//==================================================================== // 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) -> i1 func.func private @sov_bifrost_sign_hash(tensor<32xi8>, tensor<32xi8>, tensor<64xi8>) -> () func.func private @sov_blake3_hash_tensor(tensor, tensor<32xi8>) -> () func.func private @sov_zmexp_scaling_squaring(tensor, tensor, f64) -> (tensor, tensor) func.func private @sov_spe_encode_frame(tensor, tensor, tensor, tensor) -> (tensor, tensor, tensor) func.func private @sov_spe_decode_frame(tensor, tensor, tensor, tensor) -> (tensor, tensor) func.func private @sov_boolean_lens_watch(tensor, tensor<32xi8>, tensor) -> () // ═══════════════════════════════════════════════════════════════════ // 1. FUSED SPE ENCODER: Signal -> Frame Coeffs -> Eigenvalues -> Density // c_i = _HS lambda = softmax(Re(c)) rho = sum(lambda_i * psi_i) // ═══════════════════════════════════════════════════════════════════ func.func @jst_spe_encode_fused( %signal_real: tensor, %signal_imag: tensor, %frame_real: tensor, %frame_imag: tensor, %sk: tensor<32xi8>, %pk: tensor<32xi8> ) -> (tensor, tensor, tensor, 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, tensor, tensor, tensor) -> (tensor, tensor, tensor) %plasma_ok = func.call @sov_plasma_verify_tensor(%rho_real) : (tensor) -> i1 %hash = tensor.empty() : tensor<32xi8> %sig = tensor.empty() : tensor<64xi8> func.call @sov_blake3_hash_tensor(%rho_real, %hash) : (tensor, 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, tensor, tensor, 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, %rho_imag: tensor, %H_real: tensor, %H_imag: tensor, %dt: f64, %sk: tensor<32xi8>, %pk: tensor<32xi8> ) -> (tensor, tensor, 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, tensor, f64) -> (tensor, tensor) // Ut = U† (conjugate transpose) %Ut_real = linalg.transpose ins(%U_real : tensor) outs(tensor) : tensor -> tensor %Ut_imag_t = linalg.transpose ins(%U_imag : tensor) outs(tensor) : tensor -> tensor %Ut_imag = linalg.map { arith.negf } ins(%Ut_imag_t : tensor) outs(tensor) // 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 %tmp_real = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor) -> tensor %tmp_imag = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor) -> tensor // complex matmul: (Ur+iUi)(rr+iri) = Ur*rr-Ui*ri + i(Ur*ri+Ui*rr) %tr = linalg.matmul ins(%U_real, %rho_real : tensor, tensor) outs(%tmp_real : tensor) -> tensor %ti = linalg.matmul ins(%U_real, %rho_imag : tensor, tensor) outs(%tmp_imag : tensor) -> tensor %rho_out_r = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor) -> tensor %rho_out_i = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%n,%n) : tensor) -> tensor %orr = linalg.matmul ins(%tr, %Ut_real : tensor, tensor) outs(%rho_out_r : tensor) -> tensor %ori = linalg.matmul ins(%ti, %Ut_real : tensor, tensor) outs(%rho_out_i : tensor) -> tensor %plasma_ok = func.call @sov_plasma_verify_tensor(%orr) : (tensor) -> i1 %hash = tensor.empty() : tensor<32xi8> %sig = tensor.empty() : tensor<64xi8> func.call @sov_blake3_hash_tensor(%orr, %hash) : (tensor, 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, tensor, 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, %rho_imag: tensor, %q_set_real: tensor, %q_set_imag: tensor, %frame_real: tensor, %frame_imag: tensor, %sk: tensor<32xi8>, %pk: tensor<32xi8> ) -> (tensor, tensor, tensor, tensor, tensor<32xi8>, tensor<64xi8>, i1, f64) { %n = tensor.dim %q_set_real, 0 : tensor %rank = tensor.dim %rho_real, 0 : tensor %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) -> tensor // 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) { %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 %qi = tensor.extract %q_set_imag[%k, %i, %j] : tensor %rr = tensor.extract %rho_real[%i, %j] : tensor %ri = tensor.extract %rho_imag[%i, %j] : tensor // 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 scf.yield %p_up : tensor } // Normalise %sum_p = linalg.reduce ins(%probs_raw : tensor) outs(tensor.empty() : tensor) dimensions=[0] { ^bb0(%a: f64, %b: f64): linalg.yield (arith.addf %a, %b) : f64 } %sum_v = tensor.extract %sum_p[] : tensor %has_m = arith.cmpf ogt, %sum_v, %c0_f64 : i1 %probs = scf.if %has_m -> (tensor) { %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) outs(tensor.empty(%n) : tensor) scf.yield %sc : tensor } 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) -> tensor scf.yield %u : tensor } // 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) outs(tensor.empty(%n) : tensor) %h_terms = linalg.map { ^bb0(%p: f64, %l: f64): linalg.yield (arith.mulf %p, (arith.negf %l)) : f64 } ins(%probs, %log_probs : tensor, tensor) outs(tensor.empty(%n) : tensor) %h_sum = linalg.reduce ins(%h_terms : tensor) outs(tensor.empty() : tensor) dimensions=[0] { ^bb0(%a: f64, %b: f64): linalg.yield (arith.addf %a, %b) : f64 } %entropy = tensor.extract %h_sum[] : tensor // Reconstruct: signal = sum(p_k * psi_k) %sr_0 = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%rank,%rank) : tensor) -> tensor %si_0 = linalg.fill ins(%c0_f64 : f64) outs(tensor.empty(%rank,%rank) : tensor) -> tensor %sig_r, %sig_i = scf.for %k = 0 to %n step 1 iter_args(%sr = %sr_0, %si = %si_0) -> (tensor, tensor) { %pk = tensor.extract %probs[%k] : tensor %fr = tensor.extract_slice %frame_real[%k, 0, 0][1, %rank, %rank][1, 1, 1] : tensor -> tensor %fi = tensor.extract_slice %frame_imag[%k, 0, 0][1, %rank, %rank][1, 1, 1] : tensor -> tensor %ur = linalg.map { ^bb0(%acc: f64, %f: f64): linalg.yield (arith.addf %acc, (arith.mulf %pk, %f)) : f64 } ins(%sr, %fr : tensor, tensor) outs(tensor.empty(%rank,%rank) : tensor) %ui = linalg.map { ^bb0(%acc: f64, %f: f64): linalg.yield (arith.addf %acc, (arith.mulf %pk, %f)) : f64 } ins(%si, %fi : tensor, tensor) outs(tensor.empty(%rank,%rank) : tensor) scf.yield %ur, %ui : tensor, tensor } %plasma_ok = func.call @sov_plasma_verify_tensor(%sig_r) : (tensor) -> i1 %hash = tensor.empty() : tensor<32xi8> %out_sig = tensor.empty() : tensor<64xi8> func.call @sov_blake3_hash_tensor(%sig_r, %hash) : (tensor, 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, tensor, tensor, tensor, 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, %frame_hash: tensor<32xi8>, %lisp_buffer: tensor ) -> () { func.call @sov_boolean_lens_watch(%eigenvals, %frame_hash, %lisp_buffer) : (tensor, tensor<32xi8>, tensor) -> () return } // ═══════════════════════════════════════════════════════════════════ // 5. MAIN ENTRY: FULL JST PIPELINE // SPE → Jordan Stack (depth layers) → Measurement → Reconstruct // ═══════════════════════════════════════════════════════════════════ func.func @jst_main( %signal_real: tensor, %signal_imag: tensor, %frame_real: tensor, %frame_imag: tensor, %H_stack_real: tensor, %H_stack_imag: tensor, %dt_stack: tensor, %q_set_real: tensor, %q_set_imag: tensor, %sk: tensor<32xi8>, %pk: tensor<32xi8>, %lisp_buffer: tensor ) -> (tensor, tensor, tensor, tensor, 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, tensor, tensor, tensor, tensor<32xi8>, tensor<32xi8>) -> (tensor, tensor, tensor, tensor<32xi8>, tensor<64xi8>, i1) func.call @jst_boolean_lens_fused(%ev0, %h0, %lisp_buffer) : (tensor, tensor<32xi8>, tensor) -> () // 2. Jordan Evolution Stack %depth = tensor.dim %H_stack_real, 0 : tensor %rho_cur_r, %rho_cur_i = scf.for %k = 0 to %depth step 1 iter_args(%rr = %rho_r, %ri = %rho_i) -> (tensor, tensor) { %Hkr = tensor.extract_slice %H_stack_real[%k, 0, 0][1,-1,-1][1,1,1] : tensor -> tensor %Hki = tensor.extract_slice %H_stack_imag[%k, 0, 0][1,-1,-1][1,1,1] : tensor -> tensor %dtk = tensor.extract %dt_stack[%k] : tensor %nr, %ni, %hk, %sk2, %pk2 = func.call @jst_jordan_block_fused(%rr, %ri, %Hkr, %Hki, %dtk, %sk, %pk) : (tensor, tensor, tensor, tensor, f64, tensor<32xi8>, tensor<32xi8>) -> (tensor, tensor, tensor<32xi8>, tensor<64xi8>, i1) %evk, %_ = func.call @sov_spe_decode_frame(%nr, %ni, %frame_real, %frame_imag) : (tensor, tensor, tensor, tensor) -> (tensor, tensor) func.call @jst_boolean_lens_fused(%evk, %hk, %lisp_buffer) : (tensor, tensor<32xi8>, tensor) -> () scf.yield %nr, %ni : tensor, tensor } // 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, tensor, tensor, tensor, tensor, tensor, tensor<32xi8>, tensor<32xi8>) -> (tensor, tensor, tensor, tensor, tensor<32xi8>, tensor<64xi8>, i1, f64) %evf, %_2 = func.call @sov_spe_decode_frame(%sig_r, %sig_i, %frame_real, %frame_imag) : (tensor, tensor, tensor, tensor) -> (tensor, tensor) func.call @jst_boolean_lens_fused(%evf, %hm, %lisp_buffer) : (tensor, tensor<32xi8>, tensor) -> () %final_plasma = arith.andi %p0, %pm : i1 return %sig_r, %sig_i, %probs, %log_probs, %ent, %final_plasma : tensor, tensor, tensor, tensor, 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