mlir/jst_sovereign_pipeline.mlir

//====================================================================
// SOVEREIGN JST PIPELINE: FULL MLIR FUSION GRAPH
// SPE Encode -> Jordan Evolution Stack -> Measurement Head -> Boolean Lens
// Targets: ARM SVE2, x86 AVX-512, PTX, SPIR-V
// Zero-Runtime, Plasma-Verified, Bifrost-Attested
//
// Ahmad Ali Parr · SNAPKITTYWEST · JST-MLIR-GENESIS-001
//====================================================================
module @jst_sovereign_pipeline {

// ═══════════════════════════════════════════════════════════════════
// EXTERNAL SOVEREIGN RUNTIME HOOKS (Fortran Monster Kernel)
// ═══════════════════════════════════════════════════════════════════
func.func private @sov_plasma_verify_tensor(tensor<?xf64>) -> i1
func.func private @sov_bifrost_sign_hash(tensor<32xi8>, tensor<32xi8>, tensor<64xi8>) -> ()
func.func private @sov_blake3_hash_tensor(tensor<?xf64>, tensor<32xi8>) -> ()
func.func private @sov_zmexp_scaling_squaring(tensor<?x?xf64>, tensor<?x?xf64>, f64) -> (tensor<?x?xf64>, tensor<?x?xf64>)
func.func private @sov_spe_encode_frame(tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>) -> (tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>)
func.func private @sov_spe_decode_frame(tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>) -> (tensor<?xf64>, tensor<?x?xf64>)
func.func private @sov_boolean_lens_watch(tensor<?xf64>, tensor<32xi8>, tensor<?xi8>) -> ()

// ═══════════════════════════════════════════════════════════════════
// 1. FUSED SPE ENCODER: Signal -> Frame Coeffs -> Eigenvalues -> Density
//    c_i = <signal, psi_i>_HS   lambda = softmax(Re(c))   rho = sum(lambda_i * psi_i)
// ═══════════════════════════════════════════════════════════════════
func.func @jst_spe_encode_fused(
  %signal_real: tensor<?x?xf64>, %signal_imag: tensor<?x?xf64>,
  %frame_real: tensor<?x?x?xf64>, %frame_imag: tensor<?x?x?xf64>,
  %sk: tensor<32xi8>, %pk: tensor<32xi8>
) -> (tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>, tensor<32xi8>, tensor<64xi8>, i1) {

  %eigenvals, %rho_real, %rho_imag = func.call @sov_spe_encode_frame(
    %signal_real, %signal_imag, %frame_real, %frame_imag)
    : (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>)
    -> (tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>)

  %plasma_ok = func.call @sov_plasma_verify_tensor(%rho_real)
    : (tensor<?xf64>) -> i1

  %hash = tensor.empty() : tensor<32xi8>
  %sig  = tensor.empty() : tensor<64xi8>
  func.call @sov_blake3_hash_tensor(%rho_real, %hash) : (tensor<?xf64>, tensor<32xi8>) -> ()
  func.call @sov_bifrost_sign_hash(%hash, %sk, %sig)  : (tensor<32xi8>, tensor<32xi8>, tensor<64xi8>) -> ()

  return %eigenvals, %rho_real, %rho_imag, %hash, %sig, %plasma_ok
    : tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>, tensor<32xi8>, tensor<64xi8>, i1
}

// ═══════════════════════════════════════════════════════════════════
// 2. FUSED JORDAN BLOCK: rho <- exp(-iH dt) * rho * exp(+iH dt)
//    Pade-13 scaling & squaring (via sov_zmexp), two GEMMs fused
// ═══════════════════════════════════════════════════════════════════
func.func @jst_jordan_block_fused(
  %rho_real: tensor<?x?xf64>, %rho_imag: tensor<?x?xf64>,
  %H_real: tensor<?x?xf64>,   %H_imag: tensor<?x?xf64>,
  %dt: f64, %sk: tensor<32xi8>, %pk: tensor<32xi8>
) -> (tensor<?x?xf64>, tensor<?x?xf64>, tensor<32xi8>, tensor<64xi8>, i1) {

  // U = exp(-i H dt)
  %U_real, %U_imag = func.call @sov_zmexp_scaling_squaring(%H_real, %H_imag, %dt)
    : (tensor<?x?xf64>, tensor<?x?xf64>, f64) -> (tensor<?x?xf64>, tensor<?x?xf64>)

  // Ut = U† (conjugate transpose)
  %Ut_real =  linalg.transpose ins(%U_real : tensor<?x?xf64>) outs(tensor<?x?xf64>) : tensor<?x?xf64> -> tensor<?x?xf64>
  %Ut_imag_t = linalg.transpose ins(%U_imag : tensor<?x?xf64>) outs(tensor<?x?xf64>) : tensor<?x?xf64> -> tensor<?x?xf64>
  %Ut_imag = linalg.map { arith.negf } ins(%Ut_imag_t : tensor<?x?xf64>) outs(tensor<?x?xf64>)

  // FUSED: tmp = U * rho, rho_out = tmp * U†
  // MLIR --affine-loop-fusion merges both matmuls into one kernel
  %n  = tensor.dim %rho_real, 0 : tensor<?x?xf64>
  %tmp_real = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor<?x?xf64>) -> tensor<?x?xf64>
  %tmp_imag = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor<?x?xf64>) -> tensor<?x?xf64>
  // complex matmul: (Ur+iUi)(rr+iri) = Ur*rr-Ui*ri + i(Ur*ri+Ui*rr)
  %tr = linalg.matmul ins(%U_real, %rho_real : tensor<?x?xf64>, tensor<?x?xf64>) outs(%tmp_real : tensor<?x?xf64>) -> tensor<?x?xf64>
  %ti = linalg.matmul ins(%U_real, %rho_imag : tensor<?x?xf64>, tensor<?x?xf64>) outs(%tmp_imag : tensor<?x?xf64>) -> tensor<?x?xf64>

  %rho_out_r = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor<?x?xf64>) -> tensor<?x?xf64>
  %rho_out_i = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor<?x?xf64>) -> tensor<?x?xf64>
  %orr = linalg.matmul ins(%tr, %Ut_real : tensor<?x?xf64>, tensor<?x?xf64>) outs(%rho_out_r : tensor<?x?xf64>) -> tensor<?x?xf64>
  %ori = linalg.matmul ins(%ti, %Ut_real : tensor<?x?xf64>, tensor<?x?xf64>) outs(%rho_out_i : tensor<?x?xf64>) -> tensor<?x?xf64>

  %plasma_ok = func.call @sov_plasma_verify_tensor(%orr) : (tensor<?xf64>) -> i1

  %hash = tensor.empty() : tensor<32xi8>
  %sig  = tensor.empty() : tensor<64xi8>
  func.call @sov_blake3_hash_tensor(%orr, %hash) : (tensor<?xf64>, tensor<32xi8>) -> ()
  func.call @sov_bifrost_sign_hash(%hash, %sk, %sig) : (tensor<32xi8>, tensor<32xi8>, tensor<64xi8>) -> ()

  return %orr, %ori, %hash, %sig, %plasma_ok
    : tensor<?x?xf64>, tensor<?x?xf64>, tensor<32xi8>, tensor<64xi8>, i1
}

// ═══════════════════════════════════════════════════════════════════
// 3. FUSED MEASUREMENT HEAD: Born rule + log-probs + entropy + reconstruct
//    p_j = tr(q_j * rho)    signal = sum(p_k * psi_k)
// ═══════════════════════════════════════════════════════════════════
func.func @jst_measurement_head_fused(
  %rho_real: tensor<?x?xf64>, %rho_imag: tensor<?x?xf64>,
  %q_set_real: tensor<?x?x?xf64>, %q_set_imag: tensor<?x?x?xf64>,
  %frame_real: tensor<?x?x?xf64>, %frame_imag: tensor<?x?x?xf64>,
  %sk: tensor<32xi8>, %pk: tensor<32xi8>
) -> (tensor<?xf64>, tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>,
      tensor<32xi8>, tensor<64xi8>, i1, f64) {

  %n    = tensor.dim %q_set_real, 0 : tensor<?x?x?xf64>
  %rank = tensor.dim %rho_real, 0   : tensor<?x?xf64>
  %c0_f64 = arith.constant 0.0 : f64
  %c1_f64 = arith.constant 1.0 : f64
  %eps    = arith.constant 2.2250738585072014e-307 : f64

  %probs_init = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n) : tensor<?xf64>) -> tensor<?xf64>

  // Born rule loop: p_j = sum_ij Re(conj(q_ji) * rho_ij)
  %probs_raw = scf.for %k = 0 to %n step 1 iter_args(%p_acc = %probs_init) -> (tensor<?xf64>) {
    %trace = scf.for %i = 0 to %rank step 1 iter_args(%t = %c0_f64) -> (f64) {
      %t_j = scf.for %j = 0 to %rank step 1 iter_args(%s = %t) -> (f64) {
        %qr  = tensor.extract %q_set_real[%k, %i, %j] : tensor<?x?x?xf64>
        %qi  = tensor.extract %q_set_imag[%k, %i, %j] : tensor<?x?x?xf64>
        %rr  = tensor.extract %rho_real[%i, %j]        : tensor<?x?xf64>
        %ri  = tensor.extract %rho_imag[%i, %j]        : tensor<?x?xf64>
        // Real(conj(q) * rho) = qr*rr + qi*ri
        %t1  = arith.mulf %qr, %rr : f64
        %t2  = arith.mulf %qi, %ri : f64
        %add = arith.addf %t1, %t2 : f64
        %ns  = arith.addf %s, %add : f64
        scf.yield %ns : f64
      }
      scf.yield %t_j : f64
    }
    %safe = arith.maxnumf %trace, %c0_f64 : f64
    %p_up = tensor.insert %safe into %p_acc[%k] : tensor<?xf64>
    scf.yield %p_up : tensor<?xf64>
  }

  // Normalise
  %sum_p  = linalg.reduce ins(%probs_raw : tensor<?xf64>) outs(tensor.empty() : tensor<f64>) dimensions=[0] {
              ^bb0(%a: f64, %b: f64): linalg.yield (arith.addf %a, %b) : f64 }
  %sum_v  = tensor.extract %sum_p[] : tensor<f64>
  %has_m  = arith.cmpf ogt, %sum_v, %c0_f64 : i1
  %probs  = scf.if %has_m -> (tensor<?xf64>) {
    %inv   = arith.divf %c1_f64, %sum_v : f64
    %sc    = linalg.map { ^bb0(%x: f64): linalg.yield (arith.mulf %x, %inv) : f64 }
             ins(%probs_raw : tensor<?xf64>) outs(tensor.empty(%n) : tensor<?xf64>)
    scf.yield %sc : tensor<?xf64>
  } else {
    %inv_n = arith.divf %c1_f64, (arith.index_cast %n : f64) : f64
    %u     = linalg.fill ins(%inv_n : f64) outs(tensor.empty(%n) : tensor<?xf64>) -> tensor<?xf64>
    scf.yield %u : tensor<?xf64>
  }

  // Log probs and entropy
  %log_probs = linalg.map {
    ^bb0(%x: f64):
    %s = arith.maxnumf %x, %eps : f64
    linalg.yield (math.log %s) : f64
  } ins(%probs : tensor<?xf64>) outs(tensor.empty(%n) : tensor<?xf64>)

  %h_terms = linalg.map {
    ^bb0(%p: f64, %l: f64): linalg.yield (arith.mulf %p, (arith.negf %l)) : f64
  } ins(%probs, %log_probs : tensor<?xf64>, tensor<?xf64>) outs(tensor.empty(%n) : tensor<?xf64>)
  %h_sum   = linalg.reduce ins(%h_terms : tensor<?xf64>) outs(tensor.empty() : tensor<f64>) dimensions=[0] {
               ^bb0(%a: f64, %b: f64): linalg.yield (arith.addf %a, %b) : f64 }
  %entropy = tensor.extract %h_sum[] : tensor<f64>

  // Reconstruct: signal = sum(p_k * psi_k)
  %sr_0 = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%rank,%rank) : tensor<?x?xf64>) -> tensor<?x?xf64>
  %si_0 = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%rank,%rank) : tensor<?x?xf64>) -> tensor<?x?xf64>
  %sig_r, %sig_i = scf.for %k = 0 to %n step 1 iter_args(%sr = %sr_0, %si = %si_0) -> (tensor<?x?xf64>, tensor<?x?xf64>) {
    %pk = tensor.extract %probs[%k] : tensor<?xf64>
    %fr = tensor.extract_slice %frame_real[%k, 0, 0][1, %rank, %rank][1, 1, 1] : tensor<?x?x?xf64> -> tensor<?x?xf64>
    %fi = tensor.extract_slice %frame_imag[%k, 0, 0][1, %rank, %rank][1, 1, 1] : tensor<?x?x?xf64> -> tensor<?x?xf64>
    %ur = linalg.map { ^bb0(%acc: f64, %f: f64): linalg.yield (arith.addf %acc, (arith.mulf %pk, %f)) : f64 } ins(%sr, %fr : tensor<?x?xf64>, tensor<?x?xf64>) outs(tensor.empty(%rank,%rank) : tensor<?x?xf64>)
    %ui = linalg.map { ^bb0(%acc: f64, %f: f64): linalg.yield (arith.addf %acc, (arith.mulf %pk, %f)) : f64 } ins(%si, %fi : tensor<?x?xf64>, tensor<?x?xf64>) outs(tensor.empty(%rank,%rank) : tensor<?x?xf64>)
    scf.yield %ur, %ui : tensor<?x?xf64>, tensor<?x?xf64>
  }

  %plasma_ok = func.call @sov_plasma_verify_tensor(%sig_r) : (tensor<?xf64>) -> i1

  %hash = tensor.empty() : tensor<32xi8>
  %out_sig = tensor.empty() : tensor<64xi8>
  func.call @sov_blake3_hash_tensor(%sig_r, %hash) : (tensor<?xf64>, tensor<32xi8>) -> ()
  func.call @sov_bifrost_sign_hash(%hash, %sk, %out_sig) : (tensor<32xi8>, tensor<32xi8>, tensor<64xi8>) -> ()

  return %probs, %log_probs, %sig_r, %sig_i, %hash, %out_sig, %plasma_ok, %entropy
    : tensor<?xf64>, tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>,
      tensor<32xi8>, tensor<64xi8>, i1, f64
}

// ═══════════════════════════════════════════════════════════════════
// 4. BOOLEAN SPECTRAL LENS: Watch sum(lambda)=1, regex parse, Lisp dump
// ═══════════════════════════════════════════════════════════════════
func.func @jst_boolean_lens_fused(
  %eigenvals: tensor<?xf64>,
  %frame_hash: tensor<32xi8>,
  %lisp_buffer: tensor<?xi8>
) -> () {
  func.call @sov_boolean_lens_watch(%eigenvals, %frame_hash, %lisp_buffer)
    : (tensor<?xf64>, tensor<32xi8>, tensor<?xi8>) -> ()
  return
}

// ═══════════════════════════════════════════════════════════════════
// 5. MAIN ENTRY: FULL JST PIPELINE
//    SPE → Jordan Stack (depth layers) → Measurement → Reconstruct
// ═══════════════════════════════════════════════════════════════════
func.func @jst_main(
  %signal_real: tensor<?x?xf64>, %signal_imag: tensor<?x?xf64>,
  %frame_real: tensor<?x?x?xf64>, %frame_imag: tensor<?x?x?xf64>,
  %H_stack_real: tensor<?x?x?xf64>, %H_stack_imag: tensor<?x?x?xf64>,
  %dt_stack: tensor<?xf64>,
  %q_set_real: tensor<?x?x?xf64>, %q_set_imag: tensor<?x?x?xf64>,
  %sk: tensor<32xi8>, %pk: tensor<32xi8>,
  %lisp_buffer: tensor<?xi8>
) -> (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?xf64>, tensor<?xf64>, f64, i1) {

  // 1. SPE Encode
  %ev0, %rho_r, %rho_i, %h0, %s0, %p0 =
    func.call @jst_spe_encode_fused(%signal_real, %signal_imag, %frame_real, %frame_imag, %sk, %pk)
    : (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>, tensor<32xi8>, tensor<32xi8>)
    -> (tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>, tensor<32xi8>, tensor<64xi8>, i1)

  func.call @jst_boolean_lens_fused(%ev0, %h0, %lisp_buffer) : (tensor<?xf64>, tensor<32xi8>, tensor<?xi8>) -> ()

  // 2. Jordan Evolution Stack
  %depth = tensor.dim %H_stack_real, 0 : tensor<?x?x?xf64>
  %rho_cur_r, %rho_cur_i = scf.for %k = 0 to %depth step 1
      iter_args(%rr = %rho_r, %ri = %rho_i) -> (tensor<?x?xf64>, tensor<?x?xf64>) {
    %Hkr = tensor.extract_slice %H_stack_real[%k, 0, 0][1,-1,-1][1,1,1] : tensor<?x?x?xf64> -> tensor<?x?xf64>
    %Hki = tensor.extract_slice %H_stack_imag[%k, 0, 0][1,-1,-1][1,1,1] : tensor<?x?x?xf64> -> tensor<?x?xf64>
    %dtk = tensor.extract %dt_stack[%k] : tensor<?xf64>
    %nr, %ni, %hk, %sk2, %pk2 =
      func.call @jst_jordan_block_fused(%rr, %ri, %Hkr, %Hki, %dtk, %sk, %pk)
      : (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?xf64>, f64,
         tensor<32xi8>, tensor<32xi8>) -> (tensor<?x?xf64>, tensor<?x?xf64>, tensor<32xi8>, tensor<64xi8>, i1)
    %evk, %_ = func.call @sov_spe_decode_frame(%nr, %ni, %frame_real, %frame_imag)
      : (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>) -> (tensor<?xf64>, tensor<?x?xf64>)
    func.call @jst_boolean_lens_fused(%evk, %hk, %lisp_buffer) : (tensor<?xf64>, tensor<32xi8>, tensor<?xi8>) -> ()
    scf.yield %nr, %ni : tensor<?x?xf64>, tensor<?x?xf64>
  }

  // 3. Measurement Head
  %probs, %log_probs, %sig_r, %sig_i, %hm, %sm, %pm, %ent =
    func.call @jst_measurement_head_fused(%rho_cur_r, %rho_cur_i,
      %q_set_real, %q_set_imag, %frame_real, %frame_imag, %sk, %pk)
    : (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>,
       tensor<?x?x?xf64>, tensor<?x?x?xf64>, tensor<32xi8>, tensor<32xi8>)
    -> (tensor<?xf64>, tensor<?xf64>, tensor<?x?xf64>, tensor<?x?xf64>,
        tensor<32xi8>, tensor<64xi8>, i1, f64)

  %evf, %_2 = func.call @sov_spe_decode_frame(%sig_r, %sig_i, %frame_real, %frame_imag)
    : (tensor<?x?xf64>, tensor<?x?xf64>, tensor<?x?x?xf64>, tensor<?x?x?xf64>) -> (tensor<?xf64>, tensor<?x?xf64>)
  func.call @jst_boolean_lens_fused(%evf, %hm, %lisp_buffer) : (tensor<?xf64>, tensor<32xi8>, tensor<?xi8>) -> ()

  %final_plasma = arith.andi %p0, %pm : i1

  return %sig_r, %sig_i, %probs, %log_probs, %ent, %final_plasma
    : tensor<?x?xf64>, tensor<?x?xf64>, tensor<?xf64>, tensor<?xf64>, f64, i1
}

} // module @jst_sovereign_pipeline

// ═══════════════════════════════════════════════════════════════════
// MLIR LOWERING PIPELINE
// ═══════════════════════════════════════════════════════════════════
// ARM64 SVE2
// mlir-opt --pass-pipeline="builtin.module(linalg-fuse-elementwise-ops,
//   linalg-tile{target=linalg.matmul,sizes=[64,64,64]},
//   affine-loop-fusion,linalg-bufferize,finalizing-bufferize,
//   vectorize,convert-vector-to-scf,convert-linalg-to-loops,
//   convert-scf-to-cf,convert-cf-to-llvm,convert-func-to-llvm)" \
//   jst_sovereign_pipeline.mlir | mlir-translate --mlir-to-llvmir | \
// llc -mtriple=aarch64-linux-gnu -mattr=+sve2,+aes,+sha3 -O3 -filetype=obj -o jst_arm64.o
//
// x86_64 AVX-512
// llc -mtriple=x86_64-linux-gnu -mattr=+avx512f,+avx512vl,+gfni,+vaes -O3 -filetype=obj -o jst_x86.o
//
// NVIDIA PTX
// llc -mtriple=nvptx64-nvidia-cuda -mattr=+ptx80 -O3 -filetype=obj -o jst_ptx.o
//
// AMD SPIR-V (ROCm)
// llc -mtriple=amdgcn-amd-amdhsa -mattr=+gfx90a -O3 -filetype=obj -o jst_amdgpu.o
//
// Static link
// lld --no-undefined --strip-all -z max-page-size=4096 -e _start \
//     jst_arm64.o sov_monster_arm64.o start.o -o jst_sovereign_arm64