MemRef Dialect

Refer to the official documentation for more details.

Reactant.MLIR.Dialects.memref.alloc Method

alloc

The alloc operation allocates a region of memory, as specified by its memref type.

Example

%0 = memref.alloc() : memref<8x64xf32, 1>

The optional list of dimension operands are bound to the dynamic dimensions specified in its memref type. In the example below, the ssa value '%d' is bound to the second dimension of the memref (which is dynamic).

%0 = memref.alloc(%d) : memref<8x?xf32, 1>

The optional list of symbol operands are bound to the symbols of the memrefs affine map. In the example below, the ssa value '%s' is bound to the symbol 's0' in the affine map specified in the allocs memref type.

%0 = memref.alloc()[%s] : memref<8x64xf32,
                          affine_map<(d0, d1)[s0] -> ((d0 + s0), d1)>, 1>

This operation returns a single ssa value of memref type, which can be used by subsequent load and store operations.

The optional alignment attribute may be specified to ensure that the region of memory that will be indexed is aligned at the specified byte boundary.

%0 = memref.alloc()[%s] {alignment = 8} :
  memref<8x64xf32, affine_map<(d0, d1)[s0] -> ((d0 + s0), d1)>, 1>

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Reactant.MLIR.Dialects.memref.alloca Method

alloca

The alloca operation allocates memory on the stack, to be automatically released when control transfers back from the region of its closest surrounding operation with an AutomaticAllocationScope trait. The amount of memory allocated is specified by its memref and additional operands. For example:

%0 = memref.alloca() : memref<8x64xf32>

The optional list of dimension operands are bound to the dynamic dimensions specified in its memref type. In the example below, the SSA value '%d' is bound to the second dimension of the memref (which is dynamic).

%0 = memref.alloca(%d) : memref<8x?xf32>

The optional list of symbol operands are bound to the symbols of the memref's affine map. In the example below, the SSA value '%s' is bound to the symbol 's0' in the affine map specified in the allocs memref type.

%0 = memref.alloca()[%s] : memref<8x64xf32,
                           affine_map<(d0, d1)[s0] -> ((d0 + s0), d1)>>

This operation returns a single SSA value of memref type, which can be used by subsequent load and store operations. An optional alignment attribute, if specified, guarantees alignment at least to that boundary. If not specified, an alignment on any convenient boundary compatible with the type will be chosen.

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Reactant.MLIR.Dialects.memref.alloca_scope Method

alloca_scope

The memref.alloca_scope operation represents an explicitly-delimited scope for the alloca allocations. Any memref.alloca operations that are used within this scope are going to be cleaned up automatically once the control-flow exits the nested region. For example:

memref.alloca_scope {
  %myalloca = memref.alloca(): memref<4x3xf32>
  ...
}

Here, %myalloca memref is valid within the explicitly delimited scope and is automatically deallocated at the end of the given region. Conceptually, memref.alloca_scope is a passthrough operation with AutomaticAllocationScope that spans the body of the region within the operation.

memref.alloca_scope may also return results that are defined in the nested region. To return a value, one should use memref.alloca_scope.return operation:

%result = memref.alloca_scope {
  ...
  memref.alloca_scope.return %value
}

If memref.alloca_scope returns no value, the memref.alloca_scope.return can be left out, and will be inserted implicitly.

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Reactant.MLIR.Dialects.memref.alloca_scope_return Method

alloca_scope_return

memref.alloca_scope.return operation returns zero or more SSA values from the region within memref.alloca_scope. If no values are returned, the return operation may be omitted. Otherwise, it has to be present to indicate which values are going to be returned. For example:

memref.alloca_scope.return %value

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Reactant.MLIR.Dialects.memref.assume_alignment Method

assume_alignment

The assume_alignment operation takes a memref and an integer of alignment value, and internally annotates the buffer with the given alignment. If the buffer isn't aligned to the given alignment, the behavior is undefined.

This operation doesn't affect the semantics of a correct program. It's for optimization only, and the optimization is best-effort.

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Reactant.MLIR.Dialects.memref.atomic_rmw Method

atomic_rmw

The memref.atomic_rmw operation provides a way to perform a read-modify-write sequence that is free from data races. The kind enumeration specifies the modification to perform. The value operand represents the new value to be applied during the modification. The memref operand represents the buffer that the read and write will be performed against, as accessed by the specified indices. The arity of the indices is the rank of the memref. The result represents the latest value that was stored.

Example

%x = memref.atomic_rmw "addf" %value, %I[%i] : (f32, memref<10xf32>) -> f32

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Reactant.MLIR.Dialects.memref.atomic_yield Method

atomic_yield

"memref.atomic_yield" yields an SSA value from a GenericAtomicRMWOp region.

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Reactant.MLIR.Dialects.memref.cast Method

cast

The memref.cast operation converts a memref from one type to an equivalent type with a compatible shape. The source and destination types are compatible if:

a. Both are ranked memref types with the same element type, address space, and rank and:

1. Both have the same layout or both have compatible strided layouts.

2. The individual sizes (resp. offset and strides in the case of strided memrefs) may convert constant dimensions to dynamic dimensions and vice-versa.

If the cast converts any dimensions from an unknown to a known size, then it acts as an assertion that fails at runtime if the dynamic dimensions disagree with resultant destination size.

Example

// Assert that the input dynamic shape matches the destination static shape.
%2 = memref.cast %1 : memref<?x?xf32> to memref<4x4xf32>
// Erase static shape information, replacing it with dynamic information.
%3 = memref.cast %1 : memref<4xf32> to memref<?xf32>

// The same holds true for offsets and strides.

// Assert that the input dynamic shape matches the destination static stride.
%4 = memref.cast %1 : memref<12x4xf32, strided<[?, ?], offset: ?>> to
                      memref<12x4xf32, strided<[4, 1], offset: 5>>
// Erase static offset and stride information, replacing it with
// dynamic information.
%5 = memref.cast %1 : memref<12x4xf32, strided<[4, 1], offset: 5>> to
                      memref<12x4xf32, strided<[?, ?], offset: ?>>

b. Either or both memref types are unranked with the same element type, and address space.

Example

Cast to concrete shape.
    %4 = memref.cast %1 : memref<*xf32> to memref<4x?xf32>

Erase rank information.
    %5 = memref.cast %1 : memref<4x?xf32> to memref<*xf32>

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Reactant.MLIR.Dialects.memref.collapse_shape Method

collapse_shape

The memref.collapse_shape op produces a new view with a smaller rank whose sizes are a reassociation of the original view. The operation is limited to such reassociations, where subsequent, contiguous dimensions are collapsed into a single dimension. Such reassociations never require additional allocs or copies.

Collapsing non-contiguous dimensions is undefined behavior. When a group of dimensions can be statically proven to be non-contiguous, collapses of such groups are rejected in the verifier on a best-effort basis. In the general case, collapses of dynamically-sized dims with dynamic strides cannot be proven to be contiguous or non-contiguous due to limitations in the memref type.

A reassociation is defined as a continuous grouping of dimensions and is represented with an array of DenseI64ArrayAttr attribute.

Note: Only the dimensions within a reassociation group must be contiguous. The remaining dimensions may be non-contiguous.

The result memref type can be zero-ranked if the source memref type is statically shaped with all dimensions being unit extent. In such a case, the reassociation indices must be empty.

Examples:

// Dimension collapse (i, j) -> i' and k -> k'
%1 = memref.collapse_shape %0 [[0, 1], [2]] :
    memref<?x?x?xf32, stride_spec> into memref<?x?xf32, stride_spec_2>

For simplicity, this op may not be used to cast dynamicity of dimension sizes and/or strides. I.e., a result dimension must be dynamic if and only if at least one dimension in the corresponding reassociation group is dynamic. Similarly, the stride of a result dimension must be dynamic if and only if the corresponding start dimension in the source type is dynamic.

Note: This op currently assumes that the inner strides are of the source/result layout map are the faster-varying ones.

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Reactant.MLIR.Dialects.memref.copy Method

copy

Copies the data from the source to the destination memref.

Usage:

memref.copy %arg0, %arg1 : memref<?xf32> to memref<?xf32>

Source and destination are expected to have the same element type and shape. Otherwise, the result is undefined. They may have different layouts.

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Reactant.MLIR.Dialects.memref.dealloc Method

dealloc

The dealloc operation frees the region of memory referenced by a memref which was originally created by the alloc operation. The dealloc operation should not be called on memrefs which alias an alloc'd memref (e.g. memrefs returned by view operations).

Example

%0 = memref.alloc() : memref<8x64xf32, affine_map<(d0, d1) -> (d0, d1), 1>>
memref.dealloc %0 : memref<8x64xf32,  affine_map<(d0, d1) -> (d0, d1), 1>>

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Reactant.MLIR.Dialects.memref.dim Method

dim

The dim operation takes a memref and a dimension operand of type index. It returns the size of the requested dimension of the given memref. If the dimension index is out of bounds the behavior is undefined.

The specified memref type is that of the first operand.

Example

// Always returns 4, can be constant folded:
%c0 = arith.constant 0 : index
%x = memref.dim %A, %c0 : memref<4 x ? x f32>

// Returns the dynamic dimension of %A.
%c1 = arith.constant 1 : index
%y = memref.dim %A, %c1 : memref<4 x ? x f32>

// Equivalent generic form:
%x = "memref.dim"(%A, %c0) : (memref<4 x ? x f32>, index) -> index
%y = "memref.dim"(%A, %c1) : (memref<4 x ? x f32>, index) -> index

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Reactant.MLIR.Dialects.memref.dma_start Method

dma_start

Syntax

operation ::= `memref.dma_start` ssa-use`[`ssa-use-list`]` `,`
               ssa-use`[`ssa-use-list`]` `,` ssa-use `,`
               ssa-use`[`ssa-use-list`]` (`,` ssa-use `,` ssa-use)?
              `:` memref-type `,` memref-type `,` memref-type

DmaStartOp starts a non-blocking DMA operation that transfers data from a source memref to a destination memref. The source and destination memref need not be of the same dimensionality, but need to have the same elemental type. The operands include the source and destination memref's each followed by its indices, size of the data transfer in terms of the number of elements (of the elemental type of the memref), a tag memref with its indices, and optionally at the end, a stride and a number_of_elements_per_stride arguments. The tag location is used by a DmaWaitOp to check for completion. The indices of the source memref, destination memref, and the tag memref have the same restrictions as any load/store. The optional stride arguments should be of 'index' type, and specify a stride for the slower memory space (memory space with a lower memory space id), transferring chunks of number_of_elements_per_stride every stride until %num_elements are transferred. Either both or no stride arguments should be specified. If the source and destination locations overlap the behavior of this operation is not defined.

For example, a DmaStartOp operation that transfers 256 elements of a memref '%src' in memory space 0 at indices [%i, %j] to memref '%dst' in memory space 1 at indices [%k, %l], would be specified as follows:

%num_elements = arith.constant 256
%idx = arith.constant 0 : index
%tag = memref.alloc() : memref<1 x i32, affine_map<(d0) -> (d0)>, 4>
dma_start %src[%i, %j], %dst[%k, %l], %num_elements, %tag[%idx] :
  memref<40 x 128 x f32>, affine_map<(d0) -> (d0)>, 0>,
  memref<2 x 1024 x f32>, affine_map<(d0) -> (d0)>, 1>,
  memref<1 x i32>, affine_map<(d0) -> (d0)>, 2>

If %stride and %num_elt_per_stride are specified, the DMA is expected to transfer %num_elt_per_stride elements every %stride elements apart from memory space 0 until %num_elements are transferred.

dma_start %src[%i, %j], %dst[%k, %l], %num_elements, %tag[%idx], %stride,
          %num_elt_per_stride :

• TODO: add additional operands to allow source and destination striding, and

multiple stride levels.

• TODO: Consider replacing src/dst memref indices with view memrefs.

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Reactant.MLIR.Dialects.memref.dma_wait Method

dma_wait

DmaWaitOp blocks until the completion of a DMA operation associated with the tag element '%tag[%index]'. %tag is a memref, and %index has to be an index with the same restrictions as any load/store index. %num_elements is the number of elements associated with the DMA operation.

Example

mlir dma_start %src[%i, %j], %dst[%k, %l], %num_elements, %tag[%index] : memref<2048 x f32>, affine_map<(d0) -> (d0)>, 0>, memref<256 x f32>, affine_map<(d0) -> (d0)>, 1> memref<1 x i32>, affine_map<(d0) -> (d0)>, 2> ... ... dma_wait %tag[%index], %num_elements : memref<1 x i32, affine_map<(d0) -> (d0)>, 2>

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Reactant.MLIR.Dialects.memref.expand_shape Method

expand_shape

The memref.expand_shape op produces a new view with a higher rank whose sizes are a reassociation of the original view. The operation is limited to such reassociations, where a dimension is expanded into one or multiple contiguous dimensions. Such reassociations never require additional allocs or copies.

A reassociation is defined as a grouping of dimensions and is represented with an array of DenseI64ArrayAttr attributes.

Example

%r = memref.expand_shape %0 [[0, 1], [2]] output_shape [%sz0, %sz1, 32]
    : memref<?x32xf32> into memref<?x?x32xf32>

If an op can be statically proven to be invalid (e.g, an expansion from memref<10xf32> to memref<2x6xf32>), it is rejected by the verifier. If it cannot statically be proven invalid (e.g., the full example above; it is unclear whether the first source dimension is divisible by 5), the op is accepted by the verifier. However, if the op is in fact invalid at runtime, the behavior is undefined.

The source memref can be zero-ranked. In that case, the reassociation indices must be empty and the result shape may only consist of unit dimensions.

For simplicity, this op may not be used to cast dynamicity of dimension sizes and/or strides. I.e., if and only if a source dimension is dynamic, there must be a dynamic result dimension in the corresponding reassociation group. Same for strides.

The representation for the output shape supports a partially-static specification via attributes specified through the static_output_shape argument. A special sentinel value ShapedType::kDynamic encodes that the corresponding entry has a dynamic value. There must be exactly as many SSA inputs in output_shape as there are ShapedType::kDynamic entries in static_output_shape.

Note: This op currently assumes that the inner strides are of the source/result layout map are the faster-varying ones.

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Reactant.MLIR.Dialects.memref.extract_aligned_pointer_as_index Method

extract_aligned_pointer_as_index

Extracts the underlying aligned pointer as an index.

This operation is useful for lowering to lower-level dialects while still avoiding the need to define a pointer type in higher-level dialects such as the memref dialect.

This operation is intended solely as step during lowering, it has no side effects. A reverse operation that creates a memref from an index interpreted as a pointer is explicitly discouraged.

Example

  %0 = memref.extract_aligned_pointer_as_index %arg : memref<4x4xf32> -> index
  %1 = arith.index_cast %0 : index to i64
  %2 = llvm.inttoptr %1 : i64 to !llvm.ptr
  call @foo(%2) : (!llvm.ptr) ->()

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Reactant.MLIR.Dialects.memref.extract_strided_metadata Method

extract_strided_metadata

Extracts a base buffer, offset and strides. This op allows additional layers of transformations and foldings to be added as lowering progresses from higher-level dialect to lower-level dialects such as the LLVM dialect.

The op requires a strided memref source operand. If the source operand is not a strided memref, then verification fails.

This operation is also useful for completeness to the existing memref.dim op. While accessing strides, offsets and the base pointer independently is not available, this is useful for composing with its natural complement op: memref.reinterpret_cast.

Intended Use Cases:

The main use case is to expose the logic for manipulate memref metadata at a higher level than the LLVM dialect. This makes lowering more progressive and brings the following benefits:

• not all users of MLIR want to lower to LLVM and the information to e.g. lower to library calls–-like libxsmm–-or to SPIR-V was not available.

• foldings and canonicalizations can happen at a higher level in MLIR: before this op existed, lowering to LLVM would create large amounts of LLVMIR. Even when LLVM does a good job at folding the low-level IR from a performance perspective, it is unnecessarily opaque and inefficient to send unkempt IR to LLVM.

Example

  %base, %offset, %sizes:2, %strides:2 =
    memref.extract_strided_metadata %memref :
      memref<10x?xf32>, index, index, index, index, index

  // After folding, the type of %m2 can be memref<10x?xf32> and further
  // folded to %memref.
  %m2 = memref.reinterpret_cast %base to
      offset: [%offset],
      sizes: [%sizes#0, %sizes#1],
      strides: [%strides#0, %strides#1]
    : memref<f32> to memref<?x?xf32, offset: ?, strides: [?, ?]>

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Reactant.MLIR.Dialects.memref.generic_atomic_rmw Method

generic_atomic_rmw

The memref.generic_atomic_rmw operation provides a way to perform a read-modify-write sequence that is free from data races. The memref operand represents the buffer that the read and write will be performed against, as accessed by the specified indices. The arity of the indices is the rank of the memref. The result represents the latest value that was stored. The region contains the code for the modification itself. The entry block has a single argument that represents the value stored in memref[indices] before the write is performed. No side-effecting ops are allowed in the body of GenericAtomicRMWOp.

Example

%x = memref.generic_atomic_rmw %I[%i] : memref<10xf32> {
  ^bb0(%current_value : f32):
    %c1 = arith.constant 1.0 : f32
    %inc = arith.addf %c1, %current_value : f32
    memref.atomic_yield %inc : f32
}

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Reactant.MLIR.Dialects.memref.get_global Method

get_global

The memref.get_global operation retrieves the memref pointing to a named global variable. If the global variable is marked constant, writing to the result memref (such as through a memref.store operation) is undefined.

Example

%x = memref.get_global @foo : memref<2xf32>

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Reactant.MLIR.Dialects.memref.global_ Method

global_

The memref.global operation declares or defines a named global memref variable. The backing memory for the variable is allocated statically and is described by the type of the variable (which should be a statically shaped memref type). The operation is a declaration if no initial_value is specified, else it is a definition. The initial_value can either be a unit attribute to represent a definition of an uninitialized global variable, or an elements attribute to represent the definition of a global variable with an initial value. The global variable can also be marked constant using the constant unit attribute. Writing to such constant global variables is undefined.

The global variable can be accessed by using the memref.get_global to retrieve the memref for the global variable. Note that the memref for such global variable itself is immutable (i.e., memref.get_global for a given global variable will always return the same memref descriptor).

Example

// Private variable with an initial value.
memref.global "private" @x : memref<2xf32> = dense<0.0,2.0>

// Private variable with an initial value and an alignment (power of 2).
memref.global "private" @x : memref<2xf32> = dense<0.0,2.0> {alignment = 64}

// Declaration of an external variable.
memref.global "private" @y : memref<4xi32>

// Uninitialized externally visible variable.
memref.global @z : memref<3xf16> = uninitialized

// Externally visible constant variable.
memref.global constant @c : memref<2xi32> = dense<1, 4>

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Reactant.MLIR.Dialects.memref.load Method

load

The load op reads an element from a memref specified by an index list. The output of load is a new value with the same type as the elements of the memref. The arity of indices is the rank of the memref (i.e., if the memref loaded from is of rank 3, then 3 indices are required for the load following the memref identifier).

In an affine.if or affine.for body, the indices of a load are restricted to SSA values bound to surrounding loop induction variables, symbols, results of a constant operations, or the result of an affine.apply operation that can in turn take as arguments all of the aforementioned SSA values or the recursively result of such an affine.apply operation.

Example

%1 = affine.apply affine_map<(d0, d1) -> (3*d0)> (%i, %j)
%2 = affine.apply affine_map<(d0, d1) -> (d1+1)> (%i, %j)
%12 = memref.load %A[%1, %2] : memref<8x?xi32, #layout, memspace0>

// Example of an indirect load (treated as non-affine)
%3 = affine.apply affine_map<(d0) -> (2*d0 + 1)>(%12)
%13 = memref.load %A[%3, %2] : memref<4x?xi32, #layout, memspace0>

Context: The load and store operations are specifically crafted to fully resolve a reference to an element of a memref, and (in affine affine.if and affine.for operations) the compiler can follow use-def chains (e.g. through affine.apply operations) to precisely analyze references at compile-time using polyhedral techniques. This is possible because of the restrictions on dimensions and symbols in these contexts.

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Reactant.MLIR.Dialects.memref.memory_space_cast Method

memory_space_cast

This operation casts memref values between memory spaces. The input and result will be memrefs of the same types and shape that alias the same underlying memory, though, for some casts on some targets, the underlying values of the pointer stored in the memref may be affected by the cast.

The input and result must have the same shape, element type, rank, and layout.

If the source and target address spaces are the same, this operation is a noop.

Example

// Cast a GPU private memory attribution into a generic pointer
%2 = memref.memory_space_cast %1 : memref<?xf32, 5> to memref<?xf32>
// Cast a generic pointer to workgroup-local memory
%4 = memref.memory_space_cast %3 : memref<5x4xi32> to memref<5x34xi32, 3>
// Cast between two non-default memory spaces
%6 = memref.memory_space_cast %5
  : memref<*xmemref<?xf32>, 5> to memref<*xmemref<?xf32>, 3>

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Reactant.MLIR.Dialects.memref.prefetch Method

prefetch

The "prefetch" op prefetches data from a memref location described with subscript indices similar to memref.load, and with three attributes: a read/write specifier, a locality hint, and a cache type specifier as shown below:

memref.prefetch %0[%i, %j], read, locality<3>, data : memref<400x400xi32>

The read/write specifier is either 'read' or 'write', the locality hint ranges from locality<0> (no locality) to locality<3> (extremely local keep in cache). The cache type specifier is either 'data' or 'instr' and specifies whether the prefetch is performed on data cache or on instruction cache.

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Reactant.MLIR.Dialects.memref.rank Method

rank

The memref.rank operation takes a memref operand and returns its rank.

Example

%0 = memref.rank %arg0 : memref<*xf32>
%1 = memref.rank %arg1 : memref<?x?xf32>

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Reactant.MLIR.Dialects.memref.realloc Function

realloc

The realloc operation changes the size of a memory region. The memory region is specified by a 1D source memref and the size of the new memory region is specified by a 1D result memref type and an optional dynamic Value of Index type. The source and the result memref must be in the same memory space and have the same element type.

The operation may move the memory region to a new location. In this case, the content of the memory block is preserved up to the lesser of the new and old sizes. If the new size if larger, the value of the extended memory is undefined. This is consistent with the ISO C realloc.

The operation returns an SSA value for the memref.

Example

%0 = memref.realloc %src : memref<64xf32> to memref<124xf32>

The source memref may have a dynamic shape, in which case, the compiler will generate code to extract its size from the runtime data structure for the memref.

%1 = memref.realloc %src : memref<?xf32> to memref<124xf32>

If the result memref has a dynamic shape, a result dimension operand is needed to spefify its dynamic dimension. In the example below, the ssa value '%d' specifies the unknown dimension of the result memref.

%2 = memref.realloc %src(%d) : memref<?xf32> to memref<?xf32>

An optional alignment attribute may be specified to ensure that the region of memory that will be indexed is aligned at the specified byte boundary. This is consistent with the fact that memref.alloc supports such an optional alignment attribute. Note that in ISO C standard, neither alloc nor realloc supports alignment, though there is aligned_alloc but not aligned_realloc.

%3 = memref.realloc %src {alignment = 8} : memref<64xf32> to memref<124xf32>

Referencing the memref through the old SSA value after realloc is undefined behavior.

%new = memref.realloc %old : memref<64xf32> to memref<124xf32>
%4 = memref.load %new[%index]   // ok
%5 = memref.load %old[%index]   // undefined behavior

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Reactant.MLIR.Dialects.memref.reinterpret_cast Method

reinterpret_cast

Modify offset, sizes and strides of an unranked/ranked memref.

Example

memref.reinterpret_cast %ranked to
  offset: [0],
  sizes: [%size0, 10],
  strides: [1, %stride1]
: memref<?x?xf32> to memref<?x10xf32, strided<[1, ?], offset: 0>>

memref.reinterpret_cast %unranked to
  offset: [%offset],
  sizes: [%size0, %size1],
  strides: [%stride0, %stride1]
: memref<*xf32> to memref<?x?xf32, strided<[?, ?], offset: ?>>

This operation creates a new memref descriptor using the base of the source and applying the input arguments to the other metadata. In other words:

%dst = memref.reinterpret_cast %src to
  offset: [%offset],
  sizes: [%sizes],
  strides: [%strides]

means that %dst's descriptor will be:

%dst.base = %src.base
%dst.aligned = %src.aligned
%dst.offset = %offset
%dst.sizes = %sizes
%dst.strides = %strides

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Reactant.MLIR.Dialects.memref.reshape Method

reshape

The reshape operation converts a memref from one type to an equivalent type with a provided shape. The data is never copied or modified. The source and destination types are compatible if both have the same element type, same number of elements, address space and identity layout map. The following combinations are possible:

a. Source type is ranked or unranked. Shape argument has static size. Result type is ranked.

// Reshape statically-shaped memref.
%dst = memref.reshape %src(%shape)
         : (memref<4x1xf32>, memref<1xi32>) to memref<4xf32>
%dst0 = memref.reshape %src(%shape0)
         : (memref<4x1xf32>, memref<2xi32>) to memref<2x2xf32>
// Flatten unranked memref.
%dst = memref.reshape %src(%shape)
         : (memref<*xf32>, memref<1xi32>) to memref<?xf32>

b. Source type is ranked or unranked. Shape argument has dynamic size. Result type is unranked.

// Reshape dynamically-shaped 1D memref.
%dst = memref.reshape %src(%shape)
         : (memref<?xf32>, memref<?xi32>) to memref<*xf32>
// Reshape unranked memref.
%dst = memref.reshape %src(%shape)
         : (memref<*xf32>, memref<?xi32>) to memref<*xf32>

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Reactant.MLIR.Dialects.memref.store Method

store

Store a value to a memref location given by indices. The value stored should have the same type as the elemental type of the memref. The number of arguments provided within brackets need to match the rank of the memref.

In an affine context, the indices of a store are restricted to SSA values bound to surrounding loop induction variables, symbols, results of a constant operation, or the result of an affine.apply operation that can in turn take as arguments all of the aforementioned SSA values or the recursively result of such an affine.apply operation.

Example

memref.store %100, %A[%1, 1023] : memref<4x?xf32, #layout, memspace0>

Context: The load and store operations are specifically crafted to fully resolve a reference to an element of a memref, and (in polyhedral affine.if and affine.for operations) the compiler can follow use-def chains (e.g. through affine.apply operations) to precisely analyze references at compile-time using polyhedral techniques. This is possible because of the restrictions on dimensions and symbols in these contexts.

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Reactant.MLIR.Dialects.memref.subview Method

subview

The "subview" operation converts a memref type to another memref type which represents a reduced-size view of the original memref as specified by the operation's offsets, sizes and strides arguments.

The SubView operation supports the following arguments:

• source: the "base" memref on which to create a "view" memref.

• offsets: memref-rank number of offsets into the "base" memref at which to create the "view" memref.

• sizes: memref-rank number of sizes which specify the sizes of the result "view" memref type.

• strides: memref-rank number of strides that compose multiplicatively with the base memref strides in each dimension.

The representation based on offsets, sizes and strides support a partially-static specification via attributes specified through the static_offsets, static_sizes and static_strides arguments. A special sentinel value ShapedType::kDynamic encodes that the corresponding entry has a dynamic value.

A subview operation may additionally reduce the rank of the resulting view by removing dimensions that are statically known to be of size 1.

Example 1:

%0 = memref.alloc() : memref<64x4xf32, affine_map<(d0, d1) -> (d0 * 4 + d1)>>

// Create a sub-view of "base" memref '%0' with offset arguments '%c0',
// dynamic sizes for each dimension, and stride arguments '%c1'.
%1 = memref.subview %0[%c0, %c0][%size0, %size1][%c1, %c1]
  : memref<64x4xf32, affine_map<(d0, d1) -> (d0 * 4 + d1)>> to
    memref<?x?xf32, affine_map<(d0, d1)[s0, s1] -> (d0 * s1 + d1 + s0)>>

Example 2:

%0 = memref.alloc() : memref<8x16x4xf32, affine_map<(d0, d1, d2) -> (d0 * 64 + d1 * 4 + d2)>>

// Create a sub-view of "base" memref '%0' with dynamic offsets, sizes,
// and strides.
// Note that dynamic offsets are represented by the linearized dynamic
// offset symbol 's0' in the subview memref layout map, and that the
// dynamic strides operands, after being applied to the base memref
// strides in each dimension, are represented in the view memref layout
// map as symbols 's1', 's2' and 's3'.
%1 = memref.subview %0[%i, %j, %k][%size0, %size1, %size2][%x, %y, %z]
  : memref<8x16x4xf32, affine_map<(d0, d1, d2) -> (d0 * 64 + d1 * 4 + d2)>> to
    memref<?x?x?xf32,
      affine_map<(d0, d1, d2)[s0, s1, s2, s3] -> (d0 * s1 + d1 * s2 + d2 * s3 + s0)>>

Example 3:

%0 = memref.alloc() : memref<8x16x4xf32, affine_map<(d0, d1, d2) -> (d0 * 64 + d1 * 4 + d2)>>

// Subview with constant offsets, sizes and strides.
%1 = memref.subview %0[0, 2, 0][4, 4, 4][1, 1, 1]
  : memref<8x16x4xf32, affine_map<(d0, d1, d2) -> (d0 * 64 + d1 * 4 + d2)>> to
    memref<4x4x4xf32, affine_map<(d0, d1, d2) -> (d0 * 64 + d1 * 4 + d2 + 8)>>

Example 4:

%0 = memref.alloc(%arg0, %arg1) : memref<?x?xf32>

// Subview with constant size, but dynamic offsets and
// strides. The resulting memref has a static shape, but if the
// base memref has an affine map to describe the layout, the result
// memref also uses an affine map to describe the layout. The
// strides of the result memref is computed as follows:
//
// Let #map1 represents the layout of the base memref, and #map2
// represents the layout of the result memref. A #mapsubview can be
// constructed to map an index from the result memref to the base
// memref (note that the description below uses more convenient
// naming for symbols, while in affine maps, symbols are
// represented as unsigned numbers that identify that symbol in the
// given affine map.
//
// #mapsubview = (d0, d1)[o0, o1, t0, t1] -> (d0 * t0 + o0, d1 * t1 + o1)
//
// where, o0, o1, ... are offsets, and t0, t1, ... are strides. Then,
//
// #map2 = #map1.compose(#mapsubview)
//
// If the layout map is represented as
//
// #map1 = (d0, d1)[s0, s1, s2] -> (d0 * s1 + d1 * s2 + s0)
//
// then,
//
// #map2 = (d0, d1)[s0, s1, s2, o0, o1, t0, t1] ->
//              (d0 * s1 * t0 + d1 * s2 * t1 + o0 * s1 + o1 * s2 + s0)
//
// Representing this canonically
//
// #map2 = (d0, d1)[r0, r1, r2] -> (d0 * r1 + d1 * r2 + r0)
//
// where, r0 = o0 * s1 + o1 * s2 + s0, r1 = s1 * t0, r2 = s2 * t1.
%1 = memref.subview %0[%i, %j][4, 4][%x, %y] :
  : memref<?x?xf32, affine_map<(d0, d1)[s0, s1, s2] -> (d0 * s1 + d1 * s2 + s0)>> to
    memref<4x4xf32, affine_map<(d0, d1)[r0, r1, r2] -> (d0 * r1 + d1 * r2 + r0)>>

// Note that the subview op does not guarantee that the result
// memref is "inbounds" w.r.t to base memref. It is upto the client
// to ensure that the subview is accessed in a manner that is
// in-bounds.

Example 5:

// Rank-reducing subview.
%1 = memref.subview %0[0, 0, 0][1, 16, 4][1, 1, 1] :
  memref<8x16x4xf32> to memref<16x4xf32>

// Original layout:
// (d0, d1, d2) -> (64 * d0 + 16 * d1 + d2)
// Subviewed layout:
// (d0, d1, d2) -> (64 * (d0 + 3) + 4 * (d1 + 4) + d2 + 2) = (64 * d0 + 4 * d1 + d2 + 210)
// After rank reducing:
// (d0, d1) -> (4 * d0 + d1 + 210)
%3 = memref.subview %2[3, 4, 2][1, 6, 3][1, 1, 1] :
  memref<8x16x4xf32> to memref<6x3xf32, strided<[4, 1], offset: 210>>

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Reactant.MLIR.Dialects.memref.transpose Method

transpose

The transpose op produces a strided memref whose sizes and strides are a permutation of the original in memref. This is purely a metadata transformation.

Example

%1 = memref.transpose %0 (i, j) -> (j, i) : memref<?x?xf32> to memref<?x?xf32, affine_map<(d0, d1)[s0] -> (d1 * s0 + d0)>>

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Reactant.MLIR.Dialects.memref.view Method

view

The "view" operation extracts an N-D contiguous memref with empty layout map with arbitrary element type from a 1-D contiguous memref with empty layout map of i8 element type. The ViewOp supports the following arguments:

• A single dynamic byte-shift operand must be specified which represents a a shift of the base 1-D memref pointer from which to create the resulting contiguous memref view with identity layout.

• A dynamic size operand that must be specified for each dynamic dimension in the resulting view memref type.

The "view" operation gives a structured indexing form to a flat 1-D buffer. Unlike "subview" it can perform a type change. The type change behavior requires the op to have special semantics because, e.g. a byte shift of 3 cannot be represented as an offset on f64. For now, a "view" op:

1. Only takes a contiguous source memref with 0 offset and empty layout.

2. Must specify a byte_shift operand (in the future, a special integer attribute may be added to support the folded case).

3. Returns a contiguous memref with 0 offset and empty layout.

Example

// Allocate a flat 1D/i8 memref.
%0 = memref.alloc() : memref<2048xi8>

// ViewOp with dynamic offset and static sizes.
%1 = memref.view %0[%offset_1024][] : memref<2048xi8> to memref<64x4xf32>

// ViewOp with dynamic offset and two dynamic size.
%2 = memref.view %0[%offset_1024][%size0, %size1] :
  memref<2048xi8> to memref<?x4x?xf32>

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