// binary zip operation, e.g. Plus
// If allowBroadcast then one can be a sub-dimension of the other (if layout then only for rows, otherwise for cols, too).
// This also helpfully resizes the children if not yet sized.
void ComputationNodeBase::ValidateBinaryZip(bool isFinalValidationPass, bool allowBroadcast)
{
assert(m_inputs.size() == 2);
ComputationNodeBase::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
ValidateInferBinaryInputDims();
if (isFinalValidationPass)
ValidateMBLayout(Input(0), Input(1));
// result has tensor shape with dimensions being the max over both
let shape0 = GetInputSampleLayout(0);
let shape1 = GetInputSampleLayout(1);
SmallVector<size_t> dims = shape0.GetDims();
if (shape1.GetRank() > dims.size())
dims.resize(shape1.GetRank(), 1); // pad with ones
// If rank of [0] is higher than we only need to take max over rank [1].
// If rank of [1] is higher then we have padded to equal lentgh.
for (size_t k = 0; k < shape1.GetRank(); k++)
{
size_t dim1 = shape1[k];
// BUGBUG: We must consider the allowBroadcast flag here.
if (dims[k] <= 1 && dim1 != 0) // is [0] broadcasting (1) or unspecified (0)?
dims[k] = dim1; // then use dimension we broadcast to
else if (dim1 <= 1 && dims[k] != 0) // if [1] is broadcasting or unspecified
; // then dims is already correct
else if (isFinalValidationPass && dim1 != dims[k]) // no broadcasting or unspecified: they must match
InvalidArgument("%ls: Input dimensions [%s] and [%s] are not compatible.",
NodeDescription().c_str(), string(shape0).c_str(), string(shape1).c_str());
}
SetDims(TensorShape(dims), HasMBLayout());
}
// binary zip operation, e.g. Plus
// If allowBroadcast then one can be a sub-dimension of the other (if layout then only for rows, otherwise for cols, too).
// This also helpfully resizes the children if not yet sized.
void ComputationNodeBase::ValidateBinaryZip(bool isFinalValidationPass, bool allowBroadcast)
{
assert(m_inputs.size() == 2);
ComputationNodeBase::Validate(isFinalValidationPass);
InferMBLayoutFromInputsForStandardCase(isFinalValidationPass);
ValidateInferBinaryInputDims();
if (isFinalValidationPass &&
Input(0)->GetMBLayout() != Input(1)->GetMBLayout() && Input(0)->HasMBLayout() && Input(1)->HasMBLayout())
{
LogicError("%ls: Minibatch layouts are not the same between arguments and might get out of sync during runtime. If this is by design, use ReconcileDynamicAxis() to forward layouts between nodes.", NodeDescription().c_str());
}
// result has tensor shape with dimensions being the max over both
let shape0 = GetInputSampleLayout(0);
let shape1 = GetInputSampleLayout(1);
SmallVector<size_t> dims = shape0.GetDims();
if (shape1.GetRank() > dims.size())
dims.resize(shape1.GetRank(), 1); // pad with ones
// If rank of [0] is higher than we only need to take max over rank [1].
// If rank of [1] is higher then we have padded to equal lentgh.
for (size_t k = 0; k < shape1.GetRank(); k++)
{
size_t dim1 = shape1[k];
// BUGBUG: We must consider the allowBroadcast flag here.
if (dims[k] == 1) // is [0] broadcasting?
dims[k] = dim1; // then use dimension we broadcast to
else if (dim1 == 1) // if [1] is broadcasting
; // dims is already correct
else if (isFinalValidationPass && dim1 != dims[k]) // no broadcasting: they must match
InvalidArgument("%ls: Input dimensions [%s] and [%s] are not compatible.",
NodeDescription().c_str(), string(shape0).c_str(), string(shape1).c_str());
}
SetDims(TensorShape(dims), HasMBLayout());
}
// binary reduce-to-(1,1) operation, e.g. CrossEntropyWithSoftmaxNode
// Currently only called by criterion nodes.
// This function also infers child LearnableParameters. In case you wonder why this is needed for criterion nodes, there are edge cases, e.g. a
// learnable parameter being regularized by a criterion node, where the learnable parameter is fed both into that criterion node and other places.
void ComputationNodeBase::ValidateBinaryReduce(bool isFinalValidationPass)
{
ComputationNodeBase::Validate(isFinalValidationPass);
m_pMBLayout = nullptr; // this node does not hold mini-batch data
ValidateInferBinaryInputDims();
if (isFinalValidationPass)
{
if (!(Input(0)->GetSampleLayout().IsElementwiseCompatibleWith(Input(1)->GetSampleLayout())))
{
string s1 = Input(0)->GetSampleLayout();
string s2 = Input(1)->GetSampleLayout();
// BUGBUG: Allow broadcasting?
LogicError("%ls: The tensor dimensions in the inputs do not match. %s != %s", NodeDescription().c_str(), s1.c_str(), s2.c_str());
}
else if (!(Input(0)->HasMBLayout()))
LogicError("%ls: Expected MBLayout in Input 0.", NodeDescription().c_str());
else if (!(Input(1)->HasMBLayout()))
LogicError("%ls: Expected MBLayout in Input 1.", NodeDescription().c_str());
// Shape of the MBLayouts is checked at runtime.
}
SetDims(TensorShape(1), false);
}
