Tracing dialect A dialect to print program values at runtime.

Operation definition

Tracing.trace_ciphertext (::mlir::concretelang::Tracing::TraceCiphertextOp)

Prints a ciphertext.

Attributes:

Operands:

Tracing.trace_message (::mlir::concretelang::Tracing::TraceMessageOp)

Prints a message.

Attributes:

Tracing.trace_plaintext (::mlir::concretelang::Tracing::TracePlaintextOp)

Prints a plaintext.

Attributes:

Operands:

High Level Fully Homomorphic Encryption Linalg dialect A dialect for representation of high level linalg operations on fully homomorphic ciphertexts.

Operation definition

FHELinalg.add_eint_int (::mlir::concretelang::FHELinalg::AddEintIntOp)

Returns a tensor that contains the addition of a tensor of encrypted integers and a tensor of clear integers.

Performs an addition following the broadcasting rules between a tensor of encrypted integers and a tensor of clear integers. The width of the clear integers must be less than or equal to the width of encrypted integers.

Examples:

// Returns the term-by-term addition of `%a0` with `%a1`
"FHELinalg.add_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4xi5>) -> tensor<4x!FHE.eint<4>>

// Returns the term-by-term addition of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.add_eint_int"(%a0, %a1) : (tensor<4x1x4x!FHE.eint<4>>, tensor<1x4x4xi5>) -> tensor<4x4x4x!FHE.eint<4>>

// Returns the addition of a 3x3 matrix of encrypted integers and a 3x1 matrix (a column) of integers.
//
// [1,2,3]   [1]   [2,3,4]
// [4,5,6] + [2] = [6,7,8]
// [7,8,9]   [3]   [10,11,12]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.add_eint_int"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3x1xi5>) -> tensor<3x3x!FHE.eint<4>>

// Returns the addition of a 3x3 matrix of encrypted integers and a 1x3 matrix (a line) of integers.
//
// [1,2,3]             [2,4,6]
// [4,5,6] + [1,2,3] = [5,7,9]
// [7,8,9]             [8,10,12]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.add_eint_int"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<1x3xi5>) -> tensor<3x3x!FHE.eint<4>>

// Same behavior as the previous one, but as the dimension #2 is missing of operand #2.
"FHELinalg.add_eint_int(%a0, %a1)" : (tensor<3x4x!FHE.eint<4>>, tensor<3xi5>) -> tensor<4x4x4x!FHE.eint<4>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEintInt, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.add_eint (::mlir::concretelang::FHELinalg::AddEintOp)

Returns a tensor that contains the addition of two tensor of encrypted integers.

Performs an addition following the broadcasting rules between two tensors of encrypted integers. The width of the encrypted integers must be equal.

Examples:

// Returns the term-by-term addition of `%a0` with `%a1`
"FHELinalg.add_eint"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4x!FHE.eint<4>>) -> tensor<4x!FHE.eint<4>>

// Returns the term-by-term addition of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.add_eint"(%a0, %a1) : (tensor<4x1x4x!FHE.eint<4>>, tensor<1x4x4x!FHE.eint<4>>) -> tensor<4x4x4x!FHE.eint<4>>

// Returns the addition of a 3x3 matrix of encrypted integers and a 3x1 matrix (a column) of encrypted integers.
//
// [1,2,3]   [1]   [2,3,4]
// [4,5,6] + [2] = [6,7,8]
// [7,8,9]   [3]   [10,11,12]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.add_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3x1x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

// Returns the addition of a 3x3 matrix of encrypted integers and a 1x3 matrix (a line) of encrypted integers.
//
// [1,2,3]             [2,4,6]
// [4,5,6] + [1,2,3] = [5,7,9]
// [7,8,9]             [8,10,12]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.add_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<1x3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

// Same behavior as the previous one, but as the dimension #2 of operand #2 is missing.
"FHELinalg.add_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEint, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.apply_lookup_table (::mlir::concretelang::FHELinalg::ApplyLookupTableEintOp)

Returns a tensor that contains the result of a lookup table.

For each encrypted index, performs a lookup table of clear integers.

// The result of this operation, is a tensor that contains the result of a lookup table.
// i.e. %res[i, ..., k] = %lut[%t[i, ..., k]]
%res = FHELinalg.apply_lookup_table(%t, %lut): tensor<DNx...xD1x!FHE.eint<$p>>, tensor<D2^$pxi64> -> tensor<DNx...xD1x!FHE.eint<$p>>

The %lut argument must be a tensor with one dimension, where its dimension is 2^p where p is the width of the encrypted integers.

Examples:

// Returns the lookup of 3x3 matrix of encrypted indices of with 2 on a table of size 4=2² of clear integers.
//
// [0,1,2]                 [1,3,5]
// [3,0,1] lut [1,3,5,7] = [7,1,3]
// [2,3,0]                 [5,7,1]
"FHELinalg.apply_lookup_table"(%t, %lut) : (tensor<3x3x!FHE.eint<2>>, tensor<4xi64>) -> tensor<3x3x!FHE.eint<3>>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.apply_mapped_lookup_table (::mlir::concretelang::FHELinalg::ApplyMappedLookupTableEintOp)

Returns a tensor that contains the result of a lookup table, using a different lookup table for each element, specified by a map.

Performs for each encrypted index a lookup table of clear integers. Multiple lookup tables are passed, and the application of lookup tables is performed following the broadcasting rules. The precise lookup is specified by a map.

// The result of this operation, is a tensor that contains the result of the lookup on different tables.
// i.e. %res[i, ..., k] = %luts[ %map[i, ..., k] ][ %t[i, ..., k] ]
%res = FHELinalg.apply_mapped_lookup_table(%t, %luts, %map): tensor<DNx...xD1x!FHE.eint<$p>>, tensor<DM x ^$p>, tensor<DNx...xD1xindex> -> tensor<DNx...xD1x!FHE.eint<$p>>

Examples:

// Returns the lookup of 3x2 matrix of encrypted indices of width 2 on a vector of 2 tables of size 4=2^2 of clear integers.
//
// [0,1]                                 [0, 1] = [1,2]
// [3,0] lut [[1,3,5,7], [0,2,4,6]] with [0, 1] = [7,0]
// [2,3]                                 [0, 1] = [5,6]
"FHELinalg.apply_mapped_lookup_table"(%t, %luts, %map) : (tensor<3x2x!FHE.eint<2>>, tensor<2x4xi64>, tensor<3x2xindex>) -> tensor<3x2x!FHE.eint<3>>

Others examples: // [0,1] [1, 0] = [3,2] // [3,0] lut [[1,3,5,7], [0,2,4,6]] with [0, 1] = [7,0] // [2,3] [1, 0] = [4,7]

// [0,1] [0, 0] = [1,3] // [3,0] lut [[1,3,5,7], [0,2,4,6]] with [1, 1] = [6,0] // [2,3] [1, 0] = [4,7]

// [0,1] [0] = [1,3] // [3,0] lut [[1,3,5,7], [0,2,4,6]] with [1] = [6,0] // [2,3] [0] = [5,7]

// [0,1] = [1,2] // [3,0] lut [[1,3,5,7], [0,2,4,6]] with [0, 1] = [7,0] // [2,3] = [5,6]

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.apply_multi_lookup_table (::mlir::concretelang::FHELinalg::ApplyMultiLookupTableEintOp)

Returns a tensor that contains the result of a lookup table, using a different lookup table for each element.

Performs for each encrypted index a lookup table of clear integers. Multiple lookup tables are passed, and the application of lookup tables is performed following the broadcasting rules.

// The result of this operation, is a tensor that contains the result of the lookup on different tables.
// i.e. %res[i, ..., k] = [ %luts[i][%t[i]], ..., %luts[k][%t[k]] ]
%res = FHELinalg.apply_multi_lookup_table(%t, %lut): tensor<DNx...xD1x!FHE.eint<$p>>, tensor<DMx...xD1xD2^$pxi64> -> tensor<DNx...xD1x!FHE.eint<$p>>

The %luts argument should be a tensor with M dimension, where the first M-1 dimensions are broadcastable with the N dimensions of the encrypted tensor, and where the last dimension dimension is equal to 2^p where p is the width of the encrypted integers.

Examples:

// Returns the lookup of 3x2 matrix of encrypted indices of width 2 on a vector of 2 tables of size 4=2² of clear integers.
// The tables are broadcasted along the first dimension of the tensor.
//
// [0,1]                            = [1,2]
// [3,0] lut [[1,3,5,7], [0,2,4,6]] = [7,0]
// [2,3]                            = [5,6]
"FHELinalg.apply_multi_lookup_table"(%t, %luts) : (tensor<3x2x!FHE.eint<2>>, tensor<2x4xi64>) -> tensor<3x2x!FHE.eint<3>>

// Returns the lookup of a vector of 3 encrypted indices of width 2 on a vector of 3 tables of size 4=2² of clear integers.
//
// [3,0,1] lut [[1,3,5,7], [0,2,4,6], [1,2,3,4]] = [7,0,2]
"FHELinalg.apply_multi_lookup_table"(%t, %luts) : (tensor<3x!FHE.eint<2>>, tensor<3x4xi64>) -> tensor<3x!FHE.eint<3>>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.concat (::mlir::concretelang::FHELinalg::ConcatOp)

Concatenates a sequence of tensors along an existing axis.

Concatenates several tensors along a given axis.

Examples:

"FHELinalg.concat"(%a, %b) { axis = 0 } : (tensor<3x3x!FHE.eint<4>>, tensor<3x3x!FHE.eint<4>>) -> tensor<6x3x!FHE.eint<4>>
//
//        ( [1,2,3]  [1,2,3] )   [1,2,3]
// concat ( [4,5,6], [4,5,6] ) = [4,5,6]
//        ( [7,8,9]  [7,8,9] )   [7,8,9]
//                               [1,2,3]
//                               [4,5,6]
//                               [7,8,9]
//

"FHELinalg.concat"(%a, %b) { axis = 1 } : (tensor<3x3x!FHE.eint<4>>, tensor<3x3x!FHE.eint<4>>) -> tensor<3x6x!FHE.eint<4>>
//
//        ( [1,2,3]  [1,2,3] )   [1,2,3,1,2,3]
// concat ( [4,5,6], [4,5,6] ) = [4,5,6,4,5,6]
//        ( [7,8,9]  [7,8,9] )   [7,8,9,7,8,9]
//

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

FHELinalg.conv2d (::mlir::concretelang::FHELinalg::Conv2dOp)

Returns the 2D convolution of a tensor in NCHW form with weights in the form FCHW

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

FHELinalg.dot_eint_int (::mlir::concretelang::FHELinalg::Dot)

Returns the encrypted dot product between a vector of encrypted integers and a vector of clean integers.

Performs a dot product between a vector of encrypted integers and a vector of clear integers.

Examples:

// Returns the dot product of `%a0` with `%a1`
"FHELinalg.dot_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4xi5>) -> !FHE.eint<4>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.dot_eint_eint (::mlir::concretelang::FHELinalg::DotEint)

Returns the encrypted dot product between two vectors of encrypted integers.

Performs a dot product between two vectors of encrypted integers.

Examples:

// Returns the dot product of `%a0` with `%a1`
"FHELinalg.dot_eint_eint"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4x!FHE.eint<4>>) -> !FHE.eint<4>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.from_element (::mlir::concretelang::FHELinalg::FromElementOp)

Creates a tensor with a single element.

Creates a tensor with a single element.

"FHELinalg.from_element"(%a) : (Type) -> tensor<1xType>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.matmul_eint_eint (::mlir::concretelang::FHELinalg::MatMulEintEintOp)

Returns a tensor that contains the result of the matrix multiplication of a matrix of encrypted integers and a second matrix of encrypted integers.

Performs a matrix multiplication of a matrix of encrypted integers and a second matrix of encrypted integers.

The behavior depends on the arguments in the following way:

- If both arguments are 2-D,
  they are multiplied like conventional matrices.

  e.g.,

  arg0: tensor<MxN> = [...]
  arg1: tensor<NxP> = [...]

  result: tensor<MxP> = [...]

- If the first argument is a vector (1-D),
  it is treated as a matrix with a single row and standard matrix multiplication is performed.

  After standard matrix multiplication,
  the first dimension is removed from the result.

  e.g.,

  arg0: tensor<3> = [x, y, z]
  arg1: tensor<3xM> = [
      [_, _, ..., _, _],
      [_, _, ..., _, _],
      [_, _, ..., _, _],
  ]

  is treated as

  arg0: tensor<1x3> = [
      [x, y, z]
  ]
  arg1: tensor<3xM> = [
      [_, _, ..., _, _],
      [_, _, ..., _, _],
      [_, _, ..., _, _],
  ]

  and matrix multiplication is performed with the following form (1x3 @ 3xM -> 1xM)

  result: tensor<1xM> = [[_, _, ..., _, _]]

  finally, the first dimension is removed by definition so the result has the following form

  result: tensor<M>  = [_, _, ..., _, _]

- If the second argument is 1-D,
  it is treated as a matrix with a single column and standard matrix multiplication is performed.

  After standard matrix multiplication,
  the last dimension is removed from the result.

  e.g.,

  arg0: tensor<Mx3> = [
      [_, _, _],
      [_, _, _],
      ...,
      [_, _, _],
      [_, _, _],
  ]
  arg1: tensor<3> = [x, y, z]

  is treated as

  arg0: tensor<Mx3> = [
      [_, _, _],
      [_, _, _],
      ...,
      [_, _, _],
      [_, _, _],
  ]
  arg1: tensor<3x1> = [
    [x],
    [y],
    [z],
  ]

  and matrix multiplication is performed with the following form (Mx3 @ 3x1 -> Mx1)

  result: tensor<Mx1> = [
    [_],
    [_],
      ...,
    [_],
    [_],
  ]

  finally, the last dimension is removed by definition so the result has the following form

  result: tensor<M> = [_, _, _]

- If either argument is N-D where N > 2,
  the operation is treated as a collection of matrices residing in the last two indices and broadcasted accordingly.

  arg0: tensor<Kx1MxN> = [...]
  arg1: tensor<LxNxP> = [...]

  result: tensor<KxLxMxP> = [...]

"FHELinalg.matmul_eint_eint(%a, %b) : (tensor<MxNx!FHE.eint<p>>, tensor<NxPx!FHE.eint<p>'>) -> tensor<MxPx!FHE.eint<p>>"
"FHELinalg.matmul_eint_eint(%a, %b) : (tensor<KxLxMxNx!FHE.eint<p>>, tensor<KxLxNxPx!FHE.eint<p>'>) -> tensor<KxLxMxPx!FHE.eint<p>>"
"FHELinalg.matmul_eint_eint(%a, %b) : (tensor<MxNx!FHE.eint<p>>, tensor<Nx!FHE.eint<p>'>) -> tensor<Mx!FHE.eint<p>>"
"FHELinalg.matmul_eint_eint(%a, %b) : (tensor<Nx!FHE.eint<p>>, tensor<NxPx!FHE.eint<p>'>) -> tensor<Px!FHE.eint<p>>"

Examples:

// Returns the matrix multiplication of a 3x2 matrix of encrypted integers and a 2x3 matrix of integers.
//         [ 1, 2, 3]
//         [ 2, 3, 4]
//       *
// [1,2]   [ 5, 8,11]
// [3,4] = [11,18,25]
// [5,6]   [17,28,39]
//
"FHELinalg.matmul_eint_eint"(%a, %b) : (tensor<3x2x!FHE.eint<6>>, tensor<2x3x!FHE.eint<6>>) -> tensor<3x3x!FHE.eint<12>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEint

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.matmul_eint_int (::mlir::concretelang::FHELinalg::MatMulEintIntOp)

Returns a tensor that contains the result of the matrix multiplication of a matrix of encrypted integers and a matrix of clear integers.

Performs a matrix multiplication of a matrix of encrypted integers and a matrix of clear integers. The width of the clear integers must be less than or equal to the width of encrypted integers.

The behavior depends on the arguments in the following way:

- If both arguments are 2-D,
  they are multiplied like conventional matrices.

  e.g.,

  arg0: tensor<MxN> = [...]
  arg1: tensor<NxP> = [...]

  result: tensor<MxP> = [...]

- If the first argument is a vector (1-D),
  it is treated as a matrix with a single row and standard matrix multiplication is performed.

  After standard matrix multiplication,
  the first dimension is removed from the result.

  e.g.,

  arg0: tensor<3> = [x, y, z]
  arg1: tensor<3xM> = [
      [_, _, ..., _, _],
      [_, _, ..., _, _],
      [_, _, ..., _, _],
  ]

  is treated as

  arg0: tensor<1x3> = [
      [x, y, z]
  ]
  arg1: tensor<3xM> = [
      [_, _, ..., _, _],
      [_, _, ..., _, _],
      [_, _, ..., _, _],
  ]

  and matrix multiplication is performed with the following form (1x3 @ 3xM -> 1xM)

  result: tensor<1xM> = [[_, _, ..., _, _]]

  finally, the first dimension is removed by definition so the result has the following form

  result: tensor<M>  = [_, _, ..., _, _]

- If the second argument is 1-D,
  it is treated as a matrix with a single column and standard matrix multiplication is performed.

  After standard matrix multiplication,
  the last dimension is removed from the result.

  e.g.,

  arg0: tensor<Mx3> = [
      [_, _, _],
      [_, _, _],
      ...,
      [_, _, _],
      [_, _, _],
  ]
  arg1: tensor<3> = [x, y, z]

  is treated as

  arg0: tensor<Mx3> = [
      [_, _, _],
      [_, _, _],
      ...,
      [_, _, _],
      [_, _, _],
  ]
  arg1: tensor<3x1> = [
    [x],
    [y],
    [z],
  ]

  and matrix multiplication is performed with the following form (Mx3 @ 3x1 -> Mx1)

  result: tensor<Mx1> = [
    [_],
    [_],
      ...,
    [_],
    [_],
  ]

  finally, the last dimension is removed by definition so the result has the following form

  result: tensor<M> = [_, _, _]

- If either argument is N-D where N > 2,
  the operation is treated as a collection of matrices residing in the last two indices and broadcasted accordingly.

  arg0: tensor<Kx1MxN> = [...]
  arg1: tensor<LxNxP> = [...]

  result: tensor<KxLxMxP> = [...]

"FHELinalg.matmul_eint_int(%a, %b) : (tensor<MxNx!FHE.eint<p>>, tensor<NxPxip'>) -> tensor<MxPx!FHE.eint<p>>"
"FHELinalg.matmul_eint_int(%a, %b) : (tensor<KxLxMxNx!FHE.eint<p>>, tensor<KxLxNxPxip'>) -> tensor<KxLxMxPx!FHE.eint<p>>"
"FHELinalg.matmul_eint_int(%a, %b) : (tensor<MxNx!FHE.eint<p>>, tensor<Nxip'>) -> tensor<Mx!FHE.eint<p>>"
"FHELinalg.matmul_eint_int(%a, %b) : (tensor<Nx!FHE.eint<p>>, tensor<NxPxip'>) -> tensor<Px!FHE.eint<p>>"

Examples:

// Returns the matrix multiplication of a 3x2 matrix of encrypted integers and a 2x3 matrix of integers.
//         [ 1, 2, 3]
//         [ 2, 3, 4]
//       *
// [1,2]   [ 5, 8,11]
// [3,4] = [11,18,25]
// [5,6]   [17,28,39]
//
"FHELinalg.matmul_eint_int"(%a, %b) : (tensor<3x2x!FHE.eint<6>>, tensor<2x3xi7>) -> tensor<3x3x!FHE.eint<6>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEintInt

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.matmul_int_eint (::mlir::concretelang::FHELinalg::MatMulIntEintOp)

Returns a tensor that contains the result of the matrix multiplication of a matrix of clear integers and a matrix of encrypted integers.

Performs a matrix multiplication of a matrix of clear integers and a matrix of encrypted integers. The width of the clear integers must be less than or equal to the width of encrypted integers.

The behavior depends on the arguments in the following way:

- If both arguments are 2-D,
  they are multiplied like conventional matrices.

  e.g.,

  arg0: tensor<MxN> = [...]
  arg1: tensor<NxP> = [...]

  result: tensor<MxP> = [...]

- If the first argument is a vector (1-D),
  it is treated as a matrix with a single row and standard matrix multiplication is performed.

  After standard matrix multiplication,
  the first dimension is removed from the result.

  e.g.,

  arg0: tensor<3> = [x, y, z]
  arg1: tensor<3xM> = [
      [_, _, ..., _, _],
      [_, _, ..., _, _],
      [_, _, ..., _, _],
  ]

  is treated as

  arg0: tensor<1x3> = [
      [x, y, z]
  ]
  arg1: tensor<3xM> = [
      [_, _, ..., _, _],
      [_, _, ..., _, _],
      [_, _, ..., _, _],
  ]

  and matrix multiplication is performed with the following form (1x3 @ 3xM -> 1xM)

  result: tensor<1xM> = [[_, _, ..., _, _]]

  finally, the first dimension is removed by definition so the result has the following form

  result: tensor<M>  = [_, _, ..., _, _]

- If the second argument is 1-D,
  it is treated as a matrix with a single column and standard matrix multiplication is performed.

  After standard matrix multiplication,
  the last dimension is removed from the result.

  e.g.,

  arg0: tensor<Mx3> = [
      [_, _, _],
      [_, _, _],
      ...,
      [_, _, _],
      [_, _, _],
  ]
  arg1: tensor<3> = [x, y, z]

  is treated as

  arg0: tensor<Mx3> = [
      [_, _, _],
      [_, _, _],
      ...,
      [_, _, _],
      [_, _, _],
  ]
  arg1: tensor<3x1> = [
    [x],
    [y],
    [z],
  ]

  and matrix multiplication is performed with the following form (Mx3 @ 3x1 -> Mx1)

  result: tensor<Mx1> = [
    [_],
    [_],
      ...,
    [_],
    [_],
  ]

  finally, the last dimension is removed by definition so the result has the following form

  result: tensor<M> = [_, _, _]

- If either argument is N-D where N > 2,
  the operation is treated as a collection of matrices residing in the last two indices and broadcasted accordingly.

  arg0: tensor<Kx1MxN> = [...]
  arg1: tensor<LxNxP> = [...]

  result: tensor<KxLxMxP> = [...]

"FHELinalg.matmul_int_eint(%a, %b) : (tensor<MxNxip'>, tensor<NxPxFHE.eint<p>>) -> tensor<MxPx!FHE.eint<p>>"
"FHELinalg.matmul_int_eint(%a, %b) : (tensor<KxLxMxNxip'>, tensor<KxLxNxPxFHE.eint<p>>) -> tensor<KxLxMxPx!FHE.eint<p>>"
"FHELinalg.matmul_int_eint(%a, %b) : (tensor<MxNxip'>, tensor<NxFHE.eint<p>>) -> tensor<Mx!FHE.eint<p>>"
"FHELinalg.matmul_int_eint(%a, %b) : (tensor<Nxip'>, tensor<NxPxFHE.eint<p>>) -> tensor<Px!FHE.eint<p>>"

Examples:

// Returns the matrix multiplication of a 3x2 matrix of clear integers and a 2x3 matrix of encrypted integers.
//         [ 1, 2, 3]
//         [ 2, 3, 4]
//       *
// [1,2]   [ 5, 8,11]
// [3,4] = [11,18,25]
// [5,6]   [17,28,39]
//
"FHELinalg.matmul_int_eint"(%a, %b) : (tensor<3x2xi7>, tensor<2x3x!FHE.eint<6>>) -> tensor<3x3x!FHE.eint<6>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryIntEint

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.maxpool2d (::mlir::concretelang::FHELinalg::Maxpool2dOp)

Returns the 2D maxpool of a tensor in NCHW form

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

FHELinalg.mul_eint_int (::mlir::concretelang::FHELinalg::MulEintIntOp)

Returns a tensor that contains the multiplication of a tensor of encrypted integers and a tensor of clear integers.

Performs a multiplication following the broadcasting rules between a tensor of encrypted integers and a tensor of clear integers. The width of the clear integers must be less than or equal to the width of encrypted integers.

Examples:

// Returns the term-by-term multiplication of `%a0` with `%a1`
"FHELinalg.mul_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4xi5>) -> tensor<4x!FHE.eint<4>>

// Returns the term-by-term multiplication of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.mul_eint_int"(%a0, %a1) : (tensor<4x1x4x!FHE.eint<4>>, tensor<1x4x4xi5>) -> tensor<4x4x4x!FHE.eint<4>>

// Returns the multiplication of a 3x3 matrix of encrypted integers and a 3x1 matrix (a column) of integers.
//
// [1,2,3]   [1]   [1,2,3]
// [4,5,6] * [2] = [8,10,18]
// [7,8,9]   [3]   [21,24,27]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3x1xi5>) -> tensor<3x3x!FHE.eint<4>>

// Returns the multiplication of a 3x3 matrix of encrypted integers and a 1x3 matrix (a line) of integers.
//
// [1,2,3]             [2,4,6]
// [4,5,6] * [1,2,3] = [5,7,9]
// [7,8,9]             [8,10,12]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<1x3xi5>) -> tensor<3x3x!FHE.eint<4>>

// Same behavior as the previous one, but as the dimension #2 is missing of operand #2.
"FHELinalg.mul_eint_int"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3xi5>) -> tensor<3x3x!FHE.eint<4>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEintInt, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.mul_eint (::mlir::concretelang::FHELinalg::MulEintOp)

Returns a tensor that contains the multiplication of two tensor of encrypted integers.

Performs an addition following the broadcasting rules between two tensors of encrypted integers. The width of the encrypted integers must be equal.

Examples:

// Returns the term-by-term multiplication of `%a0` with `%a1`
"FHELinalg.mul_eint"(%a0, %a1) : (tensor<4x!FHE.eint<8>>, tensor<4x!FHE.eint<8>>) -> tensor<4x!FHE.eint<8>>

// Returns the term-by-term multiplication of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.mul_eint"(%a0, %a1) : (tensor<4x1x4x!FHE.eint<8>>, tensor<1x4x4x!FHE.eint<8>>) -> tensor<4x4x4x!FHE.eint<8>>

// Returns the multiplication of a 3x3 matrix of encrypted integers and a 3x1 matrix (a column) of encrypted integers.
//
// [1,2,3]   [1]   [1,2,3]
// [4,5,6] * [2] = [8,10,12]
// [7,8,9]   [3]   [21,24,27]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.mul_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<8>>, tensor<3x1x!FHE.eint<8>>) -> tensor<3x3x!FHE.eint<8>>

// Returns the multiplication of a 3x3 matrix of encrypted integers and a 1x3 matrix (a line) of encrypted integers.
//
// [1,2,3]             [1,4,9]
// [4,5,6] * [1,2,3] = [4,10,18]
// [7,8,9]             [7,16,27]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.mul_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<8>>, tensor<1x3x!FHE.eint<8>>) -> tensor<3x3x!FHE.eint<8>>

// Same behavior as the previous one, but as the dimension #2 of operand #2 is missing.
"FHELinalg.mul_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<8>>, tensor<3x!FHE.eint<8>>) -> tensor<3x3x!FHE.eint<8>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEint, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.neg_eint (::mlir::concretelang::FHELinalg::NegEintOp)

Returns a tensor that contains the negation of a tensor of encrypted integers.

Performs a negation to a tensor of encrypted integers.

Examples:

// Returns the term-by-term negation of `%a0`
"FHELinalg.neg_eint"(%a0) : (tensor<3x3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>
//
//        ( [1,2,3] )   [31,30,29]
// negate ( [4,5,6] ) = [28,27,26]
//        ( [7,8,9] )   [25,24,23]
//
// The negation is computed as `2**(p+1) - a` where p=4 here.

Traits: AlwaysSpeculatableImplTrait, TensorUnaryEint

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.round (::mlir::concretelang::FHELinalg::RoundOp)

Rounds a tensor of ciphertexts into a smaller precision.

  Assuming a ciphertext whose message is implemented over `p` bits, this
  operation rounds it to fit to `q` bits where `p>q`.

  Example:
  ```mlir
  // ok
  "FHELinalg.round"(%a): (tensor<3x!FHE.eint<6>>) -> (tensor<3x!FHE.eint<5>>)
  "FHELinalg.round"(%a): (tensor<3x!FHE.eint<5>>) -> (tensor<3x!FHE.eint<3>>)
  "FHELinalg.round"(%a): (tensor<3x!FHE.eint<3>>) -> (tensor<3x!FHE.eint<2>>)
  "FHELinalg.round"(%a): (tensor<3x!FHE.esint<3>>) -> (tensor<3x!FHE.esint<2>>)

  // error
  "FHELinalg.round"(%a): (tensor<3x!FHE.eint<6>>) -> (tensor<3x!FHE.eint<6>>)
  "FHELinalg.round"(%a): (tensor<3x!FHE.eint<4>>) -> (tensor<3x!FHE.eint<5>>)
  "FHELinalg.round"(%a): (tensor<3x!FHE.eint<4>>) -> (tensor<3x!FHE.esint<2>>)

Traits: AlwaysSpeculatableImplTrait, TensorUnaryEint

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

#### Operands:

| Operand | Description |
| :-----: | ----------- |
| `input` | 

#### Results:

| Result | Description |
| :----: | ----------- |
| `output` | 

### `FHELinalg.sub_eint_int` (::mlir::concretelang::FHELinalg::SubEintIntOp)

Returns a tensor that contains the subtraction of a tensor of clear integers from a tensor of encrypted integers.

Performs a subtraction following the broadcasting rules between a tensor of clear integers from a tensor of encrypted integers.
The width of the clear integers must be less than or equal to the width of encrypted integers.

Examples:
```mlir
// Returns the term-by-term subtraction of `%a0` with `%a1`
"FHELinalg.sub_eint_int"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4xi5>) -> tensor<4x!FHE.eint<4>>

// Returns the term-by-term subtraction of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.sub_eint_int"(%a0, %a1) : (tensor<1x4x4x!FHE.eint<4>>, tensor<4x1x4xi5>) -> tensor<4x4x4x!FHE.eint<4>>

// Returns the subtraction of a 3x3 matrix of integers and a 3x1 matrix (a column) of encrypted integers.
//
// [1,2,3]   [1]   [0,2,3]
// [4,5,6] - [2] = [2,3,4]
// [7,8,9]   [3]   [4,5,6]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.sub_eint_int"(%a0, %a1) : (tensor<3x1x!FHE.eint<4>>, tensor<3x3xi5>) -> tensor<3x3x!FHE.eint<4>>

// Returns the subtraction of a 3x3 matrix of integers and a 1x3 matrix (a line) of encrypted integers.
//
// [1,2,3]             [0,0,0]
// [4,5,6] - [1,2,3] = [3,3,3]
// [7,8,9]             [6,6,6]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.sub_eint_int"(%a0, %a1) : (tensor<1x3x!FHE.eint<4>>, tensor<3x3xi5>) -> tensor<3x3x!FHE.eint<4>>

// Same behavior as the previous one, but as the dimension #2 is missing of operand #2.
"FHELinalg.sub_eint_int"(%a0, %a1) : (tensor<3x!FHE.eint<4>>, tensor<3x3xi5>) -> tensor<3x3x!FHE.eint<4>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEintInt, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.sub_eint (::mlir::concretelang::FHELinalg::SubEintOp)

Returns a tensor that contains the subtraction of two tensor of encrypted integers.

Performs an subtraction following the broadcasting rules between two tensors of encrypted integers. The width of the encrypted integers must be equal.

Examples:

// Returns the term-by-term subtraction of `%a0` with `%a1`
"FHELinalg.sub_eint"(%a0, %a1) : (tensor<4x!FHE.eint<4>>, tensor<4x!FHE.eint<4>>) -> tensor<4x!FHE.eint<4>>

// Returns the term-by-term subtraction of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.sub_eint"(%a0, %a1) : (tensor<4x1x4x!FHE.eint<4>>, tensor<1x4x4x!FHE.eint<4>>) -> tensor<4x4x4x!FHE.eint<4>>

// Returns the subtraction of a 3x3 matrix of integers and a 3x1 matrix (a column) of encrypted integers.
//
// [1,2,3]   [1]   [0,2,3]
// [4,5,6] - [2] = [2,3,4]
// [7,8,9]   [3]   [4,5,6]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.sub_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3x1x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

// Returns the subtraction of a 3x3 matrix of integers and a 1x3 matrix (a line) of encrypted integers.
//
// [1,2,3]             [0,0,0]
// [4,5,6] - [1,2,3] = [3,3,3]
// [7,8,9]             [6,6,6]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.sub_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<1x3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

// Same behavior as the previous one, but as the dimension #2 of operand #2 is missing.
"FHELinalg.sub_eint"(%a0, %a1) : (tensor<3x3x!FHE.eint<4>>, tensor<3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryEint, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.sub_int_eint (::mlir::concretelang::FHELinalg::SubIntEintOp)

Returns a tensor that contains the subtraction of a tensor of clear integers and a tensor of encrypted integers.

Performs a subtraction following the broadcasting rules between a tensor of clear integers and a tensor of encrypted integers. The width of the clear integers must be less than or equal to the width of encrypted integers.

Examples:

// Returns the term-by-term subtraction of `%a0` with `%a1`
"FHELinalg.sub_int_eint"(%a0, %a1) : (tensor<4xi5>, tensor<4x!FHE.eint<4>>) -> tensor<4x!FHE.eint<4>>

// Returns the term-by-term subtraction of `%a0` with `%a1`, where dimensions equal to one are stretched.
"FHELinalg.sub_int_eint"(%a0, %a1) : (tensor<4x1x4xi5>, tensor<1x4x4x!FHE.eint<4>>) -> tensor<4x4x4x!FHE.eint<4>>

// Returns the subtraction of a 3x3 matrix of integers and a 3x1 matrix (a column) of encrypted integers.
//
// [1,2,3]   [1]   [0,2,3]
// [4,5,6] - [2] = [2,3,4]
// [7,8,9]   [3]   [4,5,6]
//
// The dimension #1 of operand #2 is stretched as it is equal to 1.
"FHELinalg.sub_int_eint"(%a0, %a1) : (tensor<3x3xi5>, tensor<3x1x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

// Returns the subtraction of a 3x3 matrix of integers and a 1x3 matrix (a line) of encrypted integers.
//
// [1,2,3]             [0,0,0]
// [4,5,6] - [1,2,3] = [3,3,3]
// [7,8,9]             [6,6,6]
//
// The dimension #2 of operand #2 is stretched as it is equal to 1.
"FHELinalg.sub_int_eint"(%a0, %a1) : (tensor<3x3xi5>, tensor<1x3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

// Same behavior as the previous one, but as the dimension #2 is missing of operand #2.
"FHELinalg.sub_int_eint"(%a0, %a1) : (tensor<3x3xi5>, tensor<3x!FHE.eint<4>>) -> tensor<3x3x!FHE.eint<4>>

Traits: AlwaysSpeculatableImplTrait, TensorBinaryIntEint, TensorBroadcastingRules

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.sum (::mlir::concretelang::FHELinalg::SumOp)

Returns the sum of elements of a tensor of encrypted integers along specified axes.

Attributes:

• keep_dims: boolean = false whether to keep the rank of the tensor after the sum operation if true, reduced axes will have the size of 1

• axes: I64ArrayAttr = [] list of dimension to perform the sum along think of it as the dimensions to reduce (see examples below to get an intuition)

Examples:

// Returns the sum of all elements of `%a0`
"FHELinalg.sum"(%a0) : (tensor<3x3x!FHE.eint<4>>) -> !FHE.eint<4>
//
//     ( [1,2,3] )
// sum ( [4,5,6] ) = 45
//     ( [7,8,9] )
//

// Returns the sum of all elements of `%a0` along columns
"FHELinalg.sum"(%a0) { axes = [0] } : (tensor<3x2x!FHE.eint<4>>) -> tensor<2x!FHE.eint<4>>
//
//     ( [1,2] )
// sum ( [3,4] ) = [9, 12]
//     ( [5,6] )
//

// Returns the sum of all elements of `%a0` along columns while preserving dimensions
"FHELinalg.sum"(%a0) { axes = [0], keep_dims = true } : (tensor<3x2x!FHE.eint<4>>) -> tensor<1x2x!FHE.eint<4>>
//
//     ( [1,2] )
// sum ( [3,4] ) = [[9, 12]]
//     ( [5,6] )
//

// Returns the sum of all elements of `%a0` along rows
"FHELinalg.sum"(%a0) { axes = [1] } : (tensor<3x2x!FHE.eint<4>>) -> tensor<3x!FHE.eint<4>>
//
//     ( [1,2] )
// sum ( [3,4] ) = [3, 7, 11]
//     ( [5,6] )
//

// Returns the sum of all elements of `%a0` along rows while preserving dimensions
"FHELinalg.sum"(%a0) { axes = [1], keep_dims = true } : (tensor<3x2x!FHE.eint<4>>) -> tensor<3x1x!FHE.eint<4>>
//
//     ( [1,2] )   [3]
// sum ( [3,4] ) = [7]
//     ( [5,6] )   [11]
//

Traits: AlwaysSpeculatableImplTrait, TensorUnaryEint

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

FHELinalg.to_signed (::mlir::concretelang::FHELinalg::ToSignedOp)

Cast an unsigned integer tensor to a signed one

Cast an unsigned integer tensor to a signed one. The result must have the same width and the same shape as the input.

The behavior is undefined on overflow/underflow.

Examples:

// ok
"FHELinalg.to_signed"(%x) : (tensor<3x2x!FHE.eint<2>>) -> tensor<3x2x!FHE.esint<2>>

// error
"FHELinalg.to_signed"(%x) : (tensor<3x2x!FHE.eint<2>>) -> tensor<3x2x!FHE.esint<3>>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.to_unsigned (::mlir::concretelang::FHELinalg::ToUnsignedOp)

Cast a signed integer tensor to an unsigned one

Cast a signed integer tensor to an unsigned one. The result must have the same width and the same shape as the input.

The behavior is undefined on overflow/underflow.

Examples:

// ok
"FHELinalg.to_unsigned"(%x) : (tensor<3x2x!FHE.esint<2>>) -> tensor<3x2x!FHE.eint<2>>

// error
"FHELinalg.to_unsigned"(%x) : (tensor<3x2x!FHE.esint<2>>) -> tensor<3x2x!FHE.eint<3>>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHELinalg.transpose (::mlir::concretelang::FHELinalg::TransposeOp)

Returns a tensor that contains the transposition of the input tensor.

Performs a transpose operation on an N-dimensional tensor.

Attributes:

• axes: I64ArrayAttr = [] list of dimension to perform the transposition contains a permutation of [0,1,..,N-1] where N is the number of axes think of it as a way to rearrange axes (see the example below)

"FHELinalg.transpose"(%a) : (tensor<n0xn1x...xnNxType>) -> tensor<nNx...xn1xn0xType>

Examples:

// Transpose the input tensor
// [1,2]    [1, 3, 5]
// [3,4] => [2, 4, 6]
// [5,6]
//
"FHELinalg.transpose"(%a) : (tensor<3x2xi7>) -> tensor<2x3xi7>

"FHELinalg.transpose"(%a) { axes = [1, 3, 0, 2] } : (tensor<2x3x4x5xi7>) -> tensor<3x5x2x4xi7>

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Introduction

Compilation of a Python program starts with Concrete's Python frontend, which first traces and transforms it and then converts it into an intermediate representation (IR) that is further processed by Concrete Compiler. This IR is based on the MLIR subproject of the LLVM compiler infrastructure. This document provides an overview of Concrete's FHE-specific representations based on the MLIR framework.

In contrast to traditional infrastructure for compilers, the set of operations and data types that constitute the IR, as well as the level of abstraction that the IR represents, are not fixed in MLIR and can easily be extended. All operations and data types are grouped into dialects, with each dialect representing a specific domain or a specific level of abstraction. Mixing operations and types from different dialects within the same IR is allowed and even encouraged, with all dialects--builtin or developed as an extension--being first-class citizens.

Concrete compiler takes advantage of these concepts by defining a set of dialects, capable of representing an FHE program from an abstract specification that is independent of the actual cryptosystem down to a program that can easily be mapped to function calls of a cryptographic library. The dialects for the representation of an FHE program are:

• The FHELinalg Dialect (documentation, source)

• The FHE Dialect (documentation, source)

• The TFHE Dialect (documentation, source)

• The Concrete Dialect (documentation, source)

• and for debugging purposes, the Tracing Dialect (documentation, source).

In addition, the project further defines two dialects that help expose dynamic task-parallelism and static data-flow graphs in order to benefit from multi-core, multi-accelerator and distributed systems. These are:

• The RT Dialect (documentation, source) and

• The SDFG Dialect (documentation, source).

The figure below illustrates the relationship between the dialects and their embedding into the compilation pipeline.

The following sections focus on the FHE-related dialects, i.e., on the FHELinalg Dialect, the FHE Dialect, the TFHE Dialect and the Concrete Dialect.

The FHE and FHELinalg Dialects: An abstract specification of an FHE program

The top part of the figure shows the components which are involved in the generation of the initial IR, ending with the step labelled MLIR translation. When the initial IR is passed on to Concrete Compiler through its Python bindings, all FHE-related operations are specified using either the FHE or FHELinalg Dialect. Both of these dialects provide operations and data types for the abstract specification of an FHE program, completely independently of a cryptosystem. At this point, the IR simply indicates whether an operand is encrypted (via the type FHE.eint<n>, where n stands for the precision in bits) and what operations are applied to encrypted values. Plaintext values simply use MLIR's builtin integer type in (e.g., i3 or i64).

The FHE Dialect provides scalar operations on encrypted integers, such as additions (FHE.add_eint) or multiplications (FHE.mul_eint), while the FHELinalg Dialect offers operations on tensors of encrypted integers, e.g., matrix products (FHELinalg.matmul_eint_eint) or convolutions (FHELinalg.conv2d).

In a first lowering step of the pipeline, all FHELinalg operations are lowered to operations from MLIR's builtin Linalg Dialect using scalar operations from the FHE Dialect. Consider the following example, which consists of a function that performs a multiplication of a matrix of encrypted integers and a matrix of cleartext values:

func.func @main(%arg0: tensor<4x3x!FHE.eint<2>>, %arg1: tensor<3x2xi3>) -> tensor<4x2x!FHE.eint<2>> {
  %0 = "FHELinalg.matmul_eint_int"(%arg0, %arg1) : (tensor<4x3x!FHE.eint<2>>, tensor<3x2xi3>) -> tensor<4x2x!FHE.eint<2>>
  return %0 : tensor<4x2x!FHE.eint<2>>
}

Upon conversion, the FHELinalg.matmul operation is converted to a linalg.generic operation whose body contains a scalar multiplication (FHE.mul_eint_int) and a scalar addition (FHE.add_eint_int):

#map = affine_map<(d0, d1, d2) -> (d0, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d2, d1)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1)>

func.func @main(%arg0: tensor<4x3x!FHE.eint<2>>, %arg1: tensor<3x2xi3>) -> tensor<4x2x!FHE.eint<2>> {
  %0 = "FHE.zero_tensor"() : () -> tensor<4x2x!FHE.eint<2>>
  %1 = linalg.generic {indexing_maps = [#map, #map1, #map2], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0, %arg1 : tensor<4x3x!FHE.eint<2>>, tensor<3x2xi3>) outs(%0 : tensor<4x2x!FHE.eint<2>>) {
  ^bb0(%in: !FHE.eint<2>, %in_0: i3, %out: !FHE.eint<2>):
    %2 = "FHE.mul_eint_int"(%in, %in_0) : (!FHE.eint<2>, i3) -> !FHE.eint<2>
    %3 = "FHE.add_eint"(%out, %2) : (!FHE.eint<2>, !FHE.eint<2>) -> !FHE.eint<2>
    linalg.yield %3 : !FHE.eint<2>
  } -> tensor<4x2x!FHE.eint<2>>
  return %1 : tensor<4x2x!FHE.eint<2>>
}

This is then further lowered to a nest of loops from MLIR's SCF Dialect, implementing the parallel and reduction dimensions from the linalg.generic operation above:

func.func @main(%arg0: tensor<4x3x!FHE.eint<2>>, %arg1: tensor<3x2xi3>) -> tensor<4x2x!FHE.eint<2>> {
  %c0 = arith.constant 0 : index
  %c4 = arith.constant 4 : index
  %c1 = arith.constant 1 : index
  %c2 = arith.constant 2 : index
  %c3 = arith.constant 3 : index
  %0 = "FHE.zero_tensor"() : () -> tensor<4x2x!FHE.eint<2>>
  %1 = scf.for %arg2 = %c0 to %c4 step %c1 iter_args(%arg3 = %0) -> (tensor<4x2x!FHE.eint<2>>) {
    %2 = scf.for %arg4 = %c0 to %c2 step %c1 iter_args(%arg5 = %arg3) -> (tensor<4x2x!FHE.eint<2>>) {
      %3 = scf.for %arg6 = %c0 to %c3 step %c1 iter_args(%arg7 = %arg5) -> (tensor<4x2x!FHE.eint<2>>) {
        %extracted = tensor.extract %arg0[%arg2, %arg6] : tensor<4x3x!FHE.eint<2>>
        %extracted_0 = tensor.extract %arg1[%arg6, %arg4] : tensor<3x2xi3>
        %extracted_1 = tensor.extract %arg7[%arg2, %arg4] : tensor<4x2x!FHE.eint<2>>
        %4 = "FHE.mul_eint_int"(%extracted, %extracted_0) : (!FHE.eint<2>, i3) -> !FHE.eint<2>
        %5 = "FHE.add_eint"(%extracted_1, %4) : (!FHE.eint<2>, !FHE.eint<2>) -> !FHE.eint<2>
        %inserted = tensor.insert %5 into %arg7[%arg2, %arg4] : tensor<4x2x!FHE.eint<2>>
        scf.yield %inserted : tensor<4x2x!FHE.eint<2>>
      }
      scf.yield %3 : tensor<4x2x!FHE.eint<2>>
    }
    scf.yield %2 : tensor<4x2x!FHE.eint<2>>
  }
  return %1 : tensor<4x2x!FHE.eint<2>>
}

The TFHE Dialect: Binding to the TFHE cryptosystem and parametrization

In order to obtain an executable program at the end of the compilation pipeline, the abstract specification of the FHE program must at some point be bound to a specific cryptosystem. This is the role of the TFHE Dialect, whose purpose is:

• to indicate operations to be carried out using an implementation of the TFHE cryptosystem

• to parametrize the cryptosystem with key sizes, and

• to provide a mapping between keys and encrypted values

When lowering the IR based on the FHE Dialect to the TFHE Dialect, the compiler first generates a generic form, in which FHE operations are lowered to TFHE operations and where values are converted to unparametrized TFHE.glwe values. The unparametrized form TFHE.glwe<sk?> simply indicates that a TFHE.glwe value is to be used, but without any indication of the cryptographic parameters and the actual key.

The IR below shows the example program after lowering to unparametrized TFHE:

func.func @main(%arg0: tensor<4x3x!TFHE.glwe<sk?>>, %arg1: tensor<3x2xi3>) -> tensor<4x2x!TFHE.glwe<sk?>> {
  %c0 = arith.constant 0 : index
  %c4 = arith.constant 4 : index
  %c1 = arith.constant 1 : index
  %c2 = arith.constant 2 : index
  %c3 = arith.constant 3 : index
  %0 = "TFHE.zero_tensor"() : () -> tensor<4x2x!TFHE.glwe<sk?>>
  %1 = scf.for %arg2 = %c0 to %c4 step %c1 iter_args(%arg3 = %0) -> (tensor<4x2x!TFHE.glwe<sk?>>) {
    %2 = scf.for %arg4 = %c0 to %c2 step %c1 iter_args(%arg5 = %arg3) -> (tensor<4x2x!TFHE.glwe<sk?>>) {
      %3 = scf.for %arg6 = %c0 to %c3 step %c1 iter_args(%arg7 = %arg5) -> (tensor<4x2x!TFHE.glwe<sk?>>) {
        %extracted = tensor.extract %arg0[%arg2, %arg6] : tensor<4x3x!TFHE.glwe<sk?>>
        %extracted_0 = tensor.extract %arg1[%arg6, %arg4] : tensor<3x2xi3>
        %extracted_1 = tensor.extract %arg7[%arg2, %arg4] : tensor<4x2x!TFHE.glwe<sk?>>
        %4 = arith.extsi %extracted_0 : i3 to i64
        %5 = "TFHE.mul_glwe_int"(%extracted, %4) : (!TFHE.glwe<sk?>, i64) -> !TFHE.glwe<sk?>
        %6 = "TFHE.add_glwe"(%extracted_1, %5) : (!TFHE.glwe<sk?>, !TFHE.glwe<sk?>) -> !TFHE.glwe<sk?>
        %inserted = tensor.insert %6 into %arg7[%arg2, %arg4] : tensor<4x2x!TFHE.glwe<sk?>>
        scf.yield %inserted : tensor<4x2x!TFHE.glwe<sk?>>
      }
      scf.yield %3 : tensor<4x2x!TFHE.glwe<sk?>>
    }
    scf.yield %2 : tensor<4x2x!TFHE.glwe<sk?>>
  }
  return %1 : tensor<4x2x!TFHE.glwe<sk?>>
}

All operations from the FHE dialect have been replaced with corresponding operations from the TFHE Dialect.

During subsequent parametrization, the compiler can either use a set of default parameters or can obtain a set of parameters from Concrete's optimizer. Either way, an additional pass injects the parameters into the IR, replacing all TFHE.glwe<sk?> instances with TFHE.glwe<i,d,n>, where i is a sequential identifier for a key, d the number of GLWE dimensions and n the size of the GLWE polynomial.

The result of such a parametrization for the example is given below:

func.func @main(%arg0: tensor<4x3x!TFHE.glwe<sk<0,1,512>>>, %arg1: tensor<3x2xi3>) -> tensor<4x2x!TFHE.glwe<sk<0,1,512>>> {
  %c0 = arith.constant 0 : index
  %c4 = arith.constant 4 : index
  %c1 = arith.constant 1 : index
  %c2 = arith.constant 2 : index
  %c3 = arith.constant 3 : index
  %0 = "TFHE.zero_tensor"() : () -> tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
  %1 = scf.for %arg2 = %c0 to %c4 step %c1 iter_args(%arg3 = %0) -> (tensor<4x2x!TFHE.glwe<sk<0,1,512>>>) {
    %2 = scf.for %arg4 = %c0 to %c2 step %c1 iter_args(%arg5 = %arg3) -> (tensor<4x2x!TFHE.glwe<sk<0,1,512>>>) {
      %3 = scf.for %arg6 = %c0 to %c3 step %c1 iter_args(%arg7 = %arg5) -> (tensor<4x2x!TFHE.glwe<sk<0,1,512>>>) {
        %extracted = tensor.extract %arg0[%arg2, %arg6] : tensor<4x3x!TFHE.glwe<sk<0,1,512>>>
        %extracted_0 = tensor.extract %arg1[%arg6, %arg4] : tensor<3x2xi3>
        %extracted_1 = tensor.extract %arg7[%arg2, %arg4] : tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
        %4 = arith.extsi %extracted_0 : i3 to i64
        %5 = "TFHE.mul_glwe_int"(%extracted, %4) : (!TFHE.glwe<sk<0,1,512>>, i64) -> !TFHE.glwe<sk<0,1,512>>
        %6 = "TFHE.add_glwe"(%extracted_1, %5) : (!TFHE.glwe<sk<0,1,512>>, !TFHE.glwe<sk<0,1,512>>) -> !TFHE.glwe<sk<0,1,512>>
        %inserted = tensor.insert %6 into %arg7[%arg2, %arg4] : tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
        scf.yield %inserted : tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
      }
      scf.yield %3 : tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
    }
    scf.yield %2 : tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
  }
  return %1 : tensor<4x2x!TFHE.glwe<sk<0,1,512>>>
}

In this parametrization, a single key with the ID 0 is used, with a single dimension and a polynomial of size 512.

The Concrete Dialect: Preparing bindings with a crypto library

In the next step of the pipeline, operations and types are lowered to the Concrete Dialect. This dialect provides operations, which are implemented by one of Concrete's backend libraries, but still abstracts from any technical details required for interaction with an actual library. The goal is to maintain a high-level representation with value-based semantics and actual operations instead of buffer semantics and library calls, while ensuring that all operations an effectively be lowered to a library call later in the pipeline. However, the abstract types from TFHE are already lowered to tensors of integers with a suitable shape that will hold the binary data of the encrypted values.

The result of the lowering of the example to the Concrete Dialect is shown below:

func.func @main(%arg0: tensor<4x3x513xi64>, %arg1: tensor<3x2xi3>) -> tensor<4x2x513xi64> {
  %c0 = arith.constant 0 : index
  %c4 = arith.constant 4 : index
  %c1 = arith.constant 1 : index
  %c2 = arith.constant 2 : index
  %c3 = arith.constant 3 : index
  %generated = tensor.generate  {
  ^bb0(%arg2: index, %arg3: index, %arg4: index):
    %c0_i64 = arith.constant 0 : i64
    tensor.yield %c0_i64 : i64
  } : tensor<4x2x513xi64>
  %0 = scf.for %arg2 = %c0 to %c4 step %c1 iter_args(%arg3 = %generated) -> (tensor<4x2x513xi64>) {
    %1 = scf.for %arg4 = %c0 to %c2 step %c1 iter_args(%arg5 = %arg3) -> (tensor<4x2x513xi64>) {
      %2 = scf.for %arg6 = %c0 to %c3 step %c1 iter_args(%arg7 = %arg5) -> (tensor<4x2x513xi64>) {
        %extracted_slice = tensor.extract_slice %arg0[%arg2, %arg6, 0] [1, 1, 513] [1, 1, 1] : tensor<4x3x513xi64> to tensor<513xi64>
        %extracted = tensor.extract %arg1[%arg6, %arg4] : tensor<3x2xi3>
        %extracted_slice_0 = tensor.extract_slice %arg7[%arg2, %arg4, 0] [1, 1, 513] [1, 1, 1] : tensor<4x2x513xi64> to tensor<513xi64>
        %3 = arith.extsi %extracted : i3 to i64
        %4 = "Concrete.mul_cleartext_lwe_tensor"(%extracted_slice, %3) : (tensor<513xi64>, i64) -> tensor<513xi64>
        %5 = "Concrete.add_lwe_tensor"(%extracted_slice_0, %4) : (tensor<513xi64>, tensor<513xi64>) -> tensor<513xi64>
        %inserted_slice = tensor.insert_slice %5 into %arg7[%arg2, %arg4, 0] [1, 1, 513] [1, 1, 1] : tensor<513xi64> into tensor<4x2x513xi64>
        scf.yield %inserted_slice : tensor<4x2x513xi64>
      }
      scf.yield %2 : tensor<4x2x513xi64>
    }
    scf.yield %1 : tensor<4x2x513xi64>
  }
  return %0 : tensor<4x2x513xi64>
}

Bufferization and emitting library calls

The remaining stages of the pipeline are rather technical. Before any binding to an actual Concrete backend library, the compiler first invokes MLIR's bufferization infrastructure to convert the value-based IR into an IR with buffer semantics. In particular, this means that keys and encrypted values are no longer abstract values in a mathematical sense, but values backed by a memory location that holds the actual data. This form of IR is then suitable for a pass emitting actual library calls that implement the corresponding operations from the Concrete Dialect for a specific backend.

The result for the example is given below:

func.func @main(%arg0: memref<4x3x513xi64, strided<[?, ?, ?], offset: ?>>, %arg1: memref<3x2xi3, strided<[?, ?], offset: ?>>, %arg2: !Concrete.context) -> memref<4x2x513xi64> {
  %c0_i64 = arith.constant 0 : i64
  call @_dfr_start(%c0_i64, %arg2) : (i64, !Concrete.context) -> ()
  %c0 = arith.constant 0 : index
  %c4 = arith.constant 4 : index
  %c1 = arith.constant 1 : index
  %c2 = arith.constant 2 : index
  %c513 = arith.constant 513 : index
  %c0_i64_0 = arith.constant 0 : i64
  %c3 = arith.constant 3 : index
  %alloc = memref.alloc() {alignment = 64 : i64} : memref<4x2x513xi64>
  scf.for %arg3 = %c0 to %c4 step %c1 {
    scf.for %arg4 = %c0 to %c2 step %c1 {
      scf.for %arg5 = %c0 to %c513 step %c1 {
        memref.store %c0_i64_0, %alloc[%arg3, %arg4, %arg5] : memref<4x2x513xi64>
      }
    }
  }
  scf.for %arg3 = %c0 to %c4 step %c1 {
    scf.for %arg4 = %c0 to %c2 step %c1 {
      %subview = memref.subview %alloc[%arg3, %arg4, 0] [1, 1, 513] [1, 1, 1] : memref<4x2x513xi64> to memref<513xi64, strided<[1], offset: ?>>
      scf.for %arg5 = %c0 to %c3 step %c1 {
        %subview_1 = memref.subview %arg0[%arg3, %arg5, 0] [1, 1, 513] [1, 1, 1] : memref<4x3x513xi64, strided<[?, ?, ?], offset: ?>> to memref<513xi64, strided<[?], offset: ?>>
        %0 = memref.load %arg1[%arg5, %arg4] : memref<3x2xi3, strided<[?, ?], offset: ?>>
        %1 = arith.extsi %0 : i3 to i64
        %alloc_2 = memref.alloc() {alignment = 64 : i64} : memref<513xi64>
        %cast = memref.cast %alloc_2 : memref<513xi64> to memref<?xi64, #map>
        %cast_3 = memref.cast %subview_1 : memref<513xi64, strided<[?], offset: ?>> to memref<?xi64, #map>
        func.call @memref_mul_cleartext_lwe_ciphertext_u64(%cast, %cast_3, %1) : (memref<?xi64, #map>, memref<?xi64, #map>, i64) -> ()
        %alloc_4 = memref.alloc() {alignment = 64 : i64} : memref<513xi64>
        %cast_5 = memref.cast %alloc_4 : memref<513xi64> to memref<?xi64, #map>
        %cast_6 = memref.cast %subview : memref<513xi64, strided<[1], offset: ?>> to memref<?xi64, #map>
        %cast_7 = memref.cast %alloc_2 : memref<513xi64> to memref<?xi64, #map>
        func.call @memref_add_lwe_ciphertexts_u64(%cast_5, %cast_6, %cast_7) : (memref<?xi64, #map>, memref<?xi64, #map>, memref<?xi64, #map>) -> ()
        memref.dealloc %alloc_2 : memref<513xi64>
        memref.copy %alloc_4, %subview : memref<513xi64> to memref<513xi64, strided<[1], offset: ?>>
        memref.dealloc %alloc_4 : memref<513xi64>
      }
    }
  }
  call @_dfr_stop(%c0_i64) : (i64) -> ()
  return %alloc : memref<4x2x513xi64>
}

At this stage, the IR is only composed of operations from builtin Dialects and thus amenable to lowering to LLVM-IR using the lowering passes provided by MLIR.

Dialect for the construction of static data flow graphs A dialect for the construction of static data flow graphs. The data flow graph is composed of a set of processes, connected through data streams. Special streams allow for data to be injected into and to be retrieved from the data flow graph.

Operation definition

SDFG.get (::mlir::concretelang::SDFG::Get)

Retrieves a data element from a stream

Retrieves a single data element from the specified stream (i.e., an instance of the element type of the stream).

Example:

"SDFG.get" (%stream) : (!SDFG.stream<1024xi64>) -> (tensor<1024xi64>)

Operands:

Results:

SDFG.init (::mlir::concretelang::SDFG::Init)

Initializes the streaming framework

Initializes the streaming framework. This operation must be performed before control reaches any other operation from the dialect.

Example:

"SDFG.init" : () -> !SDFG.dfg

Results:

SDFG.make_process (::mlir::concretelang::SDFG::MakeProcess)

Creates a new SDFG process

Creates a new SDFG process and connects it to the input and output streams.

Example:

%in0 = "SDFG.make_stream" { type = #SDFG.stream_kind<host_to_device> }(%dfg) : (!SDFG.dfg) -> !SDFG.stream<tensor<1024xi64>>
%in1 = "SDFG.make_stream" { type = #SDFG.stream_kind<host_to_device> }(%dfg) : (!SDFG.dfg) -> !SDFG.stream<tensor<1024xi64>>
%out = "SDFG.make_stream" { type = #SDFG.stream_kind<device_to_host> }(%dfg) : (!SDFG.dfg) -> !SDFG.stream<tensor<1024xi64>>
"SDFG.make_process" { type = #SDFG.process_kind<add_eint> }(%dfg, %in0, %in1, %out) :
  (!SDFG.dfg, !SDFG.stream<tensor<1024xi64>>, !SDFG.stream<tensor<1024xi64>>, !SDFG.stream<tensor<1024xi64>>) -> ()

Attributes:

Operands:

SDFG.make_stream (::mlir::concretelang::SDFG::MakeStream)

Returns a new SDFG stream

Returns a new SDFG stream, transporting data either between processes on the device, from the host to the device or from the device to the host. All streams are typed, allowing data to be read / written through SDFG.get and SDFG.put only using the stream's type.

Example:

"SDFG.make_stream" { name = "stream", type = #SDFG.stream_kind<host_to_device> }(%dfg)
  : (!SDFG.dfg) -> !SDFG.stream<tensor<1024xi64>>

Attributes:

Operands:

Results:

SDFG.put (::mlir::concretelang::SDFG::Put)

Writes a data element to a stream

Writes the input operand to the specified stream. The operand's type must meet the element type of the stream.

Example:

"SDFG.put" (%stream, %data) : (!SDFG.stream<1024xi64>, tensor<1024xi64>) -> ()

Operands:

SDFG.shutdown (::mlir::concretelang::SDFG::Shutdown)

Shuts down the streaming framework

Shuts down the streaming framework. This operation must be performed after any other operation from the dialect.

Example:

"SDFG.shutdown" (%dfg) : !SDFG.dfg

Operands:

SDFG.start (::mlir::concretelang::SDFG::Start)

Finalizes the creation of an SDFG and starts execution of its processes

Finalizes the creation of an SDFG and starts execution of its processes. Any creation of streams and processes must take place before control reaches this operation.

Example:

"SDFG.start"(%dfg) : !SDFG.dfg

Operands:

Attribute definition

ProcessKindAttr

Process kind

Syntax:

#SDFG.process_kind<
  ::mlir::concretelang::SDFG::ProcessKind   # value
>

Parameters:

StreamKindAttr

Stream kind

Syntax:

#SDFG.stream_kind<
  ::mlir::concretelang::SDFG::StreamKind   # value
>

Parameters:

Type definition

DFGType

An SDFG data flow graph

Syntax: !SDFG.dfg

A handle to an SDFG data flow graph

StreamType

An SDFG data stream

An SDFG stream to connect SDFG processes.

Parameters:

Runtime dialect A dialect for representation the abstraction needed for the runtime.

Operation definition

RT.await_future (::mlir::concretelang::RT::AwaitFutureOp)

Wait for a future and access its data.

The results of a dataflow task are always futures which could be further used as inputs to subsequent tasks. When the result of a task is needed in the outer execution context, the result future needs to be synchronized and its data accessed using RT.await_future.

Operands:

Results:

RT.build_return_ptr_placeholder (::mlir::concretelang::RT::BuildReturnPtrPlaceholderOp)

Results:

RT.clone_future (::mlir::concretelang::RT::CloneFutureOp)

Interfaces: AllocationOpInterface, MemoryEffectOpInterface

Operands:

Results:

RT.create_async_task (::mlir::concretelang::RT::CreateAsyncTaskOp)

Create a dataflow task.

Attributes:

Operands:

RT.dataflow_task (::mlir::concretelang::RT::DataflowTaskOp)

Dataflow task operation

RT.dataflow_task allows to specify a task that will be concurrently executed when their operands are ready. Operands are either the results of computation in other RT.dataflow_task (dataflow dependences) or obtained from the execution context (immediate operands). Operands are synchronized using futures and, in the case of immediate operands, copied when the task is created. Caution is required when the operand is a pointer as no deep copy will occur.

Example:

func @test(%0 : i64): (i64, i64) {
    // Execute right now as %0 is ready.
    %1, %2 = "RT.dataflow_task"(%0) ({
        %a = addi %0, %0 : i64
        %b = muli %0, %0 : i64
        "RT.dataflow_yield"(%a, %b) : (i64, i64) -> i64
    }) : (i64, i64) -> (i64, i64)
    // Concurrently execute both tasks below when the task above is completed.
    %3 = "RT.dataflow_task"(%1) ({
        %c = constant 1 : %i64
        %a = addi %1, %c : i64
        "RT.dataflow_yield"(%a) : (i64, i64) -> i64
    }) : (i64, i64) -> (i64, i64)
    %4 = "RT.dataflow_task"(%2) ({
        %c = constant 2 : %i64
        %a = addi %2, %c : i64
        "RT.dataflow_yield"(%a) : (i64, i64) -> i64
    }) : (i64, i64) -> (i64, i64)
    return %3, %4 : (i64, i64)
}

Traits: AutomaticAllocationScope, SingleBlockImplicitTerminator

Interfaces: AllocationOpInterface, MemoryEffectOpInterface, RegionBranchOpInterface

Operands:

Results:

RT.dataflow_yield (::mlir::concretelang::RT::DataflowYieldOp)

Dataflow yield operation

RT.dataflow_yield is a special terminator operation for blocks inside the region in RT.dataflow_task. It allows to specify the return values of a RT.dataflow_task.

Example:

%0 = constant 1 : i64
%1 = constant 2 : i64
"RT.dataflow_yield" %0, %1 : i64, i64

Traits: ReturnLike, Terminator

Operands:

RT.deallocate_future_data (::mlir::concretelang::RT::DeallocateFutureDataOp)

Operands:

RT.deallocate_future (::mlir::concretelang::RT::DeallocateFutureOp)

Operands:

RT.deref_return_ptr_placeholder (::mlir::concretelang::RT::DerefReturnPtrPlaceholderOp)

Operands:

Results:

RT.deref_work_function_argument_ptr_placeholder (::mlir::concretelang::RT::DerefWorkFunctionArgumentPtrPlaceholderOp)

Operands:

Results:

RT.make_ready_future (::mlir::concretelang::RT::MakeReadyFutureOp)

Build a ready future.

Data passed to dataflow tasks must be encapsulated in futures, including immediate operands. These must be converted into futures using RT.make_ready_future.

Interfaces: AllocationOpInterface, MemoryEffectOpInterface

Operands:

Results:

RT.register_task_work_function (::mlir::concretelang::RT::RegisterTaskWorkFunctionOp)

Register the task work-function with the runtime system.

Operands:

RT.work_function_return (::mlir::concretelang::RT::WorkFunctionReturnOp)

Operands:

Type definition

FutureType

Future with a parameterized element type

The value of a !RT.future type represents the result of an asynchronous operation.

Examples:

!RT.future<i64>

Parameters:

PointerType

Pointer to a parameterized element type

Parameters:

High Level Fully Homomorphic Encryption dialect A dialect for representation of high level operation on fully homomorphic ciphertext.

Operation definition

TFHE.batched_add_glwe_cst_int (::mlir::concretelang::TFHE::ABatchedAddGLWECstIntOp)

Batched version of AddGLWEIntOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_add_glwe_int_cst (::mlir::concretelang::TFHE::ABatchedAddGLWEIntCstOp)

Batched version of AddGLWEIntOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_add_glwe_int (::mlir::concretelang::TFHE::ABatchedAddGLWEIntOp)

Batched version of AddGLWEIntOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_add_glwe (::mlir::concretelang::TFHE::ABatchedAddGLWEOp)

Batched version of AddGLWEOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.add_glwe_int (::mlir::concretelang::TFHE::AddGLWEIntOp)

Returns the sum of a clear integer and an lwe ciphertext

Traits: AlwaysSpeculatableImplTrait

Interfaces: BatchableOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.add_glwe (::mlir::concretelang::TFHE::AddGLWEOp)

Returns the sum of two lwe ciphertexts

Traits: AlwaysSpeculatableImplTrait

Interfaces: BatchableOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_bootstrap_glwe (::mlir::concretelang::TFHE::BatchedBootstrapGLWEOp)

Batched version of KeySwitchGLWEOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.batched_keyswitch_glwe (::mlir::concretelang::TFHE::BatchedKeySwitchGLWEOp)

Batched version of KeySwitchGLWEOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.batched_mapped_bootstrap_glwe (::mlir::concretelang::TFHE::BatchedMappedBootstrapGLWEOp)

Batched version of KeySwitchGLWEOp which also batches the lookup table

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.batched_mul_glwe_cst_int (::mlir::concretelang::TFHE::BatchedMulGLWECstIntOp)

Batched version of MulGLWEIntOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_mul_glwe_int_cst (::mlir::concretelang::TFHE::BatchedMulGLWEIntCstOp)

Batched version of MulGLWEIntOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_mul_glwe_int (::mlir::concretelang::TFHE::BatchedMulGLWEIntOp)

Batched version of MulGLWEIntOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.batched_neg_glwe (::mlir::concretelang::TFHE::BatchedNegGLWEOp)

Batched version of NegGLWEOp

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.bootstrap_glwe (::mlir::concretelang::TFHE::BootstrapGLWEOp)

Programmable bootstraping of a GLWE ciphertext with a lookup table

Traits: AlwaysSpeculatableImplTrait

Interfaces: BatchableOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.encode_expand_lut_for_bootstrap (::mlir::concretelang::TFHE::EncodeExpandLutForBootstrapOp)

Encode and expand a lookup table so that it can be used for a bootstrap.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.encode_lut_for_crt_woppbs (::mlir::concretelang::TFHE::EncodeLutForCrtWopPBSOp)

Encode and expand a lookup table so that it can be used for a wop pbs.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.encode_plaintext_with_crt (::mlir::concretelang::TFHE::EncodePlaintextWithCrtOp)

Encodes a plaintext by decomposing it on a crt basis.

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.keyswitch_glwe (::mlir::concretelang::TFHE::KeySwitchGLWEOp)

Change the encryption parameters of a glwe ciphertext by applying a keyswitch

Traits: AlwaysSpeculatableImplTrait

Interfaces: BatchableOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.mul_glwe_int (::mlir::concretelang::TFHE::MulGLWEIntOp)

Returns the product of a clear integer and an lwe ciphertext

Traits: AlwaysSpeculatableImplTrait

Interfaces: BatchableOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.neg_glwe (::mlir::concretelang::TFHE::NegGLWEOp)

Negates a glwe ciphertext

Traits: AlwaysSpeculatableImplTrait

Interfaces: BatchableOpInterface, ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.sub_int_glwe (::mlir::concretelang::TFHE::SubGLWEIntOp)

Substracts an integer and a GLWE ciphertext

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

TFHE.wop_pbs_glwe (::mlir::concretelang::TFHE::WopPBSGLWEOp)

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

TFHE.zero (::mlir::concretelang::TFHE::ZeroGLWEOp)

Returns a trivial encryption of 0

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Results:

TFHE.zero_tensor (::mlir::concretelang::TFHE::ZeroTensorGLWEOp)

Returns a tensor containing trivial encryptions of 0

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Results:

Attribute definition

GLWEBootstrapKeyAttr

An attribute representing bootstrap key.

Syntax:

Parameters:

GLWEKeyswitchKeyAttr

An attribute representing keyswitch key.

Syntax:

Parameters:

GLWEPackingKeyswitchKeyAttr

An attribute representing Wop Pbs key.

Syntax:

Parameters:

Type definition

GLWECipherTextType

A GLWE ciphertext

An GLWE cipher text

Parameters:

High Level Fully Homomorphic Encryption dialect A dialect for representation of high level operation on fully homomorphic ciphertext.

Operation definition

FHE.add_eint_int (::mlir::concretelang::FHE::AddEintIntOp)

Adds an encrypted integer and a clear integer

The clear integer must have at most one more bit than the encrypted integer and the result must have the same width and the same signedness as the encrypted integer.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.add_eint (::mlir::concretelang::FHE::AddEintOp)

Adds two encrypted integers

The encrypted integers and the result must have the same width and the same signedness.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.apply_lookup_table (::mlir::concretelang::FHE::ApplyLookupTableEintOp)

Applies a clear lookup table to an encrypted integer

The width of the result can be different than the width of the operand. The lookup table must be a tensor of size 2^p where p is the width of the encrypted integer.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.and (::mlir::concretelang::FHE::BoolAndOp)

Applies an AND gate to two encrypted boolean values

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.nand (::mlir::concretelang::FHE::BoolNandOp)

Applies a NAND gate to two encrypted boolean values

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.not (::mlir::concretelang::FHE::BoolNotOp)

Applies a NOT gate to an encrypted boolean value

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.or (::mlir::concretelang::FHE::BoolOrOp)

Applies an OR gate to two encrypted boolean values

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.xor (::mlir::concretelang::FHE::BoolXorOp)

Applies an XOR gate to two encrypted boolean values

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.from_bool (::mlir::concretelang::FHE::FromBoolOp)

Cast a boolean to an unsigned integer

Examples:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.gen_gate (::mlir::concretelang::FHE::GenGateOp)

Applies a truth table based on two boolean inputs

Truth table must be a tensor of four boolean values.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.max_eint (::mlir::concretelang::FHE::MaxEintOp)

Retrieve the maximum of two encrypted integers.

Retrieve the maximum of two encrypted integers using the formula, 'max(x, y) == max(x - y, 0) + y'. The input and output types should be the same.

If `x - y`` inside the max overflows or underflows, the behavior is undefined. To support the full range, you should increase the bit-width by 1 manually.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.mul_eint_int (::mlir::concretelang::FHE::MulEintIntOp)

Multiply an encrypted integer with a clear integer

The clear integer must have one more bit than the encrypted integer and the result must have the same width and the same signedness as the encrypted integer.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.mul_eint (::mlir::concretelang::FHE::MulEintOp)

Multiplies two encrypted integers

The encrypted integers and the result must have the same width and signedness. Also, due to the current implementation, one supplementary bit of width must be provided, in addition to the number of bits needed to encode the largest output value.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.mux (::mlir::concretelang::FHE::MuxOp)

Multiplexer for two encrypted boolean inputs, based on an encrypted condition

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.neg_eint (::mlir::concretelang::FHE::NegEintOp)

Negates an encrypted integer

The result must have the same width and the same signedness as the encrypted integer.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.round (::mlir::concretelang::FHE::RoundEintOp)

Rounds a ciphertext to a smaller precision.

Assuming a ciphertext whose message is implemented over p bits, this operation rounds it to fit to q bits with p>q.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.sub_eint_int (::mlir::concretelang::FHE::SubEintIntOp)

Subtract a clear integer from an encrypted integer

The clear integer must have one more bit than the encrypted integer and the result must have the same width and the same signedness as the encrypted integer.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.sub_eint (::mlir::concretelang::FHE::SubEintOp)

Subtract an encrypted integer from an encrypted integer

The encrypted integers and the result must have the same width and the same signedness.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.sub_int_eint (::mlir::concretelang::FHE::SubIntEintOp)

Subtract an encrypted integer from a clear integer

The clear integer must have one more bit than the encrypted integer and the result must have the same width and the same signedness as the encrypted integer.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.to_bool (::mlir::concretelang::FHE::ToBoolOp)

Cast an unsigned integer to a boolean

The input must be of width one or two. Two being the current representation of an encrypted boolean, leaving one bit for the carry.

Examples:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.to_signed (::mlir::concretelang::FHE::ToSignedOp)

Cast an unsigned integer to a signed one

The result must have the same width as the input.

The behavior is undefined on overflow/underflow.

Examples:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.to_unsigned (::mlir::concretelang::FHE::ToUnsignedOp)

Cast a signed integer to an unsigned one

The result must have the same width as the input.

The behavior is undefined on overflow/underflow.

Examples:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

FHE.zero (::mlir::concretelang::FHE::ZeroEintOp)

Returns a trivial encrypted integer of 0

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Results:

FHE.zero_tensor (::mlir::concretelang::FHE::ZeroTensorOp)

Creates a new tensor with all elements initialized to an encrypted zero.

Creates a new tensor with the shape specified in the result type and initializes its elements with an encrypted zero.

Example:

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Results:

Type definition

EncryptedBooleanType

An encrypted boolean

Syntax: !FHE.ebool

An encrypted boolean.

EncryptedIntegerType

An encrypted integer

An encrypted integer with width bits to performs FHE Operations.

Examples:

Parameters:

EncryptedSignedIntegerType

An encrypted signed integer

An encrypted signed integer with width bits to performs FHE Operations.

Examples:

Parameters:

Low Level Fully Homomorphic Encryption dialect A dialect for representation of low level operation on fully homomorphic ciphertext.

Operation definition

Concrete.add_lwe_buffer (::mlir::concretelang::Concrete::AddLweBufferOp)

Returns the sum of 2 lwe ciphertexts

Operands:

Concrete.add_lwe_tensor (::mlir::concretelang::Concrete::AddLweTensorOp)

Returns the sum of 2 lwe ciphertexts

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.add_plaintext_lwe_buffer (::mlir::concretelang::Concrete::AddPlaintextLweBufferOp)

Returns the sum of a clear integer and an lwe ciphertext

Operands:

Concrete.add_plaintext_lwe_tensor (::mlir::concretelang::Concrete::AddPlaintextLweTensorOp)

Returns the sum of a clear integer and an lwe ciphertext

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.batched_add_lwe_buffer (::mlir::concretelang::Concrete::BatchedAddLweBufferOp)

Batched version of AddLweBufferOp, which performs the same operation on multiple elements

Operands:

Concrete.batched_add_lwe_tensor (::mlir::concretelang::Concrete::BatchedAddLweTensorOp)

Batched version of AddLweTensorOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.batched_add_plaintext_cst_lwe_buffer (::mlir::concretelang::Concrete::BatchedAddPlaintextCstLweBufferOp)

Batched version of AddPlaintextLweBufferOp, which performs the same operation on multiple elements

Operands:

Concrete.batched_add_plaintext_cst_lwe_tensor (::mlir::concretelang::Concrete::BatchedAddPlaintextCstLweTensorOp)

Batched version of AddPlaintextLweTensorOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.batched_add_plaintext_lwe_buffer (::mlir::concretelang::Concrete::BatchedAddPlaintextLweBufferOp)

Batched version of AddPlaintextLweBufferOp, which performs the same operation on multiple elements

Operands:

Concrete.batched_add_plaintext_lwe_tensor (::mlir::concretelang::Concrete::BatchedAddPlaintextLweTensorOp)

Batched version of AddPlaintextLweTensorOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.batched_bootstrap_lwe_buffer (::mlir::concretelang::Concrete::BatchedBootstrapLweBufferOp)

Batched version of BootstrapLweOp, which performs the same operation on multiple elements

Attributes:

Operands:

Concrete.batched_bootstrap_lwe_tensor (::mlir::concretelang::Concrete::BatchedBootstrapLweTensorOp)

Batched version of BootstrapLweOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.batched_keyswitch_lwe_buffer (::mlir::concretelang::Concrete::BatchedKeySwitchLweBufferOp)

Batched version of KeySwitchLweOp, which performs the same operation on multiple elements

Attributes:

Operands:

Concrete.batched_keyswitch_lwe_tensor (::mlir::concretelang::Concrete::BatchedKeySwitchLweTensorOp)

Batched version of KeySwitchLweOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.batched_mapped_bootstrap_lwe_buffer (::mlir::concretelang::Concrete::BatchedMappedBootstrapLweBufferOp)

Batched, mapped version of BootstrapLweOp, which performs the same operation on multiple elements

Attributes:

Operands:

Concrete.batched_mapped_bootstrap_lwe_tensor (::mlir::concretelang::Concrete::BatchedMappedBootstrapLweTensorOp)

Batched, mapped version of BootstrapLweOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.batched_mul_cleartext_cst_lwe_buffer (::mlir::concretelang::Concrete::BatchedMulCleartextCstLweBufferOp)

Batched version of MulCleartextLweBufferOp, which performs the same operation on multiple elements

Operands:

Concrete.batched_mul_cleartext_cst_lwe_tensor (::mlir::concretelang::Concrete::BatchedMulCleartextCstLweTensorOp)

Batched version of MulCleartextLweTensorOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.batched_mul_cleartext_lwe_buffer (::mlir::concretelang::Concrete::BatchedMulCleartextLweBufferOp)

Batched version of MulCleartextLweBufferOp, which performs the same operation on multiple elements

Operands:

Concrete.batched_mul_cleartext_lwe_tensor (::mlir::concretelang::Concrete::BatchedMulCleartextLweTensorOp)

Batched version of MulCleartextLweTensorOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.batched_negate_lwe_buffer (::mlir::concretelang::Concrete::BatchedNegateLweBufferOp)

Batched version of NegateLweBufferOp, which performs the same operation on multiple elements

Operands:

Concrete.batched_negate_lwe_tensor (::mlir::concretelang::Concrete::BatchedNegateLweTensorOp)

Batched version of NegateLweTensorOp, which performs the same operation on multiple elements

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Operands:

Results:

Concrete.bootstrap_lwe_buffer (::mlir::concretelang::Concrete::BootstrapLweBufferOp)

Bootstraps a LWE ciphertext with a GLWE trivial encryption of the lookup table

Attributes:

Operands:

Concrete.bootstrap_lwe_tensor (::mlir::concretelang::Concrete::BootstrapLweTensorOp)

Bootstraps an LWE ciphertext with a GLWE trivial encryption of the lookup table

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.encode_expand_lut_for_bootstrap_buffer (::mlir::concretelang::Concrete::EncodeExpandLutForBootstrapBufferOp)

Encode and expand a lookup table so that it can be used for a bootstrap

Attributes:

Operands:

Concrete.encode_expand_lut_for_bootstrap_tensor (::mlir::concretelang::Concrete::EncodeExpandLutForBootstrapTensorOp)

Encode and expand a lookup table so that it can be used for a bootstrap

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.encode_lut_for_crt_woppbs_buffer (::mlir::concretelang::Concrete::EncodeLutForCrtWopPBSBufferOp)

Encode and expand a lookup table so that it can be used for a crt wop pbs

Attributes:

Operands:

Concrete.encode_lut_for_crt_woppbs_tensor (::mlir::concretelang::Concrete::EncodeLutForCrtWopPBSTensorOp)

Encode and expand a lookup table so that it can be used for a wop pbs

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.encode_plaintext_with_crt_buffer (::mlir::concretelang::Concrete::EncodePlaintextWithCrtBufferOp)

Encodes a plaintext by decomposing it on a crt basis

Attributes:

Operands:

Concrete.encode_plaintext_with_crt_tensor (::mlir::concretelang::Concrete::EncodePlaintextWithCrtTensorOp)

Encodes a plaintext by decomposing it on a crt basis

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands:

Results:

Concrete.keyswitch_lwe_buffer (::mlir::concretelang::Concrete::KeySwitchLweBufferOp)

Performs a keyswitching operation on an LWE ciphertext

Attributes:

Operands:

Concrete.keyswitch_lwe_tensor (::mlir::concretelang::Concrete::KeySwitchLweTensorOp)

Performs a keyswitching operation on an LWE ciphertext

Traits: AlwaysSpeculatableImplTrait

Interfaces: ConditionallySpeculatable, NoMemoryEffect (MemoryEffectOpInterface)

Effects: MemoryEffects::Effect{}

Attributes:

Operands: