================================================================================
linalg.map without ins
================================================================================
func.func @map_no_inputs(%init: tensor<64xf32>) -> tensor<64xf32> {
%add = linalg.map
outs(%init:tensor<64xf32>)
() {
%0 = arith.constant 0.0: f32
linalg.yield %0: f32
}
func.return %add : tensor<64xf32>
}
--------------------------------------------------------------------------------
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================================================================================
linalg.map with ins and outs
================================================================================
func.func @map_binary_memref(%lhs: memref<64xf32>, %rhs: memref<64xf32>,
%init: memref<64xf32>) {
linalg.map
ins(%lhs, %rhs: memref<64xf32>, memref<64xf32>)
outs(%init:memref<64xf32>)
(%lhs_elem: f32, %rhs_elem: f32) {
%0 = arith.addf %lhs_elem, %rhs_elem: f32
linalg.yield %0: f32
}
func.return
}
--------------------------------------------------------------------------------
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================================================================================
linalg.generic with attribute dictionary
================================================================================
func.func @gather_like(
%arg0 : tensor<?x?xf32> {bufferization.writable = false},
%arg1 : tensor<?xi32> {bufferization.writable = false},
%arg2 : tensor<?x?xf32> {bufferization.writable = true})
-> tensor<?x?xf32>
{
%0 = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg1 : tensor<?xi32>) outs(%arg2 : tensor<?x?xf32>) {
^bb0(%arg3: i32, %arg4 : f32):
%iv1 = linalg.index 1 : index
%1 = arith.index_cast %arg3: i32 to index
%2 = tensor.extract %arg0[%1, %iv1] : tensor<?x?xf32>
linalg.yield %2 : f32
} -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}
--------------------------------------------------------------------------------
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================================================================================
tensor.collapse_shape and tensor.expand_shape without reassocation
================================================================================
func.func @tensor_reshape_zero_dim(%arg0 : tensor<1x1xf32>, %arg1 : tensor<f32>)
-> (tensor<f32>, tensor<1x1xf32>) {
%0 = tensor.collapse_shape %arg0 [] : tensor<1x1xf32> into tensor<f32>
%1 = tensor.expand_shape %0 [] : tensor<f32> into tensor<1x1xf32>
return %0, %1 : tensor<f32>, tensor<1x1xf32>
}
--------------------------------------------------------------------------------
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================================================================================
tensor.collapse_shape with reassocation
================================================================================
func.func @legal_collapsing_reshape_dynamic_tensor
(%arg0: tensor<?x?x?x4x?xf32>) -> tensor<?x?x?xf32>
{
%0 = tensor.collapse_shape %arg0 [[0], [1], [2, 3, 4]] :
tensor<?x?x?x4x?xf32> into tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}
--------------------------------------------------------------------------------
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================================================================================
tensor.gather and tensor.scatter
================================================================================
func.func @gather_scatter(%dest : tensor<4x5x6xf32>, %indices: tensor<1x3x2xindex>,
%indices_i32: tensor<1x3x2xi32>) {
%gathered = tensor.gather %dest[%indices_i32] gather_dims([1, 2]) unique:
(tensor<4x5x6xf32>, tensor<1x3x2xi32>) -> tensor<1x3x4x1x1xf32>
%rank_reduced_gathered = tensor.gather %dest[%indices] gather_dims([1, 2]) unique:
(tensor<4x5x6xf32>, tensor<1x3x2xindex>) -> tensor<1x3x4xf32>
%scattered = tensor.scatter %gathered into %dest[%indices]
scatter_dims([1, 2]) unique:
(tensor<1x3x4x1x1xf32>, tensor<4x5x6xf32>, tensor<1x3x2xindex>) -> tensor<4x5x6xf32>
%rank_reduced_scattered = tensor.scatter %rank_reduced_gathered into %dest[%indices_i32]
scatter_dims([1, 2]) unique:
(tensor<1x3x4xf32>, tensor<4x5x6xf32>, tensor<1x3x2xi32>) -> tensor<4x5x6xf32>
return
}
--------------------------------------------------------------------------------
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================================================================================
tensor.extract_slice
================================================================================
func.func @slice(%t: tensor<8x16x4xf32>, %idx : index) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%1 = tensor.extract_slice %t[%c0, %c0, %c0][%idx, %idx, %idx][%c1, %c1, %c1]
: tensor<8x16x4xf32> to tensor<?x?x?xf32>
%2 = tensor.extract_slice %t[0, 2, 0][4, 4, 4][1, 1, 1]
: tensor<8x16x4xf32> to tensor<4x4x4xf32>
%3 = tensor.extract_slice %t[0, 2, 0][4, 1, 4][1, 1, 1]
: tensor<8x16x4xf32> to tensor<4x4xf32>
return
}
--------------------------------------------------------------------------------
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================================================================================
tensor.insert_slice
================================================================================
func.func @insert_slice(
%t: tensor<8x16x4xf32>,
%td: tensor<8x?x4xf32>,
%t2: tensor<16x32x8xf32>,
%t3: tensor<4x4xf32>,
%idx : index,
%sz : index) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%1 = tensor.insert_slice %t into %t2[%c0, %c0, %c0][8, 16, 4][%c1, %c1, %c1]
: tensor<8x16x4xf32> into tensor<16x32x8xf32>
%2 = tensor.insert_slice %t into %t2[%c0, %idx, %c0][8, 16, 4][%c1, 1, %c1]
: tensor<8x16x4xf32> into tensor<16x32x8xf32>
%3 = tensor.insert_slice %t3 into %t[0, 2, 0][4, 1, 4][1, 1, 1]
: tensor<4x4xf32> into tensor<8x16x4xf32>
%4 = tensor.insert_slice %td into %t[0, %idx, 0][8, %sz, 4][1, 1, 1]
: tensor<8x?x4xf32> into tensor<8x16x4xf32>
return
}
--------------------------------------------------------------------------------
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(operation
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================================================================================
tensor.rank, tensor.generate, tensor.yield, tensor.extract
================================================================================
func.func @extract_from_tensor.generate(%idx: index, %tensor: tensor<*xf32>) -> index {
%size = tensor.rank %tensor : tensor<*xf32>
%0 = tensor.generate %size {
^bb0(%arg0: index):
%1 = tensor.dim %tensor, %arg0 : tensor<*xf32>
tensor.yield %1 : index
} : tensor<?xindex>
%1 = tensor.extract %0[%idx] : tensor<?xindex>
return %1 : index
}
--------------------------------------------------------------------------------
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(operation
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(func_dialect
(value_use)
(type
(builtin_type
(index_type))))))))))))
================================================================================
tensor.insert
================================================================================
func.func @insert(%arg0: f32, %arg1: index, %arg2: tensor<?x?x?xf32>) {
%0 = tensor.insert %arg0 into %arg2[%arg1, %arg1, %arg1] : tensor<?x?x?xf32>
return
}
--------------------------------------------------------------------------------
(toplevel
(operation
(custom_operation
(func_dialect
(symbol_ref_id)
(func_arg_list
(value_use)
(type
(builtin_type
(float_type)))
(value_use)
(type
(builtin_type
(index_type)))
(value_use)
(type
(builtin_type
(tensor_type
(dim_list
(float_type))))))
(region
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(value_use)
(type
(builtin_type
(tensor_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect)))))))))
================================================================================
tensor.from_elements and tensor.reshape
================================================================================
func.func @tensor.reshape(%t1: tensor<?x10xf32>) -> tensor<2x2x5xf32> {
%two = arith.constant 2 : i64
%five = arith.constant 5 : i64
%shape = tensor.from_elements %two, %two, %five : tensor<3xi64>
%reshaped = tensor.reshape %t1(%shape) : (tensor<?x10xf32>, tensor<3xi64>) -> tensor<2x2x5xf32>
return %reshaped : tensor<2x2x5xf32>
}
--------------------------------------------------------------------------------
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(symbol_ref_id)
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(type
(builtin_type
(tensor_type
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(operation
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(value_use)
(type
(builtin_type
(tensor_type
(dim_list
(float_type))))))))))))))
================================================================================
tensor.pad
================================================================================
func.func @pad_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
%pad_value: f32) -> tensor<6x?x?x?xf32> {
%0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
tensor.yield %pad_value : f32
} : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
return %0 : tensor<6x?x?x?xf32>
}
func.func @pad_nofold_same_static_shape(%arg0: tensor<5x6xf32>, %a: index)
-> tensor<5x6xf32> {
%cst = arith.constant 0.000000e+00 : f32
%0 = tensor.pad %arg0 nofold low[%a, 0] high[0, %a] {
^bb0(%arg1: index, %arg2: index):
tensor.yield %cst : f32
} : tensor<5x6xf32> to tensor<5x6xf32>
return %0 : tensor<5x6xf32>
}
--------------------------------------------------------------------------------
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(dim_list
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(entry_block
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(custom_operation
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(type
(builtin_type
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(operation
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(builtin_type
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(type
(builtin_type
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(type
(builtin_type
(tensor_type
(dim_list
(float_type)))))
(type
(builtin_type
(tensor_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect
(value_use)
(type
(builtin_type
(tensor_type
(dim_list
(float_type))))))))))))))
================================================================================
tensor.splat
================================================================================
func.func @test_splat_op(%s : f32) {
%v = tensor.splat %s : tensor<8xf32>
return
}
--------------------------------------------------------------------------------
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(operation
(custom_operation
(func_dialect
(symbol_ref_id)
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(builtin_type
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(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect)))))))))
================================================================================
tensor.reshape
================================================================================
func.func @tensor_reshape(%unranked: tensor<*xf32>, %shape: tensor<1xi32>) {
%dyn_vec = tensor.reshape %unranked(%shape)
: (tensor<*xf32>, tensor<1xi32>) -> tensor<?xf32>
return
}
--------------------------------------------------------------------------------
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(type
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(tensor_type
(dim_list
(float_type)))))))))
(operation
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(func_dialect)))))))))
================================================================================
tensor.pack and tensor.unpack
================================================================================
func.func @pack_nc_to_ncnc(%source: tensor<128x256xf32>, %dest: tensor<4x16x32x16xf32>) -> tensor<128x256xf32> {
%0 = tensor.pack %source inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<128x256xf32> -> tensor<4x16x32x16xf32>
%1 = tensor.empty() : tensor<128x256xf32>
%2 = tensor.unpack %0 inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %1 : tensor<4x16x32x16xf32> -> tensor<128x256xf32>
return %2 : tensor<128x256xf32>
}
--------------------------------------------------------------------------------
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(builtin_type
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(operation
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(type
(builtin_type
(tensor_type
(dim_list
(float_type))))))))))))))
================================================================================
memref.alloc, memref.cast, and memref.copy
================================================================================
func.func @memref_copy() {
%0 = memref.alloc() : memref<2xf32>
%1 = memref.cast %0 : memref<2xf32> to memref<*xf32>
%2 = memref.alloc() : memref<2xf32>
%3 = memref.cast %0 : memref<2xf32> to memref<*xf32>
memref.copy %1, %3 : memref<*xf32> to memref<*xf32>
return
}
--------------------------------------------------------------------------------
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(operation
(custom_operation
(func_dialect
(symbol_ref_id)
(func_arg_list)
(region
(entry_block
(operation
(op_result
(value_use))
(custom_operation
(memref_dialect
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(op_result
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(custom_operation
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(type
(builtin_type
(memref_type
(dim_list
(float_type)))))
(type
(builtin_type
(memref_type
(dim_list
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(value_use))
(custom_operation
(memref_dialect
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(op_result
(value_use))
(custom_operation
(memref_dialect
(value_use)
(type
(builtin_type
(memref_type
(dim_list
(float_type)))))
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(memref_dialect
(value_use)
(value_use)
(type
(builtin_type
(memref_type
(dim_list
(float_type)))))
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect)))))))))
================================================================================
memref.expand_shape and memref.collapse_shape
================================================================================
func.func @expand_collapse_shape_dynamic(%arg0: memref<?x?x?xf32>,
%arg1: memref<?x?x?xf32, strided<[?, ?, 1], offset: 0>>,
%arg2: memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>>,
%arg3: memref<?x42xf32, strided<[42, 1], offset: 0>>) {
%0 = memref.collapse_shape %arg0 [[0, 1], [2]] :
memref<?x?x?xf32> into memref<?x?xf32>
%r0 = memref.expand_shape %0 [[0, 1], [2]] :
memref<?x?xf32> into memref<?x4x?xf32>
%1 = memref.collapse_shape %arg1 [[0, 1], [2]] :
memref<?x?x?xf32, strided<[?, ?, 1], offset: 0>> into
memref<?x?xf32, strided<[?, 1], offset: 0>>
%r1 = memref.expand_shape %1 [[0, 1], [2]] :
memref<?x?xf32, strided<[?, 1], offset: 0>> into
memref<?x4x?xf32, strided<[?, ?, 1], offset: 0>>
%2 = memref.collapse_shape %arg2 [[0, 1], [2]] :
memref<?x?x?xf32, strided<[?, ?, 1], offset: ?>> into
memref<?x?xf32, strided<[?, 1], offset: ?>>
%r2 = memref.expand_shape %2 [[0, 1], [2]] :
memref<?x?xf32, strided<[?, 1], offset: ?>> into
memref<?x4x?xf32, strided<[?, ?, 1], offset: ?>>
%3 = memref.collapse_shape %arg3 [[0, 1]] :
memref<?x42xf32, strided<[42, 1], offset: 0>> into
memref<?xf32, strided<[1]>>
%r3 = memref.expand_shape %3 [[0, 1]] :
memref<?xf32, strided<[1]>> into memref<?x42xf32>
return
}
--------------------------------------------------------------------------------
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(builtin_type
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(type
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(memref_type
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(float_type))
(attribute_value
(builtin_attribute
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(type
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(integer_literal)))
(type
(builtin_type
(memref_type
(dim_list
(float_type))
(attribute_value
(builtin_attribute
(strided_layout
(integer_literal)))))))
(type
(builtin_type
(memref_type
(dim_list
(float_type))
(attribute_value
(builtin_attribute
(strided_layout)))))))))
(operation
(op_result
(value_use))
(custom_operation
(memref_dialect
(value_use)
(nested_idx_list
(nested_idx_list
(integer_literal)
(integer_literal)))
(type
(builtin_type
(memref_type
(dim_list
(float_type))
(attribute_value
(builtin_attribute
(strided_layout))))))
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect)))))))))
================================================================================
memref.realloc
================================================================================
func.func @memref_realloc_sd(%src : memref<2xf32>, %d : index) -> memref<?xf32>{
%0 = memref.realloc %src(%d) : memref<2xf32> to memref<?xf32>
return %0 : memref<?xf32>
}
--------------------------------------------------------------------------------
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(operation
(custom_operation
(func_dialect
(symbol_ref_id)
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(value_use)
(type
(builtin_type
(memref_type
(dim_list
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(value_use)
(type
(builtin_type
(index_type))))
(func_return
(type_list_attr_parens
(type
(builtin_type
(memref_type
(dim_list
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(region
(entry_block
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(custom_operation
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(builtin_type
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(type
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(operation
(custom_operation
(func_dialect
(value_use)
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))))))))
================================================================================
memref.prefetch
================================================================================
func.func @prefetch_locality_hint(%i : index) {
%0 = memref.alloc() : memref<10xf32>
memref.prefetch %0[%i], read, locality<3>, data : memref<10xf32>
return
}
--------------------------------------------------------------------------------
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(operation
(custom_operation
(func_dialect
(symbol_ref_id)
(func_arg_list
(value_use)
(type
(builtin_type
(index_type))))
(region
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(op_result
(value_use))
(custom_operation
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(type
(builtin_type
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(operation
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(value_use)
(isWrite_attr)
(localityHint_attr
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(isDataCache_attr)
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect)))))))))
================================================================================
memref.rank
================================================================================
func.func @rank(%t : memref<4x4x?xf32>) {
%0 = memref.rank %t : memref<4x4x?xf32>
return
}
--------------------------------------------------------------------------------
(toplevel
(operation
(custom_operation
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(func_arg_list
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(value_use)
(type
(builtin_type
(memref_type
(dim_list
(float_type))))))))
(operation
(custom_operation
(func_dialect)))))))))
================================================================================
memref.store
================================================================================
func.func @truncation_spillover(%arg0 : memref<?xi32>) -> index {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
%c49 = arith.constant 49 : index
%0 = scf.for %arg1 = %c0 to %c2 step %c1 iter_args(%arg2 = %c0) -> index {
%1 = arith.divsi %arg2, %c49 : index
%2 = arith.index_cast %1 : index to i32
memref.store %2, %arg0[%c0] : memref<?xi32>
%3 = arith.addi %arg2, %arg1 : index
scf.yield %3 : index
}
func.return %0 : index
}
--------------------------------------------------------------------------------
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(operation
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================================================================================
memref.reinterpret_cast
================================================================================
func.func @memref_reinterpret_cast_static_to_dynamic_sizes(%in: memref<?xf32>)
-> memref<10x?xf32, strided<[?, 1], offset: ?>> {
%out = memref.reinterpret_cast %in to
offset: [1], sizes: [10, 10], strides: [1, 1]
: memref<?xf32> to memref<10x?xf32, strided<[?, 1], offset: ?>>
return %out : memref<10x?xf32, strided<[?, 1], offset: ?>>
}
--------------------------------------------------------------------------------
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================================================================================
cf.assert
================================================================================
func.func @assert(%arg : i1) {
cf.assert %arg, "Some message in case this assertion fails."
return
}
--------------------------------------------------------------------------------
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================================================================================
vector.splat
================================================================================
func.func @signExtendConstantSplat() -> vector<4xi16> {
%c-2 = arith.constant -2 : i8
%splat = vector.splat %c-2 : vector<4xi8>
%ext = arith.extsi %splat : vector<4xi8> to vector<4xi16>
return %ext : vector<4xi16>
}
--------------------------------------------------------------------------------
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================================================================================
arith.cmpi with string parameter
================================================================================
func.func @notCmpEQ(%arg0: i8, %arg1: i8) -> i1 {
%true = arith.constant true
%cmp = arith.cmpi "eq", %arg0, %arg1 : i8
%ncmp = arith.xori %cmp, %true : i1
return %ncmp : i1
}
--------------------------------------------------------------------------------
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================================================================================
bufferization.alloc_tensor
================================================================================
func.func @test_alloc_tensor_op(%t: tensor<?x5xf32>, %sz: index)
-> tensor<?x5xf32>
{
%0 = bufferization.alloc_tensor(%sz) : tensor<?x5xf32>
%1 = bufferization.alloc_tensor() copy(%t) : tensor<?x5xf32>
%2 = bufferization.alloc_tensor() : tensor<5x6xf32>
%3 = bufferization.alloc_tensor(%sz, %sz) : tensor<?x?xf32>
%4 = bufferization.alloc_tensor() copy(%t) {escape = true} : tensor<?x5xf32>
%5 = bufferization.alloc_tensor() copy(%t) {escape = false} : tensor<?x5xf32>
%c100 = arith.constant 100 : index
%6 = bufferization.alloc_tensor() size_hint=%c100 : tensor<100x100xf64, #CSR>
%7 = bufferization.alloc_tensor(%sz) {memory_space = "foo"} : tensor<?xf32>
return %1 : tensor<?x5xf32>
}
--------------------------------------------------------------------------------
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================================================================================
vector.transfer_read and vector.transfer_write
================================================================================
func.func @vector_transfer_ops(%arg0: memref<?x?xf32>,
%arg1 : memref<?x?xvector<4x3xf32>>,
%arg2 : memref<?x?xvector<4x3xi32>>,
%arg3 : memref<?x?xvector<4x3xindex>>,
%arg4 : memref<?x?x?xf32>) {
%c3 = arith.constant 3 : index
%cst = arith.constant 3.0 : f32
%f0 = arith.constant 0.0 : f32
%c0 = arith.constant 0 : i32
%i0 = arith.constant 0 : index
%i1 = arith.constant 1 : i1
%vf0 = vector.splat %f0 : vector<4x3xf32>
%v0 = vector.splat %c0 : vector<4x3xi32>
%vi0 = vector.splat %i0 : vector<4x3xindex>
%m = arith.constant dense<[0, 0, 1, 0, 1]> : vector<5xi1>
%m2 = vector.splat %i1 : vector<4x5xi1>
%0 = vector.transfer_read %arg0[%c3, %c3], %f0 {permutation_map = affine_map<(d0, d1)->(d0)>} : memref<?x?xf32>, vector<128xf32>
%1 = vector.transfer_read %arg0[%c3, %c3], %f0 {permutation_map = affine_map<(d0, d1)->(d1, d0)>} : memref<?x?xf32>, vector<3x7xf32>
%2 = vector.transfer_read %arg0[%c3, %c3], %cst {permutation_map = affine_map<(d0, d1)->(d0)>} : memref<?x?xf32>, vector<128xf32>
%3 = vector.transfer_read %arg0[%c3, %c3], %cst {permutation_map = affine_map<(d0, d1)->(d1)>} : memref<?x?xf32>, vector<128xf32>
%4 = vector.transfer_read %arg1[%c3, %c3], %vf0 {permutation_map = affine_map<(d0, d1)->(d0, d1)>} : memref<?x?xvector<4x3xf32>>, vector<1x1x4x3xf32>
%5 = vector.transfer_read %arg1[%c3, %c3], %vf0 {in_bounds = [false, true]} : memref<?x?xvector<4x3xf32>>, vector<1x1x4x3xf32>
%6 = vector.transfer_read %arg2[%c3, %c3], %v0 : memref<?x?xvector<4x3xi32>>, vector<5x24xi8>
%7 = vector.transfer_read %arg3[%c3, %c3], %vi0 : memref<?x?xvector<4x3xindex>>, vector<5x48xi8>
%8 = vector.transfer_read %arg0[%c3, %c3], %f0, %m : memref<?x?xf32>, vector<5xf32>
%9 = vector.transfer_read %arg4[%c3, %c3, %c3], %f0, %m2 {permutation_map = affine_map<(d0, d1, d2)->(d1, d0, 0)>} : memref<?x?x?xf32>, vector<5x4x8xf32>
vector.transfer_write %0, %arg0[%c3, %c3] {permutation_map = affine_map<(d0, d1)->(d0)>} : vector<128xf32>, memref<?x?xf32>
vector.transfer_write %1, %arg0[%c3, %c3] {permutation_map = affine_map<(d0, d1)->(d1, d0)>} : vector<3x7xf32>, memref<?x?xf32>
vector.transfer_write %4, %arg1[%c3, %c3] {permutation_map = affine_map<(d0, d1)->(d0, d1)>} : vector<1x1x4x3xf32>, memref<?x?xvector<4x3xf32>>
vector.transfer_write %5, %arg1[%c3, %c3] {in_bounds = [false, false]} : vector<1x1x4x3xf32>, memref<?x?xvector<4x3xf32>>
vector.transfer_write %6, %arg2[%c3, %c3] : vector<5x24xi8>, memref<?x?xvector<4x3xi32>>
vector.transfer_write %7, %arg3[%c3, %c3] : vector<5x48xi8>, memref<?x?xvector<4x3xindex>>
vector.transfer_write %8, %arg0[%c3, %c3], %m : vector<5xf32>, memref<?x?xf32>
return
}
--------------------------------------------------------------------------------
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