1 files changed, 51 insertions, 0 deletions
diff --git a/dom/webgpu/tests/cts/checkout/src/webgpu/shader/execution/expression/unary/af_assignment.cache.ts b/dom/webgpu/tests/cts/checkout/src/webgpu/shader/execution/expression/unary/af_assignment.cache.ts
new file mode 100644
index 0000000000..7c607927fe
--- /dev/null
+++ b/dom/webgpu/tests/cts/checkout/src/webgpu/shader/execution/expression/unary/af_assignment.cache.ts
@@ -0,0 +1,51 @@
+import { kValue } from '../../../../util/constants.js';
+import { abstractFloat } from '../../../../util/conversion.js';
+import { FP } from '../../../../util/floating_point.js';
+import {
+  isSubnormalNumberF64,
+  limitedScalarF64Range,
+  scalarF64Range,
+} from '../../../../util/math.js';
+import { reinterpretU64AsF64 } from '../../../../util/reinterpret.js';
+import { makeCaseCache } from '../case_cache.js';
+
+export const d = makeCaseCache('unary/af_assignment', {
+  abstract: () => {
+    const inputs = [
+      // Values that are useful for debugging the underlying framework/shader code, since it cannot be directly unit tested.
+      0,
+      0.5,
+      0.5,
+      1,
+      -1,
+      reinterpretU64AsF64(0x7000_0000_0000_0001n), // smallest magnitude negative subnormal with non-zero mantissa
+      reinterpretU64AsF64(0x0000_0000_0000_0001n), // smallest magnitude positive subnormal with non-zero mantissa
+      reinterpretU64AsF64(0x600a_aaaa_5555_5555n), // negative subnormal with obvious pattern
+      reinterpretU64AsF64(0x000a_aaaa_5555_5555n), // positive subnormal with obvious pattern
+      reinterpretU64AsF64(0x0010_0000_0000_0001n), // smallest magnitude negative normal with non-zero mantissa
+      reinterpretU64AsF64(0x0010_0000_0000_0001n), // smallest magnitude positive normal with non-zero mantissa
+      reinterpretU64AsF64(0xf555_5555_aaaa_aaaan), // negative normal with obvious pattern
+      reinterpretU64AsF64(0x5555_5555_aaaa_aaaan), // positive normal with obvious pattern
+      reinterpretU64AsF64(0xffef_ffff_ffff_ffffn), // largest magnitude negative normal
+      reinterpretU64AsF64(0x7fef_ffff_ffff_ffffn), // largest magnitude positive normal
+      // WebGPU implementation stressing values
+      ...scalarF64Range(),
+    ];
+    return inputs.map(f => {
+      return {
+        input: abstractFloat(f),
+        expected: isSubnormalNumberF64(f) ? abstractFloat(0) : abstractFloat(f),
+      };
+    });
+  },
+  f32: () => {
+    return limitedScalarF64Range(kValue.f32.negative.min, kValue.f32.positive.max).map(f => {
+      return { input: abstractFloat(f), expected: FP.f32.correctlyRoundedInterval(f) };
+    });
+  },
+  f16: () => {
+    return limitedScalarF64Range(kValue.f16.negative.min, kValue.f16.positive.max).map(f => {
+      return { input: abstractFloat(f), expected: FP.f16.correctlyRoundedInterval(f) };
+    });
+  },
+});