| # Copyright (c) 2020-2023, ARM Limited. |
| # SPDX-License-Identifier: Apache-2.0 |
| import os |
| from copy import deepcopy |
| |
| import numpy as np |
| import serializer.tosa_serializer as ts |
| from generator.tosa_arg_gen import TosaArgGen |
| from generator.tosa_arg_gen import TosaQuantGen |
| from generator.tosa_arg_gen import TosaTensorGen |
| from generator.tosa_arg_gen import TosaTensorValuesGen |
| from generator.tosa_error_if import ErrorIf |
| from generator.tosa_error_if import TosaErrorIfArgGen |
| from generator.tosa_error_if import TosaErrorValidator |
| from generator.tosa_error_if import TosaInvalidValidator |
| from generator.tosa_utils import DTYPE_ATTRIBUTES |
| from generator.tosa_utils import get_rank_mismatch_shape |
| from generator.tosa_utils import get_wrong_output_type |
| from generator.tosa_utils import MAX_RESIZE_DIMENSION |
| from generator.tosa_utils import usableDTypes |
| from generator.tosa_utils import vect_f32_to_bf16 |
| from tosa.DType import DType |
| from tosa.Op import Op |
| |
| |
| class TosaTestGen: |
| # Maximum rank of tensor supported by test generator. |
| # This currently matches the 8K level defined in the specification. |
| TOSA_TENSOR_MAX_RANK = 6 |
| TOSA_8K_LEVEL_MAX_SCALE = 64 |
| TOSA_8K_LEVEL_MAX_KERNEL = 8192 |
| TOSA_8K_LEVEL_MAX_STRIDE = 8192 |
| |
| def __init__(self, args): |
| self.args = args |
| self.basePath = args.output_dir |
| self.random_seed = args.random_seed |
| self.ser = None |
| self.rng = np.random.default_rng(self.random_seed) |
| self.createDynamicOpLists() |
| self.initOpListDefaults() |
| self.quantGen = TosaQuantGen() |
| # Force makeShape to do a specific starting shape |
| self.targetted_shape = None |
| # Work out floating point range |
| self.random_fp_low = min(args.tensor_fp_value_range) |
| self.random_fp_high = max(args.tensor_fp_value_range) |
| |
| def createSerializer(self, opName, testPath): |
| self.testPath = os.path.join(opName, testPath) |
| |
| fullPath = os.path.join(self.basePath, self.testPath) |
| os.makedirs(fullPath, exist_ok=True) |
| # Embed const data in the flatbuffer |
| constMode = ts.ConstMode.EMBED |
| if self.args.dump_consts: |
| constMode = ts.ConstMode.EMBED_DUMP |
| self.ser = ts.TosaSerializer(fullPath, constMode) |
| |
| def getSerializer(self): |
| return self.ser |
| |
| def serialize(self, testName): |
| with open( |
| os.path.join(self.basePath, self.testPath, "{}.tosa".format(testName)), "wb" |
| ) as fd: |
| fd.write(self.ser.serialize()) |
| |
| with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: |
| fd.write(self.ser.writeJson("{}.tosa".format(testName))) |
| |
| def resetRNG(self, seed=None): |
| if seed is None: |
| seed = self.random_seed + 1 |
| self.rng = np.random.default_rng(seed) |
| |
| def getRandTensor(self, shape, dtype): |
| if dtype == DType.BOOL: |
| return np.bool_(self.rng.choice(a=[False, True], size=shape)) |
| # TOSA specific INT4 weight range from -7 to 7 |
| elif dtype == DType.INT4: |
| return np.int32(self.rng.integers(low=-7, high=8, size=shape)) |
| elif dtype == DType.INT8: |
| return np.int32(self.rng.integers(low=-128, high=128, size=shape)) |
| elif dtype == DType.UINT8: |
| return np.int32(self.rng.integers(low=0, high=256, size=shape)) |
| elif dtype == DType.INT16: |
| return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) |
| elif dtype == DType.UINT16: |
| return np.int32(self.rng.integers(low=0, high=65536, size=shape)) |
| elif ( |
| dtype == DType.INT32 or dtype == DType.SHAPE |
| ): # restricting too large value for SHAPE |
| return np.int32( |
| self.rng.integers(low=-(1 << 31), high=(1 << 31), size=shape) |
| ) |
| elif dtype == DType.INT48: |
| return np.int64( |
| self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) |
| ) |
| elif dtype == DType.FP16: |
| return np.float16( |
| self.rng.uniform( |
| low=self.random_fp_low, high=self.random_fp_high, size=shape |
| ) |
| ) |
| elif dtype == DType.BF16: |
| f32_tensor = np.float32( |
| self.rng.uniform( |
| low=self.random_fp_low, high=self.random_fp_high, size=shape |
| ) |
| ) |
| # Floor the last 16 bits of each f32 value |
| return np.float32(vect_f32_to_bf16(f32_tensor)) |
| elif dtype == DType.FP32: |
| return np.float32( |
| self.rng.uniform( |
| low=self.random_fp_low, high=self.random_fp_high, size=shape |
| ) |
| ) |
| else: |
| raise Exception("Unrecognized Dtype: {}".format(dtype)) |
| |
| def buildPlaceholderTensors(self, shape_list, dtype_list): |
| placeholders = [] |
| |
| assert len(shape_list) == len(dtype_list) |
| |
| for idx, shape in enumerate(shape_list): |
| arr = self.getRandTensor(shape, dtype_list[idx]) |
| placeholders.append(self.ser.addPlaceholder(shape, dtype_list[idx], arr)) |
| |
| return placeholders |
| |
| def buildConstTensors(self, shape_list, dtype_list): |
| consts = [] |
| |
| assert len(shape_list) == len(dtype_list) |
| |
| for idx, shape in enumerate(shape_list): |
| arr = self.getRandTensor(shape, dtype_list[idx]) |
| consts.append(self.ser.addConst(shape, dtype_list[idx], arr)) |
| |
| return consts |
| |
| def makeShape(self, rank): |
| if self.targetted_shape: |
| return np.int32(self.targetted_shape) |
| return np.int32( |
| self.rng.integers( |
| low=self.args.tensor_shape_range[0], |
| high=self.args.tensor_shape_range[1], |
| size=rank, |
| ) |
| ) |
| |
| def setTargetShape(self, shape): |
| self.targetted_shape = shape |
| |
| def randInt(self, low=0, high=256): |
| return np.int32(self.rng.integers(low=low, high=high, size=1))[0] |
| |
| def getRandNumberDType(self, dtype): |
| if dtype == DType.FP32: |
| return np.float32( |
| self.rng.uniform(low=self.random_fp_low, high=self.random_fp_high) |
| ) |
| elif dtype == DType.FP16: |
| return np.float16( |
| self.rng.uniform(low=self.random_fp_low, high=self.random_fp_high) |
| ) |
| elif dtype == DType.BF16: |
| rand_f32 = np.float32( |
| self.rng.uniform(low=self.random_fp_low, high=self.random_fp_high) |
| ) |
| return vect_f32_to_bf16(rand_f32) |
| elif dtype == DType.BOOL: |
| return self.rng.choice([False, True]) |
| # TOSA specific INT4 weight range from -7 to 7 |
| elif dtype == DType.INT4: |
| low, high = (-7, 8) |
| elif dtype == DType.INT8: |
| low, high = (-128, 128) |
| elif dtype == DType.INT16: |
| low, high = (-32768, 32768) |
| elif ( |
| dtype == DType.INT32 or dtype == DType.SHAPE |
| ): # restricting too large value for SHAPE |
| low, high = (-(1 << 31), (1 << 31)) |
| elif dtype == DType.INT48: |
| low, high = (-(1 << 47), (1 << 47)) |
| # Special size |
| return np.int64(self.rng.integers(low, high, size=1))[0] |
| else: |
| raise Exception("Unknown dtype: {}".format(dtype)) |
| |
| return np.int32(self.rng.integers(low, high, size=1))[0] |
| |
| def shapeStr(self, shape): |
| |
| sStr = [] |
| # Convert to strings |
| for i in shape: |
| sStr.append(str(i)) |
| |
| return "x".join(sStr) |
| |
| def typeStr(self, dtype): |
| if isinstance(dtype, list) or isinstance(dtype, tuple): |
| assert len(dtype) >= 2 |
| strs = [self.typeStr(t) for t in dtype] |
| # Limit types to the first 2 as the 3rd is the accumulator |
| return "x".join(strs[:2]) |
| else: |
| if dtype in DTYPE_ATTRIBUTES: |
| return DTYPE_ATTRIBUTES[dtype]["str"] |
| else: |
| raise Exception( |
| "Unknown dtype, cannot convert to string: {}".format(dtype) |
| ) |
| |
| def typeWidth(self, dtype): |
| """Get the datatype width for data types""" |
| if dtype in DTYPE_ATTRIBUTES: |
| return DTYPE_ATTRIBUTES[dtype]["width"] |
| else: |
| raise Exception(f"Unknown dtype, cannot determine width: {dtype}") |
| |
| def constrictBatchSize(self, shape): |
| # Limit the batch size unless an explicit target shape set |
| if self.args.max_batch_size and not self.args.target_shapes: |
| shape[0] = min(shape[0], self.args.max_batch_size) |
| return shape |
| |
| def makeDimension(self): |
| return self.randInt( |
| low=self.args.tensor_shape_range[0], high=self.args.tensor_shape_range[1] |
| ) |
| |
| # Argument generators |
| # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) |
| # Where the string descriptor is used to generate the test name and |
| # The build_fcn_arg_list is expanded and passed to the operator test |
| # build function |
| |
| def build_unary(self, op, a, validator_fcns=None, error_name=None, qinfo=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| # build_placeholder returns an int, ABS/other ops does not |
| if isinstance(op, int): |
| self.ser.addOperator(op, a.name, result_tens.name, None) |
| return result_tens |
| elif op["op"] == Op.IDENTITY: |
| self.ser.addOperator(op["op"], a.name, result_tens.name, None) |
| return result_tens |
| |
| # Ensure new output type has correct qinfo |
| if error_name == ErrorIf.WrongOutputType: |
| if result_tens.dtype not in [DType.INT8, DType.UINT8]: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(self, a.dtype), |
| TosaQuantGen.getZeroPoint(self, result_tens.dtype), |
| ] |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = None |
| if op["op"] == Op.NEGATE: |
| attr = ts.TosaSerializerAttribute() |
| attr.NegateAttribute(qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_binary_broadcast(self, op, a, b, validator_fcns, error_name=None): |
| result_tens = OutputShaper.binaryBroadcastOp( |
| self.ser, self.rng, a, b, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name, b.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input1=a, |
| input2=b, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| return result_tens |
| |
| def build_binary_nonbroadcast(self, op, a, b, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.binaryNonBroadcastOp(self.ser, a, b) |
| self.ser.addOperator(op["op"], [a.name, b.name], [result_tens.name]) |
| return result_tens |
| |
| def build_arithmetic_right_shift( |
| self, op, a, b, round, validator_fcns=None, error_name=None |
| ): |
| result_tens = OutputShaper.binaryBroadcastOp( |
| self.ser, self.rng, a, b, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name, b.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input1=a, |
| input2=b, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.ArithmeticRightShiftAttribute(round) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_mul(self, op, a, b, shift, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.binaryBroadcastOp( |
| self.ser, self.rng, a, b, error_name |
| ) |
| |
| # Special for multiply: |
| # Force the result to INT32 for INT types |
| if a.dtype not in (DType.FP16, DType.BF16, DType.FP32): |
| result_tens.setDtype(DType.INT32) |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [DType.INT8, DType.INT16, DType.INT48] |
| outputDType = self.rng.choice(all_dtypes) |
| result_tens.setDtype(outputDType) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name, b.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input1=a, |
| input2=b, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.MulAttribute(shift) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_table(self, op, a, table, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.tableOp(self.ser, self.rng, a, error_name) |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.TableAttribute(table) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| |
| return result_tens |
| |
| def build_select(self, op, cond, a, b, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.selectOp(self.ser, self.rng, cond, a, b, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [cond.name, a.name, b.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input1=cond, |
| input2=a, |
| input3=b, |
| input_shape=a.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator( |
| op["op"], |
| input_list, |
| output_list, |
| ) |
| return result_tens |
| |
| def build_comparison(self, op, a, b, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.binaryComparisonOp( |
| self.ser, self.rng, a, b, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name, b.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input1=a, |
| input2=b, |
| input_shape=a.shape, |
| input_dtype=a.dtype, |
| output_shape=result_tens.shape, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator( |
| op["op"], |
| input_list, |
| output_list, |
| ) |
| return result_tens |
| |
| def build_argmax(self, op, a, axis, validator_fcns, error_name): |
| result_tens = OutputShaper.argmaxOp(self.ser, self.rng, a, axis, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| axis=axis, |
| input_shape=a.shape, |
| input_dtype=a.dtype, |
| output_shape=result_tens.shape, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.AxisAttribute(axis) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_pool2d( |
| self, |
| op, |
| input, |
| accum_dtype, |
| stride, |
| pad, |
| kernel, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| result_tens = OutputShaper.pool2dOp( |
| self.ser, self.rng, input, kernel, stride, pad, error_name |
| ) |
| |
| # Ensure new output type has correct qinfo |
| if error_name == ErrorIf.WrongInputType: |
| if input.dtype not in [DType.INT8, DType.UINT8]: |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(self, input.dtype), |
| TosaQuantGen.getZeroPoint(self, result_tens.dtype), |
| ] |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [input.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=input.shape, |
| input_dtype=input.dtype, |
| output_shape=result_tens.shape, |
| output_dtype=result_tens.dtype, |
| kernel=kernel, |
| stride=stride, |
| pad=pad, |
| qinfo=qinfo, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| if qinfo is None: |
| qinfo = [0, 0] |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.PoolAttribute(kernel, stride, pad, qinfo[0], qinfo[1], accum_dtype) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_maxpool2d( |
| self, |
| op, |
| input, |
| stride, |
| pad, |
| kernel, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| # Same as build_pool2d but manually sets accum_dtype value |
| # (maxpool has no accum_dtype) |
| return self.build_pool2d( |
| op, |
| input, |
| DType.UNKNOWN, |
| stride, |
| pad, |
| kernel, |
| validator_fcns, |
| error_name, |
| qinfo, |
| ) |
| |
| def build_conv2d( |
| self, |
| op, |
| ifm, |
| filter, |
| bias, |
| accum_dtype, |
| strides, |
| padding, |
| dilations, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| assert len(padding) == 4 |
| result_tens = OutputShaper.conv2dOp( |
| self.ser, |
| self.rng, |
| ifm, |
| filter, |
| accum_dtype, |
| strides, |
| padding, |
| dilations, |
| error_name, |
| ) |
| |
| # Ensure new output type has correct qinfo |
| if error_name == ErrorIf.WrongInputType and ifm.dtype not in ( |
| DType.INT8, |
| DType.UINT8, |
| ): |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(self, ifm.dtype), |
| TosaQuantGen.getZeroPoint(self, result_tens.dtype), |
| ] |
| |
| # Invalidate Input/Output list for error_if checks. |
| input_list = [ifm.name, filter.name, bias.name] |
| output_list = [result_tens.name] |
| num_operands = sum(op["operands"]) |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_dtype=ifm.dtype, |
| weight_dtype=filter.dtype, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| input_list=input_list, |
| num_operands=num_operands, |
| output_list=output_list, |
| pad=padding, |
| stride=strides, |
| dilation=dilations, |
| input_shape=ifm.shape, |
| weight_shape=filter.shape, |
| output_shape=result_tens.shape, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_conv3d( |
| self, |
| op, |
| ifm, |
| filter, |
| bias, |
| accum_dtype, |
| strides, |
| padding, |
| dilations, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| assert len(padding) == 6 |
| result_tens = OutputShaper.conv3dOp( |
| self.ser, |
| self.rng, |
| ifm, |
| filter, |
| accum_dtype, |
| strides, |
| padding, |
| dilations, |
| error_name, |
| ) |
| |
| # Ensure new output type has correct qinfo |
| if error_name == ErrorIf.WrongInputType and ifm.dtype not in ( |
| DType.INT8, |
| DType.UINT8, |
| ): |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(self, ifm.dtype), |
| TosaQuantGen.getZeroPoint(self, result_tens.dtype), |
| ] |
| |
| # Invalidate Input/Output list for error_if checks. |
| input_list = [ifm.name, filter.name, bias.name] |
| output_list = [result_tens.name] |
| num_operands = sum(op["operands"]) |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_dtype=ifm.dtype, |
| weight_dtype=filter.dtype, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| input_list=input_list, |
| num_operands=num_operands, |
| output_list=output_list, |
| pad=padding, |
| stride=strides, |
| dilation=dilations, |
| input_shape=ifm.shape, |
| weight_shape=filter.shape, |
| output_shape=result_tens.shape, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_transpose_conv2d( |
| self, |
| op, |
| ifm, |
| filter, |
| bias, |
| accum_dtype, |
| stride, |
| out_pad, |
| output_shape, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| assert len(out_pad) == 4 |
| result_tens = OutputShaper.transposeConv2DOp( |
| self.ser, self.rng, ifm, output_shape, accum_dtype, error_name |
| ) |
| |
| # Ensure new output type has correct qinfo |
| if error_name == ErrorIf.WrongInputType and ifm.dtype not in ( |
| DType.INT8, |
| DType.UINT8, |
| ): |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(self, ifm.dtype), |
| TosaQuantGen.getZeroPoint(self, result_tens.dtype), |
| ] |
| |
| # Invalidate Input/Output list for error_if checks. |
| input_list = [ifm.name, filter.name, bias.name] |
| output_list = [result_tens.name] |
| num_operands = sum(op["operands"]) |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_dtype=ifm.dtype, |
| weight_dtype=filter.dtype, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| input_list=input_list, |
| num_operands=num_operands, |
| output_list=output_list, |
| pad=out_pad, |
| stride=stride, |
| input_shape=ifm.shape, |
| weight_shape=filter.shape, |
| output_shape=result_tens.shape, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.TransposeConvAttribute(out_pad, stride, output_shape, qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_depthwise_conv2d( |
| self, |
| op, |
| ifm, |
| filter, |
| bias, |
| accum_dtype, |
| strides, |
| padding, |
| dilations, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| result_tens = OutputShaper.depthwiseConv2dOp( |
| self.ser, |
| self.rng, |
| ifm, |
| filter, |
| accum_dtype, |
| strides, |
| padding, |
| dilations, |
| error_name, |
| ) |
| |
| # Ensure new output type has correct qinfo |
| if error_name == ErrorIf.WrongInputType and ifm.dtype not in ( |
| DType.INT8, |
| DType.UINT8, |
| ): |
| qinfo = [ |
| TosaQuantGen.getZeroPoint(self, ifm.dtype), |
| TosaQuantGen.getZeroPoint(self, result_tens.dtype), |
| ] |
| |
| # Invalidate Input/Output list for error_if checks. |
| input_list = [ifm.name, filter.name, bias.name] |
| output_list = [result_tens.name] |
| num_operands = sum(op["operands"]) |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_dtype=ifm.dtype, |
| weight_dtype=filter.dtype, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| input_list=input_list, |
| num_operands=num_operands, |
| output_list=output_list, |
| pad=padding, |
| stride=strides, |
| dilation=dilations, |
| input_shape=ifm.shape, |
| weight_shape=filter.shape, |
| output_shape=result_tens.shape, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_fully_connected( |
| self, |
| op, |
| ifm, |
| filter, |
| bias, |
| accum_dtype, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| result_tens = OutputShaper.fullyConnectedOp( |
| self.ser, self.rng, ifm, filter, accum_dtype, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [ifm.name, filter.name, bias.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=ifm.shape, |
| input_dtype=ifm.dtype, |
| weight_dtype=filter.dtype, |
| output_shape=result_tens.shape, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| accum_dtype=accum_dtype, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.FullyConnectedAttribute(qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_matmul( |
| self, op, a, b, accum_dtype, validator_fcns=None, error_name=None, qinfo=None |
| ): |
| result_tens = OutputShaper.matmulOp( |
| self.ser, self.rng, a, b, accum_dtype, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name, b.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| input_dtype=a.dtype, |
| input2_shape=b.shape, |
| input2_dtype=b.dtype, |
| output_shape=result_tens.shape, |
| output_dtype=result_tens.dtype, |
| qinfo=qinfo, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| accum_dtype=accum_dtype, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.MatMulAttribute(qinfo[0], qinfo[1]) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_reduce(self, op, a, axis, validator_fcns, error_name=None): |
| result_tens = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| axis=axis, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.AxisAttribute(axis) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_clamp(self, op, a, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
| |
| if error_name == ErrorIf.MaxSmallerMin: |
| # Make sure the numbers are different to invoke this error |
| while v[0] == v[1]: |
| v = [self.getRandNumberDType(a.dtype), self.getRandNumberDType(a.dtype)] |
| max_val = min(v) |
| min_val = max(v) |
| else: |
| max_val = max(v) |
| min_val = min(v) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| max_val=max_val, |
| min_val=min_val, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| if a.dtype in (DType.BF16, DType.FP16, DType.FP32): |
| if a.dtype == DType.FP16: |
| # Non-tensor fp16 ops take fp16 values as fp32 in reference_model |
| min_val = min_val.astype(np.float32) |
| max_val = max_val.astype(np.float32) |
| |
| attr.ClampAttribute(self.ser.builder, 0, 0, min_val, max_val) |
| else: |
| attr.ClampAttribute(self.ser.builder, min_val, max_val, 0, 0) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_leaky_relu(self, op, a, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| attr = ts.TosaSerializerAttribute() |
| |
| attr.LeakyReluAttribute(self.getRandNumberDType(DType.FP32)) |
| |
| self.ser.addOperator(op["op"], [a.name], [result_tens.name], attr) |
| return result_tens |
| |
| # Needs an additional type/input |
| def build_prelu(self, op, a, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| self.ser.addOperator(op["op"], [a.name], [result_tens.name]) |
| return result_tens |
| |
| def build_sigmoid(self, op, a, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| return result_tens |
| |
| def build_tanh(self, op, a, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| return result_tens |
| |
| def build_erf(self, op, a, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| return result_tens |
| |
| def build_concat(self, op, *a, validator_fcns=None, error_name=None): |
| if error_name != ErrorIf.WrongInputType: |
| assert type(a[-1]) == int |
| |
| # To store variable length list of input tensors we need to store axis along with it |
| axis = a[-1] |
| a = a[:-1] |
| |
| result_tens = OutputShaper.concatOp( |
| self.ser, self.rng, axis, *a, error_name=error_name |
| ) |
| |
| input_tensor_names = [] |
| for tensor in a: |
| input_tensor_names.append(tensor.name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = input_tensor_names |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| axis=axis, |
| input_shape=a[0].shape, |
| output_shape=result_tens.shape, |
| input_dtype=a[0].dtype, |
| output_dtype=result_tens.dtype, |
| inputs=a, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.AxisAttribute(axis) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_pad( |
| self, |
| op, |
| a, |
| padding, |
| pad_const_int, |
| pad_const_float, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| result_tens = OutputShaper.padOp(self.ser, self.rng, a, padding, error_name) |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.PadAttribute( |
| self.ser.builder, padding.flatten(), pad_const_int, pad_const_float |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| pad=padding, |
| qinfo=qinfo, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| input1=a, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_dim( |
| self, |
| op, |
| a, |
| axis, |
| validator_fcns=None, |
| error_name=None, |
| qinfo=None, |
| ): |
| result_tens = OutputShaper.dimOp(self.ser, self.rng, a, axis, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| axis=axis, |
| input_shape=a.shape, |
| input_dtype=a.dtype, |
| output_shape=result_tens.shape, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.AxisAttribute(axis) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_reshape(self, op, a, newShape, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.reshapeOp( |
| self.ser, self.rng, a, newShape, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.ReshapeAttribute(newShape) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_reverse(self, op, a, axis, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| axis=axis, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.AxisAttribute(axis) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_transpose(self, op, a, perms, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.transposeOp(self.ser, self.rng, a, perms, error_name) |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.TransposeAttribute(perms) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| perms=perms, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| input1=a, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_slice(self, op, a, start, size, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.sliceOp( |
| self.ser, self.rng, a, start, size, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| start=start, |
| size=size, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| input1=a, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.SliceAttribute(start, size) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_tile(self, op, a, multiples, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.tileOp(self.ser, self.rng, a, multiples, error_name) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [a.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=a.shape, |
| output_shape=result_tens.shape, |
| input_dtype=a.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| input1=a, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.TileAttribute(multiples) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_gather(self, op, values, validator_fcns=None, error_name=None): |
| |
| # Create a new indicies tensor |
| # here with data that doesn't exceed the dimensions of the values tensor |
| |
| K = values.shape[1] # K |
| W = self.randInt( |
| self.args.tensor_shape_range[0], self.args.tensor_shape_range[1] |
| ) # W |
| indicies_arr = np.int32( |
| self.rng.integers(low=0, high=K, size=[values.shape[0], W]) |
| ) # (N, W) |
| indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
| |
| result_tens = OutputShaper.gatherOp( |
| self.ser, self.rng, values, indicies, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [values.name, indicies.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=values.shape, |
| output_shape=result_tens.shape, |
| input_dtype=values.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| |
| return result_tens |
| |
| def build_scatter(self, op, values_in, input, validator_fcns=None, error_name=None): |
| |
| # Create a new indicies tensor |
| # here with data that doesn't exceed the dimensions of the values_in tensor |
| |
| K = values_in.shape[1] # K |
| W = input.shape[1] # W |
| indicies_arr = np.int32( |
| self.rng.integers(low=0, high=K, size=[values_in.shape[0], W]) |
| ) # (N, W) |
| indicies = self.ser.addConst(indicies_arr.shape, DType.INT32, indicies_arr) |
| |
| result_tens = OutputShaper.scatterOp( |
| self.ser, self.rng, values_in, indicies, input, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [values_in.name, indicies.name, input.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=values_in.shape, |
| output_shape=result_tens.shape, |
| input_dtype=values_in.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| |
| return result_tens |
| |
| def build_resize( |
| self, |
| op, |
| input, |
| mode, |
| scale, |
| offset, |
| border, |
| input_dtype, |
| output_dtype, |
| validator_fcns, |
| error_name=None, |
| ): |
| result_tens = OutputShaper.resizeOp( |
| self.ser, |
| self.rng, |
| input, |
| mode, |
| scale, |
| offset, |
| border, |
| input_dtype, |
| output_dtype, |
| error_name, |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [input.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| mode=mode, |
| scale=scale, |
| input_dtype=input_dtype, |
| output_dtype=output_dtype, |
| input_shape=input.shape, |
| output_shape=result_tens.shape, |
| offset=offset, |
| border=border, |
| input_list=input_list, |
| output_list=output_list, |
| result_tensors=[result_tens], |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| |
| attr.ResizeAttribute(scale, offset, border, mode) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def build_identityn(self, op, val, val2, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.unaryOp(self.ser, self.rng, val, error_name) |
| result_tens2 = OutputShaper.unaryOp(self.ser, self.rng, val2, error_name) |
| self.ser.addOperator( |
| op, [val.name, val2.name], [result_tens.name, result_tens2.name] |
| ) |
| return result_tens |
| |
| def build_const(self, op, val, validator_fcns=None, error_name=None): |
| self.ser.addOutputTensor(val) |
| return val |
| |
| # Type Conversion |
| def build_cast(self, op, val, out_dtype, validator_fcns=None, error_name=None): |
| result_tens = OutputShaper.typeConversionOp( |
| self.ser, self.rng, val, out_dtype, error_name |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [val.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=val.shape, |
| output_shape=result_tens.shape, |
| input_dtype=val.dtype, |
| output_dtype=result_tens.dtype, |
| result_tensors=[result_tens], |
| input_list=input_list, |
| output_list=output_list, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_list, output_list) |
| return result_tens |
| |
| def build_rescale( |
| self, |
| op, |
| val, |
| out_dtype, |
| scale32, |
| double_round, |
| per_channel, |
| validator_fcns, |
| error_name, |
| ): |
| result_tens = OutputShaper.typeConversionOp( |
| self.ser, self.rng, val, out_dtype, error_name |
| ) |
| |
| if per_channel: |
| nc = val.shape[-1] |
| else: |
| nc = 1 |
| |
| in_type_width = self.typeWidth(val.dtype) |
| out_type_width = self.typeWidth(out_dtype) |
| |
| if val.dtype == DType.INT8: |
| input_zp = self.randInt(-128, 128) |
| in_type_width += 1 |
| elif val.dtype == DType.UINT8: |
| input_zp = self.randInt(0, 256) |
| in_type_width += 1 |
| elif error_name in [ |
| ErrorIf.InputZeroPointNotZero, |
| ErrorIf.U16InputZeroPointNotValid, |
| ]: |
| input_zp = self.randInt(-128, 128) |
| if input_zp == 0: |
| input_zp = input_zp + self.rng.integers(1, 10) |
| in_type_width += 1 |
| elif val.dtype == DType.UINT16: |
| # Must come after ErrorIf.U16InputZeroPointNotValid check |
| input_zp = self.rng.choice([0, 32768]) |
| in_type_width += 1 |
| else: |
| input_zp = 0 |
| |
| if out_dtype == DType.INT8: |
| output_zp = self.randInt(-128, 128) |
| out_type_width += 1 |
| elif out_dtype == DType.UINT8: |
| output_zp = self.randInt(0, 256) |
| out_type_width += 1 |
| elif error_name in [ |
| ErrorIf.OutputZeroPointNotZero, |
| ErrorIf.U16OutputZeroPointNotValid, |
| ]: |
| output_zp = self.randInt(-128, 128) |
| if output_zp == 0: |
| output_zp = output_zp + self.rng.integers(1, 10) |
| out_type_width += 1 |
| elif out_dtype == DType.UINT16: |
| # Must come after ErrorIf.U16OutputZeroPointNotValid check |
| output_zp = self.rng.choice([0, 32768]) |
| out_type_width += 1 |
| else: |
| output_zp = 0 |
| |
| # Calculate scale based on: |
| # scale = a *(2^output_width)/(2^input_width)) |
| |
| a = np.float32(self.rng.random(size=[nc])) |
| scale_arr = a * np.float32((1 << out_type_width) / (1 << in_type_width)) |
| |
| if scale32: |
| pass |
| # Cap the scaling at 2^31 - 1 for scale32 |
| scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), (1 << 31) - 1) |
| else: |
| # Cap the scaling at 2^15 - 1 for scale16 |
| scale_arr = np.clip(scale_arr, 1.0 / (1 << 31), 32767.0) |
| |
| # print('{} {} -> {}'.format(out_type_width, in_type_width, scale_arr)) |
| |
| multiplier_arr = np.int32(np.zeros(shape=[nc])) |
| shift_arr = np.int32(np.zeros(shape=[nc])) |
| min_shift_value_arr = np.int64(np.zeros(shape=[nc])) |
| max_shift_value_arr = np.int64(np.zeros(shape=[nc])) |
| |
| for i in range(nc): |
| multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( |
| scale_arr[i], scale32 |
| ) |
| min_shift_value_arr[i] = -1 << (shift_arr[i] - 1) |
| max_shift_value_arr[i] = (1 << (shift_arr[i] - 1)) - 1 |
| |
| # print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) |
| if scale32 and error_name is None: |
| # Make sure random values are within apply_scale_32 specification |
| # REQUIRES(value >= (-1<<(shift-1)) && value < (1<<(shift-1)) |
| assert val.placeholderFilename |
| values = np.load( |
| os.path.join(self.basePath, self.testPath, val.placeholderFilename) |
| ) |
| val_adj = np.subtract(values, input_zp, dtype=np.int64) |
| val_adj = np.maximum(val_adj, min_shift_value_arr, dtype=np.int64) |
| val_adj = np.minimum(val_adj, max_shift_value_arr, dtype=np.int64) |
| val_adj = np.add(val_adj, input_zp, dtype=values.dtype) |
| if not np.all(np.array_equal(values, val_adj)): |
| # Values changed so overwrite file with new values |
| np.save( |
| os.path.join(self.basePath, self.testPath, val.placeholderFilename), |
| val_adj, |
| False, |
| ) |
| |
| # Invalidate Input/Output list for error if checks. |
| input_list = [val.name] |
| output_list = [result_tens.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_list, output_list |
| ) |
| |
| qinfo = (input_zp, output_zp) |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_dtype=val.dtype, |
| output_dtype=out_dtype, |
| input_shape=val.shape, |
| qinfo=qinfo, |
| scale32=scale32, |
| double_round=double_round, |
| input_list=input_list, |
| output_list=output_list, |
| result_tensors=[result_tens], |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.RescaleAttribute( |
| input_zp, |
| output_zp, |
| multiplier_arr, |
| shift_arr, |
| scale32, |
| double_round, |
| per_channel, |
| ) |
| |
| self.ser.addOperator(op["op"], input_list, output_list, attr) |
| return result_tens |
| |
| def _get_condition_tensor(self, op, cond, error_name): |
| if error_name == ErrorIf.CondIfCondNotMatchingBool: |
| cond_type = get_wrong_output_type(op, self.rng, DType.BOOL) |
| else: |
| cond_type = DType.BOOL |
| if error_name == ErrorIf.CondIfCondShapeNotSizeOne: |
| choice = self.rng.choice([1, 2]) |
| if choice == 1: |
| cond_shape = [2] |
| else: |
| cond_shape = [1, 2] |
| else: |
| # Must be of size 1 (rank 0) |
| cond_shape = [] |
| cond_tens = self.ser.addConst(cond_shape, cond_type, [cond]) |
| return cond_tens |
| |
| def build_cond_if_const( |
| self, op, then_tens, else_tens, cond, validator_fcns=None, error_name=None |
| ): |
| # For cond_if with constants, we're supplied with then/else tensors that we ignore |
| # (except for the generated shape) and the condition. Build Then/Else blocks |
| # and fill them with const nodes for the body. |
| |
| # Condition tensor |
| cond_tens = self._get_condition_tensor(op, cond, error_name) |
| |
| # Make then/else tensors |
| out_shape = then_tens.shape |
| |
| # Create an incorrect output shape for error_if tests |
| if error_name in [ |
| ErrorIf.CondIfOutputListThenGraphMismatch, |
| ErrorIf.CondIfOutputListElseGraphMismatch, |
| ]: |
| incorrect_shape = deepcopy(then_tens.shape) |
| for i in range(len(incorrect_shape)): |
| incorrect_shape[i] += ( |
| self.rng.choice([-3, -2, 2, 3]) |
| if incorrect_shape[i] > 3 |
| else self.rng.choice([1, 2, 4]) |
| ) |
| incorrect_arr = np.int32(self.rng.integers(0, 256, size=incorrect_shape)) |
| |
| then_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| else_arr = np.int32(self.rng.integers(0, 256, size=out_shape)) |
| |
| # And the result tensor based on any of the outputs |
| result_tens = self.ser.addOutput(out_shape, DType.INT32) |
| |
| # Create the attribute with the names of the then/else blocks |
| then_block = "THEN_BLOCK" |
| else_block = "ELSE_BLOCK" |
| attr = ts.TosaSerializerAttribute() |
| attr.CondIfAttribute(then_block, else_block) |
| |
| # Finally, build the op and the two blocks |
| self.ser.addOperator(op["op"], [cond_tens.name], [result_tens.name], attr) |
| |
| self.ser.addBasicBlock(then_block) |
| # Build the actual then/else tensors inside their blocks |
| if error_name == ErrorIf.CondIfOutputListThenGraphMismatch: |
| then_tens = self.ser.addConst(incorrect_shape, DType.INT32, incorrect_arr) |
| else: |
| then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) |
| self.ser.addOutputTensor(then_tens) |
| |
| self.ser.addBasicBlock(else_block) |
| if error_name == ErrorIf.CondIfOutputListElseGraphMismatch: |
| else_tens = self.ser.addConst(incorrect_shape, DType.INT32, incorrect_arr) |
| else: |
| else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) |
| self.ser.addOutputTensor(else_tens) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| basicBlocks=self.ser.currRegion.basicBlocks, |
| cond=cond_tens, |
| ): |
| return None |
| |
| return result_tens |
| |
| def build_cond_if_binary( |
| self, op, a, b, cond, validator_fcns=None, error_name=None |
| ): |
| # For cond_if with a binary op in the then/else blocks, take a and b and |
| # alternately add or subtract them based on the condition |
| |
| # Condition tensor |
| cond_tens = self._get_condition_tensor(op, cond, error_name) |
| |
| result_tens = self.ser.addOutput(a.shape, a.dtype) |
| |
| # Create the attribute with the names of the then/else blocks |
| then_block = "THEN_BLOCK" |
| else_block = "ELSE_BLOCK" |
| attr = ts.TosaSerializerAttribute() |
| attr.CondIfAttribute(then_block, else_block) |
| |
| if error_name in [ |
| ErrorIf.CondIfInputListThenGraphMismatch, |
| ErrorIf.CondIfInputListElseGraphMismatch, |
| ErrorIf.CondIfOutputListElseGraphMismatch, |
| ErrorIf.CondIfOutputListThenGraphMismatch, |
| ]: |
| incorrect_shape = a.shape.copy() |
| for i in range(len(incorrect_shape)): |
| incorrect_shape[i] += self.rng.choice([-3, -2, 2, 3]) |
| incorrect_block_input = deepcopy(a) |
| incorrect_block_input.shape = incorrect_shape |
| |
| # Finally, build the op and the two blocks |
| self.ser.addOperator( |
| op["op"], [cond_tens.name, a.name, b.name], [result_tens.name], attr |
| ) |
| |
| if a.dtype in (DType.FP32, DType.BF16, DType.FP16, DType.INT32): |
| then_op, else_op = Op.ADD, Op.SUB |
| elif a.dtype in (DType.INT8, DType.INT16): |
| then_op, else_op = Op.LOGICAL_RIGHT_SHIFT, Op.LOGICAL_LEFT_SHIFT |
| else: |
| assert False, f"No tests for DType: {a.dtype}" |
| |
| for block, op in ((then_block, then_op), (else_block, else_op)): |
| self.ser.addBasicBlock(block) |
| if ( |
| error_name == ErrorIf.CondIfInputListThenGraphMismatch |
| and block == then_block |
| ) or ( |
| error_name == ErrorIf.CondIfInputListElseGraphMismatch |
| and block == else_block |
| ): |
| self.ser.addInputTensor(incorrect_block_input) |
| self.ser.addInputTensor(b) |
| tens = self.ser.addOutput(a.shape, a.dtype) |
| elif ( |
| error_name == ErrorIf.CondIfOutputListThenGraphMismatch |
| and block == then_block |
| ) or ( |
| error_name == ErrorIf.CondIfOutputListElseGraphMismatch |
| and block == else_block |
| ): |
| self.ser.addInputTensor(a) |
| self.ser.addInputTensor(b) |
| tens = self.ser.addOutput(incorrect_block_input.shape, a.dtype) |
| else: |
| self.ser.addInputTensor(a) |
| self.ser.addInputTensor(b) |
| tens = self.ser.addOutput(a.shape, a.dtype) |
| self.ser.addOperator(op, [a.name, b.name], [tens.name]) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| a=a, |
| b=b, |
| basicBlocks=self.ser.currRegion.basicBlocks, |
| cond=cond_tens, |
| ): |
| return None |
| |
| return result_tens |
| |
| def build_while_loop(self, op, a, iter_val, validator_fcns=None, error_name=None): |
| iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) |
| |
| cond_block = "COND_BLOCK" |
| body_block = "BODY_BLOCK" |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.WhileLoopAttribute(cond_block, body_block) |
| |
| # Accumulator tensor |
| # acc = self.ser.addOutput(a.shape, a.dtype) |
| acc_init_val = np.int32(np.zeros(a.shape)) |
| acc = self.ser.addPlaceholder(a.shape, a.dtype, acc_init_val) |
| |
| # Intermediate/output tensors for everything going through the loop |
| iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| a_out = self.ser.addIntermediate(a.shape, a.dtype) |
| if error_name == ErrorIf.InputListOutputListMismatch: |
| incorrect_acc = deepcopy(acc) |
| for i in range(len(incorrect_acc.shape)): |
| incorrect_acc.shape[i] += self.rng.choice([-3, -2, 2, 3]) |
| acc_out = self.ser.addIntermediate(incorrect_acc.shape, acc.dtype) |
| else: |
| acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
| |
| # While_loop operator |
| self.ser.addOperator( |
| op["op"], |
| [iter.name, a.name, acc.name], |
| [iter_out.name, a_out.name, acc_out.name], |
| attr, |
| ) |
| self.ser.addOutputTensor(acc_out) |
| |
| if error_name in [ |
| ErrorIf.InputListCondGraphMismatch, |
| ErrorIf.InputListBodyGraphInputMismatch, |
| ErrorIf.InputListBodyGraphOutputMismatch, |
| ]: |
| incorrect_iter = deepcopy(iter) |
| for i in range(len(incorrect_iter.shape)): |
| incorrect_iter.shape[i] += self.rng.choice([-3, -2, 2, 3]) |
| if len(incorrect_iter.shape) == 0: |
| incorrect_iter.shape.append(self.rng.choice([-3, -2, 2, 3])) |
| |
| incorrect_acc = deepcopy(acc) |
| for i in range(len(incorrect_acc.shape)): |
| incorrect_acc.shape[i] += self.rng.choice([-3, -2, 2, 3]) |
| |
| # COND block (input: iter, output: cond_tens ) |
| self.ser.addBasicBlock(cond_block) |
| |
| if error_name == ErrorIf.InputListCondGraphMismatch: |
| self.ser.addInputTensor(incorrect_iter) |
| self.ser.addInputTensor(a) |
| self.ser.addInputTensor(incorrect_acc) |
| else: |
| self.ser.addInputTensor(iter) |
| self.ser.addInputTensor(a) |
| self.ser.addInputTensor(acc) |
| zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) |
| |
| if error_name == ErrorIf.CondGraphOutputNotMatchingBool: |
| cond_type = self.rng.choice([DType.INT8, DType.INT32, DType.FP32]) |
| else: |
| cond_type = DType.BOOL |
| if error_name == ErrorIf.CondGraphOutputShapeNotSizeOne: |
| choice = self.rng.choice([1, 2]) |
| if choice == 1: |
| cond_shape = [3] |
| else: |
| cond_shape = [1, 2] |
| else: |
| cond_shape = [] |
| cond_tens = self.ser.addOutput(cond_shape, cond_type) |
| |
| self.ser.addOperator(Op.GREATER, [iter.name, zero_tens.name], [cond_tens.name]) |
| |
| # BODY block (input: a, acc, iter, output: a, acc, iter) |
| # Note that local intermediate tensors need to be declared here for the outputs |
| self.ser.addBasicBlock(body_block) |
| |
| if error_name == ErrorIf.InputListBodyGraphInputMismatch: |
| self.ser.addInputTensor(incorrect_iter) |
| self.ser.addInputTensor(a) |
| self.ser.addInputTensor(incorrect_acc) |
| else: |
| self.ser.addInputTensor(iter) |
| self.ser.addInputTensor(a) |
| self.ser.addInputTensor(acc) |
| |
| one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) |
| |
| if error_name == ErrorIf.InputListBodyGraphOutputMismatch: |
| iter_body_out = self.ser.addIntermediate( |
| incorrect_iter.shape, incorrect_iter.dtype |
| ) |
| acc_body_out = self.ser.addIntermediate( |
| incorrect_acc.shape, incorrect_acc.dtype |
| ) |
| else: |
| iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype) |
| acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype) |
| |
| self.ser.addOperator(Op.ADD, [a.name, acc.name], [acc_body_out.name]) |
| self.ser.addOperator(Op.SUB, [iter.name, one_tens.name], [iter_body_out.name]) |
| self.ser.addOutputTensor(iter_body_out) |
| self.ser.addOutputTensor(a) |
| self.ser.addOutputTensor(acc_body_out) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| basicBlocks=self.ser.currRegion.basicBlocks, |
| ): |
| return None |
| |
| return acc_out |
| |
| def build_fft2d( |
| self, op, val1, val2, inverse, validator_fcns=None, error_name=None |
| ): |
| results = OutputShaper.fft2dOp(self.ser, self.rng, val1, val2, error_name) |
| |
| input_names = [val1.name, val2.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| |
| output_names = [res.name for res in results] |
| output_shapes = [res.shape for res in results] |
| output_dtypes = [res.dtype for res in results] |
| |
| input_names, output_names = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_names, output_names |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| inverse=inverse, |
| input1=val1, |
| input2=val2, |
| input_shape=val1.shape, |
| input_dtype=val1.dtype, |
| output_shape=output_shapes, |
| output_dtype=output_dtypes, |
| result_tensors=results, |
| input_list=input_names, |
| output_list=output_names, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| attr = ts.TosaSerializerAttribute() |
| attr.FFTAttribute(inverse) |
| |
| self.ser.addOperator(op["op"], input_names, output_names, attr) |
| return results |
| |
| def build_rfft2d(self, op, val, validator_fcns=None, error_name=None): |
| results = OutputShaper.rfft2dOp(self.ser, self.rng, val, error_name) |
| |
| input_names = [val.name] |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| |
| output_names = [res.name for res in results] |
| output_shapes = [res.shape for res in results] |
| output_dtypes = [res.dtype for res in results] |
| |
| input_names, output_names = TosaErrorIfArgGen.eiInvalidateInputOutputList( |
| self, error_name, input_names, output_names |
| ) |
| |
| if not TosaErrorValidator.evValidateErrorIfs( |
| self.ser, |
| validator_fcns, |
| error_name, |
| op=op, |
| input_shape=val.shape, |
| input_dtype=val.dtype, |
| output_shape=output_shapes, |
| output_dtype=output_dtypes, |
| result_tensors=results, |
| input_list=input_names, |
| output_list=output_names, |
| num_operands=num_operands, |
| ): |
| return None |
| |
| self.ser.addOperator(op["op"], input_names, output_names) |
| return results |
| |
| def create_filter_lists( |
| self, op, shapeFilter, rankFilter, dtypeFilter, testType, validator=None |
| ): |
| # Create a default testing rank range, 1-4 inclusive to keep test sizes reasonably small. |
| default_test_rank_range = range(1, 5) |
| if not shapeFilter: |
| shapeFilter = [None] |
| |
| # Calculate the filters based on what is requested and what the operator allows |
| rmin, rmax = op["rank"] |
| if rankFilter is not None: |
| cleanRankFilter = [] |
| # Ensure rankFilter values are allowed by operator |
| for rank in rankFilter: |
| if rank >= rmin and rank <= rmax: |
| cleanRankFilter.append(rank) |
| elif rankFilter is None and shapeFilter[0] is None: |
| # Ensure default behaviour is bounded by default range or by operator, |
| # whichever is the smaller range of ranks. |
| opRankRange = range(rmin, rmax + 1) |
| cleanRankFilter = ( |
| opRankRange |
| if len(opRankRange) <= len(default_test_rank_range) |
| else default_test_rank_range |
| ) |
| else: |
| cleanRankFilter = range(rmin, rmax + 1) |
| |
| dtypes = op["types"] |
| |
| if dtypeFilter is not None: |
| cleanDtypeFilter = [] |
| # Create list of operator dtypes filtered by requested dtypes |
| for dtype in dtypes: |
| if dtype in dtypeFilter or ( |
| isinstance(dtype, list) and dtype[0] in dtypeFilter |
| ): |
| cleanDtypeFilter.append(dtype) |
| else: |
| cleanDtypeFilter = dtypes |
| |
| if testType == "positive": |
| filterDict = { |
| "shapeFilter": shapeFilter, |
| "rankFilter": cleanRankFilter, |
| "dtypeFilter": cleanDtypeFilter, |
| } |
| return filterDict |
| elif testType == "negative": |
| if validator is not None: |
| validator_info = validator(check=False, op=op) |
| else: |
| return None |
| |
| error_arguments = validator_info["param_reqs"] |
| |
| # Set parameters as required |
| if error_arguments["rank"] is not None: |
| rankFilter = error_arguments["rank"] |
| else: |
| rankFilter = cleanRankFilter |
| |
| if error_arguments["dtype"] is not None: |
| dtypeFilter = error_arguments["dtype"] |
| else: |
| dtypeFilter = cleanDtypeFilter |
| |
| if error_arguments["shape"] is not None: |
| shapeFilter = error_arguments["shape"] |
| else: |
| shapeFilter = shapeFilter[ |
| :2 |
| ] # Reduce number of shapes to keep test numbers small |
| |
| filterDict = { |
| "shapeFilter": shapeFilter, |
| "rankFilter": rankFilter, |
| "dtypeFilter": dtypeFilter, |
| } |
| return filterDict |
| |
| def genOpTestList( |
| self, |
| opName, |
| shapeFilter=[None], |
| rankFilter=None, |
| dtypeFilter=None, |
| testType="positive", |
| ): |
| |
| try: |
| op = self.TOSA_OP_LIST[opName] |
| except KeyError: |
| raise Exception("Cannot find op with name {}".format(opName)) |
| |
| # Initialize a new random number generator |
| self.rng = np.random.default_rng(self.random_seed) |
| |
| build_fcn, tgen_fcn, tvgen_fcn, agen_fcn = op["build_fcn"] |
| |
| # Test list consists of a tuple of: |
| # (opName, testNameStr, dtype, shapeList, argumentsList) |
| testList = [] |
| if testType == "negative" and "error_if_validators" in op: |
| error_if_validators = op["error_if_validators"] |
| else: |
| error_if_validators = [None] |
| |
| for validator in error_if_validators: |
| if validator is not None: |
| error_name = validator(check=False, op=op)["error_name"] |
| else: |
| error_name = None |
| |
| filterDict = self.create_filter_lists( |
| op, shapeFilter, rankFilter, dtypeFilter, testType, validator |
| ) |
| if filterDict is None: |
| return [] |
| cleanRankFilter = filterDict["rankFilter"] |
| cleanDtypeFilter = filterDict["dtypeFilter"] |
| cleanShapeFilter = filterDict["shapeFilter"] |
| # print(f"Error: {error_name}, Filters: S {cleanShapeFilter}, R {cleanRankFilter}, T {cleanDtypeFilter}") |
| |
| for r in cleanRankFilter: |
| for t in cleanDtypeFilter: |
| for shape in cleanShapeFilter: |
| # Filter out by rank |
| if shape is not None and len(shape) != r: |
| continue |
| self.setTargetShape(shape) |
| shapeList = tgen_fcn(self, op, r, error_name) |
| |
| shapeStr = self.shapeStr(shapeList[0]) |
| typeStr = self.typeStr(t) |
| |
| # Argument lists consists of tuples of the (str, []) string representation and the build function argument list |
| argList = [] |
| if agen_fcn: |
| argList = agen_fcn(self, opName, shapeList, t, error_name) |
| else: |
| argList = [("", [])] |
| |
| for argStr, args in argList: |
| if testType == "positive": |
| if argStr: |
| testStr = "{}_{}_{}_{}".format( |
| opName, shapeStr, typeStr, argStr |
| ) |
| else: |
| testStr = "{}_{}_{}".format( |
| opName, shapeStr, typeStr |
| ) |
| elif testType == "negative": |
| if argStr: |
| testStr = "{}_ERRORIF_{}_{}_{}_{}".format( |
| opName, error_name, shapeStr, typeStr, argStr |
| ) |
| else: |
| testStr = "{}_ERRORIF_{}_{}_{}".format( |
| opName, error_name, shapeStr, typeStr |
| ) |
| |
| testList.append( |
| (opName, testStr, t, error_name, shapeList, args) |
| ) |
| |
| if testType == "positive": |
| # Remove tests which are expected to fail but don't correlate to a ERROR_IF statement |
| if "invalid_test_validators" in op: |
| invalid_test_validators = op["invalid_test_validators"] |
| clean_testList = [] |
| for test in testList: |
| remove_test = False |
| for validator_fcn in invalid_test_validators: |
| if validator_fcn( |
| opName=test[0], |
| input_dtype=test[2], |
| shapeList=test[4], |
| args=test[5], |
| ): |
| remove_test = True |
| if not remove_test: |
| clean_testList.append(test) |
| testList = clean_testList |
| |
| return testList |
| |
| def serializeTest( |
| self, opName, testStr, dtype_or_dtypeList, error_name, shapeList, testArgs |
| ): |
| try: |
| op = self.TOSA_OP_LIST[opName] |
| except KeyError: |
| raise Exception("Cannot find op with name {}".format(opName)) |
| |
| if self.args.verbose: |
| print(f"Creating {testStr}") |
| |
| # Create a serializer |
| self.createSerializer(opName, testStr) |
| |
| build_fcn, tgen_fcn, tvgen_fcn, agen_fcn = op["build_fcn"] |
| if "error_if_validators" in op: |
| error_if_validators = op["error_if_validators"] |
| else: |
| error_if_validators = None |
| |
| pCount, cCount = op["operands"] |
| num_operands = pCount + cCount |
| |
| if isinstance(dtype_or_dtypeList, list): |
| dtypeList = dtype_or_dtypeList |
| elif op["op"] == Op.CONCAT: |
| dtypeList = [dtype_or_dtypeList] * len(shapeList) |
| else: |
| dtypeList = [dtype_or_dtypeList] * (num_operands) |
| |
| if op["op"] != Op.CONCAT: |
| assert ( |
| len(shapeList) == num_operands |
| ), "shapeList length {} must match number of operands {}".format( |
| len(shapeList), num_operands |
| ) |
| assert ( |
| len(dtypeList) == num_operands |
| ), "dtypeList length {} must match number of operands {}".format( |
| len(dtypeList), num_operands |
| ) |
| |
| try: |
| qgen = op["qgen"] |
| except KeyError: |
| qgen = None |
| |
| # Build the random tensor operands and the test |
| tens = [] |
| |
| if qgen is not None: |
| qinfo = qgen(self, op, dtype_or_dtypeList, error_name) |
| else: |
| qinfo = None |
| |
| tens = tvgen_fcn(self, op, dtypeList, shapeList, testArgs, error_name) |
| |
| try: |
| if error_if_validators is None: |
| if qinfo is not None: |
| resultName = build_fcn(self, op, *tens, *testArgs, qinfo) |
| else: |
| resultName = build_fcn(self, op, *tens, *testArgs) |
| else: |
| if qinfo is not None: |
| resultName = build_fcn( |
| self, |
| op, |
| *tens, |
| *testArgs, |
| validator_fcns=error_if_validators, |
| error_name=error_name, |
| qinfo=qinfo, |
| ) |
| else: |
| resultName = build_fcn( |
| self, |
| op, |
| *tens, |
| *testArgs, |
| validator_fcns=error_if_validators, |
| error_name=error_name, |
| ) |
| except TypeError as e: |
| print(f"build_fcn: {build_fcn}\nTensors: {tens}\nArgs: {testArgs}\n") |
| raise e |
| |
| if resultName: |
| # The test is valid, serialize it |
| self.serialize("test") |
| else: |
| # The test is not valid |
| print(f"Invalid ERROR_IF test created: {opName} {testStr}") |
| |
| def createDynamicOpLists(self): |
| |
| if "conv2d_TEMPLATE" not in self.TOSA_OP_LIST: |
| # Already created these lists (can occur when class is initialized more than once) |
| return |
| |
| # Dynamically create op lists for convolutions with a list of kernel sizes |
| if not self.args.level8k: |
| KERNELS_2D = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] |
| KERNELS_3D = [[1, 1, 1], [2, 1, 1], [1, 2, 1], [1, 1, 2]] |
| else: |
| bigK = self.TOSA_8K_LEVEL_MAX_KERNEL |
| KERNELS_2D = [[1, bigK], [bigK, 2]] |
| KERNELS_3D = [[1, bigK, 1], [2, 2, bigK]] |
| |
| for k in KERNELS_2D: |
| testName = "conv2d_{}x{}".format(k[0], k[1]) |
| self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv2d_TEMPLATE"].copy() |
| self.TOSA_OP_LIST[testName]["filter"] = k |
| self.TOSA_OP_LIST[testName]["template"] = False |
| |
| testName = "depthwise_conv2d_{}x{}".format(k[0], k[1]) |
| self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| "depthwise_conv2d_TEMPLATE" |
| ].copy() |
| self.TOSA_OP_LIST[testName]["filter"] = k |
| self.TOSA_OP_LIST[testName]["template"] = False |
| |
| testName = "transpose_conv2d_{}x{}".format(k[0], k[1]) |
| self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST[ |
| "transpose_conv2d_TEMPLATE" |
| ].copy() |
| self.TOSA_OP_LIST[testName]["filter"] = k |
| self.TOSA_OP_LIST[testName]["template"] = False |
| |
| for k in KERNELS_3D: |
| testName = "conv3d_{}x{}x{}".format(k[0], k[1], k[2]) |
| self.TOSA_OP_LIST[testName] = self.TOSA_OP_LIST["conv3d_TEMPLATE"].copy() |
| self.TOSA_OP_LIST[testName]["filter"] = k |
| self.TOSA_OP_LIST[testName]["template"] = False |
| |
| # Delete any templates after having created any dynamic ops |
| # This is a two-pass operation because it's bad practice to delete |
| # keys from dictionaries while iterating |
| keyList = [] |
| for k in self.TOSA_OP_LIST: |
| try: |
| if self.TOSA_OP_LIST[k]["template"]: |
| keyList.append(k) |
| continue |
| except KeyError: |
| pass |
| |
| for k in keyList: |
| del self.TOSA_OP_LIST[k] |
| |
| def initOpListDefaults(self): |
| """Fill in default fields for ops if they aren't already specified. |
| Look for missing required fields (datastructure linting).""" |
| for op in self.TOSA_OP_LIST: |
| |
| # Required fields |
| try: |
| pl, c = self.TOSA_OP_LIST[op]["operands"] |
| except (KeyError, ValueError, TypeError): |
| raise Exception( |
| "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) |
| ) |
| |
| try: |
| fcn, tgen, tvgen, arggen = self.TOSA_OP_LIST[op]["build_fcn"] |
| except (KeyError, ValueError, TypeError): |
| raise Exception( |
| "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( |
| op |
| ) |
| ) |
| |
| try: |
| _ = self.TOSA_OP_LIST[op]["types"] |
| except KeyError: |
| raise Exception( |
| "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) |
| ) |
| |
| try: |
| _ = self.TOSA_OP_LIST[op]["op"] |
| except KeyError: |
| raise Exception( |
| "Op {} is missing the Op field in TOSA_OP_LIST".format(op) |
| ) |
| |
| # Put in default rank range, if missing |
| try: |
| _ = self.TOSA_OP_LIST[op]["rank"] |
| except KeyError: |
| self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE |
| |
| # Tensor operator list |
| # 'op': op name |
| # 'operands': tuple of (placeholder, const) operands |
| # 'rank': optional, restricts rank to tuple inclusive of (min, max), |
| # if not specified, defaults to (1, 4) |
| # 'build_fcn': tuple of the function to (build_operator(), TensorGen function, ArgGen enum) |
| # 'types': array of datatypes to be tested |
| TYPE_FP = [DType.FP32, DType.FP16, DType.BF16] |
| |
| TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 |
| TYPE_INT_FP = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ] # Excludes INT4 |
| |
| TYPE_BOOL = [DType.BOOL] |
| TYPE_FI32 = [ |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| DType.INT32, |
| ] # floating-types and INT32 |
| TYPE_FIB = [ |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.BOOL, |
| ] |
| TYPE_FI16 = [DType.FP32, DType.INT16] |
| |
| TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FP16, DType.BF16, DType.FP32] |
| |
| # List of [Input Type 1, Input Type 2, Accumulator Type] |
| TYPE_CONV = [ |
| [DType.INT8, DType.INT4, DType.INT32], |
| [DType.INT8, DType.INT8, DType.INT32], |
| [DType.INT16, DType.INT8, DType.INT48], |
| [DType.FP16, DType.FP16, DType.FP16], |
| [DType.FP16, DType.FP16, DType.FP32], |
| [DType.BF16, DType.BF16, DType.FP32], |
| [DType.FP32, DType.FP32, DType.FP32], |
| ] |
| |
| DEFAULT_RANK_RANGE = (1, TOSA_TENSOR_MAX_RANK) |
| |
| TOSA_OP_LIST = { |
| # Tensor operators |
| "argmax": { |
| "op": Op.ARGMAX, |
| "operands": (1, 0), |
| "rank": (1, 6), |
| "build_fcn": ( |
| build_argmax, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_NARROW_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evArgmaxOutputRankMismatch, |
| TosaErrorValidator.evArgmaxOutputShapeMismatch, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "avg_pool2d": { |
| "op": Op.AVG_POOL2D, |
| "operands": (1, 0), |
| "rank": (4, 4), |
| "build_fcn": ( |
| build_pool2d, |
| TosaTensorGen.tgNHWC, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agPooling, |
| ), |
| "qgen": TosaQuantGen.qgUnary, |
| "types": TYPE_NARROW_INT_FP, |
| "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), |
| "error_if_validators": ( |
| TosaErrorValidator.evKernelSmallerOne, |
| TosaErrorValidator.evStrideSmallerOne, |
| TosaErrorValidator.evPadSmallerZero, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evOutputZeroPointNotZero, |
| TosaErrorValidator.evPadLargerEqualKernel, |
| TosaErrorValidator.evPoolingOutputShapeMismatch, |
| TosaErrorValidator.evPoolingOutputShapeNonInteger, |
| ), |
| }, |
| # Templated operator. Filled in by createDynamicOpLists |
| "conv2d_TEMPLATE": { |
| "op": Op.CONV2D, |
| "operands": (1, 2), |
| "rank": (4, 4), |
| "build_fcn": ( |
| build_conv2d, |
| TosaTensorGen.tgConv2D, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agConv, |
| ), |
| "qgen": TosaQuantGen.qgConv, |
| "types": TYPE_CONV, |
| "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evWeightZeroPointNotZero, |
| TosaErrorValidator.evPadSmallerZero, |
| TosaErrorValidator.evStrideSmallerOne, |
| TosaErrorValidator.evDilationSmallerOne, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evConvOutputShapeMismatch, |
| TosaErrorValidator.evConvOutputShapeNonInteger, |
| ), |
| "template": True, |
| }, |
| # Templated operator. Filled in by createDynamicOpLists |
| "conv3d_TEMPLATE": { |
| "op": Op.CONV3D, |
| "operands": (1, 2), |
| "rank": (5, 5), |
| "build_fcn": ( |
| build_conv3d, |
| TosaTensorGen.tgConv3D, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agConv, |
| ), |
| "qgen": TosaQuantGen.qgConv, |
| "types": TYPE_CONV, |
| "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evWeightZeroPointNotZero, |
| TosaErrorValidator.evPadSmallerZero, |
| TosaErrorValidator.evStrideSmallerOne, |
| TosaErrorValidator.evDilationSmallerOne, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evConvOutputShapeMismatch, |
| TosaErrorValidator.evConvOutputShapeNonInteger, |
| ), |
| "template": True, |
| }, |
| # Templated operator. Filled in by createDynamicOpLists |
| "depthwise_conv2d_TEMPLATE": { |
| "op": Op.DEPTHWISE_CONV2D, |
| "operands": (1, 2), |
| "filter": [1, 1], |
| "rank": (4, 4), |
| "build_fcn": ( |
| build_depthwise_conv2d, |
| TosaTensorGen.tgDepthwiseConv2D, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agConv, |
| ), |
| "qgen": TosaQuantGen.qgConv, |
| "types": TYPE_CONV, |
| "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evWeightZeroPointNotZero, |
| TosaErrorValidator.evPadSmallerZero, |
| TosaErrorValidator.evStrideSmallerOne, |
| TosaErrorValidator.evDilationSmallerOne, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evConvOutputShapeMismatch, |
| TosaErrorValidator.evConvOutputShapeNonInteger, |
| ), |
| "template": True, |
| }, |
| "fully_connected": { |
| "op": Op.FULLY_CONNECTED, |
| "operands": (1, 2), |
| "rank": (2, 2), |
| "build_fcn": ( |
| build_fully_connected, |
| TosaTensorGen.tgFullyConnected, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agFullyConnected, |
| ), |
| "qgen": TosaQuantGen.qgConv, |
| "types": TYPE_CONV, |
| "error_if_validators": ( |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evWeightZeroPointNotZero, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "matmul": { |
| "op": Op.MATMUL, |
| "operands": (2, 0), |
| "rank": (3, 3), |
| "build_fcn": ( |
| build_matmul, |
| TosaTensorGen.tgMatmul, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agMatMul, |
| ), |
| "qgen": TosaQuantGen.qgMatmul, |
| "types": TYPE_NARROW_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "max_pool2d": { |
| "op": Op.MAX_POOL2D, |
| "operands": (1, 0), |
| "rank": (4, 4), |
| "build_fcn": ( |
| build_maxpool2d, |
| TosaTensorGen.tgNHWC, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agPooling, |
| ), |
| "types": TYPE_NARROW_INT_FP, |
| "invalid_test_validators": (TosaInvalidValidator.ivHeightWidthInvalid,), |
| "error_if_validators": ( |
| TosaErrorValidator.evKernelSmallerOne, |
| TosaErrorValidator.evStrideSmallerOne, |
| TosaErrorValidator.evPadSmallerZero, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evPadLargerEqualKernel, |
| TosaErrorValidator.evPoolingOutputShapeMismatch, |
| TosaErrorValidator.evPoolingOutputShapeNonInteger, |
| ), |
| }, |
| # Templated operator. Filled in by createDynamicOpLists |
| "transpose_conv2d_TEMPLATE": { |
| "op": Op.TRANSPOSE_CONV2D, |
| "operands": (1, 2), |
| "rank": (4, 4), |
| "build_fcn": ( |
| build_transpose_conv2d, |
| TosaTensorGen.tgTransposeConv2D, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agTransposeConv2D, |
| ), |
| "qgen": TosaQuantGen.qgConv, |
| "types": TYPE_CONV, |
| "invalid_test_validators": ( |
| TosaInvalidValidator.ivHeightWidthInvalid, |
| TosaInvalidValidator.ivNonPositiveOutputShape, |
| ), |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evWeightZeroPointNotZero, |
| TosaErrorValidator.evPadLargerEqualKernel, |
| TosaErrorValidator.evStrideSmallerOne, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evConvOutputShapeMismatch, |
| ), |
| "template": True, |
| }, |
| # Activation functions |
| "clamp": { |
| "op": Op.CLAMP, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_clamp, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_NARROW_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evMaxSmallerMin, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "sigmoid": { |
| "op": Op.SIGMOID, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_sigmoid, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "tanh": { |
| "op": Op.TANH, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_tanh, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "erf": { |
| "op": Op.ERF, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_erf, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| # Elementwise Binary Operators |
| "add": { |
| "op": Op.ADD, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgAddSub, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "arithmetic_right_shift": { |
| "op": Op.ARITHMETIC_RIGHT_SHIFT, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_arithmetic_right_shift, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgArithmeticRightShift, |
| TosaArgGen.agArithmeticRightShift, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "bitwise_and": { |
| "op": Op.BITWISE_AND, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "bitwise_or": { |
| "op": Op.BITWISE_OR, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "bitwise_xor": { |
| "op": Op.BITWISE_XOR, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "intdiv": { |
| "op": Op.INTDIV, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgIntDiv, |
| None, |
| ), |
| "types": [DType.INT32], |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "logical_and": { |
| "op": Op.LOGICAL_AND, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_BOOL, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "logical_left_shift": { |
| "op": Op.LOGICAL_LEFT_SHIFT, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgLogicalShift, |
| None, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "logical_right_shift": { |
| "op": Op.LOGICAL_RIGHT_SHIFT, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgLogicalShift, |
| None, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "logical_or": { |
| "op": Op.LOGICAL_OR, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_BOOL, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "logical_xor": { |
| "op": Op.LOGICAL_XOR, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_BOOL, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "maximum": { |
| "op": Op.MAXIMUM, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "minimum": { |
| "op": Op.MINIMUM, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "mul": { |
| "op": Op.MUL, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_mul, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgMul, |
| TosaArgGen.agMul, |
| ), |
| "types": TYPE_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "pow": { |
| "op": Op.POW, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "sub": { |
| "op": Op.SUB, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_binary_broadcast, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgAddSub, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "table": { |
| "op": Op.TABLE, |
| # Use the automatic generation functions to create the input array |
| # but create the table tensor in the build function, as it may be |
| # a different type from the input |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_table, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agTable, |
| ), |
| "types": [DType.INT8, DType.INT16], |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| # Elementwise Unary operators |
| "abs": { |
| "op": Op.ABS, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "bitwise_not": { |
| "op": Op.BITWISE_NOT, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_INT, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "ceil": { |
| "op": Op.CEIL, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "clz": { |
| "op": Op.CLZ, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": [DType.INT32], |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "exp": { |
| "op": Op.EXP, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "floor": { |
| "op": Op.FLOOR, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "log": { |
| "op": Op.LOG, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "logical_not": { |
| "op": Op.LOGICAL_NOT, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_BOOL, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "negate": { |
| "op": Op.NEGATE, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgNegate, |
| None, |
| ), |
| "qgen": TosaQuantGen.qgUnary, |
| "types": TYPE_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evOutputZeroPointNotZero, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "reciprocal": { |
| "op": Op.RECIPROCAL, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "rsqrt": { |
| "op": Op.RSQRT, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| # Elementwise Ternary operators |
| "select": { |
| "op": Op.SELECT, |
| "operands": (3, 0), |
| "build_fcn": ( |
| build_select, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgSelect, |
| None, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| # Comparison operators |
| "equal": { |
| "op": Op.EQUAL, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_comparison, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgEqual, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "greater_equal": { |
| "op": Op.GREATER_EQUAL, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_comparison, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| "greater": { |
| "op": Op.GREATER, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_comparison, |
| TosaTensorGen.tgBroadcastFuzz, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FI32, |
| "error_if_validators": ( |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evDimensionMismatch, |
| TosaErrorValidator.evBroadcastShapesMismatch, |
| ), |
| }, |
| # Reduction operators |
| "reduce_all": { |
| "op": Op.REDUCE_ALL, |
| "operands": (1, 0), |
| "rank": (1, 4), |
| "build_fcn": ( |
| build_reduce, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_BOOL, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evShapeOfAxisNotOne, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "reduce_any": { |
| "op": Op.REDUCE_ANY, |
| "operands": (1, 0), |
| "rank": (1, 4), |
| "build_fcn": ( |
| build_reduce, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_BOOL, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evShapeOfAxisNotOne, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "reduce_max": { |
| "op": Op.REDUCE_MAX, |
| "operands": (1, 0), |
| "rank": (1, 4), |
| "build_fcn": ( |
| build_reduce, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evShapeOfAxisNotOne, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "reduce_min": { |
| "op": Op.REDUCE_MIN, |
| "operands": (1, 0), |
| "rank": (1, 4), |
| "build_fcn": ( |
| build_reduce, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evShapeOfAxisNotOne, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "reduce_product": { |
| "op": Op.REDUCE_PRODUCT, |
| "operands": (1, 0), |
| "rank": (1, 4), |
| "build_fcn": ( |
| build_reduce, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evShapeOfAxisNotOne, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "reduce_sum": { |
| "op": Op.REDUCE_SUM, |
| "operands": (1, 0), |
| "rank": (1, 4), |
| "build_fcn": ( |
| build_reduce, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgReduceSum, |
| TosaArgGen.agAxis, |
| ), |
| "types": (DType.FP16, DType.BF16, DType.FP32, DType.INT32), |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evShapeOfAxisNotOne, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| # Data layout operators |
| "concat": { |
| "op": Op.CONCAT, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_concat, |
| TosaTensorGen.tgConcat, |
| TosaTensorValuesGen.tvgConcat, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evConcatInputRankMismatch, |
| TosaErrorValidator.evConcatShapeSumMismatch, |
| TosaErrorValidator.evConcatInputDimMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "pad": { |
| "op": Op.PAD, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_pad, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agPad, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evPadSmallerZero, |
| TosaErrorValidator.evPadOutputShapeMismatch, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongRank, |
| ), |
| }, |
| "dim": { |
| "op": Op.DIM, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_dim, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evWrongRank, |
| ), |
| }, |
| "reshape": { |
| "op": Op.RESHAPE, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_reshape, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agReshape, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evTensorSizeInputOutputMismatch, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evReshapeOutputSizeMultiInference, |
| TosaErrorValidator.evReshapeOutputSizeNonInteger, |
| ), |
| }, |
| "reverse": { |
| "op": Op.REVERSE, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_reverse, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agAxis, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evAxisSmallerZero, |
| TosaErrorValidator.evAxisLargerRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "slice": { |
| "op": Op.SLICE, |
| "operands": (1, 0), |
| "rank": (1, 6), |
| "build_fcn": ( |
| build_slice, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agSlice, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evStartSmallerZero, |
| TosaErrorValidator.evSizeSmallerEqualZero, |
| TosaErrorValidator.evStartSizeOutsideBounds, |
| TosaErrorValidator.evSizeOutputShapeMismatch, |
| TosaErrorValidator.evInputSizeStartLengthMismatch, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evRankMismatch, |
| ), |
| }, |
| "tile": { |
| "op": Op.TILE, |
| "operands": (1, 0), |
| "rank": (1, 6), |
| "build_fcn": ( |
| build_tile, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agTile, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evWrongRank, |
| ), |
| }, |
| "transpose": { |
| "op": Op.TRANSPOSE, |
| "operands": (1, 0), |
| "rank": (1, 6), |
| "build_fcn": ( |
| build_transpose, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agTranspose, |
| ), |
| "types": TYPE_FIB, |
| "error_if_validators": ( |
| TosaErrorValidator.evIndexOutsideBounds, |
| TosaErrorValidator.evIndexUsedTwice, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evRankMismatch, |
| TosaErrorValidator.evTensorSizeInputOutputMismatch, |
| ), |
| }, |
| # Data nodes |
| "const": { |
| "op": Op.CONST, |
| "operands": (0, 1), |
| "build_fcn": ( |
| build_const, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FIB + [DType.INT48], |
| }, |
| "identity": { |
| "op": Op.IDENTITY, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_unary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_FIB, |
| }, |
| # Scatter/Gather |
| "gather": { |
| "op": Op.GATHER, |
| # Only specify 'values' tensor here. 'indices' is generated in op building stage |
| "operands": (1, 0), |
| "rank": (3, 3), |
| "build_fcn": ( |
| build_gather, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": ( |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ), |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evWrongRank, |
| ), |
| }, |
| "scatter": { |
| "op": Op.SCATTER, |
| # Only specify 'values_in' tensor here. |
| # 'indices' and 'input' are generated in op building stage |
| "operands": (2, 0), |
| "rank": (3, 3), |
| "build_fcn": ( |
| build_scatter, |
| TosaTensorGen.tgScatter, |
| TosaTensorValuesGen.tvgDefault, |
| None, |
| ), |
| "types": TYPE_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evWrongRank, |
| ), |
| }, |
| # Image operations |
| "resize": { |
| "op": Op.RESIZE, |
| "operands": (1, 0), |
| "rank": (4, 4), |
| "build_fcn": ( |
| build_resize, |
| TosaTensorGen.tgNHWC, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agResize, |
| ), |
| "types": (DType.INT8, DType.INT16, DType.FP16, DType.BF16, DType.FP32), |
| "invalid_test_validators": ( |
| TosaInvalidValidator.ivWrongDataTypeOrModeResize, |
| ), |
| "error_if_validators": ( |
| TosaErrorValidator.evMaxDimExceeded, |
| TosaErrorValidator.evScaleSmallerEqualZero, |
| TosaErrorValidator.evScaleNLargerMax, |
| TosaErrorValidator.evScaleDLargerMax, |
| TosaErrorValidator.evOffsetSmallerMin, |
| TosaErrorValidator.evOffsetLargerEqualMax, |
| TosaErrorValidator.evBorderSmallerMin, |
| TosaErrorValidator.evBorderLargerEqualMax, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evBatchMismatch, |
| TosaErrorValidator.evChannelMismatch, |
| TosaErrorValidator.evResizeOutputShapeMismatch, |
| TosaErrorValidator.evResizeOutputShapeNonInteger, |
| ), |
| }, |
| # Type conversion |
| "cast": { |
| "op": Op.CAST, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_cast, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agCast, |
| ), |
| "types": ( |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.BOOL, |
| ), |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| "rescale": { |
| "op": Op.RESCALE, |
| "operands": (1, 0), |
| "build_fcn": ( |
| build_rescale, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agRescale, |
| ), |
| "types": [ |
| DType.UINT8, |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.UINT16, |
| ], |
| "error_if_validators": ( |
| TosaErrorValidator.evInputZeroPointNotZero, |
| TosaErrorValidator.evOutputZeroPointNotZero, |
| TosaErrorValidator.evU16InputZeroPointNotValid, |
| TosaErrorValidator.evU16OutputZeroPointNotValid, |
| TosaErrorValidator.evScaleTrue, |
| TosaErrorValidator.evScaleNotTrue, |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| ), |
| }, |
| # Custom |
| # Not implemented. |
| # Control flow operators |
| # Two varients of cond_if, one that generates one of two constant tensors (no |
| # inputs to the basic blocks, one output) and another that either adds or subtracts two tensors |
| # (two inputs to the basic blocks, one output) |
| "cond_if_const": { |
| "op": Op.COND_IF, |
| "operands": (0, 2), |
| "build_fcn": ( |
| build_cond_if_const, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgCondIfWhileLoop, |
| TosaArgGen.agCondIf, |
| ), |
| "types": [DType.BOOL], |
| "error_if_validators": ( |
| TosaErrorValidator.evOutputListThenGraphMismatch, |
| TosaErrorValidator.evOutputListElseGraphMismatch, |
| TosaErrorValidator.evCondIfCondNotMatchingBool, |
| TosaErrorValidator.evCondIfCondShapeNotSizeOne, |
| ), |
| }, |
| "cond_if_binary": { |
| "op": Op.COND_IF, |
| "operands": (2, 0), |
| "build_fcn": ( |
| build_cond_if_binary, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgCondIfWhileLoop, |
| TosaArgGen.agCondIf, |
| ), |
| "types": TYPE_INT_FP, |
| "error_if_validators": ( |
| TosaErrorValidator.evInputListThenGraphMismatch, |
| TosaErrorValidator.evInputListElseGraphMismatch, |
| TosaErrorValidator.evOutputListThenGraphMismatch, |
| TosaErrorValidator.evOutputListElseGraphMismatch, |
| TosaErrorValidator.evCondIfCondNotMatchingBool, |
| TosaErrorValidator.evCondIfCondShapeNotSizeOne, |
| ), |
| }, |
| # while_loop |
| "while_loop": { |
| "op": Op.WHILE_LOOP, |
| "operands": (0, 1), |
| "build_fcn": ( |
| build_while_loop, |
| TosaTensorGen.tgBasic, |
| TosaTensorValuesGen.tvgCondIfWhileLoop, |
| TosaArgGen.agWhileLoop, |
| ), |
| "types": [DType.INT32], |
| "error_if_validators": ( |
| TosaErrorValidator.evInputListOutputListMismatch, |
| TosaErrorValidator.evInputListCondGraphMismatch, |
| TosaErrorValidator.evInputListBodyGraphInputMismatch, |
| TosaErrorValidator.evInputListBodyGraphOutputMismatch, |
| TosaErrorValidator.evCondGraphOutputNotMatchingBool, |
| TosaErrorValidator.evCondGraphOutputShapeNotSizeOne, |
| ), |
| }, |
| "fft2d": { |
| "op": Op.FFT2D, |
| "operands": (2, 0), |
| "rank": (3, 3), |
| "build_fcn": ( |
| build_fft2d, |
| TosaTensorGen.tgFFT2d, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agFFT2d, |
| ), |
| "types": [DType.FP32], |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evBatchMismatch, |
| TosaErrorValidator.evKernelNotPowerOfTwo, |
| TosaErrorValidator.evFFTInputShapeMismatch, |
| TosaErrorValidator.evFFTOutputShapeMismatch, |
| ), |
| }, |
| "rfft2d": { |
| "op": Op.RFFT2D, |
| "operands": (1, 0), |
| "rank": (3, 3), |
| "build_fcn": ( |
| build_rfft2d, |
| TosaTensorGen.tgRFFT2d, |
| TosaTensorValuesGen.tvgDefault, |
| TosaArgGen.agNone, |
| ), |
| "types": [DType.FP32], |
| "error_if_validators": ( |
| TosaErrorValidator.evWrongInputType, |
| TosaErrorValidator.evWrongOutputType, |
| TosaErrorValidator.evWrongInputList, |
| TosaErrorValidator.evWrongOutputList, |
| TosaErrorValidator.evWrongRank, |
| TosaErrorValidator.evBatchMismatch, |
| TosaErrorValidator.evKernelNotPowerOfTwo, |
| TosaErrorValidator.evFFTOutputShapeMismatch, |
| ), |
| }, |
| } |
| |
| |
| class OutputShaper: |
| # Methods in this class compute the expected output shape and datatype |
| # for common classes of operations |
| def __init__(self): |
| pass |
| |
| # These methods return arguments that can be used for |
| # creating a new output tensor |
| @staticmethod |
| def binaryBroadcastOp(ser, rng, a, b, error_name=None): |
| if error_name != ErrorIf.RankMismatch: |
| assert len(a.shape) == len(b.shape) |
| assert a.dtype == b.dtype |
| |
| shape = [] |
| for i in range(len(a.shape)): |
| if a.shape[i] == 1 and error_name is None: |
| shape.append(b.shape[i]) |
| else: |
| shape.append(a.shape[i]) |
| |
| fuzz_idx = rng.integers(0, len(a.shape)) |
| if error_name == ErrorIf.DimensionMismatch: |
| shape[fuzz_idx] += 1 |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP16, |
| DType.BF16, |
| DType.FP32, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(shape, outputDType) |
| |
| @staticmethod |
| def binaryNonBroadcastOp(ser, a, b): |
| assert len(a.shape) == len(b.shape) |
| assert a.dtype == b.dtype |
| |
| shape = [] |
| for i in range(len(a.shape)): |
| assert a.shape[i] == b.shape[i] |
| shape.append(a.shape[i]) |
| |
| return ser.addOutput(shape, a.dtype) |
| |
| @staticmethod |
| def unaryOp(ser, rng, a, error_name=None): |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(a.shape, outputDType) |
| |
| @staticmethod |
| def selectOp(ser, rng, cond, a, b, error_name=None): |
| if error_name != ErrorIf.RankMismatch: |
| assert len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape) |
| assert a.dtype == b.dtype |
| |
| shape = [] |
| for i in range(len(cond.shape)): |
| if cond.shape[i] == 1 and error_name is None: |
| shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) |
| else: |
| shape.append(cond.shape[i]) |
| |
| fuzz_idx = rng.integers(0, len(a.shape)) |
| if error_name == ErrorIf.DimensionMismatch: |
| shape[fuzz_idx] += 1 |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(shape, outputDType) |
| |
| @staticmethod |
| def binaryComparisonOp(ser, rng, a, b, error_name=None): |
| if error_name != ErrorIf.RankMismatch: |
| assert len(a.shape) == len(b.shape) |
| assert a.dtype == b.dtype |
| |
| # Do broadcast |
| shape = [] |
| for i in range(len(a.shape)): |
| if a.shape[i] == 1 and len(b.shape) > i: |
| shape.append(b.shape[i]) |
| else: |
| shape.append(a.shape[i]) |
| |
| fuzz_idx = rng.integers(0, len(a.shape)) |
| if error_name == ErrorIf.DimensionMismatch: |
| shape[fuzz_idx] += 1 |
| |
| if error_name == ErrorIf.WrongOutputType: |
| wrong_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = DType.BOOL |
| |
| return ser.addOutput(shape, outputDType) |
| |
| @staticmethod |
| def reduceOp(ser, rng, a, axis, error_name=None): |
| shape = a.shape.copy() |
| if error_name not in [ |
| ErrorIf.AxisSmallerZero, |
| ErrorIf.AxisLargerRank, |
| ErrorIf.ShapeOfAxisNotOne, |
| ]: |
| shape[axis] = 1 |
| if error_name == ErrorIf.ShapeOfAxisNotOne and shape[axis] == 1: |
| shape[axis] = rng.integers(2, 10) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(shape, outputDType) |
| |
| @staticmethod |
| def argmaxOp(ser, rng, a, axis, error_name=None): |
| shape = a.shape.copy() |
| |
| if error_name not in [ErrorIf.AxisSmallerZero, ErrorIf.AxisLargerRank]: |
| del shape[axis] |
| |
| if error_name == ErrorIf.ArgmaxOutputRankMismatch: |
| remove = rng.choice([True, False]) |
| if remove and len(shape) > 1: |
| del shape[0] |
| else: |
| shape.append(1) |
| elif error_name == ErrorIf.ArgmaxOutputShapeMismatch: |
| for i in range(len(shape)): |
| shape[i] = shape[i] + rng.integers(1, 10) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([DType.INT32])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = DType.INT32 |
| |
| return ser.addOutput(shape, outputDType) |
| |
| @staticmethod |
| def conv2dOp( |
| ser, rng, ifm, filter, accum_dtype, strides, padding, dilations, error_name=None |
| ): |
| |
| # IFM: NHWC |
| # Filter: OHWI |
| # OFM: NHWC |
| |
| h = ( |
| ifm.shape[1] |
| - 1 |
| + padding[0] |
| + padding[1] |
| - (filter.shape[1] - 1) * dilations[0] |
| ) // strides[0] + 1 |
| |
| w = ( |
| ifm.shape[2] |
| - 1 |
| + padding[2] |
| + padding[3] |
| - (filter.shape[2] - 1) * dilations[1] |
| ) // strides[1] + 1 |
| |
| if error_name == ErrorIf.ConvOutputShapeMismatch: |
| choices = [1, 2, 3] |
| change = rng.choice(choices) |
| # increment in multiples of stride to not hit non-integer error case |
| if change in [1, 3]: |
| h = h + (rng.choice(choices) * strides[0]) |
| if change in [2, 3]: |
| w = w + (rng.choice(choices) * strides[1]) |
| |
| ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] |
| |
| if error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| out_dtype = DType.INT32 |
| else: |
| out_dtype = accum_dtype |
| |
| if error_name == ErrorIf.WrongOutputType: |
| if ifm.dtype == DType.FP16: |
| excludes = [DType.FP16, DType.FP32] |
| else: |
| excludes = [out_dtype] |
| wrong_dtypes = list(usableDTypes(excludes=excludes)) |
| out_dtype = rng.choice(wrong_dtypes) |
| |
| return ser.addOutput(ofm_shape, out_dtype) |
| |
| @staticmethod |
| def conv3dOp( |
| ser, rng, ifm, filter, accum_dtype, strides, padding, dilations, error_name=None |
| ): |
| |
| # IFM: NDHWC |
| # Filter: ODHWI |
| # OFM: NDHWC |
| |
| d = ( |
| ifm.shape[1] |
| - 1 |
| + padding[0] |
| + padding[1] |
| - (filter.shape[1] - 1) * dilations[0] |
| ) // strides[0] + 1 |
| |
| h = ( |
| ifm.shape[2] |
| - 1 |
| + padding[2] |
| + padding[3] |
| - (filter.shape[2] - 1) * dilations[1] |
| ) // strides[1] + 1 |
| |
| w = ( |
| ifm.shape[3] |
| - 1 |
| + padding[4] |
| + padding[5] |
| - (filter.shape[3] - 1) * dilations[2] |
| ) // strides[2] + 1 |
| |
| if error_name == ErrorIf.ConvOutputShapeMismatch: |
| choices = [1, 2, 3, 4] |
| change = rng.choice(choices) |
| # increment in multiples of stride to not hit non-integer error case |
| if change in [1, 4]: |
| d = d + (rng.choice(choices) * strides[0]) |
| if change in [2, 4]: |
| h = h + (rng.choice(choices) * strides[1]) |
| if change in [3, 4]: |
| w = w + (rng.choice(choices) * strides[2]) |
| |
| ofm_shape = [ifm.shape[0], d, h, w, filter.shape[0]] |
| |
| if error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| out_dtype = DType.INT32 |
| else: |
| out_dtype = accum_dtype |
| |
| if error_name == ErrorIf.WrongOutputType: |
| if ifm.dtype == DType.FP16: |
| excludes = [DType.FP16, DType.FP32] |
| else: |
| excludes = [out_dtype] |
| wrong_dtypes = list(usableDTypes(excludes=excludes)) |
| out_dtype = rng.choice(wrong_dtypes) |
| |
| return ser.addOutput(ofm_shape, out_dtype) |
| |
| @staticmethod |
| def depthwiseConv2dOp( |
| ser, rng, ifm, filter, accum_dtype, strides, padding, dilations, error_name=None |
| ): |
| # IFM: NHWC |
| # Filter: HWCM |
| # OFM: NHW C*M |
| |
| h = ( |
| ifm.shape[1] |
| - 1 |
| + padding[0] |
| + padding[1] |
| - (filter.shape[0] - 1) * dilations[0] |
| ) // strides[0] + 1 |
| |
| w = ( |
| ifm.shape[2] |
| - 1 |
| + padding[2] |
| + padding[3] |
| - (filter.shape[1] - 1) * dilations[1] |
| ) // strides[1] + 1 |
| |
| if error_name == ErrorIf.ConvOutputShapeMismatch: |
| choices = [1, 2, 3] |
| change = rng.choice(choices) |
| # increment in multiples of stride to not hit non-integer error case |
| if change in [1, 3]: |
| h = h + (rng.choice(choices) * strides[0]) |
| if change in [2, 3]: |
| w = w + (rng.choice(choices) * strides[1]) |
| |
| ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] |
| |
| if error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| out_dtype = DType.INT32 |
| else: |
| out_dtype = accum_dtype |
| |
| if error_name == ErrorIf.WrongOutputType: |
| if ifm.dtype == DType.FP16: |
| excludes = [DType.FP16, DType.FP32] |
| else: |
| excludes = [out_dtype] |
| wrong_dtypes = list(usableDTypes(excludes=excludes)) |
| out_dtype = rng.choice(wrong_dtypes) |
| |
| return ser.addOutput(ofm_shape, out_dtype) |
| |
| @staticmethod |
| def pool2dOp(ser, rng, ifm, kernel, stride, pad, error_name=None): |
| # input: NHWC |
| if stride[0] <= 0 or stride[1] <= 0 or min(pad) < 0: |
| # If an incorrect stride is used set dimensions to 1, test is invalid anyway. |
| h = 1 |
| w = 1 |
| else: |
| h = (ifm.shape[1] + pad[0] + pad[1] - kernel[0]) // stride[0] + 1 |
| w = (ifm.shape[2] + pad[2] + pad[3] - kernel[1]) // stride[1] + 1 |
| |
| if error_name == ErrorIf.PoolingOutputShapeMismatch: |
| choices = [1, 2, 3] |
| change = rng.choice(choices) |
| # increment in multiples of stride to not hit non-integer error case |
| if change in [1, 3]: |
| h = h + (rng.choice(choices) * stride[0]) |
| if change in [2, 3]: |
| w = w + (rng.choice(choices) * stride[1]) |
| ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([ifm.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = ifm.dtype |
| |
| return ser.addOutput(ofm_shape, outputDType) |
| |
| @staticmethod |
| def fullyConnectedOp(ser, rng, input, filter, accum_dtype, error_name=None): |
| # input: N, IC |
| # filter: OC, IC |
| # output: N, OC |
| |
| output_shape = [input.shape[0], filter.shape[0]] |
| |
| # Validated in arg_gen (also invalidated for ErrorIf) |
| out_dtype = accum_dtype |
| |
| return ser.addOutput(output_shape, out_dtype) |
| |
| @staticmethod |
| def matmulOp(ser, rng, a, b, accum_dtype, error_name=None): |
| # a: N, H, C |
| # b: N, C, W |
| # out: N, H, W |
| |
| output_shape = [a.shape[0], a.shape[1], b.shape[2]] |
| |
| if error_name == ErrorIf.WrongOutputType: |
| if a.dtype == DType.INT8: |
| incorrect_types = ( |
| DType.INT4, |
| DType.INT8, |
| DType.INT16, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ) |
| elif a.dtype == DType.INT16: |
| incorrect_types = ( |
| DType.INT4, |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ) |
| elif ( |
| a.dtype == DType.FP32 or a.dtype == DType.FP16 or a.dtype == DType.BF16 |
| ): |
| incorrect_types = ( |
| DType.INT4, |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| ) |
| out_dtype = rng.choice(a=incorrect_types) |
| elif error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| out_dtype = DType.INT32 |
| else: |
| out_dtype = accum_dtype # Validated in arg_gen |
| |
| return ser.addOutput(output_shape, out_dtype) |
| |
| @staticmethod |
| def concatOp(ser, rng, axis, *a, error_name=None): |
| input1 = a[0] |
| remaining_inputs = a[1:] |
| |
| # calculate the output shape, if possible, otherwise just use the first input shape |
| output_shape = input1.shape.copy() |
| if not ( |
| # unable to concat tensors of different ranks |
| error_name == ErrorIf.ConcatInputRankMismatch |
| # unable to concat tensors along an invalid axis |
| or error_name in [ErrorIf.AxisLargerRank, ErrorIf.AxisSmallerZero] |
| ): |
| for tensor in remaining_inputs: |
| output_shape[axis] += tensor.shape[axis] |
| |
| if error_name == ErrorIf.ConcatShapeSumMismatch: |
| output_shape[axis] += rng.integers(5, 10) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = { |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| } |
| wrong_dtypes = list(all_dtypes - set([input1.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = input1.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def padOp(ser, rng, a, padding, error_name=None): |
| |
| output_shape = a.shape.copy() |
| |
| for i in range(len(output_shape)): |
| output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] |
| |
| if error_name == ErrorIf.PadOutputShapeMismatch: |
| bad_dim = rng.choice(range(len(output_shape))) |
| output_shape[bad_dim] -= rng.choice([1, 2]) |
| elif error_name == ErrorIf.RankMismatch: |
| output_shape = get_rank_mismatch_shape(rng, output_shape) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def dimOp(ser, rng, a, axis, error_name=None): |
| output_shape = [] |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes)) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = DType.SHAPE |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def reshapeOp(ser, rng, a, shape, error_name=None): |
| output_shape = shape.copy() |
| |
| if error_name == ErrorIf.TensorSizeInputOutputMismatch: |
| for i in range(len(output_shape)): |
| output_shape[i] = output_shape[i] + rng.integers(1, 10) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def sliceOp(ser, rng, input, start, size, error_name=None): |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([input.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = input.dtype |
| |
| output_shape = size.copy() |
| if error_name == ErrorIf.SizeOutputShapeMismatch: |
| for index in range(len(output_shape)): |
| if output_shape[index] <= 2: |
| output_shape[index] = output_shape[index] + rng.choice([1, 2]) |
| else: |
| output_shape[index] = output_shape[index] + rng.choice( |
| [-2, -1, 1, 2] |
| ) |
| elif error_name == ErrorIf.InputSizeStartLengthMismatch: |
| output_shape = input.shape.copy() |
| elif error_name == ErrorIf.RankMismatch: |
| output_shape = get_rank_mismatch_shape(rng, output_shape) |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def tileOp(ser, rng, a, multiples, error_name=None): |
| |
| output_shape = a.shape.copy() |
| assert len(multiples) == len(output_shape) |
| |
| for i in range(len(output_shape)): |
| output_shape[i] = a.shape[i] * multiples[i] |
| |
| if error_name == ErrorIf.RankMismatch: |
| output_shape = get_rank_mismatch_shape(rng, output_shape) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def transposeOp(ser, rng, a, perms, error_name=None): |
| output_shape = a.shape.copy() |
| |
| assert len(perms) == len(output_shape) |
| |
| if error_name not in [ErrorIf.IndexOutsideBounds, ErrorIf.IndexUsedTwice]: |
| for i in range(len(output_shape)): |
| output_shape[i] = a.shape[perms[i]] |
| |
| if error_name == ErrorIf.TensorSizeInputOutputMismatch: |
| for i in range(len(output_shape)): |
| output_shape[i] += rng.integers(1, 10) |
| elif error_name == ErrorIf.RankMismatch: |
| output_shape = get_rank_mismatch_shape(rng, output_shape) |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([a.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = a.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def gatherOp(ser, rng, values, indices, error_name=None): |
| if error_name != ErrorIf.WrongRank: |
| assert len(values.shape) == 3 |
| assert len(indices.shape) == 2 |
| assert values.shape[0] == indices.shape[0] |
| |
| output_shape = [values.shape[0], indices.shape[1], values.shape[2]] |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([values.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = values.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def scatterOp(ser, rng, values_in, indices, input, error_name=None): |
| if error_name != ErrorIf.WrongRank: |
| assert len(values_in.shape) == 3 |
| assert len(indices.shape) == 2 |
| assert len(input.shape) == 3 |
| assert values_in.shape[0] == indices.shape[0] # N |
| assert input.shape[1] == indices.shape[1] # W |
| assert values_in.shape[2] == input.shape[2] # C |
| |
| output_shape = values_in.shape |
| |
| if error_name == ErrorIf.WrongOutputType: |
| all_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes = list(set(all_dtypes) - set([values_in.dtype])) |
| outputDType = rng.choice(wrong_dtypes) |
| else: |
| outputDType = values_in.dtype |
| |
| return ser.addOutput(output_shape, outputDType) |
| |
| @staticmethod |
| def tableOp(ser, rng, input, error_name=None): |
| # Same shape as the input, dtype dependent on input dtype |
| if error_name != ErrorIf.WrongInputType: |
| assert input.dtype == DType.INT16 or input.dtype == DType.INT8 |
| output_dtype = DType.INT32 if input.dtype == DType.INT16 else DType.INT8 |
| if error_name == ErrorIf.WrongOutputType: |
| wrong_dtypes = [ |
| DType.INT8, |
| DType.INT16, |
| DType.INT32, |
| DType.INT48, |
| DType.FP32, |
| DType.FP16, |
| DType.BF16, |
| ] |
| wrong_dtypes.remove(output_dtype) |
| output_dtype = rng.choice(wrong_dtypes) |
| return ser.addOutput(input.shape, output_dtype) |
| |
| @staticmethod |
| def resizeOp( |
| serializer, |
| rng, |
| input, |
| mode, |
| scale, |
| offset, |
| border, |
| input_dtype, |
| output_dtype, |
| error_name=None, |
| ): |
| # Calculate OH, OW |
| scale_y_n = scale[0] |
| scale_y_d = scale[1] |
| scale_x_n = scale[2] |
| scale_x_d = scale[3] |
| if error_name == ErrorIf.ScaleSmallerEqualZero: |
| scale_y_n = max(scale_y_n, 1) |
| scale_y_d = max(scale_y_d, 1) |
| scale_x_n = max(scale_x_n, 1) |
| scale_x_d = max(scale_x_d, 1) |
| |
| oh = ((input.shape[1] - 1) * scale_y_n - offset[0] + border[0]) // scale_y_d + 1 |
| ow = ((input.shape[2] - 1) * scale_x_n - offset[1] + border[1]) // scale_x_d + 1 |
| |
| if error_name is not None: |
| # Make sure the output tensor is valid, which can occur when |
| # scale, offset or border have been changed for ERROR_IFs |
| oh = max(oh, 1) |
| ow = max(ow, 1) |
| if error_name != ErrorIf.MaxDimExceeded: |
| oh = min(oh, MAX_RESIZE_DIMENSION - 1) |
| ow = min(ow, MAX_RESIZE_DIMENSION - 1) |
| |
| if error_name == ErrorIf.ResizeOutputShapeMismatch: |
| choices = [1, 2, 3] |
| change = rng.choice(choices) |
| # increment in multiples of scale_y/x_d so we don't hit non-integer error case |
| if change in [1, 3]: |
| if oh + scale_y_d >= MAX_RESIZE_DIMENSION: |
| oh -= scale_y_d |
| assert oh > 0 # Should have been caught in agResize |
| else: |
| oh += scale_y_d |
| if change in [2, 3]: |
| if ow + scale_x_d >= MAX_RESIZE_DIMENSION: |
| ow -= scale_x_d |
| assert ow > 0 # Should have been caught in agResize |
| else: |
| ow += scale_x_d |
| |
| if error_name == ErrorIf.WrongRank: |
| output_dims = [ |
| input.shape[0], |
| oh, |
| ow, |
| input.shape[0], |
| ] |
| elif error_name == ErrorIf.BatchMismatch: |
| output_dims = [ |
| input.shape[0] + rng.integers(1, 10), |
| oh, |
| ow, |
| input.shape[3], |
| ] |
| elif error_name == ErrorIf.ChannelMismatch: |
| output_dims = [ |
| input.shape[0], |
| oh, |
| ow, |
| input.shape[3] + rng.integers(1, 10), |
| ] |
| else: |
| output_dims = [input.shape[0], oh, ow, input.shape[3]] |
| |
| return serializer.addOutput(output_dims, output_dtype) |
| |
| @staticmethod |
| def typeConversionOp(ser, rng, val, out_dtype, error_name=None): |
| return ser.addOutput(val.shape, out_dtype) |
| |
| @staticmethod |
| def transposeConv2DOp(ser, rng, ifm, output_shape, accum_dtype, error_name=None): |
| if error_name == ErrorIf.ConvOutputShapeMismatch: |
| choices = [1, 2, 3] |
| change = rng.choice(choices) |
| if change in [1, 3]: |
| output_shape[1] = output_shape[1] + rng.choice(choices) |
| if change in [2, 3]: |
| output_shape[2] = output_shape[2] + rng.choice(choices) |
| |
| if error_name == ErrorIf.WrongInputType: |
| # Pick some potentially correct output dtype if input type is incorrect |
| out_dtype = DType.INT32 |
| else: |
| out_dtype = accum_dtype |
| |
| if error_name == ErrorIf.WrongOutputType: |
| if ifm.dtype == DType.FP16: |
| excludes = [DType.FP16, DType.FP32] |
| else: |
| excludes = [out_dtype] |
| wrong_dtypes = list(usableDTypes(excludes=excludes)) |
| out_dtype = rng.choice(wrong_dtypes) |
| |
| return ser.addOutput(output_shape, out_dtype) |
| |
| @staticmethod |
| def fft2dOp(serializer, rng, ifm1, ifm2, error_name=None): |
| outputs = [] |
| |
| assert ifm1.dtype == ifm2.dtype |
| input_dtype = ifm1.dtype |
| |
| if error_name != ErrorIf.FFTInputShapeMismatch: |
| assert ifm1.shape == ifm2.shape |
| |
| input_shape = ifm1.shape |
| if error_name != ErrorIf.WrongRank: |
| assert len(input_shape) == 3 |
| |
| output_shape = input_shape.copy() |
| output_dtype = input_dtype |
| |
| if error_name == ErrorIf.WrongOutputType: |
| excludes = [DType.FP32] |
| wrong_dtypes = list(usableDTypes(excludes=excludes)) |
| output_dtype = rng.choice(wrong_dtypes) |
| elif error_name == ErrorIf.BatchMismatch: |
| output_shape[0] += rng.integers(1, 10) |
| elif error_name == ErrorIf.FFTOutputShapeMismatch: |
| modify_dim = rng.choice([1, 2]) |
| output_shape[modify_dim] += rng.integers(1, 10) |
| |
| outputs.append(serializer.addOutput(output_shape, output_dtype)) |
| outputs.append(serializer.addOutput(output_shape, output_dtype)) |
| return outputs |
| |
| @staticmethod |
| def rfft2dOp(serializer, rng, value, error_name=None): |
| outputs = [] |
| |
| input_shape = value.shape |
| if error_name != ErrorIf.WrongRank: |
| assert len(input_shape) == 3 |
| |
| output_shape = [*input_shape[:-1], input_shape[-1] // 2 + 1] |
| |
| output_dtype = value.dtype |
| if error_name == ErrorIf.WrongOutputType: |
| excludes = [DType.FP32] |
| wrong_dtypes = list(usableDTypes(excludes=excludes)) |
| output_dtype = rng.choice(wrong_dtypes) |
| elif error_name == ErrorIf.BatchMismatch: |
| output_shape[0] += rng.integers(1, 10) |
| elif error_name == ErrorIf.FFTOutputShapeMismatch: |
| modify_dim = rng.choice([1, 2]) |
| output_shape[modify_dim] += rng.integers(1, 10) |
| |
| outputs.append(serializer.addOutput(output_shape, output_dtype)) |
| outputs.append(serializer.addOutput(output_shape, output_dtype)) |
| return outputs |