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Add block evaluation to TensorEvalTo and fix few small bugs
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@ -111,22 +111,28 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
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IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
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PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
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BlockAccess = true,
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BlockAccessV2 = false,
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BlockAccessV2 = true,
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PreferBlockAccess = false,
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Layout = TensorEvaluator<ArgType, Device>::Layout,
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CoordAccess = false, // to be implemented
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RawAccess = true
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};
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typedef typename internal::TensorBlock<
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CoeffReturnType, Index, internal::traits<ArgType>::NumDimensions, Layout>
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TensorBlock;
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typedef typename internal::TensorBlockReader<
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CoeffReturnType, Index, internal::traits<ArgType>::NumDimensions, Layout>
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TensorBlockReader;
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static const int NumDims = internal::traits<ArgType>::NumDimensions;
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typedef typename internal::TensorBlock<CoeffReturnType, Index, NumDims, Layout> TensorBlock;
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typedef typename internal::TensorBlockReader<CoeffReturnType, Index, NumDims, Layout> TensorBlockReader;
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//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
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typedef internal::TensorBlockNotImplemented TensorBlockV2;
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typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
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typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
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typedef typename TensorEvaluator<const ArgType, Device>::TensorBlockV2
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ArgTensorBlock;
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typedef internal::TensorBlockAssignment<
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Scalar, NumDims, typename ArgTensorBlock::XprType, Index>
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TensorBlockAssignment;
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//===--------------------------------------------------------------------===//
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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@ -164,6 +170,30 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
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m_impl.block(&eval_to_block);
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlockV2(
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TensorBlockDesc& desc, TensorBlockScratch& scratch) {
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// Add `m_buffer` as destination buffer to the block descriptor.
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desc.AddDestinationBuffer(
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/*dst_base=*/m_buffer + desc.offset(),
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/*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()),
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/*total_dst_bytes=*/
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(internal::array_prod(m_impl.dimensions())
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* sizeof(Scalar)));
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ArgTensorBlock block = m_impl.blockV2(desc, scratch);
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// If block was evaluated into a destination buffer, there is no need to do
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// an assignment.
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if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
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TensorBlockAssignment::Run(
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TensorBlockAssignment::target(
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desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()),
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m_buffer, desc.offset()),
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block.expr());
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}
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block.cleanup();
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}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
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m_impl.cleanup();
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}
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@ -238,7 +238,8 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable,
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typedef TensorBlockMapper<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout> TensorBlockMapper;
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typedef typename TensorBlock::Dimensions TensorBlockDimensions;
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typedef internal::TensorBlockDescriptor<NumDims> TensorBlockDesc;
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typedef internal::TensorBlockDescriptor<NumDims, StorageIndex>
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TensorBlockDesc;
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typedef internal::TensorBlockScratchAllocator<DefaultDevice>
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TensorBlockScratch;
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@ -231,7 +231,11 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlockV2
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blockV2(TensorBlockDesc& desc, TensorBlockScratch& scratch) const {
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eigen_assert(m_impl.data() != NULL);
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// If one of the dimensions is zero, return empty block view.
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if (desc.size() == 0) {
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return TensorBlockV2(internal::TensorBlockKind::kView, NULL,
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desc.dimensions());
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}
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// Check if we can reuse `desc` destination, or allocate new scratch buffer.
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ScalarNoConst* materialized_output =
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@ -385,6 +389,8 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
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const Index out = output_offset + output_inner_pad_before_size;
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const Index in = input_offset + output_inner_pad_before_size;
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eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);
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LinCopy::template Run<LinCopy::Kind::Linear>(
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typename LinCopy::Dst(out, 1, materialized_output),
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typename LinCopy::Src(in, 1, m_impl.data()),
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@ -131,6 +131,7 @@ static void VerifyBlockEvaluator(Expression expr, GenBlockParams gen_block) {
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// TensorEvaluator is needed to produce tensor blocks of the expression.
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auto eval = TensorEvaluator<const decltype(expr), Device>(expr, d);
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eval.evalSubExprsIfNeeded(nullptr);
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// Choose a random offsets, sizes and TensorBlockDescriptor.
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TensorBlockParams<NumDims> block_params = gen_block();
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@ -266,29 +267,6 @@ static void test_eval_tensor_reshape() {
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[&shuffled]() { return SkewedInnerBlock<Layout>(shuffled); });
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}
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template <typename T, int Layout>
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static void test_eval_tensor_reshape_with_bcast() {
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Index dim = internal::random<Index>(1, 100);
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Tensor<T, 2, Layout> lhs(1, dim);
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Tensor<T, 2, Layout> rhs(dim, 1);
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lhs.setRandom();
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rhs.setRandom();
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auto reshapeLhs = NByOne(dim);
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auto reshapeRhs = OneByM(dim);
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auto bcastLhs = OneByM(dim);
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auto bcastRhs = NByOne(dim);
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DSizes<Index, 2> dims(dim, dim);
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VerifyBlockEvaluator<T, 2, Layout>(
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lhs.reshape(reshapeLhs).broadcast(bcastLhs) +
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rhs.reshape(reshapeRhs).broadcast(bcastRhs),
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[dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
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}
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template <typename T, int NumDims, int Layout>
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static void test_eval_tensor_cast() {
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DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
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@ -355,6 +333,52 @@ static void test_eval_tensor_padding() {
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[&padded_dims]() { return SkewedInnerBlock<Layout>(padded_dims); });
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}
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template <typename T, int Layout>
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static void test_eval_tensor_reshape_with_bcast() {
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Index dim = internal::random<Index>(1, 100);
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Tensor<T, 2, Layout> lhs(1, dim);
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Tensor<T, 2, Layout> rhs(dim, 1);
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lhs.setRandom();
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rhs.setRandom();
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auto reshapeLhs = NByOne(dim);
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auto reshapeRhs = OneByM(dim);
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auto bcastLhs = OneByM(dim);
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auto bcastRhs = NByOne(dim);
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DSizes<Index, 2> dims(dim, dim);
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VerifyBlockEvaluator<T, 2, Layout>(
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lhs.reshape(reshapeLhs).broadcast(bcastLhs) +
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rhs.reshape(reshapeRhs).broadcast(bcastRhs),
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[dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
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}
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template <typename T, int Layout>
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static void test_eval_tensor_forced_eval() {
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Index dim = internal::random<Index>(1, 100);
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Tensor<T, 2, Layout> lhs(dim, 1);
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Tensor<T, 2, Layout> rhs(1, dim);
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lhs.setRandom();
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rhs.setRandom();
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auto bcastLhs = OneByM(dim);
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auto bcastRhs = NByOne(dim);
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DSizes<Index, 2> dims(dim, dim);
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VerifyBlockEvaluator<T, 2, Layout>(
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(lhs.broadcast(bcastLhs) + rhs.broadcast(bcastRhs)).eval().reshape(dims),
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[dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
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VerifyBlockEvaluator<T, 2, Layout>(
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(lhs.broadcast(bcastLhs) + rhs.broadcast(bcastRhs)).eval().reshape(dims),
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[dims]() { return RandomBlock<Layout, 2>(dims, 1, 50); });
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}
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// -------------------------------------------------------------------------- //
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// Verify that assigning block to a Tensor expression produces the same result
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// as an assignment to TensorSliceOp (writing a block is is identical to
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@ -482,6 +506,7 @@ EIGEN_DECLARE_TEST(cxx11_tensor_block_eval) {
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CALL_SUBTESTS_DIMS_LAYOUTS(test_eval_tensor_padding);
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CALL_SUBTESTS_LAYOUTS(test_eval_tensor_reshape_with_bcast);
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CALL_SUBTESTS_LAYOUTS(test_eval_tensor_forced_eval);
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CALL_SUBTESTS_DIMS_LAYOUTS(test_assign_to_tensor);
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CALL_SUBTESTS_DIMS_LAYOUTS(test_assign_to_tensor_reshape);
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