diff options
| author | Kevin Cheng <kevin.cheng@arm.com> | 2021-03-03 11:21:43 -0800 |
|---|---|---|
| committer | Kevin Cheng <kevin.cheng@arm.com> | 2021-04-27 16:01:59 -0700 |
| commit | 550ccc52de231621c0bf0c05ae2a398eec37ff51 (patch) | |
| tree | d4a5bd8d24560135784208c0fe35615b1d043249 /verif | |
| parent | cf6224e6e8ba4fc2984de3e542538c38e27c9f57 (diff) | |
| download | reference_model-550ccc52de231621c0bf0c05ae2a398eec37ff51.tar.gz | |
Replace serialization/ and verif/ with MLPlatform's serialization_lib submodule
- Remove Usage and Format
- Run black on verif/*.py scripts
Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Ie81515891eb0039540f614894f4b6b0e0e78ba74
Diffstat (limited to 'verif')
40 files changed, 1574 insertions, 3436 deletions
diff --git a/verif/tosa/ArithmeticRightShiftAttribute.py b/verif/tosa/ArithmeticRightShiftAttribute.py deleted file mode 100644 index eaa52ab..0000000 --- a/verif/tosa/ArithmeticRightShiftAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class ArithmeticRightShiftAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsArithmeticRightShiftAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ArithmeticRightShiftAttribute() - x.Init(buf, n + offset) - return x - - # ArithmeticRightShiftAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ArithmeticRightShiftAttribute - def Round(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def ArithmeticRightShiftAttributeStart(builder): builder.StartObject(1) -def ArithmeticRightShiftAttributeAddRound(builder, round): builder.PrependBoolSlot(0, round, 0) -def ArithmeticRightShiftAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Attribute.py b/verif/tosa/Attribute.py deleted file mode 100644 index 5d79a08..0000000 --- a/verif/tosa/Attribute.py +++ /dev/null @@ -1,37 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -class Attribute(object): - NONE = 0 - Pool2dAttribute = 1 - Conv2dAttribute = 2 - TransposeConv2dAttribute = 3 - ReluNAttribute = 4 - AxisAttribute = 5 - ReshapeAttribute = 6 - SliceAttribute = 7 - TileAttribute = 8 - ResizeAttribute = 9 - ClampAttribute = 10 - RescaleAttribute = 11 - MulAttribute = 12 - ArithmeticRightShiftAttribute = 13 - CondIfAttribute = 14 - WhileLoopAttribute = 15 - diff --git a/verif/tosa/AxisAttribute.py b/verif/tosa/AxisAttribute.py deleted file mode 100644 index d47eb81..0000000 --- a/verif/tosa/AxisAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class AxisAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsAxisAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = AxisAttribute() - x.Init(buf, n + offset) - return x - - # AxisAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # AxisAttribute - def Axis(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def AxisAttributeStart(builder): builder.StartObject(1) -def AxisAttributeAddAxis(builder, axis): builder.PrependInt32Slot(0, axis, 0) -def AxisAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ClampAttribute.py b/verif/tosa/ClampAttribute.py deleted file mode 100644 index ddc95cf..0000000 --- a/verif/tosa/ClampAttribute.py +++ /dev/null @@ -1,69 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class ClampAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsClampAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ClampAttribute() - x.Init(buf, n + offset) - return x - - # ClampAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ClampAttribute - def MinInt(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ClampAttribute - def MaxInt(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ClampAttribute - def MinFp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - - # ClampAttribute - def MaxFp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def ClampAttributeStart(builder): builder.StartObject(4) -def ClampAttributeAddMinInt(builder, minInt): builder.PrependInt32Slot(0, minInt, 0) -def ClampAttributeAddMaxInt(builder, maxInt): builder.PrependInt32Slot(1, maxInt, 0) -def ClampAttributeAddMinFp(builder, minFp): builder.PrependFloat32Slot(2, minFp, 0.0) -def ClampAttributeAddMaxFp(builder, maxFp): builder.PrependFloat32Slot(3, maxFp, 0.0) -def ClampAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/CondIfAttribute.py b/verif/tosa/CondIfAttribute.py deleted file mode 100644 index 0bf4566..0000000 --- a/verif/tosa/CondIfAttribute.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class CondIfAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCondIfAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CondIfAttribute() - x.Init(buf, n + offset) - return x - - # CondIfAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CondIfAttribute - def ThenBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # CondIfAttribute - def ElseBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def CondIfAttributeStart(builder): builder.StartObject(2) -def CondIfAttributeAddThenBranch(builder, thenBranch): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(thenBranch), 0) -def CondIfAttributeAddElseBranch(builder, elseBranch): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(elseBranch), 0) -def CondIfAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Conv2dAttribute.py b/verif/tosa/Conv2dAttribute.py deleted file mode 100644 index c7861a5..0000000 --- a/verif/tosa/Conv2dAttribute.py +++ /dev/null @@ -1,109 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class Conv2dAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConv2dAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Conv2dAttribute() - x.Init(buf, n + offset) - return x - - # Conv2dAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Conv2dAttribute - def Padding(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Conv2dAttribute - def PaddingAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Conv2dAttribute - def PaddingLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Conv2dAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Conv2dAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Conv2dAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Conv2dAttribute - def Dilation(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Conv2dAttribute - def DilationAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Conv2dAttribute - def DilationLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def Conv2dAttributeStart(builder): builder.StartObject(3) -def Conv2dAttributeAddPadding(builder, padding): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(padding), 0) -def Conv2dAttributeStartPaddingVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Conv2dAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def Conv2dAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Conv2dAttributeAddDilation(builder, dilation): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(dilation), 0) -def Conv2dAttributeStartDilationVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Conv2dAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ConvQuantInfo.py b/verif/tosa/ConvQuantInfo.py deleted file mode 100644 index a88bfa6..0000000 --- a/verif/tosa/ConvQuantInfo.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class ConvQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsConvQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ConvQuantInfo() - x.Init(buf, n + offset) - return x - - # ConvQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ConvQuantInfo - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ConvQuantInfo - def WeightZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def ConvQuantInfoStart(builder): builder.StartObject(2) -def ConvQuantInfoAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def ConvQuantInfoAddWeightZp(builder, weightZp): builder.PrependInt32Slot(1, weightZp, 0) -def ConvQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/CustomAttribute.py b/verif/tosa/CustomAttribute.py deleted file mode 100644 index 25f6759..0000000 --- a/verif/tosa/CustomAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class CustomAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsCustomAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = CustomAttribute() - x.Init(buf, n + offset) - return x - - # CustomAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # CustomAttribute - def Identifier(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def CustomAttributeStart(builder): builder.StartObject(1) -def CustomAttributeAddIdentifier(builder, identifier): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(identifier), 0) -def CustomAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/DType.py b/verif/tosa/DType.py deleted file mode 100644 index 2e30531..0000000 --- a/verif/tosa/DType.py +++ /dev/null @@ -1,30 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# Copyright (c) 2020-2021, 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. - - -# namespace: tosa - -class DType(object): - UNKNOWN = 0 - BOOL = 1 - UINT8 = 2 - INT4 = 3 - INT8 = 4 - INT16 = 5 - INT32 = 6 - INT48 = 7 - FLOAT = 8 - diff --git a/verif/tosa/Format.py b/verif/tosa/Format.py deleted file mode 100644 index 5db4f27..0000000 --- a/verif/tosa/Format.py +++ /dev/null @@ -1,27 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -class Format(object): - UNKNOWN = 0 - NHWC = 1 - NDHWC = 2 - OHWI = 3 - HWIM = 4 - DOHWI = 5 - diff --git a/verif/tosa/MatMulQuantInfo.py b/verif/tosa/MatMulQuantInfo.py deleted file mode 100644 index b8390a9..0000000 --- a/verif/tosa/MatMulQuantInfo.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class MatMulQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMatMulQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MatMulQuantInfo() - x.Init(buf, n + offset) - return x - - # MatMulQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # MatMulQuantInfo - def AZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # MatMulQuantInfo - def BZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def MatMulQuantInfoStart(builder): builder.StartObject(2) -def MatMulQuantInfoAddAZp(builder, aZp): builder.PrependInt32Slot(0, aZp, 0) -def MatMulQuantInfoAddBZp(builder, bZp): builder.PrependInt32Slot(1, bZp, 0) -def MatMulQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/MulAttribute.py b/verif/tosa/MulAttribute.py deleted file mode 100644 index f45b285..0000000 --- a/verif/tosa/MulAttribute.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class MulAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsMulAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = MulAttribute() - x.Init(buf, n + offset) - return x - - # MulAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # MulAttribute - def Shift(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def MulAttributeStart(builder): builder.StartObject(1) -def MulAttributeAddShift(builder, shift): builder.PrependInt32Slot(0, shift, 0) -def MulAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Op.py b/verif/tosa/Op.py deleted file mode 100644 index ea9cdfe..0000000 --- a/verif/tosa/Op.py +++ /dev/null @@ -1,91 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -class Op(object): - UNKNOWN = 0 - ARGMAX = 1 - AVG_POOL2D = 2 - CONV2D = 3 - CONV3D = 4 - DEPTHWISE_CONV2D = 5 - FULLY_CONNECTED = 6 - MATMUL = 7 - MAX_POOL2D = 8 - TRANSPOSE_CONV2D = 9 - CLAMP = 10 - RELUN = 11 - SIGMOID = 12 - TANH = 13 - ADD = 14 - ARITHMETIC_RIGHT_SHIFT = 15 - BITWISE_AND = 16 - BITWISE_OR = 17 - BITWISE_XOR = 18 - LOGICAL_AND = 19 - LOGICAL_LEFT_SHIFT = 20 - LOGICAL_RIGHT_SHIFT = 21 - LOGICAL_OR = 22 - LOGICAL_XOR = 23 - MAXIMUM = 24 - MINIMUM = 25 - MUL = 26 - POW = 27 - SUB = 28 - TABLE = 29 - ABS = 30 - BITWISE_NOT = 31 - CEIL = 32 - CLZ = 33 - EXP = 34 - FLOOR = 35 - LOG = 36 - LOGICAL_NOT = 37 - NEGATE = 38 - RECIPROCAL = 39 - RSQRT = 40 - SELECT = 41 - EQUAL = 42 - GREATER = 43 - GREATER_EQUAL = 44 - REDUCE_ANY = 45 - REDUCE_ALL = 46 - REDUCE_MAX = 47 - REDUCE_MIN = 48 - REDUCE_PRODUCT = 49 - REDUCE_SUM = 50 - CONCAT = 51 - PAD = 52 - RESHAPE = 53 - REVERSE = 54 - SLICE = 55 - TILE = 56 - TRANSPOSE = 57 - GATHER = 58 - SCATTER = 59 - RESIZE = 60 - CAST = 61 - RESCALE = 62 - CONST = 63 - PLACEHOLDER = 64 - IDENTITY = 65 - IDENTITYN = 66 - CUSTOM = 67 - COND_IF = 68 - WHILE_LOOP = 69 - diff --git a/verif/tosa/PadQuantInfo.py b/verif/tosa/PadQuantInfo.py deleted file mode 100644 index df61926..0000000 --- a/verif/tosa/PadQuantInfo.py +++ /dev/null @@ -1,45 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class PadQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPadQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = PadQuantInfo() - x.Init(buf, n + offset) - return x - - # PadQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # PadQuantInfo - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def PadQuantInfoStart(builder): builder.StartObject(1) -def PadQuantInfoAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def PadQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Pool2dAttribute.py b/verif/tosa/Pool2dAttribute.py deleted file mode 100644 index 1520de2..0000000 --- a/verif/tosa/Pool2dAttribute.py +++ /dev/null @@ -1,109 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class Pool2dAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsPool2dAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Pool2dAttribute() - x.Init(buf, n + offset) - return x - - # Pool2dAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Pool2dAttribute - def Padding(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Pool2dAttribute - def PaddingAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Pool2dAttribute - def PaddingLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Pool2dAttribute - def Kernel(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Pool2dAttribute - def KernelAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Pool2dAttribute - def KernelLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # Pool2dAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # Pool2dAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # Pool2dAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def Pool2dAttributeStart(builder): builder.StartObject(3) -def Pool2dAttributeAddPadding(builder, padding): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(padding), 0) -def Pool2dAttributeStartPaddingVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Pool2dAttributeAddKernel(builder, kernel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(kernel), 0) -def Pool2dAttributeStartKernelVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Pool2dAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def Pool2dAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def Pool2dAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/QuantInfo.py b/verif/tosa/QuantInfo.py deleted file mode 100644 index 0544cce..0000000 --- a/verif/tosa/QuantInfo.py +++ /dev/null @@ -1,26 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -class QuantInfo(object): - NONE = 0 - UnaryQuantInfo = 1 - ConvQuantInfo = 2 - MatMulQuantInfo = 3 - PadQuantInfo = 4 - diff --git a/verif/tosa/README.md b/verif/tosa/README.md deleted file mode 100644 index de8c1f9..0000000 --- a/verif/tosa/README.md +++ /dev/null @@ -1,14 +0,0 @@ -TOSA FlatBuffers python serialization library -============================================= - -Files in this directory are automatically generated by running: - -``` bash -../build/thirdparty/flatbuffers/flatc --python ../serialization/tosa.fbs -``` - -From the ``verif/`` directory. Flatc is compiled along with the *TOSA -Reference Model*. - -*Because they are automatically generated, please do not edit the -python files in this directory by hand.* diff --git a/verif/tosa/ReluNAttribute.py b/verif/tosa/ReluNAttribute.py deleted file mode 100644 index e446c03..0000000 --- a/verif/tosa/ReluNAttribute.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class ReluNAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReluNAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReluNAttribute() - x.Init(buf, n + offset) - return x - - # ReluNAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReluNAttribute - def MaxInt(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ReluNAttribute - def MaxFp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos) - return 0.0 - -def ReluNAttributeStart(builder): builder.StartObject(2) -def ReluNAttributeAddMaxInt(builder, maxInt): builder.PrependInt32Slot(0, maxInt, 0) -def ReluNAttributeAddMaxFp(builder, maxFp): builder.PrependFloat32Slot(1, maxFp, 0.0) -def ReluNAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/RescaleAttribute.py b/verif/tosa/RescaleAttribute.py deleted file mode 100644 index 0ec8c2b..0000000 --- a/verif/tosa/RescaleAttribute.py +++ /dev/null @@ -1,125 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class RescaleAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsRescaleAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = RescaleAttribute() - x.Init(buf, n + offset) - return x - - # RescaleAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # RescaleAttribute - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # RescaleAttribute - def OutputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # RescaleAttribute - def Multiplier(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # RescaleAttribute - def MultiplierAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # RescaleAttribute - def MultiplierLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # RescaleAttribute - def Shift(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # RescaleAttribute - def ShiftAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # RescaleAttribute - def ShiftLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # RescaleAttribute - def Scale32(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # RescaleAttribute - def DoubleRound(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - - # RescaleAttribute - def PerChannel(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def RescaleAttributeStart(builder): builder.StartObject(7) -def RescaleAttributeAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def RescaleAttributeAddOutputZp(builder, outputZp): builder.PrependInt32Slot(1, outputZp, 0) -def RescaleAttributeAddMultiplier(builder, multiplier): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(multiplier), 0) -def RescaleAttributeStartMultiplierVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def RescaleAttributeAddShift(builder, shift): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(shift), 0) -def RescaleAttributeStartShiftVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def RescaleAttributeAddScale32(builder, scale32): builder.PrependBoolSlot(4, scale32, 0) -def RescaleAttributeAddDoubleRound(builder, doubleRound): builder.PrependBoolSlot(5, doubleRound, 0) -def RescaleAttributeAddPerChannel(builder, perChannel): builder.PrependBoolSlot(6, perChannel, 0) -def RescaleAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ReshapeAttribute.py b/verif/tosa/ReshapeAttribute.py deleted file mode 100644 index 2c50cef..0000000 --- a/verif/tosa/ReshapeAttribute.py +++ /dev/null @@ -1,61 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class ReshapeAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsReshapeAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ReshapeAttribute() - x.Init(buf, n + offset) - return x - - # ReshapeAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ReshapeAttribute - def Shape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ReshapeAttribute - def ShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ReshapeAttribute - def ShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def ReshapeAttributeStart(builder): builder.StartObject(1) -def ReshapeAttributeAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) -def ReshapeAttributeStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ReshapeAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ResizeAttribute.py b/verif/tosa/ResizeAttribute.py deleted file mode 100644 index 35be73a..0000000 --- a/verif/tosa/ResizeAttribute.py +++ /dev/null @@ -1,173 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class ResizeAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsResizeAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = ResizeAttribute() - x.Init(buf, n + offset) - return x - - # ResizeAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # ResizeAttribute - def OutputSize(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def OutputSizeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ResizeAttribute - def OutputSizeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ResizeAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Offset(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def OffsetAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # ResizeAttribute - def OffsetLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Shift(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # ResizeAttribute - def StrideFp(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def StrideFpAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # ResizeAttribute - def StrideFpLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def OffsetFp(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Float32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # ResizeAttribute - def OffsetFpAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Float32Flags, o) - return 0 - - # ResizeAttribute - def OffsetFpLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # ResizeAttribute - def Mode(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - -def ResizeAttributeStart(builder): builder.StartObject(7) -def ResizeAttributeAddOutputSize(builder, outputSize): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(outputSize), 0) -def ResizeAttributeStartOutputSizeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def ResizeAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddOffset(builder, offset): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(offset), 0) -def ResizeAttributeStartOffsetVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddShift(builder, shift): builder.PrependInt32Slot(3, shift, 0) -def ResizeAttributeAddStrideFp(builder, strideFp): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(strideFp), 0) -def ResizeAttributeStartStrideFpVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddOffsetFp(builder, offsetFp): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(offsetFp), 0) -def ResizeAttributeStartOffsetFpVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def ResizeAttributeAddMode(builder, mode): builder.PrependUint32Slot(6, mode, 0) -def ResizeAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/ResizeMode.py b/verif/tosa/ResizeMode.py deleted file mode 100644 index 02bed51..0000000 --- a/verif/tosa/ResizeMode.py +++ /dev/null @@ -1,24 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -class ResizeMode(object): - UNKNOWN = 0 - NEAREST = 1 - BILINEAR = 2 - diff --git a/verif/tosa/SliceAttribute.py b/verif/tosa/SliceAttribute.py deleted file mode 100644 index d156a4a..0000000 --- a/verif/tosa/SliceAttribute.py +++ /dev/null @@ -1,85 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class SliceAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsSliceAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = SliceAttribute() - x.Init(buf, n + offset) - return x - - # SliceAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # SliceAttribute - def Begin(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SliceAttribute - def BeginAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SliceAttribute - def BeginLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # SliceAttribute - def Size(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # SliceAttribute - def SizeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # SliceAttribute - def SizeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def SliceAttributeStart(builder): builder.StartObject(2) -def SliceAttributeAddBegin(builder, begin): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(begin), 0) -def SliceAttributeStartBeginVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SliceAttributeAddSize(builder, size): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(size), 0) -def SliceAttributeStartSizeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def SliceAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TileAttribute.py b/verif/tosa/TileAttribute.py deleted file mode 100644 index 6385edd..0000000 --- a/verif/tosa/TileAttribute.py +++ /dev/null @@ -1,61 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class TileAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTileAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TileAttribute() - x.Init(buf, n + offset) - return x - - # TileAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TileAttribute - def Multiples(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TileAttribute - def MultiplesAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TileAttribute - def MultiplesLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TileAttributeStart(builder): builder.StartObject(1) -def TileAttributeAddMultiples(builder, multiples): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(multiples), 0) -def TileAttributeStartMultiplesVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TileAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaBasicBlock.py b/verif/tosa/TosaBasicBlock.py deleted file mode 100644 index 42a7379..0000000 --- a/verif/tosa/TosaBasicBlock.py +++ /dev/null @@ -1,123 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class TosaBasicBlock(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaBasicBlock(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaBasicBlock() - x.Init(buf, n + offset) - return x - - # TosaBasicBlock - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaBasicBlock - def Name(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # TosaBasicBlock - def Operators(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .TosaOperator import TosaOperator - obj = TosaOperator() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaBasicBlock - def OperatorsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaBasicBlock - def Tensors(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .TosaTensor import TosaTensor - obj = TosaTensor() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaBasicBlock - def TensorsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaBasicBlock - def Inputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaBasicBlock - def InputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaBasicBlock - def Outputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaBasicBlock - def OutputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TosaBasicBlockStart(builder): builder.StartObject(5) -def TosaBasicBlockAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) -def TosaBasicBlockAddOperators(builder, operators): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(operators), 0) -def TosaBasicBlockStartOperatorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockAddTensors(builder, tensors): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(tensors), 0) -def TosaBasicBlockStartTensorsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) -def TosaBasicBlockStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) -def TosaBasicBlockStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaBasicBlockEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaGraph.py b/verif/tosa/TosaGraph.py deleted file mode 100644 index 92568b9..0000000 --- a/verif/tosa/TosaGraph.py +++ /dev/null @@ -1,71 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class TosaGraph(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaGraph(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaGraph() - x.Init(buf, n + offset) - return x - - # TosaGraph - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaGraph - def Version(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - x = self._tab.Indirect(o + self._tab.Pos) - from .Version import Version - obj = Version() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaGraph - def Blocks(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - x = self._tab.Vector(o) - x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4 - x = self._tab.Indirect(x) - from .TosaBasicBlock import TosaBasicBlock - obj = TosaBasicBlock() - obj.Init(self._tab.Bytes, x) - return obj - return None - - # TosaGraph - def BlocksLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TosaGraphStart(builder): builder.StartObject(2) -def TosaGraphAddVersion(builder, version): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(version), 0) -def TosaGraphAddBlocks(builder, blocks): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(blocks), 0) -def TosaGraphStartBlocksVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaGraphEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaOperator.py b/verif/tosa/TosaOperator.py deleted file mode 100644 index ab4a160..0000000 --- a/verif/tosa/TosaOperator.py +++ /dev/null @@ -1,117 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class TosaOperator(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaOperator(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaOperator() - x.Init(buf, n + offset) - return x - - # TosaOperator - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaOperator - def Op(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # TosaOperator - def AttributeType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) - return 0 - - # TosaOperator - def Attribute(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - from flatbuffers.table import Table - obj = Table(bytearray(), 0) - self._tab.Union(obj, o) - return obj - return None - - # TosaOperator - def Inputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaOperator - def InputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaOperator - def Outputs(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.String(a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return "" - - # TosaOperator - def OutputsLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaOperator - def QuantInfoType(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint8Flags, o + self._tab.Pos) - return 0 - - # TosaOperator - def QuantInfo(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16)) - if o != 0: - from flatbuffers.table import Table - obj = Table(bytearray(), 0) - self._tab.Union(obj, o) - return obj - return None - -def TosaOperatorStart(builder): builder.StartObject(7) -def TosaOperatorAddOp(builder, op): builder.PrependUint32Slot(0, op, 0) -def TosaOperatorAddAttributeType(builder, attributeType): builder.PrependUint8Slot(1, attributeType, 0) -def TosaOperatorAddAttribute(builder, attribute): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(attribute), 0) -def TosaOperatorAddInputs(builder, inputs): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(inputs), 0) -def TosaOperatorStartInputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaOperatorAddOutputs(builder, outputs): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(outputs), 0) -def TosaOperatorStartOutputsVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaOperatorAddQuantInfoType(builder, quantInfoType): builder.PrependUint8Slot(5, quantInfoType, 0) -def TosaOperatorAddQuantInfo(builder, quantInfo): builder.PrependUOffsetTRelativeSlot(6, flatbuffers.number_types.UOffsetTFlags.py_type(quantInfo), 0) -def TosaOperatorEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TosaTensor.py b/verif/tosa/TosaTensor.py deleted file mode 100644 index 0b30266..0000000 --- a/verif/tosa/TosaTensor.py +++ /dev/null @@ -1,133 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class TosaTensor(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTosaTensor(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TosaTensor() - x.Init(buf, n + offset) - return x - - # TosaTensor - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TosaTensor - def Name(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # TosaTensor - def Shape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TosaTensor - def ShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TosaTensor - def ShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaTensor - def Type(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Uint32Flags, o + self._tab.Pos) - return 0 - - # TosaTensor - def Usage(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TosaTensor - def UsageAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint32Flags, o) - return 0 - - # TosaTensor - def UsageLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaTensor - def Format(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Uint32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TosaTensor - def FormatAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint32Flags, o) - return 0 - - # TosaTensor - def FormatLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TosaTensor - def NpyFilename(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def TosaTensorStart(builder): builder.StartObject(6) -def TosaTensorAddName(builder, name): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(name), 0) -def TosaTensorAddShape(builder, shape): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(shape), 0) -def TosaTensorStartShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaTensorAddType(builder, type): builder.PrependUint32Slot(2, type, 0) -def TosaTensorAddUsage(builder, usage): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(usage), 0) -def TosaTensorStartUsageVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaTensorAddFormat(builder, format): builder.PrependUOffsetTRelativeSlot(4, flatbuffers.number_types.UOffsetTFlags.py_type(format), 0) -def TosaTensorStartFormatVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TosaTensorAddNpyFilename(builder, npyFilename): builder.PrependUOffsetTRelativeSlot(5, flatbuffers.number_types.UOffsetTFlags.py_type(npyFilename), 0) -def TosaTensorEnd(builder): return builder.EndObject() diff --git a/verif/tosa/TransposeConv2dAttribute.py b/verif/tosa/TransposeConv2dAttribute.py deleted file mode 100644 index 043d8e8..0000000 --- a/verif/tosa/TransposeConv2dAttribute.py +++ /dev/null @@ -1,133 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class TransposeConv2dAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsTransposeConv2dAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = TransposeConv2dAttribute() - x.Init(buf, n + offset) - return x - - # TransposeConv2dAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # TransposeConv2dAttribute - def Outpad(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def OutpadAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def OutpadLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TransposeConv2dAttribute - def Stride(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def StrideAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def StrideLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TransposeConv2dAttribute - def Dilation(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def DilationAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def DilationLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - - # TransposeConv2dAttribute - def OutputShape(self, j): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - a = self._tab.Vector(o) - return self._tab.Get(flatbuffers.number_types.Int32Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 4)) - return 0 - - # TransposeConv2dAttribute - def OutputShapeAsNumpy(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Int32Flags, o) - return 0 - - # TransposeConv2dAttribute - def OutputShapeLength(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return self._tab.VectorLen(o) - return 0 - -def TransposeConv2dAttributeStart(builder): builder.StartObject(4) -def TransposeConv2dAttributeAddOutpad(builder, outpad): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(outpad), 0) -def TransposeConv2dAttributeStartOutpadVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeAddStride(builder, stride): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(stride), 0) -def TransposeConv2dAttributeStartStrideVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeAddDilation(builder, dilation): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(dilation), 0) -def TransposeConv2dAttributeStartDilationVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeAddOutputShape(builder, outputShape): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(outputShape), 0) -def TransposeConv2dAttributeStartOutputShapeVector(builder, numElems): return builder.StartVector(4, numElems, 4) -def TransposeConv2dAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/UnaryQuantInfo.py b/verif/tosa/UnaryQuantInfo.py deleted file mode 100644 index 9ae0214..0000000 --- a/verif/tosa/UnaryQuantInfo.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class UnaryQuantInfo(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsUnaryQuantInfo(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = UnaryQuantInfo() - x.Init(buf, n + offset) - return x - - # UnaryQuantInfo - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # UnaryQuantInfo - def InputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # UnaryQuantInfo - def OutputZp(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - -def UnaryQuantInfoStart(builder): builder.StartObject(2) -def UnaryQuantInfoAddInputZp(builder, inputZp): builder.PrependInt32Slot(0, inputZp, 0) -def UnaryQuantInfoAddOutputZp(builder, outputZp): builder.PrependInt32Slot(1, outputZp, 0) -def UnaryQuantInfoEnd(builder): return builder.EndObject() diff --git a/verif/tosa/Usage.py b/verif/tosa/Usage.py deleted file mode 100644 index 4c42daa..0000000 --- a/verif/tosa/Usage.py +++ /dev/null @@ -1,25 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -class Usage(object): - UNKNOWN = 0 - ACTIVATION = 1 - WEIGHT = 2 - INDEX = 3 - diff --git a/verif/tosa/Version.py b/verif/tosa/Version.py deleted file mode 100644 index e327507..0000000 --- a/verif/tosa/Version.py +++ /dev/null @@ -1,69 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class Version(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsVersion(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = Version() - x.Init(buf, n + offset) - return x - - # Version - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # Version - def _major(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Version - def _minor(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 21 - - # Version - def _patch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8)) - if o != 0: - return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos) - return 0 - - # Version - def _experimental(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10)) - if o != 0: - return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos)) - return False - -def VersionStart(builder): builder.StartObject(4) -def VersionAdd_major(builder, Major): builder.PrependInt32Slot(0, Major, 0) -def VersionAdd_minor(builder, Minor): builder.PrependInt32Slot(1, Minor, 21) -def VersionAdd_patch(builder, Patch): builder.PrependInt32Slot(2, Patch, 0) -def VersionAdd_experimental(builder, Experimental): builder.PrependBoolSlot(3, Experimental, 0) -def VersionEnd(builder): return builder.EndObject() diff --git a/verif/tosa/WhileLoopAttribute.py b/verif/tosa/WhileLoopAttribute.py deleted file mode 100644 index c37977f..0000000 --- a/verif/tosa/WhileLoopAttribute.py +++ /dev/null @@ -1,53 +0,0 @@ -# automatically generated by the FlatBuffers compiler, do not modify - -# 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. - - -# namespace: tosa - -import flatbuffers - -class WhileLoopAttribute(object): - __slots__ = ['_tab'] - - @classmethod - def GetRootAsWhileLoopAttribute(cls, buf, offset): - n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset) - x = WhileLoopAttribute() - x.Init(buf, n + offset) - return x - - # WhileLoopAttribute - def Init(self, buf, pos): - self._tab = flatbuffers.table.Table(buf, pos) - - # WhileLoopAttribute - def CondBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - - # WhileLoopAttribute - def BodyBranch(self): - o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6)) - if o != 0: - return self._tab.String(o + self._tab.Pos) - return None - -def WhileLoopAttributeStart(builder): builder.StartObject(2) -def WhileLoopAttributeAddCondBranch(builder, condBranch): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(condBranch), 0) -def WhileLoopAttributeAddBodyBranch(builder, bodyBranch): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(bodyBranch), 0) -def WhileLoopAttributeEnd(builder): return builder.EndObject() diff --git a/verif/tosa/__init__.py b/verif/tosa/__init__.py deleted file mode 100644 index ee1ab30..0000000 --- a/verif/tosa/__init__.py +++ /dev/null @@ -1,15 +0,0 @@ - -# 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. - diff --git a/verif/tosa_ref_run.py b/verif/tosa_ref_run.py index 2035147..26c64da 100644 --- a/verif/tosa_ref_run.py +++ b/verif/tosa_ref_run.py @@ -19,6 +19,7 @@ import shlex import subprocess from tosa_test_runner import TosaTestRunner, run_sh_command + class TosaRefRunner(TosaTestRunner): def __init__(self, args, runnerArgs, testDir): super().__init__(args, runnerArgs, testDir) @@ -28,28 +29,33 @@ class TosaRefRunner(TosaTestRunner): # Uses arguments from the argParser args, not the runnerArgs args = self.args - ref_cmd = [ args.ref_model_path, - '-Csubgraph_file={}'.format(self.testDesc['tosa_file']), - '-Csubgraph_dir={}'.format(self.testDir), - '-Cinput_dir={}'.format(self.testDir), - '-Coutput_dir={}'.format(self.testDir), - '-Coutput_tensor_prefix=ref-', # Naming agreement with TosaSerializer - ] + ref_cmd = [ + args.ref_model_path, + "-Csubgraph_file={}".format(self.testDesc["tosa_file"]), + "-Csubgraph_dir={}".format(self.testDir), + "-Cinput_dir={}".format(self.testDir), + "-Coutput_dir={}".format(self.testDir), + "-Coutput_tensor_prefix=ref-", # Naming agreement with TosaSerializer + ] # Build up input tensor_name/filename list inputTensors = [] - for i in range(len(self.testDesc['ifm_placeholder'])): - inputTensors.append('{}:{}'.format(self.testDesc['ifm_placeholder'][i], self.testDesc['ifm_file'][i])) + for i in range(len(self.testDesc["ifm_placeholder"])): + inputTensors.append( + "{}:{}".format( + self.testDesc["ifm_placeholder"][i], self.testDesc["ifm_file"][i] + ) + ) - ref_cmd.append('-Cinput_tensor={}'.format(','.join(inputTensors))) + ref_cmd.append("-Cinput_tensor={}".format(",".join(inputTensors))) if args.ref_debug: - ref_cmd.extend(['-dALL', '-l{}'.format(args.ref_debug)]) + ref_cmd.extend(["-dALL", "-l{}".format(args.ref_debug)]) if args.ref_intermediates: - ref_cmd.extend(['-Ddump_intermediates=1']) + ref_cmd.extend(["-Ddump_intermediates=1"]) - expectedFailure = self.testDesc['expected_failure'] + expectedFailure = self.testDesc["expected_failure"] try: run_sh_command(self.args, ref_cmd) diff --git a/verif/tosa_serializer.py b/verif/tosa_serializer.py index 136f7aa..fa1fdcb 100644 --- a/verif/tosa_serializer.py +++ b/verif/tosa_serializer.py @@ -1,5 +1,3 @@ - - # Copyright (c) 2020-2021, ARM Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -16,37 +14,57 @@ #!/usr/bin/env python3 +import os +import sys +import json 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 +from tosa import ( + TosaGraph, + TosaBasicBlock, + TosaTensor, + TosaOperator, + DType, + Op, + ResizeMode, + Version, +) + +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) 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', - 'UINT8', - 'INT4', - 'INT8', - 'INT16', - 'INT32', - 'INT48', - 'FLOAT' ] +DTypeNames = [ + "UNKNOWN", + "BOOL", + "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)) + raise Exception("Unable to parse DType name {}".format(name)) class TosaSerializerUnion: - '''This class handles encapsulating and serializing union types into flatbuffers''' + """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 @@ -105,8 +123,9 @@ class TosaSerializerUnion: return endFcn(builder) + class TosaSerializerAttribute(TosaSerializerUnion): - '''This class handles encapsulating all of the enumerated types for attributes''' + """This class handles encapsulating all of the enumerated types for attributes""" def __init__(self): super().__init__() @@ -117,12 +136,9 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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)) + 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 @@ -130,12 +146,9 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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)) + 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 @@ -143,14 +156,10 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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)) + 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 @@ -161,15 +170,13 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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)) + self.ints.append((a.AxisAttributeAddAxis, axis)) def ReshapeAttribute(self, shape): from tosa import ReshapeAttribute as a, Attribute @@ -177,8 +184,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().ReshapeAttribute self.optFcns = (a.ReshapeAttributeStart, a.ReshapeAttributeEnd) - self.intvecs.append((a.ReshapeAttributeAddShape, - shape)) + self.intvecs.append((a.ReshapeAttributeAddShape, shape)) def SliceAttribute(self, begin, size): from tosa import SliceAttribute as a, Attribute @@ -186,10 +192,8 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().SliceAttribute self.optFcns = (a.SliceAttributeStart, a.SliceAttributeEnd) - self.intvecs.append((a.SliceAttributeAddBegin, - begin)) - self.intvecs.append((a.SliceAttributeAddSize, - size)) + self.intvecs.append((a.SliceAttributeAddBegin, begin)) + self.intvecs.append((a.SliceAttributeAddSize, size)) def TileAttribute(self, multiples): from tosa import TileAttribute as a, Attribute @@ -197,29 +201,23 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().TileAttribute self.optFcns = (a.TileAttributeStart, a.TileAttributeEnd) - self.intvecs.append((a.TileAttributeAddMultiples, - multiples)) + self.intvecs.append((a.TileAttributeAddMultiples, multiples)) - def ResizeAttribute(self, output_size, stride, offset, shift, stride_fp, offset_fp, mode): + 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)) + 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 @@ -227,36 +225,27 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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.ClampAttributeAddMinInt, minint)) + self.ints.append((a.ClampAttributeAddMaxInt, maxint)) - self.ints.append((a.ClampAttributeAddMinFp, - minfp)) - self.ints.append((a.ClampAttributeAddMaxFp, - maxfp)) + 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): + 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)) + 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 @@ -264,17 +253,18 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().MulAttribute self.optFcns = (a.MulAttributeStart, a.MulAttributeEnd) - self.ints.append((a.MulAttributeAddShift, - shift)) + 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.optFcns = ( + a.ArithmeticRightShiftAttributeStart, + a.ArithmeticRightShiftAttributeEnd, + ) - self.bools.append((a.ArithmeticRightShiftAttributeAddRound, - round)) + self.bools.append((a.ArithmeticRightShiftAttributeAddRound, round)) def CustomAttribute(self, identifier): from tosa import CustomAttribute as a, Attribute @@ -282,8 +272,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): self.utype = Attribute.Attribute().CustomAttribute self.optFcns = (a.CustomAttributeStart, a.CustomAttributeEnd) - self.strings.append((a.CustomAttributeAddIdentifier, - identifier)) + self.strings.append((a.CustomAttributeAddIdentifier, identifier)) def CondIfAttribute(self, then_branch, else_branch): from tosa import CondIfAttribute as a, Attribute @@ -291,10 +280,8 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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)) + 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 @@ -302,13 +289,13 @@ class TosaSerializerAttribute(TosaSerializerUnion): 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)) + 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''' + """This class handles encapsulating all of the enumerated types for quantinfo types""" + def __init__(self): super().__init__() @@ -343,8 +330,16 @@ class TosaSerializerQuantInfo(TosaSerializerUnion): 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): + def __init__( + self, + name, + shape, + dtype, + filename=None, + placeholderFilename=None, + ): self.name = name if isinstance(shape, np.ndarray): @@ -353,8 +348,6 @@ class TosaSerializerTensor: 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 @@ -366,58 +359,35 @@ class TosaSerializerTensor: 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) + str = "TosaSerializerTensor name: {} shape: {} dtype: {} filename: {}".format( + self.name, + self.shape, + DTypeNames[self.dtype], + 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): + def __init__(self, op, inputs, outputs, attributes=None, quantInfo=None): self.op = op self.attributes = attributes self.inputs = TosaSerializer.toList(inputs) @@ -425,18 +395,22 @@ class TosaSerializerOperator: self.quantInfo = quantInfo def __str__(self): - str = 'Op {}\n----\n'.format(self.op) + str = "Op {}\n----\n".format(self.op) for i in self.inputs: - str = str + ' Input: {}\n'.format(i) + str = str + " Input: {}\n".format(i) for o in self.outputs: - str = str + ' Output: {}\n'.format(o) + 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) + 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) @@ -457,6 +431,7 @@ class TosaSerializerOperator: return TosaOperator.TosaOperatorEnd(builder) + class TosaSerializerBasicBlock: def __init__(self, name): self.name = name @@ -468,14 +443,21 @@ class TosaSerializerBasicBlock: self.inputs = [] self.outputs = [] - def addTensor(self, name, shape, dtype, usage, dformat, filename = None, placeholderFilename = None): + def addTensor( + self, + name, + shape, + dtype, + 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) + self.tensors[name] = TosaSerializerTensor( + name, shape, dtype, filename, placeholderFilename + ) return self.tensors[name] @@ -485,15 +467,27 @@ class TosaSerializerBasicBlock: 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 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) + 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) @@ -503,6 +497,7 @@ class TosaSerializerBasicBlock: TosaBasicBlock.TosaBasicBlockAddOperators(builder, fbv_operators) return TosaBasicBlock.TosaBasicBlockEnd(builder) + @unique class TensorDir(IntEnum): PLACEHOLDER = 0 @@ -510,6 +505,7 @@ class TensorDir(IntEnum): INTERMEDIATE = 2 RESULT = 3 + class TosaSerializer: def __init__(self, pathPrefix): @@ -522,7 +518,7 @@ class TosaSerializer: self.builder = flatbuffers.Builder(0) self.basicBlocks = [] - self.startBasicBlock('main') + self.startBasicBlock("main") self.pathPrefix = pathPrefix # Indicies used for adding/naming tensors @@ -533,23 +529,23 @@ class TosaSerializer: # Is this an illegal test that is expected to fail? self.expectedFailure = False - self.expectedFailureDesc = '' + self.expectedFailureDesc = "" def __str__(self): - str = '' + str = "" for bb in self.basicBlocks: str = str + bb.__str__() return str - def addPlaceholder(self, shape, dtype, usage, dformat, vals): + def addPlaceholder(self, shape, dtype, vals): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'input-{}'.format(self.currInputIdx) - filename = '{}.npy'.format(name) + 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) + tens = self.currBasicBlock.addTensor(name, shape, dtype, None, filename) # This is always an input to the block self.currBasicBlock.addInput(name) # Add the operator now @@ -560,15 +556,15 @@ class TosaSerializer: return tens - def addConst(self, shape, dtype, usage, dformat, vals): + def addConst(self, shape, dtype, vals): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'const-{}'.format(self.currInputIdx) - filename = '{}.npy'.format(name) + name = "const-{}".format(self.currInputIdx) + filename = "{}.npy".format(name) self.currInputIdx = self.currInputIdx + 1 - tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, filename) + tens = self.currBasicBlock.addTensor(name, shape, dtype, filename) # Add the operator now self.currBasicBlock.addOperator(tosa.Op.Op().CONST, [], name) @@ -576,51 +572,54 @@ class TosaSerializer: np.save(os.path.join(self.pathPrefix, filename), vals, False) return tens - def addIntermediate(self, shape, dtype, usage, dformat): + def addIntermediate(self, shape, dtype): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'layer-{}'.format(self.currLayerIdx) - filename = None # No file, so no filename + 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) + tens = self.currBasicBlock.addTensor(name, shape, dtype, 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.addTensor(tensor.name, tensor.shape, tensor.dtype) self.currBasicBlock.addInput(tensor.name) def addOutputTensor(self, tensor): self.currBasicBlock.addOutput(tensor.name) - def addOutput(self, shape, dtype, usage, dformat): + def addOutput(self, shape, dtype): if not self.currBasicBlock: - raise Exception('addTensor called without valid basic block') + raise Exception("addTensor called without valid basic block") - name = 'result-{}'.format(self.currResultIdx) + name = "result-{}".format(self.currResultIdx) self.currResultIdx = self.currResultIdx + 1 - tens = self.currBasicBlock.addTensor(name, shape, dtype, usage, dformat, None) + tens = self.currBasicBlock.addTensor(name, shape, dtype, None) self.currBasicBlock.addOutput(name) return tens - def addOperator(self, op, inputs, outputs, attributes = None, quant_info = None): + 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') + 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) + return self.currBasicBlock.addOperator( + op, inputs, outputs, attributes, quant_info + ) - def setExpectedFailure(self, desc='', val=True): + def setExpectedFailure(self, desc="", val=True): self.expectedFailure = val self.expectedFailureDesc = desc - def setExpectedFailure(self, desc='', val=True): + def setExpectedFailure(self, desc="", val=True): self.expectedFailure = val self.expectedFailureDesc = desc @@ -635,7 +634,9 @@ class TosaSerializer: Version.VersionAdd_experimental(builder, TOSA_VERSION[3]) version = Version.VersionEnd(builder) - fbv_bb = TosaSerializer.serializeObjVec(builder, self.basicBlocks, TosaGraph.TosaGraphStartBlocksVector) + fbv_bb = TosaSerializer.serializeObjVec( + builder, self.basicBlocks, TosaGraph.TosaGraphStartBlocksVector + ) TosaGraph.TosaGraphStart(builder) TosaGraph.TosaGraphAddVersion(builder, version) @@ -646,11 +647,11 @@ class TosaSerializer: 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''' + """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 + test_desc["tosa_file"] = tosa_filename ifm_name = [] ifm_shape = [] ifm_file = [] @@ -659,7 +660,7 @@ class TosaSerializer: ofm_shape = [] for b in self.basicBlocks: - if b.name == 'main': + if b.name == "main": for i in b.inputs: ifm_name.append(i) ifm_shape.append(b.tensors[i].shape) @@ -669,19 +670,19 @@ class TosaSerializer: 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 + 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 + test_desc["expected_failure_desc"] = self.expectedFailureDesc - return json.dumps(test_desc, indent=' ') + return json.dumps(test_desc, indent=" ") def startBasicBlock(self, name): self.currBasicBlock = TosaSerializerBasicBlock(name) @@ -748,7 +749,9 @@ class TosaSerializer: # 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() ] + TOSA_VERSION = [ + root.Version()._major(), + root.Version()._minor(), + root.Version()._patch(), + root.Version()._experimental(), + ] diff --git a/verif/tosa_test_gen.py b/verif/tosa_test_gen.py index ae1a5c6..b059ef5 100644 --- a/verif/tosa_test_gen.py +++ b/verif/tosa_test_gen.py @@ -32,19 +32,24 @@ import math from enum import IntEnum, Enum, unique +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) import tosa_serializer as ts from tosa_serializer import * import tosa # Convenience variables to the flatc-generated types that should be enums, but aren't DType = tosa.DType.DType() -Usage = tosa.Usage.Usage() -Format = tosa.Format.Format() -Op = tosa.Op.Op() +Op = tosa.Op.Op() ResizeMode = tosa.ResizeMode.ResizeMode() + class TosaQuantGen: - '''QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion''' + """QuantizedInfo random generator helper functions. Specify with 'qgen': in the operator defintion""" + def __init__(self): pass @@ -107,30 +112,31 @@ class TosaQuantGen: m = -m multiplier = round(m * (1 << scaleBits)) - assert(multiplier <= (1 << scaleBits)) + assert multiplier <= (1 << scaleBits) if multiplier == (1 << scaleBits): multiplier = multiplier // 2 shift = shift + 1 shift = (-shift) + scaleBits - #print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) + # print('scalefp {} scaleBits {} m {} mult {} shift {}'.format(scaleFp, scaleBits, m, multiplier, shift)) - assert(multiplier <= (1 << scaleBits)) - assert(shift >= 0 and shift <= 63) + assert multiplier <= (1 << scaleBits) + assert shift >= 0 and shift <= 63 return multiplier, shift -class TosaTensorGen(): - ''' Tensor generators create a shape list for the placeholder and const tensor - data operands for the operator. The actual random data is generated separately for each test.''' +class TosaTensorGen: + """Tensor generators create a shape list for the placeholder and const tensor + data operands for the operator. The actual random data is generated separately for each test.""" + def __init__(self): pass @staticmethod def tgBasic(testGen, opName, rank): - pl, const = opName['operands'] + pl, const = opName["operands"] shape = testGen.makeShape(rank) shape_list = [] @@ -141,9 +147,9 @@ class TosaTensorGen(): @staticmethod def tgNHWC(testGen, opName, rank): - pl, const = opName['operands'] + pl, const = opName["operands"] - assert(rank == 4) + assert rank == 4 shape = testGen.makeShape(rank) @@ -159,11 +165,11 @@ class TosaTensorGen(): @staticmethod def tgScatter(testGen, opName, rank): - pl, const = opName['operands'] + pl, const = opName["operands"] - assert(pl == 2) - assert(const == 0) - assert(rank == 3) + assert pl == 2 + assert const == 0 + assert rank == 3 values_in_shape = testGen.makeShape(rank) @@ -171,7 +177,9 @@ class TosaTensorGen(): if testGen.args.max_batch_size: values_in_shape[0] = (values_in_shape[0] % testGen.args.max_batch_size) + 1 - W = testGen.randInt(testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1]) + W = testGen.randInt( + testGen.args.tensor_shape_range[0], testGen.args.tensor_shape_range[1] + ) input_shape = [values_in_shape[0], W, values_in_shape[2]] shape_list = [] @@ -184,7 +192,7 @@ class TosaTensorGen(): def tgBroadcastFuzz(testGen, op, rank): shape = testGen.makeShape(rank) - pl, const = op['operands'] + pl, const = op["operands"] shape_list = [] @@ -204,9 +212,9 @@ class TosaTensorGen(): @staticmethod def tgConv2D(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 4) + assert rank == 4 # IFM dimensions are NHWC ifm_shape = testGen.makeShape(rank) @@ -216,7 +224,7 @@ class TosaTensorGen(): ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 # Get the filter height/width from the operator parameters - filter_hw = op['filter'] + filter_hw = op["filter"] # Generate a random OFM depth ofm_depth = testGen.makeShape(1)[0] @@ -231,9 +239,9 @@ class TosaTensorGen(): @staticmethod def tgTransposeConv2D(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 4) + assert rank == 4 # IFM dimensions are NHWC ifm_shape = testGen.makeShape(rank) @@ -243,7 +251,7 @@ class TosaTensorGen(): ifm_shape[0] = (ifm_shape[0] % testGen.args.max_batch_size) + 1 # Get the filter height/width from the operator parameters - filter_hw = op['filter'] + filter_hw = op["filter"] # Generate a random OFM depth ofm_depth = testGen.makeShape(1)[0] @@ -255,10 +263,10 @@ class TosaTensorGen(): @staticmethod def tgDepthwiseConv2D(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 4) - assert(pl == 1 and const == 2) + assert rank == 4 + assert pl == 1 and const == 2 # IFM dimensions are NHWC ifm_shape = testGen.makeShape(rank) @@ -269,11 +277,13 @@ class TosaTensorGen(): # Get the filter height/width from the operator parameters # Filter is KH, HW, C, M - filter_hw = op['filter'] + filter_hw = op["filter"] # Generate a random OFM depth, but don't let it get too big because # the output depth is M * C - filter_m = (testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4)) + 1 + filter_m = ( + testGen.makeShape(1)[0] % (testGen.args.tensor_shape_range[1] // 4) + ) + 1 # The filter dimensions are HWCM filter_shape = np.asarray([filter_hw[0], filter_hw[1], ifm_shape[3], filter_m]) @@ -285,10 +295,10 @@ class TosaTensorGen(): @staticmethod def tgFullyConnected(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 2) - assert(pl == 2 and const == 0) + assert rank == 2 + assert pl == 2 and const == 0 input_shape = testGen.makeShape(rank) filter_oc = testGen.makeShape(1)[0] @@ -300,10 +310,10 @@ class TosaTensorGen(): @staticmethod def tgMatmul(testGen, op, rank): - pl, const = op['operands'] + pl, const = op["operands"] - assert(rank == 2) - assert(pl == 2 and const == 0) + assert rank == 2 + assert pl == 2 and const == 0 a_shape = testGen.makeShape(rank) b_oc = testGen.makeShape(1)[0] @@ -311,29 +321,31 @@ class TosaTensorGen(): return [a_shape, b_shape] + class TosaArgGen: - '''Argument generators create exhaustive or random lists of attributes for operators that take - attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) - tuples where the descriptive_name is appended to the test name and the arglist is expanded - as arguments to the operator build function.''' + """Argument generators create exhaustive or random lists of attributes for operators that take + attributes or other parameters. The return value is a list of (descriptive_name, [arglist]) + tuples where the descriptive_name is appended to the test name and the arglist is expanded + as arguments to the operator build function.""" + def __init__(self): pass @staticmethod def agNone(testGen, opName, shapeList, dtype): - '''A trivial argument generator for operators that don't take any - non-tensor arguments''' - return [('', [])] + """A trivial argument generator for operators that don't take any + non-tensor arguments""" + return [("", [])] @staticmethod def agAxis(testGen, opName, shapeList, dtype): - '''Build the axis argument for operators that take a single axis''' + """Build the axis argument for operators that take a single axis""" axes = [] shape = shapeList[0] for a in range(0, len(shape)): - axes.append(('axis_{}'.format(a), [a])) + axes.append(("axis_{}".format(a), [a])) return axes @staticmethod @@ -344,8 +356,8 @@ class TosaArgGen: filter_shape = shapeList[1] # Must be rank 4 - assert(len(ifm_shape) == 4) - assert(len(filter_shape) == 4) + assert len(ifm_shape) == 4 + assert len(filter_shape) == 4 maxStride = testGen.args.max_conv_stride maxPadding = testGen.args.max_conv_padding + 1 @@ -356,20 +368,24 @@ class TosaArgGen: for padding in range(0, (maxPadding) ** 4): for dilation in range(0, maxDilation ** 2): - s = [stride // maxStride + 1, - stride % maxStride + 1] - p = [(padding // (maxPadding * 4)) % maxPadding, - (padding // (maxPadding * 2)) % maxPadding, - (padding // (maxPadding * 1)) % maxPadding, - padding % maxPadding] - d = [ dilation // maxDilation + 1, - dilation % maxDilation + 1] + s = [stride // maxStride + 1, stride % maxStride + 1] + p = [ + (padding // (maxPadding * 4)) % maxPadding, + (padding // (maxPadding * 2)) % maxPadding, + (padding // (maxPadding * 1)) % maxPadding, + padding % maxPadding, + ] + d = [dilation // maxDilation + 1, dilation % maxDilation + 1] # 4 padding parameters for regular conv2d - arg_list.append(('st{}{}_pad{}{}{}{}_dilat{}{}'.format(s[0], s[1], - p[0], p[1], p[2], p[3], - d[0], d[1]), - [ s, p, d ])) + arg_list.append( + ( + "st{}{}_pad{}{}{}{}_dilat{}{}".format( + s[0], s[1], p[0], p[1], p[2], p[3], d[0], d[1] + ), + [s, p, d], + ) + ) return arg_list @staticmethod @@ -380,8 +396,8 @@ class TosaArgGen: filter_shape = shapeList[1] # Must be rank 4 - assert(len(ifm_shape) == 4) - assert(len(filter_shape) == 4) + assert len(ifm_shape) == 4 + assert len(filter_shape) == 4 maxStride = testGen.args.max_conv_stride maxPadding = testGen.args.max_conv_padding + 1 @@ -392,27 +408,47 @@ class TosaArgGen: for out_padding in range(0, (maxPadding) ** 2): for dilation in range(0, maxDilation ** 2): - s = [stride // maxStride + 1, - stride % maxStride + 1] - p = [(out_padding // (maxPadding * 1)) % maxPadding, - out_padding % maxPadding] - d = [ dilation // maxDilation + 1, - dilation % maxDilation + 1] - - oh = (ifm_shape[1] - filter_shape[1] - (filter_shape[1] - 1) * (d[0] - 1) + \ - 2 * p[0]) // s[0] + 1 - - ow = (ifm_shape[2] - filter_shape[2] - (filter_shape[2] - 1) * (d[1] - 1) + \ - 2 * p[1]) // s[1] + 1 + s = [stride // maxStride + 1, stride % maxStride + 1] + p = [ + (out_padding // (maxPadding * 1)) % maxPadding, + out_padding % maxPadding, + ] + d = [dilation // maxDilation + 1, dilation % maxDilation + 1] + + oh = ( + ifm_shape[1] + - filter_shape[1] + - (filter_shape[1] - 1) * (d[0] - 1) + + 2 * p[0] + ) // s[0] + 1 + + ow = ( + ifm_shape[2] + - filter_shape[2] + - (filter_shape[2] - 1) * (d[1] - 1) + + 2 * p[1] + ) // s[1] + 1 # Output shape - os = [ ifm_shape[0], oh, ow, filter_shape[0] ] - - arg_list.append(('st{}{}_outpad{}{}_dilat{}{}_os{}x{}x{}x{}'.format(s[0], s[1], - p[0], p[1], - d[0], d[1], - os[0], os[1], os[2], os[3]), - [ s, p, d, os ])) + os = [ifm_shape[0], oh, ow, filter_shape[0]] + + arg_list.append( + ( + "st{}{}_outpad{}{}_dilat{}{}_os{}x{}x{}x{}".format( + s[0], + s[1], + p[0], + p[1], + d[0], + d[1], + os[0], + os[1], + os[2], + os[3], + ), + [s, p, d, os], + ) + ) return arg_list @@ -430,14 +466,14 @@ class TosaArgGen: paddings = np.zeros((rank * 2), dtype=np.int32) # Fill in the 1's - for r in (range(rank * 2)): + for r in range(rank * 2): if (v >> r) & 1: paddings[r] = 1 # Reshape back to a 2D array paddings = paddings.reshape((rank, 2)) - arg_list.append(('pad{0:b}'.format(v), [ paddings ])) + arg_list.append(("pad{0:b}".format(v), [paddings])) return arg_list @@ -446,7 +482,7 @@ class TosaArgGen: arg_list = [] shape = shapeList[0] - assert(len(shape) == 4) + assert len(shape) == 4 maxStride = testGen.args.max_pooling_stride maxKernel = testGen.args.max_pooling_kernel @@ -455,19 +491,23 @@ class TosaArgGen: for kernel in range(0, maxKernel ** 2): for stride in range(0, maxStride ** 2): for padding in range(0, maxPadding ** 4): - s = [stride // maxStride + 1, - stride % maxStride + 1] - k = [(kernel // maxKernel) + 2, - (kernel % maxKernel) + 2] - p = [(padding // (maxPadding * 4)) % maxPadding, - (padding // (maxPadding * 2)) % maxPadding, - (padding // (maxPadding * 1)) % maxPadding, - padding % maxPadding] - - arg_list.append(('st{}{}_kern{}{}_pad{}{}{}{}'.format(s[0], s[1], - k[0], k[1], - p[0], p[1], p[2], p[3]), - [k, s, p])) + s = [stride // maxStride + 1, stride % maxStride + 1] + k = [(kernel // maxKernel) + 2, (kernel % maxKernel) + 2] + p = [ + (padding // (maxPadding * 4)) % maxPadding, + (padding // (maxPadding * 2)) % maxPadding, + (padding // (maxPadding * 1)) % maxPadding, + padding % maxPadding, + ] + + arg_list.append( + ( + "st{}{}_kern{}{}_pad{}{}{}{}".format( + s[0], s[1], k[0], k[1], p[0], p[1], p[2], p[3] + ), + [k, s, p], + ) + ) return arg_list @staticmethod @@ -476,20 +516,20 @@ class TosaArgGen: # Enumerate the output types here if inDtype == DType.INT8: - dtypeList = [ DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT ] + dtypeList = [DType.BOOL, DType.INT16, DType.INT32, DType.FLOAT] elif inDtype == DType.INT16: - dtypeList = [ DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT ] + dtypeList = [DType.BOOL, DType.INT8, DType.INT32, DType.FLOAT] elif inDtype == DType.INT32: - dtypeList = [ DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT ] + dtypeList = [DType.BOOL, DType.INT8, DType.INT16, DType.FLOAT] elif inDtype == DType.BOOL: - dtypeList = [ DType.INT8, DType.INT16, DType.INT32 ] + dtypeList = [DType.INT8, DType.INT16, DType.INT32] elif inDtype == DType.FLOAT: - dtypeList = [ DType.INT8, DType.INT16, DType.INT32 ] + dtypeList = [DType.INT8, DType.INT16, DType.INT32] else: - raise Exception('Unexpected input dtype: {}'.format(inDtype)) + raise Exception("Unexpected input dtype: {}".format(inDtype)) for dtype in dtypeList: - arg_list.append(('out{}'.format(DTypeNames[dtype]), [dtype])) + arg_list.append(("out{}".format(DTypeNames[dtype]), [dtype])) return arg_list @@ -498,17 +538,26 @@ class TosaArgGen: arg_list = [] # Enumerate the output types here - for dtype in [ DType.INT8, DType.INT16, DType.INT32 ]: - for scale32 in [ False, True ]: - for double_round in [ False, True ]: - for per_channel in [ False, True ]: + for dtype in [DType.INT8, DType.INT16, DType.INT32]: + for scale32 in [False, True]: + for double_round in [False, True]: + for per_channel in [False, True]: if inDtype == DType.INT48 and scale32: # Illegal condition. Must be scale32=False continue - arg_list.append(('out{}_sc{}_dr{}_pc{}'.format(DTypeNames[dtype], int(scale32), int(double_round), int(per_channel)), - [dtype, scale32, double_round, per_channel])) + arg_list.append( + ( + "out{}_sc{}_dr{}_pc{}".format( + DTypeNames[dtype], + int(scale32), + int(double_round), + int(per_channel), + ), + [dtype, scale32, double_round, per_channel], + ) + ) return arg_list @@ -521,9 +570,9 @@ class TosaArgGen: shift = testGen.randInt(0, 32) - arg_list.append(('perm{}_shift{}'.format(p, shift), [shift])) + arg_list.append(("perm{}_shift{}".format(p, shift), [shift])) else: - arg_list.append(('shift0', [0])) + arg_list.append(("shift0", [0])) return arg_list @@ -531,8 +580,8 @@ class TosaArgGen: def agArithmeticRightShift(testGen, opName, shapeList, dtype): arg_list = [] - arg_list.append(('roundTrue', [True])) - arg_list.append(('roundFalse', [False])) + arg_list.append(("roundTrue", [True])) + arg_list.append(("roundFalse", [False])) return arg_list @@ -563,7 +612,7 @@ class TosaArgGen: for p in range(testGen.args.num_rand_permutations): newRank = testGen.randInt(1, 6) newShape = [] - if (len(factors) < newRank): + if len(factors) < newRank: continue remainingElements = totalElements @@ -572,7 +621,9 @@ class TosaArgGen: # pick rank-1 factors newShape.append(shuffledFactors[0]) remainingElements = remainingElements // shuffledFactors[0] - shuffledFactors = testGen.rng.permutation(TosaArgGen.getFactors(remainingElements)) + shuffledFactors = testGen.rng.permutation( + TosaArgGen.getFactors(remainingElements) + ) newShape.append(remainingElements) # Toss in a -1 sometimes @@ -580,11 +631,10 @@ class TosaArgGen: if minusOne < newRank: newShape[minusOne] = -1 - arg_list.append(('perm{}_rank{}'.format(p, newRank), [newShape])) + arg_list.append(("perm{}_rank{}".format(p, newRank), [newShape])) return arg_list - @staticmethod def agTranspose(testGen, opName, shapeList, dtype): arg_list = [] @@ -603,7 +653,7 @@ class TosaArgGen: break if not found: - arg_list.append(('perm{}'.format(p), [perms])) + arg_list.append(("perm{}".format(p), [perms])) return arg_list @@ -618,7 +668,7 @@ class TosaArgGen: begin = [] size = [] - valid=True + valid = True for i in range(rank): if ifm_shape[i] > 1: @@ -633,7 +683,7 @@ class TosaArgGen: size.append(1) if valid: - arg_list.append(('perm{}'.format(p), [begin, size])) + arg_list.append(("perm{}".format(p), [begin, size])) return arg_list @staticmethod @@ -652,7 +702,7 @@ class TosaArgGen: for i in range(rank): multiples.append(testGen.randInt(1, 4)) - arg_list.append(('perm{}'.format(p), [multiples])) + arg_list.append(("perm{}".format(p), [multiples])) return arg_list @@ -666,15 +716,15 @@ class TosaArgGen: # Exclude illegal {mode, type} configurations. Pick legal output types if m == ResizeMode.NEAREST and dtype == DType.INT8: - outputDTypeList = [ DType.INT32 ] + outputDTypeList = [DType.INT32] elif m == ResizeMode.NEAREST and dtype == DType.INT16: - outputDTypeList = [ DType.INT16 ] + outputDTypeList = [DType.INT16] elif m == ResizeMode.BILINEAR and dtype == DType.INT8: - outputDTypeList = [ DType.INT8 ] + outputDTypeList = [DType.INT8] elif m == ResizeMode.BILINEAR and dtype == DType.INT16: - outputDTypeList = [ DType.INT48 ] + outputDTypeList = [DType.INT48] elif dtype == DType.FLOAT: - outputDTypeList = [ DType.FLOAT ] + outputDTypeList = [DType.FLOAT] else: continue @@ -683,7 +733,7 @@ class TosaArgGen: # Randomly generate legal output dimensions and shift # and then compute the stride and offset based on them - output_dims = [ testGen.randInt(1), testGen.randInt(1) ] + output_dims = [testGen.randInt(1), testGen.randInt(1)] in_center_h = (ifm_shape[1] - 1) / 2.0 in_center_w = (ifm_shape[2] - 1) / 2.0 out_center_h = (output_dims[0] - 1) / 2.0 @@ -698,12 +748,33 @@ class TosaArgGen: shift = 0 stride = [0, 0] offset = [0, 0] - stride_fp = [ fp_stride_y, fp_stride_x] - offset_fp = [ fp_offset_y, fp_offset_x] - arg_list.append(('mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}'.format(m, output_dims[0], output_dims[1], - testGen.typeStr(outputDType), stride_fp[0], stride_fp[1], - offset_fp[0], offset_fp[1]), - [m, stride, offset, shift, stride_fp, offset_fp, output_dims, dtype, outputDType])) + stride_fp = [fp_stride_y, fp_stride_x] + offset_fp = [fp_offset_y, fp_offset_x] + arg_list.append( + ( + "mode{}_odim{}x{}_out{}_st{:.2f}x{:.2f}_off{:.2f}x{:.2f}".format( + m, + output_dims[0], + output_dims[1], + testGen.typeStr(outputDType), + stride_fp[0], + stride_fp[1], + offset_fp[0], + offset_fp[1], + ), + [ + m, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + dtype, + outputDType, + ], + ) + ) else: shift = 11 unit = float(1 << shift) @@ -712,7 +783,14 @@ class TosaArgGen: offset_y = int(round(fp_offset_y * unit)) offset_x = int(round(fp_offset_x * unit)) - while (stride_y >= 32768 or stride_x >= 32768 or offset_y >= 32768 or offset_x >= 32768 or offset_y < -32768 or offset_x < -32768): + while ( + stride_y >= 32768 + or stride_x >= 32768 + or offset_y >= 32768 + or offset_x >= 32768 + or offset_y < -32768 + or offset_x < -32768 + ): shift = shift - 1 unit = float(1 << shift) stride_y = int(round(fp_stride_y * unit)) @@ -720,16 +798,38 @@ class TosaArgGen: offset_y = int(round(fp_offset_y * unit)) offset_x = int(round(fp_offset_x * unit)) - stride = [ stride_y, stride_x] - offset = [ offset_y, offset_x] + stride = [stride_y, stride_x] + offset = [offset_y, offset_x] stride_fp = [0.0, 0.0] offset_fp = [0.0, 0.0] - arg_list.append(('mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}'.format(m, shift, output_dims[0], output_dims[1], - testGen.typeStr(outputDType), stride[0], stride[1], - offset[0], offset[1]), - [m, stride, offset, shift, stride_fp, offset_fp, output_dims, dtype, outputDType])) + arg_list.append( + ( + "mode{}_shift{}_odim{}x{}_out{}_st{}x{}_off{}x{}".format( + m, + shift, + output_dims[0], + output_dims[1], + testGen.typeStr(outputDType), + stride[0], + stride[1], + offset[0], + offset[1], + ), + [ + m, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + dtype, + outputDType, + ], + ) + ) return arg_list @@ -740,7 +840,7 @@ class TosaArgGen: arg_list = [] for c in [False, True]: - arg_list.append(('cond{}'.format(int(c)), [ c ])) + arg_list.append(("cond{}".format(int(c)), [c])) return arg_list @@ -749,10 +849,11 @@ class TosaArgGen: arg_list = [] for iter in [0, 1, 4]: - arg_list.append(('iter{}'.format(iter), [ iter ])) + arg_list.append(("iter{}".format(iter), [iter])) return arg_list + class TosaTestGen: def __init__(self, args): self.args = args @@ -777,11 +878,13 @@ class TosaTestGen: return self.ser def serialize(self, testName): - with open(os.path.join(self.basePath, self.testPath, '{}.tosa'.format(testName)), 'wb') as fd: + 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))) + with open(os.path.join(self.basePath, self.testPath, "desc.json"), "w") as fd: + fd.write(self.ser.writeJson("{}.tosa".format(testName))) def getRandTensor(self, shape, dtype): RAND_SHIFT_FACTOR = 0.5 @@ -797,20 +900,26 @@ class TosaTestGen: elif dtype == DType.INT16: return np.int32(self.rng.integers(low=-32768, high=32768, size=shape)) elif dtype == DType.INT32: - return np.int32(self.rng.integers(low=-(1 << 31), high=(1 << 31), size=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)) + return np.int64( + self.rng.integers(low=-(1 << 47), high=(1 << 47), size=shape) + ) elif dtype == DType.FLOAT: - return np.float32(self.rng.random(size=shape) - RAND_SHIFT_FACTOR * RAND_SCALE_FACTOR) + return np.float32( + self.rng.random(size=shape) - RAND_SHIFT_FACTOR * RAND_SCALE_FACTOR + ) else: - raise Exception('Unrecognized Dtype: {}'.format(dtype)) + raise Exception("Unrecognized Dtype: {}".format(dtype)) def buildPlaceholderTensors(self, shape_list, dtype): placeholders = [] for shape in shape_list: arr = self.getRandTensor(shape, dtype) - placeholders.append(self.ser.addPlaceholder(shape, dtype, Usage.ACTIVATION, [], arr)) + placeholders.append(self.ser.addPlaceholder(shape, dtype, arr)) return placeholders @@ -819,16 +928,20 @@ class TosaTestGen: for shape in shape_list: arr = self.getRandTensor(shape, dtype) - consts.append(self.ser.addConst(shape, dtype, Usage.ACTIVATION, [], arr)) + consts.append(self.ser.addConst(shape, dtype, 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)) + 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 @@ -848,13 +961,13 @@ class TosaTestGen: elif dtype == DType.INT16: low, high = (-32768, 32768) elif dtype == DType.INT32: - low, high = (-(1<<31), (1<<31)) + low, high = (-(1 << 31), (1 << 31)) elif dtype == DType.INT48: - low, high = (-(1<<47), (1<<47)) + 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)) + raise Exception("Unknown dtype: {}".format(dtype)) return np.int32(self.rng.integers(low, high, size=1))[0] @@ -865,30 +978,30 @@ class TosaTestGen: for i in shape: sStr.append(str(i)) - return 'x'.join(sStr) + return "x".join(sStr) def typeStr(self, t): if t == DType.BOOL: - return 'b' + return "b" elif t == DType.INT4: - return 'i4' + return "i4" elif t == DType.INT8: - return 'i8' + return "i8" elif t == DType.UINT8: - return 'u8' + return "u8" elif t == DType.INT16: - return 'i16' + return "i16" elif t == DType.INT32: - return 'i32' + return "i32" elif t == DType.INT48: - return 'i48' + return "i48" elif t == DType.FLOAT: - return 'float' + return "float" else: - raise Exception('Unknown dtype, cannot convert to string: {}'.format(t)) + raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) def typeWidth(self, t): - ''' Get the datatype width for integer types''' + """ Get the datatype width for integer types""" if t == DType.INT4: return 4 elif t == DType.INT8: @@ -902,7 +1015,7 @@ class TosaTestGen: elif t == DType.INT48: return 48 else: - raise Exception('Unknown dtype, cannot convert to string: {}'.format(t)) + raise Exception("Unknown dtype, cannot convert to string: {}".format(t)) # Argument generators # Returns a list of tuples (stringDescriptor, [build_fcn_arg_list]) @@ -910,8 +1023,7 @@ class TosaTestGen: # The build_fcn_arg_list is expanded and passed to the operator test # build function - - def build_unary(self, op, a, qinfo = None): + def build_unary(self, op, a, qinfo=None): result_tens = OutputShaper.unaryOp(self.ser, a) self.ser.addOperator(op, [a.name], [result_tens.name], None, qinfo) return result_tens @@ -952,7 +1064,7 @@ class TosaTestGen: def build_table(self, op, a): # Constant size, random values table_arr = self.getRandTensor([513], DType.INT16) - table_tens = self.ser.addConst(table_arr.shape, DType.INT16, Usage.INDEX, [], table_arr) + table_tens = self.ser.addConst(table_arr.shape, DType.INT16, table_arr) result_tens = OutputShaper.tableOp(self.ser, a, table_tens) self.ser.addOperator(op, [a.name, table_tens.name], [result_tens.name], None) @@ -985,43 +1097,38 @@ class TosaTestGen: self.ser.addOperator(op, [a.name], [result_tens.name], attr) return result_tens - def build_pool2d(self, op, input, kernel, stride, pad, qinfo = None): + def build_pool2d(self, op, input, kernel, stride, pad, qinfo=None): result_tens = OutputShaper.pool2dOp(self.ser, input, kernel, stride, pad) attr = ts.TosaSerializerAttribute() attr.Pool2dAttribute(kernel, stride, pad) - input.addFormat(Format.NHWC) self.ser.addOperator(op, [input.name], [result_tens.name], attr, qinfo) return result_tens def build_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): - assert(len(padding) == 4) - result_tens = OutputShaper.conv2dOp(self.ser, ifm, filter, strides, padding, dilations) + assert len(padding) == 4 + result_tens = OutputShaper.conv2dOp( + self.ser, ifm, filter, strides, padding, dilations + ) attr = ts.TosaSerializerAttribute() attr.Conv2dAttribute(padding, strides, dilations) - ifm.addFormat(Format.NHWC) - # Update the filter ordering - filter.addUsage(Usage.WEIGHT) - filter.addFormat(Format.OHWI) - - self.ser.addOperator(op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo + ) return result_tens - def build_transpose_conv2d(self, op, ifm, filter, stride, outpad, dilation, output_shape, qinfo): - assert(len(outpad) == 2) + def build_transpose_conv2d( + self, op, ifm, filter, stride, outpad, dilation, output_shape, qinfo + ): + assert len(outpad) == 2 result_tens = OutputShaper.transposeConv2DOp(self.ser, ifm, output_shape) attr = ts.TosaSerializerAttribute() attr.TransposeConv2DAttribute(outpad, stride, dilation, output_shape) - ifm.addFormat(Format.NHWC) - # Update the filter ordering - filter.addUsage(Usage.WEIGHT) - filter.addFormat(Format.OHWI) - # Create bias here since the acc_t depends on (but isn't the same as) the input dtype # The bias is OC if ifm.dtype == DType.INT8: @@ -1031,32 +1138,39 @@ class TosaTestGen: elif ifm.dtype == DType.FLOAT: bias_type = DType.FLOAT else: - raise Exception('Unsupported dtype for transpose_conv2d: {}'.format(ifm.dtype)) + raise Exception( + "Unsupported dtype for transpose_conv2d: {}".format(ifm.dtype) + ) bias_arr = self.getRandTensor([filter.shape[0]], bias_type) - bias_tens = self.ser.addConst([filter.shape[0]], bias_type, [], [], bias_arr) + bias_tens = self.ser.addConst([filter.shape[0]], bias_type, bias_arr) - self.ser.addOperator(op, [ifm.name, filter.name, bias_tens.name], [result_tens.name], attr, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias_tens.name], [result_tens.name], attr, qinfo + ) return result_tens - def build_depthwise_conv2d(self, op, ifm, filter, bias, strides, padding, dilations, qinfo): - result_tens = OutputShaper.depthwiseConv2dOp(self.ser, ifm, filter, strides, padding, dilations) + def build_depthwise_conv2d( + self, op, ifm, filter, bias, strides, padding, dilations, qinfo + ): + result_tens = OutputShaper.depthwiseConv2dOp( + self.ser, ifm, filter, strides, padding, dilations + ) attr = ts.TosaSerializerAttribute() attr.Conv2dAttribute(padding, strides, dilations) - ifm.addFormat(Format.NHWC) - filter.addUsage(Usage.WEIGHT) - filter.addFormat(Format.HWIM) - - self.ser.addOperator(op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], attr, qinfo + ) return result_tens def build_fully_connected(self, op, ifm, filter, bias, qinfo): result_tens = OutputShaper.fullyConnectedOp(self.ser, ifm, filter) - filter.addUsage(Usage.WEIGHT) - self.ser.addOperator(op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo) + self.ser.addOperator( + op, [ifm.name, filter.name, bias.name], [result_tens.name], None, qinfo + ) return result_tens def build_matmul(self, op, a, b, qinfo): @@ -1142,9 +1256,11 @@ class TosaTestGen: # Need to turn the padding array into a TOSA tensor here. # This is one of the few tensor operands that does not get # randomly generated - padding_tens = self.ser.addConst(padding.shape, DType.INT32, [], [], padding) + padding_tens = self.ser.addConst(padding.shape, DType.INT32, padding) - self.ser.addOperator(op, [a.name, padding_tens.name], [result_tens.name], None, qinfo) + self.ser.addOperator( + op, [a.name, padding_tens.name], [result_tens.name], None, qinfo + ) def build_reshape(self, op, a, newShape): result_tens = OutputShaper.reshapeOp(self.ser, a, newShape) @@ -1167,7 +1283,7 @@ class TosaTestGen: def build_transpose(self, op, a, perms): result_tens = OutputShaper.transposeOp(self.ser, a, perms) - perms_tens = self.ser.addConst([len(perms)], DType.INT32, Usage.ACTIVATION, [], np.int32(perms)) + perms_tens = self.ser.addConst([len(perms)], DType.INT32, np.int32(perms)) self.ser.addOperator(op, [a.name, perms_tens.name], [result_tens.name]) return result_tens @@ -1190,16 +1306,19 @@ class TosaTestGen: self.ser.addOperator(op, [a.name], [result_tens.name], attr) return result_tens - def build_gather(self, op, values): # 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, Usage.INDEX, [], indicies_arr) + 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, values, indicies) @@ -1212,32 +1331,65 @@ class TosaTestGen: # 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, Usage.INDEX, [], indicies_arr) + 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, values_in, indicies, input) - self.ser.addOperator(op, [values_in.name, indicies.name, input.name], [result_tens.name]) + self.ser.addOperator( + op, [values_in.name, indicies.name, input.name], [result_tens.name] + ) return result_tens - def build_resize(self, op, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype): - result_tens = OutputShaper.resizeOp(self.ser, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype) + def build_resize( + self, + op, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + ): + result_tens = OutputShaper.resizeOp( + self.ser, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + ) attr = ts.TosaSerializerAttribute() - attr.ResizeAttribute(output_dims, stride, offset, shift, stride_fp, offset_fp, mode) + attr.ResizeAttribute( + output_dims, stride, offset, shift, stride_fp, offset_fp, mode + ) self.ser.addOperator(op, [input.name], [result_tens.name], attr) return result_tens def build_identityn(self, op, val, val2): - result_tens = OutputShaper.unaryOp(self.ser, val) + result_tens = OutputShaper.unaryOp(self.ser, val) result_tens2 = OutputShaper.unaryOp(self.ser, val2) - self.ser.addOperator(op, [val.name, val2.name], [result_tens.name, result_tens2.name]) + self.ser.addOperator( + op, [val.name, val2.name], [result_tens.name, result_tens2.name] + ) return result_tens def build_placeholder(self, op, val): @@ -1287,27 +1439,30 @@ class TosaTestGen: # 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)) + # 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])) for i in range(nc): - multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift(scale_arr[i], scale32) + multiplier_arr[i], shift_arr[i] = TosaQuantGen.computeMultiplierAndShift( + scale_arr[i], scale32 + ) if shift_arr[i] < 2 or shift_arr[i] > 62: - self.ser.setExpectedFailure(True, 'OpRescale: invalid shift value') + self.ser.setExpectedFailure(True, "OpRescale: invalid shift value") - #print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) + # print('multiplier {} shift {} inzp {} outzp {}'.format(multiplier_arr, shift_arr, input_zp, output_zp)) attr = ts.TosaSerializerAttribute() - attr.RescaleAttribute(input_zp, - output_zp, - multiplier_arr, - shift_arr, - scale32, - double_round, - - per_channel) + attr.RescaleAttribute( + input_zp, + output_zp, + multiplier_arr, + shift_arr, + scale32, + double_round, + per_channel, + ) self.ser.addOperator(op, [val.name], [result_tens.name], attr) return result_tens @@ -1318,7 +1473,7 @@ class TosaTestGen: # and fill them with const nodes for the body. # Condition tensor - cond_tens = self.ser.addConst([], DType.BOOL, Usage.ACTIVATION, [], [cond]) + cond_tens = self.ser.addConst([], DType.BOOL, [cond]) # Make then/else tensors out_shape = then_tens.shape @@ -1326,11 +1481,11 @@ class TosaTestGen: else_arr = np.int32(self.rng.integers(0, 255, size=out_shape)) # And the result tensor based on any of the outputs - result_tens = self.ser.addOutput(out_shape, DType.INT32, Usage.ACTIVATION, []) + 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' + then_block = "THEN_BLOCK" + else_block = "ELSE_BLOCK" attr = ts.TosaSerializerAttribute() attr.CondIfAttribute(then_block, else_block) @@ -1339,11 +1494,11 @@ class TosaTestGen: self.ser.startBasicBlock(then_block) # Build the actual then/else tensors inside their blocks - then_tens = self.ser.addConst(out_shape, DType.INT32, Usage.ACTIVATION, [], then_arr) + then_tens = self.ser.addConst(out_shape, DType.INT32, then_arr) self.ser.addOutputTensor(then_tens) self.ser.startBasicBlock(else_block) - else_tens = self.ser.addConst(out_shape, DType.INT32, Usage.ACTIVATION, [], else_arr) + else_tens = self.ser.addConst(out_shape, DType.INT32, else_arr) self.ser.addOutputTensor(else_tens) return result_tens @@ -1353,67 +1508,71 @@ class TosaTestGen: # alternately add or subtract them based on the condition # Condition tensor - cond_tens = self.ser.addConst([], DType.BOOL, Usage.ACTIVATION, [], [cond]) + cond_tens = self.ser.addConst([], DType.BOOL, [cond]) - result_tens = self.ser.addOutput(a.shape, a.dtype, Usage.ACTIVATION, []) + result_tens = self.ser.addOutput(a.shape, a.dtype) self.ser.currBasicBlock.addOutput(result_tens.name) # Create the attribute with the names of the then/else blocks - then_block = 'THEN_BLOCK' - else_block = 'ELSE_BLOCK' + 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, [cond_tens.name, a.name, b.name], [result_tens.name], attr) + self.ser.addOperator( + op, [cond_tens.name, a.name, b.name], [result_tens.name], attr + ) self.ser.startBasicBlock(then_block) self.ser.addInputTensor(a) self.ser.addInputTensor(b) - then_tens = self.ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + then_tens = self.ser.addOutput(a.shape, a.dtype) self.ser.addOperator(Op.ADD, [a.name, b.name], [then_tens.name]) self.ser.startBasicBlock(else_block) self.ser.addInputTensor(a) self.ser.addInputTensor(b) - else_tens = self.ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + else_tens = self.ser.addOutput(a.shape, a.dtype) self.ser.addOperator(Op.SUB, [a.name, b.name], [else_tens.name]) return result_tens def build_while_loop(self, op, a, iter_val): - iter = self.ser.addPlaceholder([], DType.INT32, Usage.ACTIVATION, [], [np.int32(iter_val)]) + iter = self.ser.addPlaceholder([], DType.INT32, [np.int32(iter_val)]) - cond_block = 'COND_BLOCK' - body_block = 'BODY_BLOCK' + 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, a.usage, a.dformat) + # 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, a.usage, a.dformat, acc_init_val) + 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, iter.usage, iter.dformat) - a_out = self.ser.addIntermediate(a.shape, a.dtype, a.usage, a.dformat) - acc_out = self.ser.addIntermediate(acc.shape, acc.dtype, acc.usage, acc.dformat) + iter_out = self.ser.addIntermediate(iter.shape, iter.dtype) + a_out = self.ser.addIntermediate(a.shape, a.dtype) + acc_out = self.ser.addIntermediate(acc.shape, acc.dtype) # While_loop operator - self.ser.addOperator(op, - [iter.name, a.name, acc.name], - [iter_out.name, a_out.name, acc_out.name], attr) + self.ser.addOperator( + op, + [iter.name, a.name, acc.name], + [iter_out.name, a_out.name, acc_out.name], + attr, + ) # COND block (input: iter, output: cond_tens ) self.ser.startBasicBlock(cond_block) self.ser.addInputTensor(iter) self.ser.addInputTensor(a) self.ser.addInputTensor(acc) - zero_tens = self.ser.addConst([], DType.INT32, [], [], [np.int32(0)]) - cond_tens = self.ser.addOutput([], DType.BOOL, [], []) - self.ser.addOperator(Op.GREATER, [iter.name, zero_tens.name], - [cond_tens.name]) + zero_tens = self.ser.addConst([], DType.INT32, [np.int32(0)]) + cond_tens = self.ser.addOutput([], DType.BOOL) + 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 @@ -1421,9 +1580,9 @@ class TosaTestGen: self.ser.addInputTensor(iter) self.ser.addInputTensor(a) self.ser.addInputTensor(acc) - one_tens = self.ser.addConst([], DType.INT32, [], [], [np.int32(1)]) - iter_body_out = self.ser.addIntermediate(iter.shape, iter.dtype, iter.usage, iter.dformat) - acc_body_out = self.ser.addIntermediate(acc.shape, acc.dtype, acc.usage, acc.dformat) + one_tens = self.ser.addConst([], DType.INT32, [np.int32(1)]) + 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) @@ -1432,21 +1591,22 @@ class TosaTestGen: return acc_out - - def genOpTestList(self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None): + def genOpTestList( + self, opName, shapeFilter=[None], rankFilter=None, dtypeFilter=None + ): try: op = self.TOSA_OP_LIST[opName] except KeyError as e: - raise Exception('Cannot find op with name {}'.format(opName)) + 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, agen_fcn = op['build_fcn'] + build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] # Generate the lists of arguments - rmin, rmax = op['rank'] + rmin, rmax = op["rank"] # Test list consists of a tuple of: # (opName, testNameStr, dtype, shapeList, argumentsList) @@ -1461,7 +1621,7 @@ class TosaTestGen: if rankFilter is not None and r not in rankFilter: continue - for t in op['types']: + for t in op["types"]: # Filter tests based on dtype? if dtypeFilter is not None: @@ -1487,13 +1647,15 @@ class TosaTestGen: if agen_fcn: argList = agen_fcn(self, opName, shapeList, t) else: - argList = [('', [])] + argList = [("", [])] for argStr, args in argList: if argStr: - testStr = '{}_{}_{}_{}'.format(opName, shapeStr, typeStr, argStr) + testStr = "{}_{}_{}_{}".format( + opName, shapeStr, typeStr, argStr + ) else: - testStr = '{}_{}_{}'.format(opName, shapeStr, typeStr) + testStr = "{}_{}_{}".format(opName, shapeStr, typeStr) testList.append((opName, testStr, t, shapeList, args)) @@ -1503,16 +1665,16 @@ class TosaTestGen: try: op = self.TOSA_OP_LIST[opName] except KeyError as e: - raise Exception('Cannot find op with name {}'.format(opName)) + raise Exception("Cannot find op with name {}".format(opName)) # Create a serializer self.createSerializer(opName, testStr) - build_fcn, tgen_fcn, agen_fcn = op['build_fcn'] - pCount, cCount = op['operands'] + build_fcn, tgen_fcn, agen_fcn = op["build_fcn"] + pCount, cCount = op["operands"] try: - qgen = op['qgen'] + qgen = op["qgen"] except KeyError: qgen = None @@ -1520,8 +1682,10 @@ class TosaTestGen: tens = [] # If test is ArithmeticRightShift, force value of operand[1] to be within [0, num_bits] - if op['op'] == Op.ARITHMETIC_RIGHT_SHIFT: - assert pCount == 2 and cCount == 0, 'Op.ArithmeticRightShift must have 2 placeholders, 0 consts' + if op["op"] == Op.ARITHMETIC_RIGHT_SHIFT: + assert ( + pCount == 2 and cCount == 0 + ), "Op.ArithmeticRightShift must have 2 placeholders, 0 consts" placeholders = [] for idx, shape in enumerate(shapeList[:]): @@ -1533,10 +1697,10 @@ class TosaTestGen: elif dtype == DType.INT32: arr = np.int32(self.rng.integers(low=0, high=32, size=shape)) else: - raise Exception('OpArithmeticRightShift: invalid input dtype') + raise Exception("OpArithmeticRightShift: invalid input dtype") else: arr = self.getRandTensor(shapeList[0], dtype) - placeholders.append(self.ser.addPlaceholder(shape, dtype, Usage.ACTIVATION, [], arr)) + placeholders.append(self.ser.addPlaceholder(shape, dtype, arr)) tens.extend(placeholders) else: @@ -1550,36 +1714,44 @@ class TosaTestGen: try: if qinfo is not None: - resultName = build_fcn(self, op['op'], *tens, *testArgs, qinfo) + resultName = build_fcn(self, op["op"], *tens, *testArgs, qinfo) else: - resultName = build_fcn(self, op['op'], *tens, *testArgs) + resultName = build_fcn(self, op["op"], *tens, *testArgs) except TypeError as e: - print('build_fcn: {}\nTensors: {}\nArgs: {}\n'.format(build_fcn, tens, testArgs)) + print( + "build_fcn: {}\nTensors: {}\nArgs: {}\n".format( + build_fcn, tens, testArgs + ) + ) raise e # Save the serialized test - self.serialize('test') + self.serialize("test") def createDynamicOpLists(self): # Dynamically create op lists for convolutions with a list of kernel sizes - KERNELS = [ [1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3] ] + KERNELS = [[1, 1], [2, 2], [3, 3], [5, 5], [3, 1], [1, 3]] for k in KERNELS: - 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 + 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 # Delete any templates after having created any dynamic ops # This is a two-pass operation because it's bad practice to delete @@ -1587,7 +1759,7 @@ class TosaTestGen: keyList = [] for k in self.TOSA_OP_LIST: try: - if self.TOSA_OP_LIST[k]['template'] == True: + if self.TOSA_OP_LIST[k]["template"] == True: keyList.append(k) continue except KeyError: @@ -1597,36 +1769,46 @@ class TosaTestGen: 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).''' + """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'] + 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)) + raise Exception( + "Op {} is missing a valid operand tuple in TOSA_OP_LIST".format(op) + ) try: - fcn, tgen, arggen = self.TOSA_OP_LIST[op]['build_fcn'] + fcn, tgen, 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)) + raise Exception( + "Op {} is missing a valid build_fcn tuple in TOSA_OP_LIST".format( + op + ) + ) try: - types = self.TOSA_OP_LIST[op]['types'] + types = self.TOSA_OP_LIST[op]["types"] except KeyError as e: - raise Exception('Op {} is missing a valid type list in TOSA_OP_LIST'.format(op)) + raise Exception( + "Op {} is missing a valid type list in TOSA_OP_LIST".format(op) + ) try: - opcode = self.TOSA_OP_LIST[op]['op'] + opcode = self.TOSA_OP_LIST[op]["op"] except KeyError as e: - raise Exception('Op {} is missing the Op field in TOSA_OP_LIST'.format(op)) + raise Exception( + "Op {} is missing the Op field in TOSA_OP_LIST".format(op) + ) # Put in default rank range, if missing try: - rank = self.TOSA_OP_LIST[op]['rank'] + rank = self.TOSA_OP_LIST[op]["rank"] except KeyError: - self.TOSA_OP_LIST[op]['rank'] = self.DEFAULT_RANK_RANGE + self.TOSA_OP_LIST[op]["rank"] = self.DEFAULT_RANK_RANGE # Tensor operator list # 'op': op name @@ -1635,495 +1817,517 @@ class TosaTestGen: # 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.FLOAT ] + TYPE_FP = [DType.FLOAT] - TYPE_INT = [ DType.INT8, DType.INT16, DType.INT32 ] # Excludes INT4 - TYPE_INT_FP = [ DType.INT8, DType.INT16, DType.INT32, DType.FLOAT ] # Excludes INT4 + TYPE_INT = [DType.INT8, DType.INT16, DType.INT32] # Excludes INT4 + TYPE_INT_FP = [DType.INT8, DType.INT16, DType.INT32, DType.FLOAT] # Excludes INT4 - TYPE_BOOL = [ DType.BOOL ] - TYPE_FI32 = [ DType.FLOAT, DType.INT32 ] - TYPE_FIB = [ DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL ] - TYPE_FI16 = [ DType.FLOAT, DType.INT16 ] + TYPE_BOOL = [DType.BOOL] + TYPE_FI32 = [DType.FLOAT, DType.INT32] + TYPE_FIB = [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL] + TYPE_FI16 = [DType.FLOAT, DType.INT16] - TYPE_NARROW_INT_FP = [ DType.INT8, DType.INT16, DType.FLOAT ] + TYPE_NARROW_INT_FP = [DType.INT8, DType.INT16, DType.FLOAT] DEFAULT_RANK_RANGE = (1, 4) TOSA_OP_LIST = { # Binary ops - 'add': - { 'op': Op.ADD, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'arithmetic_right_shift': - { 'op': Op.ARITHMETIC_RIGHT_SHIFT, - 'operands': (2, 0), - 'build_fcn': (build_arithmetic_right_shift, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agArithmeticRightShift), - 'types': TYPE_INT }, - - 'bitwise_and': - { 'op': Op.BITWISE_AND, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'bitwise_or': - { 'op': Op.BITWISE_OR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'bitwise_xor': - { 'op': Op.BITWISE_XOR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'logical_and': - { 'op': Op.LOGICAL_AND, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_BOOL }, - - 'logical_left_shift': - { 'op': Op.LOGICAL_LEFT_SHIFT, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'logical_right_shift': - { 'op': Op.LOGICAL_RIGHT_SHIFT, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_INT }, - - 'logical_or': - { 'op': Op.LOGICAL_OR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_BOOL }, - - 'logical_xor': - { 'op': Op.LOGICAL_XOR, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_BOOL }, - - 'max': - { 'op': Op.MAXIMUM, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'min': - { 'op': Op.MINIMUM, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'mul': - { 'op': Op.MUL, - 'operands': (2, 0), - 'build_fcn': (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), - 'types': TYPE_INT_FP }, - - 'pow': - { 'op': Op.POW, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'sub': - { 'op': Op.SUB, - 'operands': (2, 0), - 'build_fcn': (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - '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, None), - 'types': [ DType.INT16 ] }, - - 'argmax': - { 'op': Op.ARGMAX, - 'operands': (1, 0), - 'build_fcn': (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_NARROW_INT_FP }, - + "add": { + "op": Op.ADD, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "arithmetic_right_shift": { + "op": Op.ARITHMETIC_RIGHT_SHIFT, + "operands": (2, 0), + "build_fcn": ( + build_arithmetic_right_shift, + TosaTensorGen.tgBroadcastFuzz, + TosaArgGen.agArithmeticRightShift, + ), + "types": TYPE_INT, + }, + "bitwise_and": { + "op": Op.BITWISE_AND, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "bitwise_or": { + "op": Op.BITWISE_OR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "bitwise_xor": { + "op": Op.BITWISE_XOR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "logical_and": { + "op": Op.LOGICAL_AND, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + }, + "logical_left_shift": { + "op": Op.LOGICAL_LEFT_SHIFT, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "logical_right_shift": { + "op": Op.LOGICAL_RIGHT_SHIFT, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_INT, + }, + "logical_or": { + "op": Op.LOGICAL_OR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + }, + "logical_xor": { + "op": Op.LOGICAL_XOR, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_BOOL, + }, + "max": { + "op": Op.MAXIMUM, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "min": { + "op": Op.MINIMUM, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "mul": { + "op": Op.MUL, + "operands": (2, 0), + "build_fcn": (build_mul, TosaTensorGen.tgBroadcastFuzz, TosaArgGen.agMul), + "types": TYPE_INT_FP, + }, + "pow": { + "op": Op.POW, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "sub": { + "op": Op.SUB, + "operands": (2, 0), + "build_fcn": (build_binary_broadcast, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "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, None), + "types": [DType.INT16], + }, + "argmax": { + "op": Op.ARGMAX, + "operands": (1, 0), + "build_fcn": (build_argmax, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_NARROW_INT_FP, + }, # Templated operator. Filled in by createDynamicOpLists - 'conv2d_TEMPLATE': - { 'op': Op.CONV2D, - 'operands': (1, 2), - 'rank': (4, 4), - 'build_fcn': (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP, - 'template': True }, - + "conv2d_TEMPLATE": { + "op": Op.CONV2D, + "operands": (1, 2), + "rank": (4, 4), + "build_fcn": (build_conv2d, TosaTensorGen.tgConv2D, TosaArgGen.agConv2D), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_NARROW_INT_FP, + "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, TosaArgGen.agConv2D), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP, - 'template': True }, - + "depthwise_conv2d_TEMPLATE": { + "op": Op.DEPTHWISE_CONV2D, + "operands": (1, 2), + "filter": [1, 1], + "rank": (4, 4), + "build_fcn": ( + build_depthwise_conv2d, + TosaTensorGen.tgDepthwiseConv2D, + TosaArgGen.agConv2D, + ), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_NARROW_INT_FP, + "template": True, + }, # Templated operator. Filled in by createDynamicOpLists - 'transpose_conv2d_TEMPLATE': - { 'op': Op.TRANSPOSE_CONV2D, - 'operands': (1, 1), - 'rank': (4, 4), - 'build_fcn': (build_transpose_conv2d, TosaTensorGen.tgTransposeConv2D, TosaArgGen.agTransposeConv2D), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP, - 'template': True }, - - 'fully_connected': - { 'op': Op.FULLY_CONNECTED, - 'operands': (2, 0), - 'rank': (2, 2), - 'build_fcn': (build_fully_connected, TosaTensorGen.tgFullyConnected, None), - 'qgen': TosaQuantGen.qgConv, - 'types': TYPE_NARROW_INT_FP }, - - 'matmul': - { 'op': Op.MATMUL, - 'operands': (2, 0), - 'rank': (2, 2), - 'build_fcn': (build_matmul, TosaTensorGen.tgMatmul, None), - 'qgen': TosaQuantGen.qgMatmul, - 'types': TYPE_NARROW_INT_FP }, - + "transpose_conv2d_TEMPLATE": { + "op": Op.TRANSPOSE_CONV2D, + "operands": (1, 1), + "rank": (4, 4), + "build_fcn": ( + build_transpose_conv2d, + TosaTensorGen.tgTransposeConv2D, + TosaArgGen.agTransposeConv2D, + ), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_FP, + "template": True, + }, + "fully_connected": { + "op": Op.FULLY_CONNECTED, + "operands": (2, 0), + "rank": (2, 2), + "build_fcn": (build_fully_connected, TosaTensorGen.tgFullyConnected, None), + "qgen": TosaQuantGen.qgConv, + "types": TYPE_NARROW_INT_FP, + }, + "matmul": { + "op": Op.MATMUL, + "operands": (2, 0), + "rank": (2, 2), + "build_fcn": (build_matmul, TosaTensorGen.tgMatmul, None), + "qgen": TosaQuantGen.qgMatmul, + "types": TYPE_NARROW_INT_FP, + }, # Unary operators - 'abs': - { 'op': Op.ABS, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FI32 }, - - 'bitwise_not': - { 'op': Op.BITWISE_NOT, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_INT }, - - 'ceil': - { 'op': Op.CEIL, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'clz': - { 'op': Op.CLZ, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': [ DType.INT32 ] }, - - 'exp': - { 'op': Op.EXP, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'floor': - { 'op': Op.FLOOR, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'log': - { 'op': Op.LOG, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'floor': - { 'op': Op.FLOOR, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'logical_not': - { 'op': Op.LOGICAL_NOT, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_BOOL }, - - 'negate': - { 'op': Op.NEGATE, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'qgen': TosaQuantGen.qgUnary, - 'types': TYPE_INT_FP }, - - 'reciprocal': - { 'op': Op.RECIPROCAL, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'rsqrt': - { 'op': Op.RSQRT, - 'operands': (1, 0), - 'build_fcn': (build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - + "abs": { + "op": Op.ABS, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FI32, + }, + "bitwise_not": { + "op": Op.BITWISE_NOT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_INT, + }, + "ceil": { + "op": Op.CEIL, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "clz": { + "op": Op.CLZ, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": [DType.INT32], + }, + "exp": { + "op": Op.EXP, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "floor": { + "op": Op.FLOOR, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "log": { + "op": Op.LOG, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "floor": { + "op": Op.FLOOR, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "logical_not": { + "op": Op.LOGICAL_NOT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_BOOL, + }, + "negate": { + "op": Op.NEGATE, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "qgen": TosaQuantGen.qgUnary, + "types": TYPE_INT_FP, + }, + "reciprocal": { + "op": Op.RECIPROCAL, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "rsqrt": { + "op": Op.RSQRT, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, # Ternary operators - 'select': - { 'op': Op.SELECT, - 'operands': (3, 0), - 'build_fcn': (build_select, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FIB }, - + "select": { + "op": Op.SELECT, + "operands": (3, 0), + "build_fcn": (build_select, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FIB, + }, # Comparison operators - 'equal': - { 'op': Op.EQUAL, - 'operands': (2, 0), - 'build_fcn': (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'greater_equal': - { 'op': Op.GREATER_EQUAL, - 'operands': (2, 0), - 'build_fcn': (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - - 'greater': - { 'op': Op.GREATER, - 'operands': (2, 0), - 'build_fcn': (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), - 'types': TYPE_FI32 }, - + "equal": { + "op": Op.EQUAL, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "greater_equal": { + "op": Op.GREATER_EQUAL, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, + "greater": { + "op": Op.GREATER, + "operands": (2, 0), + "build_fcn": (build_comparison, TosaTensorGen.tgBroadcastFuzz, None), + "types": TYPE_FI32, + }, # Pooling operators - 'avg_pool2d': - { 'op': Op.AVG_POOL2D, - 'operands': (1, 0), - 'rank': (4, 4), - 'build_fcn': (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), - 'qgen': TosaQuantGen.qgUnary, - 'types': TYPE_NARROW_INT_FP }, - - - 'max_pool2d': - { 'op': Op.MAX_POOL2D, - 'operands': (1, 0), - 'rank': (4, 4), - 'build_fcn': (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), - 'types': TYPE_NARROW_INT_FP }, - + "avg_pool2d": { + "op": Op.AVG_POOL2D, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), + "qgen": TosaQuantGen.qgUnary, + "types": TYPE_NARROW_INT_FP, + }, + "max_pool2d": { + "op": Op.MAX_POOL2D, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_pool2d, TosaTensorGen.tgNHWC, TosaArgGen.agPooling), + "types": TYPE_NARROW_INT_FP, + }, # Reduce operators - 'reduce_any': - { 'op': Op.REDUCE_ANY, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_BOOL }, - - 'reduce_all': - { 'op': Op.REDUCE_ALL, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_BOOL }, - - 'reduce_max': - { 'op': Op.REDUCE_MAX, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_INT_FP }, - - 'reduce_min': - { 'op': Op.REDUCE_MAX, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_INT_FP }, - - 'reduce_product': - { 'op': Op.REDUCE_PRODUCT, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FP }, - - 'reduce_sum': - { 'op': Op.REDUCE_SUM, - 'operands': (1, 0), - 'build_fcn': (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FI32 }, - + "reduce_any": { + "op": Op.REDUCE_ANY, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_BOOL, + }, + "reduce_all": { + "op": Op.REDUCE_ALL, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_BOOL, + }, + "reduce_max": { + "op": Op.REDUCE_MAX, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_INT_FP, + }, + "reduce_min": { + "op": Op.REDUCE_MAX, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_INT_FP, + }, + "reduce_product": { + "op": Op.REDUCE_PRODUCT, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FP, + }, + "reduce_sum": { + "op": Op.REDUCE_SUM, + "operands": (1, 0), + "build_fcn": (build_reduce, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FI32, + }, # Activation functions - 'clamp': - { 'op': Op.CLAMP, - 'operands': (1, 0), - 'build_fcn': (build_clamp, TosaTensorGen.tgBasic, None), - 'types': TYPE_NARROW_INT_FP }, - - 'relun': - { 'op': Op.RELUN, - 'operands': (1, 0), - 'build_fcn': (build_relun, TosaTensorGen.tgBasic, None), - 'types': TYPE_FI32 }, - - 'sigmoid': - { 'op': Op.SIGMOID, - 'operands': (1, 0), - 'build_fcn': (build_sigmoid, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - - 'tanh': - { 'op': Op.TANH, - 'operands': (1, 0), - 'build_fcn': (build_tanh, TosaTensorGen.tgBasic, None), - 'types': TYPE_FP }, - + "clamp": { + "op": Op.CLAMP, + "operands": (1, 0), + "build_fcn": (build_clamp, TosaTensorGen.tgBasic, None), + "types": TYPE_NARROW_INT_FP, + }, + "relun": { + "op": Op.RELUN, + "operands": (1, 0), + "build_fcn": (build_relun, TosaTensorGen.tgBasic, None), + "types": TYPE_FI32, + }, + "sigmoid": { + "op": Op.SIGMOID, + "operands": (1, 0), + "build_fcn": (build_sigmoid, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, + "tanh": { + "op": Op.TANH, + "operands": (1, 0), + "build_fcn": (build_tanh, TosaTensorGen.tgBasic, None), + "types": TYPE_FP, + }, # Data layout operators - 'concat': - { 'op': Op.CONCAT, - 'operands': (2, 0), - 'build_fcn': (build_concat, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FIB }, - - 'pad': - { 'op': Op.PAD, - 'operands': (1, 0), - 'build_fcn': (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), - 'qgen': TosaQuantGen.qgPad, - 'types': TYPE_FIB }, - - 'reshape': - { 'op': Op.RESHAPE, - 'operands': (1, 0), - 'build_fcn': (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), - 'types': TYPE_FIB }, - - 'reverse': - { 'op': Op.REVERSE, - 'operands': (1, 0), - 'build_fcn': (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), - 'types': TYPE_FIB }, - - 'slice': - { 'op': Op.SLICE, - 'operands': (1, 0), - 'build_fcn': (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), - 'types': TYPE_FIB }, - - 'tile': - { 'op': Op.TILE, - 'operands': (1, 0), - 'build_fcn': (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), - 'types': TYPE_FIB }, - - 'transpose': - { 'op': Op.TRANSPOSE, - 'operands': (1, 0), - 'rank': (2, 4), # Do not allow tranpose on rank=1 - 'build_fcn': (build_transpose, TosaTensorGen.tgBasic, TosaArgGen.agTranspose), - 'types': TYPE_FIB }, - + "concat": { + "op": Op.CONCAT, + "operands": (2, 0), + "build_fcn": (build_concat, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FIB, + }, + "pad": { + "op": Op.PAD, + "operands": (1, 0), + "build_fcn": (build_pad, TosaTensorGen.tgBasic, TosaArgGen.agPad), + "qgen": TosaQuantGen.qgPad, + "types": TYPE_FIB, + }, + "reshape": { + "op": Op.RESHAPE, + "operands": (1, 0), + "build_fcn": (build_reshape, TosaTensorGen.tgBasic, TosaArgGen.agReshape), + "types": TYPE_FIB, + }, + "reverse": { + "op": Op.REVERSE, + "operands": (1, 0), + "build_fcn": (build_reverse, TosaTensorGen.tgBasic, TosaArgGen.agAxis), + "types": TYPE_FIB, + }, + "slice": { + "op": Op.SLICE, + "operands": (1, 0), + "build_fcn": (build_slice, TosaTensorGen.tgBasic, TosaArgGen.agSlice), + "types": TYPE_FIB, + }, + "tile": { + "op": Op.TILE, + "operands": (1, 0), + "build_fcn": (build_tile, TosaTensorGen.tgBasic, TosaArgGen.agTile), + "types": TYPE_FIB, + }, + "transpose": { + "op": Op.TRANSPOSE, + "operands": (1, 0), + "rank": (2, 4), # Do not allow tranpose on rank=1 + "build_fcn": ( + build_transpose, + TosaTensorGen.tgBasic, + TosaArgGen.agTranspose, + ), + "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, None), - 'types': TYPE_INT_FP }, - - '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, None), - 'types': TYPE_INT_FP }, - + "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, None), + "types": TYPE_INT_FP, + }, + "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, None), + "types": TYPE_INT_FP, + }, # Image operations - 'resize': - { 'op': Op.RESIZE, - 'operands': (1, 0), - 'rank': (4, 4), - 'build_fcn': ( build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), - 'types': [ DType.INT8, DType.INT16, DType.FLOAT ] }, - - + "resize": { + "op": Op.RESIZE, + "operands": (1, 0), + "rank": (4, 4), + "build_fcn": (build_resize, TosaTensorGen.tgNHWC, TosaArgGen.agResize), + "types": [DType.INT8, DType.INT16, DType.FLOAT], + }, # Data nodes - 'placeholder': - { 'op': Op.PLACEHOLDER, - 'operands': (1, 0), - 'build_fcn': ( build_placeholder, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - - 'const': - { 'op': Op.CONST, - 'operands': (1, 0), - 'build_fcn': ( build_placeholder, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - - - 'identity': - { 'op': Op.IDENTITY, - 'operands': (1, 0), - 'build_fcn': ( build_unary, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - - - 'identityn': - { 'op': Op.IDENTITYN, - 'operands': (2, 0), - 'build_fcn': ( build_identityn, TosaTensorGen.tgBasic, None), - 'types': TYPE_FIB }, - + "placeholder": { + "op": Op.PLACEHOLDER, + "operands": (1, 0), + "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "const": { + "op": Op.CONST, + "operands": (1, 0), + "build_fcn": (build_placeholder, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "identity": { + "op": Op.IDENTITY, + "operands": (1, 0), + "build_fcn": (build_unary, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, + "identityn": { + "op": Op.IDENTITYN, + "operands": (2, 0), + "build_fcn": (build_identityn, TosaTensorGen.tgBasic, None), + "types": TYPE_FIB, + }, # Type conversion - 'cast': - { 'op': Op.CAST, - 'operands': (1, 0), - 'build_fcn': ( build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast ), - 'types': [ DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL ] }, - - 'rescale': - { 'op': Op.RESCALE, - 'operands': (1, 0), - 'build_fcn': ( build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale ), - 'types': [ DType.INT8, DType.INT16, DType.INT32, DType.INT48 ] }, - + "cast": { + "op": Op.CAST, + "operands": (1, 0), + "build_fcn": (build_cast, TosaTensorGen.tgBasic, TosaArgGen.agCast), + "types": [DType.FLOAT, DType.INT8, DType.INT16, DType.INT32, DType.BOOL], + }, + "rescale": { + "op": Op.RESCALE, + "operands": (1, 0), + "build_fcn": (build_rescale, TosaTensorGen.tgBasic, TosaArgGen.agRescale), + "types": [DType.INT8, DType.INT16, DType.INT32, DType.INT48], + }, # Custom # Not implemented. - # Control flow - # 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, TosaArgGen.agCondIf ), - 'types': [ DType.BOOL ] }, - - 'cond_if_binary': - { 'op': Op.COND_IF, - 'operands': (2, 0), - 'build_fcn': ( build_cond_if_binary, TosaTensorGen.tgBasic, TosaArgGen.agCondIf ), - 'types': TYPE_FI32 }, - + "cond_if_const": { + "op": Op.COND_IF, + "operands": (0, 2), + "build_fcn": ( + build_cond_if_const, + TosaTensorGen.tgBasic, + TosaArgGen.agCondIf, + ), + "types": [DType.BOOL], + }, + "cond_if_binary": { + "op": Op.COND_IF, + "operands": (2, 0), + "build_fcn": ( + build_cond_if_binary, + TosaTensorGen.tgBasic, + TosaArgGen.agCondIf, + ), + "types": TYPE_FI32, + }, # while_loop - 'while_loop': - { 'op': Op.WHILE_LOOP, - 'operands': (0, 1), - 'build_fcn': ( build_while_loop, TosaTensorGen.tgBasic, TosaArgGen.agWhileLoop ), - 'types': [DType.INT32] }, - - + "while_loop": { + "op": Op.WHILE_LOOP, + "operands": (0, 1), + "build_fcn": ( + build_while_loop, + TosaTensorGen.tgBasic, + TosaArgGen.agWhileLoop, + ), + "types": [DType.INT32], + }, } + class OutputShaper: # Methods in this class compute the expected output shape and datatype # for common classes of operations @@ -2134,8 +2338,8 @@ class OutputShaper: # creating a new output tensor @staticmethod def binaryBroadcastOp(ser, a, b): - assert(len(a.shape) == len(b.shape)) - assert(a.dtype == b.dtype) + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype shape = [] for i in range(len(a.shape)): @@ -2144,39 +2348,39 @@ class OutputShaper: else: shape.append(a.shape[i]) - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def binaryNonBroadcastOp(ser, a, b): - assert(len(a.shape) == len(b.shape)) - assert(a.dtype == b.dtype) + 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]) + assert a.shape[i] == b.shape[i] shape.append(a.shape[i]) - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def unaryOp(ser, a): - return ser.addOutput(a.shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(a.shape, a.dtype) @staticmethod def selectOp(ser, cond, a, b): - assert(len(a.shape) == len(b.shape) and len(a.shape) == len(cond.shape)) - assert(a.dtype == b.dtype) + 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(a.shape)): shape.append(max(cond.shape[i], a.shape[i], b.shape[i])) - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def binaryComparisonOp(ser, a, b): - assert(len(a.shape) == len(b.shape)) - assert(a.dtype == b.dtype) + assert len(a.shape) == len(b.shape) + assert a.dtype == b.dtype # Do broadcast shape = [] @@ -2187,7 +2391,7 @@ class OutputShaper: shape.append(a.shape[i]) # Force the output type to bool - return ser.addOutput(shape, DType.BOOL, a.usage, a.dformat) + return ser.addOutput(shape, DType.BOOL) @staticmethod def reduceOp(ser, a, axis): @@ -2196,13 +2400,13 @@ class OutputShaper: shape[axis] = 1 - return ser.addOutput(shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(shape, a.dtype) @staticmethod def argmaxOp(ser, a, axis): shape = a.shape.copy() del shape[axis] - return ser.addOutput(shape, DType.INT32, a.usage, a.dformat) + return ser.addOutput(shape, DType.INT32) @staticmethod def conv2dOp(ser, ifm, filter, strides, padding, dilations): @@ -2216,17 +2420,27 @@ class OutputShaper: # From H,W to T,B,L,R padding = [padding[0], padding[0], padding[1], padding[1]] - h = (ifm.shape[1] - filter.shape[1] - (filter.shape[1] - 1) * (dilations[0] - 1) + \ - padding[0] + padding[1]) // strides[0] + 1 - - w = (ifm.shape[2] - filter.shape[2] - (filter.shape[2] - 1) * (dilations[1] - 1) + \ - padding[2] + padding[3]) // strides[1] + 1 + h = ( + ifm.shape[1] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + w = ( + ifm.shape[2] + - filter.shape[2] + - (filter.shape[2] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 if h <= 0 or w <= 0: # Invalid test parameters? h = 0 w = 0 - ser.setExpectedFailure(True, 'Invalid combination of conv2d parameters') + ser.setExpectedFailure(True, "Invalid combination of conv2d parameters") ofm_shape = [ifm.shape[0], h, w, filter.shape[0]] @@ -2237,29 +2451,39 @@ class OutputShaper: elif ifm.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(ifm.dtype)) + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) if ifm.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(ofm_shape, out_dtype, ifm.usage, ifm.dformat) + return ser.addOutput(ofm_shape, out_dtype) @staticmethod def depthwiseConv2dOp(ser, ifm, filter, strides, padding, dilations): # IFM: NHWC # Filter: HWCM # OFM: NHW C*M - h = (ifm.shape[1] - filter.shape[0] - (filter.shape[0] - 1) * (dilations[0] - 1) + \ - padding[0] + padding[1]) // strides[0] + 1 - - w = (ifm.shape[2] - filter.shape[1] - (filter.shape[1] - 1) * (dilations[1] - 1) + \ - padding[2] + padding[3]) // strides[1] + 1 + h = ( + ifm.shape[1] + - filter.shape[0] + - (filter.shape[0] - 1) * (dilations[0] - 1) + + padding[0] + + padding[1] + ) // strides[0] + 1 + + w = ( + ifm.shape[2] + - filter.shape[1] + - (filter.shape[1] - 1) * (dilations[1] - 1) + + padding[2] + + padding[3] + ) // strides[1] + 1 if h <= 0 or w <= 0: # Invalid test parameters? h = 0 w = 0 - ser.setExpectedFailure(True, 'Invalid combination of conv2d parameters') + ser.setExpectedFailure(True, "Invalid combination of conv2d parameters") ofm_shape = [ifm.shape[0], h, w, filter.shape[2] * filter.shape[3]] @@ -2270,13 +2494,12 @@ class OutputShaper: elif ifm.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(ifm.dtype)) + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) if ifm.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(ofm_shape, out_dtype, ifm.usage, ifm.dformat) - + return ser.addOutput(ofm_shape, out_dtype) @staticmethod def pool2dOp(ser, ifm, kernel, stride, pad): @@ -2288,10 +2511,10 @@ class OutputShaper: # Invalid test parameters? h = 0 w = 0 - ser.setExpectedFailure(True, 'Invalid combination of pooling parameters') + ser.setExpectedFailure(True, "Invalid combination of pooling parameters") ofm_shape = [ifm.shape[0], h, w, ifm.shape[3]] - return ser.addOutput(ofm_shape, ifm.dtype, ifm.usage, ifm.dformat) + return ser.addOutput(ofm_shape, ifm.dtype) @staticmethod def fullyConnectedOp(ser, input, filter): @@ -2308,12 +2531,12 @@ class OutputShaper: elif input.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(input.dtype)) + raise Exception("Unsupported input dtype: {}".format(input.dtype)) if input.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(output_shape, out_dtype, input.usage, input.dformat) + return ser.addOutput(output_shape, out_dtype) @staticmethod def matmulOp(ser, a, b): @@ -2330,9 +2553,9 @@ class OutputShaper: elif a.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('UNsupported input dtype for matmul: {}'.format(a.dtype)) + raise Exception("UNsupported input dtype for matmul: {}".format(a.dtype)) - return ser.addOutput(output_shape, out_dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, out_dtype) @staticmethod def concatOp(ser, a, b, axis): @@ -2340,7 +2563,7 @@ class OutputShaper: output_shape = a.shape.copy() output_shape[axis] = a.shape[axis] + b.shape[axis] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def padOp(ser, a, padding): @@ -2350,7 +2573,7 @@ class OutputShaper: for i in range(len(output_shape)): output_shape[i] = padding[i][0] + padding[i][1] + output_shape[i] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def reshapeOp(ser, a, shape): @@ -2371,34 +2594,34 @@ class OutputShaper: if output_shape[i] == -1: output_shape[i] = totalElements // totalOutputElements - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def sliceOp(ser, a, begin, size): output_shape = size.copy() - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def tileOp(ser, a, multiples): output_shape = a.shape.copy() - assert(len(multiples) == len(output_shape)) + assert len(multiples) == len(output_shape) for i in range(len(output_shape)): output_shape[i] = a.shape[i] * multiples[i] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def transposeOp(ser, a, perms): output_shape = a.shape.copy() - assert(len(perms) == len(output_shape)) + assert len(perms) == len(output_shape) for i in range(len(output_shape)): output_shape[i] = a.shape[perms[i]] - return ser.addOutput(output_shape, a.dtype, a.usage, a.dformat) + return ser.addOutput(output_shape, a.dtype) @staticmethod def gatherOp(ser, values, indices): @@ -2408,72 +2631,84 @@ class OutputShaper: output_shape = [values.shape[0], indices.shape[1], values.shape[2]] - return ser.addOutput(output_shape, values.dtype, values.usage, values.dformat) + return ser.addOutput(output_shape, values.dtype) @staticmethod def scatterOp(ser, values_in, indices, input): 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 + 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 - return ser.addOutput(output_shape, values_in.dtype, values_in.usage, values_in.dformat) + return ser.addOutput(output_shape, values_in.dtype) @staticmethod def tableOp(ser, input, table): # Same shape as the input, but with the type of the table. - return ser.addOutput(input.shape, DType.INT32, input.usage, input.dformat) + return ser.addOutput(input.shape, DType.INT32) @staticmethod - def resizeOp(ser, input, mode, stride, offset, shift, stride_fp, offset_fp, output_dims, input_dtype, output_dtype): + def resizeOp( + ser, + input, + mode, + stride, + offset, + shift, + stride_fp, + offset_fp, + output_dims, + input_dtype, + output_dtype, + ): output_dims = [input.shape[0], output_dims[0], output_dims[1], input.shape[3]] if input_dtype == DType.FLOAT: if stride_fp[0] <= 0 or stride_fp[1] <= 0: - ser.setExpectedFailure(True, 'Negative or zero stride') + ser.setExpectedFailure(True, "Negative or zero stride") else: if stride[0] <= 0 or stride[1] <= 0: - ser.setExpectedFailure(True, 'Negative or zero stride') + ser.setExpectedFailure(True, "Negative or zero stride") if mode == ResizeMode.BILINEAR: if input_dtype == DType.INT8: if output_dtype != DType.INT32: - ser.setExpectedFailure(True, 'Invalid output data type') + ser.setExpectedFailure(True, "Invalid output data type") elif input_dtype == DType.INT16: if output_dtype != DType.INT48: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") elif input_dtype == DType.FLOAT: if output_dtype != DType.FLOAT: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") else: - ser.setexpectedfailure(true, 'Invalid input data type') + ser.setexpectedfailure(true, "Invalid input data type") elif mode == ResizeMode.NEAREST: if input_dtype == DType.INT8: if output_dtype != DType.INT8: - ser.setExpectedFailure(True, 'Invalid output data type') + ser.setExpectedFailure(True, "Invalid output data type") elif input_dtype == DType.INT16: if output_dtype != DType.INT16: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") elif input_dtype == DType.FLOAT: if output_dtype != DType.FLOAT: - ser.setexpectedfailure(true, 'Invalid output data type') + ser.setexpectedfailure(true, "Invalid output data type") else: - ser.setexpectedfailure(true, 'Invalid input data type') + ser.setexpectedfailure(true, "Invalid input data type") else: - ser.setexpectedfailure(true, 'Invalid resize mode') + ser.setexpectedfailure(true, "Invalid resize mode") - return ser.addOutput(output_dims, output_dtype, input.usage, input.dformat) + return ser.addOutput(output_dims, output_dtype) @staticmethod def typeConversionOp(ser, val, out_dtype): - return ser.addOutput(val.shape, out_dtype, val.usage, val.dformat) + return ser.addOutput(val.shape, out_dtype) @staticmethod def transposeConv2DOp(ser, ifm, output_shape): @@ -2484,12 +2719,12 @@ class OutputShaper: elif ifm.dtype == DType.FLOAT: out_dtype = DType.FLOAT else: - raise Exception('Unsupported input dtype: {}'.format(ifm.dtype)) + raise Exception("Unsupported input dtype: {}".format(ifm.dtype)) if output_shape[1] <= 0 or output_shape[2] <= 0: - ser.setExpectedFailure(True, 'Negative output shape') + ser.setExpectedFailure(True, "Negative output shape") if ifm.dtype == DType.INT16: ser.setExpectedFailure(True, "INT16 support is in progress") - return ser.addOutput(output_shape, out_dtype, ifm.usage, ifm.dformat) + return ser.addOutput(output_shape, out_dtype) diff --git a/verif/tosa_test_runner.py b/verif/tosa_test_runner.py index 6549192..82d447e 100644 --- a/verif/tosa_test_runner.py +++ b/verif/tosa_test_runner.py @@ -19,29 +19,34 @@ import shlex import subprocess from enum import IntEnum, unique + def run_sh_command(args, full_cmd, capture_output=False): - '''Utility function to run an external command. Optionally return captured stdout/stderr''' + """Utility function to run an external command. Optionally return captured stdout/stderr""" # Quote the command line for printing - full_cmd_esc = [ shlex.quote(x) for x in full_cmd ] + full_cmd_esc = [shlex.quote(x) for x in full_cmd] if args.verbose: - print('### Running {}'.format(' '.join(full_cmd_esc))) + print("### Running {}".format(" ".join(full_cmd_esc))) if capture_output: rc = subprocess.run(full_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) if rc.returncode != 0: - print(rc.stdout.decode('utf-8')) - print(rc.stderr.decode('utf-8')) - raise Exception('Error running command: {}.\n{}'.format(' '.join(full_cmd_esc), rc.stderr.decode('utf-8'))) + print(rc.stdout.decode("utf-8")) + print(rc.stderr.decode("utf-8")) + raise Exception( + "Error running command: {}.\n{}".format( + " ".join(full_cmd_esc), rc.stderr.decode("utf-8") + ) + ) return (rc.stdout, rc.stderr) else: rc = subprocess.run(full_cmd) if rc.returncode != 0: - raise Exception('Error running command: {}'.format(' '.join(full_cmd_esc))) + raise Exception("Error running command: {}".format(" ".join(full_cmd_esc))) -class TosaTestRunner: +class TosaTestRunner: def __init__(self, args, runnerArgs, testDir): self.args = args @@ -49,7 +54,7 @@ class TosaTestRunner: self.testDir = testDir # Load the json test file - with open(os.path.join(testDir, 'desc.json'), 'r') as fd: + with open(os.path.join(testDir, "desc.json"), "r") as fd: self.testDesc = json.load(fd) def runModel(self): diff --git a/verif/tosa_verif_build_tests.py b/verif/tosa_verif_build_tests.py index b8a24dd..15482e6 100755 --- a/verif/tosa_verif_build_tests.py +++ b/verif/tosa_verif_build_tests.py @@ -32,11 +32,18 @@ import traceback from enum import IntEnum, Enum, unique from datetime import datetime -# Include the ../shared directory in PYTHONPATH +# Include the ../scripts and ../scripts/xunit directory in PYTHONPATH parent_dir = os.path.dirname(os.path.realpath(__file__)) -sys.path.append(os.path.join(parent_dir, '..', 'scripts')) -sys.path.append(os.path.join(parent_dir, '..', 'scripts', 'xunit')) +sys.path.append(os.path.join(parent_dir, "..", "scripts")) +sys.path.append(os.path.join(parent_dir, "..", "scripts", "xunit")) + import xunit + +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) from tosa_serializer import * from tosa_test_gen import TosaTestGen import tosa @@ -44,77 +51,153 @@ import tosa # Used for parsing a comma-separated list of integers in a string # to an actual list of integers def str_to_list(in_s): - '''Converts a comma-separated list of string integers to a python list of ints''' - lst = in_s.split(',') + """Converts a comma-separated list of string integers to a python list of ints""" + lst = in_s.split(",") out_list = [] for i in lst: out_list.append(int(i)) return out_list + def auto_int(x): - '''Converts hex/dec argument values to an int''' + """Converts hex/dec argument values to an int""" return int(x, 0) + def parseArgs(): parser = argparse.ArgumentParser() - parser.add_argument('-o', dest='output_dir', type=str, default='vtest', - help='Test output directory') - - parser.add_argument('--seed', dest='random_seed', default=42, type=int, - help='Random seed for test generation') - - parser.add_argument('--filter', dest='filter', default='', type=str, - help='Filter operator test names by this expression') - - parser.add_argument('-v', '--verbose', dest='verbose', action='count', - help='Verbose operation') + parser.add_argument( + "-o", dest="output_dir", type=str, default="vtest", help="Test output directory" + ) + + parser.add_argument( + "--seed", + dest="random_seed", + default=42, + type=int, + help="Random seed for test generation", + ) + + parser.add_argument( + "--filter", + dest="filter", + default="", + type=str, + help="Filter operator test names by this expression", + ) + + parser.add_argument( + "-v", "--verbose", dest="verbose", action="count", help="Verbose operation" + ) # Constraints on tests - parser.add_argument('--tensor-dim-range', dest='tensor_shape_range', default='1,64', - type=lambda x: str_to_list(x), - help='Min,Max range of tensor shapes') - - parser.add_argument('--max-batch-size', dest='max_batch_size', default=1, type=int, - help='Maximum batch size for NHWC tests') - - parser.add_argument('--max-conv-padding', dest='max_conv_padding', default=1, type=int, - help='Maximum padding for Conv tests') - - parser.add_argument('--max-conv-dilation', dest='max_conv_dilation', default=2, type=int, - help='Maximum dilation for Conv tests') - - parser.add_argument('--max-conv-stride', dest='max_conv_stride', default=2, type=int, - help='Maximum stride for Conv tests') - - parser.add_argument('--max-pooling-padding', dest='max_pooling_padding', default=1, type=int, - help='Maximum padding for pooling tests') - - parser.add_argument('--max-pooling-stride', dest='max_pooling_stride', default=2, type=int, - help='Maximum stride for pooling tests') - - parser.add_argument('--max-pooling-kernel', dest='max_pooling_kernel', default=2, type=int, - help='Maximum padding for pooling tests') - - parser.add_argument('--num-rand-permutations', dest='num_rand_permutations', default=6, type=int, - help='Number of random permutations for a given shape/rank for randomly-sampled parameter spaces') + parser.add_argument( + "--tensor-dim-range", + dest="tensor_shape_range", + default="1,64", + type=lambda x: str_to_list(x), + help="Min,Max range of tensor shapes", + ) + + parser.add_argument( + "--max-batch-size", + dest="max_batch_size", + default=1, + type=int, + help="Maximum batch size for NHWC tests", + ) + + parser.add_argument( + "--max-conv-padding", + dest="max_conv_padding", + default=1, + type=int, + help="Maximum padding for Conv tests", + ) + + parser.add_argument( + "--max-conv-dilation", + dest="max_conv_dilation", + default=2, + type=int, + help="Maximum dilation for Conv tests", + ) + + parser.add_argument( + "--max-conv-stride", + dest="max_conv_stride", + default=2, + type=int, + help="Maximum stride for Conv tests", + ) + + parser.add_argument( + "--max-pooling-padding", + dest="max_pooling_padding", + default=1, + type=int, + help="Maximum padding for pooling tests", + ) + + parser.add_argument( + "--max-pooling-stride", + dest="max_pooling_stride", + default=2, + type=int, + help="Maximum stride for pooling tests", + ) + + parser.add_argument( + "--max-pooling-kernel", + dest="max_pooling_kernel", + default=2, + type=int, + help="Maximum padding for pooling tests", + ) + + parser.add_argument( + "--num-rand-permutations", + dest="num_rand_permutations", + default=6, + type=int, + help="Number of random permutations for a given shape/rank for randomly-sampled parameter spaces", + ) # Targetting a specific shape/rank/dtype - parser.add_argument('--target-shape', dest='target_shapes', action='append', default=[], type=lambda x: str_to_list(x), - help='Create tests with a particular input tensor shape, e.g., 1,4,4,8 (may be repeated for tests that require multiple input shapes)') - - parser.add_argument('--target-rank', dest='target_ranks', action='append', default=None, type=lambda x: auto_int(x), - help='Create tests with a particular input tensor rank') - - parser.add_argument('--target-dtype', dest='target_dtypes', action='append', default=None, type=lambda x: dtype_str_to_val(x), - help='Create test with a particular DType (may be repeated)') + parser.add_argument( + "--target-shape", + dest="target_shapes", + action="append", + default=[], + type=lambda x: str_to_list(x), + help="Create tests with a particular input tensor shape, e.g., 1,4,4,8 (may be repeated for tests that require multiple input shapes)", + ) + + parser.add_argument( + "--target-rank", + dest="target_ranks", + action="append", + default=None, + type=lambda x: auto_int(x), + help="Create tests with a particular input tensor rank", + ) + + parser.add_argument( + "--target-dtype", + dest="target_dtypes", + action="append", + default=None, + type=lambda x: dtype_str_to_val(x), + help="Create test with a particular DType (may be repeated)", + ) args = parser.parse_args() return args -def main(): +def main(): args = parseArgs() @@ -122,16 +205,23 @@ def main(): testList = [] for op in ttg.TOSA_OP_LIST: - if re.match(args.filter + '.*', op): - testList.extend(ttg.genOpTestList(op, shapeFilter=args.target_shapes, rankFilter=args.target_ranks, dtypeFilter=args.target_dtypes)) - - print('{} matching tests'.format(len(testList))) + if re.match(args.filter + ".*", op): + testList.extend( + ttg.genOpTestList( + op, + shapeFilter=args.target_shapes, + rankFilter=args.target_ranks, + dtypeFilter=args.target_dtypes, + ) + ) + + print("{} matching tests".format(len(testList))) for opName, testStr, dtype, shapeList, testArgs in testList: if args.verbose: print(testStr) ttg.serializeTest(opName, testStr, dtype, shapeList, testArgs) - print('Done creating {} tests'.format(len(testList))) + print("Done creating {} tests".format(len(testList))) -if __name__ == '__main__': +if __name__ == "__main__": exit(main()) diff --git a/verif/tosa_verif_run_ref.py b/verif/tosa_verif_run_ref.py index 2284e35..e3926ea 100755 --- a/verif/tosa_verif_run_ref.py +++ b/verif/tosa_verif_run_ref.py @@ -33,42 +33,90 @@ import importlib from enum import IntEnum, Enum, unique from datetime import datetime -# Include the ../shared directory in PYTHONPATH +# Include the ../scripts and ../scripts/xunit directory in PYTHONPATH parent_dir = os.path.dirname(os.path.realpath(__file__)) -sys.path.append(os.path.join(parent_dir, '..', 'scripts')) -sys.path.append(os.path.join(parent_dir, '..', 'scripts', 'xunit')) +sys.path.append(os.path.join(parent_dir, "..", "scripts")) +sys.path.append(os.path.join(parent_dir, "..", "scripts", "xunit")) + import xunit + +# Include the ../thirdparty/serialization_lib/python directory in PYTHONPATH +parent_dir = os.path.dirname(os.path.realpath(__file__)) +sys.path.append( + os.path.join(parent_dir, "..", "thirdparty", "serialization_lib", "python") +) import tosa from tosa_test_gen import TosaTestGen from tosa_test_runner import TosaTestRunner no_color_printing = False -#from run_tf_unit_test import LogColors, print_color, run_sh_command +# from run_tf_unit_test import LogColors, print_color, run_sh_command + def parseArgs(): parser = argparse.ArgumentParser() - parser.add_argument('-t', '--test', dest='test', type=str, nargs='+', - help='Test(s) to run') - parser.add_argument('--seed', dest='random_seed', default=42, type=int, - help='Random seed for test generation') - parser.add_argument('--ref-model-path', dest='ref_model_path', - default='build/reference_model/tosa_reference_model', type=str, - help='Path to reference model executable') - parser.add_argument('--ref-debug', dest='ref_debug', default='', type=str, - help='Reference debug flag (low, med, high)') - parser.add_argument('--ref-intermediates', dest='ref_intermediates', default=0, type=int, - help='Reference model dumps intermediate tensors') - parser.add_argument('-v', '--verbose', dest='verbose', action='count', - help='Verbose operation') - parser.add_argument('-j', '--jobs', dest='jobs', type=int, default=1, - help='Number of parallel jobs') - parser.add_argument('--sut-module', '-s', dest='sut_module', type=str, nargs='+', default=['tosa_ref_run'], - help='System under test module to load (derives from TosaTestRunner). May be repeated') - parser.add_argument('--sut-module-args', dest='sut_module_args', type=str, nargs='+', default=[], - help='System under test module arguments. Use sutmodulename:argvalue to pass an argument. May be repeated.') - parser.add_argument('--xunit-file', dest='xunit_file', type=str, default='result.xml', - help='XUnit output file') + parser.add_argument( + "-t", "--test", dest="test", type=str, nargs="+", help="Test(s) to run" + ) + parser.add_argument( + "--seed", + dest="random_seed", + default=42, + type=int, + help="Random seed for test generation", + ) + parser.add_argument( + "--ref-model-path", + dest="ref_model_path", + default="build/reference_model/tosa_reference_model", + type=str, + help="Path to reference model executable", + ) + parser.add_argument( + "--ref-debug", + dest="ref_debug", + default="", + type=str, + help="Reference debug flag (low, med, high)", + ) + parser.add_argument( + "--ref-intermediates", + dest="ref_intermediates", + default=0, + type=int, + help="Reference model dumps intermediate tensors", + ) + parser.add_argument( + "-v", "--verbose", dest="verbose", action="count", help="Verbose operation" + ) + parser.add_argument( + "-j", "--jobs", dest="jobs", type=int, default=1, help="Number of parallel jobs" + ) + parser.add_argument( + "--sut-module", + "-s", + dest="sut_module", + type=str, + nargs="+", + default=["tosa_ref_run"], + help="System under test module to load (derives from TosaTestRunner). May be repeated", + ) + parser.add_argument( + "--sut-module-args", + dest="sut_module_args", + type=str, + nargs="+", + default=[], + help="System under test module arguments. Use sutmodulename:argvalue to pass an argument. May be repeated.", + ) + parser.add_argument( + "--xunit-file", + dest="xunit_file", + type=str, + default="result.xml", + help="XUnit output file", + ) args = parser.parse_args() @@ -78,6 +126,7 @@ def parseArgs(): return args + def workerThread(task_queue, runnerList, args, result_queue): while True: try: @@ -88,21 +137,31 @@ def workerThread(task_queue, runnerList, args, result_queue): if test is None: break - msg = '' + msg = "" start_time = datetime.now() try: for runnerModule, runnerArgs in runnerList: if args.verbose: - print('Running runner {} with test {}'.format(runnerModule.__name__, test)) + print( + "Running runner {} with test {}".format( + runnerModule.__name__, test + ) + ) runner = runnerModule.TosaRefRunner(args, runnerArgs, test) try: rc = runner.runModel() except Exception as e: rc = TosaTestRunner.Result.INTERNAL_ERROR except Exception as e: - print('Internal regression error: {}'.format(e)) - print(''.join(traceback.format_exception(etype=type(e), value=e, tb=e.__traceback__))) + print("Internal regression error: {}".format(e)) + print( + "".join( + traceback.format_exception( + etype=type(e), value=e, tb=e.__traceback__ + ) + ) + ) rc = TosaTestRunner.Result.INTERNAL_ERROR end_time = datetime.now() @@ -112,25 +171,27 @@ def workerThread(task_queue, runnerList, args, result_queue): return True + def loadRefModules(args): # Returns a tuple of (runner_module, [argument list]) runnerList = [] for r in args.sut_module: if args.verbose: - print('Loading module {}'.format(r)) + print("Loading module {}".format(r)) runner = importlib.import_module(r) # Look for arguments associated with this runner - runnerArgPrefix = '{}:'.format(r) + runnerArgPrefix = "{}:".format(r) runnerArgList = [] for a in args.sut_module_args: if a.startswith(runnerArgPrefix): - runnerArgList.append(a[len(runnerArgPrefix):]) + runnerArgList.append(a[len(runnerArgPrefix) :]) runnerList.append((runner, runnerArgList)) return runnerList + def main(): args = parseArgs() @@ -143,10 +204,12 @@ def main(): for t in args.test: taskQueue.put((t)) - print('Running {} tests '.format(taskQueue.qsize())) + print("Running {} tests ".format(taskQueue.qsize())) for i in range(args.jobs): - t = threading.Thread(target=workerThread, args=(taskQueue, runnerList, args, resultQueue)) + t = threading.Thread( + target=workerThread, args=(taskQueue, runnerList, args, resultQueue) + ) t.setDaemon(True) t.start() threads.append(t) @@ -165,34 +228,40 @@ def main(): resultList.append((test, rc, msg, time_delta)) results[rc] = results[rc] + 1 - xunit_result = xunit.xunit_results('Regressions') - xunit_suite = xunit_result.create_suite('Unit tests') + xunit_result = xunit.xunit_results("Regressions") + xunit_suite = xunit_result.create_suite("Unit tests") # Sort by test name for test, rc, msg, time_delta in sorted(resultList, key=lambda tup: tup[0]): test_name = test - xt = xunit.xunit_test(test_name, 'reference') + xt = xunit.xunit_test(test_name, "reference") - xt.time = str(float(time_delta.seconds) + (float(time_delta.microseconds) * 1e-6)) + xt.time = str( + float(time_delta.seconds) + (float(time_delta.microseconds) * 1e-6) + ) - if rc == TosaTestRunner.Result.EXPECTED_PASS or rc == TosaTestRunner.Result.EXPECTED_FAILURE: + if ( + rc == TosaTestRunner.Result.EXPECTED_PASS + or rc == TosaTestRunner.Result.EXPECTED_FAILURE + ): if args.verbose: - print('{} {}'.format(rc.name, test_name)) + print("{} {}".format(rc.name, test_name)) else: xt.failed(msg) - print('{} {}'.format(rc.name, test_name)) + print("{} {}".format(rc.name, test_name)) xunit_suite.tests.append(xt) resultQueue.task_done() xunit_result.write_results(args.xunit_file) - print('Totals: ', end='') + print("Totals: ", end="") for result in TosaTestRunner.Result: - print('{} {}, '.format(results[result], result.name.lower()), end ='') + print("{} {}, ".format(results[result], result.name.lower()), end="") print() return 0 -if __name__ == '__main__': + +if __name__ == "__main__": exit(main()) |