zml: fix float8 <-> float32 conversions, support for Tensor.constant(.{}, .{ .f8 = 1.0})

Mostly:
* fix float8 <-> float32 conversions
* support for `Tensor.constant(.{}, .{ .f8 = 1.0})`

Misc:
* fix small inconsistencies between different versions of sdpa
* better error message for broadcast
* bazelrc: --config=debug

zml/dtype.zig

@@ -10,6 +10,7 @@ test {
 
 pub const DataType = enum(u8) {
     bool,
+    // Note: the support of the float8 is a bit spotty, f8e4m3b11fnuz seems to be the most supported one on Cuda.
     f8e4m3b11fnuz,
     f8e4m3fn,
     f8e4m3fnuz,

zml/floats.zig

@@ -1,3 +1,4 @@
+///! Conversion utilities between different Floating point formats.
 const std = @import("std");
 
 test {
@@ -9,12 +10,15 @@ fn allBitsOne(v: anytype) bool {
 }
 
 fn FloatType(sign_bits: u1, exponent_bits: u8, mantissa_bits: u8, innerT: type) type {
-    return packed struct(std.meta.Int(.unsigned, @intCast(sign_bits + exponent_bits + mantissa_bits))) {
+    const bit_size = sign_bits + exponent_bits + mantissa_bits;
+    if (bit_size % 8 != 0) @compileError("FloatType should have a number of bits divisible by 8");
+
+    return packed struct(std.meta.Int(.unsigned, bit_size)) {
         const Self = @This();
 
-        mantissa: std.meta.Int(.unsigned, @intCast(mantissa_bits)),
+        mantissa: std.meta.Int(.unsigned, mantissa_bits),
-        exponent: std.meta.Int(.unsigned, @intCast(exponent_bits)),
+        exponent: std.meta.Int(.unsigned, exponent_bits),
-        sign: std.meta.Int(.unsigned, @intCast(sign_bits)),
+        sign: std.meta.Int(.unsigned, sign_bits),
 
         pub fn zero() Self {
             return .{
@@ -35,34 +39,51 @@ fn FloatType(sign_bits: u1, exponent_bits: u8, mantissa_bits: u8, innerT: type)
         /// Lossy conversion from f32, similar to @floatCast
         pub fn fromF32(f: f32) Self {
             const vf32: Float32 = @bitCast(f);
-            const precision_loss = @bitSizeOf(@TypeOf(vf32.mantissa)) - mantissa_bits;
+            const exp_bias = comptime Self.expBias();
+            const exponent = @as(u16, vf32.exponent) + exp_bias -| Float32.expBias();
+            const overflow = exponent > std.math.maxInt(std.meta.Int(.unsigned, exponent_bits));
+            if (overflow) {
+                return if (@hasDecl(Self, "inf")) {
+                    return if (vf32.sign == 0) Self.inf() else Self.minusInf();
+                } else Self.nan();
+            }
             return .{
                 .sign = vf32.sign,
-                .exponent = @intCast(vf32.exponent),
+                .exponent = @intCast(exponent),
-                .mantissa = shr(vf32.mantissa, precision_loss),
+                .mantissa = truncMantissa(vf32.mantissa),
             };
         }
 
         /// Lossless conversion to f32.
         pub fn toF32(self: Self) f32 {
             var vf32: Float32 = undefined;
-            const precision_loss = @bitSizeOf(@TypeOf(vf32.mantissa)) - mantissa_bits;
+            if (@hasDecl(Self, "isInf") and self.isInf()) {
+                return if (self.sign == 0) std.math.inf(f32) else -std.math.inf(f32);
+            }
             vf32 = .{
                 .sign = self.sign,
-                .exponent = self.exponent,
+                .exponent = if (self.exponent == 0) 0 else @intCast(@as(i16, self.exponent) + Float32.expBias() - Self.expBias()),
-                .mantissa = @shlExact(@as(@TypeOf(vf32.mantissa), self.mantissa), precision_loss),
+                .mantissa = self.f32Mantissa(),
             };
             return @bitCast(vf32);
         }
 
-        fn truncMantissa(T: type, x: anytype) T {
+        fn truncMantissa(x: anytype) std.meta.FieldType(Self, .mantissa) {
-            const off = @bitSizeOf(@TypeOf(x)) - @bitSizeOf(T);
+            @setRuntimeSafety(false);
+            const off = @bitSizeOf(@TypeOf(x)) - mantissa_bits;
             return @intCast(x >> off);
         }
 
-        fn shr(x: anytype, comptime off: u8) std.meta.Int(.unsigned, @bitSizeOf(@TypeOf(x)) - off) {
+        fn f32Mantissa(self: Self) std.meta.FieldType(Float32, .mantissa) {
-            // @setRuntimeSafety(false);
+            @setRuntimeSafety(false);
-            return @intCast(x >> off);
+            const f32_mantissa_bits = @bitSizeOf(std.meta.FieldType(Float32, .mantissa));
+
+            const Res = std.meta.FieldType(Float32, .mantissa);
+            return @shlExact(@as(Res, self.mantissa), f32_mantissa_bits - mantissa_bits);
+        }
+
+        fn expBias() u8 {
+            return std.math.maxInt(std.meta.Int(.unsigned, exponent_bits - 1));
         }
 
         pub fn format(
@@ -72,7 +93,11 @@ fn FloatType(sign_bits: u1, exponent_bits: u8, mantissa_bits: u8, innerT: type)
             writer: anytype,
         ) !void {
             _ = options;
-            try writer.print("{" ++ fmt ++ "}", .{self.toF32()});
+            if (fmt.len == 1 and fmt[0] == '_') {
+                try writer.print("{{ .sign={}, .exp={}, .mantissa={} }}", .{ self.sign, self.exponent, self.mantissa });
+            } else {
+                try writer.print("{" ++ fmt ++ "}", .{self.toF32()});
+            }
         }
 
         pub usingnamespace innerT;
@@ -124,6 +149,22 @@ pub const Float8E4M3FNUZ = FloatType(1, 4, 3, struct {
     }
 });
 
+test "Float8E4" {
+    const test_case_e4: TestCase = .{
+        .lossless = &[_]f32{ 0, 1.0, -2, 1.0 / 64.0, -128 },
+        .lossy = &[_]f32{3.02344107628},
+    };
+
+    inline for (.{
+        Float8E4M3B11FNUZ,
+        Float8E4M3FN,
+        Float8E4M3FNUZ,
+    }) |Float8T| {
+        try testCustomFloat(Float8T, test_case_e4);
+        try std.testing.expectEqual(0.0, Float8T.fromF32(1.0 / 128.0).toF32());
+    }
+}
+
 pub const Float8E5M2 = FloatType(1, 5, 2, struct {
     pub fn nan() Float8E5M2 {
         return .{
@@ -172,6 +213,16 @@ pub const Float8E5M2FNUZ = FloatType(1, 5, 2, struct {
     }
 });
 
+test "Float8E5" {
+    const test_case_e5: TestCase = .{
+        .lossless = &[_]f32{ 0, 1.0, -2, 1.0 / 128.0, -128 },
+        .lossy = &[_]f32{3.02344107628},
+    };
+    inline for (.{ Float8E5M2, Float8E5M2FNUZ }) |Float8T| {
+        try testCustomFloat(Float8T, test_case_e5);
+    }
+}
+
 pub const BFloat16 = FloatType(1, 8, 7, struct {
     pub fn nan() BFloat16 {
         return .{
@@ -215,18 +266,10 @@ test BFloat16 {
     try std.testing.expectEqual(std.mem.toBytes(BFloat16.inf().neg()), [_]u8{ 0x80, 0xff });
     try std.testing.expectEqual(BFloat16.inf(), BFloat16.fromF32(std.math.inf(f32)));
 
-    const lossless = [_]f32{ 0, -2, 1.0 / 128.0, -1e64, std.math.inf(f32) };
+    try testCustomFloat(BFloat16, .{
-    for (&lossless) |v| {
+        .lossless = &[_]f32{ 0, -2, 1.0 / 128.0, -1e64, std.math.inf(f32) },
-        try std.testing.expectEqual(v, BFloat16.fromF32(v).toF32());
+        .lossy = &[_]f32{3.02344107628},
-    }
+    });
-    const lossy = [_]f32{3.02344107628};
-    for (&lossy) |x| {
-        const y = BFloat16.fromF32(x).toF32();
-        if (!std.math.approxEqRel(f32, x, y, 1e-2)) {
-            std.log.err("expected ~{d}, got {d}", .{ x, y });
-            return error.TestUnexpectedResult;
-        }
-    }
 }
 
 pub fn floatCast(T: type, x: anytype) T {
@@ -235,3 +278,25 @@ pub fn floatCast(T: type, x: anytype) T {
         else => @floatCast(x.toF32()),
     };
 }
+
+const TestCase = struct {
+    lossless: []const f32,
+    lossy: []const f32,
+    tolerance: f32 = 1e-2,
+};
+
+fn testCustomFloat(FloatT: type, test_case: TestCase) !void {
+    for (test_case.lossless) |x| {
+        try std.testing.expectEqual(x, FloatT.fromF32(x).toF32());
+    }
+    for (test_case.lossy) |x| {
+        try expectApproxEqRel(f32, x, FloatT.fromF32(x).toF32(), test_case.tolerance);
+    }
+}
+
+fn expectApproxEqRel(FloatT: type, x: FloatT, y: FloatT, tolerance: FloatT) !void {
+    if (!std.math.approxEqRel(f32, x, y, tolerance)) {
+        std.log.err("expected ~{d}, got {d}", .{ x, y });
+        return error.TestUnexpectedResult;
+    }
+}

zml/mlir.zig

@@ -18,7 +18,7 @@ pub const ext = struct {
         return mlir.RankedTensorType.init(sh.dims(), mlir.ext.Type.fromDType(ctx, sh.dtype())).as(mlir.Type).?;
     }
 
-    pub fn denseElementAttrType(dt: dtype.DataType) mlir.DenseElementsAttributeTypes {
+    pub fn denseElementAttrType(dt: dtype.DataType) ?mlir.DenseElementsAttributeTypes {
         return switch (dt) {
             .bool => .bool,
             .i8 => .i8,
@@ -33,7 +33,7 @@ pub const ext = struct {
             .f16 => .f16,
             .f32 => .f32,
             .f64 => .f64,
-            inline else => |tag| @panic("Unsupported data type: " ++ @tagName(tag)),
+            else => null,
         };
     }
 

zml/nn.zig

@@ -775,7 +775,7 @@ pub fn sdpa(q_: Tensor, k_: Tensor, v_: Tensor, opts: SdpaOpts) Tensor {
     var attn_weights = q.dot(k, .{.hd});
     // log.debug("attn_weights : {}", .{attn_weights});
     // log.debug("attn_mask : {?}", .{attn_mask});
-    if (attn_mask) |mask| attn_weights = attn_weights.add(mask.broadcastLeft(attn_weights.shape()));
+    if (attn_mask) |mask| attn_weights = attn_weights.add(mask.broad(attn_weights.shape()));
 
     attn_weights = attn_weights.convert(.f32);
     if (opts.bias) |bias| {
@@ -988,7 +988,7 @@ pub fn sdpaChunk(q_: Tensor, k_: Tensor, v_: Tensor, opts: SdpaOpts) PartialSoft
     var attn_weights = q.dot(k, .{.hd});
     // log.debug("attn_weights : {}", .{attn_weights});
     // log.debug("attn_mask : {?}", .{attn_mask});
-    if (attn_mask) |mask| attn_weights = attn_weights.add(mask.broadcastLeft(attn_weights.shape()));
+    if (attn_mask) |mask| attn_weights = attn_weights.add(mask.broad(attn_weights.shape()));
 
     if (opts.bias) |bias| {
         attn_weights = attn_weights.add(bias);

zml/nn/cuda.zig

@@ -116,16 +116,16 @@ pub fn sdpa(q_: Tensor, k_: Tensor, v_: Tensor, opts: SdpaOpts) Tensor {
     var bias = Tensor.constant(Shape.init(.{ .b = q.dim(.b), .h = q.dim(.h), .q = q.dim(.q), .k = k.dim(.k) }, q.dtype()), Data.init(q.dtype(), 0));
 
     if (opts.attn_mask) |attn_mask| {
-        const mask = attn_mask.withTags(.{ .q, .k }).broad(bias.shape());
+        bias = bias.add(attn_mask.broad(bias.shape()));
-        bias = bias.add(mask);
     }
     if (opts.bias) |b| {
         bias = bias.add(b);
     }
 
-    const loc = ctx.mlirCtx().location(@src());
+    const mlir_ctx = ctx.mlirCtx();
+    const loc = mlir_ctx.location(@src());
     const op = dialect.stablehlo.custom_call(
-        ctx.mlirCtx(),
+        mlir_ctx,
         &.{ q.value(), k.value(), v.value(), bias.value() },
         .{
             .call_target_name = "__cudnn$fmhaScaleBiasSoftmax",
@@ -135,8 +135,8 @@ pub fn sdpa(q_: Tensor, k_: Tensor, v_: Tensor, opts: SdpaOpts) Tensor {
             .output_operand_aliases = &.{},
         },
         &.{
-            mlir.ext.mlirType(ctx.mlirCtx(), q.shape()),
+            mlir.ext.mlirType(mlir_ctx, q.shape()),
-            mlir.RankedTensorType.init(&.{0}, mlir.IntegerType(.u8).init(ctx.mlirCtx()).as(mlir.Type).?).as(mlir.Type).?,
+            mlir.RankedTensorType.init(&.{0}, mlir.IntegerType(.u8).init(mlir_ctx).as(mlir.Type).?).asType(),
         },
         loc,
     );

zml/tensor.zig

@@ -205,6 +205,7 @@ pub const Tensor = struct {
     pub fn value(self: Tensor) mlir.Value {
         return self.getContext().getValueAndDonation(self)[0];
     }
+
     /// Tell PJRT compiler that memory should be reuse between the two tensors.
     /// The compiler is already aggressively reusing tensors for intermediate results,
     /// but this API allows to reuse buffer between input and output arguments
@@ -1806,13 +1807,20 @@ pub const Tensor = struct {
         const singleton_sh = Shape.init(.{}, val.dtype());
         const ctx = CompilationContext.current().mlirCtx();
         const loc = ctx.location(@src()).namedFmt(ctx, "dims={d}, value={}", .{ sh, val });
-        const result_type = mlir.ext.RankedTensorType.fromShape(ctx, singleton_sh);
+        const res_type = mlir.ext.RankedTensorType.fromShape(ctx, singleton_sh);
-        const elem_type = mlir.ext.denseElementAttrType(val.dtype());
+
-        var constant_op = dialect.stablehlo.constant(ctx, result_type, elem_type, val.constSlice(), loc);
+        var constant_op = if (mlir.ext.denseElementAttrType(val.dtype())) |elem_type|
+            dialect.stablehlo.constant(ctx, res_type, elem_type, val.constSlice(), loc)
+        else blk: {
+            // Not all dtype can be serialized in the IR. If that's not possible, use f32.
+            const val_f32 = val.as(f32);
+            break :blk dialect.stablehlo.constant(ctx, res_type, .f32, std.mem.asBytes(&val_f32), loc);
+        };
+
         if (sh.rank() > 0) {
             constant_op = dialect.stablehlo.broadcast_in_dim(ctx, constant_op.result(0), &.{}, mlir.ext.RankedTensorType.fromShape(ctx, sh).as(mlir.Type).?, loc);
         }
-        return _result(sh, constant_op.result(0));
+        return _result(sh, constant_op.result(0)).convert(val.dtype());
     }
 
     /// Embeds a buffer with concrete values into an Mlir program.
@@ -1820,7 +1828,7 @@ pub const Tensor = struct {
         const ctx = CompilationContext.current().mlirCtx();
         const result_type = mlir.ext.RankedTensorType.fromShape(ctx, val.shape());
         const loc = ctx.location(@src());
-        const elem_type = mlir.ext.denseElementAttrType(val.dtype());
+        const elem_type = mlir.ext.denseElementAttrType(val.dtype()) orelse std.debug.panic("constantTensor expects a dtype that can be serialized to MLIR, like f32 or i32, got {}", .{val.shape()});
         const constant_op = dialect.stablehlo.constant(ctx, result_type, elem_type, val.data, loc);
         return _result(val.shape(), constant_op.result(0));
     }
@@ -1849,9 +1857,13 @@ pub const Tensor = struct {
     /// To avoid use favorise `.broad(shape)` when working with tagged tensors.
     pub fn broadcast(self: Tensor, output_shape: Shape, axes_: []const i64) Tensor {
         const res_shape = output_shape.withDtype(self.dtype());
-
+        stdx.debug.assert(axes_.len == self.rank(), "broadcast expects axes_ to map all axes from self to axes of the output shape, got broadcast({}, {}, {d})", .{ self, output_shape, axes_ });
+        for (0.., axes_) |self_ax, other_ax| {
+            const d = self.dim(self_ax);
+            stdx.debug.assert(d == 1 or d == output_shape.dim(other_ax), "broadcast expects shape axes to either be 1-sized or to match the target size. got broadcast({}, {}, {d}), error on self axis {} mapping to other axis {}", .{ self, output_shape, axes_, self_ax, other_ax });
+        }
         const result_type = mlir.ext.RankedTensorType.fromShape(self.getContext().mlirCtx(), res_shape).as(mlir.Type).?;
-        const loc = self.getContext().mlirCtx().location(@src()).namedFmt(self.getContext().mlirCtx(), "broadcast({any}, axes={d})", .{ res_shape, axes_ });
+        const loc = self.getContext().mlirCtx().location(@src()).namedFmt(self.getContext().mlirCtx(), "broadcast({}, {any}, axes={d})", .{ self, res_shape, axes_ });
         const broadcast_op = dialect.stablehlo.broadcast_in_dim(self.getContext().mlirCtx(), self.value(), axes_, result_type, loc);
 
         return _result(res_shape, broadcast_op.result(0));
@@ -2425,7 +2437,7 @@ pub const Tensor = struct {
 
             break :blk ax;
         };
-        if (indices.count() == 1) {
+        if (indices.count() == 1 and !single_coord) {
             return self.dynamicUpdateSlice1d(updates, coord_axes_.get(0), indices.reshape(.{}));
         }
 
@@ -3131,6 +3143,7 @@ pub const Tensor = struct {
     }
 
     /// Updates a slice of the input Tensor along a specific axis using the given 'update' Tensor, with a start offset known at runtime.
+    /// Note this is the untagged api, if you have tags, you should use dynamicUpdateSlice directly.
     pub fn dynamicUpdateSlice1d(self: Tensor, update: Tensor, axis_: i64, offset: Tensor) Tensor {
         const placeholder = Tensor.scalar(0, .i32);
         var start_indices = [_]Tensor{placeholder} ** MAX_RANK;