Download build/webgpu/manifest.json from webgpu-kernels/ai.onnx.BitwiseAnd: direct link, hf CLI and curl.
- Browser
- Download file 3.14 kB
-
https://huggingface.co/kernels/webgpu-kernels/ai.onnx.BitwiseAnd/resolve/v1/build/webgpu/manifest.json
- Command line
-
hf download hf://webgpu-kernels/ai.onnx.BitwiseAnd@v1/build/webgpu/manifest.json
-
curl -L -o manifest.json https://huggingface.co/kernels/webgpu-kernels/ai.onnx.BitwiseAnd/resolve/v1/build/webgpu/manifest.json
3.14 kB
| { | |
| "domain": "ai.onnx", | |
| "name": "BitwiseAnd", | |
| "sinceVersion": 18, | |
| "inputs": { "a": { "onnx": "A", "dtype": "T" }, "b": { "onnx": "B", "dtype": "T" } }, | |
| "outputs": { | |
| "c": { "onnx": "C", "dtype": "T", "rank": "max(ranks.a, ranks.b)", "shape": "broadcastShape(shapes.a, shapes.b)" } | |
| }, | |
| "typeConstraints": { "T": ["uint32", "int32", "int16", "uint8", "int8"] }, | |
| "tunables": { "WORKGROUP_SIZE": { "default": 256 } }, | |
| "variants": [ | |
| { | |
| "id": "same_shape_vec4", | |
| "priority": 20, | |
| "when": ["tensorDtypes.a == tensorDtypes.b", "tensorDtypes.a == tensorDtypes.c", "sameShape(shapes.a, shapes.c)", "sameShape(shapes.b, shapes.c)", "numel(shapes.c) > 0", "numel(shapes.c) % 4 == 0"], | |
| "derive": { "vectorScalar": "\"vec4<\" ~ dtypes.T ~ \">\"" }, | |
| "passes": [ | |
| { | |
| "id": "main", | |
| "name": "BitwiseAnd.Vec4", | |
| "shader": "bitwise-binary-vec4.wgsl.jinja", | |
| "derive": { | |
| "cDtype": "tensorDtypes.c", | |
| "vec4PerThread": "4 if numel(shapes.c) * dtypeBytes(tensorDtypes.c) <= 16777216 else 1" | |
| }, | |
| "bindings": ["a", "b", "c_bitwise", "params"], | |
| "dispatch": { | |
| "x": "min(ceilDiv((ceilDiv(numel(shapes.c) / 4, 4 if numel(shapes.c) * dtypeBytes(tensorDtypes.c) <= 16777216 else 1)), (tunables.WORKGROUP_SIZE)), 65535)", | |
| "y": "ceilDiv(ceilDiv((ceilDiv(numel(shapes.c) / 4, 4 if numel(shapes.c) * dtypeBytes(tensorDtypes.c) <= 16777216 else 1)), (tunables.WORKGROUP_SIZE)), 65535)", | |
| "z": 1 | |
| } | |
| } | |
| ] | |
| }, | |
| { | |
| "id": "broadcast", | |
| "when": ["tensorDtypes.a == tensorDtypes.b", "tensorDtypes.a == tensorDtypes.c", "ranks.a <= ranks.c", "ranks.b <= ranks.c"], | |
| "derive": { "logicalDtype": "tensorDtypes.c" }, | |
| "passes": [ | |
| { | |
| "id": "main", | |
| "name": "BitwiseAnd", | |
| "shader": "bitwise-binary-broadcast.wgsl.jinja", | |
| "derive": { | |
| "aShape": "shapes.a", | |
| "bShape": "shapes.b", | |
| "cShape": "shapes.c", | |
| "aRank": "ranks.a", | |
| "bRank": "ranks.b", | |
| "cRank": "ranks.c", | |
| "itemsPerInvocation": 4 | |
| }, | |
| "bindings": ["a_2_bitwise", "b_2_bitwise", "c_2_bitwise", "params_count"], | |
| "dispatch": { | |
| "x": "min(ceilDiv((ceilDiv(numel(shapes.c), 4)), (tunables.WORKGROUP_SIZE)), 65535)", | |
| "y": "ceilDiv(ceilDiv((ceilDiv(numel(shapes.c), 4)), (tunables.WORKGROUP_SIZE)), 65535)", | |
| "z": 1 | |
| } | |
| } | |
| ] | |
| } | |
| ], | |
| "bindings": { | |
| "a": { "elementType": "$vectorScalar" }, | |
| "b": { "elementType": "$vectorScalar" }, | |
| "c_bitwise": { "elementType": "$vectorScalar", "name": "c" }, | |
| "params": { "struct": [{ "name": "count", "type": "u32", "value": "numel(shapes.c) / 4" }] }, | |
| "a_2_bitwise": { "name": "a", "elementType": "$T" }, | |
| "b_2_bitwise": { "name": "b", "elementType": "$T" }, | |
| "c_2_bitwise": { "name": "c", "elementType": "$T" }, | |
| "params_count": { "name": "params", "struct": [{ "name": "count", "type": "u32", "value": "numel(shapes.c)" }] } | |
| } | |
| } | |