MLBEDSW-4840 Move setting of input indices to tflite reader
Mapping to internal input indexing has been added to
tflite_reader.py and tosa_reader.py.
And the other way around in tflite_writer.py.
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I4d8596e747cfa7c4203884c4e785eb1977e2bcc1
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 0558e52..ffa4717 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -100,16 +100,16 @@
TensorIndices = namedtuple("TensorIndices", ["ifms", "weights", "biases"])
-NO_INDICES = TensorIndices([], [], [])
-IFM_INDICES = TensorIndices([0], [], [])
-IFM_WEIGHTS_INDICES = TensorIndices([0], [1], [])
-IFM_WEIGHTS_BIAS_INDICES = TensorIndices([0], [1], [2])
-IFM_IFM2_INDICES = TensorIndices([0, 1], [], [])
-CONV2D_BACKPROP_INDICES = TensorIndices([2], [1], [3])
-TRANSPOSE_CONV_INDICES = TensorIndices([0], [1], [3])
-CONCAT_INDICES = TensorIndices([1, 2], [], [])
-SPLIT_IFM_INDICES = TensorIndices([1], [], [])
-BLOCK_LSTM_INDICES = TensorIndices([3], [4], [])
+NNG_NO_INDICES = TensorIndices([], [], [])
+NNG_IFM_INDICES = TensorIndices([0], [], [])
+NNG_IFM_WEIGHTS_INDICES = TensorIndices([0], [1], [])
+NNG_IFM_WEIGHTS_BIAS_INDICES = TensorIndices([0], [1], [2])
+NNG_IFM_IFM2_INDICES = TensorIndices([0, 1], [], [])
+NNG_CONV2D_BACKPROP_INDICES = TensorIndices([2], [1], [3])
+NNG_TRANSPOSE_CONV_INDICES = TensorIndices([0], [1], [3])
+NNG_CONCAT_INDICES = TensorIndices([1, 2], [], [])
+NNG_SPLIT_IFM_INDICES = TensorIndices([1], [], [])
+NNG_BLOCK_LSTM_INDICES = TensorIndices([3], [4], [])
# Static information related to operation codes
@@ -117,7 +117,7 @@
__slots__ = ("id", "block_type", "indices", "is_unary")
_id = 0
- def __init__(self, block_type=NpuBlockType.Default, indices=NO_INDICES, is_unary=False):
+ def __init__(self, block_type=NpuBlockType.Default, indices=NNG_NO_INDICES, is_unary=False):
OperatorInfo._id += 1
self.id = OperatorInfo._id
self.block_type = block_type
@@ -127,37 +127,38 @@
# Internally used operation codes
class Op(Enum):
- Abs = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_INDICES, is_unary=True)
- Add = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES)
+ Abs = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_INDICES, is_unary=True)
+ Add = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
AddN = OperatorInfo()
Any = OperatorInfo()
ArgMax = OperatorInfo()
ArgMin = OperatorInfo()
- AvgPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=IFM_INDICES)
+ AvgPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=NNG_IFM_INDICES)
BatchMatMul = OperatorInfo()
BatchToSpaceND = OperatorInfo()
- BidirectionalSequenceLstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
- BidirectionalSequenceRnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
- BlockLSTM = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=BLOCK_LSTM_INDICES)
+ BidirectionalSequenceLstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
+ BidirectionalSequenceRnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
+ BlockLSTM = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_BLOCK_LSTM_INDICES)
CLZ = OperatorInfo(
- block_type=NpuBlockType.ElementWise, indices=IFM_INDICES, is_unary=True
+ block_type=NpuBlockType.ElementWise, indices=NNG_IFM_INDICES, is_unary=True
) # NPU specific operation
Call = OperatorInfo()
Cast = OperatorInfo()
Ceil = OperatorInfo()
+ Clamp = OperatorInfo(indices=NNG_IFM_INDICES) # TOSA specific
Clip = OperatorInfo() # NPU specific fused activation function for clipping between activation.min/max
- Concat = OperatorInfo(indices=CONCAT_INDICES)
+ Concat = OperatorInfo(indices=NNG_CONCAT_INDICES)
ConcatEmbeddings = OperatorInfo()
- ConcatSliceWrite = OperatorInfo(indices=IFM_INDICES)
- ConcatTFLite = OperatorInfo(indices=CONCAT_INDICES)
+ ConcatSliceWrite = OperatorInfo(indices=NNG_IFM_INDICES)
+ ConcatTFLite = OperatorInfo(indices=NNG_CONCAT_INDICES)
Const = OperatorInfo() # Constant tensor, only used in CPU subgraphs
- Conv2D = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=IFM_WEIGHTS_INDICES)
- Conv2DBackpropInput = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=CONV2D_BACKPROP_INDICES)
+ Conv2D = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=NNG_IFM_WEIGHTS_INDICES)
+ Conv2DBackpropInput = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=NNG_CONV2D_BACKPROP_INDICES)
Conv2DBackpropInputSwitchedBias = OperatorInfo(
- block_type=NpuBlockType.ConvolutionMxN, indices=TRANSPOSE_CONV_INDICES
+ block_type=NpuBlockType.ConvolutionMxN, indices=NNG_TRANSPOSE_CONV_INDICES
)
- Conv2DBias = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=IFM_WEIGHTS_BIAS_INDICES)
+ Conv2DBias = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=NNG_IFM_WEIGHTS_BIAS_INDICES)
Cos = OperatorInfo()
Cumsum = OperatorInfo()
Custom = OperatorInfo() # Custom 3rd party operator, only used in CPU subgraphs
@@ -165,26 +166,28 @@
Delegate = OperatorInfo()
Densify = OperatorInfo()
DepthToSpace = OperatorInfo()
- DepthwiseConv2DBias = OperatorInfo(block_type=NpuBlockType.ConvolutionDepthWise, indices=IFM_WEIGHTS_BIAS_INDICES)
- Dequantize = OperatorInfo(indices=IFM_INDICES)
+ DepthwiseConv2DBias = OperatorInfo(
+ block_type=NpuBlockType.ConvolutionDepthWise, indices=NNG_IFM_WEIGHTS_BIAS_INDICES
+ )
+ Dequantize = OperatorInfo(indices=NNG_IFM_INDICES)
Div = OperatorInfo()
Elu = OperatorInfo()
EmbeddingLookup = OperatorInfo()
EmbeddingLookupSparse = OperatorInfo()
Equal = OperatorInfo()
Exp = OperatorInfo()
- ExpandDims = OperatorInfo(indices=IFM_INDICES)
+ ExpandDims = OperatorInfo(indices=NNG_IFM_INDICES)
FakeQuantWithMinMaxArgs = OperatorInfo()
Fill = OperatorInfo()
Floor = OperatorInfo()
FloorDiv = OperatorInfo()
FloorMod = OperatorInfo()
- FullyConnected = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_BIAS_INDICES)
+ FullyConnected = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_BIAS_INDICES)
GatherNd = OperatorInfo()
GatherV2 = OperatorInfo()
Greater = OperatorInfo()
GreaterEqual = OperatorInfo()
- HardSwish = OperatorInfo(indices=IFM_INDICES)
+ HardSwish = OperatorInfo(indices=NNG_IFM_INDICES)
HashtableLookup = OperatorInfo()
Identity = OperatorInfo()
If = OperatorInfo()
@@ -192,7 +195,7 @@
L2Pool2D = OperatorInfo()
LRN = OperatorInfo()
LSHProjection = OperatorInfo()
- LeakyRelu = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_INDICES, is_unary=True)
+ LeakyRelu = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_INDICES, is_unary=True)
Less = OperatorInfo()
LessEqual = OperatorInfo()
Log = OperatorInfo()
@@ -200,92 +203,92 @@
LogicalAnd = OperatorInfo()
LogicalNot = OperatorInfo()
LogicalOr = OperatorInfo()
- Lstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
+ Lstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
LUT = OperatorInfo() # NPU specific, operator has LUT, only used in fused activation functions
- MatMul = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
+ MatMul = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
MatrixDiag = OperatorInfo()
MatrixSetDiag = OperatorInfo()
Max = OperatorInfo()
- MaxPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=IFM_INDICES)
- Maximum = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES)
- Mean = OperatorInfo(indices=IFM_INDICES)
+ MaxPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=NNG_IFM_INDICES)
+ Maximum = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
+ Mean = OperatorInfo(indices=NNG_IFM_INDICES)
Min = OperatorInfo()
- Minimum = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES)
+ Minimum = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
MirrorPad = OperatorInfo()
- Mul = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES)
+ Mul = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
Neg = OperatorInfo()
NonMaxSuppressionV4 = OperatorInfo()
NonMaxSuppressionV5 = OperatorInfo()
NotEqual = OperatorInfo()
OneHot = OperatorInfo()
- Pack = OperatorInfo(indices=IFM_INDICES)
- PackReshaped = OperatorInfo(indices=IFM_INDICES)
- Pad = OperatorInfo(indices=IFM_INDICES)
+ Pack = OperatorInfo(indices=NNG_IFM_INDICES)
+ PackReshaped = OperatorInfo(indices=NNG_IFM_INDICES)
+ Pad = OperatorInfo(indices=NNG_IFM_INDICES)
PadV2 = OperatorInfo()
Placeholder = OperatorInfo() # Only used in CPU subgraphs
Pow = OperatorInfo()
Prelu = OperatorInfo()
Prod = OperatorInfo()
- Quantize = OperatorInfo(indices=IFM_INDICES)
- QuantizedAvgPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=IFM_INDICES)
- QuantizedConv2D = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=IFM_WEIGHTS_INDICES)
- QuantizedMatMul = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
- QuantizedMaxPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=IFM_INDICES)
- QuantizedReshape = OperatorInfo(indices=IFM_INDICES)
+ Quantize = OperatorInfo(indices=NNG_IFM_INDICES)
+ QuantizedAvgPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=NNG_IFM_INDICES)
+ QuantizedConv2D = OperatorInfo(block_type=NpuBlockType.ConvolutionMxN, indices=NNG_IFM_WEIGHTS_INDICES)
+ QuantizedMatMul = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
+ QuantizedMaxPool = OperatorInfo(block_type=NpuBlockType.Pooling, indices=NNG_IFM_INDICES)
+ QuantizedReshape = OperatorInfo(indices=NNG_IFM_INDICES)
Range = OperatorInfo()
Rank = OperatorInfo()
- ReduceSum = OperatorInfo(block_type=NpuBlockType.ReduceSum, indices=IFM_INDICES)
- Relu = OperatorInfo(indices=IFM_INDICES)
- Relu6 = OperatorInfo(indices=IFM_INDICES)
- ReluN1To1 = OperatorInfo(indices=IFM_INDICES)
- ReluN = OperatorInfo(indices=IFM_INDICES) # TOSA specific
- Rescale = OperatorInfo(indices=IFM_INDICES) # TOSA specific
- RescaleAdd = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES)
- Reshape = OperatorInfo(indices=IFM_INDICES)
- ResizeBilinear = OperatorInfo(block_type=NpuBlockType.Pooling, indices=IFM_INDICES)
+ ReduceSum = OperatorInfo(block_type=NpuBlockType.ReduceSum, indices=NNG_IFM_INDICES)
+ Relu = OperatorInfo(indices=NNG_IFM_INDICES)
+ Relu6 = OperatorInfo(indices=NNG_IFM_INDICES)
+ ReluN1To1 = OperatorInfo(indices=NNG_IFM_INDICES)
+ ReluN = OperatorInfo(indices=NNG_IFM_INDICES) # TOSA specific
+ Rescale = OperatorInfo(indices=NNG_IFM_INDICES) # TOSA specific
+ RescaleAdd = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
+ Reshape = OperatorInfo(indices=NNG_IFM_INDICES)
+ ResizeBilinear = OperatorInfo(block_type=NpuBlockType.Pooling, indices=NNG_IFM_INDICES)
ResizeNearestNeighbor = OperatorInfo()
ReverseSequence = OperatorInfo()
ReverseV2 = OperatorInfo()
- Rnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
+ Rnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
Round = OperatorInfo()
Rsqrt = OperatorInfo()
- SHL = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES) # NPU specific operation
- SHR = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES) # NPU specific operation
+ SHL = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES) # NPU specific operation
+ SHR = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES) # NPU specific operation
ScatterNd = OperatorInfo()
SegmentSum = OperatorInfo()
Select = OperatorInfo()
SelectV2 = OperatorInfo()
Shape = OperatorInfo()
- Sigmoid = OperatorInfo(indices=IFM_INDICES)
+ Sigmoid = OperatorInfo(indices=NNG_IFM_INDICES)
SignBit = OperatorInfo()
Sin = OperatorInfo()
SkipGram = OperatorInfo()
- Slice = OperatorInfo(indices=IFM_INDICES)
- Softmax = OperatorInfo(indices=IFM_INDICES)
+ Slice = OperatorInfo(indices=NNG_IFM_INDICES)
+ Softmax = OperatorInfo(indices=NNG_IFM_INDICES)
SpaceToBatchND = OperatorInfo()
SpaceToDepth = OperatorInfo()
SparseToDense = OperatorInfo()
- Split = OperatorInfo(indices=SPLIT_IFM_INDICES)
- SplitSliceRead = OperatorInfo(indices=IFM_INDICES)
- SplitV = OperatorInfo(indices=IFM_INDICES)
+ Split = OperatorInfo(indices=NNG_SPLIT_IFM_INDICES)
+ SplitSliceRead = OperatorInfo(indices=NNG_IFM_INDICES)
+ SplitV = OperatorInfo(indices=NNG_IFM_INDICES)
Sqrt = OperatorInfo()
Square = OperatorInfo()
SquaredDifference = OperatorInfo()
- Squeeze = OperatorInfo(indices=IFM_INDICES)
- StridedSlice = OperatorInfo(indices=IFM_INDICES)
- Sub = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=IFM_IFM2_INDICES)
+ Squeeze = OperatorInfo(indices=NNG_IFM_INDICES)
+ StridedSlice = OperatorInfo(indices=NNG_IFM_INDICES)
+ Sub = OperatorInfo(block_type=NpuBlockType.ElementWise, indices=NNG_IFM_IFM2_INDICES)
SubgraphInput = OperatorInfo() # Only used in CPU subgraphs
Sum = OperatorInfo()
Svdf = OperatorInfo()
- Tanh = OperatorInfo(indices=IFM_INDICES)
+ Tanh = OperatorInfo(indices=NNG_IFM_INDICES)
Tile = OperatorInfo()
TopKV2 = OperatorInfo()
Transpose = OperatorInfo()
- UnidirectionalSequenceLstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
- UnidirectionalSequenceRnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=IFM_WEIGHTS_INDICES)
+ UnidirectionalSequenceLstm = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
+ UnidirectionalSequenceRnn = OperatorInfo(block_type=NpuBlockType.VectorProduct, indices=NNG_IFM_WEIGHTS_INDICES)
Unique = OperatorInfo()
- Unpack = OperatorInfo(indices=IFM_INDICES)
- UnpackReshaped = OperatorInfo(indices=IFM_INDICES)
+ Unpack = OperatorInfo(indices=NNG_IFM_INDICES)
+ UnpackReshaped = OperatorInfo(indices=NNG_IFM_INDICES)
Where = OperatorInfo()
While = OperatorInfo()
ZerosLike = OperatorInfo()
@@ -323,7 +326,7 @@
return self.info.block_type == NpuBlockType.ElementWise and not self.info.is_unary
def is_relu_op(self):
- return self in (Op.Relu, Op.Relu6, Op.ReluN1To1, Op.ReluN, Op.Clip)
+ return self in (Op.Relu, Op.Relu6, Op.ReluN1To1, Op.ReluN, Op.Clip, Op.Clamp)
def is_activation_op(self):
return self.is_relu_op() or self in (Op.Tanh, Op.Sigmoid, Op.Softmax, Op.LUT, Op.HardSwish)
@@ -408,7 +411,7 @@
act.max = 1.0
elif op_type == Op.HardSwish:
act.min = 0.0
- if op_type == Op.Clip:
+ if op_type == Op.Clamp:
assert min is not None and max is not None
act.min = min
act.max = max