NDArrayView::NDArrayView(const NDShape& viewShape, const SparseIndexType* colStarts, const SparseIndexType* rowIndices, const ElementType* nonZeroValues, size_t numNonZeroValues, const DeviceDescriptor& device, bool readOnly/* = false*/)
: NDArrayView(AsDataType<ElementType>(), device, StorageFormat::SparseCSC, viewShape, false, AllocateTensorView<ElementType>(viewShape, StorageFormat::SparseCSC, device))
{
if ((colStarts == nullptr) || (rowIndices == nullptr) || (nonZeroValues == nullptr) || (numNonZeroValues == 0) || (numNonZeroValues > viewShape.TotalSize()))
InvalidArgument("Invalid sparse CSC format initial data specified for NDArrayView construction");
auto sparseMatrix = GetWritableMatrix<ElementType>(1);
sparseMatrix->SetMatrixFromCSCFormat(colStarts, rowIndices, nonZeroValues, numNonZeroValues, sparseMatrix->GetNumRows(), sparseMatrix->GetNumCols());
m_isReadOnly = readOnly;
}
void CNTKEvalExtended<ElemType>::ForwardPassT(const std::vector<ValueBuffer<ElemType, ValueContainer> >& inputs, std::vector<ValueBuffer<ElemType, ValueContainer> >& outputs, bool resetRNN)
{
if (!m_started)
RuntimeError("ForwardPass() called before StartForwardEvaluation()");
if (inputs.size() != (size_t)std::distance(m_inputMatrices.begin(), m_inputMatrices.end()))
RuntimeError("Expected %d inputs, but got %d.", (int)std::distance(m_inputMatrices.begin(), m_inputMatrices.end()), (int)inputs.size());
if (outputs.size() != m_outputNodes.size())
RuntimeError("Expected %d outputs, but got %d.", (int)m_outputNodes.size(), (int)outputs.size());
size_t i = 0;
for (auto& inputNode : m_inputNodes)
{
// const cast: The matrix class takes this over without copying and could theoretically change the contents,
// though it doesn't in this case.
auto& buffer = const_cast<ValueBuffer<ElemType, ValueContainer>&>(inputs[i]);
auto matrix = dynamic_pointer_cast<Matrix<ElemType>>(inputNode->ValuePtr());
auto type = matrix->GetMatrixType();
size_t numRows = inputNode->GetSampleLayout().GetNumElements();
if (buffer.m_buffer.data() == nullptr)
RuntimeError("Input %ls: Buffer is not allocated.", m_inputNodes[i]->GetName().c_str());
if (type == MatrixType::DENSE)
{
if (buffer.m_buffer.size() % numRows != 0)
RuntimeError("Input %ls: Expected input data to be a multiple of %" PRIu64 ", but it is %" PRIu64 ".",
m_inputNodes[i]->GetName().c_str(), numRows, buffer.m_buffer.size());
if (buffer.m_buffer.size() == 0)
RuntimeError("Input %ls: Expected at least one element.", m_inputNodes[i]->GetName().c_str());
}
else if (type == MatrixType::SPARSE)
{
if (buffer.m_colIndices.data() == nullptr)
RuntimeError("Input %ls: Due to sparse input format, expected colIndices array, but was nullptr.", m_inputNodes[i]->GetName().c_str());
if (buffer.m_indices.data() == nullptr)
RuntimeError("Input %ls: Due to sparse input format, expected Indices array, but was nullptr.", m_inputNodes[i]->GetName().c_str());
if (buffer.m_colIndices.size() < 2)
RuntimeError("Input %ls: Expected at least one element (2 entries in colIndices array).", m_inputNodes[i]->GetName().c_str());
if (buffer.m_colIndices[0] != 0)
RuntimeError("Input %ls: First element of column indices must be 0", m_inputNodes[i]->GetName().c_str());
if (buffer.m_colIndices[buffer.m_colIndices.size() - 1] != buffer.m_indices.size())
RuntimeError("Input %ls: Last element of column indices must be equal to the size of indices (%ld), but was %d",
m_inputNodes[i]->GetName().c_str(), buffer.m_indices.size(),
buffer.m_colIndices[buffer.m_colIndices.size() - 1]);
}
int numCols = type == MatrixType::DENSE ? buffer.m_buffer.size() / numRows : buffer.m_colIndices.size() - 1;
if (numCols < 1)
RuntimeError("Input: the number of column must be greater than or equal to 1.");
inputNode->GetMBLayout()->Init(1, numCols);
// SentinelValueIndicatingUnspecifedSequenceBeginIdx is used to specify the lower bound of look-back step of recurrent nodes
inputNode->GetMBLayout()->AddSequence(0, 0, resetRNN ? 0 : SentinelValueIndicatingUnspecifedSequenceBeginIdx, numCols);
if (type == MatrixType::DENSE)
matrix->SetValue(numRows, numCols, matrix->GetDeviceId(), buffer.m_buffer.data(), matrixFlagNormal);
else if (type == MatrixType::SPARSE)
{
// In the sparse case the m_data layout is identical to CUDA's CSC layout
// (see http://docs.nvidia.com/cuda/cusparse/#compressed-sparse-column-format-csc).
matrix->SetMatrixFromCSCFormat(buffer.m_colIndices.data(), buffer.m_indices.data(), buffer.m_buffer.data(),
buffer.m_buffer.size(), numRows, numCols);
}
++i;
}
ComputationNetwork::BumpEvalTimeStamp(m_inputNodes);
this->m_net->ForwardProp(m_outputNodes);
for (size_t i2 = 0; i2 < m_outputNodes.size(); ++i2)
{
auto node = m_outputNodes[i2];
shared_ptr<Matrix<ElemType>> outputMatrix = dynamic_pointer_cast<Matrix<ElemType>>(node->ValuePtr());
auto pMBLayout = node->GetMBLayout();
if (!pMBLayout)
{
pMBLayout = make_shared<MBLayout>();
pMBLayout->InitAsFrameMode(1); // treat this as if we have one single sample
}
const auto& seq = pMBLayout->GetAllSequences();
if (seq.size() != 1)
RuntimeError("Only 1 output sequence supported by this API");
ValueContainer<ElemType>& vec = outputs[i2].m_buffer;
size_t numElements = outputMatrix->GetNumElements();
if (vec.capacity() < numElements)
{
// Bad luck - we can't reallocate memory of an external object at this point.
RuntimeError("Not enough space in output buffer for output '%ls'.", node->GetName().c_str());
}
vec.resize(numElements);
ElemType* data = const_cast<ElemType*>(vec.data());
outputMatrix->CopyToArray(data, numElements);
}
}
