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Tensor block evaluation cost model

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This commit is contained in:
Eugene Zhulenev 2019-12-18 20:07:00 +00:00 committed by Rasmus Munk Larsen
parent 72166d0e6e
commit ae07801dd8
13 changed files with 194 additions and 126 deletions
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91
unsupported/Eigen/CXX11/src/Tensor/TensorBlock.h
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@ -73,14 +73,68 @@ EIGEN_STRONG_INLINE DSizes<std::ptrdiff_t, sizeof...(Indices)> strides(
enum class TensorBlockShapeType { kUniformAllDims, kSkewedInnerDims };
struct TensorBlockResourceRequirements {
TensorBlockShapeType shape_type;
size_t size;
TensorBlockShapeType shape_type; // target block shape
size_t size; // target block size
TensorOpCost cost_per_coeff; // cost of computing a single block element
template <typename Scalar>
EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
TensorBlockShapeType shape_type, size_t size_in_bytes,
TensorOpCost cost) {
const size_t size = numext::maxi(size_t(1), size_in_bytes / sizeof(Scalar));
return {shape_type, size, cost};
}
template <typename Scalar>
EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements withShapeAndSize(
TensorBlockShapeType shape_type, size_t size_in_bytes) {
// This default cost per coefficient is valid for most materialized tensor
// block evaluation implementations, because they typically just read
// coefficients from the underlying tensor storage, and write to the tensor
// block buffer (scratch or destination memory, reads and writes have linear
// access pattern). We ignore the fixed cost of block evaluation, because in
// practice it should negligible.
//
// Lazy block evaluation adds the cost of calling a functor for each
// coefficient.
//
// All non-trivial block evaluation implementations must provide their own
// cost approximation (e.g. shuffling inner dimension has a much higher cost
// because it reads memory randomly, although the total number of moved
// bytes is the same).
return withShapeAndSize<Scalar>(shape_type, size_in_bytes,
{/*bytes_loaded=*/sizeof(Scalar),
/*bytes_stored=*/sizeof(Scalar),
/*compute_cycles=*/0});
}
template <typename Scalar>
EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements skewed(
size_t size_in_bytes) {
return withShapeAndSize<Scalar>(TensorBlockShapeType::kSkewedInnerDims,
size_in_bytes);
}
template <typename Scalar>
EIGEN_DEVICE_FUNC static TensorBlockResourceRequirements uniform(
size_t size_in_bytes) {
return withShapeAndSize<Scalar>(TensorBlockShapeType::kUniformAllDims,
size_in_bytes);
}
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE TensorBlockResourceRequirements
merge(const TensorBlockResourceRequirements &lhs,
const TensorBlockResourceRequirements &rhs) {
return {merge(lhs.shape_type, rhs.shape_type), merge(rhs.size, lhs.size)};
merge(const TensorBlockResourceRequirements& lhs,
const TensorBlockResourceRequirements& rhs) {
return {merge(lhs.shape_type, rhs.shape_type), // shape_type
merge(lhs.size, rhs.size), // size
merge(lhs.cost_per_coeff, rhs.cost_per_coeff)}; // cost_per_coeff
}
EIGEN_DEVICE_FUNC TensorBlockResourceRequirements& addCostPerCoeff(
TensorOpCost cost) {
cost_per_coeff += cost;
return *this;
}
// This is a resource requirement that should be returned from expressions
@ -88,10 +142,10 @@ struct TensorBlockResourceRequirements {
// expression with raw buffer access).
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE TensorBlockResourceRequirements any() {
return {TensorBlockShapeType::kUniformAllDims, 1};
return {TensorBlockShapeType::kUniformAllDims, 1, {0, 0, 0}};
}
private:
private:
using Requirements = TensorBlockResourceRequirements;
EIGEN_DEVICE_FUNC
@ -100,13 +154,19 @@ private:
}
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE TensorBlockShapeType merge(TensorBlockShapeType lhs,
TensorBlockShapeType rhs) {
static EIGEN_STRONG_INLINE TensorBlockShapeType
merge(TensorBlockShapeType lhs, TensorBlockShapeType rhs) {
return (lhs == TensorBlockShapeType::kSkewedInnerDims ||
rhs == TensorBlockShapeType::kSkewedInnerDims)
? TensorBlockShapeType::kSkewedInnerDims
: TensorBlockShapeType::kUniformAllDims;
}
EIGEN_DEVICE_FUNC
static EIGEN_STRONG_INLINE TensorOpCost merge(TensorOpCost lhs_cost,
TensorOpCost rhs_cost) {
return lhs_cost + rhs_cost;
}
};
// -------------------------------------------------------------------------- //
@ -131,8 +191,9 @@ class TensorBlockDescriptor {
class DestinationBuffer {
public:
enum DestinationBufferKind : int {
// The above explicit specification of "int" as the enum basetype is needed
// to get around a HIPCC link error ("the field type is not amp-compatible")
// The above explicit specification of "int" as the enum basetype is
// needed to get around a HIPCC link error ("the field type is not
// amp-compatible")
// which is issued for class members with the enum type.
// TODO(rocm):
// remove the "int" basetype once HIPCC has been fixed to not error out
@ -299,8 +360,8 @@ class TensorBlockMapper {
return m_block_dimensions;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
BlockDescriptor blockDescriptor(IndexType block_index) const {
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockDescriptor
blockDescriptor(IndexType block_index) const {
static const bool isColMajor = Layout == static_cast<int>(ColMajor);
IndexType offset = 0;
@ -761,7 +822,6 @@ class TensorMaterializedBlock {
template <typename UnaryOp, typename ArgTensorBlock>
class TensorCwiseUnaryBlock {
static const bool NoArgBlockAccess =
internal::is_void<typename ArgTensorBlock::XprType>::value;
@ -793,7 +853,6 @@ class TensorCwiseUnaryBlock {
template <typename BinaryOp, typename LhsTensorBlock, typename RhsTensorBlock>
class TensorCwiseBinaryBlock {
static const bool NoArgBlockAccess =
internal::is_void<typename LhsTensorBlock::XprType>::value ||
internal::is_void<typename RhsTensorBlock::XprType>::value;
@ -840,7 +899,6 @@ class TensorCwiseBinaryBlock {
template <typename BlockFactory, typename ArgTensorBlock>
class TensorUnaryExprBlock {
typedef typename ArgTensorBlock::XprType ArgXprType;
static const bool NoArgBlockAccess = internal::is_void<ArgXprType>::value;
@ -872,7 +930,6 @@ class TensorUnaryExprBlock {
template <typename BlockFactory, typename Arg1TensorBlock,
typename Arg2TensorBlock, typename Arg3TensorBlock>
class TensorTernaryExprBlock {
typedef typename Arg1TensorBlock::XprType Arg1XprType;
typedef typename Arg2TensorBlock::XprType Arg2XprType;
typedef typename Arg3TensorBlock::XprType Arg3XprType;

8
unsupported/Eigen/CXX11/src/Tensor/TensorBroadcasting.h
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@ -620,12 +620,10 @@ struct TensorEvaluator<const TensorBroadcastingOp<Broadcast, ArgType>, Device>
internal::TensorBlockResourceRequirements getResourceRequirements() const {
// TODO(wuke): Targeting L1 size is 30% faster than targeting L{-1} on large
// tensors. But this might need further tuning.
const size_t target_block_size = numext::maxi<size_t>(
1, m_device.firstLevelCacheSize() / sizeof(Scalar));
const size_t target_size = m_device.firstLevelCacheSize();
return internal::TensorBlockResourceRequirements::merge(
{internal::TensorBlockShapeType::kSkewedInnerDims, target_block_size},
m_impl.getResourceRequirements());
m_impl.getResourceRequirements(),
internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock

6
unsupported/Eigen/CXX11/src/Tensor/TensorChipping.h
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@ -296,11 +296,9 @@ struct TensorEvaluator<const TensorChippingOp<DimId, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
const size_t target_block_size =
numext::maxi<size_t>(1, m_device.lastLevelCacheSize() / sizeof(Scalar));
const size_t target_size = m_device.lastLevelCacheSize();
return internal::TensorBlockResourceRequirements::merge(
{internal::TensorBlockShapeType::kSkewedInnerDims, target_block_size},
internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
m_impl.getResourceRequirements());
}

21
unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
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@ -521,7 +521,9 @@ struct TensorEvaluator<const TensorCwiseUnaryOp<UnaryOp, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
return m_argImpl.getResourceRequirements();
static const double functor_cost = internal::functor_traits<UnaryOp>::Cost;
return m_argImpl.getResourceRequirements().addCostPerCoeff(
{0, 0, functor_cost / PacketSize});
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
@ -654,9 +656,11 @@ struct TensorEvaluator<const TensorCwiseBinaryOp<BinaryOp, LeftArgType, RightArg
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
static const double functor_cost = internal::functor_traits<BinaryOp>::Cost;
return internal::TensorBlockResourceRequirements::merge(
m_leftImpl.getResourceRequirements(),
m_rightImpl.getResourceRequirements());
m_rightImpl.getResourceRequirements())
.addCostPerCoeff({0, 0, functor_cost / PacketSize});
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock
@ -934,11 +938,16 @@ struct TensorEvaluator<const TensorSelectOp<IfArgType, ThenArgType, ElseArgType>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
auto then_req = m_thenImpl.getResourceRequirements();
auto else_req = m_elseImpl.getResourceRequirements();
auto merged_req =
internal::TensorBlockResourceRequirements::merge(then_req, else_req);
merged_req.cost_per_coeff =
then_req.cost_per_coeff.cwiseMax(else_req.cost_per_coeff);
return internal::TensorBlockResourceRequirements::merge(
m_condImpl.getResourceRequirements(),
internal::TensorBlockResourceRequirements::merge(
m_thenImpl.getResourceRequirements(),
m_elseImpl.getResourceRequirements()));
m_condImpl.getResourceRequirements(), merged_req);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock

7
unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
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@ -245,8 +245,8 @@ TensorExecutorTilingContext<TensorBlockMapper> GetTensorExecutorTilingContext(
evaluator.getResourceRequirements();
// Update target block size based on cost model.
TensorOpCost cost = evaluator.costPerCoeff(Vectorizable);
double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(1, cost);
double taskSize = TensorCostModel<ThreadPoolDevice>::taskSize(
1, requirements.cost_per_coeff);
requirements.size = static_cast<size_t>(1.0 / taskSize);
TensorBlockMapper block_mapper(
@ -259,7 +259,8 @@ TensorExecutorTilingContext<TensorBlockMapper> GetTensorExecutorTilingContext(
align *
divup<size_t>(block_size * sizeof(typename Evaluator::Scalar), align);
return {block_mapper, cost * block_size, aligned_blocksize};
return {block_mapper, requirements.cost_per_coeff * block_size,
aligned_blocksize};
}
template <typename Evaluator, typename StorageIndex, bool Vectorizable>

8
unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h
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@ -166,10 +166,10 @@ struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device>
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
const size_t target_block_size = numext::maxi<size_t>(
1, m_device.firstLevelCacheSize() / sizeof(Scalar));
return {internal::TensorBlockShapeType::kSkewedInnerDims,
target_block_size};
const size_t target_size = m_device.firstLevelCacheSize();
// TODO(ezhulenev): Generator should have a cost.
return internal::TensorBlockResourceRequirements::skewed<Scalar>(
target_size);
}
struct BlockIteratorState {

5
unsupported/Eigen/CXX11/src/Tensor/TensorMorphing.h
View File

@ -634,10 +634,9 @@ struct TensorEvaluator<const TensorSlicingOp<StartIndices, Sizes, ArgType>, Devi
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
const size_t target_block_size =
numext::maxi<size_t>(1, m_device.lastLevelCacheSize() / sizeof(Scalar));
const size_t target_size = m_device.lastLevelCacheSize();
return internal::TensorBlockResourceRequirements::merge(
{internal::TensorBlockShapeType::kSkewedInnerDims, target_block_size},
internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
m_impl.getResourceRequirements());
}

5
unsupported/Eigen/CXX11/src/Tensor/TensorPadding.h
View File

@ -229,10 +229,9 @@ struct TensorEvaluator<const TensorPaddingOp<PaddingDimensions, ArgType>, Device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
const size_t target_block_size =
numext::maxi<size_t>(1, m_device.lastLevelCacheSize() / sizeof(Scalar));
const size_t target_size = m_device.lastLevelCacheSize();
return internal::TensorBlockResourceRequirements::merge(
{internal::TensorBlockShapeType::kSkewedInnerDims, target_block_size},
internal::TensorBlockResourceRequirements::skewed<Scalar>(target_size),
m_impl.getResourceRequirements());
}

10
unsupported/Eigen/CXX11/src/Tensor/TensorReverse.h
View File

@ -246,10 +246,12 @@ struct TensorEvaluator<const TensorReverseOp<ReverseDimensions, ArgType>, Device
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
internal::TensorBlockResourceRequirements getResourceRequirements() const {
const size_t target_block_size =
numext::maxi<size_t>(1, m_device.lastLevelCacheSize() / sizeof(Scalar));
return {internal::TensorBlockShapeType::kSkewedInnerDims,
target_block_size};
const size_t target_size = m_device.lastLevelCacheSize();
// Block evaluation reads underlying memory in reverse order, and default
// cost model does not properly catch this in bytes stored/loaded.
return internal::TensorBlockResourceRequirements::skewed<Scalar>(
target_size)
.addCostPerCoeff({0, 0, 24});
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock

21
unsupported/Eigen/CXX11/src/Tensor/TensorShuffling.h
View File

@ -249,14 +249,21 @@ struct TensorEvaluator<const TensorShufflingOp<Shuffle, ArgType>, Device>
static const int inner_dim =
Layout == static_cast<int>(ColMajor) ? 0 : NumDims - 1;
const size_t target_block_size = numext::maxi<size_t>(
1, m_device.firstLevelCacheSize() / sizeof(Scalar));
const size_t target_size = m_device.firstLevelCacheSize();
const bool inner_dim_shuffled = m_shuffle[inner_dim] != inner_dim;
return {inner_dim_shuffled
? internal::TensorBlockShapeType::kUniformAllDims
: internal::TensorBlockShapeType::kSkewedInnerDims,
target_block_size};
// Shuffled inner dimensions leads to a random memory access, which is not
// captured by default cost model bytes loaded/stored. We add this cost
// explicitly. The number of cycles picked based on the benchmarks.
// TODO(ezhulenev): This number was picked based on a very questionable
// benchmarks, add benchmarks that are representative of real workloads.
using BlockRequirements = internal::TensorBlockResourceRequirements;
if (inner_dim_shuffled) {
return BlockRequirements::uniform<Scalar>(target_size)
.addCostPerCoeff({0, 0, NumDims * 28});
} else {
return BlockRequirements::skewed<Scalar>(target_size);
}
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorBlock

119
unsupported/test/cxx11_tensor_block_access.cpp
View File

@ -21,6 +21,7 @@ using Eigen::RowMajor;
using Eigen::ColMajor;
using Eigen::internal::TensorBlockShapeType;
static TensorOpCost zeroCost() { return {0, 0, 0}; }
template<typename T>
static const T& choose(int layout, const T& col, const T& row) {
@ -73,7 +74,7 @@ static void test_block_mapper_sanity()
// Test uniform blocks.
TensorBlockMapper uniform_block_mapper(
tensor_dims, {TensorBlockShapeType::kUniformAllDims, 100});
tensor_dims, {TensorBlockShapeType::kUniformAllDims, 100, zeroCost()});
VERIFY_IS_EQUAL(uniform_block_mapper.blockCount(), 100);
VERIFY_IS_EQUAL(uniform_block_mapper.blockTotalSize(), 100);
@ -85,7 +86,7 @@ static void test_block_mapper_sanity()
// Test skewed to inner dims blocks.
TensorBlockMapper skewed_block_mapper(
tensor_dims, {TensorBlockShapeType::kSkewedInnerDims, 100});
tensor_dims, {TensorBlockShapeType::kSkewedInnerDims, 100, zeroCost()});
VERIFY_IS_EQUAL(skewed_block_mapper.blockCount(), 100);
VERIFY_IS_EQUAL(skewed_block_mapper.blockTotalSize(), 100);
@ -130,7 +131,8 @@ static void test_block_mapper_maps_every_element() {
std::set<Index> coeff_set;
// Try different combinations of block types and sizes.
TensorBlockMapper block_mapper(dims, {RandomShape(), RandomTargetSize(dims)});
TensorBlockMapper block_mapper(
dims, {RandomShape(), RandomTargetSize(dims), zeroCost()});
for (int i = 0; i < block_mapper.blockCount(); ++i) {
auto block = block_mapper.blockDescriptor(i);
@ -233,9 +235,8 @@ static void test_uniform_block_shape()
// Test shape 'UniformAllDims' with uniform 'max_coeff count'.
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 5 * 5 * 5 * 5 * 5;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
for (int i = 0; i < 5; ++i) {
VERIFY_IS_EQUAL(5, block.dimensions()[i]);
@ -248,9 +249,8 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 7 * 5 * 5 * 5 * 5;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[0]);
for (int i = 1; i < 5; ++i) {
@ -260,9 +260,8 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 5 * 5 * 5 * 5 * 6;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(6, block.dimensions()[4]);
for (int i = 3; i >= 0; --i) {
@ -276,9 +275,8 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 11 * 5 * 5 * 5 * 5;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(11, block.dimensions()[0]);
for (int i = 1; i < 5; ++i) {
@ -288,9 +286,8 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 5 * 5 * 5 * 5 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
for (int i = 3; i >= 0; --i) {
@ -304,9 +301,8 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(7, 5, 6, 17, 7);
const Index max_coeff_count = 7 * 5 * 6 * 7 * 5;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[0]);
VERIFY_IS_EQUAL(5, block.dimensions()[1]);
@ -317,9 +313,8 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(7, 5, 6, 9, 7);
const Index max_coeff_count = 5 * 5 * 5 * 6 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
VERIFY_IS_EQUAL(6, block.dimensions()[3]);
@ -333,9 +328,8 @@ static void test_uniform_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(7, 5, 6, 17, 7);
const Index max_coeff_count = 7 * 5 * 6 * 17 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[0]);
VERIFY_IS_EQUAL(5, block.dimensions()[1]);
@ -346,9 +340,8 @@ static void test_uniform_block_shape()
} else {
DSizes<Index, 5> dims(7, 5, 6, 9, 7);
const Index max_coeff_count = 7 * 5 * 6 * 9 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count});
TensorBlockMapper block_mapper(dims, {TensorBlockShapeType::kUniformAllDims,
max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
VERIFY_IS_EQUAL(9, block.dimensions()[3]);
@ -369,9 +362,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 10 * 1 * 1 * 1 * 1;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(10, block.dimensions()[0]);
for (int i = 1; i < 5; ++i) {
@ -381,9 +374,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 1 * 1 * 1 * 1 * 6;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(6, block.dimensions()[4]);
for (int i = 3; i >= 0; --i) {
@ -396,9 +389,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 11 * 1 * 1 * 1 * 1;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(11, block.dimensions()[0]);
for (int i = 1; i < 5; ++i) {
@ -408,9 +401,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 1 * 1 * 1 * 1 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
for (int i = 3; i >= 0; --i) {
@ -424,9 +417,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 11 * 3 * 1 * 1 * 1;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(11, block.dimensions()[0]);
VERIFY_IS_EQUAL(3, block.dimensions()[1]);
@ -437,9 +430,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 1 * 1 * 1 * 15 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
VERIFY_IS_EQUAL(15, block.dimensions()[3]);
@ -454,9 +447,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 11 * 5 * 5 * 1 * 1;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(11, block.dimensions()[0]);
VERIFY_IS_EQUAL(5, block.dimensions()[1]);
@ -468,9 +461,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 1 * 1 * 5 * 17 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
VERIFY_IS_EQUAL(17, block.dimensions()[3]);
@ -485,9 +478,9 @@ static void test_skewed_inner_dim_block_shape()
if (Layout == ColMajor) {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(11, block.dimensions()[0]);
VERIFY_IS_EQUAL(5, block.dimensions()[1]);
@ -498,9 +491,9 @@ static void test_skewed_inner_dim_block_shape()
} else {
DSizes<Index, 5> dims(11, 5, 6, 17, 7);
const Index max_coeff_count = 11 * 5 * 6 * 17 * 7;
TensorBlockMapper
block_mapper(dims, {TensorBlockShapeType::kSkewedInnerDims,
max_coeff_count});
TensorBlockMapper block_mapper(
dims,
{TensorBlockShapeType::kSkewedInnerDims, max_coeff_count, zeroCost()});
TensorBlock block = block_mapper.blockDescriptor(0);
VERIFY_IS_EQUAL(7, block.dimensions()[4]);
VERIFY_IS_EQUAL(17, block.dimensions()[3]);
@ -524,7 +517,8 @@ static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
DSizes<Index, 1> dims(0);
for (size_t max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
TensorBlockMapper block_mapper(dims, {block_shape, max_coeff_count});
TensorBlockMapper block_mapper(
dims, {block_shape, max_coeff_count, zeroCost()});
VERIFY_IS_EQUAL(block_mapper.blockCount(), 0);
VERIFY(block_mapper.blockTotalSize() >= 1);
}
@ -537,7 +531,8 @@ static void test_empty_dims(const internal::TensorBlockShapeType block_shape)
for (int dim2 = 0; dim2 < 3; ++dim2) {
DSizes<Index, 2> dims(dim1, dim2);
for (size_t max_coeff_count = 0; max_coeff_count < 2; ++max_coeff_count) {
TensorBlockMapper block_mapper(dims, {block_shape, max_coeff_count});
TensorBlockMapper block_mapper(
dims, {block_shape, max_coeff_count, zeroCost()});
if (dim1 * dim2 == 0) {
VERIFY_IS_EQUAL(block_mapper.blockCount(), 0);
}

3
unsupported/test/cxx11_tensor_block_eval.cpp
View File

@ -64,7 +64,8 @@ static TensorBlockParams<NumDims> SkewedInnerBlock(
using BlockMapper = internal::TensorBlockMapper<NumDims, Layout, Index>;
BlockMapper block_mapper(dims,
{internal::TensorBlockShapeType::kSkewedInnerDims,
internal::random<size_t>(1, dims.TotalSize())});
internal::random<size_t>(1, dims.TotalSize()),
{0, 0, 0}});
Index total_blocks = block_mapper.blockCount();
Index block_index = internal::random<Index>(0, total_blocks - 1);

10
unsupported/test/cxx11_tensor_block_io.cpp
View File

@ -75,8 +75,8 @@ static void test_block_io_copy_data_from_source_to_target() {
// Construct a tensor block mapper.
using TensorBlockMapper =
internal::TensorBlockMapper<NumDims, Layout, Index>;
TensorBlockMapper block_mapper(dims, {RandomBlockShape(),
RandomTargetBlockSize(dims)});
TensorBlockMapper block_mapper(
dims, {RandomBlockShape(), RandomTargetBlockSize(dims), {0, 0, 0}});
// We will copy data from input to output through this buffer.
Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());
@ -146,8 +146,10 @@ static void test_block_io_copy_using_reordered_dimensions() {
// NOTE: Tensor block mapper works with shuffled dimensions.
using TensorBlockMapper =
internal::TensorBlockMapper<NumDims, Layout, Index>;
TensorBlockMapper block_mapper(output_tensor_dims, {RandomBlockShape(),
RandomTargetBlockSize(output_tensor_dims)});
TensorBlockMapper block_mapper(output_tensor_dims,
{RandomBlockShape(),
RandomTargetBlockSize(output_tensor_dims),
{0, 0, 0}});
// We will copy data from input to output through this buffer.
Tensor<T, NumDims, Layout> block(block_mapper.blockDimensions());