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README.md +57 -0
build/webgpu/bench.json +98 -0
build/webgpu/manifest.json +78 -0
build/webgpu/metadata.json +19 -0
build/webgpu/test.json +257 -0
build/webgpu/unary-scalar.wgsl.jinja +226 -0
build/webgpu/unary-vec4.wgsl.jinja +219 -0

README.md CHANGED Viewed

@@ -1,3 +1,60 @@
 ---
 license: apache-2.0
 ---

 ---
+library_name: kernels
 license: apache-2.0
+tags:
+- kernel
+- webgpu
+- wgsl
 ---
+# ai.onnx.Sin
+`ai.onnx`  ·  standard ONNX operator  ·  ONNX opset ≥ 7
+## Description
+Computes the sine of each element of the input tensor, elementwise. The output has the same shape and type as the input.
+See the [ONNX `Sin` spec](https://onnx.ai/onnx/operators/onnx__Sin.html) for the reference semantics.
+## Inputs
+| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
+| --- | --- | --- | --- | --- | --- | --- |
+| `input` | `x` | `T` | — | — | Angles in radians whose sine is computed elementwise. | required |
+## Outputs
+| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
+| --- | --- | --- | --- | --- | --- | --- |
+| `output` | `y` | `T` | same as `input` | same as `input` | The sine of the input tensor computed elementwise. | required |
+## Type constraints
+| Variable | Allowed dtypes |
+| --- | --- |
+| `T` | `float32`, `float16` |
+## Files
+- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, provenance)
+- [`manifest.json`](build/webgpu/manifest.json) — the op contract (source of truth)
+- [`test.json`](build/webgpu/test.json) — correctness cases
+- [`bench.json`](build/webgpu/bench.json) — benchmark + tuning cases
+- [`unary-scalar.wgsl.jinja`](build/webgpu/unary-scalar.wgsl.jinja)
+- [`unary-vec4.wgsl.jinja`](build/webgpu/unary-vec4.wgsl.jinja)
+## Use with `@huggingface/kernels`
+The loader derives every required output's shape and logical dtype from the manifest contract and this call.
+It then allocates the result tensors automatically.
+The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model version.
+Replace each `*Data` placeholder with a typed array containing the corresponding input data.
+```js
+import { getKernel } from "@huggingface/kernels";
+const kernel = await getKernel("webgpu-kernels/ai.onnx.Sin", { version: 1 });
+const { y } = await kernel({ x: { data: xData, shape: [3] } });
+```

build/webgpu/bench.json ADDED Viewed

	@@ -0,0 +1,98 @@

+{
+  "op": "ai.onnx.Sin",
+  "cases": [
+    {
+      "name": "1m_f32",
+      "inputs": { "x": { "dtype": "float32", "shape": [1048576] } },
+      "outputs": { "y": { "dtype": "float32", "shape": [1048576] } }
+    },
+    {
+      "name": "sin-f32-1m-vec4",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 1048576 },
+      "inputs": {
+        "x": {
+          "shape": [1048576],
+          "dtype": "float32",
+          "dist": "uniform",
+          "seed": 631,
+          "scale": 6.283185307179586,
+          "offset": -3.141592653589793
+        }
+      },
+      "outputs": { "y": { "shape": [1048576], "dtype": "float32" } },
+      "bench": {
+        "primary": true,
+        "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }]
+      }
+    },
+    {
+      "name": "sin-f32-1m-plus-1-scalar-fallback",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 1048577 },
+      "inputs": {
+        "x": {
+          "shape": [1048577],
+          "dtype": "float32",
+          "dist": "uniform",
+          "seed": 632,
+          "scale": 6.283185307179586,
+          "offset": -3.141592653589793
+        }
+      },
+      "outputs": { "y": { "shape": [1048577], "dtype": "float32" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "sin-f16-1m-vec4",
+      "preset": "smoke",
+      "vars": { "dtype": "float16", "count": 1048576 },
+      "inputs": {
+        "x": {
+          "shape": [1048576],
+          "dtype": "float16",
+          "dist": "uniform",
+          "seed": 633,
+          "scale": 6.283185307179586,
+          "offset": -3.141592653589793
+        }
+      },
+      "outputs": { "y": { "shape": [1048576], "dtype": "float16" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "sin-f16-1m-plus-1-scalar-fallback",
+      "preset": "smoke",
+      "vars": { "dtype": "float16", "count": 1048577 },
+      "inputs": {
+        "x": {
+          "shape": [1048577],
+          "dtype": "float16",
+          "dist": "uniform",
+          "seed": 634,
+          "scale": 6.283185307179586,
+          "offset": -3.141592653589793
+        }
+      },
+      "outputs": { "y": { "shape": [1048577], "dtype": "float16" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "sin-f32-8m-vec4-sustained",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 8388608 },
+      "inputs": {
+        "x": {
+          "shape": [8388608],
+          "dtype": "float32",
+          "dist": "uniform",
+          "seed": 635,
+          "scale": 6.283185307179586,
+          "offset": -3.141592653589793
+        }
+      },
+      "outputs": { "y": { "shape": [8388608], "dtype": "float32" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    }
+  ]
+}

build/webgpu/manifest.json ADDED Viewed

	@@ -0,0 +1,78 @@

+{
+  "domain": "ai.onnx",
+  "name": "Sin",
+  "sinceVersion": 7,
+  "description": "Computes the sine of each element of the input tensor, elementwise. The output has the same shape and type as the input.",
+  "inputs": [{ "role": "input", "dtype": "T", "description": "Angles in radians whose sine is computed elementwise." }],
+  "outputs": [
+    {
+      "role": "output",
+      "dtype": "T",
+      "rank": "ranks.input",
+      "description": "The sine of the input tensor computed elementwise.",
+      "shape": "shapes.input"
+    }
+  ],
+  "typeConstraints": { "T": ["float32", "float16"] },
+  "args": {
+    "x": { "kind": "tensor", "semantic": "input", "role": "input" },
+    "y": { "kind": "tensor", "semantic": "output", "role": "output" }
+  },
+  "tunables": { "WORKGROUP_SIZE": 256 },
+  "variants": [
+    {
+      "id": "same_layout_vec4",
+      "when": ["numel(shapes.x) > 0", "numel(shapes.x) % 4 == 0", "numel(shapes.x) == numel(shapes.y)", "f16Ok(dtypes.T)"],
+      "constants": {
+        "scalar": "dtypes.T",
+        "usesF16": "dtypes.T == \"f16\"",
+        "vectorScalar": "\"vec4<\" ~ dtypes.T ~ \">\""
+      },
+      "passes": [
+        {
+          "id": "main",
+          "name": "Sin.vec4",
+          "source": { "shader": "unary-vec4.wgsl.jinja", "inputs": { "op": "\"sin\"" } },
+          "bindings": [
+            { "name": "x", "arg": "x", "buffer": { "type": "read-only-storage" }, "elementType": "$vectorScalar" },
+            { "name": "y", "arg": "y", "buffer": { "type": "storage" }, "elementType": "$vectorScalar" },
+            {
+              "name": "params",
+              "semantic": "kernel.params",
+              "buffer": { "type": "uniform" },
+              "struct": {
+                "name": "Params",
+                "fields": [{ "name": "count", "type": "u32", "value": "numel(shapes.y) / 4" }]
+              }
+            }
+          ],
+          "dispatch": { "threads": "numel(shapes.y) / 4", "workgroupSize": "tunables.WORKGROUP_SIZE" }
+        }
+      ],
+      "priority": 20
+    },
+    {
+      "id": "elementwise",
+      "when": ["numel(shapes.x) == numel(shapes.y)", "f16Ok(dtypes.T)"],
+      "constants": { "scalar": "dtypes.T", "usesF16": "dtypes.T == \"f16\"" },
+      "passes": [
+        {
+          "id": "main",
+          "name": "Sin",
+          "source": { "shader": "unary-scalar.wgsl.jinja", "inputs": { "op": "\"sin\"", "itemsPerInvocation": 4 } },
+          "bindings": [
+            { "name": "x", "arg": "x", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
+            { "name": "y", "arg": "y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
+            {
+              "name": "params",
+              "semantic": "kernel.params",
+              "buffer": { "type": "uniform" },
+              "struct": { "name": "Params", "fields": [{ "name": "count", "type": "u32", "value": "numel(shapes.y)" }] }
+            }
+          ],
+          "dispatch": { "threads": "ceilDiv(numel(shapes.y), 4)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
+        }
+      ]
+    }
+  ]
+}

build/webgpu/metadata.json ADDED Viewed

	@@ -0,0 +1,19 @@

+{
+  "name": "ai.onnx.Sin",
+  "id": "_ai_onnx_sin_webgpu_c7e0592",
+  "version": 1,
+  "license": "Apache-2.0",
+  "backend": { "type": "webgpu" },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "bench.json": "UOV9gwFw9jnme1cEK/nz1bt9ipju32MueHfRdIRt6es=",
+      "manifest.json": "lvniX/6EzV7L5v6AW9Hj79TOZHWdAqO28c95M26jdTE=",
+      "test.json": "YWbR8aUbOj0MA7AZt4qhnRqG8tegECpDx1fA4HA2ae0=",
+      "unary-scalar.wgsl.jinja": "7jBrpk29+fb0h+uKME4UDlhIALpJxVg/4GCuxsQEEyE=",
+      "unary-vec4.wgsl.jinja": "rNqiHPIA51+d8IiR66QdEzBZZvNtoBvCKZoQeyNCLX8="
+    }
+  },
+  "provenance": { "kernel": { "sha": "2e7068faf55e7f43df740015f6d1ee49391a41c5", "dirty": false } },
+  "webgpu": { "manifestSpec": "1.0", "specialized": true, "opPath": "ops/ai.onnx.Sin" }
+}

build/webgpu/test.json ADDED Viewed

	@@ -0,0 +1,257 @@

+{
+  "op": "ai.onnx.Sin",
+  "cases": [
+    {
+      "name": "f32_values",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [6],
+          "data": { "kind": "values", "values": [-3.0, -1.0, 0.0, 0.5, 1.0, 3.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "f32_subnormal_identity_tail_gpu_gap",
+      "skipGpu": {
+        "category": "permanent",
+        "reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero (f32 and f16); the kernel cannot preserve denormal inputs/outputs bit-exactly."
+      },
+      "provenance": {
+        "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
+        "test": "MathOpTest.SinFloat",
+        "notes": "For tiny finite inputs sin(x) rounds back to x in float32; zero-flushing erases the signed tail."
+      },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [4],
+          "data": { "kind": "values", "values": [-1e-39, -1e-40, 1e-40, 1e-39] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0 } }
+    },
+    {
+      "name": "f32_subnormal_identity_tail_scalar_gpu_gap",
+      "skipGpu": {
+        "category": "permanent",
+        "reason": "Portable WGSL floating-point semantics do not guarantee preservation of the subnormal values required by this fixture. Backend evidence: WebGPU/Metal flushes subnormals to zero (f32 and f16); the kernel cannot preserve denormal inputs/outputs bit-exactly."
+      },
+      "provenance": {
+        "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
+        "test": "MathOpTest.SinFloat",
+        "notes": "Scalar-path companion: subnormal inputs are valid finite Sin outputs."
+      },
+      "inputs": {
+        "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1e-40, 0.0, 1e-40] } }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0 } }
+    },
+    {
+      "name": "f16_values",
+      "inputs": {
+        "x": {
+          "dtype": "float16",
+          "shape": [2, 3],
+          "data": { "kind": "values", "values": [-2.0, -0.5, 0.0, 0.5, 1.0, 2.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [2, 3] } },
+      "tolerance": 0.001
+    },
+    {
+      "name": "large_argument_range_reduction",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [8],
+          "data": {
+            "kind": "values",
+            "values": [1000000.0, 10000000.0, 10000000000000.0, 100000000000000000000.0, -1000000000000000.0, -123456.78, 314159.265, 2500000000.0]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.0001 } }
+    },
+    {
+      "name": "ort_float_opset22",
+      "provenance": {
+        "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
+        "test": "MathOpTest.Sin_Opset22"
+      },
+      "inputs": {
+        "x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [1.1, -1.1, 2.2, -2.2] } }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "ort_nonfinite_values",
+      "provenance": {
+        "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
+        "test": "MathOpTest.Sin_Opset22",
+        "notes": "Extends ORT's Sin opset-22 case with signed infinities, signed zero, and NaN."
+      },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [7],
+          "data": { "kind": "values", "values": ["-Infinity", -1.0, 0.0, 0.0, 1.0, "Infinity", "NaN"] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [7], "tolerance": 0.000001, "allowNaN": true } }
+    },
+    {
+      "name": "onnx_backend_example",
+      "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_sin_example" },
+      "inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } },
+      "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "onnx_backend_sin",
+      "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_sin" },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [3, 4, 5],
+          "data": {
+            "kind": "values",
+            "values": [1.764052391052246, 0.40015721321105957, 0.978738009929657, 2.2408931255340576, 1.8675580024719238, -0.9772778749465942, 0.9500884413719177, -0.15135720372200012, -0.10321885347366333, 0.4105985164642334, 0.14404356479644775, 1.4542734622955322, 0.7610377073287964, 0.12167501449584961, 0.44386324286460876, 0.3336743414402008, 1.4940791130065918, -0.2051582634449005, 0.3130677044391632, -0.8540957570075989, -2.5529897212982178, 0.653618574142456, 0.8644362092018127, -0.7421650290489197, 2.269754648208618, -1.4543657302856445, 0.04575851559638977, -0.18718385696411133, 1.5327792167663574, 1.4693588018417358, 0.154947429895401, 0.37816253304481506, -0.8877857327461243, -1.980796456336975, -0.34791216254234314, 0.15634897351264954, 1.2302906513214111, 1.202379822731018, -0.38732680678367615, -0.302302747964859, -1.0485529899597168, -1.420017957687378, -1.7062702178955078, 1.950775384902954, -0.5096521973609924, -0.4380742907524109, -1.2527953386306763, 0.7774903774261475, -1.6138978004455566, -0.21274028718471527, -0.8954665660858154, 0.38690251111984253, -0.5108051300048828, -1.18063223361969, -0.02818222902715206, 0.4283318817615509, 0.06651721894741058, 0.30247190594673157, -0.6343221068382263, -0.3627411723136902]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [3, 4, 5], "tolerance": 0.00001 } }
+    },
+    {
+      "name": "onnx_backend_sin_example",
+      "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_sin_example" },
+      "inputs": { "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-1.0, 0.0, 1.0] } } },
+      "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 0.00001 } }
+    },
+    {
+      "name": "vec4_f16_lanes",
+      "inputs": {
+        "x": {
+          "dtype": "float16",
+          "shape": [16],
+          "data": {
+            "kind": "values",
+            "values": [-100.0, -50.0, -20.0, -10.0, -6.0, -3.0, -1.5, -0.5, 0.0, 0.5, 1.5, 3.0, 6.0, 10.0, 50.0, 100.0]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [16], "tolerance": 0.001, "relTolerance": 0.002 } }
+    },
+    {
+      "name": "vec4_f32_nonfinite",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [8],
+          "data": { "kind": "values", "values": ["-Infinity", -1.0, 0.0, 0.5, 1.0, "Infinity", "NaN", 2.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.000001, "allowNaN": true } }
+    },
+    {
+      "name": "empty_input_zero_dim",
+      "inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } },
+      "outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } }
+    },
+    {
+      "name": "f16_finite_scalar_fallback_non_mult4",
+      "inputs": {
+        "x": {
+          "dtype": "float16",
+          "shape": [10],
+          "data": { "kind": "values", "values": [-3.0, -2.0, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0, 3.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [10], "tolerance": 0.002, "relTolerance": 0.002 } }
+    },
+    {
+      "name": "f16_finite_vec4_realistic_range",
+      "inputs": {
+        "x": {
+          "dtype": "float16",
+          "shape": [4, 8],
+          "data": {
+            "kind": "values",
+            "values": [-3.140625, -2.75, -2.359375, -1.96875, -1.5703125, -1.179688, -0.785156, -0.392578, 0.0, 0.392578, 0.785156, 1.179688, 1.5703125, 1.96875, 2.359375, 2.75, 3.140625, -3.0, -2.5, -2.0, -1.5, -1.0, -0.5, 0.25, 0.75, 1.25, 1.75, 2.25, 2.5, 2.75, 3.0, 3.125]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [4, 8], "tolerance": 0.002, "relTolerance": 0.002 } }
+    },
+    {
+      "name": "f32_finite_scalar_fallback_non_mult4",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [7],
+          "data": {
+            "kind": "values",
+            "values": [-3.141592653589793, -1.5707963267948966, -0.7853981633974483, 0.0, 0.7853981633974483, 1.5707963267948966, 3.141592653589793]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [7], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "f16_large_argument_range_reduction",
+      "inputs": {
+        "x": {
+          "dtype": "float16",
+          "shape": [8],
+          "data": {
+            "kind": "values",
+            "values": [10000.0, 20000.0, 30000.0, 40000.0, -10000.0, -20000.0, 50000.0, 65504.0]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [8], "tolerance": 0.01, "relTolerance": 0.01 } }
+    },
+    {
+      "name": "f32_reduce_threshold_boundary",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [8],
+          "data": {
+            "kind": "values",
+            "values": [9999.0, 9999.5, 10000.0, 10000.5, 10001.0, -9999.0, -10000.0, -10001.0]
+          }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [8], "tolerance": 0.001 } }
+    },
+    {
+      "name": "f32_vec4_sustained_4096",
+      "provenance": {
+        "notes": "Compact sibling for sustained f32 Sin vec4 benchmarks; keeps generated angles in a modest range while exercising packed throughput."
+      },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [4096],
+          "data": { "kind": "fillFloat32", "sinStep": 0.017, "cosStep": 0.011, "scale": 1.5 }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [4096], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "f32_scalar_tail_4097",
+      "provenance": {
+        "notes": "Compact sibling for the f32 Sin scalar-tail benchmark; odd numel forces the elementwise fallback path."
+      },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [4097],
+          "data": { "kind": "fillFloat32", "sinStep": 0.019, "cosStep": 0.007, "scale": 1.5 }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [4097], "tolerance": 0.000001 } }
+    }
+  ]
+}

build/webgpu/unary-scalar.wgsl.jinja ADDED Viewed

	@@ -0,0 +1,226 @@

+{% macro flat_tail_open() %}
+@compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  // 2D-folded flat index: gid.y carries the high bits when the element count exceeds the
+  // maxComputeWorkgroupsPerDimension limit (the dispatch caps x and spills the rest into y).
+  let invocation = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
+  // Tail-safe scalar x4 keeps vector-like dispatch density without requiring
+  // the logical tensor length (or its storage binding) to be vec4 aligned.
+  let begin = invocation * {{ source.itemsPerInvocation }}u;
+  let end = min(begin + {{ source.itemsPerInvocation }}u, params.count);
+  for (var i = begin; i < end; i = i + 1u) {
+{%- endmacro %}
+{% macro flat_tail_close() %}
+  }
+{% endmacro %}
+// Scalar unary fallback. Each branch retains the operation's numeric hardening,
+// including Payne-Hanek trigonometric range reduction and NaN/overflow guards.
+{% if usesF16 %}
+enable f16;
+{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+{% macro emit_is_nan_f32() %}
+fn is_nan_f32(value: f32) -> bool {
+  let bits = bitcast<u32>(value);
+  return (bits & 0x7f800000u) == 0x7f800000u && (bits & 0x007fffffu) != 0u;
+}{% endmacro %}
+{% macro emit_is_inf_f32() %}
+fn is_inf_f32(value: f32) -> bool {
+  return (bitcast<u32>(value) & 0x7fffffffu) == 0x7f800000u;
+}{% endmacro %}
+{% macro emit_nan_from_inf_f32() %}
+fn nan_from_inf_f32(value: f32) -> f32 {
+  return bitcast<f32>(bitcast<u32>(value) | 0x00400000u);
+}{% endmacro %}
+{% macro emit_reduce_pio2() %}
+fn reduce_pio2(ax: f32) -> Pio2 {
+  let ix = bitcast<u32>(ax);
+  let e = (ix >> 23u) & 0xffu;
+  let mant = (ix & 0x7fffffu) | 0x800000u;   // 24-bit mantissa
+  let p = i32(e) - 150;                        // ax = mant * 2^p
+  // 96-bit accumulator a2:a1:a0 holding (ax * 2/pi  mod 4) * 2^94.
+  var a0 = 0u; var a1 = 0u; var a2 = 0u;
+  for (var k = 0u; k < 14u; k = k + 1u) {
+    let shift = p - 24 * (i32(k) + 1) + 94;
+    if (shift <= -48 || shift >= 96) { continue; }
+    // prod = mant * TWO_OVER_PI[k]  (<= 48 bits) -> phi32:plo
+    let t = TWO_OVER_PI[k];
+    let mlo = mant & 0xffffu; let mhi = mant >> 16u;
+    let tlo = t & 0xffffu;    let thi = t >> 16u;
+    let ll = mlo * tlo;
+    let mid = mlo * thi + mhi * tlo;            // <= 25 bits, no overflow
+    let hh = mhi * thi;
+    let plo = ll + ((mid & 0xffffu) << 16u);
+    let carry = select(0u, 1u, plo < ll);
+    let phi32 = hh + (mid >> 16u) + carry;      // <= 16 bits
+    // Place (phi32:plo) << shift as a 64-bit value (v1:v0) at offset off >= 0.
+    var v0 = 0u; var v1 = 0u; var off = 0;
+    if (shift < 0) {
+      let s = u32(-shift);                      // 1..47
+      if (s < 32u) {
+        v0 = (plo >> s) | (phi32 << (32u - s));
+        v1 = phi32 >> s;
+      } else {
+        v0 = phi32 >> (s - 32u);
+        v1 = 0u;
+      }
+      off = 0;
+    } else {
+      v0 = plo; v1 = phi32; off = shift;        // 0..95
+    }
+    // Spread (v1:v0) across 96-bit limbs starting at bit `off`.
+    let li = off / 32; let b = u32(off % 32);
+    var w0 = 0u; var w1 = 0u; var w2 = 0u;
+    if (b == 0u) { w0 = v0; w1 = v1; w2 = 0u; }
+    else {
+      w0 = v0 << b;
+      w1 = (v1 << b) | (v0 >> (32u - b));
+      w2 = v1 >> (32u - b);
+    }
+    var t0 = 0u; var t1 = 0u; var t2 = 0u;
+    if (li == 0) { t0 = w0; t1 = w1; t2 = w2; }
+    else if (li == 1) { t1 = w0; t2 = w1; }
+    else { t2 = w0; }
+    // 96-bit add acc += t (bits above 2^96 are multiples of 4, so discarded).
+    let n0 = a0 + t0; let c0 = select(0u, 1u, n0 < a0);
+    let n1a = a1 + t1; let c1a = select(0u, 1u, n1a < a1);
+    let n1 = n1a + c0; let c1b = select(0u, 1u, n1 < n1a);
+    let n2 = a2 + t2 + c1a + c1b;
+    a0 = n0; a1 = n1; a2 = n2;
+  }
+  var n = a2 >> 30u;                            // integer part mod 4
+  var phi = f32(a2 & 0x3fffffffu) * (1.0 / 1073741824.0)
+          + f32(a1) * (1.0 / 4611686018427387904.0);   // fraction in [0,1)
+  if (phi >= 0.5) { phi = phi - 1.0; n = n + 1u; }
+  var out: Pio2;
+  out.octant = n & 3u;
+  out.r = phi * PIO2_F;
+  return out;
+}{% endmacro %}
+{% macro emit_reduce_pio2_fast() %}
+fn reduce_pio2_fast(ax: f32) -> Pio2 {
+  // Cody-Waite reduction for ordinary magnitudes. Splitting pi/2 keeps the
+  // residual accurate even when n is thousands; larger inputs use Payne-Hanek.
+  let nFloat = floor(fma(ax, INV_PIO2_F, 0.5));
+  var residual = fma(-nFloat, PIO2_HI_F, ax);
+  residual = fma(-nFloat, PIO2_LO_F, residual);
+  var out: Pio2;
+  out.octant = u32(nFloat) & 3u;
+  out.r = residual;
+  return out;
+}{% endmacro %}
+{% macro emit_trig_reduction_support() %}
+{% set needPreciseTrigCentered = false %}
+{% set needPreciseTrigTwoPi = false %}
+// Backend-stable f32 sine/cosine core.
+//
+// Shader transcendental accuracy is implementation-defined, and some portable
+// backends are only accurate to roughly 1e-4. Each path retains the most accurate
+// available phase representation, reduces it to [-pi, pi], then uses these
+// polynomials. The half-pi core is degree 13 for sine and degree 12 for cosine;
+// truncation error is well below one f32 ULP over its documented interval.
+{% if needPreciseTrigCentered %}
+const PRECISE_TRIG_PI: f32 = 3.141592653589793;
+{% endif %}
+{% if needPreciseTrigTwoPi %}
+const PRECISE_TRIG_TWO_PI: f32 = 6.283185307179586;
+{% endif %}
+{% if needPreciseTrigCentered %}
+const PRECISE_TRIG_HALF_PI: f32 = 1.5707963267948966;
+{% endif %}
+// Input must be in [-pi/2, pi/2]. Returns (cos(x), sin(x)).
+fn precise_sincos_half_pi(x: f32) -> vec2<f32> {
+  let x2 = x * x;
+  var sinPolynomial = 1.6059043836821613e-10;
+  sinPolynomial = fma(sinPolynomial, x2, -2.505210838544172e-8);
+  sinPolynomial = fma(sinPolynomial, x2, 2.7557319223985893e-6);
+  sinPolynomial = fma(sinPolynomial, x2, -1.984126984126984e-4);
+  sinPolynomial = fma(sinPolynomial, x2, 8.333333333333333e-3);
+  sinPolynomial = fma(sinPolynomial, x2, -1.6666666666666666e-1);
+  let sine = x * fma(sinPolynomial, x2, 1.0);
+  var cosPolynomial = 2.08767569878681e-9;
+  cosPolynomial = fma(cosPolynomial, x2, -2.755731922398589e-7);
+  cosPolynomial = fma(cosPolynomial, x2, 2.48015873015873e-5);
+  cosPolynomial = fma(cosPolynomial, x2, -1.388888888888889e-3);
+  cosPolynomial = fma(cosPolynomial, x2, 4.1666666666666664e-2);
+  cosPolynomial = fma(cosPolynomial, x2, -5.0e-1);
+  let cosine = fma(cosPolynomial, x2, 1.0);
+  return vec2<f32>(cosine, sine);
+}
+{% if needPreciseTrigCentered %}
+// Input must be in [-pi, pi]. Returns (cos(x), sin(x)).
+fn precise_sincos_centered(x: f32) -> vec2<f32> {
+  var folded = x;
+  var cosineSign = 1.0;
+  if (folded > PRECISE_TRIG_HALF_PI) {
+    folded = PRECISE_TRIG_PI - folded;
+    cosineSign = -1.0;
+  } else if (folded < -PRECISE_TRIG_HALF_PI) {
+    folded = -PRECISE_TRIG_PI - folded;
+    cosineSign = -1.0;
+  }
+  let value = precise_sincos_half_pi(folded);
+  return vec2<f32>(cosineSign * value.x, value.y);
+}
+{% endif %}
+// 24-bit words of 2/pi used by the exact Payne-Hanek large-input reducer.
+const TWO_OVER_PI: array<u32, 14> = array<u32, 14>(
+  0xa2f983u, 0x6e4e44u, 0x1529fcu, 0x2757d1u, 0xf534ddu, 0xc0db62u,
+  0x95993cu, 0x439041u, 0xfe5163u, 0xabdebbu, 0xc561b7u, 0x246e3au,
+  0x424dd2u, 0xe00649u
+);
+const PIO2_F: f32 = 1.5707963267948966;
+const INV_PIO2_F: f32 = 0.6366197723675814;
+const PIO2_HI_F: f32 = 1.570796251296997;
+const PIO2_LO_F: f32 = 7.549789415861596e-8;
+const REDUCE_THRESHOLD: f32 = 1.0e4;
+struct Pio2 { octant: u32, r: f32 };
+{{ emit_reduce_pio2_fast() }}
+{{ emit_reduce_pio2() }}
+{%- endmacro %}
+{% macro emit_sin_accurate() %}
+fn sin_accurate(x: f32) -> f32 {
+  if (is_nan_f32(x)) {
+    return x;
+  }
+  if (is_inf_f32(x)) {
+    return nan_from_inf_f32(x);
+  }
+  let ax = abs(x);
+  var red: Pio2;
+  if (ax < REDUCE_THRESHOLD) { red = reduce_pio2_fast(ax); }
+  else { red = reduce_pio2(ax); }
+  let reduced = precise_sincos_half_pi(red.r);
+  var s: f32;
+  switch (red.octant) {
+    case 0u: { s = reduced.y; }
+    case 1u: { s = reduced.x; }
+    case 2u: { s = -reduced.y; }
+    default: { s = -reduced.x; }
+  }
+  return select(s, -s, x < 0.0);   // sin is odd
+}{% endmacro %}
+{{ emit_trig_reduction_support() }}
+{{ emit_is_nan_f32() }}
+{{ emit_is_inf_f32() }}
+{{ emit_nan_from_inf_f32() }}
+{{ emit_sin_accurate() }}
+{{ flat_tail_open() }}
+  y[i] = {{ scalar }}(sin_accurate(f32(x[i])));
+{{ flat_tail_close() -}}
+}

build/webgpu/unary-vec4.wgsl.jinja ADDED Viewed

	@@ -0,0 +1,219 @@

+// Loads and stores vec4<T> (128 bits) while retaining scalar per-component
+// arithmetic, including per-component helper calls for guard-heavy operations.
+{% if usesF16 %}
+enable f16;
+{% endif %}
+{{ env.wgsl.resourceDeclarations }}
+{% macro emit_is_nan_f32() %}
+fn is_nan_f32(value: f32) -> bool {
+  let bits = bitcast<u32>(value);
+  return (bits & 0x7f800000u) == 0x7f800000u && (bits & 0x007fffffu) != 0u;
+}{% endmacro %}
+{% macro emit_is_inf_f32() %}
+fn is_inf_f32(value: f32) -> bool {
+  return (bitcast<u32>(value) & 0x7fffffffu) == 0x7f800000u;
+}{% endmacro %}
+{% macro emit_nan_from_inf_f32() %}
+fn nan_from_inf_f32(value: f32) -> f32 {
+  return bitcast<f32>(bitcast<u32>(value) | 0x00400000u);
+}{% endmacro %}
+{% macro emit_reduce_pio2() %}
+fn reduce_pio2(ax: f32) -> Pio2 {
+  let ix = bitcast<u32>(ax);
+  let e = (ix >> 23u) & 0xffu;
+  let mant = (ix & 0x7fffffu) | 0x800000u;   // 24-bit mantissa
+  let p = i32(e) - 150;                        // ax = mant * 2^p
+  // 96-bit accumulator a2:a1:a0 holding (ax * 2/pi  mod 4) * 2^94.
+  var a0 = 0u; var a1 = 0u; var a2 = 0u;
+  for (var k = 0u; k < 14u; k = k + 1u) {
+    let shift = p - 24 * (i32(k) + 1) + 94;
+    if (shift <= -48 || shift >= 96) { continue; }
+    // prod = mant * TWO_OVER_PI[k]  (<= 48 bits) -> phi32:plo
+    let t = TWO_OVER_PI[k];
+    let mlo = mant & 0xffffu; let mhi = mant >> 16u;
+    let tlo = t & 0xffffu;    let thi = t >> 16u;
+    let ll = mlo * tlo;
+    let mid = mlo * thi + mhi * tlo;            // <= 25 bits, no overflow
+    let hh = mhi * thi;
+    let plo = ll + ((mid & 0xffffu) << 16u);
+    let carry = select(0u, 1u, plo < ll);
+    let phi32 = hh + (mid >> 16u) + carry;      // <= 16 bits
+    // Place (phi32:plo) << shift as a 64-bit value (v1:v0) at offset off >= 0.
+    var v0 = 0u; var v1 = 0u; var off = 0;
+    if (shift < 0) {
+      let s = u32(-shift);                      // 1..47
+      if (s < 32u) {
+        v0 = (plo >> s) | (phi32 << (32u - s));
+        v1 = phi32 >> s;
+      } else {
+        v0 = phi32 >> (s - 32u);
+        v1 = 0u;
+      }
+      off = 0;
+    } else {
+      v0 = plo; v1 = phi32; off = shift;        // 0..95
+    }
+    // Spread (v1:v0) across 96-bit limbs starting at bit `off`.
+    let li = off / 32; let b = u32(off % 32);
+    var w0 = 0u; var w1 = 0u; var w2 = 0u;
+    if (b == 0u) { w0 = v0; w1 = v1; w2 = 0u; }
+    else {
+      w0 = v0 << b;
+      w1 = (v1 << b) | (v0 >> (32u - b));
+      w2 = v1 >> (32u - b);
+    }
+    var t0 = 0u; var t1 = 0u; var t2 = 0u;
+    if (li == 0) { t0 = w0; t1 = w1; t2 = w2; }
+    else if (li == 1) { t1 = w0; t2 = w1; }
+    else { t2 = w0; }
+    // 96-bit add acc += t (bits above 2^96 are multiples of 4, so discarded).
+    let n0 = a0 + t0; let c0 = select(0u, 1u, n0 < a0);
+    let n1a = a1 + t1; let c1a = select(0u, 1u, n1a < a1);
+    let n1 = n1a + c0; let c1b = select(0u, 1u, n1 < n1a);
+    let n2 = a2 + t2 + c1a + c1b;
+    a0 = n0; a1 = n1; a2 = n2;
+  }
+  var n = a2 >> 30u;                            // integer part mod 4
+  var phi = f32(a2 & 0x3fffffffu) * (1.0 / 1073741824.0)
+          + f32(a1) * (1.0 / 4611686018427387904.0);   // fraction in [0,1)
+  if (phi >= 0.5) { phi = phi - 1.0; n = n + 1u; }
+  var out: Pio2;
+  out.octant = n & 3u;
+  out.r = phi * PIO2_F;
+  return out;
+}{% endmacro %}
+{% macro emit_reduce_pio2_fast() %}
+fn reduce_pio2_fast(ax: f32) -> Pio2 {
+  // Cody-Waite reduction for ordinary magnitudes. Splitting pi/2 keeps the
+  // residual accurate even when n is thousands; larger inputs use Payne-Hanek.
+  let nFloat = floor(fma(ax, INV_PIO2_F, 0.5));
+  var residual = fma(-nFloat, PIO2_HI_F, ax);
+  residual = fma(-nFloat, PIO2_LO_F, residual);
+  var out: Pio2;
+  out.octant = u32(nFloat) & 3u;
+  out.r = residual;
+  return out;
+}{% endmacro %}
+{% macro emit_trig_reduction_support() %}
+{% set needPreciseTrigCentered = false %}
+{% set needPreciseTrigTwoPi = false %}
+// Backend-stable f32 sine/cosine core.
+//
+// Shader transcendental accuracy is implementation-defined, and some portable
+// backends are only accurate to roughly 1e-4. Each path retains the most accurate
+// available phase representation, reduces it to [-pi, pi], then uses these
+// polynomials. The half-pi core is degree 13 for sine and degree 12 for cosine;
+// truncation error is well below one f32 ULP over its documented interval.
+{% if needPreciseTrigCentered %}
+const PRECISE_TRIG_PI: f32 = 3.141592653589793;
+{% endif %}
+{% if needPreciseTrigTwoPi %}
+const PRECISE_TRIG_TWO_PI: f32 = 6.283185307179586;
+{% endif %}
+{% if needPreciseTrigCentered %}
+const PRECISE_TRIG_HALF_PI: f32 = 1.5707963267948966;
+{% endif %}
+// Input must be in [-pi/2, pi/2]. Returns (cos(x), sin(x)).
+fn precise_sincos_half_pi(x: f32) -> vec2<f32> {
+  let x2 = x * x;
+  var sinPolynomial = 1.6059043836821613e-10;
+  sinPolynomial = fma(sinPolynomial, x2, -2.505210838544172e-8);
+  sinPolynomial = fma(sinPolynomial, x2, 2.7557319223985893e-6);
+  sinPolynomial = fma(sinPolynomial, x2, -1.984126984126984e-4);
+  sinPolynomial = fma(sinPolynomial, x2, 8.333333333333333e-3);
+  sinPolynomial = fma(sinPolynomial, x2, -1.6666666666666666e-1);
+  let sine = x * fma(sinPolynomial, x2, 1.0);
+  var cosPolynomial = 2.08767569878681e-9;
+  cosPolynomial = fma(cosPolynomial, x2, -2.755731922398589e-7);
+  cosPolynomial = fma(cosPolynomial, x2, 2.48015873015873e-5);
+  cosPolynomial = fma(cosPolynomial, x2, -1.388888888888889e-3);
+  cosPolynomial = fma(cosPolynomial, x2, 4.1666666666666664e-2);
+  cosPolynomial = fma(cosPolynomial, x2, -5.0e-1);
+  let cosine = fma(cosPolynomial, x2, 1.0);
+  return vec2<f32>(cosine, sine);
+}
+{% if needPreciseTrigCentered %}
+// Input must be in [-pi, pi]. Returns (cos(x), sin(x)).
+fn precise_sincos_centered(x: f32) -> vec2<f32> {
+  var folded = x;
+  var cosineSign = 1.0;
+  if (folded > PRECISE_TRIG_HALF_PI) {
+    folded = PRECISE_TRIG_PI - folded;
+    cosineSign = -1.0;
+  } else if (folded < -PRECISE_TRIG_HALF_PI) {
+    folded = -PRECISE_TRIG_PI - folded;
+    cosineSign = -1.0;
+  }
+  let value = precise_sincos_half_pi(folded);
+  return vec2<f32>(cosineSign * value.x, value.y);
+}
+{% endif %}
+// 24-bit words of 2/pi used by the exact Payne-Hanek large-input reducer.
+const TWO_OVER_PI: array<u32, 14> = array<u32, 14>(
+  0xa2f983u, 0x6e4e44u, 0x1529fcu, 0x2757d1u, 0xf534ddu, 0xc0db62u,
+  0x95993cu, 0x439041u, 0xfe5163u, 0xabdebbu, 0xc561b7u, 0x246e3au,
+  0x424dd2u, 0xe00649u
+);
+const PIO2_F: f32 = 1.5707963267948966;
+const INV_PIO2_F: f32 = 0.6366197723675814;
+const PIO2_HI_F: f32 = 1.570796251296997;
+const PIO2_LO_F: f32 = 7.549789415861596e-8;
+const REDUCE_THRESHOLD: f32 = 1.0e4;
+struct Pio2 { octant: u32, r: f32 };
+{{ emit_reduce_pio2_fast() }}
+{{ emit_reduce_pio2() }}
+{%- endmacro %}
+{% macro emit_sin_accurate() %}
+fn sin_accurate(x: f32) -> f32 {
+  if (is_nan_f32(x)) {
+    return x;
+  }
+  if (is_inf_f32(x)) {
+    return nan_from_inf_f32(x);
+  }
+  let ax = abs(x);
+  var red: Pio2;
+  if (ax < REDUCE_THRESHOLD) { red = reduce_pio2_fast(ax); }
+  else { red = reduce_pio2(ax); }
+  let reduced = precise_sincos_half_pi(red.r);
+  var s: f32;
+  switch (red.octant) {
+    case 0u: { s = reduced.y; }
+    case 1u: { s = reduced.x; }
+    case 2u: { s = -reduced.y; }
+    default: { s = -reduced.x; }
+  }
+  return select(s, -s, x < 0.0);   // sin is odd
+}{% endmacro %}
+{{ emit_is_nan_f32() }}
+{{ emit_trig_reduction_support() }}
+{{ emit_is_inf_f32() }}
+{{ emit_nan_from_inf_f32() }}
+{{ emit_sin_accurate() }}
+@compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
+fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
+  // 2D-folded flat index: gid.y carries the high bits when the element count exceeds the
+  // maxComputeWorkgroupsPerDimension limit (the dispatch caps x and spills the rest into y).
+  let i = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
+  if (i >= params.count) {
+    return;
+  }
+  let xv = x[i];
+  let fv = vec4<f32>(xv);
+  y[i] = {{ vectorScalar }}(vec4<f32>(sin_accurate(fv.x), sin_accurate(fv.y), sin_accurate(fv.z), sin_accurate(fv.w)));
+}