCALE |
| ) |
| |
| # Offsets and border within the scale |
| offset_y = rng.randInt(low=-scale_y_n, high=(16 * scale_y_n)) |
| offset_x = rng.randInt(low=-scale_x_n, high=(16 * scale_x_n)) |
| border_y = rng.randInt(low=(-16 * scale_y_n), high=scale_y_n) |
| border_x = rng.randInt(low=(-16 * scale_x_n), high=scale_x_n) |
| |
| scale = (scale_y_n, scale_y_d, scale_x_n, scale_x_d) |
| offset = (offset_y, offset_x) |
| border = (border_y, border_x) |
| return scale, offset, border |
| |
| def get_level_8k_params(): |
| # Create 64x scale - 64/1 to 2048/32 |
| scale_d = rng.randInt( |
| low=1, high=(1 << 11) / testGen.TOSA_8K_LEVEL_MAX_SCALE |
| ) |
| scale_n = scale_d * testGen.TOSA_8K_LEVEL_MAX_SCALE |
| # Create half to fifth scaling |
| scale_d_alt = rng.randInt(low=2, high=6) |
| scale_n_alt = 1 |
| switch = rng.choice((False, True)) |
| if switch: |
| scale = (scale_n_alt, scale_d_alt, scale_n, scale_d) |
| else: |
| scale = (scale_n, scale_d, scale_n_alt, scale_d_alt) |
| |
| offset_y = rng.choice((-scale[0], 0, (16 * scale[0]) - 1)) |
| offset_x = rng.choice((-scale[2], 0, (16 * scale[2]) - 1)) |
| offset = (offset_y, offset_x) |
| border_y = rng.choice((-16 * scale[0], 0, scale[0] - 1)) |
| border_x = rng.choice((-16 * scale[2], 0, scale[2] - 1)) |
| border = (border_y, border_x) |
| return scale, offset, border |
| |
| 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.FP16: |
| outputDTypeList = [DType.FP16] |
| elif dtype == DType.BF16: |
| outputDTypeList = [DType.BF16] |
| elif dtype == DType.FP32: |
| outputDTypeList = [DType.FP32] |
| elif dtype == DType.FP8E4M3: |
| outputDTypeList = [DType.FP8E4M3] |
| elif dtype == DType.FP8E5M2: |
| outputDTypeList = [DType.FP8E5M2] |
| 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 |
| |
| arg_str = "mode{}_out{}_sc{}x{}x{}x{}_off{}x{}_bor{}x{}" |
| |
| for outputDType in outputDTypeList: |
| perm = 0 |
| while perm < testGen.args.num_rand_permutations: |
| # Random choice of type of params we are testing |
| if not testGen.args.level8k: |
| _rnd_param_fn = rng.choice( |
| ( |
| get_rand_params, |
| get_upscale_downscale_params, |
| get_aspect_ratio_resize_params, |
| ) |
| ) |
| scale, offset, border = _rnd_param_fn() |
| else: |
| scale, offset, border = get_level_8k_params() |
| |
| # Expand params for bounds-checking |
| (scale_y_n, scale_y_d, scale_x_n, scale_x_d) = scale |
| (offset_y, offset_x) = offset |
| (border_y, border_x) = border |
| |
| # Make sure output dimensions OH and OW are integers |
| partial_output_y = ( |
| (ifm_shape[1] - 1) * scale_y_n - offset_y + border_y |
| ) |
| partial_output_x = ( |
| (ifm_shape[2] - 1) * scale_x_n - offset_x + border_x |
| ) |
| if error_name == ErrorIf.ResizeOutputShapeNonInteger: |
| # Look for non-integer test |
| if ( |
| partial_output_y % scale_y_d == 0 |
| and partial_output_x % scale_x_d == 0 |
| ): |
| # Skip this test as it doesn't produce NonInteger output |
| if perm > 0: |
| perm += 1 |
| continue |
| else: |
| # Alter the scaling factors to make the output integer |
| while partial_output_y % scale_y_d != 0: |
| scale_y_d -= 1 |
| while partial_output_x % scale_x_d != 0: |
| scale_x_d -= 1 |
| # Make sure we are still within max scaling |
| if ( |
| scale_y_n / scale_y_d |
| ) > testGen.TOSA_8K_LEVEL_MAX_SCALE or ( |
| scale_x_n / scale_x_d |
| ) > testGen.TOSA_8K_LEVEL_MAX_SCALE: |
| # Skip the test as it is using too large a scaling factor |
| if perm > 0: |
| perm += 1 |
| continue |
| |
| output_y = partial_output_y // scale_y_d + 1 |
| output_x = partial_output_x // scale_x_d + 1 |
| |
| if ( |
| output_y >= testGen.args.max_resize_output_dim |
| or output_x >= testGen.args.max_resize_output_dim |
| ) and error_name is None: |
| # Skip positive test if output dim will be too high |
| # Avoid high test latency and OOM issues |
| if not testGen.args.level8k or perm > 0: |
| perm += 1 |
| continue |
| |
| if ( |
| output_y <= 0 |
| or output_y >= gtu.MAX_RESIZE_DIMENSION |
| or output_x <= 0 |
| or output_x >= gtu.MAX_RESIZE_DIMENSION |
| ): |
| # Output dimensions out of scope |
| if error_name is not None and perm > 0: |
| # As long as we have one ERROR_IF test, don't worry |
| # about creating all the other permutations |
| perm += 1 |
| continue |
| |
| if error_name == ErrorIf.ResizeOutputShapeMismatch and ( |
| ( |
| output_y + scale_y_d >= gtu.MAX_RESIZE_DIMENSION |
| and output_y - scale_y_d < 1 |
| ) |
| or ( |
| output_x + scale_x_d >= gtu.MAX_RESIZE_DIMENSION |
| and output_x - scale_x_d < 1 |
| ) |
| ): |
| # Can't create a negative test with these params as it |
| # will create invalid output size |
| if perm > 0: |
| perm += 1 |
| continue |
| |
| scale = [scale_y_n, scale_y_d, scale_x_n, scale_x_d] |
| offset = [offset_y, offset_x] |
| border = [border_y, border_x] |
| |
| # Common for all data types |
| if error_name is not None: |
| ( |
| scale, |
| offset, |
| border, |
| outputDTypeNew, |
| ) = TosaErrorIfArgGen.eiResizeErrorIf( |
| rng, |
| error_name, |
| mode, |
| dtype, |
| shapeList, |
| outputDType, |
| scale, |
| offset, |
| border, |
| ) |
| else: |
| outputDTypeNew = outputDType |
| |
| arg_to_append = ( |
| arg_str.format( |
| "N" if mode == ResizeMode.NEAREST else "B", |
| testGen.typeStr(outputDTypeNew), |
| scale[0], |
| scale[1], |
| scale[2], |
| scale[3], |
| offset[0], |
| offset[1], |
| border[0], |
| border[1], |
| ), |
| { |
| "mode": mode, |
| "scale": scale, |
| "offset": offset, |
| "border": border, |
| "output_dtype": outputDTypeNew, |
| }, |
| ) |
| if arg_to_append in arg_list: |
| # Skip already generated test params |
| continue |
| |
| # Valid permutation |
| perm += 1 |
| arg_list.append(arg_to_append) |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| @staticmethod |
| def agTable(testGen, rng, opName, shapeList, dtype, error_name=None): |
| arg_list = [] |
| |
| if dtype == DType.INT8: |
| table = np.int32(rng.integers(low=-128, high=128, size=[256])).tolist() |
| else: # INT16 |
| table = np.int32(rng.integers(low=-32768, high=32768, size=[513])).tolist() |
| # Make sure all slopes are within REQUIRE min/max 16-bit int |
| for idx in range(len(table) - 1): |
| slope = table[idx + 1] - table[idx] |
| # Alter the next table entry to force the slope to be ok |
| if slope > 32767: |
| table[idx + 1] -= slope - 32767 |
| if slope < -32768: |
| table[idx + 1] -= slope + 32768 |
| slope = table[idx + 1] - table[idx] |
| assert slope <= 32767 and slope >= -32768 |
| arg_list.append( |
| ( |
| "", |
| {"table": table}, |
| ) |
| ) |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| def agCondIf(testGen, rng, 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)), {"condition": c})) |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |
| |
| def agWhileLoop(testGen, rng, opName, shapeList, dtype, error_name=None): |
| # While loop: 0 iterations, 1, more than 1 |
| arg_list = [] |
| |
| for iterations in [0, 1, 4]: |
| arg_list.append(("iter{}".format(iterations), {"iterations": iterations})) |
| |
| # Now add data generator types |
| arg_list = TosaArgGen._add_data_generators( |
| testGen, |
| opName, |
| shapeList, |
| dtype, |
| arg_list, |
| error_name, |
| ) |
| # Return list of tuples: (arg_str, args_dict) |
| return arg_list |