const std::string ComputationNodeBase::ShapeDescription() const { return msra::strfun::strprintf("[%s%s%ls]", string(m_sampleLayout).c_str(), HasMBLayout() ? " x " : "", HasMBLayout() ? GetMBLayout()->GetAxisName() : L""); }
/*virtual*/ void GatherPackedNode<ElemType>::Validate(bool isFinalValidationPass) /*override*/ { ComputationNodeBase::Validate(isFinalValidationPass); // inherit MBLayout from indexData m_pMBLayout = Input(INDEXDATA)->GetMBLayout(); if (isFinalValidationPass && (!Input(INDEXDATA)->HasMBLayout())) LogicError("%ls requires first argument (index data) to have a time dimension.", NodeDescription().c_str()); bool sourceHasTimeDimension = Input(SOURCEDATA)->HasMBLayout(); if (isFinalValidationPass && Input(INDEXDATA)->GetSampleLayout().GetNumElements() != 1) InvalidArgument("%ls requires the first argument (index data) to be a scalar time sequence.", NodeDescription().c_str()); // inherit tensor dimension from sourceData, minus the last (column or time) dimension. TODO this needs to become simpler... if (sourceHasTimeDimension) SetDims(Input(SOURCEDATA)->GetSampleLayout(), HasMBLayout()); else { SmallVector<size_t> layout = { 1 }; // Scalar if (Input(SOURCEDATA)->GetSampleLayout().GetRank() > 1) { auto srcLayout = Input(SOURCEDATA)->GetSampleLayout().GetDims(); layout.assign(srcLayout.begin(), srcLayout.end() - 1); } SetDims(TensorShape(layout), 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) 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()); }
// same as GetTensorSliceFor() except that 'fr' refers to a single column, and result will not have seq/time axes // This is needed by TimesNode when the left argument has to be broken up into individual matrices/GEMM calls. // To enable its first argument to have an MBLayout, it needs to un-pad if we have an MBLayout but only refer to a single sequence and time step. TensorShape ComputationNodeBase::GetOneSampleTensorSliceFor(size_t rank, const FrameRange& fr) const { TensorShape result = GetTensorSliceFor(rank, fr); // undo the adding of (seq, time) axes that was done by GetTensorShape() if (!fr.IsOneColumnWrt(GetMBLayout())) LogicError("GetOneSampleTensorSliceFor: Requires 'fr' to refer to a single sample."); if (HasMBLayout()) result.TrimRankInPlace(rank); // Note: This function will verify once again that the extra dimensions have been reduced to [1 x 1] return result; }
// form the actual tensor that describes the full object TensorShape ComputationNodeBase::GetTensorShape(size_t rank) const { // If we have an MB layout then add the necessary sequence and time axes. If we have none, then absorb the column dimension. TensorShape tensorShape = GetSampleLayout(); // TODO: Do we need to expect this tensor to have arbitrary strides? In case it came out of a Slice, Reshape, or Transpose op in-place? if (HasMBLayout()) { size_t i = (rank != SIZE_MAX) ? rank : tensorShape.GetRank(); tensorShape.AppendInPlace(i++, GetMBLayout()->GetNumParallelSequences()); tensorShape.AppendInPlace(i++, GetMBLayout()->GetNumTimeSteps()); } return tensorShape; }
/*virtual*/ void PackedIndexNode<ElemType>::Validate(bool isFinalValidationPass) /*override*/ { ComputationNodeBase::Validate(isFinalValidationPass); // inherit both MBLayout and sample dimension (scalar) from indexData // Because we map (per-seq) index sequence to (packed) index sequence. Target is only for index calculation. m_pMBLayout = Input(INDEXDATA)->GetMBLayout(); if (isFinalValidationPass && (!Input(INDEXDATA)->HasMBLayout() || !Input(SOURCEDATA)->HasMBLayout())) LogicError("%ls %ls operation requires both inputs to be minibatch data (must have MBLayouts).", NodeName().c_str(), OperationName().c_str()); if (isFinalValidationPass && Input(INDEXDATA)->GetSampleLayout().GetNumElements() != 1) InvalidArgument("%ls %ls operation requires the second argument (indexData) to be a scalar sequence.", NodeName().c_str(), OperationName().c_str()); SetDims(Input(INDEXDATA)->GetSampleLayout(), HasMBLayout()); }
/*virtual*/ void GatherPackedNode<ElemType>::Validate(bool isFinalValidationPass) /*override*/ { ComputationNodeBase::Validate(isFinalValidationPass); // inherit MBLayout from indexData m_pMBLayout = Input(INDEXDATA)->GetMBLayout(); if (isFinalValidationPass && (!Input(INDEXDATA)->HasMBLayout() || !Input(SOURCEDATA)->HasMBLayout())) LogicError("%ls %ls operation requires both inputs to be minibatch data (must have MBLayouts).", NodeName().c_str(), OperationName().c_str()); if (isFinalValidationPass && Input(INDEXDATA)->GetSampleLayout().GetNumElements() != 1) InvalidArgument("%ls %ls operation requires the first argument (indexData) to be a scalar sequence.", NodeName().c_str(), OperationName().c_str()); // inherit tensor dimension from sourceData SetDims(Input(SOURCEDATA)->GetSampleLayout(), HasMBLayout()); }
/*virtual*/ void ScatterPackedNode<ElemType>::Validate(bool isFinalValidationPass) /*override*/ { ComputationNodeBase::Validate(isFinalValidationPass); // inherit MBLayout from layoutData (that's the only thing we use it for) m_pMBLayout = Input(LAYOUTDATA)->GetMBLayout(); if (isFinalValidationPass && (!Input(LAYOUTDATA)->HasMBLayout() || !Input(INDEXDATA)->HasMBLayout() || !Input(SOURCEDATA)->HasMBLayout())) LogicError("%ls %ls operation requires all inputs to be minibatch data (must have MBLayouts).", NodeName().c_str(), OperationName().c_str()); if (isFinalValidationPass && Input(INDEXDATA)->GetSampleLayout().GetNumElements() != 1) InvalidArgument("%ls %ls operation requires the second argument (indexData) to be a scalar sequence.", NodeName().c_str(), OperationName().c_str()); // TODO: We also know that indexData and sourceData must have the same MBLayout. But that is checked at runtime. // inherit tensor dimension from sourceData SetDims(Input(SOURCEDATA)->GetSampleLayout(), 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()); }
// N-nary zip operation, e.g. for TernaryZip for clip() // 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::ValidateNaryZip(bool isFinalValidationPass, bool allowBroadcast, size_t numInputs) { assert(m_inputs.size() == numInputs); ComputationNodeBase::Validate(isFinalValidationPass); InferMBLayoutFromInputsForStandardCase(isFinalValidationPass); ValidateInferNaryInputDims(numInputs); // check minibatch layout consistency for all possible pairs (n choose 2) if (isFinalValidationPass) for (size_t i = 0; i < numInputs; i++) for (size_t j = i + 1; j < numInputs; j++) ValidateMBLayout(Input(i), Input(j)); // result has tensor shape with dimensions being the max over all inputs let shape0 = GetInputSampleLayout(0); // dims is max over all inputs size_t maxRank = shape0.GetRank(); for (size_t i = 1; i < numInputs; i++) { let shape = GetInputSampleLayout(i); if (shape.GetRank() > maxRank) maxRank = shape.GetRank(); } SmallVector<size_t> dims = shape0.GetDims(); dims.resize(maxRank, 1); // pad with 1 // first check for invalid dimensions for (size_t k = 0; k < maxRank; k++) { size_t maxDim = 0; TensorShape maxShape = shape0; // arbitrary; this is just used for the error message for (size_t i = 0; i < numInputs; i++) { let currentShape = GetInputSampleLayout(i); size_t currentRank = currentShape.GetRank(); // make sure that the rank of this input is bigger than the current index (otherwise, these are implied singleton dimensions that do not need to be checked) if (currentRank > k) { size_t currentDim = currentShape[k]; if (currentDim > 1 && maxDim != currentDim && maxDim > 1) // 1=broadcasting, 0=not known yet, meant to be inferred { InvalidArgument("%ls: Input dimensions [%s] and [%s] are not compatible.", NodeDescription().c_str(), string(maxShape).c_str(), string(currentShape).c_str()); } else if (currentDim > maxDim) { maxDim = currentDim; maxShape = currentShape; } } } } // now set up the right dims for (size_t k = 0; k < maxRank; k++) { for (size_t i = 0; i < numInputs; i++) { let shape = GetInputSampleLayout(i); if (shape.GetRank() > k) { size_t dim = shape[k]; if (dims[k] <= 1 && dim != 0) dims[k] = dim; } } } SetDims(TensorShape(dims), HasMBLayout()); }