{ "domain": "com.microsoft", "name": "GatherBlockQuantized", "sinceVersion": 1, "description": "Gathers rows from a block-wise quantized weight matrix and dequantizes them. This inference implementation supports the standard `gather_axis = 0`, `quantize_axis = 1` matrix subset with uint8 `data`, 4-bit packed or 8-bit values, rank-1 non-negative in-bounds int64 `indices` projected to uint32 WebGPU storage, and float32 scales/output. Higher-rank gathers, negative indices, int32 indices, int4/uint4 data, 2-bit data, float16/bfloat16 output, and non-default axes are not implemented.", "inputs": [ { "role": "data", "dtype": "T1", "rank": 2, "description": "Constant uint8 weight matrix. With `bits = 4`, each byte stores two values low-nibble first; with `bits = 8`, each byte stores one value." }, { "role": "indices", "dtype": "Tind", "rank": 1, "description": "Non-negative logical int64 indices selecting rows from axis 0 of `data`. Every index must be less than the row count; values use checked uint32 WebGPU storage." }, { "role": "scales", "dtype": "T2", "rank": 2, "description": "Per-block dequantization scale factors of shape `(rows, ceil(output_columns / block_size))`." }, { "role": "zero_points", "dtype": "T1", "rank": 2, "optional": true, "description": "Optional uint8 zero points. At 4 bits two zero points are packed per byte along the quantized axis, low-nibble first; at 8 bits the shape matches `scales`. If absent, uint8 data uses 2^(bits-1)." } ], "outputs": [ { "role": "output", "dtype": "T2", "rank": 2, "shape": "[dim(shapes.indices, 0), dim(shapes.data, 1) * (8 / attrs.bits)]", "description": "Dequantized floating-point output rows corresponding to the gathered indices." } ], "attributes": { "bits": 4, "block_size": 128, "gather_axis": 0, "quantize_axis": 1 }, "attributeConstraints": { "bits": { "values": [4, 8] }, "gather_axis": { "values": [0] }, "quantize_axis": { "values": [1] } }, "attributeDescriptions": { "bits": "Bits per quantized value. The schema default is 4; this implementation supports 4 or 8.", "block_size": "Number of values sharing a scale. Defaults to 128 and must be a power of two at least 16.", "gather_axis": "Axis from which values are gathered. This matrix implementation supports the standard default, axis 0.", "quantize_axis": "Axis split into quantization blocks. This matrix implementation supports the standard default, axis 1." }, "typeConstraints": { "T1": ["uint8"], "T2": ["float32"], "Tind": ["int64"] }, "args": { "dataT": { "kind": "tensor", "semantic": "data", "role": "input" }, "indicesT": { "kind": "tensor", "semantic": "indices", "role": "input", "dtype": "uint32", "narrowing": "checked" }, "scalesT": { "kind": "tensor", "semantic": "scales", "role": "input" }, "zeroPointsT": { "kind": "tensor", "semantic": "zero_points", "role": "input", "required": false }, "outputT": { "kind": "tensor", "semantic": "output", "role": "output" } }, "derive": { "deviceWorkgroupCap": "min(device.limits.maxComputeInvocationsPerWorkgroup, device.limits.maxComputeWorkgroupSizeX)", "bits": "attrs.bits", "blockSize": "attrs.block_size", "blockSizeOk": "blockSize >= 16 and pow2ceil(blockSize) == blockSize", "outBlocks": "ceilDiv(dim(shapes.output, 1), blockSize)", "zeroPointCols": "ceilDiv(outBlocks, 2) if bits == 4 else outBlocks", "workgroupSize": "min(tunables.WORKGROUP_SIZE, deviceWorkgroupCap)", "commonShapeValid": "blockSizeOk and ranks.data == 2 and ranks.indices == 1 and ranks.scales == 2 and ranks.output == 2 and tensorDtypes.indices == \"uint32\" and tensorDtypes.scales == \"float32\" and tensorDtypes.output == \"float32\" and dim(shapes.output, 0) == dim(shapes.indices, 0) and dim(shapes.scales, 0) == dim(shapes.data, 0) and dim(shapes.scales, 1) == outBlocks", "q4ShapeValid": "commonShapeValid and tensorDtypes.data == \"uint8\" and dim(shapes.output, 1) == dim(shapes.data, 1) * 2", "q8ShapeValid": "commonShapeValid and tensorDtypes.data == \"uint8\" and dim(shapes.output, 1) == dim(shapes.data, 1)", "zeroPointsValid": "present.zeroPointsT and ranks.zero_points == 2 and tensorDtypes.zero_points == \"uint8\" and dim(shapes.zero_points, 0) == dim(shapes.data, 0) and dim(shapes.zero_points, 1) == zeroPointCols", "noZeroMode": "not present.zeroPointsT", "zeroMode": "zeroPointsValid", "workgroupFits": "workgroupSize > 0", "foldedDispatchFits": "ceil(ceil(numel(shapes.output) / device.limits.maxComputeWorkgroupsPerDimension) / workgroupSize) <= device.limits.maxComputeWorkgroupsPerDimension" }, "tunables": { "WORKGROUP_SIZE": 64 }, "bindingSets": { "noZero": [ { "name": "data", "arg": "dataT", "semantic": "data", "buffer": { "type": "read-only-storage" }, "elementType": "$dataElement" }, { "name": "indices", "arg": "indicesT", "semantic": "indices", "buffer": { "type": "read-only-storage" }, "elementType": "$indexScalar" }, { "name": "scales", "arg": "scalesT", "semantic": "scales", "buffer": { "type": "read-only-storage" }, "elementType": "$scaleScalar" }, { "name": "output", "arg": "outputT", "semantic": "output", "buffer": { "type": "storage" }, "elementType": "$outputElement" }, { "name": "params", "semantic": "kernel.params", "buffer": { "type": "uniform" }, "struct": { "name": "Params", "fields": [ { "name": "indexCount", "type": "u32", "value": "dim(shapes.indices, 0)" }, { "name": "cols", "type": 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