Send back the complete code with all the fixes. Fix each of ...

Prompt

Send back the complete code with all the fixes. Fix each of the listed errors one by one, making sure to actually correct them so that there are 0 errors remaining. Keep the original imports, since the files exist. Write out every single character; do not abbreviate anything. Fix every error. There must be exactly one file. Do not write anything else; just output the complete code, and it must not contain any comments. Never, under any circumstances, use simplified, substitute, dummy, simulated, or fake code. Write the entire file as complete, unabridged, production-ready code in a single code block. It must be 100% error-free, a complete, error-free file. If no list of errors is provided, you must find all the errors and fix them. const std = @import("std"); const builtin = @import("builtin"); const tensor = @import("../core/tensor.zig"); const types = @import("../core/types.zig"); const Tensor = tensor.Tensor; pub const OFTB = struct { pub const FRACTAL_SCALE: f32 = 0.7071067811865476; pub const FRACTAL_SCALE_SQ: f32 = 0.5000000000000001; pub const LOG_DET_JACOBIAN: f32 = 0.0; pub const resonance_order: usize = 8; dim: usize, global_diffusion: bool, layout: ?types.RSFDiffusionLayout, pub fn init(d: usize) OFTB { std.debug.assert(d != 0); std.debug.assert(d <= std.math.maxInt(usize) / 2); return OFTB{ .dim = d, .global_diffusion = false, .layout = null, }; } pub fn initDiffusing(d: usize) !OFTB { return initWithDiffusion(d, true); } pub fn initWithDiffusion(d: usize, global_diffusion: bool) !OFTB { if (d == 0) return error.InvalidDimension; if (d > std.math.maxInt(usize) / 2) return error.DimensionOverflow; if (!global_diffusion) { return OFTB{ .dim = d, .global_diffusion = false, .layout = null }; } const row_len = d * 2; const layout = types.rsfDiffusionLayout(row_len) orelse return error.InvalidDiffusionLayout; if (!tensor.diffusionLayoutIsApplicable(row_len, layout)) return error.InvalidDiffusionLayout; return OFTB{ .dim = d, .global_diffusion = true, .layout = layout }; } pub fn deinit(self: *OFTB) void { self.* = undefined; } pub fn diffusionEnabled(self: OFTB) bool { return self.global_diffusion and self.layout != null; } pub fn diffusionLayout(self: OFTB) ?types.RSFDiffusionLayout { return self.layout; } pub fn rowLen(self: OFTB) usize { return self.dim * 2; } pub fn vectorLen() usize { if (comptime builtin.cpu.arch == .x86_64 and std.Target.x86.featureSetHas(builtin.cpu.features, .avx512f)) { return 16; } return 8; } pub fn applyRotationSliceInPlace(self: OFTB, data: []f32) void { if (self.dim == 0) return; const total = self.dim * 2; if (data.len != total) return; const half = self.dim; const x1 = data[0..half]; const x2 = data[half..][0..half]; const scale: f32 = FRACTAL_SCALE; const VLEN: usize = comptime vectorLen(); var i: usize = 0; while (i + VLEN <= half) : (i += VLEN) { const va: @Vector(VLEN, f32) = x1[i..][0..VLEN].*; const vb: @Vector(VLEN, f32) = x2[i..][0..VLEN].*; const vscale: @Vector(VLEN, f32) = @splat(scale); x1[i..][0..VLEN].* = (va - vb) * vscale; x2[i..][0..VLEN].* = (va + vb) * vscale; } while (i < half) : (i += 1) { const a = x1[i]; const b = x2[i]; x1[i] = (a - b) * scale; x2[i] = (a + b) * scale; } } pub fn applyRotationAdjointSliceInPlace(self: OFTB, data: []f32) void { if (self.dim == 0) return; const total = self.dim * 2; if (data.len != total) return; const half = self.dim; const g1 = data[0..half]; const g2 = data[half..][0..half]; const scale: f32 = FRACTAL_SCALE; const VLEN: usize = comptime vectorLen(); var i: usize = 0; while (i + VLEN <= half) : (i += VLEN) { const va: @Vector(VLEN, f32) = g1[i..][0..VLEN].*; const vb: @Vector(VLEN, f32) = g2[i..][0..VLEN].*; const vscale: @Vector(VLEN, f32) = @splat(scale); g1[i..][0..VLEN].* = (va + vb) * vscale; g2[i..][0..VLEN].* = (vb - va) * vscale; } while (i < half) : (i += 1) { const a = g1[i]; const b = g2[i]; g1[i] = (a + b) * scale; g2[i] = (b - a) * scale; } } pub fn applyDiffusionSliceInPlace(self: OFTB, data: []f32) !void { const layout = self.layout orelse return error.DimensionMismatch; if (data.len != self.dim * 2) return error.DimensionMismatch; if (!self.diffusionEnabled()) return; // f64 accumulation: the f32 butterfly drifts past the 1e-7 involution // bound on long rows (measured 8.9e-7 at row_len = 98304). try tensor.diffuseRowAccurateInPlace(data, layout); } pub fn fastWalshHadamardTransformInPlace(data: []f32) !void { if (data.len == 0 or (data.len & (data.len - 1)) != 0) return error.InvalidDimension; try tensor.hadamardBlockInPlace(data); } pub fn mixRadixBlocksInPlace(self: OFTB, data: []f32) !void { const layout = self.layout orelse return; if (data.len != layout.row_len) return error.DimensionMismatch; // Q_1 = I: the radix mix is a no-op for r = 1. if (layout.radix == 1) return; try tensor.mixRadixBlocksInPlace(data, layout); } pub fn diffuseSliceInPlace(self: OFTB, data: []f32) !void { if (!self.diffusionEnabled()) return; const layout = self.layout.?; if (data.len != layout.row_len) return error.DimensionMismatch; try tensor.globalDiffuseRowStack(data, layout); } pub fn logDetContribution(self: OFTB) f32 { _ = self; return LOG_DET_JACOBIAN; } pub fn rotationIsIdentityAfterOrder(self: OFTB, data: []f32) !bool { if (data.len != self.dim * 2) return error.DimensionMismatch; const copy = try std.heap.smp_allocator.dupe(f32, data); defer std.heap.smp_allocator.free(copy); var check = OFTB{ .dim = self.dim, .global_diffusion = false, .layout = null }; var i: usize = 0; while (i < resonance_order) : (i += 1) check.applyRotationSliceInPlace(copy); for (copy, data) |a, b| if (@abs(a - b) > 1.0e-6) return false; return true; } pub fn applyDiffusionRowsInPlace(self: OFTB, rows: []f32, count: usize) void { const layout = self.layout orelse return; const total = self.dim * 2; var r: usize = 0; while (r < count) : (r += 1) { const base = r * total; if (base + total > rows.len) return; tensor.globalDiffuseRowUnchecked(rows[base .. base + total], layout); } } pub fn forwardInPlace(self: OFTB, x: *Tensor) !void { if (self.dim == 0) return error.InvalidDimension; if (self.dim > std.math.maxInt(usize) / 2) return error.DimensionOverflow; const total = self.dim * 2; if (x.data.len != total) return error.DimensionMismatch; try self.forwardSliceInPlace(x.data); } pub fn forwardSliceInPlace(self: OFTB, data: []f32) !void { if (self.dim == 0) return; const total = self.dim * 2; if (data.len != total) return; self.applyRotationSliceInPlace(data); try self.diffuseSliceInPlace(data); } pub fn backwardInPlace(self: OFTB, grad: []f32) !void { if (self.dim == 0) return error.InvalidDimension; if (self.dim > std.math.maxInt(usize) / 2) return error.DimensionOverflow; const total = self.dim * 2; if (grad.len != total) return error.DimensionMismatch; try self.backwardSliceInPlace(grad); } pub fn backwardSliceInPlace(self: OFTB, grad: []f32) !void { if (self.dim == 0) return; const total = self.dim * 2; if (grad.len != total) return; try self.diffuseSliceInPlace(grad); self.applyRotationAdjointSliceInPlace(grad); } pub fn inverseInPlace(self: OFTB, x: *Tensor) !void { if (self.dim == 0) return error.InvalidDimension; if (self.dim > std.math.maxInt(usize) / 2) return error.DimensionOverflow; const total = self.dim * 2; if (x.data.len != total) return error.DimensionMismatch; try self.backwardSliceInPlace(x.data); } pub fn inverseSliceInPlace(self: OFTB, data: []f32) !void { try self.backwardSliceInPlace(data); } pub fn forwardBackwardFusedInPlace(self: OFTB, activation: []f32, grad: []f32) !void { try self.forwardSliceInPlace(activation); try self.backwardSliceInPlace(grad); } pub fn symplecticReversalInPlace(self: OFTB, activation: []f32, grad: []f32) !void { if (self.dim == 0) return; const total = self.dim * 2; if (activation.len != total or grad.len != total) return; try self.applyDiffusionSliceInPlace(activation); try self.applyDiffusionSliceInPlace(grad); const half = self.dim; const a1 = activation[0..half]; const a2 = activation[half..][0..half]; const g1 = grad[0..half]; const g2 = grad[half..][0..half]; const scale: f32 = FRACTAL_SCALE; const VLEN: usize = comptime vectorLen(); var i: usize = 0; while (i + VLEN <= half) : (i += VLEN) { const wa: @Vector(VLEN, f32) = a1[i..][0..VLEN].*; const wb: @Vector(VLEN, f32) = a2[i..][0..VLEN].*; const wscale: @Vector(VLEN, f32) = @splat(scale); a1[i..][0..VLEN].* = (wa + wb) * wscale; a2[i..][0..VLEN].* = (wb - wa) * wscale; const va: @Vector(VLEN, f32) = g1[i..][0..VLEN].*; const vb: @Vector(VLEN, f32) = g2[i..][0..VLEN].*; g1[i..][0..VLEN].* = (va + vb) * wscale; g2[i..][0..VLEN].* = (vb - va) * wscale; } while (i < half) : (i += 1) { const a = a1[i]; const b = a2[i]; a1[i] = (a + b) * scale; a2[i] = (b - a) * scale; const ga = g1[i]; const gb = g2[i]; g1[i] = (ga + gb) * scale; g2[i] = (gb - ga) * scale; } } pub fn logDeterminantJacobian(self: OFTB) f32 { _ = self; return LOG_DET_JACOBIAN; } pub fn logDeterminantAdjointShift(_: OFTB) f32 { return 1.0; } pub fn isSymplectic(_: OFTB) bool { return true; } pub fn isOrthogonal(_: OFTB) bool { return true; } pub fn diffusionIsInvolution(self: OFTB, data: []const f32, allocator: std.mem.Allocator) !bool { if (!self.diffusionEnabled()) return true; if (data.len != self.dim * 2) return error.DimensionMismatch; const copy = try allocator.dupe(f32, data); defer allocator.free(copy); try self.diffuseSliceInPlace(copy); try self.diffuseSliceInPlace(copy); for (copy, data) |a, b| if (@abs(a - b) > 1.0e-7) return false; return true; } pub fn resonanceOrder(_: OFTB) usize { return resonance_order; } pub fn forwardRows(self: OFTB, rows: *Tensor) !void { if (self.dim == 0) return error.InvalidDimension; if (rows.shape.dims.len != 2) return error.DimensionMismatch; const total = self.dim * 2; if (rows.shape.dims[1] != total) return error.DimensionMismatch; if (!rows.shape.isContiguous()) return error.DimensionMismatch; const row_count = rows.shape.dims[0]; var r: usize = 0; while (r < row_count) : (r += 1) { try self.forwardSliceInPlace(rows.data[r * total ..][0..total]); } } pub fn inverseRows(self: OFTB, rows: *Tensor) !void { if (self.dim == 0) return error.InvalidDimension; if (rows.shape.dims.len != 2) return error.DimensionMismatch; const total = self.dim * 2; if (rows.shape.dims[1] != total) return error.DimensionMismatch; if (!rows.shape.isContiguous()) return error.DimensionMismatch; const row_count = rows.shape.dims[0]; var r: usize = 0; while (r < row_count) : (r += 1) { try self.backwardSliceInPlace(rows.data[r * total ..][0..total]); } } }; pub fn mixForward(oftb: OFTB, x: *Tensor) !void { try oftb.forwardInPlace(x); } pub fn mixBackward(oftb: OFTB, grad: []f32) !void { try oftb.backwardInPlace(grad); } pub fn mixInverse(oftb: OFTB, x: *Tensor) !void { try oftb.inverseInPlace(x); } comptime { _ = OFTB; }