| |
| |
| # Copyright (c) 2020, ARM Limited. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| #!/usr/bin/env python3 |
| |
| import flatbuffers |
| import numpy as np |
| from enum import Enum, IntEnum, unique |
| from tosa import TosaGraph, TosaBasicBlock, TosaTensor, TosaOperator, DType, Format, Usage, Op, ResizeMode, Version |
| import tosa |
| import os |
| import json |
| |
| # With the way flatc generates its python types, there is no programatic way |
| # to get string names for the integer types. Manually maintain a string table |
| # here. |
| DTypeNames = [ 'UNKNOWN', |
| 'BOOL', |
| 'AINT8', |
| 'UINT8', |
| 'INT4', |
| 'INT8', |
| 'INT16', |
| 'INT32', |
| 'INT48', |
| 'FLOAT' ] |
| |
| def dtype_str_to_val(name): |
| |
| for i in range(len(DTypeNames)): |
| if name.casefold() == DTypeNames[i].casefold(): |
| return i |
| raise Exception('Unable to parse DType name {}'.format(name)) |
| |
| |
| class TosaSerializerUnion: |
| '''This class handles encapsulating and serializing union types into flatbuffers''' |
| def __init__(self): |
| |
| # A tuple of the start and end functions. Set by the options constructors below |
| self.optFcns = None |
| |
| # The type from the tosa.Options enumeration. Set by the options constructors below. |
| self.utype = None |
| |
| # Each of these lists is a tuple of the add function and the |
| # value being added. Set by the options constructors below. |
| self.ints = [] |
| self.bools = [] |
| self.floats = [] |
| self.strings = [] |
| self.intvecs = [] |
| self.fpvecs = [] |
| |
| def serialize(self, builder): |
| |
| # We have to build strings and vectors first |
| strList = [] |
| intVecList = [] |
| fpVecList = [] |
| |
| for fcn, val in self.strings: |
| strList.append((fcn, builder.CreateString(val))) |
| |
| for fcn, val in self.intvecs: |
| intVecList.append((fcn, TosaSerializer.serializeInt32Vec(builder, val))) |
| |
| for fcn, val in self.fpvecs: |
| fpVecList.append((fcn, TosaSerializer.serializeFpVec(builder, val))) |
| |
| startFcn, endFcn = self.optFcns |
| |
| # Then serialize the options object from the list of primitives and |
| # other serialized values |
| startFcn(builder) |
| for fcn, val in self.ints: |
| fcn(builder, val) |
| |
| for fcn, val in self.bools: |
| fcn(builder, val) |
| |
| for fcn, val in self.floats: |
| fcn(builder, val) |
| |
| for fcn, val in strList: |
| fcn(builder, val) |
| |
| for fcn, val in intVecList: |
| fcn(builder, val) |
| |
| for fcn, val in fpVecList: |
| fcn(builder, val) |
| |
| return endFcn(builder) |
| |
| class TosaSerializerAttribute(TosaSerializerUnion): |
| '''This class handles encapsulating all of the enumerated types for attributes''' |
| |
| def __init__(self): |
| super().__init__() |
| |
| def Pool2dAttribute(self, kernel, stride, padding): |
| from tosa import Pool2dAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().Pool2dAttribute |
| |
| self.optFcns = (a.Pool2dAttributeStart, a.Pool2dAttributeEnd) |
| self.intvecs.append((a.Pool2dAttributeAddPadding, |
| padding)) |
| self.intvecs.append((a.Pool2dAttributeAddKernel, |
| kernel)) |
| self.intvecs.append((a.Pool2dAttributeAddStride, |
| stride)) |
| |
| def Conv2dAttribute(self, padding, stride, dilation): |
| from tosa import Conv2dAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().Conv2dAttribute |
| self.optFcns = (a.Conv2dAttributeStart, a.Conv2dAttributeEnd) |
| |
| self.intvecs.append((a.Conv2dAttributeAddPadding, |
| padding)) |
| self.intvecs.append((a.Conv2dAttributeAddStride, |
| stride)) |
| self.intvecs.append((a.Conv2dAttributeAddDilation, |
| dilation)) |
| |
| def TransposeConv2DAttribute(self, outpad, stride, dilation, output_shape): |
| from tosa import TransposeConv2dAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().TransposeConv2dAttribute |
| self.optFcns = (a.TransposeConv2dAttributeStart, a.TransposeConv2dAttributeEnd) |
| |
| self.intvecs.append((a.TransposeConv2dAttributeAddOutpad, |
| outpad)) |
| self.intvecs.append((a.TransposeConv2dAttributeAddStride, |
| stride)) |
| self.intvecs.append((a.TransposeConv2dAttributeAddDilation, |
| dilation)) |
| self.intvecs.append((a.TransposeConv2dAttributeAddOutputShape, |
| output_shape)) |
| |
| def ReluNAttribute(self, maxint, maxfp): |
| from tosa import ReluNAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().ReluNAttribute |
| self.optFcns = (a.ReluNAttributeStart, a.ReluNAttributeEnd) |
| |
| self.ints.append((a.ReluNAttributeAddMaxInt, maxint)) |
| self.ints.append((a.ReluNAttributeAddMaxFp, maxfp)) |
| |
| |
| def AxisAttribute(self, axis): |
| from tosa import AxisAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().AxisAttribute |
| self.optFcns = (a.AxisAttributeStart, a.AxisAttributeEnd) |
| |
| self.ints.append((a.AxisAttributeAddAxis, |
| axis)) |
| |
| def ReshapeAttribute(self, shape): |
| from tosa import ReshapeAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().ReshapeAttribute |
| self.optFcns = (a.ReshapeAttributeStart, a.ReshapeAttributeEnd) |
| |
| self.intvecs.append((a.ReshapeAttributeAddShape, |
| shape)) |
| |
| def SliceAttribute(self, begin, size): |
| from tosa import SliceAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().SliceAttribute |
| self.optFcns = (a.SliceAttributeStart, a.SliceAttributeEnd) |
| |
| self.intvecs.append((a.SliceAttributeAddBegin, |
| begin)) |
| self.intvecs.append((a.SliceAttributeAddSize, |
| size)) |
| |
| def TileAttribute(self, multiples): |
| from tosa import TileAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().TileAttribute |
| self.optFcns = (a.TileAttributeStart, a.TileAttributeEnd) |
| |
| self.intvecs.append((a.TileAttributeAddMultiples, |
| multiples)) |
| |
| def ResizeAttribute(self, output_size, stride, offset, shift, stride_fp, offset_fp, mode): |
| from tosa import ResizeAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().ResizeAttribute |
| self.optFcns = (a.ResizeAttributeStart, a.ResizeAttributeEnd) |
| |
| self.intvecs.append((a.ResizeAttributeAddOutputSize, |
| output_size)) |
| self.intvecs.append((a.ResizeAttributeAddStride, |
| stride)) |
| self.intvecs.append((a.ResizeAttributeAddOffset, |
| offset)) |
| self.ints.append((a.ResizeAttributeAddShift, |
| shift)) |
| self.fpvecs.append((a.ResizeAttributeAddStrideFp, |
| stride_fp)) |
| self.fpvecs.append((a.ResizeAttributeAddOffsetFp, |
| offset_fp)) |
| self.ints.append((a.ResizeAttributeAddMode, |
| mode)) |
| |
| def ClampAttribute(self, minint, maxint, minfp, maxfp): |
| from tosa import ClampAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().ClampAttribute |
| self.optFcns = (a.ClampAttributeStart, a.ClampAttributeEnd) |
| |
| self.ints.append((a.ClampAttributeAddMinInt, |
| minint)) |
| self.ints.append((a.ClampAttributeAddMaxInt, |
| maxint)) |
| |
| self.ints.append((a.ClampAttributeAddMinFp, |
| minfp)) |
| self.ints.append((a.ClampAttributeAddMaxFp, |
| maxfp)) |
| |
| def RescaleAttribute(self, input_zp, output_zp, multiplier, shift, scale32, double_round, per_channel): |
| from tosa import RescaleAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().RescaleAttribute |
| self.optFcns = (a.RescaleAttributeStart, a.RescaleAttributeEnd) |
| |
| self.ints.append((a.RescaleAttributeAddInputZp, |
| input_zp)) |
| self.ints.append((a.RescaleAttributeAddOutputZp, |
| output_zp)) |
| self.intvecs.append((a.RescaleAttributeAddMultiplier, |
| multiplier)) |
| self.intvecs.append((a.RescaleAttributeAddShift, |
| shift)) |
| self.bools.append((a.RescaleAttributeAddScale32, |
| scale32)) |
| self.bools.append((a.RescaleAttributeAddDoubleRound, |
| double_round)) |
| self.bools.append((a.RescaleAttributeAddPerChannel, |
| per_channel)) |
| |
| def MulAttribute(self, shift): |
| from tosa import MulAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().MulAttribute |
| self.optFcns = (a.MulAttributeStart, a.MulAttributeEnd) |
| |
| self.ints.append((a.MulAttributeAddShift, |
| shift)) |
| |
| def ArithmeticRightShiftAttribute(self, round): |
| from tosa import ArithmeticRightShiftAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().ArithmeticRightShiftAttribute |
| self.optFcns = (a.ArithmeticRightShiftAttributeStart, a.ArithmeticRightShiftAttributeEnd) |
| |
| self.bools.append((a.ArithmeticRightShiftAttributeAddRound, |
| round)) |
| |
| def CustomAttribute(self, identifier): |
| from tosa import CustomAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().CustomAttribute |
| self.optFcns = (a.CustomAttributeStart, a.CustomAttributeEnd) |
| |
| self.strings.append((a.CustomAttributeAddIdentifier, |
| identifier)) |
| |
| def CondIfAttribute(self, then_branch, else_branch): |
| from tosa import CondIfAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().CondIfAttribute |
| self.optFcns = (a.CondIfAttributeStart, a.CondIfAttributeEnd) |
| |
| self.strings.append((a.CondIfAttributeAddThenBranch, |
| then_branch)) |
| self.strings.append((a.CondIfAttributeAddElseBranch, |
| else_branch)) |
| |
| def WhileLoopAttribute(self, cond_branch, body_branch): |
| from tosa import WhileLoopAttribute as a, Attribute |
| |
| self.utype = Attribute.Attribute().WhileLoopAttribute |
| self.optFcns = (a.WhileLoopAttributeStart, a.WhileLoopAttributeEnd) |
| |
| self.strings.append((a.WhileLoopAttributeAddCondBranch, |
| cond_branch)) |
| self.strings.append((a.WhileLoopAttributeAddBodyBranch, |
| body_branch)) |
| |
| class TosaSerializerQuantInfo(TosaSerializerUnion): |
| '''This class handles encapsulating all of the enumerated types for quantinfo types''' |
| def __init__(self): |
| super().__init__() |
| |
| def ConvQuantInfo(self, input_zp, weight_zp): |
| from tosa import ConvQuantInfo as q, QuantInfo |
| |
| self.utype = QuantInfo.QuantInfo().ConvQuantInfo |
| self.optFcns = (q.ConvQuantInfoStart, q.ConvQuantInfoEnd) |
| self.ints.append((q.ConvQuantInfoAddInputZp, input_zp)) |
| self.ints.append((q.ConvQuantInfoAddWeightZp, weight_zp)) |
| |
| def UnaryQuantInfo(self, input_zp, output_zp): |
| from tosa import UnaryQuantInfo as q, QuantInfo |
| |
| self.utype = QuantInfo.QuantInfo().UnaryQuantInfo |
| self.optFcns = (q.UnaryQuantInfoStart, q.UnaryQuantInfoEnd) |
| self.ints.append((q.UnaryQuantInfoAddInputZp, input_zp)) |
| self.ints.append((q.UnaryQuantInfoAddOutputZp, output_zp)) |
| |
| def MatMulQuantInfo(self, a_zp, b_zp): |
| from tosa import MatMulQuantInfo as q, QuantInfo |
| |
| self.utype = QuantInfo.QuantInfo().MatMulQuantInfo |
| self.optFcns = (q.MatMulQuantInfoStart, q.MatMulQuantInfoEnd) |
| self.ints.append((q.MatMulQuantInfoAddAZp, a_zp)) |
| self.ints.append((q.MatMulQuantInfoAddBZp, b_zp)) |
| |
| def PadQuantInfo(self, input_zp): |
| from tosa import PadQuantInfo as q, QuantInfo |
| |
| self.utype = QuantInfo.QuantInfo().PadQuantInfo |
| self.optFcns = (q.PadQuantInfoStart, q.PadQuantInfoEnd) |
| self.ints.append((q.PadQuantInfoAddInputZp, input_zp)) |
| |
| class TosaSerializerTensor: |
| def __init__(self, name, shape, dtype, usage, dformat, filename = None, placeholderFilename = None): |
| self.name = name |
| |
| if isinstance(shape, np.ndarray): |
| shape = shape.astype(int).tolist() |
| shape = list(map(int, shape)) |
| |
| self.shape = shape |
| self.dtype = dtype |
| self.usage = TosaSerializer.toList(usage) |
| self.dformat = TosaSerializer.toList(dformat) |
| |
| # Filename for const tensors. This gets written to the .tosa serialization |
| self.filename = filename |
| |
| # Filename for placeholder tensors. These get generated by the test generation |
| # process and are written to disk, but are considered input tensors by the network |
| # so they do not appear in the TOSA serialiazation. However, if we want to form a unit |
| # test around these input tensors, we can get the filename from here. |
| self.placeholderFilename = placeholderFilename |
| |
| def __str__(self): |
| str = 'TosaSerializerTensor name: {} shape: {} dtype: {} Usage: {} format {} filename: {}'.format( |
| self.name, self.shape, DTypeNames[self.dtype], self.usage, self.dformat, self.filename) |
| return str |
| |
| def addUsage(self, usage): |
| self.usage.append(usage) |
| |
| def addFormat(self, format): |
| self.dformat.append(format) |
| |
| def setDtype(self, dtype): |
| self.dtype = dtype |
| |
| def merge(self, name, shape, dtype, usage, dformat, filename = None): |
| # Merge in additional usage/formats to the list |
| found = 0 |
| for i in self.usage: |
| if i == usage: |
| found = 1 |
| break |
| if not found: |
| self.usage.append(usage) |
| |
| found = 0 |
| for i in self.dformat: |
| if i == dformat: |
| found = 1 |
| break |
| if not found: |
| self.dformat.append(dformat) |
| |
| def serialize(self, builder): |
| fb_name = builder.CreateString(self.name) |
| if self.filename: |
| fb_filename = builder.CreateString(self.filename) |
| fb_shapes = TosaSerializer.serializeInt32Vec(builder, self.shape) |
| fb_usage = TosaSerializer.serializeInt32Vec(builder, self.usage) |
| fb_dformat = TosaSerializer.serializeInt32Vec(builder, self.dformat) |
| |
| TosaTensor.TosaTensorStart(builder) |
| TosaTensor.TosaTensorAddName(builder, fb_name) |
| TosaTensor.TosaTensorAddShape(builder, fb_shapes) |
| TosaTensor.TosaTensorAddType(builder, self.dtype) |
| TosaTensor.TosaTensorAddUsage(builder, fb_usage) |
| TosaTensor.TosaTensorAddFormat(builder, fb_dformat) |
| if self.filename: |
| TosaTensor.TosaTensorAddNpyFilename(builder, fb_filename) |
| |
| return TosaTensor.TosaTensorEnd(builder) |
| |
| class TosaSerializerOperator: |
| def __init__(self, op, inputs, outputs, attributes = None, quantInfo = None): |
| self.op = op |
| self.attributes = attributes |
| self.inputs = TosaSerializer.toList(inputs) |
| self.outputs = TosaSerializer.toList(outputs) |
| self.quantInfo = quantInfo |
| |
| def __str__(self): |
| str = 'Op {}\n----\n'.format(self.op) |
| |
| for i in self.inputs: |
| str = str + ' Input: {}\n'.format(i) |
| for o in self.outputs: |
| str = str + ' Output: {}\n'.format(o) |
| |
| return str |
| |
| def serialize(self, builder): |
| fb_inputs = TosaSerializer.serializeStrVec(builder, self.inputs, TosaOperator.TosaOperatorStartInputsVector) |
| fb_outputs = TosaSerializer.serializeStrVec(builder, self.outputs, TosaOperator.TosaOperatorStartOutputsVector) |
| # Need to serialize quant_info and attributes enums still |
| if self.attributes is not None: |
| fb_attributes = self.attributes.serialize(builder) |
| |
| if self.quantInfo is not None: |
| fb_qinfo = self.quantInfo.serialize(builder) |
| |
| TosaOperator.TosaOperatorStart(builder) |
| TosaOperator.TosaOperatorAddOp(builder, self.op) |
| TosaOperator.TosaOperatorAddInputs(builder, fb_inputs) |
| TosaOperator.TosaOperatorAddOutputs(builder, fb_outputs) |
| if self.attributes is not None: |
| TosaOperator.TosaOperatorAddAttributeType(builder, self.attributes.utype) |
| TosaOperator.TosaOperatorAddAttribute(builder, fb_attributes) |
| if self.quantInfo is not None: |
| TosaOperator.TosaOperatorAddQuantInfoType(builder, self.quantInfo.utype) |
| TosaOperator.TosaOperatorAddQuantInfo(builder, fb_qinfo) |
| |
| return TosaOperator.TosaOperatorEnd(builder) |
| |
| class TosaSerializerBasicBlock: |
| def __init__(self, name): |
| self.name = name |
| self.operators = [] |
| |
| # Dict assures uniqueness, but allows us to look up by name |
| self.tensors = dict() |
| |
| self.inputs = [] |
| self.outputs = [] |
| |
| def addTensor(self, name, shape, dtype, usage, dformat, filename = None, placeholderFilename = None): |
| try: |
| # Someone already added this tensor. |
| # We may have to add more usages and formats |
| tens = self.tensors[name] |
| filename = tens.merge(name, shape, dtype, usage, dformat, filename) |
| except KeyError: |
| self.tensors[name] = TosaSerializerTensor(name, shape, dtype, usage, dformat, filename, placeholderFilename) |
| |
| return self.tensors[name] |
| |
| def addInput(self, name): |
| self.inputs.append(name) |
| |
| def addOutput(self, name): |
| self.outputs.append(name) |
| |
| def addOperator(self, op, inputs, outputs, attributes = None, quant_info = None): |
| self.operators.append(TosaSerializerOperator(op, inputs, outputs, attributes, quant_info)) |
| |
| def serialize(self, builder): |
| fb_name = builder.CreateString(self.name) |
| fbv_inputs = TosaSerializer.serializeStrVec(builder, list(self.inputs), TosaBasicBlock.TosaBasicBlockStartInputsVector) |
| fbv_outputs = TosaSerializer.serializeStrVec(builder, list(self.outputs), TosaBasicBlock.TosaBasicBlockStartOutputsVector) |
| fbv_tensors = TosaSerializer.serializeObjVec(builder, list(self.tensors.values()), TosaBasicBlock.TosaBasicBlockStartTensorsVector) |
| fbv_operators = TosaSerializer.serializeObjVec(builder, self.operators, TosaBasicBlock.TosaBasicBlockStartOperatorsVector) |
| |
| TosaBasicBlock.TosaBasicBlockStart(builder) |
| TosaBasicBlock.TosaBasicBlockAddName(builder, fb_name) |
| TosaBasicBlock.TosaBasicBlockAddInputs(builder, fbv_inputs) |
| TosaBasicBlock.TosaBasicBlockAddOutputs(builder, fbv_outputs) |
| TosaBasicBlock.TosaBasicBlockAddTensors(builder, fbv_tensors) |
| TosaBasicBlock.TosaBasicBlockAddOperators(builder, fbv_operators) |
| return TosaBasicBlock.TosaBasicBlockEnd(builder) |
| |
| @unique |
| class TensorDir(IntEnum): |
| PLACEHOLDER = 0 |
| CONST = 1 |
| INTERMEDIATE = 2 |
| RESULT = 3 |
| |
| class TosaSerializer: |
| def __init__(self, pathPrefix): |
| |
| # Get the global TOSA version if not already defined |
| try: |
| TOSA_VERSION |
| except NameError: |
| TosaSerializer.setTosaVersion() |
| |
| self.builder = flatbuffers.Builder(0) |
| |
| self.basicBlocks = [] |
| self.startBasicBlock('main') |
| self.pathPrefix = pathPrefix |
| |
| # Indicies used for adding/naming tensors |
| self.currInputIdx = 0 |
| self.currConstIdx = 0 |
| self.currLayerIdx = 1 |
| self.currResultIdx = 0 |
| |
| # Is this an illegal test that is expected to fail? |
| self.expectedFailure = False |
| self.expectedFailureDesc = '' |
| |
| def __str__(self): |
| str = '' |
| for bb in self.basicBlocks: |
| str = str + bb.__str__() |
| return str |
| |
| def addPlaceholder(self, shape, dtype, usage, dformat, vals): |
| if not self.currBasicBlock: |
| raise Exception('addTensor called without valid basic block') |
| |
| name = 'input-{}'.format(self.currInputIdx) |
| filename = '{}.npy'.format(name) |
| self.currInputIdx = self.currInputIdx + 1 |
| |
| tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, None, filename) |
| # This is always an input to the block |
| self.currBasicBlock.addInput(name) |
| # Add the operator now |
| self.currBasicBlock.addOperator(tosa.Op.Op().PLACEHOLDER, [], name) |
| |
| if vals is not None: |
| np.save(os.path.join(self.pathPrefix, filename), vals, False) |
| |
| return tens |
| |
| def addConst(self, shape, dtype, usage, dformat, vals): |
| if not self.currBasicBlock: |
| raise Exception('addTensor called without valid basic block') |
| |
| name = 'const-{}'.format(self.currInputIdx) |
| filename = '{}.npy'.format(name) |
| self.currInputIdx = self.currInputIdx + 1 |
| |
| tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, filename) |
| # Add the operator now |
| self.currBasicBlock.addOperator(tosa.Op.Op().CONST, [], name) |
| |
| if vals is not None: |
| np.save(os.path.join(self.pathPrefix, filename), vals, False) |
| return tens |
| |
| def addIntermediate(self, shape, dtype, usage, dformat): |
| |
| if not self.currBasicBlock: |
| raise Exception('addTensor called without valid basic block') |
| |
| name = 'layer-{}'.format(self.currLayerIdx) |
| filename = None # No file, so no filename |
| self.currLayerIdx = self.currLayerIdx + 1 |
| |
| tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, filename) |
| |
| return tens |
| |
| def addInputTensor(self, tensor): |
| self.currBasicBlock.addOperator(tosa.Op.Op().PLACEHOLDER, [], tensor.name) |
| self.currBasicBlock.addTensor(tensor.name, tensor.shape, tensor.dtype, tensor.usage, tensor.dformat) |
| self.currBasicBlock.addInput(tensor.name) |
| |
| def addOutputTensor(self, tensor): |
| self.currBasicBlock.addOutput(tensor.name) |
| |
| def addOutput(self, shape, dtype, usage, dformat): |
| if not self.currBasicBlock: |
| raise Exception('addTensor called without valid basic block') |
| |
| name = 'result-{}'.format(self.currResultIdx) |
| self.currResultIdx = self.currResultIdx + 1 |
| |
| tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, None) |
| self.currBasicBlock.addOutput(name) |
| return tens |
| |
| def addOperator(self, op, inputs, outputs, attributes = None, quant_info = None): |
| |
| if op == tosa.Op.Op().PLACEHOLDER or \ |
| op == tosa.Op.Op().CONST: |
| raise Exception('Use addPlaceholderTensor() or addConstTensor() to add PLACEHOLDER and CONST ops') |
| |
| return self.currBasicBlock.addOperator(op, inputs, outputs, attributes, quant_info) |
| |
| def setExpectedFailure(self, desc='', val=True): |
| self.expectedFailure = val |
| self.expectedFailureDesc = desc |
| |
| def setExpectedFailure(self, desc='', val=True): |
| self.expectedFailure = val |
| self.expectedFailureDesc = desc |
| |
| def serialize(self): |
| |
| builder = self.builder |
| |
| Version.VersionStart(builder) |
| Version.VersionAdd_major(builder, TOSA_VERSION[0]) |
| Version.VersionAdd_minor(builder, TOSA_VERSION[1]) |
| Version.VersionAdd_patch(builder, TOSA_VERSION[2]) |
| Version.VersionAdd_experimental(builder, TOSA_VERSION[3]) |
| version = Version.VersionEnd(builder) |
| |
| fbv_bb = TosaSerializer.serializeObjVec(builder, self.basicBlocks, TosaGraph.TosaGraphStartBlocksVector) |
| |
| TosaGraph.TosaGraphStart(builder) |
| TosaGraph.TosaGraphAddVersion(builder, version) |
| TosaGraph.TosaGraphAddBlocks(builder, fbv_bb) |
| graph = TosaGraph.TosaGraphEnd(builder) |
| |
| self.builder.Finish(graph) |
| return self.builder.Output() |
| |
| def writeJson(self, tosa_filename): |
| '''Write a json test file so that it is fairly easy to pick up the test |
| and generate commands for third party tool''' |
| test_desc = dict() |
| |
| test_desc['tosa_file'] = tosa_filename |
| ifm_name = [] |
| ifm_shape = [] |
| ifm_file = [] |
| ofm_name = [] |
| ofm_file = [] |
| ofm_shape = [] |
| |
| for b in self.basicBlocks: |
| if b.name == 'main': |
| for i in b.inputs: |
| ifm_name.append(i) |
| ifm_shape.append(b.tensors[i].shape) |
| ifm_file.append(b.tensors[i].placeholderFilename) |
| for o in b.outputs: |
| ofm_name.append(o) |
| ofm_shape.append(b.tensors[o].shape) |
| # Make up an OFM filename here. One isn't generated until the reference tool is |
| # run, so any name is a good name |
| ofm_file.append('ref-{}.npy'.format(o)) |
| |
| test_desc['ifm_placeholder'] = ifm_name |
| test_desc['ifm_file'] = ifm_file |
| test_desc['ifm_shape'] = ifm_shape |
| test_desc['ofm_name'] = ofm_name |
| test_desc['ofm_shape'] = ofm_shape |
| test_desc['ofm_file'] = ofm_file |
| test_desc['expected_failure'] = self.expectedFailure |
| if self.expectedFailureDesc: |
| test_desc['expected_failure_desc'] = self.expectedFailureDesc |
| |
| return json.dumps(test_desc, indent=' ') |
| |
| def startBasicBlock(self, name): |
| self.currBasicBlock = TosaSerializerBasicBlock(name) |
| self.basicBlocks.append(self.currBasicBlock) |
| |
| @staticmethod |
| def serializeStrVec(builder, vec, start_fcn): |
| fb_strs = [builder.CreateString(i) for i in vec] |
| start_fcn(builder, len(fb_strs)) |
| for s in fb_strs[::-1]: |
| builder.PrependUOffsetTRelative(s) |
| return builder.EndVector(len(fb_strs)) |
| |
| @staticmethod |
| def serializeInt32Vec(builder, vec): |
| builder.StartVector(4, len(vec), 4) |
| for v in vec[::-1]: |
| builder.PrependInt32(v) |
| return builder.EndVector(len(vec)) |
| |
| @staticmethod |
| def serializeFpVec(builder, vec): |
| builder.StartVector(4, len(vec), 4) |
| for v in vec[::-1]: |
| builder.PrependFloat32(v) |
| return builder.EndVector(len(vec)) |
| |
| @staticmethod |
| def serializeObjVec(builder, vec, start_fcn): |
| serialized_vec = [] |
| for v in vec[::-1]: |
| serialized_vec.append(v.serialize(builder)) |
| |
| start_fcn(builder, len(vec)) |
| for v in serialized_vec: |
| builder.PrependUOffsetTRelative(v) |
| return builder.EndVector(len(vec)) |
| |
| @staticmethod |
| def toList(val): |
| if isinstance(val, list): |
| return val |
| else: |
| return [val] |
| |
| @staticmethod |
| def setTosaVersion(): |
| # Create a dummy flatbuffers file with the default version information |
| # There does not appear to be a better way to get a constant from a |
| # flatbuffer schema file |
| builder = flatbuffers.Builder(0) |
| Version.VersionStart(builder) |
| ver = Version.VersionEnd(builder) |
| TosaGraph.TosaGraphStart(builder) |
| TosaGraph.TosaGraphAddVersion(builder, ver) |
| gr = TosaGraph.TosaGraphEnd(builder) |
| builder.Finish(gr) |
| |
| out = builder.Output() |
| |
| gr = TosaGraph.TosaGraph() |
| root = gr.GetRootAsTosaGraph(out, 0) |
| |
| # Store the version as a global variable so that it only needs to be |
| # generated once per process. |
| global TOSA_VERSION |
| TOSA_VERSION = [root.Version()._major(), |
| root.Version()._minor(), |
| root.Version()._patch(), |
| root.Version()._experimental() ] |