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refine the implementation

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This commit is contained in:
Haicheng Wu
2021-09-08 13:14:08 +00:00
parent 4e8af93da1
commit 59e2aa505a
45 changed files with 1593 additions and 706 deletions
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72
include/cutlass/conv/threadblock/conv2d_dgrad_filter_tile_access_iterator_analytic.h
View File

@@ -59,7 +59,8 @@ template <
typename Shape_,
typename Element_,
typename ThreadMap_,
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dDgradFilterTileAccessIteratorAnalytic;
@@ -70,13 +71,15 @@ class Conv2dDgradFilterTileAccessIteratorAnalytic;
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_
>
class Conv2dDgradFilterTileAccessIteratorAnalytic <
Shape_,
Element_,
ThreadMap_,
conv::StrideSupport::kStrided
conv::StrideSupport::kStrided,
AccessType_
> {
public:
@@ -88,7 +91,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -97,7 +100,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"DGRAD requires elements of size 8b or larger.");
@@ -107,14 +115,13 @@ public:
using Params = Conv2dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
// For a fixed filter position (r,s) find and fill offset_k_, offset_c_ in strided and contiguous dimension
@@ -162,8 +169,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -213,7 +222,7 @@ public:
TensorCoord coord = at();
return coord.n() < problem_size_.K && coord.c() < problem_size_.C;
return coord.n() < problem_size_.K && (coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.C;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -223,13 +232,19 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dDgradFilterTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -249,7 +264,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
@@ -263,13 +278,15 @@ public:
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_
>
class Conv2dDgradFilterTileAccessIteratorAnalytic <
Shape_,
Element_,
ThreadMap_,
conv::StrideSupport::kUnity
conv::StrideSupport::kUnity,
AccessType_
>{
public:
@@ -281,7 +298,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -290,7 +307,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"DGRAD requires elements of size 8b or larger.");
@@ -306,6 +328,7 @@ private:
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
// For a fixed filter position (r,s) find and fill offset_k_, offset_c_ in strided and contiguous dimension
@@ -348,8 +371,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -395,7 +420,7 @@ public:
TensorCoord coord = at();
return coord.n() < problem_size_.K && coord.c() < problem_size_.C;
return coord.n() < problem_size_.K && (coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.C;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -405,13 +430,18 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dDgradFilterTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -431,7 +461,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
@@ -446,5 +476,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

59
include/cutlass/conv/threadblock/conv2d_dgrad_filter_tile_access_iterator_optimized.h
View File

@@ -60,7 +60,8 @@ template <
typename Shape_,
typename Element_,
typename ThreadMap_,
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dDgradFilterTileAccessIteratorOptimized;
@@ -71,13 +72,15 @@ class Conv2dDgradFilterTileAccessIteratorOptimized;
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_
>
class Conv2dDgradFilterTileAccessIteratorOptimized <
Shape_,
Element_,
ThreadMap_,
conv::StrideSupport::kUnity
conv::StrideSupport::kUnity,
AccessType_
> {
public:
@@ -89,7 +92,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -98,9 +101,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
//
// Parameters structure
//
@@ -141,9 +147,10 @@ private:
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
uint32_t predicates_;
uint32_t predicates_[kAccessesPerVector];
int filter_rs_;
int filter_k_;
@@ -169,7 +176,7 @@ public:
params_(params),
problem_size_(problem_size),
pointer_(reinterpret_cast<char const *>(ptr)),
predicates_(0),
predicates_{0},
filter_rs_(0),
filter_k_(0) {
@@ -186,11 +193,15 @@ public:
int filter_k = filter_k_ + s * ThreadMap::Delta::kStrided;
int filter_c = column + c * ThreadMap::Delta::kContiguous;
uint32_t pred = ((filter_k < problem_size_.K && filter_c < problem_size_.C) ? 1u : 0);
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < kAccessesPerVector; ++v) {
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
predicates_ |= (pred << pred_idx);
uint32_t pred = ((filter_k < problem_size_.K && (filter_c + v * AccessType::kElements) < problem_size_.C) ? 1u : 0);
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
predicates_[v] |= (pred << pred_idx);
}
}
}
@@ -204,8 +215,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -234,7 +247,11 @@ public:
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
if (filter_k_ + s * ThreadMap::Delta::kStrided >= problem_size_.K) {
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
predicates_ = (predicates_ & (~kClearMask));
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < kAccessesPerVector; ++v) {
predicates_[v] = (predicates_[v] & (~kClearMask));
}
}
}
@@ -245,19 +262,25 @@ public:
CUTLASS_HOST_DEVICE
bool valid() {
LongIndex pred_idx = iteration_contiguous_ + iteration_strided_ * ThreadMap::Iterations::kContiguous;
return (predicates_ & (1u << pred_idx));
return (predicates_[iteration_vector_] & (1u << pred_idx));
}
/// Returns a pointer to the vector starting at the current coordinate
CUTLASS_HOST_DEVICE
AccessType const *get() const {
return reinterpret_cast<AccessType const *>(pointer_ +
iteration_contiguous_ * ThreadMap::Delta::kContiguous * sizeof_bits<Element>::value / 8);
iteration_contiguous_ * ThreadMap::Delta::kContiguous * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dDgradFilterTileAccessIteratorOptimized &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -282,7 +305,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
@@ -297,5 +320,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

76
include/cutlass/conv/threadblock/conv2d_dgrad_output_gradient_tile_access_iterator_analytic.h
View File

@@ -59,7 +59,8 @@ template <
typename Shape_,
typename Element_,
typename ThreadMap_,
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kStrided
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kStrided,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dDgradOutputGradientTileAccessIteratorAnalytic;
/////////////////////////////////////////////////////////////////////////////////////////////////
@@ -69,13 +70,15 @@ class Conv2dDgradOutputGradientTileAccessIteratorAnalytic;
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_
>
class Conv2dDgradOutputGradientTileAccessIteratorAnalytic <
Shape_,
Element_,
ThreadMap_,
conv::StrideSupport::kStrided
conv::StrideSupport::kStrided,
AccessType_
> {
public:
@@ -86,7 +89,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -95,7 +98,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"DGRAD requires elements of size 8b or greater.");
@@ -112,14 +120,13 @@ public:
using Params = Conv2dDgradOutputGradientTileAccessIteratorAnalyticParams;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
int filter_k_;
@@ -211,8 +218,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -277,7 +286,7 @@ public:
coord.n() < problem_size_.N &&
coord.h() >= 0 && coord.h() < problem_size_.P &&
coord.w() >= 0 && coord.w() < problem_size_.Q &&
coord.c() < problem_size_.K;
(coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.K;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -287,12 +296,18 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dDgradOutputGradientTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -312,14 +327,14 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
if (problem_size.K % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
return Status::kSuccess;
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
// Conv2dDgradOutputGradientTileAccessIteratorAnalytic for unity strides can be optimized by
@@ -327,13 +342,15 @@ public:
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_
>
class Conv2dDgradOutputGradientTileAccessIteratorAnalytic <
Shape_,
Element_,
ThreadMap_,
conv::StrideSupport::kUnity
conv::StrideSupport::kUnity,
AccessType_
> {
public:
@@ -344,7 +361,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -353,7 +370,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"DGRAD requires elements of size 8b or greater.");
@@ -368,8 +390,6 @@ public:
// Parameters structure
//
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
struct Params {
Layout layout;
@@ -395,6 +415,7 @@ private:
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
int filter_k_;
@@ -446,8 +467,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -497,7 +520,6 @@ public:
}
/// Returns true if the current coordinate is within the output tensor Dy
CUTLASS_HOST_DEVICE
bool valid() const {
@@ -507,7 +529,7 @@ public:
return coord.n() < problem_size_.N &&
coord.h() >= 0 && coord.h() < problem_size_.P &&
coord.w() >= 0 && coord.w() < problem_size_.Q &&
coord.c() < problem_size_.K;
(coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.K;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -517,12 +539,18 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dDgradOutputGradientTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -548,7 +576,7 @@ public:
}
// check alignment constraint on iterator's contiguous dimension
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
if (problem_size.K % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
@@ -556,7 +584,9 @@ public:
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace threadblock
} // namespace conv
} // namespace cutlass

132
include/cutlass/conv/threadblock/conv2d_dgrad_output_gradient_tile_access_iterator_optimized.h
View File

@@ -61,7 +61,8 @@ template <
typename Shape_,
typename Element_,
typename ThreadMap_,
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity
conv::StrideSupport StrideSupport_ = conv::StrideSupport::kUnity,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dDgradOutputGradientTileAccessIteratorOptimized;
/////////////////////////////////////////////////////////////////////////////////////////////////
@@ -74,14 +75,16 @@ class Conv2dDgradOutputGradientTileAccessIteratorOptimized;
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_
>
class Conv2dDgradOutputGradientTileAccessIteratorOptimized <
Shape_,
Element_,
ThreadMap_,
conv::StrideSupport::kUnity
> {
conv::StrideSupport::kUnity,
AccessType_
> {
public:
//
@@ -93,7 +96,7 @@ public:
using Layout = layout::TensorNHWC;
using TensorCoord = typename Layout::TensorCoord;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using Index = typename Layout::Index;
using LongIndex = typename Layout::LongIndex;
@@ -101,7 +104,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kUnity;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
using Mask = uint64_t;
//
@@ -116,14 +124,13 @@ public:
using Params = Conv2dDgradOutputGradientIteratorOptimizedParams;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Conv2dDgradOutputGradientIteratorOptimizedParams const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
// One pointer per access
char const *pointer_[ThreadMap::Iterations::kStrided];
@@ -133,7 +140,7 @@ private:
int filter_s_;
int filter_k_;
Index masks_[ThreadMap::Iterations::kStrided][2];
Index masks_[ThreadMap::Iterations::kStrided][kAccessesPerVector][2];
public:
@@ -201,7 +208,11 @@ public:
int p = offset_h[s_idx] + problem_size_.pad_h - r_ * problem_size_.dilation_h;
bool pred = (offset_n[s_idx] < problem_size_.N && p >= 0 && p < problem_size_.P);
masks_[s_idx][0] |= (pred << r);
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
masks_[s_idx][v_idx][0] |= (pred << r);
}
}
}
@@ -218,12 +229,17 @@ public:
int q = offset_w[s_idx] + problem_size_.pad_w - s_ * problem_size_.dilation_w;
bool pred = (q >= 0 && q < problem_size_.Q);
masks_[s_idx][1] |= (pred << s);
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
masks_[s_idx][v_idx][1] |= (pred << s);
}
}
}
if (filter_k_ >= problem_size.K) {
clear_mask();
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask(v_idx, filter_k_ >= problem_size.K);
}
set_iteration_index(0);
@@ -269,62 +285,15 @@ private:
}
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask_(bool clear) {
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
// We are using inline PTX assembly here to avoid an CUDA C++ compilation
// artifact in which control flow instructions are generated. Instead, our
// intent is to predicate the mov instructions.
#if defined(__CUDA_ARCH__)
asm volatile(
"{\n"
" .reg .pred p;\n"
" .reg .u32 m;"
" mov.u32 m, %2;"
" setp.ne.b32 p, %1, 0;\n"
" @p mov.u32 m, 0;\n"
" mov.u32 %0, m;\n"
"}\n"
:
"=r"(masks_[s][0])
:
"r"((int)clear),
"r"(masks_[s][0])
);
asm volatile(
"{\n"
" .reg .pred p;\n"
" .reg .u32 m;"
" mov.u32 m, %2;"
" setp.ne.b32 p, %1, 0;\n"
" @p mov.u32 m, 0;\n"
" mov.u32 %0, m;\n"
"}\n"
:
"=r"(masks_[s][1])
:
"r"((int)clear),
"r"(masks_[s][1])
);
#else
if (clear) {
masks_[s][0] = 0;
masks_[s][1] = 0;
}
#endif
}
}
public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of element
@@ -359,16 +328,32 @@ public:
filter_k_ += params_.filter_k_delta;
}
clear_mask_(filter_k_ >= problem_size_.K);
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask(v_idx, (filter_k_ + v_idx * AccessType::kElements) >= problem_size_.K);
}
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask() {
void clear_mask(bool clear = true) {
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
masks_[s][0] = Mask(0);
masks_[s][1] = Mask(0);
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < kAccessesPerVector; ++v) {
masks_[s][v][0] = clear ? Mask(0) : masks_[s][v][0];
masks_[s][v][1] = clear ? Mask(0) : masks_[s][v][1];
}
}
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask(int v, bool clear = true) {
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
masks_[s][v][0] = clear ? Mask(0) : masks_[s][v][0];
masks_[s][v][1] = clear ? Mask(0) : masks_[s][v][1];
}
}
@@ -376,20 +361,25 @@ public:
bool valid() {
return
(masks_[iteration_strided_][0] & (Index(1) << filter_r_)) &&
(masks_[iteration_strided_][1] & (Index(1) << filter_s_));
(masks_[iteration_strided_][iteration_vector_][0] & (Index(1) << filter_r_)) &&
(masks_[iteration_strided_][iteration_vector_][1] & (Index(1) << filter_s_));
}
/// Returns a pointer to the vector starting at the current coordinate
CUTLASS_HOST_DEVICE
AccessType const *get() const {
return reinterpret_cast<AccessType const *>(pointer_[iteration_strided_]);
return reinterpret_cast<AccessType const *>(pointer_[iteration_strided_]) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dDgradOutputGradientTileAccessIteratorOptimized &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
@@ -416,7 +406,7 @@ public:
}
// check alignment constraint on iterator's contiguous dimension
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
if (problem_size.K % AccessType::kElements) {
return Status::kErrorNotSupported;
}

31
include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h
View File

@@ -60,7 +60,8 @@ template <
typename Shape_,
typename Element_,
typename Layout_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dFpropActivationTileAccessIteratorAnalytic {
public:
@@ -74,7 +75,7 @@ public:
using Layout = Layout_;
using TensorCoord = typename Layout::TensorCoord;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using Index = typename Layout::Index;
using LongIndex = typename Layout::LongIndex;
@@ -82,7 +83,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
//
// Simplifying assertions
//
@@ -95,14 +101,13 @@ public:
using Params = Conv2dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
int filter_c_;
@@ -156,8 +161,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -214,7 +221,7 @@ public:
return coord.n() < problem_size_.N &&
coord.h() >= 0 && coord.h() < problem_size_.H &&
coord.w() >= 0 && coord.w() < problem_size_.W &&
coord.c() < problem_size_.C;
(coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.C;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -224,7 +231,7 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
AccessType const *ptr = reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
AccessType const *ptr = reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
return ptr;
}
@@ -232,6 +239,12 @@ public:
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dFpropActivationTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -252,7 +265,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}

97
include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h
View File

@@ -61,7 +61,7 @@ template <
typename Element_,
typename Layout_,
typename ThreadMap_,
int AccessSize = ThreadMap_::kElementsPerAccess
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dFpropActivationTileAccessIteratorOptimized {
public:
@@ -75,7 +75,7 @@ public:
using Layout = Layout_;
using TensorCoord = typename Layout::TensorCoord;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, AccessSize>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using Index = typename Layout::Index;
using LongIndex = typename Layout::LongIndex;
@@ -86,6 +86,11 @@ public:
using Mask = uint64_t;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
//
// Simplifying assertions
//
@@ -98,8 +103,6 @@ public:
using Params = Conv2dFpropActivationIteratorOptimizedParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
@@ -213,10 +216,10 @@ public:
}
}
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask_(filter_c_ + v_idx * AccessSize >= problem_size_.C, v_idx);
}
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask(v_idx, filter_c_ + v_idx * AccessType::kElements >= problem_size_.C);
}
set_iteration_index(0);
}
@@ -260,56 +263,7 @@ private:
pointer_[s] += byte_offset;
}
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask_(bool clear, int index) {
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
// We are using inline PTX assembly here to avoid an CUDA C++ compilation
// artifact in which control flow instructions are generated. Instead, our
// intent is to predicate the mov instructions.
#if defined(__CUDA_ARCH__)
asm volatile(
"{\n"
" .reg .pred p;\n"
" .reg .u32 m;"
" mov.u32 m, %2;"
" setp.ne.b32 p, %1, 0;\n"
" @p mov.u32 m, 0;\n"
" mov.u32 %0, m;\n"
"}\n"
:
"=r"(masks_[s][index][0])
:
"r"((int)clear),
"r"(masks_[s][index][0])
);
asm volatile(
"{\n"
" .reg .pred p;\n"
" .reg .u32 m;"
" mov.u32 m, %2;"
" setp.ne.b32 p, %1, 0;\n"
" @p mov.u32 m, 0;\n"
" mov.u32 %0, m;\n"
"}\n"
:
"=r"(masks_[s][index][1])
:
"r"((int)clear),
"r"(masks_[s][index][1])
);
#else
if (clear) {
masks_[s][index][0] = 0;
masks_[s][index][1] = 0;
}
#endif
}
}
public:
/// Overrides the internal iteration index
@@ -354,23 +308,33 @@ public:
filter_c_ += params_.filter_c_delta;
}
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask_(filter_c_ + v_idx * AccessSize >= problem_size_.C, v_idx);
}
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask(v_idx, filter_c_ + v_idx * AccessType::kElements >= problem_size_.C);
}
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask() {
void clear_mask(bool clear = true) {
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < kAccessesPerVector; ++v) {
masks_[s][v][0] = Mask(0);
masks_[s][v][1] = Mask(0);
masks_[s][v][0] = clear ? 0 : masks_[s][v][0];
masks_[s][v][1] = clear ? 0 : masks_[s][v][1];
}
}
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask(int v, bool clear = true) {
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
masks_[s][v][0] = clear ? 0 : masks_[s][v][0];
masks_[s][v][1] = clear ? 0 : masks_[s][v][1];
}
}
CUTLASS_HOST_DEVICE
@@ -396,7 +360,6 @@ public:
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
@@ -419,7 +382,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % AccessSize) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}

33
include/cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h
View File

@@ -59,7 +59,8 @@ template <
typename Shape_,
typename Element_,
typename Layout_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dFpropFilterTileAccessIteratorAnalytic {
public:
@@ -72,7 +73,7 @@ public:
using Element = Element_;
using Layout = Layout_;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -81,7 +82,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
//
// Simplifying assertions
//
@@ -94,14 +100,13 @@ public:
using Params = Conv2dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
int filter_r_;
@@ -142,8 +147,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -187,7 +194,7 @@ public:
TensorCoord coord = at();
return coord.n() < problem_size_.K &&
coord.c() < problem_size_.C;
(coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.C;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -197,12 +204,18 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dFpropFilterTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -223,7 +236,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
if (problem_size.K % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
@@ -250,5 +263,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

57
include/cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h
View File

@@ -61,7 +61,7 @@ template <
typename Element_,
typename Layout_,
typename ThreadMap_,
int AccessSize = ThreadMap_::kElementsPerAccess
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dFpropFilterTileAccessIteratorOptimized{
public:
@@ -74,7 +74,7 @@ public:
using Element = Element_;
using Layout = Layout_;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, AccessSize>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -83,15 +83,18 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
//
// Simplifying assertions
//
static_assert(ThreadMap::Iterations::kContiguous == 1,
"Require Iterations::kContiguous == 1");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
//
@@ -170,6 +173,7 @@ public:
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
uint32_t pred = ((column + s * ThreadMap::Delta::kStrided < problem_size_.K) ? 1u : 0);
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
predicates_[v_idx] |= (pred << s);
@@ -178,7 +182,7 @@ public:
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask_(filter_c_ + v_idx * AccessSize >= problem_size_.C, v_idx);
clear_mask(v_idx, filter_c_ + v_idx * AccessType::kElements >= problem_size_.C);
}
pointer_ += (
@@ -188,41 +192,11 @@ public:
set_iteration_index(0);
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask_(bool clear, int index) {
// We are using inline PTX assembly here to avoid an CUDA C++ compilation
// artifact in which control flow instructions are generated. Instead, our
// intent is to predicate the mov instructions.
#if defined(__CUDA_ARCH__)
asm volatile(
"{\n"
" .reg .pred p;\n"
" .reg .u32 m;"
" mov.u32 m, %2;"
" setp.ne.b32 p, %1, 0;\n"
" @p mov.u32 m, 0;\n"
" mov.u32 %0, m;\n"
"}\n"
:
"=r"(predicates_[index])
:
"r"((int)clear),
"r"(predicates_[index])
);
#else
if (clear) {
predicates_[index] = 0;
}
#endif
}
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
@@ -246,15 +220,21 @@ public:
next = params_.inc_next_c;
filter_c_ += params_.filter_c_delta;
}
CUTLASS_PRAGMA_UNROLL
for (int v_idx = 0; v_idx < kAccessesPerVector; ++v_idx) {
clear_mask_(filter_c_ + v_idx * AccessSize >= problem_size_.C, v_idx);
clear_mask(v_idx, filter_c_ + v_idx * AccessType::kElements >= problem_size_.C);
}
pointer_ += next;
}
/// Clears the predicates
CUTLASS_HOST_DEVICE
void clear_mask(int v, bool clear = true) {
predicates_[v] = clear ? 0u : predicates_[v];
}
/// Returns true if the current coordinate is within the filter tensor W
CUTLASS_HOST_DEVICE
bool valid() {
@@ -274,7 +254,6 @@ public:
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
@@ -301,7 +280,7 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % AccessSize) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}

11
include/cutlass/conv/threadblock/conv2d_tile_iterator.h
View File

@@ -68,6 +68,7 @@ public:
using Params = typename TileAccessIterator::Params;
static int const kConvDim = TileAccessIterator::kConvDim;
using ConvProblemSize = typename TileAccessIterator::ConvProblemSize;
static int const kAccessesPerVector = TileAccessIterator::kAccessesPerVector;
/// Fragment object to be loaded or stored
using Fragment = cutlass::Array<
@@ -130,18 +131,20 @@ public:
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
CUTLASS_PRAGMA_UNROLL
for (int c = 0; c < ThreadMap::Iterations::kContiguous; ++c) {
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < tile_access_iterator_.kAccessesPerVector; ++v) {
for (int v = 0; v < kAccessesPerVector; ++v) {
int idx = v + kAccessesPerVector * (c + s * ThreadMap::Iterations::kContiguous);
cutlass::arch::global_load<
AccessType,
sizeof(AccessType)
>(
frag_ptr[(c + s * ThreadMap::Iterations::kContiguous) * tile_access_iterator_.kAccessesPerVector + v],
frag_ptr[idx],
tile_access_iterator_.get() + pointer_offset,
tile_access_iterator_.valid()
);
++tile_access_iterator_;
}
}

53
include/cutlass/conv/threadblock/conv2d_wgrad_activation_tile_access_iterator_analytic.h
View File

@@ -58,7 +58,8 @@ namespace threadblock {
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dWgradActivationTileAccessIteratorAnalytic {
public:
@@ -70,7 +71,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -79,7 +80,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
@@ -89,14 +95,13 @@ public:
using Params = Conv2dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
// Filter postion (r,s,c) in contiguous dimension stays constant for each gemm_iteration_k
@@ -149,8 +154,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -173,9 +180,19 @@ public:
/// by the iterator.
CUTLASS_HOST_DEVICE
TensorCoord at() const {
int r, s, c;
int r = filter_r_[iteration_contiguous_];
int s = filter_s_[iteration_contiguous_];
if (kAccessesPerVector == 1) {
r = filter_r_[iteration_contiguous_];
s = filter_s_[iteration_contiguous_];
c = filter_c_[iteration_contiguous_];
} else {
c = (filter_c_[iteration_contiguous_] + iteration_vector_ * AccessType::kElements) % problem_size_.C;
int wrap_c = (filter_c_[iteration_contiguous_] + iteration_vector_ * AccessType::kElements) / problem_size_.C;
s = (filter_s_[iteration_contiguous_] + wrap_c) % problem_size_.S;
int wrap_s = (filter_s_[iteration_contiguous_] + wrap_c) / problem_size_.S;
r = filter_r_[iteration_contiguous_] + wrap_s;
}
if (problem_size_.mode == Mode::kConvolution) {
r = (problem_size_.R - 1 - r);
@@ -184,14 +201,14 @@ public:
int n = offset_npq_[iteration_strided_] / (problem_size_.P * problem_size_.Q);
int residual = offset_npq_[iteration_strided_] % (problem_size_.P * problem_size_.Q);
int p = residual / problem_size_.Q;
int q = residual % problem_size_.Q;
int h = p * problem_size_.stride_h - problem_size_.pad_h + r * problem_size_.dilation_h;
int w = q * problem_size_.stride_w - problem_size_.pad_w + s * problem_size_.dilation_w;
return TensorCoord(n, h, w, filter_c_[iteration_contiguous_]);
return TensorCoord(n, h, w, c);
}
/// Returns true if the current coordinate is within the activation tensor x
@@ -201,8 +218,7 @@ public:
return coord.n() < problem_size_.N &&
coord.h() >= 0 && coord.h() < problem_size_.H &&
coord.w() >= 0 && coord.w() < problem_size_.W &&
coord.c() < problem_size_.C;
coord.w() >= 0 && coord.w() < problem_size_.W;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -218,6 +234,12 @@ public:
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dWgradActivationTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -237,13 +259,12 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
if (problem_size.K % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
return Status::kSuccess;
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////

68
include/cutlass/conv/threadblock/conv2d_wgrad_activation_tile_access_iterator_optimized.h
View File

@@ -57,7 +57,8 @@ namespace threadblock {
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dWgradActivationTileAccessIteratorOptimized {
public:
@@ -69,7 +70,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -78,7 +79,12 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
@@ -88,14 +94,13 @@ public:
using Params = Conv2dWgradActivationIteratorOptimizedParams;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Conv2dWgradActivationIteratorOptimizedParams const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
// Precomputed effective filter postion (r,s) in contiguous dimension stays constant for each gemm_iteration_k
@@ -153,9 +158,8 @@ public:
s = (problem_size_.S - 1 - s);
}
precomputed_filter_r_[c] = - problem_size_.pad_h + r * problem_size_.dilation_h;
precomputed_filter_s_[c] = - problem_size_.pad_w + s * problem_size_.dilation_w;
precomputed_filter_r_[c] = -problem_size_.pad_h + r * problem_size_.dilation_h;
precomputed_filter_s_[c] = -problem_size_.pad_w + s * problem_size_.dilation_w;
}
// initialize n, p, q offset for every strided iteration
@@ -170,8 +174,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -194,6 +200,31 @@ public:
/// by the iterator.
CUTLASS_HOST_DEVICE
TensorCoord at() const {
int r = precomputed_filter_r_[iteration_contiguous_];
int s = precomputed_filter_s_[iteration_contiguous_];
int c = filter_c_[iteration_contiguous_];
if (kAccessesPerVector > 1) {
int wrap_c;
params_.c_divmod(wrap_c, c, c + iteration_vector_ * AccessType::kElements);
if (problem_size_.mode == Mode::kConvolution) {
s -= (problem_size_.dilation_w * wrap_c);
int wrap_s = (s == -problem_size_.pad_w - problem_size_.dilation_w);
s = wrap_s ? (-problem_size_.pad_w + (problem_size_.S - 1) * problem_size_.dilation_w): s;
r -= (problem_size_.dilation_h * wrap_s);
} else {
s += (problem_size_.dilation_w * wrap_c);
int wrap_s = (s == (-problem_size_.pad_w + problem_size_.S * problem_size_.dilation_w));
s = wrap_s ? -problem_size_.pad_w : s;
r += (problem_size_.dilation_h * wrap_s);
}
}
// The subseqnet fast_divmod() operations are equivalent to the following logical computation:
//
@@ -209,10 +240,10 @@ public:
params_.pq_divmod(n, residual, offset_npq_[iteration_strided_]);
params_.q_divmod(p, q, residual);
int h = p * problem_size_.stride_h + precomputed_filter_r_[iteration_contiguous_];
int w = q * problem_size_.stride_w + precomputed_filter_s_[iteration_contiguous_];
int h = p * problem_size_.stride_h + r;
int w = q * problem_size_.stride_w + s;
return TensorCoord(n, h, w, filter_c_[iteration_contiguous_]);
return TensorCoord(n, h, w, c);
}
/// Returns true if the current coordinate is within the activation tensor x
@@ -222,8 +253,7 @@ public:
return coord.n() < problem_size_.N &&
coord.h() >= 0 && coord.h() < problem_size_.H &&
coord.w() >= 0 && coord.w() < problem_size_.W &&
coord.c() < problem_size_.C;
coord.w() >= 0 && coord.w() < problem_size_.W;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -239,6 +269,12 @@ public:
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dWgradActivationTileAccessIteratorOptimized &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -258,14 +294,14 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.K % (128/sizeof_bits<Element>::value)) {
if (problem_size.K % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
return Status::kSuccess;
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace threadblock

35
include/cutlass/conv/threadblock/conv2d_wgrad_output_gradient_tile_access_iterator_analytic.h
View File

@@ -58,7 +58,8 @@ namespace threadblock {
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dWgradOutputGradientTileAccessIteratorAnalytic {
public:
@@ -70,7 +71,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -80,6 +81,11 @@ public:
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
@@ -89,14 +95,13 @@ public:
using Params = Conv2dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
int filter_k_[ThreadMap::Iterations::kContiguous];
@@ -143,8 +148,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -187,7 +194,7 @@ public:
return coord.n() < problem_size_.N &&
coord.h() < problem_size_.P &&
coord.w() < problem_size_.Q &&
coord.c() < problem_size_.K;
(coord.c() + iteration_vector_ * AccessType::kElements) < problem_size_.K;
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -197,12 +204,18 @@ public:
TensorCoord coord = at();
LongIndex offset = params_.layout(coord);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8);
return reinterpret_cast<AccessType const *>(pointer_ + offset * sizeof_bits<Element>::value / 8) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dWgradOutputGradientTileAccessIteratorAnalytic &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -222,14 +235,14 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
return Status::kSuccess;
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace threadblock
@@ -237,5 +250,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

62
include/cutlass/conv/threadblock/conv2d_wgrad_output_gradient_tile_access_iterator_optimized.h
View File

@@ -57,7 +57,8 @@ namespace threadblock {
template <
typename Shape_,
typename Element_,
typename ThreadMap_
typename ThreadMap_,
typename AccessType_ = cutlass::AlignedArray<Element_, ThreadMap_::kElementsPerAccess>
>
class Conv2dWgradOutputGradientTileAccessIteratorOptimized {
public:
@@ -69,7 +70,7 @@ public:
using Element = Element_;
using Layout = layout::TensorNHWC;
using ThreadMap = ThreadMap_;
using AccessType = AlignedArray<Element, ThreadMap::kElementsPerAccess>;
using AccessType = AccessType_;
using TensorRef = cutlass::TensorRef<Element, Layout>;
using TensorCoord = typename Layout::TensorCoord;
using Index = typename Layout::Index;
@@ -79,6 +80,11 @@ public:
static int const kConvDim = 2;
using ConvProblemSize = typename conv::Conv2dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static_assert(!(ThreadMap::kElementsPerAccess % AccessType::kElements),
"Vectors implied by the thread map must be divisible by the access type.");
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
@@ -88,17 +94,16 @@ public:
using Params = Conv2dWgradOutputGradientIteratorOptimizedParams;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Conv2dWgradOutputGradientIteratorOptimizedParams const &params_;
Conv2dProblemSize const &problem_size_;
LongIndex iteration_contiguous_;
LongIndex iteration_strided_;
LongIndex iteration_vector_;
char const *pointer_;
uint32_t predicates_;
uint32_t predicates_[kAccessesPerVector];
int filter_k_;
int offset_npq_;
@@ -115,7 +120,7 @@ public:
params_(params),
problem_size_(problem_size),
pointer_(reinterpret_cast<char const *>(ptr)),
predicates_(0),
predicates_{0},
filter_k_(0),
offset_npq_(0) {
@@ -132,13 +137,16 @@ public:
int filter_k = filter_k_ + c * ThreadMap::Delta::kContiguous;
int offset_npq = offset_npq_ + s * ThreadMap::Delta::kStrided;
bool predicate = valid_(at_(offset_npq, filter_k));
uint32_t pred = (predicate ? 1u : 0);
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
predicates_ |= (pred << pred_idx);
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < kAccessesPerVector; ++v) {
bool predicate = valid_(at_(offset_npq, filter_k + v * AccessType::kElements));
uint32_t pred = (predicate ? 1u : 0);
int pred_idx = c + s * ThreadMap::Iterations::kContiguous;
predicates_[v] |= (pred << pred_idx);
}
}
}
@@ -165,8 +173,10 @@ public:
/// Overrides the internal iteration index
CUTLASS_HOST_DEVICE
void set_iteration_index(Index index) {
iteration_contiguous_ = index % ThreadMap::Iterations::kContiguous;
iteration_strided_ = index / ThreadMap::Iterations::kContiguous;
iteration_vector_ = index % kAccessesPerVector;
int residual_access = index / kAccessesPerVector;
iteration_contiguous_ = residual_access % ThreadMap::Iterations::kContiguous;
iteration_strided_ = residual_access / ThreadMap::Iterations::kContiguous;
}
/// Adds a pointer offset in units of Element
@@ -185,7 +195,11 @@ public:
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
if (offset_npq_ + s * ThreadMap::Delta::kStrided >= params_.NPQ) {
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
predicates_ = (predicates_ & (~kClearMask));
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < kAccessesPerVector; ++v) {
predicates_[v] = (predicates_[v] & (~kClearMask));
}
}
}
@@ -231,7 +245,7 @@ public:
bool valid() const {
LongIndex pred_idx = iteration_contiguous_ + iteration_strided_ * ThreadMap::Iterations::kContiguous;
return (predicates_ & (1u << pred_idx));
return (predicates_[iteration_vector_] & (1u << pred_idx));
}
/// Returns a pointer to the vector starting at the current coordinate
@@ -242,12 +256,18 @@ public:
pointer_ +
iteration_strided_ * params_.offset_next_strided +
iteration_contiguous_ * params_.offset_next_contiguous
);
) + iteration_vector_;
}
/// Increments to the next memory access
CUTLASS_HOST_DEVICE
Conv2dWgradOutputGradientTileAccessIteratorOptimized &operator++() {
++iteration_vector_;
if (iteration_vector_ < kAccessesPerVector) {
return *this;
}
iteration_vector_ = 0;
++iteration_contiguous_;
if (iteration_contiguous_ < ThreadMap::Iterations::kContiguous) {
return *this;
@@ -267,14 +287,14 @@ public:
static Status can_implement(Conv2dProblemSize const &problem_size) {
// check alignment constraint on iterator's contiguous dimension
if (problem_size.C % (128/sizeof_bits<Element>::value)) {
if (problem_size.C % AccessType::kElements) {
return Status::kErrorInvalidProblem;
}
return Status::kSuccess;
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace threadblock
@@ -282,5 +302,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

5
include/cutlass/conv/threadblock/conv3d_dgrad_filter_tile_access_iterator_analytic.h
View File

@@ -79,11 +79,10 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
static_assert(sizeof_bits<Element>::value >= 8,
"DGRAD requires elements of size 8b or larger.");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
@@ -261,5 +260,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

8
include/cutlass/conv/threadblock/conv3d_dgrad_filter_tile_access_iterator_optimized.h
View File

@@ -82,8 +82,7 @@ public:
static StrideSupport const kStrideSupport = StrideSupport_;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
static int const kAccessesPerVector = 1;
//
// Parameters structure
@@ -217,7 +216,8 @@ public:
CUTLASS_PRAGMA_UNROLL
for (int s = 0; s < ThreadMap::Iterations::kStrided; ++s) {
if (filter_k_ + s * ThreadMap::Delta::kStrided >= problem_size_.K) {
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
uint32_t kClearMask = ((1u << ThreadMap::Iterations::kContiguous) - 1) << (s * ThreadMap::Iterations::kContiguous);
predicates_ = (predicates_ & (~kClearMask));
}
}
@@ -281,5 +281,3 @@ public:
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

7
include/cutlass/conv/threadblock/conv3d_dgrad_output_gradient_tile_access_iterator_analytic.h
View File

@@ -93,11 +93,10 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
static_assert(sizeof_bits<Element>::value >= 8,
"DGRAD requires elements of size 8b or greater.");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Simpligying assertions
@@ -328,11 +327,11 @@ public:
}
};
/////////////////////////////////////////////////////////////////////////////////////////////////
} // namespace threadblock
} // namespace conv
} // namespace cutlass
/////////////////////////////////////////////////////////////////////////////////////////////////

5
include/cutlass/conv/threadblock/conv3d_dgrad_output_gradient_tile_access_iterator_optimized.h
View File

@@ -86,7 +86,7 @@ public:
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
using Coord3D = Coord<3>;
static int const kAccessesPerVector = 1;
using Mask = uint64_t;
//
@@ -101,8 +101,6 @@ public:
using Params = Conv3dDgradOutputGradientIteratorOptimizedParams;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;
@@ -403,7 +401,6 @@ public:
}
clear_mask_(filter_k_ >= problem_size_.K);
}

3
include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_analytic.h
View File

@@ -81,6 +81,7 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
//
// Simplifying assertions
@@ -94,8 +95,6 @@ public:
using Params = Conv3dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;

4
include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_optimized.h
View File

@@ -82,7 +82,7 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
using Mask = uint64_t;
//
@@ -97,8 +97,6 @@ public:
using Params = Conv3dFpropActivationIteratorOptimizedParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Conv3dFpropActivationIteratorOptimizedParams<Layout> const &params_;

3
include/cutlass/conv/threadblock/conv3d_fprop_filter_tile_access_iterator_analytic.h
View File

@@ -80,6 +80,7 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
//
// Simplifying assertions
@@ -93,8 +94,6 @@ public:
using Params = Conv3dAnalyticParams<Layout>;
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
private:
Params const &params_;

5
include/cutlass/conv/threadblock/conv3d_fprop_filter_tile_access_iterator_optimized.h
View File

@@ -82,6 +82,7 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
//
// Simplifying assertions
@@ -89,8 +90,6 @@ public:
static_assert(ThreadMap::Iterations::kContiguous == 1,
"Require Iterations::kContiguous == 1");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
//
@@ -156,7 +155,7 @@ public:
params_(params),
problem_size_(problem_size),
pointer_(reinterpret_cast<char const *>(ptr)),
predicates_(0),
predicates_{0},
filter_trs_(0),
filter_c_(0) {

4
include/cutlass/conv/threadblock/conv3d_wgrad_activation_tile_access_iterator_analytic.h
View File

@@ -79,11 +79,11 @@ public:
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
//

4
include/cutlass/conv/threadblock/conv3d_wgrad_activation_tile_access_iterator_optimized.h
View File

@@ -79,12 +79,10 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
//

4
include/cutlass/conv/threadblock/conv3d_wgrad_output_gradient_tile_access_iterator_analytic.h
View File

@@ -78,12 +78,10 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
//

4
include/cutlass/conv/threadblock/conv3d_wgrad_output_gradient_tile_access_iterator_optimized.h
View File

@@ -79,12 +79,10 @@ public:
static StrideSupport const kStrideSupport = conv::StrideSupport::kStrided;
static int const kConvDim = 3;
using ConvProblemSize = typename conv::Conv3dProblemSize;
static int const kAccessesPerVector = 1;
static_assert(sizeof_bits<Element>::value >= 8,
"WGRAD requires elements of size 8b or greater.");
static int const kAccessesPerVector = ThreadMap::kElementsPerAccess / AccessType::kElements;
//
// Parameters structure
//

18
include/cutlass/conv/threadblock/implicit_gemm_multistage.h
View File

@@ -216,6 +216,7 @@ public:
for (int v = 0; v < IteratorA::kAccessesPerVector; ++v) {
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA>(
dst_ptr + v, iterator_A.get(), iterator_A.valid());
++iterator_A;
}
@@ -244,6 +245,7 @@ public:
for (int v = 0; v < IteratorB::kAccessesPerVector; ++v) {
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB>(
dst_ptr + v, iterator_B.get(), iterator_B.valid());
++iterator_B;
}
++this->smem_iterator_B_;
@@ -289,16 +291,17 @@ public:
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < IteratorA::kAccessesPerVector; ++v) {
int const kSrcBytes =
sizeof_bits<typename IteratorA::Element>::value *
IteratorA::ThreadMap::kElementsPerAccess /
IteratorA::kAccessesPerVector / 8;
int const kSrcBytes =
sizeof_bits<typename IteratorA::Element>::value *
IteratorA::ThreadMap::kElementsPerAccess /
IteratorA::kAccessesPerVector / 8;
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA>(
dst_ptr + v, iterator_A.get(), iterator_A.valid());
++iterator_A;
}
++this->smem_iterator_A_;
}
@@ -313,17 +316,18 @@ public:
this->smem_iterator_B_.get());
CUTLASS_PRAGMA_UNROLL
for (int v = 0; v < IteratorB::kAccessesPerVector; ++v) {
for (int v = 0; v < IteratorA::kAccessesPerVector; ++v) {
int const kSrcBytes =
sizeof_bits<typename IteratorB::Element>::value *
IteratorB::ThreadMap::kElementsPerAccess /
IteratorB::kAccessesPerVector / 8;
cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB>(
dst_ptr + v, iterator_B.get(), iterator_B.valid());
++iterator_B;
}
++this->smem_iterator_B_;
}