IVGCVSW-5325 Fix non-channel per axis quantization
Signed-off-by: Finn Williams <Finn.Williams@arm.com>
Change-Id: Ie0cf69b2cd76d6ecedab43d3d9ae267d23bbc052
diff --git a/src/backends/reference/workloads/BaseIterator.hpp b/src/backends/reference/workloads/BaseIterator.hpp
index 0165ec7..a10f383 100644
--- a/src/backends/reference/workloads/BaseIterator.hpp
+++ b/src/backends/reference/workloads/BaseIterator.hpp
@@ -45,9 +45,10 @@
virtual IType Get() const = 0;
- virtual std::vector<float> DecodeTensor(uint32_t size,
- uint32_t channelStep = 1,
- uint32_t channelMultiplier = 1) = 0;
+ virtual std::vector<float>
+ DecodeTensor(const TensorShape &tensorShape,
+ const unsigned int channelMultiplier = 1,
+ bool isDepthwise = false) = 0;
};
template<typename IType>
@@ -133,11 +134,13 @@
{
return armnn::Dequantize(*m_Iterator, m_Scale, m_Offset);
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -169,11 +172,13 @@
{
return armnn::Dequantize(*m_Iterator, m_Scale, m_Offset);
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -205,11 +210,13 @@
{
return armnn::Dequantize(*m_Iterator, m_Scale, m_Offset);
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -241,13 +248,13 @@
{
return armnn::Dequantize(*m_Iterator, m_Scale, m_Offset);
}
-
-
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -281,11 +288,13 @@
armnnUtils::FloatingPointConverter::ConvertBFloat16ToFloat32(m_Iterator, 1, &val);
return val;
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -318,11 +327,13 @@
armnnUtils::FloatingPointConverter::ConvertFloat16To32(m_Iterator, 1, &val);
return val;
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -353,9 +364,12 @@
{
return *m_Iterator;
}
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -378,11 +392,13 @@
{
return static_cast<float>(*m_Iterator) * m_Scale;
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -413,11 +429,13 @@
{
return static_cast<float>(*m_Iterator);
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -444,11 +462,13 @@
{
return *m_Iterator;
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -475,11 +495,13 @@
{
return *m_Iterator;
}
-
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor (const TensorShape& tensorShape,
+ const unsigned int channelMultiplier,
+ const bool isDepthwise) override
{
- IgnoreUnused(channelStepSize, channelMultiplier);
+ IgnoreUnused(channelMultiplier, isDepthwise);
+ const unsigned int size = tensorShape.GetNumElements();
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
@@ -782,42 +804,49 @@
{
public:
QSymm8PerAxisDecoder(const int8_t* data, const std::vector<float>& scale, unsigned int axisFactor)
- : PerAxisIterator(data, axisFactor), m_Scale(scale) {}
+ : PerAxisIterator(data, axisFactor), m_Scales(scale) {}
float Get() const override
{
- return armnn::Dequantize(*m_Iterator, m_Scale[m_AxisIndex], 0);
+ return armnn::Dequantize(*m_Iterator, m_Scales[m_AxisIndex], 0);
}
// Get scale of the current value
float GetScale() const
{
- return m_Scale[m_AxisIndex];
+ return m_Scales[m_AxisIndex];
}
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor(const TensorShape &tensorShape,
+ const unsigned int channelMultiplier,
+ bool isDepthwise) override
{
- uint32_t channels = static_cast<uint32_t>(m_Scale.size());
- uint32_t channelSteps = size / (channelStepSize * channelMultiplier);
+ const uint32_t size = tensorShape.GetNumElements();
+ const uint32_t scaleSize = static_cast<uint32_t>(m_Scales.size());
+
+ const uint32_t stepSize = isDepthwise ?
+ tensorShape[2] * tensorShape[3] : tensorShape.GetNumElements() / tensorShape[0];
+
+ const uint32_t stepNum = size / (stepSize * channelMultiplier);
uint32_t scale;
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
- // channelMultiplier is only used in depthwise convolutions and in other cases will cancel out
- // channelStepSize is the length of a contiguous section of a channel within a tensor
- // channelSteps is the number of those steps/blocks in the tensor
+ // channelMultiplier is only used in depthwise convolutions and in other cases will have no effect
+ // stepSize is the length of a contiguous area sharing a quantization scale within a tensor
+ // stepNum is the number of those steps/blocks in the tensor
for (uint32_t mult = 0; mult < channelMultiplier; ++mult)
{
- for (uint32_t channelStep = 0; channelStep < channelSteps; ++channelStep)
+ for (uint32_t step = 0; step < stepNum; ++step)
{
- scale = (channelMultiplier * channelStep + mult) % channels;
- for (uint32_t i = 0; i < channelStepSize; ++i)
+ scale = (channelMultiplier * step + mult) % scaleSize;
+ for (uint32_t i = 0; i < stepSize; ++i)
{
- unsigned int index = mult * channelStepSize * channelMultiplier +
- channelStep * channelStepSize + i;
+ unsigned int index = mult * stepSize * channelMultiplier +
+ step * stepSize + i;
this->operator[](index);
- decodedTensor.emplace_back(armnn::Dequantize(*m_Iterator, m_Scale[scale], 0));
+ decodedTensor.emplace_back(armnn::Dequantize(*m_Iterator, m_Scales[scale], 0));
}
}
}
@@ -825,7 +854,7 @@
}
private:
- std::vector<float> m_Scale;
+ std::vector<float> m_Scales;
};
class QSymm8PerAxisEncoder : public PerAxisIterator<int8_t, Encoder<float>>
@@ -871,27 +900,34 @@
return m_Scales[m_AxisIndex];
}
- std::vector<float> DecodeTensor(uint32_t size, uint32_t channelStepSize, uint32_t channelMultiplier) override
+ std::vector<float> DecodeTensor(const TensorShape &tensorShape,
+ const unsigned int channelMultiplier,
+ bool isDepthwise) override
{
- uint32_t channels = static_cast<uint32_t>(m_Scales.size());
- uint32_t channelSteps = size / (channelStepSize * channelMultiplier);
+ const uint32_t size = tensorShape.GetNumElements();
+ const uint32_t scaleSize = static_cast<uint32_t>(m_Scales.size());
+
+ const uint32_t stepSize = isDepthwise ?
+ tensorShape[2] * tensorShape[3] : tensorShape.GetNumElements() / tensorShape[0];
+
+ const uint32_t stepNum = size / (stepSize * channelMultiplier);
uint32_t scale;
std::vector<float> decodedTensor;
decodedTensor.reserve(size);
- // channelMultiplier is only used in depthwise convolutions and in other cases will cancel out
- // channelStepSize is the length of a contiguous section of a channel within a tensor
- // channelSteps is the number of those steps/blocks in the tensor
+ // channelMultiplier is only used in depthwise convolutions and in other cases will have no effect
+ // stepSize is the length of a contiguous area sharing a quantization scale within a tensor
+ // stepNum is the number of those steps/blocks in the tensor
for (uint32_t mult = 0; mult < channelMultiplier; ++mult)
{
- for (uint32_t channelStep = 0; channelStep < channelSteps; ++channelStep)
+ for (uint32_t step = 0; step < stepNum; ++step)
{
- scale = (channelMultiplier * channelStep + mult) % channels;
- for (uint32_t i = 0; i < channelStepSize; ++i)
+ scale = (channelMultiplier * step + mult) % scaleSize;
+ for (uint32_t i = 0; i < stepSize; ++i)
{
- unsigned int index = mult * channelStepSize * channelMultiplier +
- channelStep * channelStepSize + i;
+ unsigned int index = mult * stepSize * channelMultiplier +
+ step * stepSize + i;
this->operator[](index);
decodedTensor.emplace_back(armnn::Dequantize(*m_Iterator, m_Scales[scale], 0));
}
diff --git a/src/backends/reference/workloads/ConvImpl.cpp b/src/backends/reference/workloads/ConvImpl.cpp
index f11c351..d784553 100644
--- a/src/backends/reference/workloads/ConvImpl.cpp
+++ b/src/backends/reference/workloads/ConvImpl.cpp
@@ -108,30 +108,11 @@
const unsigned int filterHeight = depthwise ? rFilterShape[2] : rFilterShape[heightIndex];
const unsigned int filterWidth = depthwise ? rFilterShape[3] : rFilterShape[widthIndex];
- const std::vector<float> inputVec = rInputDecoder.DecodeTensor(rInputShape.GetNumElements());
+ const std::vector<float> inputVec = rInputDecoder.DecodeTensor(rInputShape);
+ const std::vector<float> filterVec = rFilterDecoder.DecodeTensor(rFilterShape, depthMultiplier, depthwise);
- uint32_t channelStepSize;
- if (depthwise)
- {
- channelStepSize = filterHeight * filterWidth;
- }
- else
- {
- if (dataLayoutIndexed.GetDataLayout() == DataLayout::NHWC)
- {
- channelStepSize = rFilterShape[3];
- }
- else
- {
- channelStepSize = rFilterShape[1] * rFilterShape[2] * rFilterShape[3];
- }
- }
-
- const std::vector<float> filterVec = rFilterDecoder.DecodeTensor(rFilterShape.GetNumElements(),
- channelStepSize,
- depthMultiplier);
- const std::vector<float> biasVec = biasEnabled ?
- pBiasDecoder->DecodeTensor(outputChannels) : std::vector<float>();
+ const TensorShape biasShape{outputChannels};
+ const std::vector<float> biasVec = biasEnabled ? pBiasDecoder->DecodeTensor(biasShape) : std::vector<float>();
unsigned int depthwiseMultiplierIdx = 0;
for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
diff --git a/src/backends/reference/workloads/FullyConnected.cpp b/src/backends/reference/workloads/FullyConnected.cpp
index 61c8e88..9ec9ea6 100644
--- a/src/backends/reference/workloads/FullyConnected.cpp
+++ b/src/backends/reference/workloads/FullyConnected.cpp
@@ -24,10 +24,11 @@
// Perform FullyConnected implementation
unsigned int outputSize = rOutputShape[1];
- const std::vector<float> decodedInputs = rInputDecoder.DecodeTensor(rInputShape.GetNumElements());
- const std::vector<float> decodedWeights = rWeightDecoder.DecodeTensor(rWeightsShape.GetNumElements());
- const std::vector<float> decodedBiases = biasEnabled ?
- rBiasDecoder.DecodeTensor(outputSize) : std::vector<float>();
+ const std::vector<float> decodedInputs = rInputDecoder.DecodeTensor(rInputShape);
+ const std::vector<float> decodedWeights = rWeightDecoder.DecodeTensor(rWeightsShape);
+
+ const TensorShape biasShape{outputSize};
+ const std::vector<float> decodedBiases = biasEnabled ? rBiasDecoder.DecodeTensor(biasShape) : std::vector<float>();
for (unsigned int n = 0; n < rInputShape[0]; n++)
diff --git a/src/backends/reference/workloads/Pooling2d.cpp b/src/backends/reference/workloads/Pooling2d.cpp
index 2bc3b4f..be6ff38 100644
--- a/src/backends/reference/workloads/Pooling2d.cpp
+++ b/src/backends/reference/workloads/Pooling2d.cpp
@@ -180,7 +180,7 @@
throw armnn::InvalidArgumentException("Unsupported padding type");
}
- const std::vector<float> decodedInputVec = rInputDecoder.DecodeTensor(inputInfo.GetNumElements());
+ const std::vector<float> decodedInputVec = rInputDecoder.DecodeTensor(inputInfo.GetShape());
for (int n = 0; n < batchSize; n++)
{
diff --git a/src/backends/reference/workloads/TransposeConvolution2d.cpp b/src/backends/reference/workloads/TransposeConvolution2d.cpp
index c34a309..7408e92 100644
--- a/src/backends/reference/workloads/TransposeConvolution2d.cpp
+++ b/src/backends/reference/workloads/TransposeConvolution2d.cpp
@@ -52,12 +52,8 @@
std::vector<float> outputBuffer(outputShape.GetNumElements(), 0);
- const std::vector<float> inputVec = inputDecoder.DecodeTensor(inputShape.GetNumElements());
-
- const unsigned channelStep = weightsWidth * weightsHeight * weightsDepth;
-
- const std::vector<float> filterVec =
- weightsDecoder.DecodeTensor(weightsShape.GetNumElements(), channelStep);
+ const std::vector<float> inputVec = inputDecoder.DecodeTensor(inputShape);
+ const std::vector<float> filterVec = weightsDecoder.DecodeTensor(weightsShape);
for (unsigned int batch = 0u; batch < numBatches; ++batch)
{