Removed all inline for (0..total) for either builtin or for loop without inline

This is to prevent giant binary for Tensor with a lot of Scalar

src/Dimensions.zig

@@ -51,17 +51,17 @@ data: std.EnumArray(Dimension, comptime_int),
 /// Unspecified dimensions default to 0.
 pub fn init(comptime init_val: ArgOpts) Self {
     var s = Self{ .data = std.EnumArray(Dimension, comptime_int).initFill(0) };
-    inline for (std.meta.fields(@TypeOf(init_val))) |f|
+    for (std.meta.fields(@TypeOf(init_val))) |f|
         s.data.set(@field(Dimension, f.name), @field(init_val, f.name));
     return s;
 }
 
 pub fn initFill(comptime val: comptime_int) Self {
-    return .{ .data = std.EnumArray(Dimension, comptime_int).initFill(val) };
+    comptime return .{ .data = std.EnumArray(Dimension, comptime_int).initFill(val) };
 }
 
 pub fn get(comptime self: Self, comptime key: Dimension) comptime_int {
-    return self.data.get(key);
+    comptime return self.data.get(key);
 }
 
 pub fn set(comptime self: *Self, comptime key: Dimension, comptime val: i8) void {
@@ -70,40 +70,40 @@ pub fn set(comptime self: *Self, comptime key: Dimension, comptime val: i8) void
 
 pub fn argsOpt(self: Self) ArgOpts {
     var args: ArgOpts = undefined;
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         @field(args, @tagName(d)) = self.get(d);
-    return args;
+    comptime return args;
 }
 
 /// Add exponents component-wise. Used internally by `mul`.
 pub fn add(comptime a: Self, comptime b: Self) Self {
     var result = Self.initFill(0);
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) + b.get(d));
-    return result;
+    comptime return result;
 }
 
 /// Subtract exponents component-wise. Used internally by `div`.
 pub fn sub(comptime a: Self, comptime b: Self) Self {
     var result = Self.initFill(0);
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) - b.get(d));
-    return result;
+    comptime return result;
 }
 
 /// Multiply exponents by a scalar integer. Used internally by `pow` in Scalar.
 pub fn scale(comptime a: Self, comptime exp: comptime_int) Self {
     var result = Self.initFill(0);
-    inline for (std.enums.values(Dimension)) |d|
+    for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) * exp);
-    return result;
+    comptime return result;
 }
 
 pub fn div(comptime a: Self, comptime exp: comptime_int) Self {
     var result = Self.initFill(0);
     inline for (std.enums.values(Dimension)) |d|
         result.set(d, a.get(d) / exp);
-    return result;
+    comptime return result;
 }
 
 /// Returns true if every dimension exponent is equal. Used to enforce type compatibility in `add`, `sub`, `to`.

src/Scales.zig

@@ -65,7 +65,7 @@ pub const UnitScale = enum(isize) {
     }
 
     pub inline fn getFactor(self: @This()) comptime_float {
-        return comptime switch (self) {
+        comptime return switch (self) {
             // Standard SI Exponents
             inline .P, .T, .G, .M, .k, .h, .da, .none, .d, .c, .m, .u, .n, .p, .f => std.math.pow(f64, 10.0, @floatFromInt(@intFromEnum(self))),
 
@@ -83,7 +83,7 @@ pub const UnitScale = enum(isize) {
             inline .lb => 453.59237,
             inline .st => 6350.29318,
 
-            inline else => @floatFromInt(@intFromEnum(self)),
+            else => @floatFromInt(@intFromEnum(self)),
         };
     }
 };
@@ -103,11 +103,11 @@ pub fn init(comptime init_val: ArgOpts) Self {
         else
             s.data.set(@field(Dimension, f.name), @field(init_val, f.name));
     }
-    return s;
+    return comptime s;
 }
 
 pub fn initFill(comptime val: UnitScale) Self {
-    return comptime .{ .data = std.EnumArray(Dimension, UnitScale).initFill(val) };
+    comptime return .{ .data = std.EnumArray(Dimension, UnitScale).initFill(val) };
 }
 
 pub fn get(comptime self: Self, comptime key: Dimension) UnitScale {
@@ -115,7 +115,7 @@ pub fn get(comptime self: Self, comptime key: Dimension) UnitScale {
 }
 
 pub fn set(comptime self: *Self, comptime key: Dimension, comptime val: UnitScale) void {
-    comptime self.data.set(key, val);
+    self.data.set(key, val);
 }
 
 pub fn argsOpt(self: Self) ArgOpts {
@@ -144,5 +144,5 @@ pub inline fn getFactor(comptime s: Self, comptime d: Dimensions) comptime_float
                 factor /= base;
         }
     }
-    return comptime factor;
+    comptime return factor;
 }

src/Tensor.zig

@@ -8,14 +8,14 @@ const Dimension = Dimensions.Dimension;
 // Comptime utilities
 // ─────────────────────────────────────────────────────────────────────────────
 
-pub fn shapeTotal(comptime shape: []const usize) usize {
+pub fn shapeTotal(comptime shape: []const comptime_int) usize {
-    var t: usize = 1;
+    var t: comptime_int = 1;
     for (shape) |s| t *= s;
     return t;
 }
 
 /// Check if two shapes are strictly identical.
-pub fn shapeEql(comptime a: []const usize, comptime b: []const usize) bool {
+pub fn shapeEql(comptime a: []const comptime_int, comptime b: []const comptime_int) bool {
     if (a.len != b.len) return false;
     for (a, 0..) |v, i|
         if (v != b[i]) return false;
@@ -25,21 +25,21 @@ pub fn shapeEql(comptime a: []const usize, comptime b: []const usize) bool {
 /// Row-major (C-order) strides: strides[i] = product(shape[i+1..]).
 ///   e.g. shape {3, 4} → strides {4, 1}
 ///        shape {2, 3, 4} → strides {12, 4, 1}
-pub fn shapeStrides(comptime shape: []const usize) [shape.len]usize {
+pub fn shapeStrides(comptime shape: []const comptime_int) [shape.len]comptime_int {
-    var st: [shape.len]usize = undefined;
+    var st: [shape.len]comptime_int = undefined;
     if (shape.len == 0) return st;
     st[shape.len - 1] = 1;
     if (shape.len > 1) {
-        var i: usize = shape.len - 1;
+        var i: comptime_int = shape.len - 1;
         while (i > 0) : (i -= 1) st[i - 1] = st[i] * shape[i];
     }
     return st;
 }
 
 /// Return a copy of `shape` with the element at `axis` removed.
-pub fn shapeRemoveAxis(comptime shape: []const usize, comptime axis: usize) [shape.len - 1]usize {
+pub fn shapeRemoveAxis(comptime shape: []const comptime_int, comptime axis: comptime_int) [shape.len - 1]comptime_int {
-    var out: [shape.len - 1]usize = undefined;
+    var out: [shape.len - 1]comptime_int = undefined;
-    var j: usize = 0;
+    var j: comptime_int = 0;
     for (shape, 0..) |v, i| {
         if (i != axis) {
             out[j] = v;
@@ -50,8 +50,8 @@ pub fn shapeRemoveAxis(comptime shape: []const usize, comptime axis: usize) [sha
 }
 
 /// Concatenate two compile-time slices.
-pub fn shapeCat(comptime a: []const usize, comptime b: []const usize) [a.len + b.len]usize {
+pub fn shapeCat(comptime a: []const comptime_int, comptime b: []const comptime_int) [a.len + b.len]comptime_int {
-    var out: [a.len + b.len]usize = undefined;
+    var out: [a.len + b.len]comptime_int = undefined;
     for (a, 0..) |v, i| out[i] = v;
     for (b, 0..) |v, i| out[a.len + i] = v;
     return out;
@@ -60,11 +60,11 @@ pub fn shapeCat(comptime a: []const usize, comptime b: []const usize) [a.len + b
 /// Decode a flat row-major index into N-D coordinates.
 /// Called only in comptime contexts (all arguments are comptime).
 pub fn decodeFlatCoords(
-    comptime flat: usize,
+    comptime flat: comptime_int,
-    comptime n: usize,
+    comptime n: comptime_int,
-    comptime strd: [n]usize,
+    comptime strd: [n]comptime_int,
 ) [n]usize {
-    var coords: [n]usize = undefined;
+    var coords: [n]comptime_int = undefined;
     var tmp = flat;
     for (0..n) |i| {
         coords[i] = if (strd[i] == 0) 0 else tmp / strd[i];
@@ -116,7 +116,7 @@ fn finerScales(comptime T1: type, comptime T2: type) Scales {
     const s1: Scales = T1.scales;
     const s2: Scales = T2.scales;
     comptime var out = Scales.initFill(.none);
-    inline for (std.enums.values(Dimension)) |dim| {
+    for (std.enums.values(Dimension)) |dim| {
         const scale1 = comptime s1.get(dim);
         const scale2 = comptime s2.get(dim);
         out.set(dim, if (comptime d1.get(dim) == 0 and d2.get(dim) == 0)
@@ -201,7 +201,7 @@ pub fn Tensor(
     comptime T: type,
     comptime d_opt: Dimensions.ArgOpts,
     comptime s_opt: Scales.ArgOpts,
-    comptime shape_: []const usize,
+    comptime shape_: []const comptime_int,
 ) type {
     comptime {
         if (shape_.len == 0) @compileError("Tensor shape must have at least 1 dimension (rank >= 1).");
@@ -223,10 +223,10 @@ pub fn Tensor(
         pub const ValueType: type = T;
         pub const dims: Dimensions = Dimensions.init(d_opt);
         pub const scales: Scales = Scales.init(s_opt);
-        pub const shape: []const usize = shape_;
+        pub const shape: []const comptime_int = shape_;
-        pub const rank: usize = shape_.len;
+        pub const rank: comptime_int = shape_.len;
-        pub const total: usize = _total;
+        pub const total: comptime_int = _total;
-        pub const strides_arr: [shape_.len]usize = _strides;
+        pub const strides_arr: [shape_.len]comptime_int = _strides;
         pub const ISTENSOR = true;
 
         /// Convert N-D coords (row-major) to flat index — fully comptime.
@@ -359,9 +359,7 @@ pub fn Tensor(
             const rr_base = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
             const rr: Vec = broadcastToVec(RhsNorm, rr_base);
             if (comptime isInt(T)) {
-                var result: Vec = undefined;
+                return .{ .data = @divTrunc(l, rr) };
-                inline for (0..total) |i| result[i] = @divTrunc(l[i], rr[i]);
-                return .{ .data = result };
             } else {
                 return .{ .data = l / rr };
             }
@@ -379,19 +377,27 @@ pub fn Tensor(
             scales.argsOpt(),
             shape_,
         ) {
-            if (comptime isInt(T)) {
+            if (comptime exp == 0) return .{ .data = @splat(1) };
-                var result: Vec = undefined;
+            if (comptime exp == 1) return self;
-                inline for (0..total) |i|
+
-                    result[i] = std.math.powi(T, self.data[i], exp) catch std.math.maxInt(T);
+            var base = self.data;
-                return .{ .data = result };
-            } else {
-                const abs_exp = comptime @abs(exp);
             var result: Vec = @splat(1);
-                comptime var i = 0;
+            comptime var e = @abs(exp);
-                inline while (i < abs_exp) : (i += 1) result *= self.data;
+
-                if (comptime exp < 0) result = @as(Vec, @splat(1)) / result;
+            // $O(\log n)$ Exponentiation by squaring applied to the entire vector
-                return .{ .data = result };
+            inline while (e > 0) {
+                if (e % 2 == 1) {
+                    result = if (comptime isInt(T)) result *| base else result * base;
                 }
+                e /= 2;
+                if (e > 0) {
+                    base = if (comptime isInt(T)) base *| base else base * base;
+                }
+            }
+            if (comptime !isInt(T) and exp < 0) {
+                result = @as(Vec, @splat(1)) / result;
+            }
+            return .{ .data = result };
         }
 
         /// Square root of every element.  All dimension exponents must be even.
@@ -404,15 +410,16 @@ pub fn Tensor(
             if (comptime !dims.isSquare())
                 @compileError("Cannot take sqrt of " ++ dims.str() ++ ": exponents must be even.");
             if (comptime @typeInfo(T) == .float) {
-                return .{ .data = @sqrt(self.data) };
+                return .{ .data = @sqrt(self.data) }; // Float is natively vectorized!
             } else {
-                var result: Vec = undefined;
+                const arr: [total]T = self.data; // Add this!
+                var res_arr: [total]T = undefined;
                 const UnsignedT = @Int(.unsigned, @typeInfo(T).int.bits);
-                inline for (0..total) |i| {
+                for (0..total) |i| {
-                    const v = self.data[i];
+                    const v = arr[i];
-                    result[i] = if (v < 0) 0 else @as(T, @intCast(std.math.sqrt(@as(UnsignedT, @intCast(v)))));
+                    res_arr[i] = if (v < 0) 0 else @as(T, @intCast(std.math.sqrt(@as(UnsignedT, @intCast(v)))));
                 }
-                return .{ .data = result };
+                return .{ .data = res_arr };
             }
         }
 
@@ -433,28 +440,34 @@ pub fn Tensor(
 
             if (comptime Self == ActualDest) return self;
 
-            const ratio = comptime (scales.getFactor(dims) / ActualDest.scales.getFactor(ActualDest.dims));
+            // Run validation checks FIRST before dealing with types
-            const DestT = ActualDest.ValueType;
-            const DestVec = @Vector(total, DestT);
-
-            // If ratio is 1, just handle type conversion
-            if (comptime ratio == 1.0) {
-                if (comptime T == DestT) return .{ .data = self.data };
-                return .{
-                    .data = switch (comptime @typeInfo(DestT)) {
-                        .float => @floatFromInt(self.data), // or @floatCast
-                        .int => @intFromFloat(self.data), // or @intCast
-                        else => unreachable,
-                    },
-                };
-            }
-
             if (comptime !dims.eql(ActualDest.dims))
                 @compileError("Dimension mismatch in to: " ++ dims.str() ++ " vs " ++ ActualDest.dims.str());
             if (comptime Dest.total != 1 and !shapeEql(shape_, Dest.shape))
                 @compileError("Shape mismatch in to: destination type must have the identical shape, or be a scalar.");
 
-            if (comptime Self == ActualDest) return self;
+            const ratio = comptime (scales.getFactor(dims) / ActualDest.scales.getFactor(ActualDest.dims));
+            const DestT = ActualDest.ValueType;
+            const DestVec = @Vector(total, DestT);
+
+            // If ratio is 1, handle type conversion correctly based on BOTH source and dest types
+            if (comptime ratio == 1.0) {
+                const T_info = @typeInfo(T);
+                const Dest_info = @typeInfo(DestT);
+
+                return .{
+                    .data = if (comptime T_info == .int and Dest_info == .int)
+                        @as(DestVec, @intCast(self.data))
+                    else if (comptime T_info == .float and Dest_info == .float)
+                        @as(DestVec, @floatCast(self.data))
+                    else if (comptime T_info == .int and Dest_info == .float)
+                        @as(DestVec, @floatFromInt(self.data))
+                    else if (comptime T_info == .float and Dest_info == .int)
+                        @as(DestVec, @intFromFloat(self.data)) // Or @intFromFloat(@round(self.data)) if you want rounding
+                    else
+                        unreachable,
+                };
+            }
 
             if (comptime T == DestT) {
                 if (comptime @typeInfo(T) == .float)
@@ -466,34 +479,34 @@ pub fn Tensor(
                 } else {
                     const div_val: T = comptime @intFromFloat(@round(1.0 / ratio));
                     const half: T = comptime @divTrunc(div_val, 2);
-                    var result: DestVec = undefined;
+
-                    inline for (0..total) |i| {
+                    if (comptime @typeInfo(T).int.signedness == .unsigned) {
-                        const val = self.data[i];
+                        return .{ .data = @divTrunc(self.data + @as(Vec, @splat(half)), @as(Vec, @splat(div_val))) };
-                        result[i] = if (val >= 0)
+                    } else {
-                            @divTrunc(val + half, div_val)
+                        // Vectorized branchless negative handling
-                        else
+                        const is_pos = self.data >= @as(Vec, @splat(0));
-                            @divTrunc(val - half, div_val);
+                        const offsets = @select(T, is_pos, @as(Vec, @splat(half)), @as(Vec, @splat(-half)));
+                        return .{ .data = @divTrunc(self.data + offsets, @as(Vec, @splat(div_val))) };
                     }
-                    return .{ .data = result };
                 }
             }
 
-            var result: DestVec = undefined;
+            // Cross-type fully vectorized casting with scales
-            inline for (0..total) |i| {
+            const FVec = @Vector(total, f64);
-                const float_val: f64 = switch (comptime @typeInfo(T)) {
+            const float_vec: FVec = switch (comptime @typeInfo(T)) {
-                    .float => @floatCast(self.data[i]),
+                .float => @floatCast(self.data),
-                    .int => @floatFromInt(self.data[i]),
+                .int => @floatFromInt(self.data),
                 else => unreachable,
             };
-                const scaled = float_val * ratio;
+
-                result[i] = switch (comptime @typeInfo(DestT)) {
+            const scaled = float_vec * @as(FVec, @splat(ratio));
-                    .float => @floatCast(scaled),
+
-                    .int => @intFromFloat(@round(scaled)),
+            return switch (comptime @typeInfo(DestT)) {
+                .float => .{ .data = @floatCast(scaled) },
+                .int => .{ .data = @intFromFloat(@round(scaled)) },
                 else => unreachable,
             };
         }
-            return .{ .data = result };
-        }
 
         const CmpResult = if (total == 1) bool else [total]bool;
 
@@ -592,7 +605,7 @@ pub fn Tensor(
             const sa = shapeRemoveAxis(shape_, axis_a);
             const sb = shapeRemoveAxis(OT.shape, axis_b);
             const rs_raw = shapeCat(&sa, &sb);
-            const rs: []const usize = if (rs_raw.len == 0) &.{1} else &rs_raw;
+            const rs: []const comptime_int = if (rs_raw.len == 0) &.{1} else &rs_raw;
             break :blk Tensor(
                 T,
                 dims.add(OT.dims).argsOpt(),
@@ -606,7 +619,7 @@ pub fn Tensor(
             const sa = comptime shapeRemoveAxis(shape_, axis_a);
             const sb = comptime shapeRemoveAxis(OT.shape, axis_b);
             const rs_raw = comptime shapeCat(&sa, &sb);
-            const rs: []const usize = comptime if (rs_raw.len == 0) &.{1} else &rs_raw;
+            const rs: []const comptime_int = comptime if (rs_raw.len == 0) &.{1} else &rs_raw;
 
             const ResultType = Tensor(
                 T,
@@ -617,34 +630,85 @@ pub fn Tensor(
 
             const SelfNorm = Tensor(T, dims.argsOpt(), finerScales(Self, OT).argsOpt(), shape_);
             const OtherNorm = Tensor(T, OT.dims.argsOpt(), finerScales(Self, OT).argsOpt(), OT.shape);
+
             const a_data = if (comptime Self == SelfNorm) self.data else self.to(SelfNorm).data;
             const b_data = if (comptime OT == OtherNorm) other.data else other.to(OtherNorm).data;
 
+            // FAST PATH: Dot Product
+            if (comptime rank == 1 and OT.rank == 1 and axis_a == 0 and axis_b == 0) {
+                if (comptime !isInt(T)) {
+                    return .{ .data = @splat(@reduce(.Add, a_data * b_data)) };
+                } else {
+                    // For integers, we do a vectorized saturating multiply,
+                    // then convert to an array to do a saturating sum
+                    const mul_arr: [total]T = a_data *| b_data;
+                    var acc: T = 0;
+                    for (mul_arr) |val| acc +|= val;
+                    return .{ .data = @splat(acc) };
+                }
+            }
+
+            // --- ZERO-COST COERCION TO ARRAYS FOR RUNTIME INDEXING ---
+            const a_arr: [total]T = a_data;
+            const b_arr: [OT.total]T = b_data;
+
+            // FAST PATH: 2D Matrix Multiplication
+            if (comptime rank == 2 and OT.rank == 2 and axis_a == 1 and axis_b == 0) {
+                const rows = shape_[0];
+                const cols = OT.shape[1];
+                const inner = shape_[1];
+
+                // Create a mutable array for the result, NOT a Tensor struct
+                var res_arr: [ResultType.total]T = undefined;
+
+                for (0..rows) |i| {
+                    for (0..cols) |j| {
+                        var acc: T = 0;
+                        for (0..inner) |id| {
+                            const a_flat = i * _strides[0] + id * _strides[1];
+                            const b_flat = id * OT.strides_arr[0] + j * OT.strides_arr[1];
+
+                            // Use a_arr and b_arr here
+                            if (comptime isInt(T)) acc +|= a_arr[a_flat] *| b_arr[b_flat] else acc += a_arr[a_flat] * b_arr[b_flat];
+                        }
+                        // Write to the array
+                        res_arr[i * cols + j] = acc;
+                    }
+                }
+                // Return the initialized Tensor struct
+                return .{ .data = res_arr };
+            }
+
+            // FALLBACK PATH
             const rs_raw_strides = comptime shapeStrides(&rs_raw);
 
-            var result: ResultType = .{ .data = @splat(0) };
+            // Create a mutable array for the result
+            var result_arr: [ResultType.total]T = undefined;
 
-            inline for (0..ResultType.total) |res_flat| {
+            for (0..ResultType.total) |res_flat| {
-                const res_coords = comptime decodeFlatCoords(res_flat, rs_raw.len, rs_raw_strides);
+                const res_coords = decodeFlatCoords(res_flat, rs_raw.len, rs_raw_strides);
 
-                const a_free: [sa.len]usize = comptime res_coords[0..sa.len].*;
+                var a_free: [sa.len]usize = undefined;
-                const b_free: [sb.len]usize = comptime res_coords[sa.len..].*;
+                for (0..sa.len) |i| a_free[i] = res_coords[i];
+                var b_free: [sb.len]usize = undefined;
+                for (0..sb.len) |i| b_free[i] = res_coords[sa.len + i];
 
                 var acc: T = 0;
-                inline for (0..k) |ki| {
+                for (0..k) |ki| {
-                    const a_coords = comptime insertAxis(rank, axis_a, ki, &a_free);
+                    const a_coords = insertAxis(rank, axis_a, ki, &a_free);
-                    const b_coords = comptime insertAxis(OT.rank, axis_b, ki, &b_free);
+                    const b_coords = insertAxis(OT.rank, axis_b, ki, &b_free);
-                    const a_flat = comptime encodeFlatCoords(&a_coords, rank, _strides);
+                    const a_flat = encodeFlatCoords(&a_coords, rank, _strides);
-                    const b_flat = comptime encodeFlatCoords(&b_coords, OT.rank, OT.strides_arr);
+                    const b_flat = encodeFlatCoords(&b_coords, OT.rank, OT.strides_arr);
 
-                    if (comptime isInt(T))
+                    // Use a_arr and b_arr here
-                        acc +|= a_data[a_flat] *| b_data[b_flat]
+                    if (comptime isInt(T)) acc +|= a_arr[a_flat] *| b_arr[b_flat] else acc += a_arr[a_flat] * b_arr[b_flat];
-                    else
-                        acc += a_data[a_flat] * b_data[b_flat];
                 }
-                result.data[res_flat] = acc;
+                // Write to the array
+                result_arr[res_flat] = acc;
             }
-            return result;
+
+            // Return the initialized Tensor struct
+            return .{ .data = result_arr };
         }
 
         /// 3D Cross Product. Only defined for Rank-1 tensors of length 3.
@@ -724,7 +788,8 @@ pub fn Tensor(
                 }
             } else {
                 try writer.writeAll("(");
-                inline for (0..total) |i| {
+                const max_to_print = 6;
+                inline for (0..@min(total, max_to_print)) |i| {
                     if (i > 0) try writer.writeAll(", ");
                     switch (@typeInfo(T)) {
                         .float, .comptime_float => try writer.printFloat(self.data[i], options),
@@ -736,6 +801,8 @@ pub fn Tensor(
                         }),
                         else => unreachable,
                     }
+                    if (comptime i == max_to_print - 1 and total != max_to_print - 1)
+                        try writer.writeAll(", ...");
                 }
                 try writer.writeAll(")");
             }

src/benchmark.zig

@@ -23,10 +23,10 @@ pub fn main(init: std.process.Init) !void {
 
     try bench_Scalar(&stdout_writer.interface);
     try stdout_writer.flush();
-    // try bench_vsNative(&stdout_writer.interface);
+    try bench_vsNative(&stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
-    // try bench_crossTypeVsNative(&stdout_writer.interface);
+    try bench_crossTypeVsNative(&stdout_writer.interface);
-    // try stdout_writer.flush();
+    try stdout_writer.flush();
     try bench_Vector(&stdout_writer.interface);
     try stdout_writer.flush();
 }