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predicated_tile_iterator.h
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predicated_tile_iterator.h
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/*! \file
\brief Epilogue for threadblock scoped GEMMs using Tensor Ops.
The epilogue rearranges the result of a matrix product through shared memory to match canonical
tensor layouts in global memory. Epilogues support conversion and reduction operations.
*/
#pragma once
#include "cutlass/cutlass.h"
#include "cutlass/numeric_types.h"
#include "cutlass/array.h"
#include "cutlass/layout/matrix.h"
#include "cutlass/layout/tensor.h"
#include "cutlass/layout/permute.h"
#include "cutlass/matrix_shape.h"
#include "cutlass/tensor_ref.h"
#include "cutlass/transform/pitch_linear_thread_map.h"
#include "cutlass/epilogue/threadblock/output_tile_thread_map.h"
#include "cutlass/arch/arch.h"
#include "cutlass/arch/memory.h"
#include "cutlass/epilogue/threadblock/predicated_tile_iterator_params.h"
////////////////////////////////////////////////////////////////////////////////
namespace cutlass {
////////////////////////////////////////////////////////////////////////////////
namespace epilogue {
namespace threadblock {
////////////////////////////////////////////////////////////////////////////////
/// Tile iterator used to load and store output tile from global memory in epilogue.
///
/// Satisfies: ReadableTileIterator | PredicatedTileIterator | ForwardTileIterator
///
template <
typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
typename Element_, ///< Element data type
bool ScatterD = false, ///< Scatter D operand or not
typename PermuteDLayout = layout::NoPermute, ///< Permute D operand or not
bool UseCUDAStore = false
>
class PredicatedTileIterator {
public:
using ThreadMap = ThreadMap_;
using Shape = typename ThreadMap::Shape;
using Element = Element_;
using Layout = layout::RowMajor;
using TensorRef = TensorRef<Element, Layout>;
using ConstTensorRef = typename TensorRef::ConstTensorRef;
using Index = typename Layout::Index;
using LongIndex = typename Layout::LongIndex;
using TensorCoord = MatrixCoord;
static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
static int const kThreads = ThreadMap::kThreads;
static int const kIterations = ThreadMap::Count::kTile;
static_assert( ThreadMap::Iterations::kRow > 0,"ThreadMap::Iterations::kRow must be > 0");
static_assert( ThreadMap::Iterations::kGroup > 0,"ThreadMap::Iterations::kGroup must be > 0");
static_assert( ThreadMap::Iterations::kCluster > 0,"ThreadMap::Iterations::kCluster must be > 0");
static_assert( ThreadMap::Iterations::kColumn > 0,"ThreadMap::Iterations::kColumn must be > 0");
/// Fragment object
using Fragment = Array<
Element,
ThreadMap::Iterations::kColumn *
ThreadMap::Iterations::kRow *
ThreadMap::Iterations::kGroup *
ThreadMap::Iterations::kCluster * ThreadMap::kElementsPerAccess>;
/// Memory access size
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
//
// Parameters struct
//
/// Uses a non-template class
struct Params : PredicatedTileIteratorParams {
using Base = PredicatedTileIteratorParams;
CUTLASS_HOST_DEVICE
Params() { }
CUTLASS_HOST_DEVICE
Params(Layout const &layout):
PredicatedTileIteratorParams(
layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
make_OutputTileThreadMapDesc<ThreadMap>()
)
{ }
CUTLASS_HOST_DEVICE
Params(Base const &base) :
Base(base) { }
};
/// Mask object
struct Mask {
static int const kCount = ThreadMap::Iterations::kColumn;
/// Predicate state
bool predicates[kCount];
//
// Mask
//
CUTLASS_HOST_DEVICE
Mask() {
enable();
}
///< Efficiently disables all accesses guarded by mask
CUTLASS_HOST_DEVICE void clear() {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < kCount; ++i) {
predicates[i] = false;
}
}
///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
CUTLASS_DEVICE void enable() {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < kCount; ++i) {
predicates[i] = true;
}
}
};
private:
//
// Data members
//
/// Parameters structure containing reference and precomputed state.
PredicatedTileIteratorParams params_;
/// Byte-level pointer. This pointer is usually for both load() and store(), unless PermuteD is performed. When having PermuteD, byte_pointer_ is only for load().
uint8_t *byte_pointer_;
/// Byte-level pointer for store(). Due to PermuteD Op, store_byte_pointer_ may be with different address computation compared to byte_pointer_.
uint8_t *store_byte_pointer_;
/// Array of boolean values to contain steady-state predicates
Mask mask_;
/// Extent of the matrix tile in rows
Index extent_row_;
/// Extent of the matrix tile in rows
Index extent_column_;
/// A thread's starting row position (assuming steady-state predicates have been computed)
Index thread_start_row_;
/// A thread's starting column
Index thread_start_column_;
/// Internal state counter
int state_[3];
/// Scatter indices
int const *indices_;
/// Whether to perform Permute Op
bool PermuteD;
/// PermuteDLayout
mutable PermuteDLayout permute_layout_;
//
// Static asserts about internal strides
//
static_assert(sizeof(extent_row_) == 4, "Expected 32b extents");
static_assert(sizeof(thread_start_row_) == 4, "Expected 32b extents");
static_assert(sizeof(PredicatedTileIteratorParams::stride) == 8, "Expected 64b strides");
private:
//
// Methods
//
public:
//
// Methods
//
/// Constructor
CUTLASS_DEVICE
PredicatedTileIterator(
PredicatedTileIteratorParams const & params,
Element *pointer,
TensorCoord extent,
int thread_idx,
TensorCoord threadblock_offset = TensorCoord(),
int const *indices = nullptr
):
params_(params), indices_(indices)
{
TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) + threadblock_offset;
extent_row_ = extent.row();
extent_column_ = extent.column();
thread_start_row_ = thread_offset.row();
thread_start_column_ = thread_offset.column();
// Initialize predicates
CUTLASS_PRAGMA_UNROLL
for (int c = 0; c < ThreadMap::Iterations::kColumn; ++c) {
mask_.predicates[c] = ((thread_offset.column()
+ ThreadMap::Delta::kColumn * c) < extent.column());
}
// Null pointer performs no accesses
if (!pointer) {
mask_.clear();
}
if (ScatterD && !indices) {
mask_.clear();
}
// Initialize byte_pointer_
byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
LongIndex(thread_offset.row()) * LongIndex(params_.stride) +
LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
if (ScatterD) {
byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
}
// store_byte_pointer_ is set to be the same with byte_pointer_ unless PermuteD is used.
store_byte_pointer_ = byte_pointer_;
// Initialize PermuteD. If PermuteD is true, store_byte_pointer_ is initialized accordingly.
if (platform::is_same<PermuteDLayout, layout::NoPermute>::value) {
PermuteD = false;
}else{
PermuteD = true;
store_byte_pointer_ = reinterpret_cast<uint8_t *>(pointer);
permute_layout_ = PermuteDLayout(extent,
params_.stride * kElementsPerAccess / sizeof(AccessType));
}
// Initialize internal state counter
state_[0] = state_[1] = state_[2] = 0;
}
/// Adds a pointer offset in units of Element
CUTLASS_HOST_DEVICE
void add_pointer_offset(LongIndex pointer_offset) {
store_byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
}
/// Loads a fragment from memory
CUTLASS_DEVICE
void load_with_byte_offset(Fragment &frag, int64_t byte_offset) const {
uint8_t *byte_pointer = byte_pointer_;
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
CUTLASS_PRAGMA_UNROLL
for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
CUTLASS_PRAGMA_UNROLL
for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
CUTLASS_PRAGMA_UNROLL
for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
int frag_row_idx =
(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
int row_offset = row * ThreadMap::Delta::kRow
+ group * ThreadMap::Delta::kGroup
+ cluster * ThreadMap::Delta::kCluster;
bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);
if (ScatterD && row_guard) {
assert(indices_);
memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset +
LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
}
CUTLASS_PRAGMA_UNROLL
for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
bool guard = row_guard && mask_.predicates[column];
cutlass::arch::global_load<
AccessType,
sizeof(AccessType)
>(
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
column],
(void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
kElementsPerAccess],
guard);
}
if (row + 1 < ThreadMap::Iterations::kRow) {
if (!ScatterD) {
byte_pointer += params_.increment_row;
}
}
}
if (group + 1 < ThreadMap::Iterations::kGroup) {
byte_pointer += params_.increment_group;
}
}
if (cluster + 1 < ThreadMap::Iterations::kCluster) {
byte_pointer += params_.increment_cluster;
}
}
}
/// Loads a fragment from memory
CUTLASS_DEVICE
void load(Fragment &frag) const {
load_with_byte_offset(frag, 0);
}
/// Stores a fragment to memory
CUTLASS_DEVICE
void store_with_byte_offset(Fragment const &frag, int64_t byte_offset) const {
uint8_t *byte_pointer = store_byte_pointer_;
AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
CUTLASS_PRAGMA_UNROLL
for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
CUTLASS_PRAGMA_UNROLL
for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
CUTLASS_PRAGMA_UNROLL
for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
int frag_row_idx =
(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
int row_offset = row * ThreadMap::Delta::kRow
+ group * ThreadMap::Delta::kGroup
+ cluster * ThreadMap::Delta::kCluster;
bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);
if (ScatterD && row_guard) {
assert(indices_);
memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset +
LongIndex(indices_[row_offset + thread_start_row_]) * LongIndex(params_.stride));
}
CUTLASS_PRAGMA_UNROLL
for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
bool guard = row_guard && mask_.predicates[column];
int col_offset = column * ThreadMap::Delta::kColumn;
if (PermuteD) {
int col = col_offset + thread_start_column_;
int row = row_offset + thread_start_row_;
TensorCoord init_coord(row, col);
// Locate memory_pointer
memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset
+ permute_layout_(init_coord) * sizeof(AccessType) / kElementsPerAccess);
}
if (UseCUDAStore) {
if (guard) {
memory_pointer[0] =
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column];
}
} else {
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn + column],
(void *)&memory_pointer[0],
guard);
}
if (!PermuteD) {
memory_pointer += (ThreadMap::Delta::kColumn / kElementsPerAccess);
}
}
if (row + 1 < ThreadMap::Iterations::kRow) {
if (!ScatterD && !PermuteD) {
byte_pointer += params_.increment_row;
}
}
}
if (group + 1 < ThreadMap::Iterations::kGroup) {
byte_pointer += params_.increment_group;
}
}
if (cluster + 1 < ThreadMap::Iterations::kCluster) {
byte_pointer += params_.increment_cluster;
}
}
}
/// Stores a fragment to memory
CUTLASS_DEVICE
void store(Fragment const &frag) const {
store_with_byte_offset(frag, 0);
}
/// Loads a fragment from memory
CUTLASS_DEVICE
void downsample_load_with_byte_offset(Fragment &frag, int64_t byte_offset, int convolution_P, int convolution_Q, int add_P, int add_Q, int problem_N) const {
uint8_t *byte_pointer = byte_pointer_;
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
CUTLASS_PRAGMA_UNROLL
for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
CUTLASS_PRAGMA_UNROLL
for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
CUTLASS_PRAGMA_UNROLL
for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
int frag_row_idx =
(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
int row_offset = row * ThreadMap::Delta::kRow
+ group * ThreadMap::Delta::kGroup
+ cluster * ThreadMap::Delta::kCluster;
bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
int output_row = row_offset + thread_start_row_;
int output_N = output_row / (convolution_P * convolution_Q);
int output_PQ = output_row % (convolution_P * convolution_Q);
int output_P = output_PQ / convolution_Q;
int output_Q = output_PQ % convolution_Q;
int input_row = output_N * 2 * convolution_P * 2 * convolution_Q +
(2 * output_P + add_P) * 2 * convolution_Q + 2 * output_Q + add_Q;
int64_t byte_offset = (input_row-output_row)*problem_N*sizeof(float);
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);
CUTLASS_PRAGMA_UNROLL
for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
bool guard = row_guard && mask_.predicates[column];
cutlass::arch::global_load<
AccessType,
sizeof(AccessType)
>(
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
column],
(void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
kElementsPerAccess],
guard);
}
if (row + 1 < ThreadMap::Iterations::kRow) {
byte_pointer += params_.increment_row;
}
}
if (group + 1 < ThreadMap::Iterations::kGroup) {
byte_pointer += params_.increment_group;
}
}
if (cluster + 1 < ThreadMap::Iterations::kCluster) {
byte_pointer += params_.increment_cluster;
}
}
}
/// Loads a fragment from memory
CUTLASS_DEVICE
void upsample_load_with_byte_offset(Fragment &frag, int64_t byte_offset, int convolution_P, int convolution_Q, int add_P, int add_Q, int problem_N) const {
uint8_t *byte_pointer = byte_pointer_;
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
CUTLASS_PRAGMA_UNROLL
for (int cluster = 0; cluster < ThreadMap::Iterations::kCluster; ++cluster) {
CUTLASS_PRAGMA_UNROLL
for (int group = 0; group < ThreadMap::Iterations::kGroup; ++group) {
CUTLASS_PRAGMA_UNROLL
for (int row = 0; row < ThreadMap::Iterations::kRow; ++row) {
int frag_row_idx =
(row + ThreadMap::Iterations::kRow * (group + ThreadMap::Iterations::kGroup * cluster));
int row_offset = row * ThreadMap::Delta::kRow
+ group * ThreadMap::Delta::kGroup
+ cluster * ThreadMap::Delta::kCluster;
bool row_guard = ((row_offset + thread_start_row_) < extent_row_);
int output_row = row_offset + thread_start_row_;
int output_N = output_row / (convolution_P * convolution_Q);
int output_PQ = output_row % (convolution_P * convolution_Q);
int output_P = output_PQ / convolution_Q;
int output_Q = output_PQ % convolution_Q;
int row_add_P = add_P;
int row_add_Q = add_Q;
if (output_P > convolution_P - 2) row_add_P = 0;
if (output_Q > convolution_Q - 2) row_add_Q = 0;
int input_row = output_N * (convolution_P/2) * (convolution_Q/2) +
((output_P + row_add_P)/2) * (convolution_Q/2) + (output_Q + row_add_Q)/2;
int64_t byte_offset = (input_row-output_row)*problem_N*sizeof(float);
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer + byte_offset);
CUTLASS_PRAGMA_UNROLL
for (int column = 0; column < ThreadMap::Iterations::kColumn; ++column) {
bool guard = row_guard && mask_.predicates[column];
cutlass::arch::global_load<
AccessType,
sizeof(AccessType)
>(
frag_ptr[frag_row_idx * ThreadMap::Iterations::kColumn +
column],
(void *)&memory_pointer[column * ThreadMap::Delta::kColumn /
kElementsPerAccess],
guard);
}
if (row + 1 < ThreadMap::Iterations::kRow) {
byte_pointer += params_.increment_row;
}
}
if (group + 1 < ThreadMap::Iterations::kGroup) {
byte_pointer += params_.increment_group;
}
}
if (cluster + 1 < ThreadMap::Iterations::kCluster) {
byte_pointer += params_.increment_cluster;
}
}
}
CUTLASS_DEVICE
MatrixCoord thread_start() const {
return MatrixCoord(thread_start_row_, thread_start_column_);
}
/// Need to get the thread start row from the tile iterator
CUTLASS_DEVICE
int32_t thread_start_row() const {
return thread_start_row_;
}
/// Need to get the thread start row from the tile iterator
CUTLASS_DEVICE
int32_t thread_start_column() const {
return thread_start_column_;
}
/// Extent of the matrix in rows
CUTLASS_DEVICE
Index extent_row() const {
return extent_row_;
}
/// Extent of the matrix in columns
CUTLASS_DEVICE
Index extent_column() const {
return extent_column_;
}
/// Advances to the next position to load or store
CUTLASS_HOST_DEVICE
PredicatedTileIterator &operator++() {
++state_[0];
if (!ScatterD && !PermuteD) {
store_byte_pointer_ += params_.advance_row;
}
if (!ScatterD) {
byte_pointer_ += params_.advance_row;
}
thread_start_row_ += ThreadMap::Shape::kRow;
if (state_[0] == ThreadMap::Count::kRow) {
state_[0] = 0;
++state_[1];
byte_pointer_ += params_.advance_group;
store_byte_pointer_ += params_.advance_group;
thread_start_row_ += (ThreadMap::Shape::kGroup - 1) *
ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
if (state_[1] == ThreadMap::Count::kGroup) {
state_[1] = 0;
++state_[2];
byte_pointer_ += params_.advance_cluster;
store_byte_pointer_ += params_.advance_cluster;
thread_start_row_ += ThreadMap::Count::kGroup *
ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
if (state_[2] == ThreadMap::Count::kCluster) {
state_[2] = 0;
byte_pointer_ += params_.advance_tile;
store_byte_pointer_ += params_.advance_tile;
thread_start_row_ += ThreadMap::Shape::kGroup * ThreadMap::Shape::kRow
* ThreadMap::Shape::kCluster * ThreadMap::Shape::kTile;
}
}
}
return *this;
}
/// Advances a number of positions to load or store
CUTLASS_HOST_DEVICE
PredicatedTileIterator &operator+=(int increment)
{
// Row
state_[0] += increment;
int increment_row = state_[0] / ThreadMap::Count::kRow;
state_[0] = state_[0] % ThreadMap::Count::kRow;
byte_pointer_ += (params_.advance_row * increment);
store_byte_pointer_ += (params_.advance_row * increment);
thread_start_row_ += (ThreadMap::Shape::kRow * increment);
// Group
state_[1] += increment_row;
int increment_group = state_[1] / ThreadMap::Count::kGroup;
state_[1] = state_[1] % ThreadMap::Count::kGroup;
byte_pointer_ += (params_.advance_group * increment_row);
store_byte_pointer_ += (params_.advance_group * increment_row);
thread_start_row_ +=
(ThreadMap::Shape::kGroup - 1) *
ThreadMap::Shape::kRow *
ThreadMap::Count::kRow *
increment_row;
// Cluster
state_[2] += increment_group;
int increment_cluster = state_[2] / ThreadMap::Count::kCluster;
state_[2] = state_[2] % ThreadMap::Count::kCluster;
byte_pointer_ += (params_.advance_cluster * increment_group);
store_byte_pointer_ += (params_.advance_cluster * increment_group);
thread_start_row_ +=
ThreadMap::Count::kGroup *
ThreadMap::Shape::kGroup *
ThreadMap::Count::kRow *
ThreadMap::Shape::kRow *
increment_group;
// Tile
byte_pointer_ += (params_.advance_tile * increment_cluster);
store_byte_pointer_ += (params_.advance_tile * increment_cluster);
thread_start_row_ +=
ThreadMap::Shape::kGroup *
ThreadMap::Shape::kRow *
ThreadMap::Shape::kCluster *
ThreadMap::Shape::kTile *
increment_cluster;
return *this;
}
///< Efficiently disables all accesses guarded by mask
CUTLASS_DEVICE void clear_mask() {
mask_.clear();
}
///< Efficiently enables all accesses guarded by mask
CUTLASS_DEVICE void enable_mask() {
mask_.enable();
}
///< Sets the mask
CUTLASS_DEVICE void get_mask(Mask &mask) const {
mask = mask_;
}
///< Sets the mask
CUTLASS_DEVICE void set_mask(Mask const &mask) {
mask_ = mask;
}
};
////////////////////////////////////////////////////////////////////////////////
/// Tile iterator used to load output tile from global memory in epilogue.
///
/// Satisfies: ReadableTileIterator | InterleavedPredicatedTileIterator | ForwardTileIterator
///
template <
typename ThreadMap_, ///< Thread map (conept: OutputTileThreadMap)
typename Element_, ///< Element data type
int InterleavedN ///< Number of Interleaved N
>
class InterleavedPredicatedTileIterator {
public:
using ThreadMap = ThreadMap_;
using Element = Element_;
using Layout = layout::ColumnMajorInterleaved<InterleavedN>;
using TensorRef = TensorRef<Element, Layout>;
using ConstTensorRef = typename TensorRef::ConstTensorRef;
using Index = typename Layout::Index;
using LongIndex = typename Layout::LongIndex;
using TensorCoord = layout::PitchLinearCoord;
static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
static int const kThreads = ThreadMap::kThreads;
static int const kIterations = ThreadMap::Iterations::kCount;
/// Fragment object
using Fragment = Array<Element, ThreadMap::kElementsPerAccess>;
/// Memory access size
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
/// Uses a non-template class
struct Params : InterleavedPredicatedTileIteratorParams {
using Base = InterleavedPredicatedTileIteratorParams;
CUTLASS_HOST_DEVICE
Params() { }
CUTLASS_HOST_DEVICE
Params(Layout const &layout):
Base(
layout.stride(0) * int(sizeof(AccessType)) / kElementsPerAccess,
make_InterleavedPredicatedTileIteratorDesc<Element, ThreadMap>()
) { }
CUTLASS_HOST_DEVICE
Params(Base const &base) :
Base(base) { }
};
/// Mask object
struct Mask {
static int const kCount = (ThreadMap::Iterations::kContiguous < 8)
? 8
: ThreadMap::Iterations::kContiguous;
/// Predicate state
bool predicates[kCount];
//
// Mask
//
CUTLASS_HOST_DEVICE
Mask() {
enable();
}
///< Efficiently disables all accesses guarded by mask
CUTLASS_HOST_DEVICE void clear() {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < kCount; ++i) {
predicates[i] = false;
}
}
///< CUTLASS_HOST_DEVICE enables all accesses guarded by mask
CUTLASS_DEVICE void enable() {
CUTLASS_PRAGMA_UNROLL
for (int i = 0; i < kCount; ++i) {
predicates[i] = true;
}
}
};
private:
//
// Data members
//
/// Parameters structure containing reference and precomputed state.
Params params_;
/// Byte-level pointer
uint8_t *byte_pointer_;
/// Array of boolean values to contain steady-state predicates
Mask mask_;
/// Extent of the matrix tile in columns
Index extent_col_;
/// A thread's starting column position (assuming steady-state predicates have
/// been computed)
Index thread_start_col_;
/// Internal iteration counter
int iteration_contiguous_;
int iteration_strided_;
private:
//
// Methods
//
public:
//
// Methods
//
/// Constructor
CUTLASS_DEVICE
InterleavedPredicatedTileIterator(
Params const & params,
Element *pointer,
TensorCoord extent,
int thread_idx,
TensorCoord threadblock_offset,
int const *indices = nullptr ///< gather/scatter indices, note no support for gather/scatter at this specialization
):
params_(params) {
TensorCoord thread_offset = ThreadMap::initial_offset(thread_idx) +
TensorCoord(threadblock_offset.contiguous() * InterleavedN,
threadblock_offset.strided() / InterleavedN);
extent_col_ = extent.strided() / InterleavedN;
thread_start_col_ = thread_offset.strided();
// Initialize predicates
CUTLASS_PRAGMA_UNROLL
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
mask_.predicates[c] =
((thread_offset.contiguous() + ThreadMap::Delta::kContiguous * c) <
(extent.contiguous() * InterleavedN));
}
// Initialize pointer
byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
LongIndex(thread_offset.strided()) * LongIndex(params_.stride) +
LongIndex(thread_offset.contiguous()) * sizeof(AccessType) / kElementsPerAccess;
// Initialize internal state counter
iteration_contiguous_ = iteration_strided_ = 0;
}
/// Adds a pointer offset in units of Element
CUTLASS_HOST_DEVICE
void add_pointer_offset(LongIndex pointer_offset) {
byte_pointer_ += pointer_offset * sizeof_bits<Element>::value / 8;
}
/// Loads a fragment from memory
CUTLASS_DEVICE
void load(Fragment &frag) {
uint8_t *byte_pointer = byte_pointer_;
AccessType *frag_ptr = reinterpret_cast<AccessType *>(&frag);
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);
int col_offset = iteration_strided_ * ThreadMap::Delta::kStrided;
bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
bool guard = col_guard && mask_.predicates[iteration_contiguous_];
cutlass::arch::global_load<
AccessType,
sizeof(AccessType)
>(
*frag_ptr,
(void *)memory_pointer,
guard);
}
/// Stores a fragment to memory
CUTLASS_DEVICE
void store(Fragment const &frag) {
uint8_t *byte_pointer = byte_pointer_;
AccessType const *frag_ptr = reinterpret_cast<AccessType const *>(&frag);
AccessType *memory_pointer = reinterpret_cast<AccessType *>(byte_pointer);
int col_offset = iteration_strided_ * ThreadMap::Delta::kStrided;
bool col_guard = ((thread_start_col_ + col_offset) < extent_col_);
bool guard = col_guard && mask_.predicates[iteration_contiguous_];
cutlass::arch::global_store<AccessType, sizeof(AccessType)>(
*frag_ptr, (void *)memory_pointer, guard);
}
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(int iteration) {
iteration_contiguous_ = iteration % ThreadMap::Iterations::kContiguous;
iteration_strided_ = iteration / ThreadMap::Iterations::kContiguous;
}
/// Advances to the next position to load or store
CUTLASS_HOST_DEVICE
InterleavedPredicatedTileIterator &operator++() {
++iteration_contiguous_;
byte_pointer_ += params_.advance_row;
if (iteration_contiguous_ == ThreadMap::Iterations::kContiguous) {
iteration_contiguous_ = 0;
++iteration_strided_;
byte_pointer_ += params_.advance_column;
if (iteration_strided_ == ThreadMap::Iterations::kStrided) {
iteration_strided_ = 0;
}
}