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README.md +57 -0
build/webgpu/bench.json +57 -0
build/webgpu/manifest.json +80 -0
build/webgpu/metadata.json +19 -0
build/webgpu/test.json +148 -0
build/webgpu/unary-scalar.wgsl.jinja +26 -0
build/webgpu/unary-vec4.wgsl.jinja +19 -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.Exp
+`ai.onnx`  ·  standard ONNX operator  ·  ONNX opset ≥ 13
+## Description
+Computes `exp(x)` elementwise for every element of the input tensor. The output has the same shape and type as the input.
+See the [ONNX `Exp` spec](https://onnx.ai/onnx/operators/onnx__Exp.html) for the reference semantics.
+## Inputs
+| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
+| --- | --- | --- | --- | --- | --- | --- |
+| `input` | `x` | `T` | — | — | Values used as exponents of `e` in the elementwise exponential. | required |
+## Outputs
+| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
+| --- | --- | --- | --- | --- | --- | --- |
+| `output` | `y` | `T` | same as `input` | same as `input` | Output tensor containing the elementwise exponential of the input. | 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.Exp", { version: 1 });
+const { y } = await kernel({ x: { data: xData, shape: [] } });
+```

build/webgpu/bench.json ADDED Viewed

	@@ -0,0 +1,57 @@

+{
+  "op": "ai.onnx.Exp",
+  "tunableSpace": { "WORKGROUP_SIZE": [64, 128, 256] },
+  "cases": [
+    {
+      "name": "exp-f32-1m",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 1048576 },
+      "inputs": { "x": { "shape": [1048576], "dtype": "float32", "dist": "normal", "seed": 620, "scale": 0.1 } },
+      "outputs": { "y": { "shape": [1048576], "dtype": "float32" } },
+      "bench": {
+        "primary": true,
+        "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }]
+      }
+    },
+    {
+      "name": "exp-f32-8m",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 8388608 },
+      "inputs": { "x": { "shape": [8388608], "dtype": "float32", "seed": 7009, "dist": "normal", "scale": 0.1 } },
+      "outputs": { "y": { "shape": [8388608], "dtype": "float32" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "exp-f32-16m",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 16777216 },
+      "inputs": { "x": { "shape": [16777216], "dtype": "float32", "seed": 7011, "dist": "normal", "scale": 0.1 } },
+      "outputs": { "y": { "shape": [16777216], "dtype": "float32" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "exp-f16-8m",
+      "preset": "smoke",
+      "vars": { "dtype": "float16", "count": 8388608 },
+      "inputs": { "x": { "shape": [8388608], "dtype": "float16", "seed": 7010, "dist": "normal", "scale": 0.1 } },
+      "outputs": { "y": { "shape": [8388608], "dtype": "float16" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "exp-f32-1m-plus-1-scalar",
+      "preset": "smoke",
+      "vars": { "dtype": "float32", "count": 1048577 },
+      "inputs": { "x": { "shape": [1048577], "dtype": "float32", "dist": "normal", "seed": 621, "scale": 0.1 } },
+      "outputs": { "y": { "shape": [1048577], "dtype": "float32" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    },
+    {
+      "name": "exp-f16-1m-plus-1-scalar",
+      "preset": "smoke",
+      "vars": { "dtype": "float16", "count": 1048577 },
+      "inputs": { "x": { "shape": [1048577], "dtype": "float16", "dist": "normal", "seed": 622, "scale": 0.1 } },
+      "outputs": { "y": { "shape": [1048577], "dtype": "float16" } },
+      "bench": { "metrics": [{ "type": "bandwidth", "value": "args.count * dtypeBytes(args.dtype) * 2" }] }
+    }
+  ]
+}

build/webgpu/manifest.json ADDED Viewed

	@@ -0,0 +1,80 @@

+{
+  "domain": "ai.onnx",
+  "name": "Exp",
+  "sinceVersion": 13,
+  "description": "Computes `exp(x)` elementwise for every element of the input tensor. The output has the same shape and type as the input.",
+  "inputs": [
+    { "role": "input", "dtype": "T", "description": "Values used as exponents of `e` in the elementwise exponential." }
+  ],
+  "outputs": [
+    {
+      "role": "output",
+      "dtype": "T",
+      "rank": "ranks.input",
+      "description": "Output tensor containing the elementwise exponential of the input.",
+      "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": "Exp.vec4",
+          "source": { "shader": "unary-vec4.wgsl.jinja", "inputs": { "op": "\"exp\"" } },
+          "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": "scalar",
+      "when": ["numel(shapes.x) == numel(shapes.y)", "f16Ok(dtypes.T)"],
+      "constants": { "scalar": "dtypes.T", "usesF16": "dtypes.T == \"f16\"" },
+      "passes": [
+        {
+          "id": "main",
+          "name": "Exp",
+          "source": { "shader": "unary-scalar.wgsl.jinja", "inputs": { "op": "\"exp\"", "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.Exp",
+  "id": "_ai_onnx_exp_webgpu_0a43009",
+  "version": 1,
+  "license": "Apache-2.0",
+  "backend": { "type": "webgpu" },
+  "digest": {
+    "algorithm": "sha256",
+    "files": {
+      "bench.json": "bDB6nvxDbLKgaCODtOOts1Z5Su/8+vP35e5KDn56fiU=",
+      "manifest.json": "+4WWeMreaihmDLYJv9k+qzTgio5ykz3P4qtbSn2avhc=",
+      "test.json": "AqFvYeLz6IF603TGdetqW4JAhc09e4kCQ+uK0R8Rhgs=",
+      "unary-scalar.wgsl.jinja": "xFU7bx8sOm0IUqpXX3/rL2fkyv/b1e0ej/74VFwlLCw=",
+      "unary-vec4.wgsl.jinja": "XSKLHhqloVC3wytaLx7c1GObxRF4CEG7GmVdu4UnAWM="
+    }
+  },
+  "provenance": { "kernel": { "sha": "2e7068faf55e7f43df740015f6d1ee49391a41c5", "dirty": false } },
+  "webgpu": { "manifestSpec": "1.0", "specialized": true, "opPath": "ops/ai.onnx.Exp" }
+}

build/webgpu/test.json ADDED Viewed

	@@ -0,0 +1,148 @@

+{
+  "op": "ai.onnx.Exp",
+  "cases": [
+    {
+      "name": "vector",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [32],
+          "data": { "kind": "fillFloat32", "sinStep": 0.1, "cosStep": 0.2, "scale": 0.1 }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [32], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "rank0_scalar",
+      "inputs": { "x": { "dtype": "float32", "shape": [], "data": { "kind": "values", "values": [1.25] } } },
+      "outputs": { "y": { "dtype": "float32", "shape": [], "tolerance": 0.000001 } }
+    },
+    {
+      "name": "overflow_and_special_values",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [6],
+          "data": { "kind": "values", "values": [100.0, -100.0, "Infinity", "-Infinity", "NaN", 0.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [6], "tolerance": 0.000001, "allowNaN": true } }
+    },
+    {
+      "name": "f32_subnormal_underflow_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.Exp_float",
+        "notes": "exp(-90..-100) is subnormal but nonzero in float32; flushing the tail to zero loses valid probability mass."
+      },
+      "inputs": {
+        "x": { "dtype": "float32", "shape": [4], "data": { "kind": "values", "values": [-88.0, -90.0, -95.0, -100.0] } }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [4], "tolerance": 1e-45 } }
+    },
+    {
+      "name": "f32_subnormal_underflow_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.Exp_float",
+        "notes": "Scalar-path companion: exp of large negative finite inputs can produce valid nonzero subnormal outputs."
+      },
+      "inputs": {
+        "x": { "dtype": "float32", "shape": [3], "data": { "kind": "values", "values": [-90.0, -95.0, -100.0] } }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [3], "tolerance": 1e-45 } }
+    },
+    {
+      "name": "f16_values",
+      "inputs": {
+        "x": { "dtype": "float16", "shape": [5], "data": { "kind": "values", "values": [-5.0, -1.0, 0.0, 1.0, 5.0] } }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [5], "tolerance": 0.05 } }
+    },
+    {
+      "name": "f32_near_overflow_boundary",
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [5],
+          "data": { "kind": "values", "values": [80.0, 87.0, 88.0, 88.5, 89.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [5] } }
+    },
+    {
+      "name": "ort_float_2x2",
+      "provenance": {
+        "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
+        "test": "MathOpTest.Exp_float"
+      },
+      "inputs": {
+        "x": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 10.0] } }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [2, 2], "tolerance": 0.003 } }
+    },
+    {
+      "name": "ort_float16_2x2_projection",
+      "provenance": {
+        "source": "onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc",
+        "test": "MathOpTest.Exp_float",
+        "notes": "Float16 projection of ORT's float Exp node values."
+      },
+      "inputs": {
+        "x": { "dtype": "float16", "shape": [2, 2], "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 10.0] } }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [2, 2], "relTolerance": 0.002 } }
+    },
+    {
+      "name": "onnx_backend_example",
+      "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_exp_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_exp",
+      "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_exp" },
+      "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_exp_example",
+      "provenance": { "source": "cmake/external/onnx/onnx/backend/test/data/node/test_exp_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": "empty_input_zero_dim",
+      "inputs": { "x": { "dtype": "float32", "shape": [0], "data": { "kind": "values", "values": [] } } },
+      "outputs": { "y": { "dtype": "float32", "shape": [0], "tolerance": 0 } }
+    },
+    {
+      "name": "f16_overflow_to_infinity",
+      "inputs": {
+        "x": {
+          "dtype": "float16",
+          "shape": [8],
+          "data": { "kind": "values", "values": [0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0] }
+        }
+      },
+      "outputs": { "y": { "dtype": "float16", "shape": [8], "relTolerance": 0.002 } }
+    }
+  ]
+}

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

	@@ -0,0 +1,26 @@

+{% 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 }}
+{{ flat_tail_open() }}
+  y[i] = {{ scalar }}(exp(f32(x[i])));
+{{ flat_tail_close() -}}
+}

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

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

+// 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 }}
+@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];
+  y[i] = {{ vectorScalar }}(exp(vec4<f32>(xv)));
+}