Individual Transform Operations

Individual Transform Operations#

The transformation engine is built from individual transform types that each handle specific coordinate conversions.

What Are Transforms?#

Transform operations convert coordinates between different dimensional spaces. Each transform operates between two coordinate spaces:

Lower Dimension Space: The source coordinate system
Upper Dimension Space: The target coordinate system

Transform Direction#

Transforms work bidirectionally:

Forward Transform: Converts coordinates from the lower dimension to the upper dimension
Inverse Transform: Converts coordinates back from the upper dimension to the lower dimension

Zero-Copy Logical Operations#

Critical Understanding: All transform operations happen in logical coordinate space only. This is a zero-copy system and there is no data copying or movement involved.

Data Storage: The actual tensor data remains stored in memory in linear fashion, exactly as specified by the original tensor shape and strides at creation time. See CK Tile buffer view for more information about raw memory access.
Logical Mapping: Transforms create different logical views of the same underlying data and only change how access coordinates are interpreted. See Tensor Views - Multi-Dimensional Structure for more information about tensor views.

Index Calculation Operations#

The transform system provides two operations for coordinate conversion:

calculate_lower_index(): Takes a coordinate from the upper dimension space and transforms it to get the corresponding index or coordinate in the lower dimension space. This calculates where to find the actual tensor element using the transformed coordinate system.
calculate_upper_index(): Takes a coordinate from the lower dimension space and transforms it back to get the corresponding coordinate in the upper dimension space. This performs the inverse transformation to recover the original coordinate representation.

These operations enable bidirectional navigation between different coordinate representations of the same underlying tensor data.

Transform System Architecture#

MergeTransform#

MergeTransform collapses multiple dimensions from the lower coordinate space into a single dimension in the upper coordinate space, effectively reducing the dimensionality of the tensor representation while preserving data relationships. This transform is fundamental to the tile distribution system.

C++ Implementation:

using namespace ck_tile;

// Create MergeTransform for 4x5 tensor (20 elements total)
auto transform = make_merge_transform(make_tuple(4, 5));

// Forward: Lower (2D) → Upper (1D) - Manual calculation
int row = 2, col = 3;
int linear_index = row * 5 + col;  // Result: 13
printf("2D coord [%d, %d] → Linear index %d\n", row, col, linear_index);
printf("Calculation: %d×5 + %d = %d\n", row, col, linear_index);

// Inverse: Upper (1D) → Lower (2D) - Using transform
multi_index<1> upper_coord;
upper_coord[number<0>{}] = 13;

multi_index<2> lower_coord;
transform.calculate_lower_index(lower_coord, upper_coord);

printf("Linear index %d → 2D coord [%d, %d]\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(lower_coord[number<0>{}]),
       static_cast<int>(lower_coord[number<1>{}]));
printf("Calculation: 13 ÷ 5 = %d remainder %d\n",
       static_cast<int>(lower_coord[number<0>{}]),
       static_cast<int>(lower_coord[number<1>{}]));

UnmergeTransform#

UnmergeTransform expands coordinates from a single dimension in the lower coordinate space into multiple dimensions in the upper coordinate space, effectively increasing the dimensionality of the tensor representation while preserving all data relationships.

C++ Implementation:

using namespace ck_tile;

// Create UnmergeTransform for 3x4x2 tensor (24 elements total)
auto transform = make_unmerge_transform(make_tuple(3, 4, 2));

// Forward: Lower (1D) → Upper (3D) - Manual calculation
int linear_index = 14;
int dim0 = linear_index / (4 * 2);      // 14 / 8 = 1
int remainder = linear_index % (4 * 2);  // 14 % 8 = 6
int dim1 = remainder / 2;               // 6 / 2 = 3
int dim2 = remainder % 2;               // 6 % 2 = 0

printf("Linear index %d → 3D coord [%d, %d, %d]\n",
       linear_index, dim0, dim1, dim2);
printf("Calculation: 14 = %d×8 + %d×2 + %d\n", dim0, dim1, dim2);

// Inverse: Upper (3D) → Lower (1D) - Using transform
multi_index<3> upper_coord;
upper_coord[number<0>{}] = 1;
upper_coord[number<1>{}] = 3;
upper_coord[number<2>{}] = 0;

multi_index<1> lower_coord;
transform.calculate_lower_index(lower_coord, upper_coord);

printf("3D coord [%d, %d, %d] → Linear index %d\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(upper_coord[number<1>{}]),
       static_cast<int>(upper_coord[number<2>{}]),
       static_cast<int>(lower_coord[number<0>{}]));
printf("Calculation: %d×8 + %d×2 + %d = %d\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(upper_coord[number<1>{}]),
       static_cast<int>(upper_coord[number<2>{}]),
       static_cast<int>(lower_coord[number<0>{}]));

EmbedTransform#

EmbedTransform expands linear indices from the lower coordinate space into multi-dimensional coordinates in the upper coordinate space using configurable strides, enabling flexible strided tensor layouts and sub-tensor views within larger buffers.

C++ Implementation:

using namespace ck_tile;

// Create embed transform for 2x3 tensor with strides [12, 1]
// This is commonly used in :ref:`descriptors <ck_tile_descriptors>`
auto transform = make_embed_transform(make_tuple(2, 3), make_tuple(12, 1));

// Forward: Linear → 2D (Manual calculation)
int linear_idx = 14;
int row = linear_idx / 12;        // 14 / 12 = 1
int remainder = linear_idx % 12;  // 14 % 12 = 2
int col = remainder / 1;          // 2 / 1 = 2
printf("Linear index %d → 2D coord [%d, %d]\n", linear_idx, row, col);

// Inverse: 2D → Linear (Using transform)
multi_index<2> upper_coord;
upper_coord[number<0>{}] = 1;
upper_coord[number<1>{}] = 2;

multi_index<1> lower_coord;
transform.calculate_lower_index(lower_coord, upper_coord);
printf("2D coord [%d, %d] → Linear index %d\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(upper_coord[number<1>{}]),
       static_cast<int>(lower_coord[number<0>{}]));

ReplicateTransform#

ReplicateTransform creates a higher-dimensional tensor by replicating (broadcasting) a lower-dimensional tensor. It’s essentially a broadcasting operation that takes a tensor with fewer dimensions and logically replicates it across new dimensions without data duplication. An example is taking a scalar (0-dimensional) input and broadcasting it across multiple dimensions, enabling efficient broadcasting patterns where a single value appears at every position in a multi-dimensional coordinate space.

C++ Implementation:

using namespace ck_tile;

// Create replicate transform for 3x4 broadcasting
auto transform = make_replicate_transform(make_tuple(3, 4));

// Inverse: Upper (2D) → Lower (0D) - Using transform
// Any 2D coordinate maps to empty scalar coordinate
multi_index<2> upper_coord;
upper_coord[number<0>{}] = 1;
upper_coord[number<1>{}] = 2;

multi_index<0> lower_coord;  // Empty coordinate (0 dimensions)
transform.calculate_lower_index(lower_coord, upper_coord);
printf("2D [%d, %d] → Empty scalar [] (always empty)\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(upper_coord[number<1>{}]));

// Forward: Scalar → 2D (Conceptual - no coordinate calculation needed)
// Broadcasting: Single scalar value appears at ALL positions
printf("Broadcasting: Scalar value appears at every [i,j] where 0≤i<3, 0≤j<4\n");
printf("Total positions: 3×4 = 12 positions, all contain same scalar value\n");

// Test multiple coordinates - all map to empty scalar
int test_coords[][2] = {{0, 0}, {1, 2}, {2, 3}};
for(int i = 0; i < 3; i++)
{
    multi_index<2> test_upper;
    test_upper[number<0>{}] = test_coords[i][0];
    test_upper[number<1>{}] = test_coords[i][1];

    multi_index<0> test_lower;
    transform.calculate_lower_index(test_lower, test_upper);
    printf("2D [%d, %d] → Empty scalar []\n",
           test_coords[i][0], test_coords[i][1]);
}

OffsetTransform#

OffsetTransform shifts coordinates by a fixed offset, creating a translated view of the coordinate space. It performs translation operations where each coordinate in the upper space is mapped to a coordinate in the lower space by adding a constant offset.

C++ Implementation:

using namespace ck_tile;

// Create offset transform for coordinate translation
// CK Tile formula: lower = upper + offset
auto transform = make_offset_transform(48, 16);

// Using Transform: Original → Translated (adds offset)
multi_index<1> upper_coord;
upper_coord[number<0>{}] = 5;  // Original index 5

multi_index<1> lower_coord;
transform.calculate_lower_index(lower_coord, upper_coord);
printf("Original index %d → Translated index %d\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(lower_coord[number<0>{}]));
printf("Calculation: %d + 16 = %d\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(lower_coord[number<0>{}]));

// Manual Reverse: Translated → Original (subtract offset)
int translated_idx = 21;
int original_idx = translated_idx - 16;
printf("Translated index %d → Original index %d\n", translated_idx, original_idx);

// Test multiple coordinates
int test_indices[] = {0, 10, 20, 47};
for(int i = 0; i < 4; i++)
{
    multi_index<1> test_upper;
    test_upper[number<0>{}] = test_indices[i];

    multi_index<1> test_lower;
    transform.calculate_lower_index(test_lower, test_upper);
    printf("Original %d → Translated %d\n",
           test_indices[i], static_cast<int>(test_lower[number<0>{}]));
}

PassThroughTransform - Identity#

No-op transform that passes coordinates unchanged. The PassThrough transform is the simplest coordinate transformation in CK Tile, implementing a perfect identity mapping where input coordinates are passed through unchanged to the output. This transform is essential as a placeholder in transformation chains and for dimensions that require no modification.

C++ Implementation:

using namespace ck_tile;

// Identity transform - no change
int length = 60;

auto transform = make_pass_through_transform(length);

printf("Length: %d\n", length);

// Forward: Upper → Lower (identity)
multi_index<1> upper_coord;
upper_coord[number<0>{}] = 25;

multi_index<1> lower_coord;
transform.calculate_lower_index(lower_coord, upper_coord);

printf("\nForward: [%d] → [%d] (unchanged)\n",
       static_cast<int>(upper_coord[number<0>{}]),
       static_cast<int>(lower_coord[number<0>{}]));

// Reverse: Lower → Upper (identity)
multi_index<1> lower_input;
lower_input[number<0>{}] = 42;

multi_index<1> upper_result;
// Note: PassThrough is bidirectional identity, so we can use same function
transform.calculate_lower_index(upper_result, lower_input);

printf("Reverse: [%d] → [%d] (unchanged)\n",
       static_cast<int>(lower_input[number<0>{}]),
       static_cast<int>(upper_result[number<0>{}]));

PadTransform#

PadTransform adds padding to tensor dimensions, mapping coordinates from upper dimension space (with padding) to lower dimension space (original data).

C++ Implementation:

using namespace ck_tile;

// PadTransform for coordinate padding
int low_length = 3;   // Original dimension length
int left_pad = 1;     // Padding on left
int right_pad = 1;    // Padding on right

auto transform = make_pad_transform(low_length, left_pad, right_pad);

printf("Low length: %d\n", low_length);
printf("Left pad: %d\n", left_pad);
printf("Right pad: %d\n", right_pad);
printf("Upper length: %d (total with padding)\n", low_length + left_pad + right_pad);

// Test coordinate mapping
int test_coords[] = {0, 1, 2, 3, 4};
for(int i = 0; i < 5; i++)
{
    multi_index<1> upper;
    upper[number<0>{}] = test_coords[i];

    multi_index<1> lower;
    transform.calculate_lower_index(lower, upper);

    int adjusted_idx = static_cast<int>(lower[number<0>{}]);
    bool is_valid = (adjusted_idx >= 0 && adjusted_idx < low_length);

    printf("Upper %d → Lower %d (%s)\n",
           test_coords[i], adjusted_idx,
           is_valid ? "valid" : "padding");
}

Additional Transform Types#

XorTransform#

XorTransform applies a 2D XOR mapping for specialized memory access patterns. It performs XOR operations on coordinates to create transformed memory layouts for specific algorithmic optimizations, particularly useful for avoiding LDS bank conflicts.

SliceTransform#

SliceTransform extracts a sub-region from a tensor dimension.

ModuloTransform#

ModuloTransform applies cyclic wrapping to coordinates using modulo operations.

Summary#

Individual transforms provide:

Modularity: Each transform does one thing
Composability: Chain transforms for complex mappings (see Tensor Adaptors - Chaining Transformations)
Efficiency: Compile-time optimization in C++
Flexibility: Handle any coordinate conversion need

These transforms enable you to:

Create custom tensor views
Implement efficient data access patterns
Handle padding and boundaries correctly
Optimize memory layouts for GPU access

The C++ implementations in Composable Kernel provide:

Zero-overhead abstractions through templates
Compile-time composition and optimization
Support for complex coordinate transformations
Integration with GPU kernel generation
Foundation for tile windows and load/store traits

Next Steps#

Tensor Adaptors - Chaining Transformations - How to chain transforms together
Tensor Descriptors - Complete Tensor Specifications - Complete tensor descriptions with transforms
Tile Window - Data Access Gateway - Using transforms for efficient data loading
Thread Mapping - Connecting to Hardware - How transforms enable thread-to-data mapping
A Block GEMM on MI300 - Practical application in GEMM kernels

Individual Transform Operations

Contents

Individual Transform Operations#

What Are Transforms?#

Transform Direction#

Zero-Copy Logical Operations#

Index Calculation Operations#

Transform System Architecture#

MergeTransform#

UnmergeTransform#

EmbedTransform#

ReplicateTransform#

OffsetTransform#

PassThroughTransform - Identity#

PadTransform#

Additional Transform Types#

XorTransform#

SliceTransform#

ModuloTransform#

Summary#

Next Steps#