add a new epilogue for the case that the output is not packed

include/cutlass/epilogue/threadblock/predicated_tile_iterator_conv.h

@@ -0,0 +1,562 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2024 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"
+#include "cutlass/conv/conv2d_problem_size.h"
+#include "cutlass/conv/conv3d_problem_size.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace epilogue {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Tile iterator used to load and store output tile from global memory in epilogue.
+///
+/// Satisfies: ReadableTileIterator | PredicatedTileIteratorConv | 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,
+  int Rank = 4
+>
+class PredicatedTileIteratorConv {
+public:
+  using ThreadMap = ThreadMap_;
+  using Shape = typename ThreadMap::Shape;
+
+  using Element = Element_;
+
+  static int const kRank = Rank;
+  using Layout = typename platform::conditional<kRank == 4,
+                                       layout::TensorNHWC,
+                                       layout::TensorNDHWC>::type;
+
+  using Stride = typename Layout::Stride;
+  static int const kStrideRank = Layout::kStrideRank;
+
+  using TensorRef = TensorRef<Element, Layout>;
+  using ConstTensorRef = typename TensorRef::ConstTensorRef;
+
+  using MappedLayout = layout::RowMajor;
+  using Index = typename MappedLayout::Index;
+  using LongIndex = typename MappedLayout::LongIndex;
+  using TensorCoord = typename MappedLayout::TensorCoord;
+
+  static int const kElementsPerAccess = ThreadMap::kElementsPerAccess;
+  static int const kThreads = ThreadMap::kThreads;
+  static int const kIterations = ThreadMap::Count::kTile;
+
+  static bool constexpr PermuteD = !layout::is_trivial_permute<PermuteDLayout>;
+
+  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;
+
+    /// Fast divmod objects divided by tensor extents
+    FastDivmod divmod[kStrideRank - 1];
+    Stride tensor_stride;
+
+    CUTLASS_HOST_DEVICE
+    Params() { }
+
+    CUTLASS_HOST_DEVICE
+    Params(Layout const &layout, conv::Conv2dProblemSize const &problem_size):
+      PredicatedTileIteratorParams(
+        layout.stride()[0] * int(sizeof(AccessType)) / kElementsPerAccess,
+        make_OutputTileThreadMapDesc<ThreadMap>()
+      ) {
+      divmod[0] = FastDivmod(problem_size.Q);
+      divmod[1] = FastDivmod(problem_size.P);
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kStrideRank; ++i) {
+        tensor_stride[i] = layout.stride()[i];
+      }
+    }
+
+    CUTLASS_HOST_DEVICE
+    Params(Layout const &layout, conv::Conv3dProblemSize const &problem_size):
+      PredicatedTileIteratorParams(
+        layout.stride()[0] * int(sizeof(AccessType)) / kElementsPerAccess,
+        make_OutputTileThreadMapDesc<ThreadMap>()
+      ) {
+      divmod[0] = FastDivmod(problem_size.Q);
+      divmod[1] = FastDivmod(problem_size.P);
+      divmod[2] = FastDivmod(problem_size.Z);
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int i = 0; i < kStrideRank; ++i) {
+        tensor_stride[i] = layout.stride()[i];
+      }
+    }
+
+    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.
+  Params 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_;
+
+  /// 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];
+
+  //
+  // 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
+  PredicatedTileIteratorConv(
+    Params const & params,
+    Element *pointer,
+    TensorCoord extent,
+    int thread_idx,
+    TensorCoord threadblock_offset = TensorCoord()
+  ):
+    params_(params)
+  {
+
+    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();
+    }
+
+    // Initialize byte_pointer_
+    byte_pointer_ = reinterpret_cast<uint8_t *>(pointer) +
+      LongIndex(thread_offset.column()) * sizeof(AccessType) / kElementsPerAccess;
+
+    // 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) {
+    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);
+
+          Stride tensor_coord = CoordinateDecompositionLittleEndian<kStrideRank>(row_offset + thread_start_row_, params_.divmod);
+
+          LongIndex tensor_offset = dot(tensor_coord, params_.tensor_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 + tensor_offset / kElementsPerAccess],
+                guard);
+          }
+        }
+      }
+    }
+  }
+
+  /// 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 = 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_);
+
+          Stride tensor_coord = CoordinateDecompositionLittleEndian<kStrideRank>((row_offset + thread_start_row_), params_.divmod);
+
+          LongIndex tensor_offset = dot(tensor_coord, params_.tensor_stride);
+
+          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];
+
+            if (UseCUDAStore) {
+              if (guard) {
+                memory_pointer[tensor_offset / kElementsPerAccess] =
+                    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[tensor_offset / kElementsPerAccess],
+                  guard);
+            }
+
+            memory_pointer += (ThreadMap::Delta::kColumn / kElementsPerAccess);
+          }
+        }
+      }
+    }
+  }
+
+  /// Stores a fragment to memory
+  CUTLASS_DEVICE
+  void store(Fragment const &frag) const {
+
+    store_with_byte_offset(frag, 0);
+  }
+
+  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
+  PredicatedTileIteratorConv &operator++() {
+
+    ++state_[0];
+
+    thread_start_row_ += ThreadMap::Shape::kRow;
+
+    if (state_[0] == ThreadMap::Count::kRow) {
+
+      state_[0] = 0;
+      ++state_[1];
+
+      thread_start_row_ += (ThreadMap::Shape::kGroup - 1) *
+        ThreadMap::Shape::kRow * ThreadMap::Count::kRow;
+
+      if (state_[1] == ThreadMap::Count::kGroup) {
+
+        state_[1] = 0;
+        ++state_[2];
+
+        thread_start_row_ += ThreadMap::Count::kGroup *
+          ThreadMap::Shape::kGroup * ThreadMap::Count::kRow * ThreadMap::Shape::kRow;
+
+        if (state_[2] == ThreadMap::Count::kCluster) {
+          state_[2] = 0;
+
+          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
+  PredicatedTileIteratorConv &operator+=(int increment)
+  {
+    // Row
+    state_[0] += increment;
+    int increment_row = state_[0] / ThreadMap::Count::kRow;
+    state_[0] = state_[0] % ThreadMap::Count::kRow;
+
+    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;
+
+    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;
+
+    thread_start_row_ +=
+        ThreadMap::Count::kGroup *
+        ThreadMap::Shape::kGroup *
+        ThreadMap::Count::kRow *
+        ThreadMap::Shape::kRow *
+        increment_group;
+
+    // Tile
+    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;
+  }
+};
+
+////////////////////////////////////////////////////////////////////////////////
+
+} // namespace threadblock
+} // namespace epilogue
+} // namespace cutlass
+
+////////////////////////////////////////////////////////////////////////////////