Add block evaluation to TensorEvalTo and fix few small bugs

This commit is contained in:
Eugene Zhulenev 2019-10-07 15:34:26 -07:00
parent 3afb640b56
commit f74ab8cb8d
4 changed files with 95 additions and 33 deletions

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@ -111,22 +111,28 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
IsAligned = TensorEvaluator<ArgType, Device>::IsAligned,
PacketAccess = TensorEvaluator<ArgType, Device>::PacketAccess,
BlockAccess = true,
BlockAccessV2 = false,
BlockAccessV2 = true,
PreferBlockAccess = false,
Layout = TensorEvaluator<ArgType, Device>::Layout,
CoordAccess = false, // to be implemented
RawAccess = true
};
typedef typename internal::TensorBlock<
CoeffReturnType, Index, internal::traits<ArgType>::NumDimensions, Layout>
TensorBlock;
typedef typename internal::TensorBlockReader<
CoeffReturnType, Index, internal::traits<ArgType>::NumDimensions, Layout>
TensorBlockReader;
static const int NumDims = internal::traits<ArgType>::NumDimensions;
typedef typename internal::TensorBlock<CoeffReturnType, Index, NumDims, Layout> TensorBlock;
typedef typename internal::TensorBlockReader<CoeffReturnType, Index, NumDims, Layout> TensorBlockReader;
//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
typedef internal::TensorBlockNotImplemented TensorBlockV2;
typedef internal::TensorBlockDescriptor<NumDims, Index> TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<Device> TensorBlockScratch;
typedef typename TensorEvaluator<const ArgType, Device>::TensorBlockV2
ArgTensorBlock;
typedef internal::TensorBlockAssignment<
Scalar, NumDims, typename ArgTensorBlock::XprType, Index>
TensorBlockAssignment;
//===--------------------------------------------------------------------===//
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
@ -164,6 +170,30 @@ struct TensorEvaluator<const TensorEvalToOp<ArgType, MakePointer_>, Device>
m_impl.block(&eval_to_block);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void evalBlockV2(
TensorBlockDesc& desc, TensorBlockScratch& scratch) {
// Add `m_buffer` as destination buffer to the block descriptor.
desc.AddDestinationBuffer(
/*dst_base=*/m_buffer + desc.offset(),
/*dst_strides=*/internal::strides<Layout>(m_impl.dimensions()),
/*total_dst_bytes=*/
(internal::array_prod(m_impl.dimensions())
* sizeof(Scalar)));
ArgTensorBlock block = m_impl.blockV2(desc, scratch);
// If block was evaluated into a destination buffer, there is no need to do
// an assignment.
if (block.kind() != internal::TensorBlockKind::kMaterializedInOutput) {
TensorBlockAssignment::Run(
TensorBlockAssignment::target(
desc.dimensions(), internal::strides<Layout>(m_impl.dimensions()),
m_buffer, desc.offset()),
block.expr());
}
block.cleanup();
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() {
m_impl.cleanup();
}

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@ -238,7 +238,8 @@ class TensorExecutor<Expression, DefaultDevice, Vectorizable,
typedef TensorBlockMapper<ScalarNoConst, StorageIndex, NumDims, Evaluator::Layout> TensorBlockMapper;
typedef typename TensorBlock::Dimensions TensorBlockDimensions;
typedef internal::TensorBlockDescriptor<NumDims> TensorBlockDesc;
typedef internal::TensorBlockDescriptor<NumDims, StorageIndex>
TensorBlockDesc;
typedef internal::TensorBlockScratchAllocator<DefaultDevice>
TensorBlockScratch;

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@ -231,7 +231,11 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlockV2
blockV2(TensorBlockDesc& desc, TensorBlockScratch& scratch) const {
eigen_assert(m_impl.data() != NULL);
// If one of the dimensions is zero, return empty block view.
if (desc.size() == 0) {
return TensorBlockV2(internal::TensorBlockKind::kView, NULL,
desc.dimensions());
}
// Check if we can reuse `desc` destination, or allocate new scratch buffer.
ScalarNoConst* materialized_output =
@ -385,6 +389,8 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
const Index out = output_offset + output_inner_pad_before_size;
const Index in = input_offset + output_inner_pad_before_size;
eigen_assert(output_inner_copy_size == 0 || m_impl.data() != NULL);
LinCopy::template Run<LinCopy::Kind::Linear>(
typename LinCopy::Dst(out, 1, materialized_output),
typename LinCopy::Src(in, 1, m_impl.data()),

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@ -131,6 +131,7 @@ static void VerifyBlockEvaluator(Expression expr, GenBlockParams gen_block) {
// TensorEvaluator is needed to produce tensor blocks of the expression.
auto eval = TensorEvaluator<const decltype(expr), Device>(expr, d);
eval.evalSubExprsIfNeeded(nullptr);
// Choose a random offsets, sizes and TensorBlockDescriptor.
TensorBlockParams<NumDims> block_params = gen_block();
@ -266,29 +267,6 @@ static void test_eval_tensor_reshape() {
[&shuffled]() { return SkewedInnerBlock<Layout>(shuffled); });
}
template <typename T, int Layout>
static void test_eval_tensor_reshape_with_bcast() {
Index dim = internal::random<Index>(1, 100);
Tensor<T, 2, Layout> lhs(1, dim);
Tensor<T, 2, Layout> rhs(dim, 1);
lhs.setRandom();
rhs.setRandom();
auto reshapeLhs = NByOne(dim);
auto reshapeRhs = OneByM(dim);
auto bcastLhs = OneByM(dim);
auto bcastRhs = NByOne(dim);
DSizes<Index, 2> dims(dim, dim);
VerifyBlockEvaluator<T, 2, Layout>(
lhs.reshape(reshapeLhs).broadcast(bcastLhs) +
rhs.reshape(reshapeRhs).broadcast(bcastRhs),
[dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
}
template <typename T, int NumDims, int Layout>
static void test_eval_tensor_cast() {
DSizes<Index, NumDims> dims = RandomDims<NumDims>(10, 20);
@ -355,6 +333,52 @@ static void test_eval_tensor_padding() {
[&padded_dims]() { return SkewedInnerBlock<Layout>(padded_dims); });
}
template <typename T, int Layout>
static void test_eval_tensor_reshape_with_bcast() {
Index dim = internal::random<Index>(1, 100);
Tensor<T, 2, Layout> lhs(1, dim);
Tensor<T, 2, Layout> rhs(dim, 1);
lhs.setRandom();
rhs.setRandom();
auto reshapeLhs = NByOne(dim);
auto reshapeRhs = OneByM(dim);
auto bcastLhs = OneByM(dim);
auto bcastRhs = NByOne(dim);
DSizes<Index, 2> dims(dim, dim);
VerifyBlockEvaluator<T, 2, Layout>(
lhs.reshape(reshapeLhs).broadcast(bcastLhs) +
rhs.reshape(reshapeRhs).broadcast(bcastRhs),
[dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
}
template <typename T, int Layout>
static void test_eval_tensor_forced_eval() {
Index dim = internal::random<Index>(1, 100);
Tensor<T, 2, Layout> lhs(dim, 1);
Tensor<T, 2, Layout> rhs(1, dim);
lhs.setRandom();
rhs.setRandom();
auto bcastLhs = OneByM(dim);
auto bcastRhs = NByOne(dim);
DSizes<Index, 2> dims(dim, dim);
VerifyBlockEvaluator<T, 2, Layout>(
(lhs.broadcast(bcastLhs) + rhs.broadcast(bcastRhs)).eval().reshape(dims),
[dims]() { return SkewedInnerBlock<Layout, 2>(dims); });
VerifyBlockEvaluator<T, 2, Layout>(
(lhs.broadcast(bcastLhs) + rhs.broadcast(bcastRhs)).eval().reshape(dims),
[dims]() { return RandomBlock<Layout, 2>(dims, 1, 50); });
}
// -------------------------------------------------------------------------- //
// Verify that assigning block to a Tensor expression produces the same result
// as an assignment to TensorSliceOp (writing a block is is identical to
@ -482,6 +506,7 @@ EIGEN_DECLARE_TEST(cxx11_tensor_block_eval) {
CALL_SUBTESTS_DIMS_LAYOUTS(test_eval_tensor_padding);
CALL_SUBTESTS_LAYOUTS(test_eval_tensor_reshape_with_bcast);
CALL_SUBTESTS_LAYOUTS(test_eval_tensor_forced_eval);
CALL_SUBTESTS_DIMS_LAYOUTS(test_assign_to_tensor);
CALL_SUBTESTS_DIMS_LAYOUTS(test_assign_to_tensor_reshape);