Passes

-align-tensor-sizes

Resize tensors into tensors with a fixed size final dimension

This pass resizes input tensors with arbitrary sizes into tensors with whose final dimensions has a fixed size. All input tensors are required to be one-dimensional. The --size option specifies the size of the final dimension of the output tensors, and is required to be a power of two.

To align the tensors in the input IR, the pass first zero pads the input to the nearest power of two before replicating or splitting it into the output shape determined by size. The resulting transformation is described in a SIMDPackingAttr encoding attribute on the final tensor.

For example, with size=16,a tensor with 7 elements will be zero-padded to 8 elements, and then replicated twice to fill a tensor with size 16. The SIMDPackingAttr will encode the input shape, the number of elements that were zero-padded, and the output shape.

Input:

%0 = tensor.empty() : tensor<7xi32>

Output:

%0 = tensor.empty() : tensor<16xi32, #tensor_ext.simd_packing<in = [7], padding = [1], out = [16]>>

A tensor with 30 elements will be zero padded with 2 elements and split into two tensors of size 16.

Input:

%0 = tensor.empty() : tensor<30xi32>

Output:

%0 = tensor.empty() : tensor<2x16xi32, #tensor_ext.simd_packing<in = [30], padding = [2], out = [16]>>

Note that this pass does not insert any new operations like reshape, but rather transforms the IR to use tensors with a fixed dimension. This pass may be used to align the sizes of tensors that represent plaintexts and ciphertexts in RLWE schemes that support SIMD slots and operations.

Options

-size : Power of two size of the final dimension of the output tensors.

-apply-folders

Apply all folding patterns from canonicalize

This pass applies all registered folding patterns greedily to the input IR. This is useful when running a full canonicalize is too slow, but applying folders before canonicalize is sufficient to simplify the IR for later passes, or even sufficient to then subsequently run a full canonicalize pass.

This is used to prepare an IR for insert-rotate after fully unrolling loops.

-bgv-to-lwe

Lower bgv to lwe dialect.

This pass lowers the bgv dialect to lwe dialect.

-bgv-to-openfhe

Lower bgv to openfhe dialect.

This pass lowers the bgv dialect to Openfhe dialect.

-cggi-boolean-vectorize

Group different logic gates with the packed API

This pass groups independent logic gates into a single call of the packed operations. Pass is based on the straight-line-vectorizer, but is fundamentally different. This pass combines any type of boolean gates and is not restricted to combining the same type of gate operand.

Pass is intended for the FPT tfhe-rs API, where packed_gates function get a the boolean gates are passed as a string vector and a left and right vector of ciphertexts. Each boolean gates specified in gates is then applied element wise.

let outputs_ct = fpga_key.packed_gates(&gates, &ref_to_ct_lefts, &ref_to_ct_rights);

Options

-parallelism : Parallelism factor for batching. 0 is infinite parallelism

-cggi-set-default-parameters

Set default parameters for CGGI ops

This pass adds default parameters to all CGGI ops as cggi_params named attributes, overriding any existing attribute set with that name.

This pass is primarily for testing purposes, and as a parameter provider before a proper parameter selection mechanism is added. This pass should not be used in production.

The specific parameters are hard-coded in lib/Dialect/CGGI/Transforms/SetDefaultParameters.cpp.

-cggi-to-jaxite

Lower cggi to jaxite dialect.

-cggi-to-tfhe-rust-bool

Lower cggi to tfhe_rust_bool dialect.

-cggi-to-tfhe-rust

Lower cggi to tfhe_rust dialect.

-ckks-to-openfhe

Lower ckks to openfhe dialect.

This pass lowers the ckks dialect to Openfhe dialect.

-collapse-insertion-chains

Collapse chains of extract/insert ops into rotate ops when possible

This pass is a cleanup pass for insert-rotate. That pass sometimes leaves behind a chain of insertion operations like this:

%extracted = tensor.extract %14[%c5] : tensor<16xi16>
%inserted = tensor.insert %extracted into %dest[%c0] : tensor<16xi16>
%extracted_0 = tensor.extract %14[%c6] : tensor<16xi16>
%inserted_1 = tensor.insert %extracted_0 into %inserted[%c1] : tensor<16xi16>
%extracted_2 = tensor.extract %14[%c7] : tensor<16xi16>
%inserted_3 = tensor.insert %extracted_2 into %inserted_1[%c2] : tensor<16xi16>
...
%extracted_28 = tensor.extract %14[%c4] : tensor<16xi16>
%inserted_29 = tensor.insert %extracted_28 into %inserted_27[%c15] : tensor<16xi16>
yield %inserted_29 : tensor<16xi16>

In many cases, this chain will insert into every index of the dest tensor, and the extracted values all come from consistently aligned indices of the same source tensor. In this case, the chain can be collapsed into a single rotate.

Each index used for insertion or extraction must be constant; this may require running --canonicalize or --sccp before this pass to apply folding rules (use --sccp if you need to fold constant through control flow).

-convert-elementwise-to-affine

This pass lowers ElementwiseMappable operations to Affine loops.

This pass lowers ElementwiseMappable operations over tensors to affine loop nests that instead apply the operation to the underlying scalar values.

Usage: ‘–convert-elementwise-to-affine=convert-ops=arith.mulf ' restrict conversion to mulf op from arith dialect.

‘–convert-elementwise-to-affine=convert-ops=arith.addf,arith.divf convert-dialects=bgv’ restrict conversion to addf and divf ops from arith dialect and all of the ops in bgv dialect.

–convert-elementwise-to-affine=convert-dialects=arith restrict conversion to arith dialect so ops only from arith dialect is processed.

–convert-elementwise-to-affine=convert-ops=arith.addf,arith.mulf restrict conversion only to these two ops - addf and mulf - from arith dialect.

Options

-convert-ops      : comma-separated list of ops to run this pass on 
-convert-dialects : comma-separated list of dialects to run this pass on 

-convert-if-to-select

Convert scf.if operations on secret conditions to arith.select operations.

Conversion for If-operations that evaluate secret condition to alternative select operations.

-convert-polynomial-mul-to-ntt

Rewrites polynomial operations to their NTT equivalents

Applies a rewrite pattern to convert polynomial multiplication to the equivalent using the number-theoretic transforms (NTT) when possible.

Polynomial multiplication can be rewritten as polynomial.NTT on each operand, followed by modulo elementwise multiplication of the point-value representation and then the inverse-NTT back to coefficient representation.

-convert-secret-extract-to-static-extract

Convert tensor.extract operations on secret index to static extract operations.

Converts tensor.extract operations that read value at secret index to alternative static tensor.extract operations that extracts value at each index and conditionally selects the value extracted at the secret index.

Note: Running this pass alone does not result in a data-oblivious program; we have to run the --convert-if-to-select pass to the resulting program to convert the secret-dependent If-operation to a Select-operation.

Example input: mlir func.func @main(%secretTensor: !secret.secret<tensor<32xi16>>, %secretIndex: !secret.secret<index>)) -> !secret.secret<i16> { ... %0 = secret.generic ins(%secretTensor, %secretIndex : !secret.secret<tensor<32xi16>>, !secret.secret<index>) { ^bb0(%tensor: tensor<32xi16>, %index: index): // Violation: tensor.extract loads value at secret index %extractedValue = tensor.extract %tensor[%index] : tensor<16xi32> ... }

Output:
```mlir
func.func @main(%secretTensor: !secret.secret<tensor<32xi16>>, %secretIndex: !secret.secret<index>)) -> !secret.secret<i16> {
  ...
  %0 = secret.generic ins(%secretTensor, %secretIndex : !secret.secret<tensor<32xi16>>, !secret.secret<index>) {
  ^bb0(%tensor: tensor<32xi16>, %index: index):
    %extractedValue = affine.for %i=0 to 16 iter_args(%arg= %dummyValue) -> (i32) {
      // 1. Check if %i matches %index
      %cond = arith.cmpi eq, %i, %index : index
      // 2. Extract value at %i
      %value = tensor.extract %tensor[%i] : tensor<16xi32>
      // 3. If %i matches %index, yield %value extracted in (2), else yield %dummyValue
      %result = scf.if %cond -> (i32) {
        scf.yield %value : i32
      } else{
        scf.yield %arg : i32
      }
      // 4. Yield result from (3)
      affine.yield %result : i32

} … }

```

-convert-secret-for-to-static-for

Convert secret scf.for ops to affine.for ops with constant bounds.

Conversion for For-operation that evaluate secret bound(s) to alternative affine For-operation with constant bound(s).

It replaces data-dependent bounds with an If-operation to check the bounds, and conditionally execute and yield values from the For-operation’s body. Note: Running this pass alone does not result in a data-oblivious program; we have to run the --convert-if-to-select pass to the resulting program to convert the secret-dependent If-operation to a Select-operation.

Example input:

  func.func @main(%secretTensor: !secret.secret<tensor<16xi32>>, %secretLower: !secret.secret<index>, %secretUpper: !secret.secret<index>) -> !secret.secret<i32> {
   ...
   %0 = secret.generic ins(%secretTensor, %secretLower, %secretUpper : !secret.secret<tensor<16xi32>>, !secret.secret<index>, !secret.secret<index>){
    ^bb0(%tensor: tensor<16xi32>, %lower : index, %upper : index ):
      ...
      %1 = scf.for %i = %lower to %upper step %step iter_args(%arg = %val) -> (i32) {
        %extracted = tensor.extract %input[%i] : tensor<16xi32>
        %sum = arith.addi %extracted, %arg : i32
        scf.yield %sum : i32
      } {lower = 0, upper = 16}
      secret.yield %1 : i32
  } -> !secret.secret<i32>
  return %0 : !secret.secret<i32>

Output:

  func.func @main(%secretTensor: !secret.secret<tensor<16xi32>>, %secretIndex: !secret.secret<index> {secret.secret}) -> !secret.secret<i32> {
   ...
   %0 = secret.generic ins(%secretTensor, %secretLower, %secretUpper : !secret.secret<tensor<16xi32>>, !secret.secret<index>, !secret.secret<index>){
    ^bb0(%tensor: tensor<16xi32>, %lower : index, %upper : index ):
      ...
      %1 = affine.for %i = 0 to 16 step %step iter_args(%arg = %val) -> (i32) {
        %lowerCond = arith.cmpi sge, %i, %index : index
        %upperCond = arith.cmpi slt, %i, %index : index
        %cond = arith.andi %lowerCond, %upperCond : i1
        %result = scf.if(%cond) -> (i32) {
          %extracted = tensor.extract %input[%i] : tensor<16xi32>
          %sum = arith.addi %extracted, %arg : i32
          scf.yield %sum : i32
        } else {
          scf.yield %arg : i32
        }
        affine.yield %result : i32
      } {lower = 0, upper = 16}
      secret.yield %1 : i32
  } -> !secret.secret<i32>
  return %0 : !secret.secret<i32>

-convert-secret-insert-to-static-insert

Convert tensor.insert operations on secret index to static insert operations.

Converts tensor.insert operations that write to secret index to alternative static tensor.insert operations that inserts the inserted value at each index and conditionally selects the newly produced tensor that contains the value at the secret index.

Note: Running this pass alone does not result in a data-oblivious program; we have to run the --convert-if-to-select pass to the resulting program to convert the secret-dependent If-operation to a Select-operation.

Example input:

func.func @main(%secretTensor: !secret.secret<tensor<32xi16>>, %secretIndex: !secret.secret<index>)) -> !secret.secret<i16> {
  ...
  %0 = secret.generic ins(%secretTensor, %secretIndex : !secret.secret<tensor<32xi16>>, !secret.secret<index>) {
  ^bb0(%tensor: tensor<32xi16>, %index: index):
    // Violation: tensor.insert writes value at secret index
    %inserted = tensor.insert %newValue into %tensor[%index] : tensor<16xi32>
  ...
}

Output:

func.func @main(%secretTensor: !secret.secret<tensor<32xi16>>, %secretIndex: !secret.secret<index>)) -> !secret.secret<i16> {
  ...
  %0 = secret.generic ins(%secretTensor, %secretIndex : !secret.secret<tensor<32xi16>>, !secret.secret<index>) {
  ^bb0(%tensor: tensor<32xi16>, %index: index):
    %inserted = affine.for %i=0 to 16 iter_args(%inputArg = %tensor) -> tensor<16xi32> {
      // 1. Check if %i matches the %index
      %cond = arith.cmpi eq, %i, %index : index
      // 2. Insert %newValue and produce %newTensor
      %newTensor = tensor.insert %value into %inputArg[%i] : tensor<16xi32>
      // 3. If %i matches %inputIndex, yield %newTensor, else yield unchanged input tensor
      %finalTensor = scf.if %cond -> (i32) {
        scf.yield %newTensor : tensor<16xi32>
      } else{
        scf.yield %inputArg : tensor<16xi32>
      }
      // 4. Yield final tensor
      affine.yield %finalTensor : tensor<16xi32>
}
  ...
}

-convert-secret-while-to-static-for

Convert secret scf.while ops to affine.for ops that have constant bounds.

Convert scf.while with a secret condition to affine.for with constant bounds. It replaces the scf.condition operation found in the scf.while loop with an scf.if operation that conditionally executes operations in the while operation’s body and yields values.

A “max_iter” attribute should be specified as part of the secret-dependent scf.while operation to successfully transform to a secret-independent affine.for operation. This attribute determines the maximum number of iterations for the new affine.for operation.

Note: Running this pass alone does not result in a data-oblivious program; we have to run the --convert-if-to-select pass to the resulting program to convert the secret-dependent If-operation to a Select-operation.

Example input:

// C-like code
int main(int secretInput) {
  while (secretInput > 100) {
    secretInput = secretInput * secretInput;
  }
  return secretInput;
}

// MLIR
func.func @main(%secretInput: !secret.secret<i16>) -> !secret.secret<i16> {
  %c100 = arith.constant 100 : i16
  %0 = secret.generic ins(%secretInput : !secret.secret<i16>) {
  ^bb0(%input: i16):
    %1 = scf.while (%arg1 = %input) : (i16) -> i16 {
      %2 = arith.cmpi sgt, %arg1, %c100 : i16
      scf.condition(%2) %arg1 : i16
    } do {
    ^bb0(%arg1: i16):
      %3 = arith.muli %arg1, %arg1 : i16
      scf.yield %3 : i16
    } attributes {max_iter = 16 : i64}
    secret.yield %1 : i16
  } -> !secret.secret<i16>
  return %0 : !secret.secret<i16>
}

Output:

func.func @main(%secretInput: !secret.secret<i16>) -> !secret.secret<i16> {
  %c100 = arith.constant 100 : i16
  %0 = secret.generic ins(%secretInput : !secret.secret<i16>) {
  ^bb0(%input: i16):
    %1 = affine.for 0 to 16 iter_args(%arg1 = %input) -> (i16) {
      %2 = arith.cmpi sgt, %arg1, %c100 : i16
      %3 = scf.if (%2) -> i16{
        %4 = arith.muli %arg1, %arg1 : i16
        scf.yield %4 : i16
      } else {
        scf.yield %arg1 : i16
      }
      affine.yield %3 : i16
    } attributes {max_iter = 16 : i64}
    secret.yield %1 : i16
  } -> !secret.secret<i16>
  return %0 : !secret.secret<i16>
}

-convert-tensor-to-scalars

Effectively ‘unrolls’ tensors of static shape to scalars.

This pass will convert a static-shaped tensor type to a TypeRange containing product(dim) copies of the element type of the tensor. This pass currently includes two patterns:

1. It converts tensor.from_elements operations to the corresponding scalar inputs.
2. It converts tensor.insert operations by updating the ValueRange corresponding to the converted input and updating it with the scalar to be inserted.

It also applies folders greedily to simplify, e.g., extract(from_elements).

Note: The pass is designed to be run on an IR, where the only operations with tensor typed operands are tensor “management” operations such as insert/extract, with all other operations (e.g., arith operations) already taking (extracted) scalar inputs. For example, an IR where elementwise operations have been converted to scalar operations via --convert-elementwise-to-affine.

The pass might insert new tensor.from_elements operations or manually create the scalar ValueRange via inserting tensor.extract operations if any operations remain that operate on tensors. The pass currently applies irrespective of tensor size, i.e., might be very slow for large tensors.

TODO (#1023): Extend this pass to support more tensor operations, e.g., tensor.slice

Options

-max-size : Limits `unrolling` to tensors with at most max-size elements

-expand-copy

Expands memref.copy ops to explicit affine loads and stores

This pass removes memref copy operations by expanding them to affine loads and stores. This pass introduces affine loops over the dimensions of the MemRef, so must be run prior to any affine loop unrolling in a pipeline.

Input

module {
  func.func @memref_copy() {
    %alloc = memref.alloc() : memref<2x3xi32>
    %alloc_0 = memref.alloc() : memref<2x3xi32>
    memref.copy %alloc, %alloc_0 : memref<1x1xi32> to memref<1x1xi32>
  }
}

Output

module {
  func.func @memref_copy() {
    %alloc = memref.alloc() : memref<2x3xi32>
    %alloc_0 = memref.alloc() : memref<2x3xi32>
    affine.for %arg0 = 0 to 2 {
      affine.for %arg1 = 0 to 3 {
        %1 = affine.load %alloc[%arg0, %arg1] : memref<2x3xi32>
        affine.store %1, %alloc_0[%arg0, %arg1] : memref<2x3xi32>
      }
    }
  }
}

When --disable-affine-loop=true is set, then the output becomes

module {
  func.func @memref_copy() {
    %alloc = memref.alloc() : memref<2x3xi32>
    %alloc_0 = memref.alloc() : memref<2x3xi32>
    %c0 = arith.constant 0 : index
    %c1 = arith.constant 1 : index
    %c2 = arith.constant 2 : index
    %0 = affine.load %alloc[%c0, %c0] : memref<2x3xi32>
    affine.store %0, %alloc_0[%c0, %c0] : memref<2x3xi32>
    %1 = affine.load %alloc[%c0, %c1] : memref<2x3xi32>
    affine.store %1, %alloc_0[%c0, %c1] : memref<2x3xi32>
    %2 = affine.load %alloc[%c0, %c2] : memref<2x3xi32>
    affine.store %2, %alloc_0[%c0, %c2] : memref<2x3xi32>
    [...]
  }
}

Options

-disable-affine-loop : Use this to control to disable using affine loops

-extract-loop-body

Extracts logic of a loop bodies into functions.

This pass extracts logic in the inner body of for loops into functions.

This pass requires that tensors are lowered to memref. It expects that a loop body contains a number of affine.load statements used as inputs to the extracted function, and a single affine.store used as the extracted function’s output.

Input

module {
  func.func @loop_body() {
    %c-128_i8 = arith.constant -128 : i8
    %c127_i8 = arith.constant 127 : i8
    %alloc_7 = memref.alloc() {alignment = 64 : i64} : memref<25x20x8xi8>
    affine.for %arg1 = 0 to 25 {
      affine.for %arg2 = 0 to 20 {
        affine.for %arg3 = 0 to 8 {
          %98 = affine.load %alloc_6[%arg1, %arg2, %arg3] : memref<25x20x8xi8>
          %99 = arith.cmpi slt, %arg0, %c-128_i8 : i8
          %100 = arith.select %99, %c-128_i8, %arg0 : i8
          %101 = arith.cmpi sgt, %arg0, %c127_i8 : i8
          %102 = arith.select %101, %c127_i8, %100 : i8
          affine.store %102, %alloc_7[%arg1, %arg2, %arg3] : memref<25x20x8xi8>
        }
      }
    }
  }
}

Output

module {
  func.func @loop_body() {
    %alloc_7 = memref.alloc() {alignment = 64 : i64} : memref<25x20x8xi8>
    affine.for %arg1 = 0 to 25 {
      affine.for %arg2 = 0 to 20 {
        affine.for %arg3 = 0 to 8 {
          %98 = affine.load %alloc_6[%arg1, %arg2, %arg3] : memref<25x20x8xi8>
          %102 = func.call @__for_loop(%98) : (i8) -> i8
          affine.store %102, %alloc_7[%arg1, %arg2, %arg3] : memref<25x20x8xi8>
        }
      }
    }
  }
  func.func private @__for_loop(%arg0: i8) -> i8 {
    %c-128_i8 = arith.constant -128 : i8
    %c127_i8 = arith.constant 127 : i8
    %99 = arith.cmpi slt, %arg0, %c-128_i8 : i8
    %100 = arith.select %99, %c-128_i8, %arg0 : i8
    %101 = arith.cmpi sgt, %arg0, %c127_i8 : i8
    %102 = arith.select %101, %c127_i8, %100 : i8
    return %102 : i8
  }
}

Options

-min-loop-size : Use this to control the minimum loop size to apply this pass
-min-body-size : Use this to control the minimum loop body size to apply this pass

-forward-insert-to-extract

Forward inserts to extracts within a single block

This pass is similar to forward-store-to-load pass where store ops are forwarded load ops; here instead tensor.insert ops are forwarded to tensor.extract ops.

Does not support complex control flow within a block, nor ops with arbitrary subregions.

-forward-store-to-load

Forward stores to loads within a single block

This pass is a simplified version of mem2reg and similar passes. It analyzes an operation, finding all basic blocks within that op that have memrefs whose stores can be forwarded to loads.

Does not support complex control flow within a block, nor ops with arbitrary subregions.

-full-loop-unroll

Fully unroll all loops

Scan the IR for affine.for loops and unroll them all.

-insert-rotate

Vectorize arithmetic FHE operations using HECO-style heuristics

This pass implements the SIMD-vectorization passes from the HECO paper.

The pass operates by identifying arithmetic operations that can be suitably combined into a combination of cyclic rotations and vectorized operations on tensors. It further identifies a suitable “slot target” for each operation and heuristically aligns the operations to reduce unnecessary rotations.

This pass by itself does not eliminate any operations, but instead inserts well-chosen rotations so that, for well-structured code (like unrolled affine loops), a subsequent --cse and --canonicalize pass will dramatically reduce the IR. As such, the pass is designed to be paired with the canonicalization patterns in tensor_ext, as well as the collapse-insertion-chains pass, which cleans up remaining insertion and extraction ops after the main simplifications are applied.

Unlike HECO, this pass operates on plaintext types and tensors, along with the HEIR-specific tensor_ext dialect for its cyclic rotate op. It is intended to be run before lowering to a scheme dialect like bgv.

-linalg-canonicalizations

This pass canonicalizes the linalg.transpose operation of a constant into a transposed constant.

This pass canonicalizes the linalg.transpose operation of a constant into a transposed constant.

-linalg-to-tensor-ext

Lower linalg.matmul to arith and tensor_ext dialects.

This pass lowers the linalg.matmul to a mixture of affine, tensor, and via the Halevi-Shoup and squat matrix multiplication algorithms.

-lwe-add-client-interface

Add client interfaces to (R)LWE encrypted functions

This pass adds encrypt and decrypt functions for each compiled function in the IR. These functions maintain the same interface as the original function, while the compiled function may lose some of this information by the lowerings to ciphertext types (e.g., a scalar ciphertext, when lowered through RLWE schemes, must be encoded as a tensor).

Options

-use-public-key          : If true, generate a client interface that uses a public key for encryption.
-one-value-per-helper-fn : If true, split encryption helpers into separate functions for each SSA value.

-lwe-set-default-parameters

Set default parameters for LWE ops

This pass adds default parameters to all lwe types as the lwe_params attribute, and for lwe ops as the params attribute, overriding any existing attributes set with those names.

This pass is primarily for testing purposes, and as a parameter provider before a proper parameter selection mechanism is added. This pass should not be used in production.

The specific parameters are hard-coded in lib/Dialect/LWE/Transforms/SetDefaultParameters.cpp.

-lwe-to-polynomial

Lower lwe to polynomial dialect.

This pass lowers the lwe dialect to polynomial dialect.

-memref-global-replace

MemrefGlobalReplacePass forwards global memrefs accessors to arithmetic values

This pass forwards constant global MemRef values to referencing affine loads. This pass requires that the MemRef global values are initialized as constants and that the affine load access indices are constants (i.e. not variadic). Unroll affine loops prior to running this pass.

MemRef removal is required to remove any memory allocations from the input model (for example, TensorFlow models contain global memory holding model weights) to support FHE transpilation.

Input

module {
  memref.global "private" constant @__constant_8xi16 : memref<2x4xi16> = dense<[[-10, 20, 3, 4], [5, 6, 7, 8]]>
  func.func @main() -> i16 {
    %c1 = arith.constant 1 : index
    %c2 = arith.constant 2 : index
    %0 = memref.get_global @__constant_8xi16 : memref<2x4xi16>
    %1 = affine.load %0[%c1, %c1 + %c2] : memref<2x4xi16>
    return %1 : i16
  }
}

Output

module {
  func.func @main() -> i16 {
    %c1 = arith.constant 1 : index
    %c2 = arith.constant 2 : index
    %c8_i16 = arith.constant 8 : i16
    return %c8_i16 : i16
  }
}

-openfhe-configure-crypto-context

Configure the crypto context in OpenFHE

This pass generates helper functions to generate and configure the OpenFHE crypto context for the given function. Generating the crypto context sets the appropriate encryption parameters, while the configuration generates the necessary evaluation keys (relinearization and rotation keys).

For example, for an MLIR function @my_func, the generated helpers have the following signatures

func.func  @my_func__generate_crypto_context() -> !openfhe.crypto_context

func.func  @my_func__configure_crypto_context(!openfhe.crypto_context, !openfhe.private_key) -> !openfhe.crypto_context

Options

-entry-function : Default entry function name of entry function.

-operation-balancer

This pass balances addition and multiplication operations.

This pass examines a tree or graph of add and multiplication operations and balances them to minimize the depth of the tree. This exposes better parallelization and reducing the multiplication depth can decrease the parameters used in FHE, which improves performance. This pass is not necessarily optimal, as there may be intermediate computations that this pass does not optimally minimize the depth for.

The algorithm is to analyze a graph of addition operations and do a depth-first search for the operands (from the last computed values in the graph). If there are intermediate computations that are used more than once, then the pass treats that computation as its own tree to balance instead of trying to minimize the global depth of the tree.

This pass only runs on addition and multiplication operations on the arithmetic dialect that are encapsulated inside a secret.generic.

This pass was inspired by section 2.6 of ‘EVA Improved: Compiler and Extension Library for CKKS’ by Chowdhary et al.

-optimize-relinearization

Optimize placement of relinearization ops

This pass defers relinearization ops as late as possible in the IR. This is more efficient in cases where multiplication operations are followed by additions, such as in a dot product. Because relinearization also adds error, deferring it can reduce the need for bootstrapping.

In this pass, we use an integer linear program to determine the optimal relinearization strategy. It solves an ILP for each func op in the IR.

The assumptions of this pass include:

• All return values of functions must be linearized.
• All ciphertext arguments to an op must have the same key basis
• Rotation op inputs must have be linearized.

For an ILP model specification, see the docs at the HEIR website. The model is an adaptation of the ILP described in a blog post by Jeremy Kun.

-polynomial-to-standard

Lower polynomial to standard MLIR dialects.

This pass lowers the polynomial dialect to standard MLIR, a mixture of affine, tensor, and arith.

-remove-unused-memref

Cleanup any unused memrefs

Scan the IR for unused memrefs and remove them.

This pass looks for locally allocated memrefs that are never used and deletes them. This pass can be used as a cleanup pass from other IR simplifications that forward stores to loads.

-rotate-and-reduce

Use a logarithmic number of rotations to reduce a tensor.

This pass identifies when a commutative, associative binary operation is used to reduce all of the entries of a tensor to a single value, and optimizes the operations by using a logarithmic number of reduction operations.

In particular, this pass identifies an unrolled set of operations of the form (the binary ops may come in any order):

%0 = tensor.extract %t[0] : tensor<8xi32>
%1 = tensor.extract %t[1] : tensor<8xi32>
%2 = tensor.extract %t[2] : tensor<8xi32>
%3 = tensor.extract %t[3] : tensor<8xi32>
%4 = tensor.extract %t[4] : tensor<8xi32>
%5 = tensor.extract %t[5] : tensor<8xi32>
%6 = tensor.extract %t[6] : tensor<8xi32>
%7 = tensor.extract %t[7] : tensor<8xi32>
%8 = arith.addi %0, %1 : i32
%9 = arith.addi %8, %2 : i32
%10 = arith.addi %9, %3 : i32
%11 = arith.addi %10, %4 : i32
%12 = arith.addi %11, %5 : i32
%13 = arith.addi %12, %6 : i32
%14 = arith.addi %13, %7 : i32

and replaces it with a logarithmic number of rotate and addi operations:

%0 = tensor_ext.rotate %t, 4 : tensor<8xi32>
%1 = arith.addi %t, %0 : tensor<8xi32>
%2 = tensor_ext.rotate %1, 2 : tensor<8xi32>
%3 = arith.addi %1, %2 : tensor<8xi32>
%4 = tensor_ext.rotate %3, 1 : tensor<8xi32>
%5 = arith.addi %3, %4 : tensor<8xi32>

-secret-capture-generic-ambient-scope

Capture the ambient scope used in a secret.generic

For each value used in the body of a secret.generic op, which is defined in the ambient scope outside the generic, add it to the argument list of the generic.

-secret-distribute-generic

Distribute generic ops through their bodies.

Converts generic ops whose region contains many ops into smaller sequences of generic ops whose regions contain a single op, dropping the generic part from any resulting generic ops that have no secret.secret inputs. If the op has associated regions, and the operands are not secret, then the generic is distributed recursively through the op’s regions as well.

This pass is intended to be used as part of a front-end pipeline, where a program that operates on a secret type annotates the input to a region as secret, and then wraps the contents of the region in a single large secret.generic, then uses this pass to simplify it.

The distribute-through option allows one to specify a comma-separated list of op names (e.g., distribute-thorugh="affine.for,scf.if"), which limits the distribution to only pass through those ops. If unset, all ops are distributed through when possible.

Options

-distribute-through : comma-separated list of ops that should be distributed through

-secret-extract-generic-body

Extract the bodies of all generic ops into functions

This pass extracts the body of all generic ops into functions, and replaces the generic bodies with call ops. Used as a sub-operation in some passes, and extracted into its own pass for testing purposes.

This pass works best when --secret-generic-absorb-constants is run before it so that the extracted function contains any constants used in the generic op’s body.

-secret-forget-secrets

Convert secret types to standard types

Drop the secret<...> type from the IR, replacing it with the contained type and the corresponding cleartext computation.

-secret-generic-absorb-constants

Copy constants into a secret.generic body

For each constant value used in the body of a secret.generic op, which is defined in the ambient scope outside the generic, add it’s definition into the generic body.

-secret-generic-absorb-dealloc

Copy deallocs of internal memrefs into a secret.generic body

For each memref allocated and used only within a body of a secret.generic op, add it’s dealloc of the memref into its generic body.

-secret-merge-adjacent-generics

Merge two adjacent generics into a single generic

This pass merges two immedaitely sequential generics into a single generic. Useful as a sub-operation in some passes, and extracted into its own pass for testing purposes.

-secret-to-bgv

Lower secret to bgv dialect.

This pass lowers an IR with secret.generic blocks containing arithmetic operations to operations on ciphertexts with the BGV dialect.

The pass assumes that the secret.generic regions have been distributed through arithmetic operations so that only one ciphertext operation appears per generic block. It also requires that canonicalize was run so that non-secret values used are removed from the secret.generic’s block arguments.

The pass requires that all types are tensors of a uniform shape matching the dimension of the ciphertext space specified my poly-mod-degree.

Options

-poly-mod-degree      : Default degree of the cyclotomic polynomial modulus to use for ciphertext space.
-coefficient-mod-bits : Default number of bits of the prime coefficient modulus to use for the ciphertext space.

-secret-to-cggi

Lower secret to cggi dialect.

This pass lowers the secret dialect to cggi dialect.

-secret-to-ckks

Lower secret to ckks dialect.

This pass lowers an IR with secret.generic blocks containing arithmetic operations to operations on ciphertexts with the CKKS dialect.

The pass assumes that the secret.generic regions have been distributed through arithmetic operations so that only one ciphertext operation appears per generic block. It also requires that canonicalize was run so that non-secret values used are removed from the secret.generic’s block arguments.

The pass requires that all types are tensors of a uniform shape matching the dimension of the ciphertext space specified my poly-mod-degree.

Options

-poly-mod-degree      : Default degree of the cyclotomic polynomial modulus to use for ciphertext space.
-coefficient-mod-bits : Default number of bits of the prime coefficient modulus to use for the ciphertext space.

-secretize

Adds secret argument attributes to entry function

Adds a secret.secret attribute argument to each argument in the entry function of an MLIR module. By default, the function is main. This may be overridden with the option -entry-function=top_level_func.

Options

-entry-function : entry function of the module

-straight-line-vectorize

A vectorizer for straight line programs.

This pass ignores control flow and only vectorizes straight-line programs within a given region.

Options

-dialect : Use this to restrict the dialect whose ops should be vectorized.

-tosa-to-secret-arith

Lower tosa.sigmoid to secret arith dialects.

This pass lowers the tosa.sigmoid dialect to the polynomial approximation -0.004 * x^3 + 0.197 * x + 0.5 (composed of arith, affine, and tensor operations).

This polynomial approximation of sigmoid only works over the range [-5, 5] and is taken from the paper ‘Logisitic regression over encrypted data from fully homomorphic encryption’ by Chen et al..

-unroll-and-forward

Loop unrolls and forwards stores to loads.

This pass processes the first function in a given module, and, starting from the first loop, iteratively does the following:

1. Fully unroll the loop.
2. Scan for load ops. For each load op with a statically-inferrable access index:
3. Backtrack to the original memref alloc
4. Find all store ops at the corresponding index (possibly transitively through renames/subviews of the underlying alloc).
5. Find the last store that occurs and forward it to the load.
6. If the original memref is an input memref, then forward through any renames to make the target load load directly from the argument memref (instead of any subviews, say)
7. Apply the same logic to any remaining loads not inside any for loop.

This pass requires that tensors are lowered to memref, and only supports affine loops with affine.load/store ops.

Memrefs that result from memref.get_global ops are excluded from forwarding, even if they are loaded with a static index, and are instead handled by memref-global-replace, which should be run after this pass.

-wrap-generic

Wraps regions using secret args in secret.generic bodies

This pass wraps function regions of func.func that use secret arguments in secret.generic bodies.

Secret arguments are annotated using a secret.secret argument attribute. This pass converts these to secret types and then inserts a secret.generic body to hold the functions region. The output type is also converted to a secret.

Example input:

  func.func @main(%arg0: i32 {secret.secret}) -> i32 {
    %0 = arith.constant 100 : i32
    %1 = arith.addi %0, %arg0 : i32
    return %1 : i32
  }

Output:

  func.func @main(%arg0: !secret.secret<i32>) -> !secret.secret<i32> {
    %0 = secret.generic ins(%arg0 : !secret.secret<i32>) {
    ^bb0(%arg1: i32):
      %1 = arith.constant 100 : i32
      %2 = arith.addi %0, %arg1 : i32
      secret.yield %2 : i32
    } -> !secret.secret<i32>
    return %0 : !secret.secret<i32>
  }

-yosys-optimizer

Invoke Yosys to perform circuit optimization.

This pass invokes Yosys to convert an arithmetic circuit to an optimized boolean circuit that uses the arith and comb dialects.

Note that booleanization changes the function signature: multi-bit integers are transformed to a tensor of booleans, for example, an i8 is converted to tensor<8xi1>.

The optimizer will be applied to each secret.generic op containing arithmetic ops that can be optimized.

Optional parameters:

• abc-fast: Run the abc optimizer in “fast” mode, getting faster compile time at the expense of a possibly larger output circuit.
• unroll-factor: Before optimizing the circuit, unroll loops by a given factor. If unset, this pass will not unroll any loops.
• print-stats: Prints statistics about the optimized circuits.
• mode={Boolean,LUT}: Map gates to boolean gates or lookup table gates.
• use-submodules: Extract the body of a generic op into submodules. Useful for large programs with generics that can be isolated. This should not be used when distributing generics through loops to avoid index arguments in the function body.

Statistics

total circuit size : The total circuit size for all optimized circuits, after optimization is done.