Changed TEnsor to use *const
This commit is contained in:
adrien
parent
bb6dd59b9a
commit
26ff02c50f
143
src/Tensor.zig
143
src/Tensor.zig
@ -139,8 +139,8 @@ inline fn RhsTensorType(comptime T: type, comptime Rhs: type) type {
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/// Take the anyvalue coming from operation and if it is a Tensor, return it.
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/// If it is a float or int, return a Tensor(T, .{}, .{}, .{1}).splat(r).
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inline fn toRhsTensor(comptime T: type, r: anytype) RhsTensorType(T, @TypeOf(r)) {
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const Rhs = @TypeOf(r);
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inline fn toRhsTensor(comptime T: type, r: anytype) *const RhsTensorType(T, @TypeOf(r.*)) {
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const Rhs = @TypeOf(r.*);
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if (comptime isTensor(Rhs)) return r;
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const scalar: T = switch (@typeInfo(Rhs)) {
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.comptime_int => switch (comptime @typeInfo(T)) {
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@ -252,7 +252,7 @@ pub fn Tensor(
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inline fn RhsT(comptime Rhs: type) type {
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return RhsTensorType(T, Rhs);
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}
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inline fn rhs(r: anytype) RhsT(@TypeOf(r)) {
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inline fn rhs(r: anytype) *const RhsT(@TypeOf(r.*)) {
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return toRhsTensor(T, r);
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}
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@ -265,20 +265,20 @@ pub fn Tensor(
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/// Element-wise add. Dimensions must match; scales resolve to finer.
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/// RHS must have the same shape as self, or total == 1 (broadcast).
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pub inline fn add(self: Self, r: anytype) Tensor(
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pub inline fn add(self: *const Self, r: anytype) *const Tensor(
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T,
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dims.argsOpt(),
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finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
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shape_,
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) {
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const rhs_t = rhs(r);
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const RhsType = @TypeOf(r);
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const RhsType = @TypeOf(rhs_t.*);
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if (comptime !dims.eql(RhsType.dims))
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@compileError("Dimension mismatch in add: " ++ dims.str() ++ " vs " ++ RhsType.dims.str());
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if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
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@compileError("Shape mismatch in add: element-wise operations require identical shapes, or a scalar RHS.");
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if (comptime total == 1 and scales.eql(RhsType.scales)) // Here rhs_t has to be {1} too
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return .{ .data = if (comptime isInt(T)) self.data +| rhs_t.data else self.data + rhs_t.data };
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return &.{ .data = if (comptime isInt(T)) self.data +| rhs_t.data else self.data + rhs_t.data };
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const TargetType = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
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const l: Vec = if (comptime Self == TargetType) self.data else self.to(TargetType).data;
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@ -287,25 +287,25 @@ pub fn Tensor(
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const rn = if (comptime RhsType == RhsNorm) rhs_t else rhs_t.to(RhsNorm);
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break :blk broadcastToVec(RhsNorm, rn);
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};
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return .{ .data = if (comptime isInt(T)) l +| rr else l + rr };
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return &.{ .data = if (comptime isInt(T)) l +| rr else l + rr };
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}
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/// Element-wise sub. Dimensions must match; scales resolve to finer.
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/// RHS must have the same shape as self, or total == 1 (broadcast).
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pub inline fn sub(self: Self, r: anytype) Tensor(
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pub inline fn sub(self: *const Self, r: anytype) *const Tensor(
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T,
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dims.argsOpt(),
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finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
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shape_,
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) {
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const rhs_t = rhs(r);
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const RhsType = @TypeOf(rhs_t);
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const RhsType = @TypeOf(rhs_t.*);
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if (comptime !dims.eql(RhsType.dims))
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@compileError("Dimension mismatch in sub: " ++ dims.str() ++ " vs " ++ RhsType.dims.str());
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if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
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@compileError("Shape mismatch in sub: element-wise operations require identical shapes, or a scalar RHS.");
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if (comptime total == 1 and scales.eql(RhsType.scales)) // Here rhs_t has to be {1} too
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return .{ .data = if (comptime isInt(T)) self.data -| rhs_t.data else self.data - rhs_t.data };
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return &.{ .data = if (comptime isInt(T)) self.data -| rhs_t.data else self.data - rhs_t.data };
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const TargetType = Tensor(T, dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), shape_);
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const l: Vec = if (comptime Self == TargetType) self.data else self.to(TargetType).data;
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@ -314,19 +314,19 @@ pub fn Tensor(
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const rn = if (comptime RhsType == RhsNorm) rhs_t else rhs_t.to(RhsNorm);
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break :blk broadcastToVec(RhsNorm, rn);
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};
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return .{ .data = if (comptime isInt(T)) l -| rr else l - rr };
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return &.{ .data = if (comptime isInt(T)) l -| rr else l - rr };
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}
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/// Element-wise multiply. Dimension exponents summed.
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/// Shape {1} RHS is automatically broadcast across all elements.
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pub inline fn mul(self: Self, r: anytype) Tensor(
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pub inline fn mul(self: *const Self, r: anytype) *const Tensor(
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T,
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dims.add(RhsT(@TypeOf(r)).dims).argsOpt(),
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finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
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shape_,
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) {
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const rhs_q = rhs(r);
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const RhsType = @TypeOf(rhs_q);
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const RhsType = @TypeOf(rhs_q.*);
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if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
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@compileError("Shape mismatch in mul: element-wise operations require identical shapes, or a scalar RHS.");
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@ -334,20 +334,20 @@ pub fn Tensor(
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const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
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const l: Vec = if (comptime Self == SelfNorm) self.data else self.to(SelfNorm).data;
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const rr_base = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
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const rr: Vec = broadcastToVec(RhsNorm, rr_base);
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return .{ .data = if (comptime isInt(T)) l *| rr else l * rr };
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const rr: Vec = broadcastToVec(RhsNorm, rr_base.*);
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return &.{ .data = if (comptime isInt(T)) l *| rr else l * rr };
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}
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/// Element-wise divide. Dimension exponents subtracted.
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/// Shape {1} RHS is automatically broadcast across all elements.
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pub inline fn div(self: Self, r: anytype) Tensor(
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pub inline fn div(self: *const Self, r: anytype) *const Tensor(
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T,
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dims.sub(RhsT(@TypeOf(r)).dims).argsOpt(),
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finerScales(Self, RhsT(@TypeOf(r))).argsOpt(),
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shape_,
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) {
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const rhs_q = rhs(r);
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const RhsType = @TypeOf(rhs_q);
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const RhsType = @TypeOf(rhs_q.*);
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if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
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@compileError("Shape mismatch in div: element-wise operations require identical shapes, or a scalar RHS.");
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@ -355,17 +355,17 @@ pub fn Tensor(
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const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
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const l: Vec = if (comptime Self == SelfNorm) self.data else self.to(SelfNorm).data;
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const rr_base = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
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const rr: Vec = broadcastToVec(RhsNorm, rr_base);
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const rr: Vec = broadcastToVec(RhsNorm, rr_base.*);
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if (comptime isInt(T)) {
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return .{ .data = @divTrunc(l, rr) };
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return &.{ .data = @divTrunc(l, rr) };
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} else {
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return .{ .data = l / rr };
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return &.{ .data = l / rr };
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}
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}
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/// Absolute value of every element.
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pub inline fn abs(self: Self) Self {
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return .{ .data = @bitCast(@abs(self.data)) };
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pub inline fn abs(self: *const Self) *const Self {
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return &.{ .data = @bitCast(@abs(self.data)) };
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}
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/// Raise every element to a comptime integer exponent.
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@ -375,7 +375,7 @@ pub fn Tensor(
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scales.argsOpt(),
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shape_,
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) {
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if (comptime exp == 0) return .{ .data = @splat(1) };
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if (comptime exp == 0) return &.{ .data = @splat(1) };
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if (comptime exp == 1) return self;
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var base = self.data;
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@ -395,7 +395,7 @@ pub fn Tensor(
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if (comptime !isInt(T) and exp < 0) {
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result = @as(Vec, @splat(1)) / result;
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}
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return .{ .data = result };
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return &.{ .data = result };
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}
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/// Square root of every element. All dimension exponents must be even.
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@ -408,7 +408,7 @@ pub fn Tensor(
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if (comptime !dims.isSquare())
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@compileError("Cannot take sqrt of " ++ dims.str() ++ ": exponents must be even.");
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if (comptime @typeInfo(T) == .float) {
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return .{ .data = @sqrt(self.data) }; // Float is natively vectorized!
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return &.{ .data = @sqrt(self.data) }; // Float is natively vectorized!
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} else {
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const arr: [total]T = self.data; // Add this!
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var res_arr: [total]T = undefined;
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@ -417,13 +417,13 @@ pub fn Tensor(
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const v = arr[i];
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res_arr[i] = if (v < 0) 0 else @as(T, @intCast(std.math.sqrt(@as(UnsignedT, @intCast(v)))));
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}
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return .{ .data = res_arr };
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return &.{ .data = res_arr };
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}
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}
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/// Negate every element.
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pub inline fn negate(self: Self) Self {
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return .{ .data = -self.data };
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return &.{ .data = -self.data };
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}
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/// Convert to a compatible Tensor type.
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@ -431,9 +431,9 @@ pub fn Tensor(
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/// • Dest.shape must equal self.shape, or Dest.total == 1 (scalar pattern).
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/// • Scale ratio is computed fully at comptime; only a SIMD multiply at runtime.
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pub inline fn to(
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self: Self,
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self: *const Self,
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comptime Dest: type,
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) Tensor(Dest.ValueType, Dest.dims.argsOpt(), Dest.scales.argsOpt(), shape_) {
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) *const Tensor(Dest.ValueType, Dest.dims.argsOpt(), Dest.scales.argsOpt(), shape_) {
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const ActualDest = Tensor(Dest.ValueType, Dest.dims.argsOpt(), Dest.scales.argsOpt(), shape_);
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if (comptime Self == ActualDest) return self;
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@ -449,14 +449,14 @@ pub fn Tensor(
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const DestVec = @Vector(total, DestT);
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if (comptime ratio == 1.0 and T == DestT)
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return .{ .data = self.data };
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return &.{ .data = self.data };
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// If ratio is 1, handle type conversion correctly based on BOTH source and dest types
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if (comptime ratio == 1.0) {
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const T_info = @typeInfo(T);
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const Dest_info = @typeInfo(DestT);
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return .{
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return &.{
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.data = if (comptime T_info == .int and Dest_info == .int)
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@as(DestVec, @intCast(self.data))
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else if (comptime T_info == .float and Dest_info == .float)
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@ -472,22 +472,22 @@ pub fn Tensor(
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if (comptime T == DestT) {
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if (comptime @typeInfo(T) == .float)
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return .{ .data = self.data * @as(DestVec, @splat(@as(T, @floatCast(ratio)))) };
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return &.{ .data = self.data * @as(DestVec, @splat(@as(T, @floatCast(ratio)))) };
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if (comptime ratio >= 1.0) {
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const mult: T = comptime @intFromFloat(@round(ratio));
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return .{ .data = self.data *| @as(Vec, @splat(mult)) };
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return &.{ .data = self.data *| @as(Vec, @splat(mult)) };
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} else {
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const div_val: T = comptime @intFromFloat(@round(1.0 / ratio));
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const half: T = comptime @divTrunc(div_val, 2);
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if (comptime @typeInfo(T).int.signedness == .unsigned) {
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return .{ .data = @divTrunc(self.data + @as(Vec, @splat(half)), @as(Vec, @splat(div_val))) };
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return &.{ .data = @divTrunc(self.data + @as(Vec, @splat(half)), @as(Vec, @splat(div_val))) };
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} else {
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// Vectorized branchless negative handling
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const is_pos = self.data >= @as(Vec, @splat(0));
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const offsets = @select(T, is_pos, @as(Vec, @splat(half)), @as(Vec, @splat(-half)));
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return .{ .data = @divTrunc(self.data + offsets, @as(Vec, @splat(div_val))) };
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return &.{ .data = @divTrunc(self.data + offsets, @as(Vec, @splat(div_val))) };
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}
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}
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}
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@ -503,8 +503,8 @@ pub fn Tensor(
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const scaled = float_vec * @as(FVec, @splat(ratio));
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return switch (comptime @typeInfo(DestT)) {
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.float => .{ .data = @floatCast(scaled) },
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.int => .{ .data = @intFromFloat(@round(scaled)) },
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.float => &.{ .data = @floatCast(scaled) },
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.int => &.{ .data = @intFromFloat(@round(scaled)) },
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else => unreachable,
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};
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}
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@ -516,8 +516,8 @@ pub fn Tensor(
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}
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/// Resolve both sides to the finer scale, broadcasting shape {1} RHS if needed.
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inline fn resolveScalePair(self: Self, rhs_q: anytype) struct { l: Vec, r: Vec } {
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const RhsType = @TypeOf(rhs_q);
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inline fn resolveScalePair(self: *const Self, rhs_q: anytype) struct { l: Vec, r: Vec } {
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const RhsType = @TypeOf(rhs_q.*);
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if (comptime RhsType.total != 1 and !shapeEql(shape_, RhsType.shape))
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@compileError("Shape mismatch in comparison: element-wise operations require identical shapes, or a scalar RHS.");
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@ -526,74 +526,74 @@ pub fn Tensor(
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const rr: Vec = blk: {
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const RhsNorm = Tensor(T, RhsType.dims.argsOpt(), finerScales(Self, RhsType).argsOpt(), RhsType.shape);
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const rn = if (comptime RhsType == RhsNorm) rhs_q else rhs_q.to(RhsNorm);
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break :blk broadcastToVec(RhsNorm, rn);
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break :blk broadcastToVec(RhsNorm, rn.*);
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};
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return .{ .l = l, .r = rr };
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}
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pub inline fn eq(self: Self, r: anytype) CmpResult {
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pub inline fn eq(self: *const Self, r: anytype) CmpResult {
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const rhs_q = rhs(r);
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if (comptime !dims.eql(@TypeOf(rhs_q).dims))
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if (comptime !dims.eql(@TypeOf(rhs_q.*).dims))
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@compileError("Dimension mismatch in ne.");
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const p = resolveScalePair(self, rhs_q);
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return cmpResult(p.l == p.r);
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}
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pub inline fn ne(self: Self, r: anytype) CmpResult {
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pub inline fn ne(self: *const Self, r: anytype) CmpResult {
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const rhs_q = rhs(r);
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if (comptime !dims.eql(@TypeOf(rhs_q).dims))
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if (comptime !dims.eql(@TypeOf(rhs_q.*).dims))
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@compileError("Dimension mismatch in ne.");
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const p = resolveScalePair(self, rhs_q);
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return cmpResult(p.l != p.r);
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}
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pub inline fn gt(self: Self, r: anytype) CmpResult {
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pub inline fn gt(self: *const Self, r: anytype) CmpResult {
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const rhs_q = rhs(r);
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if (comptime !dims.eql(@TypeOf(rhs_q).dims))
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if (comptime !dims.eql(@TypeOf(rhs_q.*).dims))
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@compileError("Dimension mismatch in gt.");
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const p = resolveScalePair(self, rhs_q);
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return cmpResult(p.l > p.r);
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}
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pub inline fn gte(self: Self, r: anytype) CmpResult {
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pub inline fn gte(self: *const Self, r: anytype) CmpResult {
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const rhs_q = rhs(r);
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if (comptime !dims.eql(@TypeOf(rhs_q).dims))
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if (comptime !dims.eql(@TypeOf(rhs_q.*).dims))
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@compileError("Dimension mismatch in gte.");
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const p = resolveScalePair(self, rhs_q);
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return cmpResult(p.l >= p.r);
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}
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pub inline fn lt(self: Self, r: anytype) CmpResult {
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pub inline fn lt(self: *const Self, r: anytype) CmpResult {
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const rhs_q = rhs(r);
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if (comptime !dims.eql(@TypeOf(rhs_q).dims))
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if (comptime !dims.eql(@TypeOf(rhs_q.*).dims))
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@compileError("Dimension mismatch in lt.");
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const p = resolveScalePair(self, rhs_q);
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return cmpResult(p.l < p.r);
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}
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pub inline fn lte(self: Self, r: anytype) CmpResult {
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pub inline fn lte(self: *const Self, r: anytype) CmpResult {
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const rhs_q = rhs(r);
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if (comptime !dims.eql(@TypeOf(rhs_q).dims))
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if (comptime !dims.eql(@TypeOf(rhs_q.*).dims))
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@compileError("Dimension mismatch in lte.");
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const p = resolveScalePair(self, rhs_q);
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return cmpResult(p.l <= p.r);
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}
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/// True iff every element is equal after scale resolution.
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pub inline fn eqAll(self: Self, other: anytype) bool {
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if (comptime !dims.eql(@TypeOf(other).dims))
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pub inline fn eqAll(self: *const Self, other: anytype) bool {
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if (comptime !dims.eql(@TypeOf(other.*).dims))
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@compileError("Dimension mismatch in eqAll.");
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const p = resolveScalePair(self, other);
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return @reduce(.And, p.l == p.r);
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}
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/// True iff any element differs after scale resolution.
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pub inline fn neAll(self: Self, other: anytype) bool {
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pub inline fn neAll(self: *const Self, other: anytype) bool {
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return !self.eqAll(other);
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}
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pub inline fn contract(
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self: Self,
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self: *const Self,
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other: anytype,
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comptime axis_a: usize,
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comptime axis_b: usize,
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@ -607,7 +607,7 @@ pub fn Tensor(
|
||||
const sb = shapeRemoveAxis(OT.shape, axis_b);
|
||||
const rs_raw = shapeCat(&sa, &sb);
|
||||
const rs: []const comptime_int = if (rs_raw.len == 0) &.{1} else &rs_raw;
|
||||
break :blk Tensor(
|
||||
break :blk *const Tensor(
|
||||
T,
|
||||
dims.add(OT.dims).argsOpt(),
|
||||
finerScales(Self, OT).argsOpt(),
|
||||
@ -645,7 +645,7 @@ pub fn Tensor(
|
||||
const mul_arr: [total]T = a_data *| b_data;
|
||||
var acc: T = 0;
|
||||
for (mul_arr) |val| acc +|= val;
|
||||
return .{ .data = @splat(acc) };
|
||||
return &.{ .data = @splat(acc) };
|
||||
}
|
||||
}
|
||||
|
||||
@ -677,7 +677,7 @@ pub fn Tensor(
|
||||
}
|
||||
}
|
||||
// Return the initialized Tensor struct
|
||||
return .{ .data = res_arr };
|
||||
return &.{ .data = res_arr };
|
||||
}
|
||||
|
||||
// FALLBACK PATH
|
||||
@ -709,12 +709,12 @@ pub fn Tensor(
|
||||
}
|
||||
|
||||
// Return the initialized Tensor struct
|
||||
return .{ .data = result_arr };
|
||||
return &.{ .data = result_arr };
|
||||
}
|
||||
|
||||
/// 3D Cross Product. Only defined for Rank-1 tensors of length 3.
|
||||
/// Result dimensions are the sum of input dimensions.
|
||||
pub inline fn cross(self: Self, other: anytype) Tensor(
|
||||
pub inline fn cross(self: *const Self, other: anytype) *const Tensor(
|
||||
T,
|
||||
dims.add(RhsT(@TypeOf(other)).dims).argsOpt(),
|
||||
finerScales(Self, RhsT(@TypeOf(other))).argsOpt(),
|
||||
@ -743,16 +743,16 @@ pub fn Tensor(
|
||||
res[2] = (l[0] * r[1]) - (l[1] * r[0]);
|
||||
}
|
||||
|
||||
return .{ .data = res };
|
||||
return &.{ .data = res };
|
||||
}
|
||||
|
||||
/// Sum of squared elements. Cheaper than length(); use for ordering.
|
||||
pub inline fn lengthSqr(self: Self) T {
|
||||
pub inline fn lengthSqr(self: *const Self) T {
|
||||
return @reduce(.Add, self.data * self.data);
|
||||
}
|
||||
|
||||
/// Euclidean length (L2 norm).
|
||||
pub inline fn length(self: Self) T {
|
||||
pub inline fn length(self: *const Self) T {
|
||||
const sq = self.lengthSqr();
|
||||
if (comptime @typeInfo(T) == .int) {
|
||||
const UnsignedT = @Int(.unsigned, @typeInfo(T).int.bits);
|
||||
@ -762,17 +762,17 @@ pub fn Tensor(
|
||||
}
|
||||
|
||||
/// Product of all elements. Result has shape {1}; dimension exponent * total.
|
||||
pub inline fn product(self: Self) Tensor(
|
||||
pub inline fn product(self: *const Self) *const Tensor(
|
||||
T,
|
||||
dims.scale(@as(comptime_int, total)).argsOpt(),
|
||||
scales.argsOpt(),
|
||||
&.{1},
|
||||
) {
|
||||
return .{ .data = .{@reduce(.Mul, self.data)} };
|
||||
return &.{ .data = .{@reduce(.Mul, self.data)} };
|
||||
}
|
||||
|
||||
pub fn formatNumber(
|
||||
self: Self,
|
||||
self: *const Self,
|
||||
writer: *std.Io.Writer,
|
||||
options: std.fmt.Number,
|
||||
) !void {
|
||||
@ -1393,3 +1393,12 @@ test "Tensor strides_arr correctness" {
|
||||
try std.testing.expectEqual(4, T3.strides_arr[1]);
|
||||
try std.testing.expectEqual(1, T3.strides_arr[2]);
|
||||
}
|
||||
|
||||
test "Big Tensor" {
|
||||
const Tens = Tensor(f32, .{}, .{}, &.{1_000_000});
|
||||
const t1 = Tens.splat(2);
|
||||
const t2 = Tens.splat(3);
|
||||
const t3 = t1.add(t2);
|
||||
|
||||
try std.testing.expectApproxEqAbs(5, t3.data[0], 0.0001);
|
||||
}
|
||||
|
||||
@ -26,11 +26,11 @@ pub fn main(init: std.process.Init) !void {
|
||||
try bench_vsNative(&stdout_writer.interface);
|
||||
try stdout_writer.flush();
|
||||
// try bench_crossTypeVsNative(&stdout_writer.interface);
|
||||
try stdout_writer.flush();
|
||||
try bench_Vector(&stdout_writer.interface);
|
||||
try stdout_writer.flush();
|
||||
try bench_HighDimTensor(&stdout_writer.interface);
|
||||
try stdout_writer.flush();
|
||||
// try stdout_writer.flush();
|
||||
// try bench_Vector(&stdout_writer.interface);
|
||||
// try stdout_writer.flush();
|
||||
// try bench_HighDimTensor(&stdout_writer.interface);
|
||||
// try stdout_writer.flush();
|
||||
}
|
||||
|
||||
fn getTime() Io.Timestamp {
|
||||
@ -180,8 +180,8 @@ fn bench_vsNative(writer: *std.Io.Writer) !void {
|
||||
}
|
||||
}.f;
|
||||
|
||||
const Types = .{ f64, i64, i128, f32, f64 };
|
||||
const TNames = .{ "f64", "i64", "i128", "f32", "f64" };
|
||||
const Types = .{ i32, i64, i128, f32, f64 };
|
||||
const TNames = .{ "i32", "i64", "i128", "f32", "f64" };
|
||||
// Expanded Ops to match bench_Scalar
|
||||
const Ops = .{ "add", "sub", "mul", "div", "abs", "eq", "gt" };
|
||||
|
||||
@ -203,7 +203,6 @@ fn bench_vsNative(writer: *std.Io.Writer) !void {
|
||||
|
||||
const M = Tensor(T, .{}, .{}, &.{1});
|
||||
|
||||
std.mem.doNotOptimizeAway({
|
||||
for (0..SAMPLES) |_| {
|
||||
// --- 1. Benchmark Native ---
|
||||
const n_start = getTime();
|
||||
@ -232,8 +231,8 @@ fn bench_vsNative(writer: *std.Io.Writer) !void {
|
||||
native_total_ns += @as(f64, @floatFromInt(n_start.durationTo(n_end).toNanoseconds()));
|
||||
|
||||
const v_start = getTime();
|
||||
const va = getValT(T, 10);
|
||||
const vb = getValT(T, 2);
|
||||
const va = @Vector(1, T){getValT(T, 10)};
|
||||
const vb = @Vector(1, T){getValT(T, 2)};
|
||||
for (0..ITERS) |_| {
|
||||
// Native logic branch
|
||||
_ = if (comptime std.mem.eql(u8, op_name, "add"))
|
||||
@ -245,7 +244,7 @@ fn bench_vsNative(writer: *std.Io.Writer) !void {
|
||||
else if (comptime std.mem.eql(u8, op_name, "div"))
|
||||
if (comptime @typeInfo(T) == .int) @divTrunc(va, vb) else va / vb
|
||||
else if (comptime std.mem.eql(u8, op_name, "abs"))
|
||||
if (comptime @typeInfo(T) == .int) @abs(va) else @as(T, @abs(va))
|
||||
if (comptime @typeInfo(T) == .int) @as(T, @intCast(@abs(va[0]))) else @abs(va)
|
||||
else if (comptime std.mem.eql(u8, op_name, "eq"))
|
||||
va == vb
|
||||
else if (comptime std.mem.eql(u8, op_name, "gt"))
|
||||
@ -263,26 +262,25 @@ fn bench_vsNative(writer: *std.Io.Writer) !void {
|
||||
for (0..ITERS) |_| {
|
||||
// Scalar logic branch
|
||||
_ = if (comptime std.mem.eql(u8, op_name, "add"))
|
||||
qa.add(qb)
|
||||
&qa.add(&qb)
|
||||
else if (comptime std.mem.eql(u8, op_name, "sub"))
|
||||
qa.sub(qb)
|
||||
&qa.sub(&qb)
|
||||
else if (comptime std.mem.eql(u8, op_name, "mul"))
|
||||
qa.mul(qb)
|
||||
&qa.mul(&qb)
|
||||
else if (comptime std.mem.eql(u8, op_name, "div"))
|
||||
qa.div(qb)
|
||||
&qa.div(&qb)
|
||||
else if (comptime std.mem.eql(u8, op_name, "abs"))
|
||||
qa.abs()
|
||||
&qa.abs()
|
||||
else if (comptime std.mem.eql(u8, op_name, "eq"))
|
||||
qa.eq(qb)
|
||||
&qa.eq(&qb)
|
||||
else if (comptime std.mem.eql(u8, op_name, "gt"))
|
||||
qa.gt(qb)
|
||||
&qa.gt(&qb)
|
||||
else
|
||||
unreachable;
|
||||
}
|
||||
const q_end = getTime();
|
||||
tensor_total_ns += @as(f64, @floatFromInt(q_start.durationTo(q_end).toNanoseconds()));
|
||||
}
|
||||
});
|
||||
|
||||
const avg_n = (native_total_ns / SAMPLES) / @as(f64, @floatFromInt(ITERS));
|
||||
const avg_v = (vector_total_ns / SAMPLES) / @as(f64, @floatFromInt(ITERS));
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user