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README.md +14 -10
build/webgpu/bench.json +11 -3
build/webgpu/manifest.json +278 -323
build/webgpu/metadata.json +25 -12
build/webgpu/pool-global-reduction.wgsl.jinja +49 -47
build/webgpu/pool-ncl1d-x4.wgsl.jinja +6 -11
build/webgpu/pool-window-nd.wgsl.jinja +11 -14
build/webgpu/pool-window-unroll.wgsl.jinja +26 -29
build/webgpu/pool2d-nchw-k2s2-vec4.wgsl.jinja +14 -16
build/webgpu/pool2d-nchw-separable.wgsl.jinja +9 -22
build/webgpu/test.json +42 -10

README.md CHANGED Viewed

@@ -18,15 +18,15 @@ See the [ONNX `LpPool` spec](https://onnx.ai/onnx/operators/onnx__LpPool.html) f
 ## Inputs
-| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
 | --- | --- | --- | --- | --- | --- | --- |
-| `X` | `x` | `T` | — | — | Input tensor of shape `(N x C x D1 x ... x Dn)`; for images the spatial axes are `H` and `W`. | required |
 ## Outputs
-| Name | Bind key | Logical dtype | Rank | Shape | Description | Presence |
 | --- | --- | --- | --- | --- | --- | --- |
-| `Y` | `y` | `T` | same as `X` | derived; see description | Output tensor after Lp pooling; spatial dimensions vary with kernel, stride, and pad settings. | required |
 ## Attributes
@@ -34,13 +34,13 @@ Attributes and default values (overridable per request):
 | Attribute | Default | Description |
 | --- | --- | --- |
-| `p` | `2` | The exponent of the Lp norm used for pooling; default `2` gives Euclidean (L2) pooling. |
 | `auto_pad` | `"NOTSET"` | Deprecated auto-padding mode (`NOTSET`, `SAME_UPPER`, `SAME_LOWER`, or `VALID`). It cannot be used together with `pads`. |
 | `ceil_mode` | `0` | When non-zero, uses `ceil` instead of `floor` to compute output spatial dimensions. |
 | `kernel_shape` | — | Required kernel shape, with one positive value per spatial axis. |
-| `strides` | — | Stride along each spatial axis. When omitted, every stride is 1. |
 | `pads` | — | Padding at the beginning and end of each spatial axis, ordered as `[begin_0, ..., begin_n, end_0, ..., end_n]`. When omitted, every pad is 0. |
-| `dilations` | — | Dilation along each spatial axis. When omitted, every dilation is 1. |
 ## Type constraints
@@ -50,7 +50,7 @@ Attributes and default values (overridable per request):
 ## 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
@@ -63,10 +63,14 @@ Attributes and default values (overridable per request):
 ## 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.

 ## Inputs
+| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
 | --- | --- | --- | --- | --- | --- | --- |
+| `x` | `X` | `T` | — | — | Input tensor of shape `(N x C x D1 x ... x Dn)`; for images the spatial axes are `H` and `W`. | required |
 ## Outputs
+| Name | Upstream name | Logical dtype | Rank | Shape | Description | Presence |
 | --- | --- | --- | --- | --- | --- | --- |
+| `y` | `Y` | `T` | same as `x` | derived | Output tensor after Lp pooling; spatial dimensions vary with kernel, stride, and pad settings. | required |
 ## Attributes
 | Attribute | Default | Description |
 | --- | --- | --- |
 | `auto_pad` | `"NOTSET"` | Deprecated auto-padding mode (`NOTSET`, `SAME_UPPER`, `SAME_LOWER`, or `VALID`). It cannot be used together with `pads`. |
 | `ceil_mode` | `0` | When non-zero, uses `ceil` instead of `floor` to compute output spatial dimensions. |
+| `dilations` | — | Dilation along each spatial axis. When omitted, every dilation is 1. |
 | `kernel_shape` | — | Required kernel shape, with one positive value per spatial axis. |
+| `p` | `2` | The exponent of the Lp norm used for pooling; default `2` gives Euclidean (L2) pooling. |
 | `pads` | — | Padding at the beginning and end of each spatial axis, ordered as `[begin_0, ..., begin_n, end_0, ..., end_n]`. When omitted, every pad is 0. |
+| `strides` | — | Stride along each spatial axis. When omitted, every stride is 1. |
 ## Type constraints
 ## Files
+- [`metadata.json`](build/webgpu/metadata.json) — kernel metadata (id, digests, per-variant templates, 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
 ## Use with `@huggingface/kernels`
+```sh
+npm install --save-exact @huggingface/kernels@0.0.1-preview.2
+```
+Required output shapes and logical data types are inferred from the supplied inputs and attributes; result tensors are allocated automatically.
 The `version: 1` option selects the published kernel contract; it is independent of any operator opset, contrib `since_version`, or model version.
+It follows the `v1` branch as fixes land. To pin exact artifact bytes, pass a 40-character commit `revision` instead of `version`.
 Replace each `*Data` placeholder with a typed array containing the corresponding input data.

build/webgpu/bench.json CHANGED Viewed

@@ -1,5 +1,4 @@
 {
-  "op": "ai.onnx.LpPool",
   "cases": [
     {
       "name": "nchw_8_32_64_64_k3",
@@ -103,8 +102,8 @@
       "name": "global-lppool-p2-rank5-serial-reduction-pathology",
       "preset": "stress",
       "provenance": {
-        "source": "authored for variant coverage",
-        "notes": "Realistic rank-5 global L2 pool that verifies global_window_parallel coverage and its feature-tier fallbacks."
       },
       "vars": { "batch": 1, "channels": 32, "inD": 32, "inH": 64, "inW": 64 },
       "attrs": { "p": 2, "kernel_shape": [32, 64, 64] },
@@ -131,6 +130,15 @@
       "inputs": { "x": { "dtype": "float32", "shape": [1, 64, 224, 224], "dist": "normal", "seed": 409, "scale": 2 } },
       "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 112, 112], "dist": "empty" } },
       "bench": { "metrics": [{ "type": "bandwidth", "value": "numel(shapes.x) * 4" }] }
     }
   ],
   "tunableSpace": { "WORKGROUP_SIZE": [64, 128, 256] }

 {
   "cases": [
     {
       "name": "nchw_8_32_64_64_k3",
       "name": "global-lppool-p2-rank5-serial-reduction-pathology",
       "preset": "stress",
       "provenance": {
+        "source": "synthetic benchmark",
+        "notes": "A realistic rank-5 global L2 pool measures the parallel global-window reduction across feature tiers."
       },
       "vars": { "batch": 1, "channels": 32, "inD": 32, "inH": 64, "inW": 64 },
       "attrs": { "p": 2, "kernel_shape": [32, 64, 64] },
       "inputs": { "x": { "dtype": "float32", "shape": [1, 64, 224, 224], "dist": "normal", "seed": 409, "scale": 2 } },
       "outputs": { "y": { "dtype": "float32", "shape": [1, 64, 112, 112], "dist": "empty" } },
       "bench": { "metrics": [{ "type": "bandwidth", "value": "numel(shapes.x) * 4" }] }
+    },
+    {
+      "name": "lppool-f32-b1c512-20x20-k5s1p2-separable-floor",
+      "provenance": {
+        "notes": "A 5x5 window contains 25 elements, the minimum window size admitted by the separable route for this operator."
+      },
+      "attrs": { "p": 2, "kernel_shape": [5, 5], "strides": [1, 1], "pads": [2, 2, 2, 2] },
+      "inputs": { "x": { "dtype": "float32", "shape": [1, 512, 20, 20], "dist": "uniform" } },
+      "outputs": { "y": { "dtype": "float32", "shape": [1, 512, 20, 20], "dist": "empty" } }
     }
   ],
   "tunableSpace": { "WORKGROUP_SIZE": [64, 128, 256] }

build/webgpu/manifest.json CHANGED Viewed

@@ -2,32 +2,16 @@
   "domain": "ai.onnx",
   "name": "LpPool",
   "sinceVersion": 18,
-  "description": "Applies Lp pooling over a spatial input tensor by computing the Lp norm within each kernel window and writing the result to the output. Output spatial dimensions are determined by the kernel size, strides, padding, and `ceil_mode`; `p` controls which norm is used (e.g. `p=1` for sum-of-absolutes, `p=2` for Euclidean).",
-  "inputs": [
-    {
-      "role": "X",
-      "dtype": "T",
-      "description": "Input tensor of shape `(N x C x D1 x ... x Dn)`; for images the spatial axes are `H` and `W`."
-    }
-  ],
-  "outputs": [
-    {
-      "role": "Y",
-      "dtype": "T",
-      "description": "Output tensor after Lp pooling; spatial dimensions vary with kernel, stride, and pad settings.",
-      "rank": "ranks.X",
-      "shape": "poolOutputShape"
-    }
-  ],
-  "attributes": { "p": 2, "auto_pad": "NOTSET", "ceil_mode": 0 },
-  "attributeDescriptions": {
-    "p": "The exponent of the Lp norm used for pooling; default `2` gives Euclidean (L2) pooling.",
-    "auto_pad": "Deprecated auto-padding mode (`NOTSET`, `SAME_UPPER`, `SAME_LOWER`, or `VALID`). It cannot be used together with `pads`.",
-    "kernel_shape": "Required kernel shape, with one positive value per spatial axis.",
-    "strides": "Stride along each spatial axis. When omitted, every stride is 1.",
-    "pads": "Padding at the beginning and end of each spatial axis, ordered as `[begin_0, ..., begin_n, end_0, ..., end_n]`. When omitted, every pad is 0.",
-    "dilations": "Dilation along each spatial axis. When omitted, every dilation is 1.",
-    "ceil_mode": "When non-zero, uses `ceil` instead of `floor` to compute output spatial dimensions."
   },
   "attributeConstraints": {
     "auto_pad": { "values": ["NOTSET", "SAME_UPPER", "SAME_LOWER", "VALID"] },
@@ -35,12 +19,9 @@
     "kernel_shape": { "required": true }
   },
   "typeConstraints": { "T": ["float32", "float16"] },
-  "args": {
-    "x": { "kind": "tensor", "semantic": "X", "role": "x" },
-    "y": { "kind": "tensor", "semantic": "Y", "role": "y" }
-  },
   "derive": {
-    "spatialRank": "ranks.X - 2",
     "kernelShapeLengthOk": "not has(attrs, \"kernel_shape\") or (attrs.kernel_shape | length) == spatialRank",
     "stridesLengthOk": "not has(attrs, \"strides\") or (attrs.strides | length) == spatialRank",
     "dilationsLengthOk": "not has(attrs, \"dilations\") or (attrs.dilations | length) == spatialRank",
@@ -60,317 +41,177 @@
     "padBack": "attrs.pads[spatialRank] if padsLengthOk and has(attrs, \"pads\") and spatialRank == 3 else 0",
     "padBottom": "attrs.pads[2 * spatialRank - 2] if padsLengthOk and has(attrs, \"pads\") and spatialRank >= 2 else 0",
     "padRight": "attrs.pads[2 * spatialRank - 1] if padsLengthOk and has(attrs, \"pads\") and spatialRank >= 1 else 0",
-    "poolBaseOk": "f16Ok(dtypes.T) and attrs.p > 0 and ranks.X >= 3 and ranks.X <= 5 and ranks.Y == ranks.X and has(attrs, \"kernel_shape\") and kernelShapeLengthOk and stridesLengthOk and dilationsLengthOk and padsLengthOk and kernelD > 0 and kernelH > 0 and kernelW > 0 and strideD > 0 and strideH > 0 and strideW > 0 and dilationD > 0 and dilationH > 0 and dilationW > 0 and padFront >= 0 and padTop >= 0 and padLeft >= 0 and padBack >= 0 and padBottom >= 0 and padRight >= 0 and (attrs.auto_pad == \"NOTSET\" or not has(attrs, \"pads\")) and dim(shapes.Y, 0) == dim(shapes.X, 0) and dim(shapes.Y, 1) == dim(shapes.X, 1)",
-    "explicitOutW": "min(ceil((dim(shapes.X, ranks.X - 1) + padLeft + padRight - ((kernelW - 1) * dilationW + 1)) / strideW) + 1, ceil((dim(shapes.X, ranks.X - 1) + padLeft) / strideW)) if attrs.ceil_mode != 0 else floor((dim(shapes.X, ranks.X - 1) + padLeft + padRight - ((kernelW - 1) * dilationW + 1)) / strideW) + 1",
-    "explicitOutH": "min(ceil((dim(shapes.X, ranks.X - 2) + padTop + padBottom - ((kernelH - 1) * dilationH + 1)) / strideH) + 1, ceil((dim(shapes.X, ranks.X - 2) + padTop) / strideH)) if attrs.ceil_mode != 0 else floor((dim(shapes.X, ranks.X - 2) + padTop + padBottom - ((kernelH - 1) * dilationH + 1)) / strideH) + 1",
-    "explicitOutD": "(min(ceil((dim(shapes.X, 2) + padFront + padBack - ((kernelD - 1) * dilationD + 1)) / strideD) + 1, ceil((dim(shapes.X, 2) + padFront) / strideD)) if attrs.ceil_mode != 0 else floor((dim(shapes.X, 2) + padFront + padBack - ((kernelD - 1) * dilationD + 1)) / strideD) + 1) if ranks.X == 5 else 0",
-    "inferredOutW": "ceil(dim(shapes.X, ranks.X - 1) / strideW) if attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\" else explicitOutW",
-    "inferredOutH": "ceil(dim(shapes.X, ranks.X - 2) / strideH) if attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\" else explicitOutH",
-    "inferredOutD": "ceil(dim(shapes.X, 2) / strideD) if attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\" else explicitOutD",
-    "poolOutputShape": "[dim(shapes.X, 0), dim(shapes.X, 1), inferredOutW] if ranks.X == 3 else ([dim(shapes.X, 0), dim(shapes.X, 1), inferredOutH, inferredOutW] if ranks.X == 4 else [dim(shapes.X, 0), dim(shapes.X, 1), inferredOutD, inferredOutH, inferredOutW])",
-    "autoPadTotalH": "max(0, (dim(shapes.Y, 2) - 1) * strideH + ((kernelH - 1) * dilationH + 1) - dim(shapes.X, 2)) if ranks.X == 4 else 0",
-    "autoPadTotalW": "max(0, (dim(shapes.Y, 3) - 1) * strideW + ((kernelW - 1) * dilationW + 1) - dim(shapes.X, 3)) if ranks.X == 4 else 0",
-    "effectivePadH": "(floor(autoPadTotalH / 2) if attrs.auto_pad == \"SAME_UPPER\" else autoPadTotalH - floor(autoPadTotalH / 2)) if ranks.X == 4 and (attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\") else padTop",
-    "effectivePadW": "(floor(autoPadTotalW / 2) if attrs.auto_pad == \"SAME_UPPER\" else autoPadTotalW - floor(autoPadTotalW / 2)) if ranks.X == 4 and (attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\") else padLeft",
-    "globalWindowParallelCovered": "poolBaseOk and numel(shapes.Y) == dim(shapes.X, 0) * dim(shapes.X, 1) and numel(shapes.Y) <= device.limits.maxComputeWorkgroupsPerDimension and attrs.auto_pad == \"NOTSET\" and strideD == 1 and strideH == 1 and strideW == 1 and dilationD == 1 and dilationH == 1 and dilationW == 1 and padFront == 0 and padTop == 0 and padLeft == 0 and padBack == 0 and padBottom == 0 and padRight == 0 and ((ranks.X == 3 and kernelW == dim(shapes.X, 2)) or (ranks.X == 4 and kernelH == dim(shapes.X, 2) and kernelW == dim(shapes.X, 3)) or (ranks.X == 5 and kernelD == dim(shapes.X, 2) and kernelH == dim(shapes.X, 3) and kernelW == dim(shapes.X, 4)))",
-    "separableTmpElements": "dim(shapes.X, 0) * dim(shapes.X, 1) * dim(shapes.X, 2) * dim(shapes.Y, 3)",
-    "separableFits": "separableTmpElements * 4 <= min(device.limits.maxStorageBufferBindingSize, device.limits.maxBufferSize) and ceilDiv(separableTmpElements, tunables.WORKGROUP_SIZE) <= device.limits.maxComputeWorkgroupsPerDimension * device.limits.maxComputeWorkgroupsPerDimension"
   },
-  "constants": { "usesF16": "dtypes.T == \"f16\"", "scalar": "dtypes.T" },
-  "bindingSets": {
-    "globalReduction": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$xElem" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": { "name": "Params", "fields": [{ "name": "spatial", "type": "u32", "value": "inner(shapes.X, 1)" }] }
-      }
-    ],
-    "rank2Vec4": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$xVec4" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [{ "name": "tileCount", "type": "u32", "value": "numel(shapes.Y) / 2" }]
-        }
-      }
-    ],
-    "flatDirect": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": { "name": "Params", "fields": [{ "name": "count", "type": "u32", "value": "numel(shapes.Y)" }] }
-      }
-    ],
-    "rank1X4": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$xVec4" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [
-            {
-              "name": "blockCount",
-              "type": "u32",
-              "value": "dim(shapes.X, 0) * dim(shapes.X, 1) * ceilDiv(dim(shapes.Y, 2), 4)"
-            }
-          ]
-        }
-      }
-    ],
-    "rank1Direct": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [
-            { "name": "channels", "type": "u32", "value": "dim(shapes.X, 1)" },
-            { "name": "inW", "type": "u32", "value": "dim(shapes.X, 2)" },
-            { "name": "outW", "type": "u32", "value": "dim(shapes.Y, 2)" },
-            { "name": "kernelW", "type": "u32", "value": "kernelW" },
-            { "name": "strideW", "type": "u32", "value": "strideW" },
-            { "name": "dilationW", "type": "u32", "value": "dilationW" },
-            { "name": "padW", "type": "i32", "value": "padLeft" },
-            { "name": "p", "type": "f32", "value": "attrs.p" },
-            { "name": "count", "type": "u32", "value": "numel(shapes.Y)" }
-          ]
-        }
-      }
-    ],
-    "rank3Direct": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [
-            { "name": "channels", "type": "u32", "value": "dim(shapes.X, 1)" },
-            { "name": "inD", "type": "u32", "value": "dim(shapes.X, 2)" },
-            { "name": "inH", "type": "u32", "value": "dim(shapes.X, 3)" },
-            { "name": "inW", "type": "u32", "value": "dim(shapes.X, 4)" },
-            { "name": "outD", "type": "u32", "value": "dim(shapes.Y, 2)" },
-            { "name": "outH", "type": "u32", "value": "dim(shapes.Y, 3)" },
-            { "name": "outW", "type": "u32", "value": "dim(shapes.Y, 4)" },
-            { "name": "kernelD", "type": "u32", "value": "kernelD" },
-            { "name": "kernelH", "type": "u32", "value": "kernelH" },
-            { "name": "kernelW", "type": "u32", "value": "kernelW" },
-            { "name": "strideD", "type": "u32", "value": "strideD" },
-            { "name": "strideH", "type": "u32", "value": "strideH" },
-            { "name": "strideW", "type": "u32", "value": "strideW" },
-            { "name": "dilationD", "type": "u32", "value": "dilationD" },
-            { "name": "dilationH", "type": "u32", "value": "dilationH" },
-            { "name": "dilationW", "type": "u32", "value": "dilationW" },
-            { "name": "padD", "type": "i32", "value": "padFront" },
-            { "name": "padH", "type": "i32", "value": "padTop" },
-            { "name": "padW", "type": "i32", "value": "padLeft" },
-            { "name": "p", "type": "f32", "value": "attrs.p" },
-            { "name": "count", "type": "u32", "value": "numel(shapes.Y)" }
-          ]
-        }
-      }
-    ],
-    "rank2Direct": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [
-            { "name": "channels", "type": "u32", "value": "dim(shapes.X, 1)" },
-            { "name": "inH", "type": "u32", "value": "dim(shapes.X, 2)" },
-            { "name": "inW", "type": "u32", "value": "dim(shapes.X, 3)" },
-            { "name": "outH", "type": "u32", "value": "dim(shapes.Y, 2)" },
-            { "name": "outW", "type": "u32", "value": "dim(shapes.Y, 3)" },
-            { "name": "kernelH", "type": "u32", "value": "kernelH" },
-            { "name": "kernelW", "type": "u32", "value": "kernelW" },
-            { "name": "strideH", "type": "u32", "value": "strideH" },
-            { "name": "strideW", "type": "u32", "value": "strideW" },
-            { "name": "dilationH", "type": "u32", "value": "dilationH" },
-            { "name": "dilationW", "type": "u32", "value": "dilationW" },
-            { "name": "padH", "type": "i32", "value": "effectivePadH" },
-            { "name": "padW", "type": "i32", "value": "effectivePadW" },
-            { "name": "p", "type": "f32", "value": "attrs.p" },
-            { "name": "count", "type": "u32", "value": "numel(shapes.Y)" }
-          ]
-        }
-      }
-    ],
-    "rank2SeparableW": [
-      { "name": "x", "arg": "x", "semantic": "X", "buffer": { "type": "read-only-storage" }, "elementType": "$scalar" },
-      { "name": "tmp", "semantic": "tmp", "buffer": { "type": "storage" }, "elementType": "f32" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [
-            { "name": "inH", "type": "u32", "value": "dim(shapes.X, 2)" },
-            { "name": "inW", "type": "u32", "value": "dim(shapes.X, 3)" },
-            { "name": "outW", "type": "u32", "value": "dim(shapes.Y, 3)" },
-            { "name": "kernelW", "type": "u32", "value": "kernelW" },
-            { "name": "strideW", "type": "u32", "value": "strideW" },
-            { "name": "dilationW", "type": "u32", "value": "dilationW" },
-            { "name": "padW", "type": "i32", "value": "effectivePadW" },
-            { "name": "p", "type": "f32", "value": "attrs.p" },
-            {
-              "name": "tmpCount",
-              "type": "u32",
-              "value": "dim(shapes.X, 0) * dim(shapes.X, 1) * dim(shapes.X, 2) * dim(shapes.Y, 3)"
-            }
-          ]
-        }
-      }
-    ],
-    "rank2SeparableH": [
-      { "name": "tmp", "semantic": "tmp", "buffer": { "type": "read-only-storage" }, "elementType": "f32" },
-      { "name": "y", "arg": "y", "semantic": "Y", "buffer": { "type": "storage" }, "elementType": "$scalar" },
-      {
-        "name": "params",
-        "semantic": "kernel.params",
-        "buffer": { "type": "uniform" },
-        "struct": {
-          "name": "Params",
-          "fields": [
-            { "name": "inH", "type": "u32", "value": "dim(shapes.X, 2)" },
-            { "name": "outH", "type": "u32", "value": "dim(shapes.Y, 2)" },
-            { "name": "outW", "type": "u32", "value": "dim(shapes.Y, 3)" },
-            { "name": "kernelH", "type": "u32", "value": "kernelH" },
-            { "name": "strideH", "type": "u32", "value": "strideH" },
-            { "name": "dilationH", "type": "u32", "value": "dilationH" },
-            { "name": "padH", "type": "i32", "value": "effectivePadH" },
-            { "name": "p", "type": "f32", "value": "attrs.p" },
-            { "name": "count", "type": "u32", "value": "numel(shapes.Y)" }
-          ]
-        }
-      }
-    ]
   },
-  "tunables": { "WORKGROUP_SIZE": 256, "SEPARABLE_MIN_WINDOW": 42 },
   "variants": [
     {
       "id": "global_window_parallel",
       "priority": 50,
       "when": ["globalWindowParallelCovered"],
-      "constants": {
-        "xElem": "(\"vec4<f16>\" if dtypes.T == \"f16\" else \"vec4<f32>\") if inner(shapes.X, 1) % 4 == 0 else dtypes.T"
       },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.GlobalWindowParallel",
-          "source": {
-            "shader": "pool-global-reduction.wgsl.jinja",
-            "inputs": {
-              "op": "\"lp\"",
-              "vec4": "inner(shapes.X, 1) % 4 == 0",
-              "p": "attrs.p",
-              "workgroupSize": "tunables.WORKGROUP_SIZE",
-              "useSubgroups": "device.features.has(\"subgroups\")"
-            }
           },
-          "bindings": "globalReduction",
-          "dispatch": { "x": "numel(shapes.Y)" }
         }
       ]
     },
     {
       "id": "nchw2d_k2s2_vec4",
       "priority": 30,
-      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.X == 4", "kernelH == 2", "kernelW == 2", "strideH == 2", "strideW == 2", "dilationH == 1", "dilationW == 1", "padTop == 0", "padLeft == 0", "padBottom == 0", "padRight == 0", "attrs.ceil_mode == 0", "dim(shapes.X, 3) % 4 == 0", "dim(shapes.Y, 2) == floor(dim(shapes.X, 2) / 2)", "dim(shapes.Y, 3) == floor(dim(shapes.X, 3) / 2)", "dim(shapes.Y, 2) > 0", "dim(shapes.Y, 3) > 0"],
-      "constants": { "xVec4": "\"vec4<f16>\" if dtypes.T == \"f16\" else \"vec4<f32>\"" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.Nchw2dK2s2Vec4",
-          "source": {
-            "shader": "pool2d-nchw-k2s2-vec4.wgsl.jinja",
-            "inputs": {
-              "op": "\"lp\"",
-              "outH": "dim(shapes.Y, 2)",
-              "outW": "dim(shapes.Y, 3)",
-              "outWHalf": "floor(dim(shapes.Y, 3) / 2)",
-              "inWVec4": "floor(dim(shapes.X, 3) / 4)",
-              "inPlaneVec4": "floor(dim(shapes.X, 2) * dim(shapes.X, 3) / 4)",
-              "p": "attrs.p"
-            }
           },
-          "bindings": "rank2Vec4",
-          "dispatch": { "threads": "numel(shapes.Y) / 2", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     },
     {
       "id": "nchw2d_unrolled",
       "priority": 25,
-      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.X == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0", "kernelH <= 3", "kernelW <= 3", "dilationH >= 1", "dilationW >= 1", "dim(shapes.Y, 2) > 0", "dim(shapes.Y, 3) > 0"],
       "passes": [
         {
           "id": "main",
           "name": "LpPool.Nchw2dUnrolled",
-          "source": {
-            "shader": "pool-window-unroll.wgsl.jinja",
-            "inputs": {
-              "op": "\"lp\"",
-              "kernelH": "kernelH",
-              "kernelW": "kernelW",
-              "strideH": "strideH",
-              "strideW": "strideW",
-              "dilationH": "dilationH",
-              "dilationW": "dilationW",
-              "padTop": "padTop",
-              "padLeft": "padLeft",
-              "inH": "dim(shapes.X, 2)",
-              "inW": "dim(shapes.X, 3)",
-              "outH": "dim(shapes.Y, 2)",
-              "outW": "dim(shapes.Y, 3)",
-              "p": "attrs.p"
-            }
           },
-          "bindings": "flatDirect",
-          "dispatch": { "threads": "numel(shapes.Y)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     },
     {
       "id": "ncl1d_k4s2_p2_x4",
       "priority": 30,
-      "when": ["poolBaseOk", "ranks.X == 3", "attrs.auto_pad == \"NOTSET\"", "attrs.p == 2", "kernelW == 4", "strideW == 2", "dilationW == 1", "padLeft == 0", "padRight == 0", "attrs.ceil_mode == 0", "dim(shapes.X, 2) % 4 == 0", "dim(shapes.Y, 2) == floor((dim(shapes.X, 2) - 4) / 2) + 1"],
-      "constants": { "xVec4": "\"vec4<f16>\" if dtypes.T == \"f16\" else \"vec4<f32>\"" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.Ncl1dK4s2P2X4",
-          "source": {
-            "shader": "pool-ncl1d-x4.wgsl.jinja",
-            "inputs": { "op": "\"lp\"", "inW": "dim(shapes.X, 2)", "outW": "dim(shapes.Y, 2)" }
-          },
-          "bindings": "rank1X4",
           "dispatch": {
-            "threads": "dim(shapes.X, 0) * dim(shapes.X, 1) * ceilDiv(dim(shapes.Y, 2), 4)",
-            "workgroupSize": "tunables.WORKGROUP_SIZE"
           }
         }
       ]
@@ -378,82 +219,196 @@
     {
       "id": "ncl1d",
       "priority": 10,
-      "when": ["poolBaseOk", "ranks.X == 3", "kernelW > 0", "strideW > 0"],
-      "constants": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCL1d",
-          "source": { "shader": "pool-window-nd.wgsl.jinja", "inputs": { "op": "\"lp\"", "spatialRank": 1 } },
-          "bindings": "rank1Direct",
-          "dispatch": { "threads": "numel(shapes.Y)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     },
     {
       "id": "nchw2d_separable",
       "priority": 22,
-      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.X == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0", "kernelH > 1", "kernelW > 1", "kernelH * kernelW >= tunables.SEPARABLE_MIN_WINDOW", "separableFits"],
-      "constants": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "intermediates": [{ "id": "tmp", "dtype": "float32", "shape": "[separableTmpElements]" }],
       "passes": [
         {
           "id": "reduce_w",
           "name": "LpPool.SeparableW",
-          "source": { "shader": "pool2d-nchw-separable.wgsl.jinja", "inputs": { "op": "\"lp\"", "axis": "\"w\"" } },
-          "bindings": "rank2SeparableW",
-          "dispatch": { "threads": "separableTmpElements", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         },
         {
           "id": "reduce_h",
           "name": "LpPool.SeparableH",
-          "source": { "shader": "pool2d-nchw-separable.wgsl.jinja", "inputs": { "op": "\"lp\"", "axis": "\"h\"" } },
-          "bindings": "rank2SeparableH",
-          "dispatch": { "threads": "numel(shapes.Y)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     },
     {
       "id": "nchw2d",
-      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.X == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0"],
-      "constants": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCHW2d",
-          "source": { "shader": "pool-window-nd.wgsl.jinja", "inputs": { "op": "\"lp\"", "spatialRank": 2 } },
-          "bindings": "rank2Direct",
-          "dispatch": { "threads": "numel(shapes.Y)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     },
     {
       "id": "nchw2d_autopad",
       "priority": 15,
-      "when": ["(attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\")", "poolBaseOk", "ranks.X == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0", "dim(shapes.Y, 2) == ceil(dim(shapes.X, 2) / strideH)", "dim(shapes.Y, 3) == ceil(dim(shapes.X, 3) / strideW)"],
-      "constants": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCHW2dAutoPad",
-          "source": { "shader": "pool-window-nd.wgsl.jinja", "inputs": { "op": "\"lp\"", "spatialRank": 2 } },
-          "bindings": "rank2Direct",
-          "dispatch": { "threads": "numel(shapes.Y)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     },
     {
       "id": "ncdhw3d",
       "priority": 10,
       "supersededBy": ["global_window_parallel"],
-      "when": ["poolBaseOk", "ranks.X == 5", "kernelD > 0", "kernelH > 0", "kernelW > 0", "strideD > 0", "strideH > 0", "strideW > 0"],
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCDHW3d",
-          "source": { "shader": "pool-window-nd.wgsl.jinja", "inputs": { "op": "\"lp\"", "spatialRank": 3 } },
-          "bindings": "rank3Direct",
-          "dispatch": { "threads": "numel(shapes.Y)", "workgroupSize": "tunables.WORKGROUP_SIZE" }
         }
       ]
     }

   "domain": "ai.onnx",
   "name": "LpPool",
   "sinceVersion": 18,
+  "inputs": { "x": { "onnx": "X", "dtype": "T" } },
+  "outputs": { "y": { "onnx": "Y", "dtype": "T", "rank": "ranks.x", "shape": "poolOutputShape" } },
+  "attributes": {
+    "p": { "default": 2 },
+    "auto_pad": { "default": "NOTSET" },
+    "ceil_mode": { "default": 0 },
+    "kernel_shape": {},
+    "strides": {},
+    "pads": {},
+    "dilations": {}
   },
   "attributeConstraints": {
     "auto_pad": { "values": ["NOTSET", "SAME_UPPER", "SAME_LOWER", "VALID"] },
     "kernel_shape": { "required": true }
   },
   "typeConstraints": { "T": ["float32", "float16"] },
+  "tunables": { "WORKGROUP_SIZE": { "default": 256 }, "SEPARABLE_MIN_WINDOW": { "default": 25 } },
   "derive": {
+    "spatialRank": "ranks.x - 2",
     "kernelShapeLengthOk": "not has(attrs, \"kernel_shape\") or (attrs.kernel_shape | length) == spatialRank",
     "stridesLengthOk": "not has(attrs, \"strides\") or (attrs.strides | length) == spatialRank",
     "dilationsLengthOk": "not has(attrs, \"dilations\") or (attrs.dilations | length) == spatialRank",
     "padBack": "attrs.pads[spatialRank] if padsLengthOk and has(attrs, \"pads\") and spatialRank == 3 else 0",
     "padBottom": "attrs.pads[2 * spatialRank - 2] if padsLengthOk and has(attrs, \"pads\") and spatialRank >= 2 else 0",
     "padRight": "attrs.pads[2 * spatialRank - 1] if padsLengthOk and has(attrs, \"pads\") and spatialRank >= 1 else 0",
+    "poolBaseOk": "f16Ok(dtypes.T) and attrs.p > 0 and ranks.x >= 3 and ranks.x <= 5 and ranks.y == ranks.x and has(attrs, \"kernel_shape\") and kernelShapeLengthOk and stridesLengthOk and dilationsLengthOk and padsLengthOk and kernelD > 0 and kernelH > 0 and kernelW > 0 and strideD > 0 and strideH > 0 and strideW > 0 and dilationD > 0 and dilationH > 0 and dilationW > 0 and padFront >= 0 and padTop >= 0 and padLeft >= 0 and padBack >= 0 and padBottom >= 0 and padRight >= 0 and (attrs.auto_pad == \"NOTSET\" or not has(attrs, \"pads\")) and dim(shapes.y, 0) == dim(shapes.x, 0) and dim(shapes.y, 1) == dim(shapes.x, 1)",
+    "explicitOutW": "min(ceil((dim(shapes.x, ranks.x - 1) + padLeft + padRight - ((kernelW - 1) * dilationW + 1)) / strideW) + 1, ceil((dim(shapes.x, ranks.x - 1) + padLeft) / strideW)) if attrs.ceil_mode != 0 else floor((dim(shapes.x, ranks.x - 1) + padLeft + padRight - ((kernelW - 1) * dilationW + 1)) / strideW) + 1",
+    "explicitOutH": "min(ceil((dim(shapes.x, ranks.x - 2) + padTop + padBottom - ((kernelH - 1) * dilationH + 1)) / strideH) + 1, ceil((dim(shapes.x, ranks.x - 2) + padTop) / strideH)) if attrs.ceil_mode != 0 else floor((dim(shapes.x, ranks.x - 2) + padTop + padBottom - ((kernelH - 1) * dilationH + 1)) / strideH) + 1",
+    "explicitOutD": "(min(ceil((dim(shapes.x, 2) + padFront + padBack - ((kernelD - 1) * dilationD + 1)) / strideD) + 1, ceil((dim(shapes.x, 2) + padFront) / strideD)) if attrs.ceil_mode != 0 else floor((dim(shapes.x, 2) + padFront + padBack - ((kernelD - 1) * dilationD + 1)) / strideD) + 1) if ranks.x == 5 else 0",
+    "inferredOutW": "ceil(dim(shapes.x, ranks.x - 1) / strideW) if attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\" else explicitOutW",
+    "inferredOutH": "ceil(dim(shapes.x, ranks.x - 2) / strideH) if attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\" else explicitOutH",
+    "inferredOutD": "ceil(dim(shapes.x, 2) / strideD) if attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\" else explicitOutD",
+    "poolOutputShape": "[dim(shapes.x, 0), dim(shapes.x, 1), inferredOutW] if ranks.x == 3 else ([dim(shapes.x, 0), dim(shapes.x, 1), inferredOutH, inferredOutW] if ranks.x == 4 else [dim(shapes.x, 0), dim(shapes.x, 1), inferredOutD, inferredOutH, inferredOutW])",
+    "autoPadTotalH": "max(0, (dim(shapes.y, ranks.x - 2) - 1) * strideH + ((kernelH - 1) * dilationH + 1) - dim(shapes.x, ranks.x - 2)) if ranks.x >= 4 else 0",
+    "autoPadTotalW": "max(0, (dim(shapes.y, ranks.x - 1) - 1) * strideW + ((kernelW - 1) * dilationW + 1) - dim(shapes.x, ranks.x - 1))",
+    "effectivePadH": "(floor(autoPadTotalH / 2) if attrs.auto_pad == \"SAME_UPPER\" else autoPadTotalH - floor(autoPadTotalH / 2)) if (attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\") else padTop",
+    "effectivePadW": "(floor(autoPadTotalW / 2) if attrs.auto_pad == \"SAME_UPPER\" else autoPadTotalW - floor(autoPadTotalW / 2)) if (attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\") else padLeft",
+    "globalWindowParallelCovered": "poolBaseOk and numel(shapes.y) == dim(shapes.x, 0) * dim(shapes.x, 1) and numel(shapes.y) <= min(device.limits.maxComputeWorkgroupsPerDimension, 65535) and attrs.auto_pad == \"NOTSET\" and strideD == 1 and strideH == 1 and strideW == 1 and dilationD == 1 and dilationH == 1 and dilationW == 1 and padFront == 0 and padTop == 0 and padLeft == 0 and padBack == 0 and padBottom == 0 and padRight == 0 and ((ranks.x == 3 and kernelW == dim(shapes.x, 2)) or (ranks.x == 4 and kernelH == dim(shapes.x, 2) and kernelW == dim(shapes.x, 3)) or (ranks.x == 5 and kernelD == dim(shapes.x, 2) and kernelH == dim(shapes.x, 3) and kernelW == dim(shapes.x, 4)))",
+    "separableTmpElements": "dim(shapes.x, 0) * dim(shapes.x, 1) * dim(shapes.x, 2) * dim(shapes.y, 3)",
+    "separableFits": "separableTmpElements * 4 <= min(device.limits.maxStorageBufferBindingSize, device.limits.maxBufferSize) and ceilDiv(separableTmpElements, tunables.WORKGROUP_SIZE) <= min(device.limits.maxComputeWorkgroupsPerDimension, 65535) * min(device.limits.maxComputeWorkgroupsPerDimension, 65535)",
+    "autoPadTotalD": "max(0, (dim(shapes.y, 2) - 1) * strideD + ((kernelD - 1) * dilationD + 1) - dim(shapes.x, 2)) if ranks.x == 5 else 0",
+    "effectivePadD": "(floor(autoPadTotalD / 2) if attrs.auto_pad == \"SAME_UPPER\" else autoPadTotalD - floor(autoPadTotalD / 2)) if (attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\") else padFront",
+    "scalar": "dtypes.T"
   },
+  "bindings": {
+    "x_2": { "name": "x", "buffer": "read-only-storage", "elementType": "$xVec4" },
+    "params": {
+      "buffer": "uniform",
+      "struct": [
+        { "name": "channels", "type": "u32", "value": "dim(shapes.x, 1)" },
+        { "name": "inH", "type": "u32", "value": "dim(shapes.x, 2)" },
+        { "name": "inW", "type": "u32", "value": "dim(shapes.x, 3)" },
+        { "name": "outH", "type": "u32", "value": "dim(shapes.y, 2)" },
+        { "name": "outW", "type": "u32", "value": "dim(shapes.y, 3)" },
+        { "name": "kernelH", "type": "u32", "value": "kernelH" },
+        { "name": "kernelW", "type": "u32", "value": "kernelW" },
+        { "name": "strideH", "type": "u32", "value": "strideH" },
+        { "name": "strideW", "type": "u32", "value": "strideW" },
+        { "name": "dilationH", "type": "u32", "value": "dilationH" },
+        { "name": "dilationW", "type": "u32", "value": "dilationW" },
+        { "name": "padH", "type": "i32", "value": "effectivePadH" },
+        { "name": "padW", "type": "i32", "value": "effectivePadW" },
+        { "name": "p", "type": "f32", "value": "attrs.p" },
+        { "name": "count", "type": "u32", "value": "numel(shapes.y)" }
+      ]
+    }
   },
   "variants": [
     {
       "id": "global_window_parallel",
       "priority": 50,
       "when": ["globalWindowParallelCovered"],
+      "derive": {
+        "xElem": "(\"vec4<f16>\" if dtypes.T == \"f16\" else \"vec4<f32>\") if inner(shapes.x, 1) % 4 == 0 else dtypes.T"
       },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.GlobalWindowParallel",
+          "shader": "pool-global-reduction.wgsl.jinja",
+          "subgroupCollectivesWidth": "portable",
+          "derive": {
+            "op": "\"lp\"",
+            "vec4": "inner(shapes.x, 1) % 4 == 0",
+            "p": "attrs.p",
+            "workgroupSizeSpec": "tunables.WORKGROUP_SIZE",
+            "useSubgroups": "device.features.has(\"subgroups\")"
           },
+          "bindings": [
+            { "arg": "x", "elementType": "$xElem" },
+            "y",
+            { "name": "params", "struct": [{ "name": "spatial", "type": "u32", "value": "inner(shapes.x, 1)" }] }
+          ],
+          "dispatch": { "x": "numel(shapes.y)" }
         }
       ]
     },
     {
       "id": "nchw2d_k2s2_vec4",
       "priority": 30,
+      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.x == 4", "kernelH == 2", "kernelW == 2", "strideH == 2", "strideW == 2", "dilationH == 1", "dilationW == 1", "padTop == 0", "padLeft == 0", "padBottom == 0", "padRight == 0", "attrs.ceil_mode == 0", "dim(shapes.x, 3) % 4 == 0", "dim(shapes.y, 2) == floor(dim(shapes.x, 2) / 2)", "dim(shapes.y, 3) == floor(dim(shapes.x, 3) / 2)", "dim(shapes.y, 2) > 0", "dim(shapes.y, 3) > 0"],
+      "derive": { "xVec4": "\"vec4<f16>\" if dtypes.T == \"f16\" else \"vec4<f32>\"" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.Nchw2dK2s2Vec4",
+          "shader": "pool2d-nchw-k2s2-vec4.wgsl.jinja",
+          "derive": {
+            "op": "\"lp\"",
+            "outH": "dim(shapes.y, 2)",
+            "outW": "dim(shapes.y, 3)",
+            "outWHalf": "floor(dim(shapes.y, 3) / 2)",
+            "inWVec4": "floor(dim(shapes.x, 3) / 4)",
+            "inPlaneVec4": "floor(dim(shapes.x, 2) * dim(shapes.x, 3) / 4)",
+            "p": "attrs.p"
           },
+          "bindings": [
+            "x_2",
+            "y",
+            { "name": "params", "struct": [{ "name": "tileCount", "type": "u32", "value": "numel(shapes.y) / 2" }] }
+          ],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y) / 2), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y) / 2), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     },
     {
       "id": "nchw2d_unrolled",
       "priority": 25,
+      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.x == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0", "kernelH <= 3", "kernelW <= 3", "dilationH >= 1", "dilationW >= 1", "dim(shapes.y, 2) > 0", "dim(shapes.y, 3) > 0"],
       "passes": [
         {
           "id": "main",
           "name": "LpPool.Nchw2dUnrolled",
+          "shader": "pool-window-unroll.wgsl.jinja",
+          "derive": {
+            "op": "\"lp\"",
+            "kernelHSpec": "kernelH",
+            "kernelWSpec": "kernelW",
+            "strideHSpec": "strideH",
+            "strideWSpec": "strideW",
+            "dilationHSpec": "dilationH",
+            "dilationWSpec": "dilationW",
+            "padTopSpec": "padTop",
+            "padLeftSpec": "padLeft",
+            "inH": "dim(shapes.x, 2)",
+            "inW": "dim(shapes.x, 3)",
+            "outH": "dim(shapes.y, 2)",
+            "outW": "dim(shapes.y, 3)",
+            "p": "attrs.p"
           },
+          "bindings": [
+            "x",
+            "y",
+            { "name": "params", "struct": [{ "name": "count", "type": "u32", "value": "numel(shapes.y)" }] }
+          ],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     },
     {
       "id": "ncl1d_k4s2_p2_x4",
       "priority": 30,
+      "when": ["poolBaseOk", "ranks.x == 3", "attrs.auto_pad == \"NOTSET\"", "attrs.p == 2", "kernelW == 4", "strideW == 2", "dilationW == 1", "padLeft == 0", "padRight == 0", "attrs.ceil_mode == 0", "dim(shapes.x, 2) % 4 == 0", "dim(shapes.y, 2) == floor((dim(shapes.x, 2) - 4) / 2) + 1"],
+      "derive": { "xVec4": "\"vec4<f16>\" if dtypes.T == \"f16\" else \"vec4<f32>\"" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.Ncl1dK4s2P2X4",
+          "shader": "pool-ncl1d-x4.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "inW": "dim(shapes.x, 2)", "outW": "dim(shapes.y, 2)" },
+          "bindings": [
+            "x_2",
+            "y",
+            {
+              "name": "params",
+              "struct": [
+                {
+                  "name": "blockCount",
+                  "type": "u32",
+                  "value": "dim(shapes.x, 0) * dim(shapes.x, 1) * ceilDiv(dim(shapes.y, 2), 4)"
+                }
+              ]
+            }
+          ],
           "dispatch": {
+            "x": "min(ceilDiv((dim(shapes.x, 0) * dim(shapes.x, 1) * ceilDiv(dim(shapes.y, 2), 4)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((dim(shapes.x, 0) * dim(shapes.x, 1) * ceilDiv(dim(shapes.y, 2), 4)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
           }
         }
       ]
     {
       "id": "ncl1d",
       "priority": 10,
+      "when": ["poolBaseOk", "ranks.x == 3", "kernelW > 0", "strideW > 0"],
+      "derive": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCL1d",
+          "shader": "pool-window-nd.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "spatialRankSpec": 1 },
+          "bindings": [
+            "x",
+            "y",
+            {
+              "name": "params",
+              "struct": [
+                { "name": "channels", "type": "u32", "value": "dim(shapes.x, 1)" },
+                { "name": "inW", "type": "u32", "value": "dim(shapes.x, 2)" },
+                { "name": "outW", "type": "u32", "value": "dim(shapes.y, 2)" },
+                { "name": "kernelW", "type": "u32", "value": "kernelW" },
+                { "name": "strideW", "type": "u32", "value": "strideW" },
+                { "name": "dilationW", "type": "u32", "value": "dilationW" },
+                { "name": "padW", "type": "i32", "value": "effectivePadW" },
+                { "name": "p", "type": "f32", "value": "attrs.p" },
+                { "name": "count", "type": "u32", "value": "numel(shapes.y)" }
+              ]
+            }
+          ],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     },
     {
       "id": "nchw2d_separable",
       "priority": 22,
+      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.x == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0", "kernelH > 1", "kernelW > 1", "kernelH * kernelW >= tunables.SEPARABLE_MIN_WINDOW", "strideH < kernelH", "separableFits"],
+      "derive": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "intermediates": [{ "id": "tmp", "dtype": "float32", "shape": "[separableTmpElements]" }],
       "passes": [
         {
           "id": "reduce_w",
           "name": "LpPool.SeparableW",
+          "shader": "pool2d-nchw-separable.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "axisSpec": "\"w\"", "windowArea": "kernelH * kernelW" },
+          "bindings": [
+            "x",
+            { "name": "tmp", "buffer": "storage", "elementType": "f32" },
+            {
+              "name": "params",
+              "struct": [
+                { "name": "inH", "type": "u32", "value": "dim(shapes.x, 2)" },
+                { "name": "inW", "type": "u32", "value": "dim(shapes.x, 3)" },
+                { "name": "outW", "type": "u32", "value": "dim(shapes.y, 3)" },
+                { "name": "kernelW", "type": "u32", "value": "kernelW" },
+                { "name": "strideW", "type": "u32", "value": "strideW" },
+                { "name": "dilationW", "type": "u32", "value": "dilationW" },
+                { "name": "padW", "type": "i32", "value": "effectivePadW" },
+                { "name": "p", "type": "f32", "value": "attrs.p" },
+                {
+                  "name": "tmpCount",
+                  "type": "u32",
+                  "value": "dim(shapes.x, 0) * dim(shapes.x, 1) * dim(shapes.x, 2) * dim(shapes.y, 3)"
+                }
+              ]
+            }
+          ],
+          "dispatch": {
+            "x": "min(ceilDiv((separableTmpElements), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((separableTmpElements), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         },
         {
           "id": "reduce_h",
           "name": "LpPool.SeparableH",
+          "shader": "pool2d-nchw-separable.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "axisSpec": "\"h\"", "windowArea": "kernelH * kernelW" },
+          "bindings": [
+            { "name": "tmp", "buffer": "read-only-storage", "elementType": "f32" },
+            "y",
+            {
+              "name": "params",
+              "struct": [
+                { "name": "inH", "type": "u32", "value": "dim(shapes.x, 2)" },
+                { "name": "outH", "type": "u32", "value": "dim(shapes.y, 2)" },
+                { "name": "outW", "type": "u32", "value": "dim(shapes.y, 3)" },
+                { "name": "kernelH", "type": "u32", "value": "kernelH" },
+                { "name": "strideH", "type": "u32", "value": "strideH" },
+                { "name": "dilationH", "type": "u32", "value": "dilationH" },
+                { "name": "padH", "type": "i32", "value": "effectivePadH" },
+                { "name": "p", "type": "f32", "value": "attrs.p" },
+                { "name": "count", "type": "u32", "value": "numel(shapes.y)" }
+              ]
+            }
+          ],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     },
     {
       "id": "nchw2d",
+      "when": ["(attrs.auto_pad != \"SAME_UPPER\" and attrs.auto_pad != \"SAME_LOWER\")", "poolBaseOk", "ranks.x == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0"],
+      "derive": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCHW2d",
+          "shader": "pool-window-nd.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "spatialRankSpec": 2 },
+          "bindings": ["x", "y", "params"],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     },
     {
       "id": "nchw2d_autopad",
       "priority": 15,
+      "when": ["(attrs.auto_pad == \"SAME_UPPER\" or attrs.auto_pad == \"SAME_LOWER\")", "poolBaseOk", "ranks.x == 4", "kernelH > 0", "kernelW > 0", "strideH > 0", "strideW > 0", "dim(shapes.y, 2) == ceil(dim(shapes.x, 2) / strideH)", "dim(shapes.y, 3) == ceil(dim(shapes.x, 3) / strideW)"],
+      "derive": { "pIs1": "attrs.p == 1", "pIs2": "attrs.p == 2" },
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCHW2dAutoPad",
+          "shader": "pool-window-nd.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "spatialRankSpec": 2 },
+          "bindings": ["x", "y", "params"],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     },
     {
       "id": "ncdhw3d",
       "priority": 10,
+      "when": ["poolBaseOk", "ranks.x == 5", "kernelD > 0", "kernelH > 0", "kernelW > 0", "strideD > 0", "strideH > 0", "strideW > 0"],
       "supersededBy": ["global_window_parallel"],
       "passes": [
         {
           "id": "main",
           "name": "LpPool.NCDHW3d",
+          "shader": "pool-window-nd.wgsl.jinja",
+          "derive": { "op": "\"lp\"", "spatialRankSpec": 3 },
+          "bindings": [
+            "x",
+            "y",
+            {
+              "name": "params",
+              "struct": [
+                { "name": "channels", "type": "u32", "value": "dim(shapes.x, 1)" },
+                { "name": "inD", "type": "u32", "value": "dim(shapes.x, 2)" },
+                { "name": "inH", "type": "u32", "value": "dim(shapes.x, 3)" },
+                { "name": "inW", "type": "u32", "value": "dim(shapes.x, 4)" },
+                { "name": "outD", "type": "u32", "value": "dim(shapes.y, 2)" },
+                { "name": "outH", "type": "u32", "value": "dim(shapes.y, 3)" },
+                { "name": "outW", "type": "u32", "value": "dim(shapes.y, 4)" },
+                { "name": "kernelD", "type": "u32", "value": "kernelD" },
+                { "name": "kernelH", "type": "u32", "value": "kernelH" },
+                { "name": "kernelW", "type": "u32", "value": "kernelW" },
+                { "name": "strideD", "type": "u32", "value": "strideD" },
+                { "name": "strideH", "type": "u32", "value": "strideH" },
+                { "name": "strideW", "type": "u32", "value": "strideW" },
+                { "name": "dilationD", "type": "u32", "value": "dilationD" },
+                { "name": "dilationH", "type": "u32", "value": "dilationH" },
+                { "name": "dilationW", "type": "u32", "value": "dilationW" },
+                { "name": "padD", "type": "i32", "value": "effectivePadD" },
+                { "name": "padH", "type": "i32", "value": "effectivePadH" },
+                { "name": "padW", "type": "i32", "value": "effectivePadW" },
+                { "name": "p", "type": "f32", "value": "attrs.p" },
+                { "name": "count", "type": "u32", "value": "numel(shapes.y)" }
+              ]
+            }
+          ],
+          "dispatch": {
+            "x": "min(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "y": "ceilDiv(ceilDiv((numel(shapes.y)), (tunables.WORKGROUP_SIZE)), 65535)",
+            "z": 1
+          }
         }
       ]
     }

build/webgpu/metadata.json CHANGED Viewed

@@ -1,23 +1,36 @@
 {
   "name": "ai.onnx.LpPool",
-  "id": "_ai_onnx_lppool_webgpu_cd77da5",
   "version": 1,
   "license": "Apache-2.0",
   "backend": { "type": "webgpu" },
   "digest": {
     "algorithm": "sha256",
     "files": {
-      "bench.json": "w6aN2KgWABkA/ms17d5eRGnNY4W16SbuGU0Bv4Ua+PM=",
-      "manifest.json": "u27G/WowR9lMUG+lwNF4ur0POWFvxA1Pj1D/why0G3w=",
-      "pool-global-reduction.wgsl.jinja": "6v3XBuPgokHWHa3DRfhk8PyQ2km8soKnuJ9NNZUgSTo=",
-      "pool-ncl1d-x4.wgsl.jinja": "uZW9X2c3ilCWzT0YCg9/fs/L3wNTS5j4aRO31UQpFbA=",
-      "pool-window-nd.wgsl.jinja": "h6hIDb6qxMwApe1Wcw4x9cEnjWY1WpYWk3vR8ecYF/w=",
-      "pool-window-unroll.wgsl.jinja": "HVLK0vaS22ommayIv3VISzdyU3X1lgtFYG9N5WCOUUM=",
-      "pool2d-nchw-k2s2-vec4.wgsl.jinja": "Oq+ar9au4O/oG5ZU8MtSJYLKGXB/GupLNnYHEgmuaUI=",
-      "pool2d-nchw-separable.wgsl.jinja": "0Av3hMIG4Rnrut2XitTZrqU8l7ih1QV0/w01vyAsbe4=",
-      "test.json": "5NKe4iEQZXXCRAfrhel4tIqfaUs+/u9uZCKdPbPU4oI="
     }
   },
-  "provenance": { "kernel": { "sha": "2e7068faf55e7f43df740015f6d1ee49391a41c5", "dirty": false } },
-  "webgpu": { "manifestSpec": "1.0", "specialized": true, "opPath": "ops/ai.onnx.LpPool" }
 }

 {
   "name": "ai.onnx.LpPool",
+  "id": "_ai_onnx_lppool_webgpu_441a712",
   "version": 1,
   "license": "Apache-2.0",
   "backend": { "type": "webgpu" },
   "digest": {
     "algorithm": "sha256",
     "files": {
+      "bench.json": "9MTx0K098K5uU8eYf1fyGZZj9qAm9WyXRQ0ri8CA5PQ=",
+      "manifest.json": "Vl52UNLWlE34s3r8t6aom9dk2ugID2Rbj8mwiQEAahY=",
+      "pool-global-reduction.wgsl.jinja": "KkncfRhVcz/NAm+o15UOS2NoX9dhg4DxKHxodJoDBtI=",
+      "pool-ncl1d-x4.wgsl.jinja": "D2FL4TG9Pkkxz0t/fpXhDAZnp6BFd7bMFPpJFDgx5C8=",
+      "pool-window-nd.wgsl.jinja": "gngVMHmnqDaysakzQPGhBmBXq8FGKjMW8b3CJ28EvxY=",
+      "pool-window-unroll.wgsl.jinja": "dgNv0uSyrzk2atSx0NZUSoDedRM88Six/G+mz1lCstc=",
+      "pool2d-nchw-k2s2-vec4.wgsl.jinja": "x1vTe17pXOTwtxqcVfAdxP21Zeu8xjlUMn1T7aQr/8M=",
+      "pool2d-nchw-separable.wgsl.jinja": "NlzeGgsP6kN2rq7AwvgLfOzDqhRmv8DAnyKFtJXPoSA=",
+      "test.json": "9sh7zuhm5EUBaqoqldehtqYLO2cENj3u4/+pEGwTVjQ="
     }
   },
+  "provenance": { "kernel": { "sha": "91d990483a174128daf7673f3f37a7c890493ae1", "dirty": false } },
+  "webgpu": {
+    "manifestSpec": "2.0",
+    "variants": {
+      "global_window_parallel": ["pool-global-reduction.wgsl.jinja"],
+      "nchw2d_k2s2_vec4": ["pool2d-nchw-k2s2-vec4.wgsl.jinja"],
+      "nchw2d_unrolled": ["pool-window-unroll.wgsl.jinja"],
+      "ncl1d_k4s2_p2_x4": ["pool-ncl1d-x4.wgsl.jinja"],
+      "ncl1d": ["pool-window-nd.wgsl.jinja"],
+      "nchw2d_separable": ["pool2d-nchw-separable.wgsl.jinja"],
+      "nchw2d": ["pool-window-nd.wgsl.jinja"],
+      "nchw2d_autopad": ["pool-window-nd.wgsl.jinja"],
+      "ncdhw3d": ["pool-window-nd.wgsl.jinja"]
+    }
+  }
 }

build/webgpu/pool-global-reduction.wgsl.jinja CHANGED Viewed

@@ -1,7 +1,5 @@
-{% if usesF16 %}
-enable f16;
-{% endif %}
-{% if source.useSubgroups %}
 enable subgroups;
 {% endif %}
 {{ env.wgsl.resourceDeclarations }}
@@ -9,53 +7,44 @@ enable subgroups;
 // Workgroup-parallel global pooling: one workgroup reduces one contiguous
 // (n, c) spatial plane (NC[D]HW layout makes the plane contiguous).
 // Threads stride the plane (vec4 loads when the plane size is divisible by 4),
-// then combine the per-thread partials. With subgroups: a subgroup collective
-// + a shared-memory fold of the per-subgroup partials. Without subgroups, a
-// full workgroup tree keeps workgroup-per-plane parallelism
-// instead of falling to the one-thread-per-plane serial scalar kernel. The
-// epilogue finalizes average / max / Lp. Sum reassociates;
-// max is exact.
-const WG: u32 = {{ source.workgroupSize }}u;
-{% if source.useSubgroups %}
-// WebGPU subgroups are at least 4 wide, so a workgroup holds at most WG / 4 subgroups.
-const MAX_SUBGROUPS: u32 = WG / 4u;
-var<workgroup> wg_partials: array<f32, MAX_SUBGROUPS>;
 {% else %}
 var<workgroup> wg_tree: array<f32, WG>;
 {% endif %}
-{% if source.op == "lp" and source.p != 1 and source.p != 2 %}
-const P: f32 = f32({{ source.p }});
 {% endif %}
 @compute @workgroup_size(WG)
 fn main(@builtin(workgroup_id) wid: vec3<u32>,
         @builtin(local_invocation_id) lid: vec3<u32>
-{%- if source.useSubgroups %},
         @builtin(subgroup_size) sg_size: u32
 {%- endif %}) {
   let plane = wid.x;
   let tid = lid.x;
-{% if source.useSubgroups %}
-  // Seed cross-subgroup slots with the reduction identity so unwritten slots
-  // (when the workgroup has fewer than MAX_SUBGROUPS subgroups) are harmless.
-  if (tid < MAX_SUBGROUPS) {
-    wg_partials[tid] = 0.0;
-  }
-  workgroupBarrier();
-{% endif %}
   var partial = 0.0;
-{% if source.vec4 %}
   let spatialVec = params.spatial / 4u;
   let base = plane * spatialVec;
   var accv = vec4<f32>(0.0);
   for (var i = tid; i < spatialVec; i = i + WG) {
     let v = vec4<f32>(x[base + i]);
-{% if source.p == 2 %}
     accv = accv + v * v;
-{% elif source.p == 1 %}
     accv = accv + abs(v);
 {% else %}
     accv = accv + pow(abs(v), vec4<f32>(P));
@@ -66,9 +55,9 @@ fn main(@builtin(workgroup_id) wid: vec3<u32>,
   let base = plane * params.spatial;
   for (var i = tid; i < params.spatial; i = i + WG) {
     let v = f32(x[base + i]);
-{% if source.p == 2 %}
     partial = partial + v * v;
-{% elif source.p == 1 %}
     partial = partial + abs(v);
 {% else %}
     partial = partial + pow(abs(v), P);
@@ -76,21 +65,34 @@ fn main(@builtin(workgroup_id) wid: vec3<u32>,
   }
 {% endif %}
-{% if source.useSubgroups %}
-  let sg_sum = subgroupAdd(partial);
-  if (subgroupElect()) {
-    wg_partials[tid / sg_size] = sg_sum;
   }
-  workgroupBarrier();
   if (tid == 0u) {
-    let nsg = min((WG + sg_size - 1u) / sg_size, MAX_SUBGROUPS);
-    var total = 0.0;
-    for (var i = 0u; i < nsg; i = i + 1u) {
-      total = total + wg_partials[i];
-    }
-{% if source.p == 2 %}
     y[plane] = {{ scalar }}(sqrt(total));
-{% elif source.p == 1 %}
     y[plane] = {{ scalar }}(total);
 {% else %}
     y[plane] = {{ scalar }}(pow(total, 1.0 / P));
@@ -108,9 +110,9 @@ fn main(@builtin(workgroup_id) wid: vec3<u32>,
   }
   if (tid == 0u) {
     let total = wg_tree[0];
-{% if source.p == 2 %}
     y[plane] = {{ scalar }}(sqrt(total));
-{% elif source.p == 1 %}
     y[plane] = {{ scalar }}(total);
 {% else %}
     y[plane] = {{ scalar }}(pow(total, 1.0 / P));

+{% set p = p | default(0) %}
+{% if useSubgroups %}
 enable subgroups;
 {% endif %}
 {{ env.wgsl.resourceDeclarations }}
 // Workgroup-parallel global pooling: one workgroup reduces one contiguous
 // (n, c) spatial plane (NC[D]HW layout makes the plane contiguous).
 // Threads stride the plane (vec4 loads when the plane size is divisible by 4),
+// then combine the per-thread partials. With subgroups: a subgroup collective,
+// then one shared-memory slot per invocation (the subgroup partial from its
+// elected lane, the identity from every other lane) that every subgroup folds.
+// Without subgroups, a full workgroup tree combines the per-thread partials.
+// The epilogue applies the selected Lp root to the f32 sum of |x|^p. The tree
+// changes the association of additions.
+const WG: u32 = {{ workgroupSizeSpec }}u;
+{% if useSubgroups %}
+// One slot per invocation: the cross-subgroup fold below indexes by
+// local_invocation_id, never by a subgroup ordinal, so no subgroup width or
+// partition can overflow or alias it.
+var<workgroup> wg_partials: array<f32, WG>;
 {% else %}
 var<workgroup> wg_tree: array<f32, WG>;
 {% endif %}
+{% if op == "lp" and p != 1 and p != 2 %}
+const P: f32 = f32({{ p }});
 {% endif %}
 @compute @workgroup_size(WG)
 fn main(@builtin(workgroup_id) wid: vec3<u32>,
         @builtin(local_invocation_id) lid: vec3<u32>
+{%- if useSubgroups %},
         @builtin(subgroup_size) sg_size: u32
 {%- endif %}) {
   let plane = wid.x;
   let tid = lid.x;
   var partial = 0.0;
+{% if vec4 %}
   let spatialVec = params.spatial / 4u;
   let base = plane * spatialVec;
   var accv = vec4<f32>(0.0);
   for (var i = tid; i < spatialVec; i = i + WG) {
     let v = vec4<f32>(x[base + i]);
+{% if p == 2 %}
     accv = accv + v * v;
+{% elif p == 1 %}
     accv = accv + abs(v);
 {% else %}
     accv = accv + pow(abs(v), vec4<f32>(P));
   let base = plane * params.spatial;
   for (var i = tid; i < params.spatial; i = i + WG) {
     let v = f32(x[base + i]);
+{% if p == 2 %}
     partial = partial + v * v;
+{% elif p == 1 %}
     partial = partial + abs(v);
 {% else %}
     partial = partial + pow(abs(v), P);
   }
 {% endif %}
+{% if useSubgroups %}
+  var total = subgroupAdd(partial);
+  // Cross-subgroup fold that assumes nothing about which invocations share a
+  // subgroup, how many subgroups there are, or which of a subgroup's lanes are
+  // active: every invocation owns the slot at its own index, the elected lane
+  // publishes its subgroup partial there and every other lane publishes the
+  // reduction identity. Each subgroup then folds all WG slots — lane `rank`,
+  // its dense position among the active lanes, walks slots rank, rank + count,
+  // ... — and one more collective merges the lane partials, so every slot is
+  // merged exactly once at any legal width and partition. A one-subgroup
+  // workgroup skips shared memory entirely; that test reads the `subgroup_size`
+  // builtin, which is uniform, because a collective's result is not uniform to
+  // WGSL's analysis and may not guard a barrier.
+  if (sg_size != WG) {
+    let rank = subgroupExclusiveAdd(1u);
+    let count = subgroupAdd(1u);
+    wg_partials[tid] = select(0.0, total, rank == 0u);
+    workgroupBarrier();
+    var acc = 0.0;
+    for (var i = rank; i < WG; i = i + count) {
+      acc = acc + wg_partials[i];
+    }
+    total = subgroupAdd(acc);
   }
   if (tid == 0u) {
+{% if p == 2 %}
     y[plane] = {{ scalar }}(sqrt(total));
+{% elif p == 1 %}
     y[plane] = {{ scalar }}(total);
 {% else %}
     y[plane] = {{ scalar }}(pow(total, 1.0 / P));
   }
   if (tid == 0u) {
     let total = wg_tree[0];
+{% if p == 2 %}
     y[plane] = {{ scalar }}(sqrt(total));
+{% elif p == 1 %}
     y[plane] = {{ scalar }}(total);
 {% else %}
     y[plane] = {{ scalar }}(pow(total, 1.0 / P));

build/webgpu/pool-ncl1d-x4.wgsl.jinja CHANGED Viewed

@@ -1,21 +1,16 @@
-{% if usesF16 %}
-enable f16;
-{% endif %}
 {{ env.wgsl.resourceDeclarations }}
-// Four adjacent NCL outputs per invocation for non-overlapping max k2/s2 and
-// overlapping L2 k4/s2 windows. Both layouts consume aligned vec4 input blocks.
-// The L2 route reuses shared middle samples instead of loading every window
-// independently.
-const IN_W: u32 = {{ source.inW }}u;
-const OUT_W: u32 = {{ source.outW }}u;
 const BLOCKS_PER_PLANE: u32 = (OUT_W + 3u) / 4u;
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
 fn main(
   @builtin(global_invocation_id) gid: vec3<u32>,
-  @builtin(num_workgroups) nwg: vec3<u32>,
 ) {
-  let block = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
   if (block >= params.blockCount) { return; }
   let plane = block / BLOCKS_PER_PLANE;

 {{ env.wgsl.resourceDeclarations }}
+// Four adjacent NCL L2-pool outputs per invocation for overlapping k4/s2
+// windows. Aligned vec4 input words share the middle samples of neighboring
+// windows.
+const IN_W: u32 = {{ inW }}u;
+const OUT_W: u32 = {{ outW }}u;
 const BLOCKS_PER_PLANE: u32 = (OUT_W + 3u) / 4u;
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
 fn main(
   @builtin(global_invocation_id) gid: vec3<u32>,
 ) {
+  let block = gid.x + gid.y * {{ DISPATCH_FOLD_WIDTH }}u * {{ tunables.WORKGROUP_SIZE }}u;
   if (block >= params.blockCount) { return; }
   let plane = block / BLOCKS_PER_PLANE;

build/webgpu/pool-window-nd.wgsl.jinja CHANGED Viewed

@@ -1,21 +1,18 @@
-{% if usesF16 %}
-enable f16;
-{% endif %}
 {{ env.wgsl.resourceDeclarations }}
-// Direct {{ source.spatialRank }}D {{ source.op }}-pool traversal for a channels-first tensor.
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
-fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
-  let index = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
   if (index >= params.count) { return; }
   let ow = index % params.outW;
   var t = index / params.outW;
-{% if source.spatialRank >= 2 %}
   let oh = t % params.outH;
   t /= params.outH;
 {% endif %}
-{% if source.spatialRank == 3 %}
   let od = t % params.outD;
   t /= params.outD;
 {% endif %}
@@ -24,12 +21,12 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups)
   var acc = 0.0;
-{% if source.spatialRank == 3 %}
   for (var kd = 0u; kd < params.kernelD; kd += 1u) {
     let id = i32(od * params.strideD + kd * params.dilationD) - params.padD;
     if (id < 0 || id >= i32(params.inD)) { continue; }
 {% endif %}
-{% if source.spatialRank >= 2 %}
     for (var kh = 0u; kh < params.kernelH; kh += 1u) {
       let ih = i32(oh * params.strideH + kh * params.dilationH) - params.padH;
       if (ih < 0 || ih >= i32(params.inH)) { continue; }
@@ -37,9 +34,9 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups)
       for (var kw = 0u; kw < params.kernelW; kw += 1u) {
         let iw = i32(ow * params.strideW + kw * params.dilationW) - params.padW;
         if (iw < 0 || iw >= i32(params.inW)) { continue; }
-{% if source.spatialRank == 1 %}
         let xIndex = (batch * params.channels + channel) * params.inW + u32(iw);
-{% elif source.spatialRank == 2 %}
         let xIndex = ((batch * params.channels + channel) * params.inH + u32(ih)) * params.inW + u32(iw);
 {% else %}
         let xIndex = (((batch * params.channels + channel) * params.inD + u32(id)) * params.inH + u32(ih)) * params.inW + u32(iw);
@@ -53,10 +50,10 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups)
         acc += pow(abs(f32(x[xIndex])), params.p);
 {% endif %}
       }
-{% if source.spatialRank >= 2 %}
     }
 {% endif %}
-{% if source.spatialRank == 3 %}
   }
 {% endif %}

 {{ env.wgsl.resourceDeclarations }}
+// Direct spatial-pooling traversal for a channels-first tensor.
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+  let index = gid.x + gid.y * {{ DISPATCH_FOLD_WIDTH }}u * {{ tunables.WORKGROUP_SIZE }}u;
   if (index >= params.count) { return; }
   let ow = index % params.outW;
   var t = index / params.outW;
+{% if spatialRankSpec >= 2 %}
   let oh = t % params.outH;
   t /= params.outH;
 {% endif %}
+{% if spatialRankSpec == 3 %}
   let od = t % params.outD;
   t /= params.outD;
 {% endif %}
   var acc = 0.0;
+{% if spatialRankSpec == 3 %}
   for (var kd = 0u; kd < params.kernelD; kd += 1u) {
     let id = i32(od * params.strideD + kd * params.dilationD) - params.padD;
     if (id < 0 || id >= i32(params.inD)) { continue; }
 {% endif %}
+{% if spatialRankSpec >= 2 %}
     for (var kh = 0u; kh < params.kernelH; kh += 1u) {
       let ih = i32(oh * params.strideH + kh * params.dilationH) - params.padH;
       if (ih < 0 || ih >= i32(params.inH)) { continue; }
       for (var kw = 0u; kw < params.kernelW; kw += 1u) {
         let iw = i32(ow * params.strideW + kw * params.dilationW) - params.padW;
         if (iw < 0 || iw >= i32(params.inW)) { continue; }
+{% if spatialRankSpec == 1 %}
         let xIndex = (batch * params.channels + channel) * params.inW + u32(iw);
+{% elif spatialRankSpec == 2 %}
         let xIndex = ((batch * params.channels + channel) * params.inH + u32(ih)) * params.inW + u32(iw);
 {% else %}
         let xIndex = (((batch * params.channels + channel) * params.inD + u32(id)) * params.inH + u32(ih)) * params.inW + u32(iw);
         acc += pow(abs(f32(x[xIndex])), params.p);
 {% endif %}
       }
+{% if spatialRankSpec >= 2 %}
     }
 {% endif %}
+{% if spatialRankSpec == 3 %}
   }
 {% endif %}

build/webgpu/pool-window-unroll.wgsl.jinja CHANGED Viewed

@@ -1,31 +1,28 @@
 // Compile-time-unrolled 2D NCHW pooling for small windows. Accumulation remains
-// kh-major/kw-minor, matching the runtime-loop fallbacks.
-{% if usesF16 %}
-enable f16;
-{% endif %}
 {{ env.wgsl.resourceDeclarations }}
-const OUT_W: u32 = {{ source.outW }}u;
-const OUT_H: u32 = {{ source.outH }}u;
-const IN_H: i32 = {{ source.inH }};
-const IN_W: i32 = {{ source.inW }};
-const IN_W_U: u32 = {{ source.inW }}u;
-const IN_PLANE: u32 = {{ source.inH * source.inW }}u;
-const STRIDE_H: u32 = {{ source.strideH }}u;
-const STRIDE_W: u32 = {{ source.strideW }}u;
-const PAD_TOP: i32 = {{ source.padTop }};
-const PAD_LEFT: i32 = {{ source.padLeft }};
-{% if source.op == "lp" and source.p != 1 and source.p != 2 %}
-const P: f32 = f32({{ source.p }});
 {% endif %}
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
 fn main(
-  @builtin(global_invocation_id) gid: vec3<u32>,
-  @builtin(num_workgroups) nwg: vec3<u32>
 ) {
   // Fold very large flat outputs into the dispatch's second dimension.
-  let index = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
   if (index >= params.count) {
     return;
   }
@@ -37,23 +34,23 @@ fn main(
   let planeBase = plane * IN_PLANE;
   let startH = i32(oh * STRIDE_H) - PAD_TOP;
   let startW = i32(ow * STRIDE_W) - PAD_LEFT;
-{% for kh in range(source.kernelH) %}
-  let ih_{{ kh }} = startH + {{ kh * source.dilationH }};
 {% endfor %}
-{% for kw in range(source.kernelW) %}
-  let iw_{{ kw }} = startW + {{ kw * source.dilationW }};
 {% endfor %}
   var result = 0.0;
-{% for kh in range(source.kernelH) %}
   if (ih_{{ kh }} >= 0 && ih_{{ kh }} < IN_H) {
     let rowBase{{ kh }} = planeBase + u32(ih_{{ kh }}) * IN_W_U;
-{% for kw in range(source.kernelW) %}
     if (iw_{{ kw }} >= 0 && iw_{{ kw }} < IN_W) {
       let value_{{ kh }}_{{ kw }} = f32(x[rowBase{{ kh }} + u32(iw_{{ kw }})]);
-{% if source.op == "lp" and source.p == 2 %}
       result += value_{{ kh }}_{{ kw }} * value_{{ kh }}_{{ kw }};
-{% elif source.op == "lp" and source.p == 1 %}
       result += abs(value_{{ kh }}_{{ kw }});
 {% else %}
       result += pow(abs(value_{{ kh }}_{{ kw }}), P);
@@ -64,9 +61,9 @@ fn main(
 {% endfor %}
-{% if source.op == "lp" and source.p == 2 %}
   let output = sqrt(result);
-{% elif source.op == "lp" and source.p == 1 %}
   let output = result;
 {% else %}
   let output = pow(result, 1.0 / P);

 // Compile-time-unrolled 2D NCHW pooling for small windows. Accumulation remains
+// kh-major/kw-minor, matching the loop-based implementations.
+{% set p = p | default(0) %}
 {{ env.wgsl.resourceDeclarations }}
+const OUT_W: u32 = {{ outW }}u;
+const OUT_H: u32 = {{ outH }}u;
+const IN_H: i32 = {{ inH }};
+const IN_W: i32 = {{ inW }};
+const IN_W_U: u32 = {{ inW }}u;
+const IN_PLANE: u32 = {{ inH * inW }}u;
+const STRIDE_H: u32 = {{ strideHSpec }}u;
+const STRIDE_W: u32 = {{ strideWSpec }}u;
+const PAD_TOP: i32 = {{ padTopSpec }};
+const PAD_LEFT: i32 = {{ padLeftSpec }};
+{% if op == "lp" and p != 1 and p != 2 %}
+const P: f32 = f32({{ p }});
 {% endif %}
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
 fn main(
+  @builtin(global_invocation_id) gid: vec3<u32>
 ) {
   // Fold very large flat outputs into the dispatch's second dimension.
+  let index = gid.x + gid.y * {{ DISPATCH_FOLD_WIDTH }}u * {{ tunables.WORKGROUP_SIZE }}u;
   if (index >= params.count) {
     return;
   }
   let planeBase = plane * IN_PLANE;
   let startH = i32(oh * STRIDE_H) - PAD_TOP;
   let startW = i32(ow * STRIDE_W) - PAD_LEFT;
+{% for kh in range(kernelHSpec) %}
+  let ih_{{ kh }} = startH + {{ kh * dilationHSpec }};
 {% endfor %}
+{% for kw in range(kernelWSpec) %}
+  let iw_{{ kw }} = startW + {{ kw * dilationWSpec }};
 {% endfor %}
   var result = 0.0;
+{% for kh in range(kernelHSpec) %}
   if (ih_{{ kh }} >= 0 && ih_{{ kh }} < IN_H) {
     let rowBase{{ kh }} = planeBase + u32(ih_{{ kh }}) * IN_W_U;
+{% for kw in range(kernelWSpec) %}
     if (iw_{{ kw }} >= 0 && iw_{{ kw }} < IN_W) {
       let value_{{ kh }}_{{ kw }} = f32(x[rowBase{{ kh }} + u32(iw_{{ kw }})]);
+{% if op == "lp" and p == 2 %}
       result += value_{{ kh }}_{{ kw }} * value_{{ kh }}_{{ kw }};
+{% elif op == "lp" and p == 1 %}
       result += abs(value_{{ kh }}_{{ kw }});
 {% else %}
       result += pow(abs(value_{{ kh }}_{{ kw }}), P);
 {% endfor %}
+{% if op == "lp" and p == 2 %}
   let output = sqrt(result);
+{% elif op == "lp" and p == 1 %}
   let output = result;
 {% else %}
   let output = pow(result, 1.0 / P);

build/webgpu/pool2d-nchw-k2s2-vec4.wgsl.jinja CHANGED Viewed

@@ -1,6 +1,4 @@
-{% if usesF16 %}
-enable f16;
-{% endif %}
 {{ env.wgsl.resourceDeclarations }}
 // 2D NCHW LpPool specialised for the dense 2x2 / stride-2 / no-pad /
@@ -11,27 +9,27 @@ enable f16;
 // |x|^p over the 2x2 window, same four elements / same count as the scalar
 // fallback) and reduced by the p-th root; p=2 specialises to sum-of-squares +
 // sqrt, p=1 to sum-of-abs.
-const OUT_W: u32 = {{ source.outW }}u;        // == IN_W / 2 (no pad, stride 2)
-const OUT_H: u32 = {{ source.outH }}u;
-const OUT_W_HALF: u32 = {{ source.outWHalf }}u;
-const IN_W_VEC4: u32 = {{ source.inWVec4 }}u; // input row length in vec4 units
-const IN_PLANE_VEC4: u32 = {{ source.inPlaneVec4 }}u;
-{% if source.p != 1 and source.p != 2 %}
-const P: f32 = f32({{ source.p }});
 {% endif %}
-{% if source.p == 2 %}
 fn term(v: {{ scalar }}) -> f32 { let f = f32(v); return f * f; }
-{% elif source.p == 1 %}
 fn term(v: {{ scalar }}) -> f32 { return abs(f32(v)); }
 {% else %}
 fn term(v: {{ scalar }}) -> f32 { return pow(abs(f32(v)), P); }
 {% endif %}
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
-fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
   // 2D-folded flat tile index (one tile = two output columns).
-  let tile = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
   if (tile >= params.tileCount) {
     return;
   }
@@ -52,10 +50,10 @@ fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups)
   let acc1 = term(r0.z) + term(r0.w) + term(r1.z) + term(r1.w);
   let outBase = (plane * OUT_H + oh) * OUT_W + tileCol * 2u;
-{% if source.p == 2 %}
   y[outBase] = {{ scalar }}(sqrt(acc0));
   y[outBase + 1u] = {{ scalar }}(sqrt(acc1));
-{% elif source.p == 1 %}
   y[outBase] = {{ scalar }}(acc0);
   y[outBase + 1u] = {{ scalar }}(acc1);
 {% else %}

+{% set p = p | default(0) %}
 {{ env.wgsl.resourceDeclarations }}
 // 2D NCHW LpPool specialised for the dense 2x2 / stride-2 / no-pad /
 // |x|^p over the 2x2 window, same four elements / same count as the scalar
 // fallback) and reduced by the p-th root; p=2 specialises to sum-of-squares +
 // sqrt, p=1 to sum-of-abs.
+const OUT_W: u32 = {{ outW }}u;        // == IN_W / 2 (no pad, stride 2)
+const OUT_H: u32 = {{ outH }}u;
+const OUT_W_HALF: u32 = {{ outWHalf }}u;
+const IN_W_VEC4: u32 = {{ inWVec4 }}u; // input row length in vec4 units
+const IN_PLANE_VEC4: u32 = {{ inPlaneVec4 }}u;
+{% if p != 1 and p != 2 %}
+const P: f32 = f32({{ p }});
 {% endif %}
+{% if p == 2 %}
 fn term(v: {{ scalar }}) -> f32 { let f = f32(v); return f * f; }
+{% elif p == 1 %}
 fn term(v: {{ scalar }}) -> f32 { return abs(f32(v)); }
 {% else %}
 fn term(v: {{ scalar }}) -> f32 { return pow(abs(f32(v)), P); }
 {% endif %}
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
   // 2D-folded flat tile index (one tile = two output columns).
+  let tile = gid.x + gid.y * {{ DISPATCH_FOLD_WIDTH }}u * {{ tunables.WORKGROUP_SIZE }}u;
   if (tile >= params.tileCount) {
     return;
   }
   let acc1 = term(r0.z) + term(r0.w) + term(r1.z) + term(r1.w);
   let outBase = (plane * OUT_H + oh) * OUT_W + tileCol * 2u;
+{% if p == 2 %}
   y[outBase] = {{ scalar }}(sqrt(acc0));
   y[outBase + 1u] = {{ scalar }}(sqrt(acc1));
+{% elif p == 1 %}
   y[outBase] = {{ scalar }}(acc0);
   y[outBase + 1u] = {{ scalar }}(acc1);
 {% else %}

build/webgpu/pool2d-nchw-separable.wgsl.jinja CHANGED Viewed

@@ -1,31 +1,18 @@
-{% if usesF16 %}
-enable f16;
-{% endif %}
 {{ env.wgsl.resourceDeclarations }}
 // Separable NCHW pooling: one spatial axis per pass.
 //
-// Both reductions here are associative, so a kernelH x kernelW window
-// factorizes into a reduction along W followed by one along H. The direct
-// kernel reads kernelH*kernelW inputs per output; separating them reads kernelW
-// into an intermediate cell that neighbouring outputs then share, plus kernelH
-// per output.
-//
-// The intermediate is f32 because the direct kernel also accumulates in f32
-// and casts once at the end, including for i32 input.
-//
-// Max pooling is bit-identical to the direct kernel because max is idempotent
-// as well as associative. Lp pooling is within tolerance but not bit-identical:
-// summing each row first regroups f32 additions relative to the direct kernel.
-//
-// `axis` selects the pass: "w" reduces x into tmp, and "h" reduces tmp into y.
-// AveragePool is not wired here because its denominator would require valid-tap
-// counts to travel alongside the sum.
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
-fn main(@builtin(global_invocation_id) gid: vec3<u32>, @builtin(num_workgroups) nwg: vec3<u32>) {
-  let index = gid.x + gid.y * nwg.x * {{ tunables.WORKGROUP_SIZE }}u;
-{% if source.axis == "w" %}
   if (index >= params.tmpCount) { return; }
   // One cell per (batch, channel, input row, output column).
   let ow = index % params.outW;

 {{ env.wgsl.resourceDeclarations }}
 // Separable NCHW pooling: one spatial axis per pass.
 //
+// A kernelH x kernelW window factorizes into a width reduction followed by a
+// height reduction. The width pass writes one f32 intermediate per input row
+// and output column; the height pass completes the selected pooling operation.
+// `axis == "w"` writes `tmp`, and `axis == "h"` writes `y`.
+// Both passes accumulate |x|^p in f32; the height pass applies the final
+// p-root. Row-first accumulation changes the association of f32 additions.
 @compute @workgroup_size({{ tunables.WORKGROUP_SIZE }})
+fn main(@builtin(global_invocation_id) gid: vec3<u32>) {
+  let index = gid.x + gid.y * {{ DISPATCH_FOLD_WIDTH }}u * {{ tunables.WORKGROUP_SIZE }}u;
+{% if axisSpec == "w" %}
   if (index >= params.tmpCount) { return; }
   // One cell per (batch, channel, input row, output column).
   let ow = index % params.outW;

build/webgpu/test.json CHANGED Viewed

@@ -1,5 +1,4 @@
 {
-  "op": "ai.onnx.LpPool",
   "fixtureArrays": {
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     "lp2_2d_kernel2_stride1_input_x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
@@ -563,9 +562,7 @@
     },
     {
       "name": "autopad_same_upper_stride2_p3",
-      "provenance": {
-        "notes": "General-p regression for the auto-pad layout; p=1 and p=2 specialize the exponent out of the shader."
-      },
       "attrs": { "auto_pad": "SAME_UPPER", "p": 3, "kernel_shape": [3, 3], "strides": [2, 2] },
       "inputs": {
         "x": {
@@ -638,9 +635,7 @@
     },
     {
       "name": "f32_2d_k2s2_p2_unrolled_2x16x32x32",
-      "provenance": {
-        "notes": "Compact f32 sibling for the k2/s2 p=2 LpPool unrolled benchmark; preserves dense NCHW planes and the stride-2 2x2 reduction shape."
-      },
       "attrs": { "p": 2, "kernel_shape": [2, 2], "strides": [2, 2] },
       "inputs": {
         "x": {
@@ -654,7 +649,7 @@
     {
       "name": "f32_3d_ncdhw_k2s2_p2_volumetric_1x4x8x8x8",
       "provenance": {
-        "notes": "Compact f32 sibling for the volumetric k2/s2 p=2 LpPool benchmark; covers the rank-5 NCDHW 3D pooling path with nontrivial channel and depth work."
       },
       "attrs": { "p": 2, "kernel_shape": [2, 2, 2], "strides": [2, 2, 2] },
       "inputs": {
@@ -773,7 +768,7 @@
       "name": "lppool3d_depth_dilation2_compact",
       "provenance": {
         "source": "ONNX LpPool-22 volumetric dilation semantics",
-        "notes": "Covers a non-default depth dilation. Existing 3D LpPool fixtures varied kernel/stride but kept dilationD=1."
       },
       "attrs": { "p": 2, "kernel_shape": [2, 1, 1], "dilations": [2, 1, 1] },
       "inputs": {
@@ -788,7 +783,7 @@
     {
       "name": "ncl1d_k4s2_p2_x4_tail_two_planes",
       "provenance": {
-        "notes": "Exercises four-output L2 reuse plus the three-output final block without reading beyond the final plane. Expected values come from the TypeScript LpPool reference."
       },
       "attrs": { "p": 2, "kernel_shape": [4], "strides": [2] },
       "inputs": {
@@ -862,6 +857,43 @@
         }
       },
       "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7], "tolerance": 0.01 } }
     }
   ]
 }

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     "lp2_2d_kernel2_stride1_input_x": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]
     },
     {
       "name": "autopad_same_upper_stride2_p3",
+      "provenance": { "notes": "SAME_UPPER with p=3 exercises the general exponent path and auto-pad layout." },
       "attrs": { "auto_pad": "SAME_UPPER", "p": 3, "kernel_shape": [3, 3], "strides": [2, 2] },
       "inputs": {
         "x": {
     },
     {
       "name": "f32_2d_k2s2_p2_unrolled_2x16x32x32",
+      "provenance": { "notes": "A compact dense float32 NCHW input exercises unrolled k2/s2 p=2 LpPool." },
       "attrs": { "p": 2, "kernel_shape": [2, 2], "strides": [2, 2] },
       "inputs": {
         "x": {
     {
       "name": "f32_3d_ncdhw_k2s2_p2_volumetric_1x4x8x8x8",
       "provenance": {
+        "notes": "A compact float32 rank-5 NCDHW input exercises volumetric k2/s2 p=2 pooling with nontrivial channel and depth work."
       },
       "attrs": { "p": 2, "kernel_shape": [2, 2, 2], "strides": [2, 2, 2] },
       "inputs": {
       "name": "lppool3d_depth_dilation2_compact",
       "provenance": {
         "source": "ONNX LpPool-22 volumetric dilation semantics",
+        "notes": "Depth dilation 2 spaces the two volumetric pooling taps across three input planes."
       },
       "attrs": { "p": 2, "kernel_shape": [2, 1, 1], "dilations": [2, 1, 1] },
       "inputs": {
     {
       "name": "ncl1d_k4s2_p2_x4_tail_two_planes",
       "provenance": {
+        "notes": "Two planes exercise four-output L2 reuse followed by a three-output final block. Distinct generated values make any read beyond the final plane observable."
       },
       "attrs": { "p": 2, "kernel_shape": [4], "strides": [2] },
       "inputs": {
         }
       },
       "outputs": { "y": { "dtype": "float16", "shape": [1, 2, 7], "tolerance": 0.01 } }
+    },
+    {
+      "name": "autopad_same_upper_rank3_effective_pad",
+      "provenance": {
+        "notes": "A rank-3 one-dimensional SAME_UPPER pool resolves padding per spatial axis. Its windows are (pad,1,2), (2,3,4), and (4,5,pad), producing sqrt(5), sqrt(29), and sqrt(41)."
+      },
+      "attrs": { "auto_pad": "SAME_UPPER", "p": 2, "kernel_shape": [3], "strides": [2] },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [1, 1, 5],
+          "data": { "kind": "values", "values": [1.0, 2.0, 3.0, 4.0, 5.0] }
+        }
+      },
+      "outputs": {
+        "y": {
+          "dtype": "float32",
+          "shape": [1, 1, 3],
+          "data": { "kind": "values", "values": [2.23606797749979, 5.385164807134504, 6.4031242374328485] },
+          "tolerance": 0.0001
+        }
+      }
+    },
+    {
+      "name": "autopad_same_upper_rank5_effective_pad",
+      "provenance": {
+        "notes": "A rank-5 SAME_UPPER pool derives leading padding independently for depth, row, and column. Every output changes if any spatial-axis pad is incorrect."
+      },
+      "attrs": { "auto_pad": "SAME_UPPER", "p": 2, "kernel_shape": [3, 3, 3], "strides": [2, 2, 2] },
+      "inputs": {
+        "x": {
+          "dtype": "float32",
+          "shape": [1, 1, 3, 3, 3],
+          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.17, "scale": 2.0 }
+        }
+      },
+      "outputs": { "y": { "dtype": "float32", "shape": [1, 1, 2, 2, 2], "tolerance": 0.0001 } }
     }
   ]
 }