'iree_linalg_ext' Dialectlink

IREE Linalg Extensions.

A dialect designed for experimenting with non-structured operations that cannot be represented efficiently/directly by the Linalg dialect.

'iree_linalg_ext' Dialect
- Operations

Operationslink

Data tiling opslink

Operations for working with data layouts, padding, encodings, and other properties useful for tiling computations across iteration space dimensions.

`iree_linalg_ext.pack` (LinalgExt::PackOp)link

Pack operation

Syntax:

operation ::= `iree_linalg_ext.pack` attr-dict
              $inputs
              (`padding_value` `(` $padding_value^ `:` type($padding_value) `)`)?
              (`outer_dims_perm` `=` $outer_dims_perm^)?
              `inner_dims_pos` `=` $inner_dims_pos
              `inner_tiles` `=`
              custom<DynamicIndexList>($inner_tiles, $static_inner_tiles)
              `into` $outputs `:` `(` type($inputs) type($outputs) `)`
              (`->` type($results)^)?

The pack operation converts an input into a tiled and packed layout. The dimensions to be tiled are obtained from inner_dims_pos and the size of the tile is obtained from inner_tiles. The dimensions listed in inner_dims_pos do not need to be contiguous in which case the tile will get transposed. We handle only full tiles if padding_value is not set; it is UB if the tile does not perfectly divide the dimension. If padding_value is set, it will pad along high dimensions, i.e., it pads at the bottom and on the right if the input has rank 2, and the result type shape, will be dynamic in any dimension if and only if the input shape is. As optional input, the operation takes outer_dims_perm that allows to permute the tiled loops.

Example KC_to_KCck:

iree_linalg_ext.pack %arg0 inner_dims_pos = [1, 0]
  inner_tiles = [32, 8] into %arg1 : (memref<128x256xf32> memref<16x8x32x8xf32>)

Example NC_to_NCnc:

iree_linalg_ext.pack %arg0 inner_dims_pos = [0, 1]
  inner_tiles = [8, 32] into %arg1 : (memref<128x256xf32> memref<16x8x8x32xf32>)

Example KC_to_CKkc

iree_linalg_ext.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1]
  inner_tiles = [32, 8] into %arg1 : (memref<128x256xf32> memref<32x4x32x8xf32>)

In all cases, dimension at position 0 in the input memref (128) is tiled with a factor of 8, while dimension at position 1 (256) is tiled with a factor of 32. In the KC_to_KCck example, the point loops are interchanged, while in the KC_to_CKkc example the tiled loops.

Example NC_to_NCnc with padding:

iree_linalg_ext.pack %arg padding_value(%pad : f32) inner_dims_pos = [0, 1]
  inner_tiles = [8, 2] into %arg1 : (memref<13x15xf32> memref<2x8x8x2xf32>)

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, LinalgExtOp, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`outer_dims_perm`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`inner_dims_pos`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`static_inner_tiles`	::mlir::DenseI64ArrayAttr	i64 dense array attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values
`inner_tiles`	variadic of index
`padding_value`	any type

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.unpack` (LinalgExt::UnPackOp)link

Unpack operation

Syntax:

operation ::= `iree_linalg_ext.unpack` attr-dict
              $inputs
              (`outer_dims_perm` `=` $outer_dims_perm^)?
              `inner_dims_pos` `=` $inner_dims_pos
              `inner_tiles` `=`
              custom<DynamicIndexList>($inner_tiles, $static_inner_tiles)
              `into` $outputs `:` `(` type($inputs) type($outputs) `)`
              (`->` type($results)^)?

The unpack operation converts a tiled and packed input to an unpacked output. See pack for more details on inner_tiles and dims_pos; it is UB if the tile does not perfectly divide the dimension. Optionally, the operation also supports permuting the tiled loops.

Example KCck_to_KC:

iree_linalg_ext.unpack %arg0 dims_pos = [1, 0]
  inner_tiles = [32, 8] into %arg1 : (memref<16x8x32x8xf32> memref<128x256xf32>)

Example NCnc_to_NC:

iree_linalg_ext.unpack %arg0 dims_pos = [0, 1]
  inner_tiles = [8, 32] into %arg1 : (memref<16x8x8x32xf32> memref<128x256xf32>)

Example CKkc_to_KC:

iree_linalg_ext.unpack %arg1 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1]
  inner_tiles = [32, 8] into %arg0 : (memref<32x4x32x8xf32> memref<128x256xf32>)

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, LinalgExtOp, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`outer_dims_perm`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`inner_dims_pos`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`static_inner_tiles`	::mlir::DenseI64ArrayAttr	i64 dense array attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values
`inner_tiles`	variadic of index

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

Non-structured opslink

`iree_linalg_ext.attention` (LinalgExt::AttentionOp)link

Attention operator

Syntax:

operation ::= `iree_linalg_ext.attention` attr-dict
              `ins` `(` $query `,` $key `,` $value `,` $scale `:` type($query) `,` type($key) `,` type($value) `,` type($scale) `)`
              `outs` `(` $outputs `:` type($outputs) `)`
              (`->` type($results)^)?

Computes the scaled dot product attention function:

attention(Q, K, V, scale) = softmax(Q @ K.T * scale) @ V

Here Q, K, V are given tensors and scale is a scalar value specifying the scale to use.

For self-attention, all inputs and the result have the same shape BxNxd where B is the batch dimension, N is the sequence length and d is head dimension. Typically N >>> d. Usually, this operator also performs masking and dropout, but we leave that out of the current implementation. For cross-attention, the query and output have the same shape (BxNxd), while the key and value differ in sequence length (they have shape BxLxd, where L != N).

This operator after tiling results in a tiled result as per FlashAttention 2 and optionally results in the current max and sum statistics while processing the current tile.

With indexing_maps we can also infer when variables are transposed. for example, if V has indexing_map of (m, k1, k2, n) -> (n, k2) instead of (m, k1, k2, n) -> (k2, n), then we'd know that V is transposed (V.T), and we can expect computation to look like:

attention(Q, K, V, scale) = softmax(Q @ K.T * scale) @ V.T

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`indexing_maps`	::mlir::ArrayAttr	AffineMap array attribute

Operands:link

Operand	Description
`query`	shaped of any type values
`key`	shaped of any type values
`value`	shaped of any type values
`scale`	floating-point
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.fft` (LinalgExt::FftOp)link

Fft operator

Syntax:

operation ::= `iree_linalg_ext.fft` attr-dict (`ins` `(` $inputs^ `:` type($inputs) `)`)?
              `outs` `(` $outputs `:` type($outputs) `)`
              (`:` type($results)^)?

Apply 1D FFT to innermost dim. This is an iterative FFT, not recurrsive. Thus, the bit reversal is assumed applied on the input. The op carries an input -- stage, which indicates the level of reduction loop in the algorithm. It represents the computation body. For more details, see "Data reordering, bit reversal, and in-place algorithms" section in https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm

The size of innermost dim is expected to be a power of 2.

It is optional to carry coefficient tensors/buffers as inputs. In this context, they will be the second and third inputs.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Operands:link

Operand	Description
`inputs`	variadic of any type
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.im2col` (LinalgExt::Im2colOp)link

Im2col operation for convolutions

Syntax:

operation ::= `iree_linalg_ext.im2col` attr-dict
              `strides` `=` $strides
              `dilations` `=` $dilations
              `kernel_size` `=`
              custom<DynamicIndexList>($kernel_size, $static_kernel_size)
              `m_offset` `=`
              custom<DynamicIndexList>($m_offset, $static_m_offset)
              `k_offset` `=`
              custom<DynamicIndexList>($k_offset, $static_k_offset)
              `batch_pos` `=` $batch_pos
              `m_pos` `=` $m_pos
              `k_pos` `=` $k_pos
              `ins` `(` $input `:` type($input) `)`
              `outs` `(` $output `:` type($output) `)`
              (`->` type($results)^)?

Im2col op for convolutions. The operation performs a transformation on the input to convert it from a convolution input to an equivalent gemm input. The op is defined by its input, output, some conv metadata, and some indexing metadata. The strides, dilations, and kernel_size are taken from the convolution from which this op is generated, and they define how the input operand is indexed when the operation is decomposed. The shape of the output should be tensor<BxMxK>, and the m_pos, k_pos, and batch_pos indicate which input dimensions map to which output dimensions.

The k_offset is an offset within the output K dimension from which the iteration space of the operation begins. This is used for tiling, since the tiled implementation must leave the output K dimension untiled. Similarly, m_offset is the offset within the output M dimension from which the iteration space of the operation begins. The iteration space is the full output shape of the im2col op, so if the im2col op were tiled to loops with a scalar inner tile, it would look like the following:

  %im2col = iree_linalg_ext.im2col
      strides = [1, 1] dilations = [1, 1] kernel_size = [3, 3]
      m_offset = [0] k_offset = [0]
      batch_pos = [0] m_pos = [1, 2] k_pos = [3]
      ins(%in : tensor<2x34x34x640xf32>)
      outs(%out : tensor<2x1024x5760xf32>) -> tensor<2x1024x5760xf32>

becomes:

  scf.for %arg0 = %c0 to %c2 step %c1
    scf.for %arg1 = %c0 to %c1024 step %c1
      scf.for %arg2 = %c0 to %c5760 step %c1
        %im2col = iree_linalg_ext.im2col
            strides = [1, 1] dilations = [1, 1] kernel_size = [3, 3]
            m_offset = [%arg1] k_offset = [%arg2]
            batch_pos = [0] m_pos = [1, 2] k_pos = [3]
            ins(%in_tile : tensor<1x34x34x640xf32>)
            outs(%out_tile : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>

Then, when the tiled op is decomposed, it becomes a loop over the iteration space of the im2col op, whith an extract_slice from the %in_tile followed by an insert_slice to the %out_tile. The indices for the extract slice are computed using the m_offset and k_offset as: (b, m, k) -> (b, M / 32 + K / (6403), M % 32 + K % (6403) / 640, K % 640) Where (b, m, k) are the indices of the tiled op's iteration space, and M = m + m_offset and K = k + K_offset.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: AggregatedOpInterface, DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`strides`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`dilations`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`static_kernel_size`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`static_m_offset`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`static_k_offset`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`batch_pos`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`m_pos`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`k_pos`	::mlir::DenseI64ArrayAttr	i64 dense array attribute

Operands:link

Operand	Description
`input`	shaped of any type values
`output`	shaped of any type values
`kernel_size`	variadic of index
`m_offset`	variadic of index
`k_offset`	variadic of index

Results:link

Result	Description
`results`	variadic of shaped of any type values

`iree_linalg_ext.online_attention` (LinalgExt::OnlineAttentionOp)link

Online Attention operator

Syntax:

operation ::= `iree_linalg_ext.online_attention` attr-dict
              `ins` `(` $query `,` $key `,` $value `,` $scale `:` type($query) `,` type($key) `,` type($value) `,` type($scale) `)`
              `outs` `(` $output `,` $max `,` $sum `:` type($output) `,` type($max) `,` type($sum) `)`
              (`->` type($results)^)?

Traditional scaled dot product attention computes:

attention(Q, K, V, scale) = softmax(Q @ K.T * scale) @ V

Online Attention on the other hand, uses an online normalizer instead of softmax:

online_attention(Q, K, V, scale, running_max, running_sum) = online_normalizer(Q @ K.T * scale, running_max, running_sum) @ V

The advantage of this online_normalizer is that it can be tiled along it's reduction dimension, making the online_attention operator: - Tilable along softmax reduction dimension - Associative along softmax reduction dimension - Commutative along softmax associative dimension

Note: The results of online_attention need to be combined after computing it over the entire softmax reduction dimension by: x, _, sum : results x = (1 / sum) * x

Interfaces: AggregatedOpInterface, DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`indexing_maps`	::mlir::ArrayAttr	AffineMap array attribute

Operands:link

Operand	Description
`query`	shaped of any type values
`key`	shaped of any type values
`value`	shaped of any type values
`scale`	floating-point
`output`	shaped of any type values
`max`	shaped of any type values
`sum`	shaped of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.scan` (LinalgExt::ScanOp)link

Scan operator

Syntax:

operation ::= `iree_linalg_ext.scan` attr-dict
              `dimension` `(` $dimension `)`
              `inclusive` `(` $inclusive `)`
              `ins` `(` $inputs `:` type($inputs) `)`
              `outs` `(` $outputs `:` type($outputs) `)`
              $region (`->` type($results)^)?

Computes the inclusive/exclusive scan along a given dimension.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`dimension`	::mlir::IntegerAttr	64-bit signless integer attribute
`inclusive`	::mlir::BoolAttr	bool attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.scatter` (LinalgExt::ScatterOp)link

Scatter operator

Syntax:

operation ::= `iree_linalg_ext.scatter` attr-dict `dimension_map` `=` $dimension_map
              `unique_indices` `(` $unique_indices `)`
              (`ins` `(` $inputs^ `:` type($inputs) `)`)?
              `outs` `(` $outputs `:` type($outputs) `)`
              $region (`->` type($results)^)?

Based on XLA operation semantics, takes two inputs (update and indices) and outputs value (original). The operation updates the value at the slices specified by indices by combining the current value with the value in updates using the computation specified in region. The region specifies a binary operation of signature (T, T) -> T, where T is the element-type of updates (and original). The first argument correspond the value to be updated (i.e. from updates), and the second the current value (i.e. value from original).

The indices is a 2D tensor/memref type. The first dim is the number of updates, and the second dim is index depth. The index depth should always be static.

The first dim of updates and indices is identical, since they represent the number of updates.

The rank of the original/result is at least index_depth + rank(%updates) - 1. The first index_depth indices are derived from indices and the shape of update value has the last rank(%original) - index_depth values match %(originals) last dimensions, with the previous dims extending from the index offsets.

The dimension_map attributes describes which index value maps to which dimension in the destionation. It cannot contain duplicate values, must have as many entries as index depth, and values must be within the rank of the destination.

The unique_indices attribute carries the information whether all the indices are unique. If there are repeated indices, the first iteration loop will be marked as reduction.

The shapes definition follows tensorflow operations execept that it force batch dims to be 1D. See more information in https://www.tensorflow.org/api_docs/python/tf/tensor_scatter_nd_update

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, LinalgFusionOpInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`dimension_map`	::mlir::DenseI64ArrayAttr	i64 dense array attribute
`unique_indices`	::mlir::BoolAttr	bool attribute

Operands:link

Operand	Description
`inputs`	variadic of ranked tensor or memref of any type values
`outputs`	variadic of ranked tensor or memref of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.sort` (LinalgExt::SortOp)link

Sort operator

Syntax:

operation ::= `iree_linalg_ext.sort` attr-dict
              `dimension` `(` $dimension `)`
              (`ins` `(` $inputs^ `:` type($inputs) `)`)?
              `outs` `(` $outputs `:` type($outputs) `)`
              $region (`->` type($results)^)?

Based on XLA operation semantics, sorts the given operands at the given dimension with the given comparator.

See https://www.tensorflow.org/xla/operation_semantics#sort.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`dimension`	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:link

Operand	Description
`inputs`	variadic of any type
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

`iree_linalg_ext.topk` (LinalgExt::TopkOp)link

Top-K operator

Syntax:

operation ::= `iree_linalg_ext.topk` attr-dict
              `dimension` `(` $dimension `)`
              `ins` `(` $inputs `:` type($inputs) `)`
              `outs` `(` $outputs `:` type($outputs) `)`
              $region (`->` type($results)^)?

A Top-K operation for N-D tensors. Reduces the target dimension from the input size N down to K elements based on the supplied binary region.

Accepts an N-D tensor input consisting of values and an optioanl N-D tensor for indices of those values (i32 type). If input indices aren't provided, the index mapping is inferred based on the k dim. Both input values/indices tensors and output values/indicies tensors must have the same shape. Top-K is computed along the target dimension (from dimension()). Returns two output tensors of values and the indicies of Top-K results. The output dimensions must match the input save for the dimension that is reduced to K results.

Region accepts lhs=[next N input] and rhs=[exiting K output] and yeilds an i1. If true, the two values are swapped: - For Top-K compoarision: > - For Min-K comparision: < Note: when the two values are equal, the first occurence is always selected.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, LinalgExtOp, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`dimension`	::mlir::IntegerAttr	64-bit signless integer attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`results`	variadic of ranked tensor of any type values

Utility opslink

`iree_linalg_ext.yield` (LinalgExt::YieldOp)link

LinalgExt yield op

Syntax:

operation ::= `iree_linalg_ext.yield` attr-dict ($operands^ `:` type($operands))?

iree_linalg_ext.yield is a special terminator operation for blocks inside regions in iree_linalg_ext ops.

Traits: AlwaysSpeculatableImplTrait, ReturnLike, Terminator

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface), RegionBranchTerminatorOpInterface

Effects: MemoryEffects::Effect{}

Operands:link

Operand	Description
`operands`	variadic of any type

Winograd opslink

`iree_linalg_ext.winograd.filter_transform` (LinalgExt::WinogradFilterTransformOp)link

Winograd Filter Transform operator

Syntax:

operation ::= `iree_linalg_ext.winograd.filter_transform` attr-dict
              `output_tile_size` `(` $output_tile_size `)`
              `kernel_size` `(` $kernel_size `)`
              `kernel_dimensions` `(` $kernel_dimensions `)`
              `ins` `(` $inputs `:` type($inputs) `)`
              `outs` `(` $outputs `:` type($outputs) `)`
              (`->` type($result)^)?

This operator is part of the first step in converting a convolution to its Winograd equivalent. Given a tile of a convolution filter (F), this operator computes matmul(G, matmul(F, transpose(B))). The filter tile is assumed to be the full m x m convolutional kernel, and the result of the transformation on this tile is a square with each side of size m + r - 1, where the output tile size is r x r. G is a constant 2-d matrix of shape (m + r - 1) x m. The input to the operator is a filter of shape (H, W, C, F) or (F, C, H, W) and the output is an operator of shape (m + r - 1, m + r - 1, C, F). The result of this operator is first collapsed and then fed to a batch matmul op.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`output_tile_size`	::mlir::IntegerAttr	64-bit signless integer attribute
`kernel_size`	::mlir::IntegerAttr	64-bit signless integer attribute
`kernel_dimensions`	::mlir::DenseI64ArrayAttr	i64 dense array attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`result`	variadic of ranked tensor of any type values

`iree_linalg_ext.winograd.input_transform` (LinalgExt::WinogradInputTransformOp)link

Winograd Input Transform operator

Syntax:

operation ::= `iree_linalg_ext.winograd.input_transform` attr-dict
              `output_tile_size` `(` $output_tile_size `)`
              `kernel_size` `(` $kernel_size `)`
              `image_dimensions` `(` $image_dimensions `)`
              `ins` `(` $inputs `:` type($inputs) `)`
              `outs` `(` $outputs `:` type($outputs) `)`
              (`->` type($result)^)?

This operator is part of the first step in converting a convolution to its Winograd equivalent. Given a tile of an input image (I), this operator computes matmul(tranpose(B), matmul(I, B)). The input tile is assumed to be square with each side of size m + r - 1, where the convolutional kernel is m x m and the output tile size is r x r. B is a constant 2-d square matrix of the same shape as the input tile I. The input to the operator is an image of shape (N, H, W, C) or (N, C, H, W) and the output is an operator of shape (m + r - 1, m + r - 1, N, H', W', C) where H' = ceil((H - m + 1)/r) and W' = ceil((W - m + 1)/r). The result of this operator is first collapsed and then fed to a batch matmul op.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`output_tile_size`	::mlir::IntegerAttr	64-bit signless integer attribute
`kernel_size`	::mlir::IntegerAttr	64-bit signless integer attribute
`image_dimensions`	::mlir::DenseI64ArrayAttr	i64 dense array attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`result`	variadic of ranked tensor of any type values

`iree_linalg_ext.winograd.output_transform` (LinalgExt::WinogradOutputTransformOp)link

Winograd Output Transform operator

Syntax:

operation ::= `iree_linalg_ext.winograd.output_transform` attr-dict
              `output_tile_size` `(` $output_tile_size `)`
              `kernel_size` `(` $kernel_size `)`
              `image_dimensions` `(` $image_dimensions `)`
              `ins` `(` $inputs `:` type($inputs) `)`
              `outs` `(` $outputs `:` type($outputs) `)`
              (`->` type($result)^)?

This operator is the last transform in converting a convolution to its Winograd equivalent. After convolution in the Winograd domain (which turns into an elementwise product for a single channel and batch matrix multiplication for many channels), this operator converts the output back into the original domain. Given a tile of the output (O) in the Winograd domain, this operator computes matmul(transpose(A), matmul(O, A)). The output tile is square with each side of size m + r - 1, where the convolutional kernel is m x m and the output tile size is r x r. A is a constant 2-d matrix of shape (m + r - 1) x r. The input to the operator is a tensor of shape (m + r - 1, m + r - 1, N, H', W', C) and the output is a tensor of shape (N, H, W, C) or (N, C, H, W) where H = r H' and W = r W'. This operator is followed by a tensor.extract_slice which extracts only the non-padded part of the output.

Traits: AttrSizedOperandSegments, SingleBlockImplicitTerminator<::mlir::iree_compiler::IREE::LinalgExt::YieldOp>, SingleBlock

Interfaces: DestinationStyleOpInterface, LinalgExtInterface, MemoryEffectOpInterface, ReifyRankedShapedTypeOpInterface, TilingInterface

Attributes:link

Attribute	MLIR Type	Description
`output_tile_size`	::mlir::IntegerAttr	64-bit signless integer attribute
`kernel_size`	::mlir::IntegerAttr	64-bit signless integer attribute
`image_dimensions`	::mlir::DenseI64ArrayAttr	i64 dense array attribute

Operands:link

Operand	Description
`inputs`	variadic of shaped of any type values
`outputs`	variadic of shaped of any type values

Results:link

Result	Description
`result`	variadic of ranked tensor of any type values

'iree_linalg_ext' Dialectlink

Operationslink

Data tiling opslink

iree_linalg_ext.pack (LinalgExt::PackOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.unpack (LinalgExt::UnPackOp)link

Attributes:link

Operands:link

Results:link

Non-structured opslink

iree_linalg_ext.attention (LinalgExt::AttentionOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.fft (LinalgExt::FftOp)link

Operands:link

Results:link

iree_linalg_ext.im2col (LinalgExt::Im2colOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.online_attention (LinalgExt::OnlineAttentionOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.scan (LinalgExt::ScanOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.scatter (LinalgExt::ScatterOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.sort (LinalgExt::SortOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.topk (LinalgExt::TopkOp)link

Attributes:link

Operands:link

Results:link

Utility opslink

iree_linalg_ext.yield (LinalgExt::YieldOp)link

Operands:link

Winograd opslink

iree_linalg_ext.winograd.filter_transform (LinalgExt::WinogradFilterTransformOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.winograd.input_transform (LinalgExt::WinogradInputTransformOp)link

Attributes:link

Operands:link

Results:link

iree_linalg_ext.winograd.output_transform (LinalgExt::WinogradOutputTransformOp)link

Attributes:link

Operands:link

Results:link

`iree_linalg_ext.pack` (LinalgExt::PackOp)link

`iree_linalg_ext.unpack` (LinalgExt::UnPackOp)link

`iree_linalg_ext.attention` (LinalgExt::AttentionOp)link

`iree_linalg_ext.fft` (LinalgExt::FftOp)link

`iree_linalg_ext.im2col` (LinalgExt::Im2colOp)link

`iree_linalg_ext.online_attention` (LinalgExt::OnlineAttentionOp)link

`iree_linalg_ext.scan` (LinalgExt::ScanOp)link

`iree_linalg_ext.scatter` (LinalgExt::ScatterOp)link

`iree_linalg_ext.sort` (LinalgExt::SortOp)link

`iree_linalg_ext.topk` (LinalgExt::TopkOp)link

`iree_linalg_ext.yield` (LinalgExt::YieldOp)link

`iree_linalg_ext.winograd.filter_transform` (LinalgExt::WinogradFilterTransformOp)link

`iree_linalg_ext.winograd.input_transform` (LinalgExt::WinogradInputTransformOp)link

`iree_linalg_ext.winograd.output_transform` (LinalgExt::WinogradOutputTransformOp)link