diff options
Diffstat (limited to 'verif/generator/tosa_arg_gen.py')
| -rw-r--r-- | verif/generator/tosa_arg_gen.py | 580 |
1 files changed, 292 insertions, 288 deletions
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py index a2ef5bf..83487a1 100644 --- a/verif/generator/tosa_arg_gen.py +++ b/verif/generator/tosa_arg_gen.py @@ -30,48 +30,48 @@ class TosaQuantGen: pass @staticmethod - def getZeroPoint(testGen, dtype, error_name=None): + def getZeroPoint(rng, zeropoint, dtype, error_name=None): if dtype == DType.INT8: - if testGen.args.zeropoint is not None: - return min(127, max(-128, testGen.args.zeropoint)) - return testGen.randInt(-128, 128) + if zeropoint is not None: + return min(127, max(-128, zeropoint)) + return rng.randInt(-128, 128) elif dtype == DType.UINT8: - if testGen.args.zeropoint is not None: - return min(255, max(0, testGen.args.zeropoint)) - return testGen.randInt(0, 256) + 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 = testGen.randInt(-128, 128) + zero_point = rng.randInt(-128, 128) if zero_point == 0: zero_point = 1 return zero_point return 0 @staticmethod - def qgUnary(testGen, op, dtype, error_name=None): + def qgUnary(rng, zeropoint, op, dtype, error_name=None): if error_name == ErrorIf.InputZeroPointNotZero: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtype, error_name), - TosaQuantGen.getZeroPoint(testGen, dtype), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), ] elif error_name == ErrorIf.OutputZeroPointNotZero: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtype), - TosaQuantGen.getZeroPoint(testGen, dtype, error_name), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), ] else: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtype), - TosaQuantGen.getZeroPoint(testGen, dtype), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), ] return qinfo @staticmethod - def qgConv(testGen, op, dtype_or_dtypeList, error_name=None): + 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 @@ -81,32 +81,32 @@ class TosaQuantGen: if error_name == ErrorIf.InputZeroPointNotZero: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtypeList[0], error_name), - TosaQuantGen.getZeroPoint(testGen, dtypeList[1]), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0], error_name), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1]), ] elif error_name == ErrorIf.WeightZeroPointNotZero: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtypeList[0]), - TosaQuantGen.getZeroPoint(testGen, dtypeList[1], error_name), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0]), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1], error_name), ] else: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtypeList[0]), - TosaQuantGen.getZeroPoint(testGen, dtypeList[1]), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[0]), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtypeList[1]), ] return qinfo @staticmethod - def qgMatmul(testGen, op, dtype, error_name=None): + def qgMatmul(rng, zeropoint, op, dtype, error_name=None): if error_name == ErrorIf.InputZeroPointNotZero: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtype, error_name), - TosaQuantGen.getZeroPoint(testGen, dtype, error_name), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype, error_name), ] else: qinfo = [ - TosaQuantGen.getZeroPoint(testGen, dtype), - TosaQuantGen.getZeroPoint(testGen, dtype), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), + TosaQuantGen.getZeroPoint(rng, zeropoint, dtype), ] return qinfo @@ -166,9 +166,9 @@ class TosaTensorGen: pass @staticmethod - def tgBasic(testGen, opName, rank, error_name=None): - pl, const = opName["operands"] - shape = testGen.makeShape(rank) + 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: @@ -181,20 +181,20 @@ class TosaTensorGen: # 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(rank + testGen.rng.choice([1, 2, 3])) + shape = testGen.makeShape(rng, rank + rng.choice([1, 2, 3])) elif i != 1: - shape = testGen.makeShape(rank + testGen.rng.choice([-1, 1])) + shape = testGen.makeShape(rng, rank + rng.choice([-1, 1])) return shape_list @staticmethod - def tgNHWC(testGen, opName, rank, error_name=None): - pl, const = opName["operands"] + def tgNHWC(testGen, rng, op, rank, error_name=None): + pl, const = op["operands"] if error_name != ErrorIf.WrongRank: assert rank == 4 - shape = testGen.makeShape(rank) + shape = testGen.makeShape(rng, rank) shape = testGen.constrictBatchSize(shape) # Constrict the overall size of the shape when creating ERROR_IF tests @@ -208,7 +208,7 @@ class TosaTensorGen: return shape_list @staticmethod - def tgGather(testGen, opName, rank, error_name=None): + def tgGather(testGen, rng, opName, rank, error_name=None): pl, const = opName["operands"] assert pl == 2 @@ -216,18 +216,18 @@ class TosaTensorGen: if error_name != ErrorIf.WrongRank: assert rank == 3 - values_shape = testGen.makeShape(rank) + values_shape = testGen.makeShape(rng, rank) values_shape = testGen.constrictBatchSize(values_shape) N = values_shape[0] - W = testGen.makeDimension() + W = testGen.makeDimension(rng) indices_shape = [N, W] shape_list = [values_shape, indices_shape] return shape_list @staticmethod - def tgScatter(testGen, opName, rank, error_name=None): + def tgScatter(testGen, rng, opName, rank, error_name=None): pl, const = opName["operands"] assert pl == 3 @@ -235,7 +235,7 @@ class TosaTensorGen: if error_name != ErrorIf.WrongRank: assert rank == 3 - values_in_shape = testGen.makeShape(rank) + values_in_shape = testGen.makeShape(rng, rank) values_in_shape = testGen.constrictBatchSize(values_in_shape) N = values_in_shape[0] @@ -246,7 +246,7 @@ class TosaTensorGen: # 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 = testGen.randInt(W_min, W_max) if W_min < W_max else W_min + W = rng.randInt(W_min, W_max) if W_min < W_max else W_min input_shape = [N, W, C] @@ -258,14 +258,14 @@ class TosaTensorGen: return shape_list @staticmethod - def _get_broadcast_shapes(testGen, num_shapes, rank, error_name=None): - shape = testGen.makeShape(rank) + 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 = testGen.randInt(0 if error_name is None else 1, num_shapes) - fuzz_idx = testGen.randInt(0, rank) + 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() @@ -278,13 +278,13 @@ class TosaTensorGen: if i == bcast_idx: if error_name == ErrorIf.RankMismatch: # Add one rank to the shape (or more for rank of 1) - extra_ranks = testGen.rng.choice([1, 2, 3]) if rank == 1 else 1 + extra_ranks = rng.choice([1, 2, 3]) if rank == 1 else 1 shape_bcast = np.concatenate( - (shape_bcast, testGen.makeShape(extra_ranks)) + (shape_bcast, testGen.makeShape(rng, extra_ranks)) ) if rank != 1: # Either keep the extra rank, or remove it - new_len = testGen.rng.choice([-2, len(shape_bcast)]) + new_len = rng.choice([-2, len(shape_bcast)]) shape_bcast = shape_bcast[:new_len] elif error_name == ErrorIf.BroadcastShapesMismatch: shape_bcast[fuzz_idx] += 2 @@ -296,30 +296,32 @@ class TosaTensorGen: return shape_list @staticmethod - def tgBroadcastFuzz(testGen, op, rank, error_name=None): + def tgBroadcastFuzz(testGen, rng, op, rank, error_name=None): pl, const = op["operands"] num_shapes = pl + const return TosaTensorGen._get_broadcast_shapes( - testGen, num_shapes, rank, error_name + testGen, rng, num_shapes, rank, error_name ) @staticmethod - def tgMul(testGen, op, rank, error_name=None): + 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, 2, rank, error_name) + 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, op, rank, error_name=None): + 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(rank) + ifm_shape = testGen.makeShape(rng, rank) ifm_shape = testGen.constrictBatchSize(ifm_shape) # Constrict the overall size of the shape when creating ERROR_IF tests @@ -332,7 +334,7 @@ class TosaTensorGen: filter_hw = op["filter"] # Generate a random OFM depth - ofm_depth = testGen.makeDimension() + 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]]) @@ -343,14 +345,14 @@ class TosaTensorGen: return [ifm_shape, filter_shape, bias_shape] @staticmethod - def tgConv3D(testGen, op, rank, error_name=None): + 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(rank) + ifm_shape = testGen.makeShape(rng, rank) ifm_shape = testGen.constrictBatchSize(ifm_shape) # Constrict the overall size of the shape when creating ERROR_IF tests @@ -363,7 +365,7 @@ class TosaTensorGen: filter_dhw = op["filter"] # Generate a random OFM channel - ofm_channel = testGen.makeDimension() + ofm_channel = testGen.makeDimension(rng) # The filter dimensions are ODHWI filter_shape = np.asarray( @@ -376,14 +378,14 @@ class TosaTensorGen: return [ifm_shape, filter_shape, bias_shape] @staticmethod - def tgTransposeConv2D(testGen, op, rank, error_name=None): + 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(rank) + ifm_shape = testGen.makeShape(rng, rank) ifm_shape = testGen.constrictBatchSize(ifm_shape) # Constrict the overall size of the shape when creating ERROR_IF tests @@ -396,7 +398,7 @@ class TosaTensorGen: filter_hw = op["filter"] # Generate a random OFM depth - ofm_depth = testGen.makeDimension() + 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]]) @@ -407,7 +409,7 @@ class TosaTensorGen: return [ifm_shape, filter_shape, bias_shape] @staticmethod - def tgDepthwiseConv2D(testGen, op, rank, error_name=None): + def tgDepthwiseConv2D(testGen, rng, op, rank, error_name=None): pl, const = op["operands"] if error_name != ErrorIf.WrongRank: @@ -415,7 +417,7 @@ class TosaTensorGen: assert pl == 1 and const == 2 # IFM dimensions are NHWC - ifm_shape = testGen.makeShape(rank) + ifm_shape = testGen.makeShape(rng, rank) ifm_shape = testGen.constrictBatchSize(ifm_shape) # Constrict the overall size of the shape when creating ERROR_IF tests @@ -431,7 +433,7 @@ class TosaTensorGen: # Generate a random OFM depth, but don't let it get too big because # the output depth is M * C filter_m = ( - testGen.makeDimension() % (testGen.args.tensor_shape_range[1] // 4) + testGen.makeDimension(rng) % (testGen.args.tensor_shape_range[1] // 4) ) + 1 # The filter dimensions are HWCM @@ -443,7 +445,7 @@ class TosaTensorGen: return [ifm_shape, filter_shape, bias_shape] @staticmethod - def tgFFT2d(testGen, op, rank, error_name=None): + def tgFFT2d(testGen, rng, op, rank, error_name=None): pl, const = op["operands"] if error_name != ErrorIf.WrongRank: @@ -451,7 +453,7 @@ class TosaTensorGen: assert pl == 2 and const == 0 # IFM dimensions are NHW - ifm_shape = testGen.makeShape(rank) + 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)) @@ -466,7 +468,7 @@ class TosaTensorGen: 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 = testGen.rng.choice(inc_choices) + selected_inc = rng.choice(inc_choices) ifm_shape[1] += selected_inc[0] ifm_shape[2] += selected_inc[1] @@ -474,15 +476,15 @@ class TosaTensorGen: ifm_shapes = [ifm_shape.copy(), ifm_shape.copy()] if error_name == ErrorIf.FFTInputShapeMismatch: - modify_shape = testGen.rng.choice([0, 1]) + modify_shape = rng.choice([0, 1]) # Only modify kernel (H, W) - modify_dim = testGen.rng.choice([1, 2]) + modify_dim = rng.choice([1, 2]) ifm_shapes[modify_shape][modify_dim] *= 2 return [ifm_shapes[0], ifm_shapes[1]] @staticmethod - def tgRFFT2d(testGen, op, rank, error_name=None): + def tgRFFT2d(testGen, rng, op, rank, error_name=None): pl, const = op["operands"] if error_name != ErrorIf.WrongRank: @@ -490,7 +492,7 @@ class TosaTensorGen: assert pl == 1 and const == 0 # IFM dimensions are NHW - ifm_shape = testGen.makeShape(rank) + 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)) @@ -506,7 +508,7 @@ class TosaTensorGen: 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 = testGen.rng.choice(inc_choices) + selected_inc = rng.choice(inc_choices) ifm_shape[1] += selected_inc[0] ifm_shape[2] += selected_inc[1] @@ -515,19 +517,19 @@ class TosaTensorGen: return [ifm_shape] @staticmethod - def tgFullyConnected(testGen, op, rank, error_name=None): + 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(rank) + 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 = testGen.rng.integers( + filter_oc = rng.integers( low=testGen.args.tensor_shape_range[0], high=testGen.args.tensor_shape_range[1], size=1, @@ -539,14 +541,14 @@ class TosaTensorGen: return [input_shape, filter_shape, bias_shape] @staticmethod - def tgMatmul(testGen, op, rank, error_name=None): + 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(rank) + a_shape = testGen.makeShape(rng, rank) # Constrict the overall size of the shape when creating ERROR_IF tests if error_name: @@ -554,7 +556,7 @@ class TosaTensorGen: # Get a random number for b_oc even if target shape is defined b_oc = np.int32( - testGen.rng.integers( + rng.integers( low=testGen.args.tensor_shape_range[0], high=testGen.args.tensor_shape_range[1], size=1, @@ -568,24 +570,24 @@ class TosaTensorGen: return [a_shape, b_shape] @staticmethod - def tgConcat(testGen, opName, rank, error_name=None): - pl, const = opName["operands"] - shape = testGen.makeShape(rank) + 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 = testGen.randInt(0, 4) + 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 = testGen.rng.choice([True, False]) + remove = rng.choice([True, False]) wrongShape = shape.copy() if remove and len(shape) > 1: wrongShape = wrongShape[1:] else: wrongShape = list(wrongShape) - wrongShape.append(testGen.rng.integers(1, 10)) + wrongShape.append(rng.integers(1, 10)) shape_list.append(wrongShape) else: @@ -594,7 +596,7 @@ class TosaTensorGen: return shape_list @staticmethod - def tgConcatConstInput(testGen, shapeList, axis, error_name=None): + def tgConcatConstInput(rng, shapeList, axis, error_name=None): if error_name in [ ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank, @@ -610,7 +612,7 @@ class TosaTensorGen: for shape in shapeList[1:]: # Negative test shapeLists are created individually for each test, # so no need to copy the shape before altering it. - shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10) + 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) @@ -630,7 +632,7 @@ class TosaTensorGen: # invalidate dimensions if error_name == ErrorIf.ConcatInputDimMismatch: - shape[(axis + 1) % len(shape)] += testGen.rng.integers(5, 10) + shape[(axis + 1) % len(shape)] += rng.integers(5, 10) else: shape[axis] = remaining_length @@ -672,12 +674,12 @@ class TosaTensorValuesGen: } @staticmethod - def _get_data_range(testGen, dtype, highValueLookup, lowValueLookup=None): + 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 = testGen.getDTypeRange(dtype, high_inclusive=True) + 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] @@ -703,7 +705,7 @@ class TosaTensorValuesGen: @staticmethod def tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name=None + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None ): # Variable inputs versus constants pCount, cCount = testGen.TOSA_OP_LIST[opName]["operands"] @@ -742,8 +744,8 @@ class TosaTensorValuesGen: ): # 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 + # 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]) @@ -756,7 +758,7 @@ class TosaTensorValuesGen: else: assert False, "Unsupported fixed_data type" else: - arr = testGen.getRandTensor(shape, dtype, data_range) + arr = rng.randTensor(shape, dtype, data_range) if roundMode: arr = np.round(arr) if idx < pCount: @@ -802,8 +804,7 @@ class TosaTensorValuesGen: info["data"] = [int(i) for i in argsDict["fixed_data"][idx]] tens_meta["fixed_data_info"] = info else: - # TODO - generate seed for this generator based on test - info["rng_seed"] = 42 + info["rng_seed"] = rng.seed data_range = None if "data_range_list" in argsDict: @@ -814,9 +815,7 @@ class TosaTensorValuesGen: data_range = argsDict["data_range"] if data_range is None: - data_range = testGen.getDTypeRange( - dtypeList[idx], high_inclusive=True - ) + 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: @@ -836,7 +835,7 @@ class TosaTensorValuesGen: elif dg_type == gtu.DataGenType.FULL_RANGE: info = {} info["start_val"] = int( - testGen.randInt(0, gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["fullset"]) + rng.randInt(0, gtu.DTYPE_ATTRIBUTES[dtypeList[idx]]["fullset"]) ) tens_meta["full_range_info"] = info else: @@ -883,7 +882,9 @@ class TosaTensorValuesGen: return TosaTensorValuesGen.TVGInfo(tens_ser_list, tens_data) @staticmethod - def tvgNegate(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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] @@ -896,7 +897,7 @@ class TosaTensorValuesGen: max_val = (1 << 31) - 1 min_val = -max_val arr = np.int32( - testGen.rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0]) + rng.integers(low=min_val, high=(max_val + 1), size=shapeList[0]) ) tens_ser_list = [] tens_ser_list.append( @@ -906,7 +907,7 @@ class TosaTensorValuesGen: else: # ERROR_IF or floating point test return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) # Set the ADD/SUB data range to half the largest value to avoid infinities @@ -917,7 +918,9 @@ class TosaTensorValuesGen: } @staticmethod - def tvgAddSub(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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 @@ -929,8 +932,8 @@ class TosaTensorValuesGen: tens_ser_list = [] add = op["op"] in (Op.ADD, Op.ADD_SHAPE) data_range = testGen.args.tensor_shape_range - a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0], data_range) - b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1], data_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: @@ -985,18 +988,18 @@ class TosaTensorValuesGen: else: # ERROR_IF or floating point test data_range = TosaTensorValuesGen._get_data_range( - testGen, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_ADDSUB + rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_ADDSUB ) if data_range: argsDict["data_range"] = data_range return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod def tvgCondIfWhileLoop( - testGen, opName, dtypeList, shapeList, argsDict, error_name=None + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None ): if dtypeList[0] in ( DType.INT32, @@ -1012,11 +1015,9 @@ class TosaTensorValuesGen: tens_ser_list = [] for idx, shape in enumerate(shapeList[:]): if dtypeList[0] == DType.INT32: - arr = testGen.getRandTensor(shapeList[idx], DType.INT16) + arr = rng.randTensor(shapeList[idx], DType.INT16) else: - arr = np.int32( - testGen.rng.integers(low=0, high=32, size=shapeList[idx]) - ) + 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) @@ -1030,12 +1031,12 @@ class TosaTensorValuesGen: return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) else: return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod def tvgArithmeticRightShift( - testGen, opName, dtypeList, shapeList, argsDict, error_name=None + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None ): op = testGen.TOSA_OP_LIST[opName] pCount, cCount = op["operands"] @@ -1048,34 +1049,38 @@ class TosaTensorValuesGen: for idx, shape in enumerate(shapeList[:]): if idx == 1: if dtypeList[idx] == DType.INT8: - arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape)) + arr = np.int32(rng.integers(low=0, high=8, size=shape)) elif dtypeList[idx] == DType.INT16: - arr = np.int32(testGen.rng.integers(low=0, high=16, size=shape)) + arr = np.int32(rng.integers(low=0, high=16, size=shape)) elif dtypeList[idx] == DType.INT32: - arr = np.int32(testGen.rng.integers(low=0, high=32, size=shape)) + arr = np.int32(rng.integers(low=0, high=32, size=shape)) elif error_name == ErrorIf.WrongInputType: - arr = np.int32(testGen.rng.integers(low=0, high=8, size=shape)) + arr = np.int32(rng.integers(low=0, high=8, size=shape)) else: raise Exception("OpArithmeticRightShift: invalid input dtype") else: - arr = testGen.getRandTensor(shape, dtypeList[idx]) + 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, opName, dtypeList, shapeList, argsDict, error_name=None): + 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, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgRescale(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgRescale( + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None + ): scale32 = argsDict["scale"] multiplier_arr = argsDict["multiplier"] shift_arr = argsDict["shift"] @@ -1091,11 +1096,11 @@ class TosaTensorValuesGen: argsDict["fixed_data"] = [None, multiplier_arr, shift_arr] return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgPad(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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 @@ -1104,11 +1109,11 @@ class TosaTensorValuesGen: argsDict["fixed_data"] = [None, pad_values] return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgSlice(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgSlice(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): dtypeList[1] = DType.SHAPE shapeList[1] = [len(argsDict["start"])] dtypeList[2] = DType.SHAPE @@ -1117,30 +1122,34 @@ class TosaTensorValuesGen: argsDict["fixed_data"] = [None, argsDict["start"], argsDict["size"]] return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgTile(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgSelect(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgIntDiv(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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"] @@ -1154,8 +1163,8 @@ class TosaTensorValuesGen: # 1. divisor == 0 # 2. dividend == -(1<<31) and divisor == -1 while True: - dividend_arr = testGen.getRandTensor(shapeList[0], dtypeList[0]) - divisor_arr = testGen.getRandTensor(shapeList[1], dtypeList[1]) + dividend_arr = rng.randTensor(shapeList[0], dtypeList[0]) + divisor_arr = rng.randTensor(shapeList[1], dtypeList[1]) if (divisor_arr == 0).any(): continue @@ -1175,7 +1184,7 @@ class TosaTensorValuesGen: return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) else: return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) # Set the MUL data range to the square root of the largest value @@ -1187,7 +1196,7 @@ class TosaTensorValuesGen: } @staticmethod - def tvgMul(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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, @@ -1195,7 +1204,7 @@ class TosaTensorValuesGen: ): # ERROR_IF or floating point test data_range = TosaTensorValuesGen._get_data_range( - testGen, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL + rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_MUL ) if data_range: argsDict["data_range"] = data_range @@ -1208,10 +1217,9 @@ class TosaTensorValuesGen: argsDict["fixed_data"] = [None, None, [argsDict["shift"]]] return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) else: - # Integer test op = testGen.TOSA_OP_LIST[opName] pCount, cCount = op["operands"] @@ -1231,7 +1239,9 @@ class TosaTensorValuesGen: elif error_name == ErrorIf.WrongInputType: num_bits = 8 else: - raise Exception("OpMul: invalid input dtype") + raise Exception( + f"OpMul: invalid input dtype {gtu.DTYPE_ATTRIBUTES[dtypeList[0]]['str']}" + ) for idx, shape in enumerate(shapeList[:]): if dtypeList[idx] == DType.SHAPE: @@ -1241,12 +1251,8 @@ class TosaTensorValuesGen: low = -(2 ** (num_bits - 1)) high = (2 ** (num_bits - 1)) - 1 - a_arr = np.int32( - testGen.rng.integers(low=low, high=high, size=shapeList[0]) - ) - b_arr = np.int32( - testGen.rng.integers(low=low, high=high, size=shapeList[1]) - ) + 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: @@ -1292,7 +1298,9 @@ class TosaTensorValuesGen: return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) @staticmethod - def tvgConcat(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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 @@ -1302,12 +1310,10 @@ class TosaTensorValuesGen: op = testGen.TOSA_OP_LIST[opName] if op["op"] == Op.CONCAT_SHAPE: # Set the axis to 0 - shapeList = TosaTensorGen.tgConcatConstInput( - testGen, shapeList, 0, error_name - ) + shapeList = TosaTensorGen.tgConcatConstInput(rng, shapeList, 0, error_name) else: shapeList = TosaTensorGen.tgConcatConstInput( - testGen, shapeList, argsDict["axis"], error_name + rng, shapeList, argsDict["axis"], error_name ) # Override default pCount/cCount for operator @@ -1315,20 +1321,20 @@ class TosaTensorValuesGen: argsDict["c_count"] = len(shapeList) - count return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod def tvgLogicalShift( - testGen, opName, dtypeList, shapeList, argsDict, error_name=None + 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 = testGen.getRandTensor(shapeList[0], dtypeList[0]) - shift_arr = np.int32(testGen.rng.integers(low=0, high=32, size=shapeList[1])) + 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) @@ -1340,7 +1346,7 @@ class TosaTensorValuesGen: return TosaTensorValuesGen.TVGInfo(tens_ser_list, None) @staticmethod - def tvgEqual(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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] @@ -1349,8 +1355,8 @@ class TosaTensorValuesGen: pCount == 2 and cCount == 0 ), "Op.EQUAL must have 2 placeholders, 0 consts" - a_arr = testGen.getRandTensor(shapeList[0], dtypeList[0]) - b_arr = testGen.getRandTensor(shapeList[1], dtypeList[1]) + 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 @@ -1362,9 +1368,7 @@ class TosaTensorValuesGen: for axis in range(0, len(shapeList[0])): # Index can be up to the largest dimension in both shapes index = np.int32( - testGen.rng.integers( - 0, max(shapeList[0][axis], shapeList[1][axis]) - ) + 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)) @@ -1383,11 +1387,13 @@ class TosaTensorValuesGen: else: # ERROR_IF or floating point test return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgReduceSum(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgReduceSum( + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None + ): dtype = dtypeList[0] if dtype == DType.INT32: op = testGen.TOSA_OP_LIST[opName] @@ -1399,7 +1405,7 @@ class TosaTensorValuesGen: # summation of any axis range_val = int((1 << 31) / max(shapeList[0])) values_arr = np.int32( - testGen.rng.integers(low=-range_val, high=range_val, size=shapeList[0]) + rng.integers(low=-range_val, high=range_val, size=shapeList[0]) ) tens_ser_list = [] tens_ser_list.append( @@ -1419,18 +1425,18 @@ class TosaTensorValuesGen: / max(shapeList[0]) } data_range = TosaTensorValuesGen._get_data_range( - testGen, dtype, highval_lookup + rng, dtype, highval_lookup ) assert data_range is not None argsDict["data_range"] = data_range return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod def tvgReduceProduct( - testGen, opName, dtypeList, shapeList, argsDict, error_name=None + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None ): dtype = dtypeList[0] if error_name is None: @@ -1442,20 +1448,20 @@ class TosaTensorValuesGen: 1 / max(shapeList[0]), ) } - data_range = TosaTensorValuesGen._get_data_range( - testGen, dtype, highval_lookup - ) + 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, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgResize(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgResize( + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None + ): data_range = TosaTensorValuesGen._get_data_range( - testGen, + rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE, ) @@ -1476,7 +1482,7 @@ class TosaTensorValuesGen: argsDict["fixed_data"] = [None, scale_values, offset_values, border_values] return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) # Set the POW exponent high data range @@ -1537,10 +1543,10 @@ class TosaTensorValuesGen: } @staticmethod - def tvgPow(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgPow(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): if error_name is not None: return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) dtype = dtypeList[0] # Different ranges for POW @@ -1548,25 +1554,25 @@ class TosaTensorValuesGen: if test_set == 0: # Positive base with fractional exponent base_range = TosaTensorValuesGen._get_data_range( - testGen, + rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE, TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE, ) exp_range = TosaTensorValuesGen._get_data_range( - testGen, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP + rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP ) exp_round = False else: # Integer exponent exp_range = TosaTensorValuesGen._get_data_range( - testGen, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP + rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_EXP ) exp_round = True if test_set == 1: # Positive base base_range = TosaTensorValuesGen._get_data_range( - testGen, + rng, dtype, TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE, TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE, @@ -1576,7 +1582,7 @@ class TosaTensorValuesGen: # Negative base # Supply new look up tables with negative values base_range = TosaTensorValuesGen._get_data_range( - testGen, + rng, dtype, {dtype: -TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_POW_BASE[dtype]}, {dtype: -TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_POW_BASE[dtype]}, @@ -1593,15 +1599,17 @@ class TosaTensorValuesGen: ) argsDict["data_range_list"] = data_range_list return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgLogRsqrt(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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( - testGen, + rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE, TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE, @@ -1610,7 +1618,7 @@ class TosaTensorValuesGen: argsDict["data_range"] = data_range return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) # Set the EXP data range to the log of the largest to smallest values @@ -1627,9 +1635,9 @@ class TosaTensorValuesGen: } @staticmethod - def tvgExp(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgExp(testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None): data_range = TosaTensorValuesGen._get_data_range( - testGen, + rng, dtypeList[0], TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE_EXP, TosaTensorValuesGen.TVG_FLOAT_LOW_VALUE_EXP, @@ -1638,12 +1646,12 @@ class TosaTensorValuesGen: argsDict["data_range"] = data_range return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod def tvgFullyConnected( - testGen, opName, dtypeList, shapeList, argsDict, error_name=None + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None ): dtype = dtypeList[0] if ( @@ -1658,26 +1666,24 @@ class TosaTensorValuesGen: highval_lookup = { dtype: math.pow(TosaTensorValuesGen.TVG_FLOAT_HIGH_VALUE[dtype], 1 / IC) } - data_range = TosaTensorValuesGen._get_data_range( - testGen, dtype, highval_lookup - ) + 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, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgCast(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + 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 = testGen.getDTypeRange(out_dtype, high_inclusive=True) + out_range = rng.dTypeRange(out_dtype, high_inclusive=True) highval_lookup = {in_dtype: out_range[1]} data_range = TosaTensorValuesGen._get_data_range( - testGen, + rng, in_dtype, highval_lookup, ) @@ -1686,11 +1692,13 @@ class TosaTensorValuesGen: argsDict["data_range"] = data_range return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgGather(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgGather( + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None + ): K = shapeList[0][1] # Fix the type of the indices tensor @@ -1709,11 +1717,11 @@ class TosaTensorValuesGen: for idx, shape in enumerate(shapeList): dtype = dtypeList[idx] if idx != 1: - arr = testGen.getRandTensor(shape, dtype) + 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 = testGen.getRandTensor(shape, dtype, (0, K)) + 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)) @@ -1729,11 +1737,13 @@ class TosaTensorValuesGen: argsDict["data_range_list"] = data_range_list return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @staticmethod - def tvgScatter(testGen, opName, dtypeList, shapeList, argsDict, error_name=None): + def tvgScatter( + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name=None + ): K = shapeList[0][1] W = shapeList[2][1] @@ -1760,7 +1770,7 @@ class TosaTensorValuesGen: for idx, shape in enumerate(shapeList): dtype = dtypeList[idx] if idx != 1: - arr = testGen.getRandTensor(shape, dtype) + arr = rng.randTensor(shape, dtype) tens_ser_list.append(testGen.ser.addPlaceholder(shape, dtype, arr)) else: # Create the indices array @@ -1769,7 +1779,7 @@ class TosaTensorValuesGen: for n in range(shape[0]): # Get a shuffled list of output indices (0 to K-1) and # limit length to W - arr.append(testGen.rng.permutation(K)[: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( @@ -1789,7 +1799,7 @@ class TosaTensorValuesGen: argsDict["data_range_list"] = data_range_list return TosaTensorValuesGen.tvgLazyGenDefault( - testGen, opName, dtypeList, shapeList, argsDict, error_name + testGen, rng, opName, dtypeList, shapeList, argsDict, error_name ) @@ -1881,7 +1891,7 @@ class TosaArgGen: return new_arg_list @staticmethod - def agNone(testGen, opName, shapeList, dtype, error_name=None): + 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( @@ -1896,7 +1906,7 @@ class TosaArgGen: return arg_list @staticmethod - def agPow(testGen, opName, shapeList, dtype, error_name=None): + 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( @@ -1911,17 +1921,17 @@ class TosaArgGen: return arg_list @staticmethod - def agAxis(testGen, opName, shapeList, dtype, error_name=None): + 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 = [testGen.rng.integers(-5, 0)] + axes = [rng.integers(-5, 0)] elif error_name == ErrorIf.AxisLargerRank: # Set too large axis - axes = [testGen.rng.integers(len(shape) + 1, len(shape) + 10)] + axes = [rng.integers(len(shape) + 1, len(shape) + 10)] else: # Create tests for each dimension axes = range(0, len(shape)) @@ -1967,7 +1977,7 @@ class TosaArgGen: return sparsity @staticmethod - def agConv(testGen, opName, shapeList, dtypes, error_name=None): + def agConv(testGen, rng, opName, shapeList, dtypes, error_name=None): # Used by CONV2D, CONV3D and DEPTHWISE_CONV2D arg_list = [] @@ -2005,13 +2015,13 @@ class TosaArgGen: # Generate comprehensive argument lists # - except for named errors, which use specific invalid value(s) if error_name == ErrorIf.PadSmallerZero: - p_vals = [testGen.rng.choice(range(-5, 0))] + 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 = [testGen.rng.choice(range(-5, 0))] + s_vals = [rng.choice(range(-5, 0))] else: # Stride must be greater than 1 to force non-integer error startStride = ( @@ -2022,7 +2032,7 @@ class TosaArgGen: ] strides = {x for x in itertools.product(*([s_vals] * k_rank))} if error_name == ErrorIf.DilationSmallerOne: - d_vals = [testGen.rng.choice(range(-5, 1))] + 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))} @@ -2195,13 +2205,13 @@ class TosaArgGen: return arg_list @staticmethod - def agFullyConnected(testGen, opName, shapeList, dtypes, error_name=None): + 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, testGen.rng, input_dtype) + 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 @@ -2230,7 +2240,7 @@ class TosaArgGen: return arg_list @staticmethod - def agMatMul(testGen, opName, shapeList, dtype, error_name=None): + def agMatMul(testGen, rng, opName, shapeList, dtype, error_name=None): # Get valid accumulate type(s) if dtype == DType.INT8: accum_dtypes = [DType.INT32] @@ -2249,7 +2259,7 @@ class TosaArgGen: if error_name == ErrorIf.WrongOutputType: # Get incorrect output dtype for ErrorIf case - accum_dtypes = [gtu.get_wrong_output_type(opName, testGen.rng, dtype)] + 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] @@ -2283,7 +2293,7 @@ class TosaArgGen: return arg_list @staticmethod - def agTransposeConv2D(testGen, opName, shapeList, dtypes, error_name=None): + def agTransposeConv2D(testGen, rng, opName, shapeList, dtypes, error_name=None): arg_list = [] if testGen.args.level8k and error_name is not None: @@ -2310,9 +2320,7 @@ class TosaArgGen: 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 = [ - testGen.rng.choice(range(max_filter_size - 10, max_filter_size)) - ] + p_vals = [rng.choice(range(max_filter_size - 10, max_filter_size))] else: p_vals = [ x @@ -2323,7 +2331,7 @@ class TosaArgGen: 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 = [testGen.rng.choice(range(-5, 0))] + 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))} @@ -2440,7 +2448,7 @@ class TosaArgGen: return arg_list @staticmethod - def agPad(testGen, opName, shapeList, dtype, error_name=None): + 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 @@ -2454,11 +2462,11 @@ class TosaArgGen: shape_pad_values = itertools.product(*([axis_pad_values] * rank)) if dtype in [DType.BOOL, DType.INT8, DType.INT16, DType.INT32]: - pad_const_int = testGen.getRandNumberDType(dtype) + pad_const_int = rng.randNumberDType(dtype) pad_const_fp = 0 elif gtu.dtypeIsFloat(dtype): pad_const_int = 0 - pad_const_fp = testGen.getRandNumberDType(dtype) + pad_const_fp = rng.randNumberDType(dtype) else: return [] @@ -2516,7 +2524,7 @@ class TosaArgGen: return arg_list @staticmethod - def agPooling(testGen, opName, shapeList, dtype, error_name=None): + def agPooling(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] shape = shapeList[0] @@ -2658,7 +2666,7 @@ class TosaArgGen: ErrorIf.PadLargerEqualKernel, ]: sNew, pNew, kNew = TosaErrorIfArgGen.eiPoolingErrorIf( - testGen, error_name, s, p, k + rng, error_name, s, p, k ) if None not in [sNew, pNew, kNew] and n % sparsity == 0: arg_list.append( @@ -2722,12 +2730,12 @@ class TosaArgGen: return arg_list @staticmethod - def agCast(testGen, opName, shapeList, inDtype, error_name=None): + 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(testGen, inDtype) + dtypeList = TosaErrorIfArgGen.eiCastErrorIf(inDtype) elif inDtype == DType.INT8: dtypeList = [ DType.BOOL, @@ -2811,7 +2819,7 @@ class TosaArgGen: return arg_list @staticmethod - def agRescale(testGen, opName, shapeList, inDtype, error_name=None): + def agRescale(testGen, rng, opName, shapeList, inDtype, error_name=None): arg_list = [] # Enumerate the output types here @@ -2906,7 +2914,7 @@ class TosaArgGen: # Calculate scale based on: # scale = a *(2^output_width)/(2^input_width)) - a = np.float32(testGen.rng.random(size=[nc])) + a = np.float32(rng.random(size=[nc])) scale_arr = a * np.float32( (1 << out_type_width) / (1 << in_type_width) ) @@ -2965,13 +2973,13 @@ class TosaArgGen: return arg_list @staticmethod - def agMul(testGen, opName, shapeList, dtype, error_name=None): + 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 = testGen.randInt(0, 32) + shift = rng.randInt(0, 32) arg_list.append(("perm{}_shift{}".format(p, shift), {"shift": shift})) else: arg_list.append(("perm0_shift0", {"shift": 0})) @@ -2988,7 +2996,7 @@ class TosaArgGen: return arg_list @staticmethod - def agArithmeticRightShift(testGen, opName, shapeList, dtype, error_name=None): + def agArithmeticRightShift(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] for round in (True, False): @@ -3009,7 +3017,7 @@ class TosaArgGen: return arg_list @staticmethod - def agFFT2d(testGen, opName, shapeList, dtype, error_name=None): + def agFFT2d(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] shape = shapeList[0] @@ -3037,7 +3045,7 @@ class TosaArgGen: return arg_list @staticmethod - def agRFFT2d(testGen, opName, shapeList, dtype, error_name=None): + def agRFFT2d(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] shape = shapeList[0] @@ -3074,7 +3082,7 @@ class TosaArgGen: return factors @staticmethod - def agReshape(testGen, opName, shapeList, dtype, error_name=None): + def agReshape(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] origShape = shapeList[0] @@ -3085,7 +3093,7 @@ class TosaArgGen: # 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 = testGen.randInt(1, (testGen.TOSA_TENSOR_MAX_RANK + 1)) + newRank = rng.randInt(1, (testGen.TOSA_TENSOR_MAX_RANK + 1)) if len(factors) < newRank: continue @@ -3095,12 +3103,12 @@ class TosaArgGen: # Generate the new shape of the chosen new rank newShape = [] remainingElements = totalElements - shuffledFactors = testGen.rng.permutation(factors) + shuffledFactors = rng.permutation(factors) for i in range(1, newRank): # pick rank-1 factors newShape.append(shuffledFactors[0]) remainingElements = remainingElements // shuffledFactors[0] - shuffledFactors = testGen.rng.permutation( + shuffledFactors = rng.permutation( TosaArgGen.getFactors(remainingElements) ) newShape.append(remainingElements) @@ -3136,7 +3144,7 @@ class TosaArgGen: return arg_list @staticmethod - def agTranspose(testGen, opName, shapeList, dtype, error_name=None): + def agTranspose(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] ifm_shape = shapeList[0] @@ -3151,7 +3159,7 @@ class TosaArgGen: elif error_name == ErrorIf.IndexUsedTwice: # Create list with a duplicated index perm_range = list(range(len(ifm_shape))) - index_choice = testGen.rng.choice(range(len(perm_range))) + 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)] @@ -3163,7 +3171,7 @@ class TosaArgGen: limit = min(len(permutations), testGen.args.num_rand_permutations) # Get random permutation generator that uses all permutations - random_permutations = testGen.rng.permutation(permutations) + random_permutations = rng.permutation(permutations) # Create list of required amount of permutations arg_list = [ @@ -3183,7 +3191,7 @@ class TosaArgGen: return arg_list @staticmethod - def agSlice(testGen, opName, shapeList, dtype, error_name=None): + def agSlice(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] ifm_shape = shapeList[0] @@ -3197,8 +3205,8 @@ class TosaArgGen: for i in range(rank): if ifm_shape[i] > 1: - start.append(testGen.randInt(0, ifm_shape[i])) - size.append(testGen.randInt(0, ifm_shape[i] - start[i])) + 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: @@ -3210,7 +3218,7 @@ class TosaArgGen: if valid: # If ERROR_IF test required then incorrect start, size will be returned start, size = TosaErrorIfArgGen.eiSliceErrorIf( - testGen, error_name, ifm_shape, start, size + rng, error_name, ifm_shape, start, size ) arg_list.append(("perm{}".format(p), {"start": start, "size": size})) # Now add data generator types @@ -3226,7 +3234,7 @@ class TosaArgGen: return arg_list @staticmethod - def agTile(testGen, opName, shapeList, dtype, error_name=None): + def agTile(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] ifm_shape = shapeList[0] @@ -3246,7 +3254,7 @@ class TosaArgGen: elif max(ifm_shape) > 1000: multiples.append(2) else: - multiples.append(testGen.randInt(1, 4)) + multiples.append(rng.randInt(1, 4)) arg_list.append(("perm{}".format(p), {"multiples": multiples})) # Now add data generator types @@ -3262,15 +3270,15 @@ class TosaArgGen: return arg_list @staticmethod - def agResize(testGen, opName, shapeList, dtype, error_name=None): + 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 = testGen.rng.choice(common_aspect_ratios) - invert = testGen.rng.choice((False, True)) - letterbox = testGen.rng.choice((False, True)) + 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] @@ -3279,13 +3287,13 @@ class TosaArgGen: if letterbox: max_border = scale_y_n - border_y = testGen.randInt(low=0, high=max_border) + border_y = rng.randInt(low=0, high=max_border) border_x = 0 else: # Pillarboxing border_y = 0 max_border = scale_x_n - border_x = testGen.randInt(low=0, high=max_border) + 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) @@ -3296,13 +3304,13 @@ class TosaArgGen: def get_upscale_downscale_params(): valid_params = False while not valid_params: - upscale = testGen.rng.choice((False, True)) + upscale = rng.choice((False, True)) # True if sampling begins from (0,0). Otherwise (-0.5,-0.5) - origin_sampling = testGen.rng.choice((False, True)) + origin_sampling = rng.choice((False, True)) if upscale: - shift = testGen.randInt(low=1, high=4) + 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 @@ -3328,16 +3336,16 @@ class TosaArgGen: if not valid_scale_y_ds: scale_y_d = 1 else: - scale_y_d = testGen.rng.choice(valid_scale_y_ds) + scale_y_d = rng.choice(valid_scale_y_ds) if not valid_scale_x_ds: scale_x_d = 1 else: - scale_x_d = testGen.rng.choice(valid_scale_x_ds) + scale_x_d = rng.choice(valid_scale_x_ds) border_x = border_y = 0 - offset_y = testGen.randInt(0, 16 * scale_y_n) - offset_x = testGen.randInt(0, 16 * scale_x_n) + 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) @@ -3356,11 +3364,11 @@ class TosaArgGen: return scale_d # Scale - scale_y_n = testGen.randInt(low=1, high=(1 << 11)) - scale_x_n = testGen.randInt(low=1, high=(1 << 11)) + scale_y_n = rng.randInt(low=1, high=(1 << 11)) + scale_x_n = rng.randInt(low=1, high=(1 << 11)) - scale_y_d = testGen.randInt(low=1, high=(16 * scale_y_n)) - scale_x_d = testGen.randInt(low=1, high=(16 * scale_x_n)) + 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 @@ -3370,10 +3378,10 @@ class TosaArgGen: ) # Offsets and border within the scale - offset_y = testGen.randInt(low=-scale_y_n, high=(16 * scale_y_n)) - offset_x = testGen.randInt(low=-scale_x_n, high=(16 * scale_x_n)) - border_y = testGen.randInt(low=(-16 * scale_y_n), high=scale_y_n) - border_x = testGen.randInt(low=(-16 * scale_x_n), high=scale_x_n) + 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) @@ -3382,24 +3390,24 @@ class TosaArgGen: def get_level_8k_params(): # Create 64x scale - 64/1 to 2048/32 - scale_d = testGen.randInt( + 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 = testGen.randInt(low=2, high=6) + scale_d_alt = rng.randInt(low=2, high=6) scale_n_alt = 1 - switch = testGen.rng.choice((False, True)) + 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 = testGen.rng.choice((-scale[0], 0, (16 * scale[0]) - 1)) - offset_x = testGen.rng.choice((-scale[2], 0, (16 * scale[2]) - 1)) + 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 = testGen.rng.choice((-16 * scale[0], 0, scale[0] - 1)) - border_x = testGen.rng.choice((-16 * scale[2], 0, scale[2] - 1)) + 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 @@ -3437,7 +3445,7 @@ class TosaArgGen: while perm < testGen.args.num_rand_permutations: # Random choice of type of params we are testing if not testGen.args.level8k: - _rnd_param_fn = testGen.rng.choice( + _rnd_param_fn = rng.choice( ( get_rand_params, get_upscale_downscale_params, @@ -3541,7 +3549,7 @@ class TosaArgGen: border, outputDTypeNew, ) = TosaErrorIfArgGen.eiResizeErrorIf( - testGen, + rng, error_name, mode, dtype, @@ -3596,17 +3604,13 @@ class TosaArgGen: return arg_list @staticmethod - def agTable(testGen, opName, shapeList, dtype, error_name=None): + def agTable(testGen, rng, opName, shapeList, dtype, error_name=None): arg_list = [] if dtype == DType.INT8: - table = np.int32( - testGen.rng.integers(low=-128, high=128, size=[256]) - ).tolist() + table = np.int32(rng.integers(low=-128, high=128, size=[256])).tolist() else: # INT16 - table = np.int32( - testGen.rng.integers(low=-32768, high=32768, size=[513]) - ).tolist() + 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] @@ -3635,7 +3639,7 @@ class TosaArgGen: # Return list of tuples: (arg_str, args_dict) return arg_list - def agCondIf(testGen, opName, shapeList, dtype, error_name=None): + 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 @@ -3656,7 +3660,7 @@ class TosaArgGen: # Return list of tuples: (arg_str, args_dict) return arg_list - def agWhileLoop(testGen, opName, shapeList, dtype, error_name=None): + def agWhileLoop(testGen, rng, opName, shapeList, dtype, error_name=None): # While loop: 0 iterations, 1, more than 1 arg_list = [] |