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com.microsoft.QMoE / build /webgpu /test.json
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{
 "cases": [
 {
 "name": "q4_colwise_default_relu",
 "provenance": {
 "notes": "Exercises every exact default at once: 4-bit integer weights, k=1, ReLU, column-wise scales, dense routing, and the raw uint8-packed weight layout."
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [2, 4],
 "data": { "kind": "values", "values": [1.0, -2.0, 0.5, 3.0, -1.0, 2.0, 4.0, -0.5] }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [2, 3],
 "data": { "kind": "values", "values": [0.1, 1.2, -0.4, 2.0, -1.0, 0.5] }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 4, 2],
 "data": { "kind": "cycle", "values": [154, 104, 124, 185, 39, 211, 78] }
 },
 "fc1ScalesT": { "dtype": "float32", "shape": [3, 4], "data": { "kind": "linspace", "start": 0.05, "end": 0.3 } },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 4, 2],
 "data": { "kind": "cycle", "values": [78, 211, 39, 185, 124, 104, 154] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 4],
 "data": { "kind": "linspace", "start": 0.04, "end": 0.22 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [2, 4], "tolerance": 0.00002 } }
 },
 {
 "name": "q4_top1_tie_selects_higher_expert",
 "provenance": {
 "notes": "Descending `(logit, expert_index)` ordering with selected-weight normalization: equal logits select expert 1, whose output is [16, 32], rather than the all-zero expert 0."
 },
 "attrs": { "normalize_routing_weights": 1 },
 "inputs": {
 "inputT": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [1.0, 1.0] } },
 "routerT": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, 0.0] } },
 "fc1T": { "dtype": "uint8", "shape": [2, 2, 1], "data": { "kind": "values", "values": [136, 136, 170, 170] } },
 "fc1ScalesT": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "constant", "value": 1.0 } },
 "fc2T": { "dtype": "uint8", "shape": [2, 2, 1], "data": { "kind": "values", "values": [136, 136, 170, 204] } },
 "fc2ScalesT": { "dtype": "float32", "shape": [2, 2], "data": { "kind": "constant", "value": 1.0 } }
 },
 "outputs": {
 "outputT": {
 "dtype": "float32",
 "shape": [1, 2],
 "data": { "kind": "values", "values": [16.0, 32.0] },
 "tolerance": 0
 }
 }
 },
 {
 "name": "q8_colwise_swiglu",
 "provenance": {
 "notes": "Covers 8-bit integer weights and the supported interleaved SwiGLU mode with non-default alpha and beta."
 },
 "attrs": {
 "activation_alpha": 1.1,
 "activation_beta": 0.05,
 "activation_type": "swiglu",
 "expert_weight_bits": 8,
 "swiglu_fusion": 1
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [3, 4],
 "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.3 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [3, 3],
 "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 6, 4],
 "data": { "kind": "cycle", "values": [131, 96, 174, 118, 205, 83, 149] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [3, 6],
 "data": { "kind": "linspace", "start": 0.006, "end": 0.042 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 4, 3],
 "data": { "kind": "cycle", "values": [149, 83, 205, 118, 174, 96, 131] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 4],
 "data": { "kind": "linspace", "start": 0.008, "end": 0.037 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
 },
 {
 "name": "q4_blockwise_topk2_swiglu",
 "provenance": {
 "notes": "Two complete 16-element quantization blocks on both reductions, with two selected experts and stable top-k softmax mixing."
 },
 "attrs": {
 "activation_type": "swiglu",
 "block_size": 16,
 "k": 2,
 "normalize_routing_weights": 1,
 "swiglu_fusion": 1,
 "weights_prepacked": 0
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [4, 32],
 "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.37, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [4, 3],
 "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.23, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 64, 16],
 "data": { "kind": "cycle", "values": [154, 104, 124, 185, 39, 211, 78] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [3, 64, 2],
 "data": { "kind": "linspace", "start": 0.04, "end": 0.33 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 32, 16],
 "data": { "kind": "cycle", "values": [78, 211, 39, 185, 124, 104, 154] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 32, 2],
 "data": { "kind": "linspace", "start": 0.06, "end": 0.27 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [4, 32], "tolerance": 0.00005 } }
 },
 {
 "name": "q4_rank3_input",
 "provenance": {
 "notes": "Locks the standard rank-3 input form and flattening of batch and sequence into router rows."
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [2, 2, 4],
 "data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.13, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [4, 2],
 "data": { "kind": "fillFloat32", "sinStep": 0.41, "cosStep": 0.17, "scale": 0.8 }
 },
 "fc1T": { "dtype": "uint8", "shape": [2, 4, 2], "data": { "kind": "cycle", "values": [25, 118, 203, 71, 154] } },
 "fc1ScalesT": { "dtype": "float32", "shape": [2, 4], "data": { "kind": "linspace", "start": 0.04, "end": 0.2 } },
 "fc2T": { "dtype": "uint8", "shape": [2, 4, 2], "data": { "kind": "cycle", "values": [97, 44, 218, 131, 76] } },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [2, 4],
 "data": { "kind": "linspace", "start": 0.03, "end": 0.18 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [2, 2, 4], "tolerance": 0.00002 } }
 },
 {
 "name": "q8_swiglu_limit",
 "provenance": { "notes": "Exercises the optional finite SwiGLU clamp and the explicit 8-bit path." },
 "attrs": {
 "activation_alpha": 0.9,
 "activation_beta": -0.02,
 "activation_type": "swiglu",
 "expert_weight_bits": 8,
 "swiglu_fusion": 1,
 "swiglu_limit": 0.75
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [2, 4],
 "data": { "kind": "values", "values": [2.0, -3.0, 1.0, 4.0, -1.0, 0.5, 3.0, -2.0] }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [2, 2],
 "data": { "kind": "values", "values": [1.0, 0.0, -1.0, 2.0] }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [2, 8, 4],
 "data": { "kind": "cycle", "values": [131, 96, 174, 118, 205, 83, 149] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [2, 8],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.12 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [2, 4, 4],
 "data": { "kind": "cycle", "values": [149, 83, 205, 118, 174, 96, 131] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [2, 4],
 "data": { "kind": "linspace", "start": 0.03, "end": 0.11 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [2, 4], "tolerance": 0.00002 } }
 },
 {
 "name": "q4_zero_inter",
 "provenance": {
 "notes": "A zero intermediate dimension has an exact zero result without binding empty weight buffers in a shader. No token, routing or scale value can reach the result once the intermediate width is zero, so the fill values are uniform."
 },
 "attrs": { "activation_type": "swiglu", "swiglu_fusion": 1 },
 "inputs": {
 "inputT": { "dtype": "float32", "shape": [3, 4], "data": { "kind": "constant", "value": 0.3 } },
 "routerT": { "dtype": "float32", "shape": [3, 3], "data": { "kind": "constant", "value": 0.7 } },
 "fc1T": { "dtype": "uint8", "shape": [3, 0, 2], "data": { "kind": "values", "values": [] } },
 "fc1ScalesT": { "dtype": "float32", "shape": [3, 0], "data": { "kind": "values", "values": [] } },
 "fc2T": { "dtype": "uint8", "shape": [3, 4, 0], "data": { "kind": "values", "values": [] } },
 "fc2ScalesT": { "dtype": "float32", "shape": [3, 4], "data": { "kind": "constant", "value": 0.02 } }
 },
 "outputs": {
 "outputT": { "dtype": "float32", "shape": [3, 4], "data": { "kind": "constant", "value": 0.0 }, "tolerance": 0 }
 }
 },
 {
 "name": "empty_zero_dim",
 "provenance": {
 "notes": "Zero tokens in the rank-2 form must retain the routed quantized shader identities while every output dispatch is elided."
 },
 "inputs": {
 "inputT": { "dtype": "float32", "shape": [0, 4], "data": { "kind": "values", "values": [] } },
 "routerT": { "dtype": "float32", "shape": [0, 3], "data": { "kind": "values", "values": [] } },
 "fc1T": { "dtype": "uint8", "shape": [3, 4, 2], "data": { "kind": "constant", "value": 120 } },
 "fc1ScalesT": { "dtype": "float32", "shape": [3, 4], "data": { "kind": "constant", "value": 0.05 } },
 "fc2T": { "dtype": "uint8", "shape": [3, 4, 2], "data": { "kind": "constant", "value": 130 } },
 "fc2ScalesT": { "dtype": "float32", "shape": [3, 4], "data": { "kind": "constant", "value": 0.05 } }
 },
 "outputs": {
 "outputT": { "dtype": "float32", "shape": [0, 4], "data": { "kind": "values", "values": [] }, "tolerance": 0 }
 }
 },
 {
 "name": "q4_colwise_relu_deep_reduction",
 "provenance": {
 "notes": "A 128-element reduction on both projections exercises the cooperative multi-lane schedule for q4 weights."
 },
 "attrs": { "weights_prepacked": 0 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [2, 128],
 "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [2, 2],
 "data": { "kind": "fillFloat32", "sinStep": 0.41, "cosStep": 0.17, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [2, 128, 64],
 "data": { "kind": "cycle", "values": [154, 104, 124, 185, 39, 211, 78] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [2, 128],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.19 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [2, 128, 64],
 "data": { "kind": "cycle", "values": [78, 211, 39, 185, 124, 104, 154] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [2, 128],
 "data": { "kind": "linspace", "start": 0.03, "end": 0.21 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00005 } }
 },
 {
 "name": "q4_blockwise_swiglu_topk2_deep_reduction",
 "provenance": {
 "notes": "Interleaved SwiGLU with two selected experts over a 128-long reduction and four 32-wide quantization blocks per row."
 },
 "attrs": {
 "activation_type": "swiglu",
 "block_size": 32,
 "k": 2,
 "normalize_routing_weights": 1,
 "swiglu_fusion": 1,
 "weights_prepacked": 0
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [3, 128],
 "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.37, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [3, 3],
 "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.23, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 256, 64],
 "data": { "kind": "cycle", "values": [97, 182, 45, 213, 131, 68, 159, 22, 241] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [3, 256, 4],
 "data": { "kind": "linspace", "start": 0.04, "end": 0.33 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 128, 64],
 "data": { "kind": "cycle", "values": [241, 22, 159, 68, 131, 213, 45, 182, 97] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 128, 4],
 "data": { "kind": "linspace", "start": 0.06, "end": 0.27 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [3, 128], "tolerance": 0.00005 } }
 },
 {
 "name": "q8_swiglu_deep_reduction",
 "provenance": {
 "notes": "Eight-bit codes over the same 128-long reduction: one stored word per logical column instead of a shared nibble pair."
 },
 "attrs": { "activation_type": "swiglu", "expert_weight_bits": 8, "swiglu_fusion": 1, "weights_prepacked": 0 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [2, 128],
 "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.11, "scale": 0.35 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [2, 2],
 "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.47, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [2, 256, 128],
 "data": { "kind": "cycle", "values": [113, 26, 201, 88, 167, 54, 229, 141, 73] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [2, 256],
 "data": { "kind": "linspace", "start": 0.01, "end": 0.09 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [2, 128, 128],
 "data": { "kind": "cycle", "values": [73, 141, 229, 54, 167, 88, 201, 26, 113] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [2, 128],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.08 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00005 } }
 },
 {
 "name": "q8_swiglu_decode_gemv_odd_reduction",
 "provenance": {
 "notes": "An 8-bit decode on the cooperative GEMV schedule with an ODD hidden size. The reduction steps two columns at a time -- one stored word per column at 8 bits, one word for both at 4 -- so an odd length leaves a final column that only 8-bit weights can produce."
 },
 "attrs": { "activation_type": "swiglu", "expert_weight_bits": 8, "swiglu_fusion": 1, "weights_prepacked": 0 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [1, 129],
 "data": { "kind": "fillFloat32", "sinStep": 0.261, "cosStep": 0.11, "scale": 0.35 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [1, 2],
 "data": { "kind": "fillFloat32", "sinStep": 0.341, "cosStep": 0.47, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [2, 256, 129],
 "data": { "kind": "cycle", "values": [113, 26, 201, 88, 167, 54, 229, 141, 73] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [2, 256],
 "data": { "kind": "linspace", "start": 0.01, "end": 0.09 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [2, 129, 128],
 "data": { "kind": "cycle", "values": [73, 141, 229, 54, 167, 88, 201, 26, 113] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [2, 129],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.08 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [1, 129], "tolerance": 0.00005 } }
 },
 {
 "name": "q4_grouped_prefill_relu",
 "provenance": {
 "notes": "Enough routed slots per expert to fill grouped tiles, so one dequantized weight tile is reused across a tile of slots."
 },
 "attrs": { "weights_prepacked": 0 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [96, 64],
 "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [96, 3],
 "data": { "kind": "fillFloat32", "sinStep": 0.33999999999999997, "cosStep": 0.24, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 64, 32],
 "data": { "kind": "cycle", "values": [97, 182, 45, 213, 131, 68, 159, 22, 241] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [3, 64],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.21 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 64, 32],
 "data": { "kind": "cycle", "values": [241, 22, 159, 68, 131, 213, 45, 182, 97] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 64],
 "data": { "kind": "linspace", "start": 0.03, "end": 0.18 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [96, 64], "tolerance": 0.00005 } }
 },
 {
 "name": "q4_grouped_prefill_swiglu_topk2",
 "provenance": {
 "notes": "Enough routed slots per expert to fill grouped tiles, so one dequantized weight tile is reused across a tile of slots."
 },
 "attrs": {
 "activation_type": "swiglu",
 "k": 2,
 "normalize_routing_weights": 1,
 "swiglu_fusion": 1,
 "weights_prepacked": 0
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [48, 64],
 "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.37, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [48, 3],
 "data": { "kind": "fillFloat32", "sinStep": 0.4, "cosStep": 0.32, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 128, 32],
 "data": { "kind": "cycle", "values": [97, 182, 45, 213, 131, 68, 159, 22, 241] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [3, 128],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.21 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 64, 32],
 "data": { "kind": "cycle", "values": [241, 22, 159, 68, 131, 213, 45, 182, 97] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 64],
 "data": { "kind": "linspace", "start": 0.03, "end": 0.18 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [48, 64], "tolerance": 0.00005 } }
 },
 {
 "name": "q4_grouped_prefill_partial_tiles",
 "provenance": {
 "notes": "Three experts share 64 routed slots across 32-row tiles, so most lanes address padding and the store guard discards more rows than it writes."
 },
 "attrs": {
 "activation_type": "swiglu",
 "k": 2,
 "normalize_routing_weights": 1,
 "swiglu_fusion": 1,
 "weights_prepacked": 0
 },
 "inputs": {
 "inputT": {
 "dtype": "float32",
 "shape": [32, 64],
 "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.41, "scale": 0.4 }
 },
 "routerT": {
 "dtype": "float32",
 "shape": [32, 3],
 "data": { "kind": "fillFloat32", "sinStep": 0.34, "cosStep": 0.28, "scale": 0.6 }
 },
 "fc1T": {
 "dtype": "uint8",
 "shape": [3, 128, 32],
 "data": { "kind": "cycle", "values": [97, 182, 45, 213, 131, 68, 159, 22, 241] }
 },
 "fc1ScalesT": {
 "dtype": "float32",
 "shape": [3, 128],
 "data": { "kind": "linspace", "start": 0.02, "end": 0.21 }
 },
 "fc2T": {
 "dtype": "uint8",
 "shape": [3, 64, 32],
 "data": { "kind": "cycle", "values": [241, 22, 159, 68, 131, 213, 45, 182, 97] }
 },
 "fc2ScalesT": {
 "dtype": "float32",
 "shape": [3, 64],
 "data": { "kind": "linspace", "start": 0.03, "end": 0.18 }
 }
 },
 "outputs": { "outputT": { "dtype": "float32", "shape": [32, 64], "tolerance": 0.00005 } }
 },
 {
 "name": "q8_grouped_prefill_swiglu_blockwise",
 "provenance": {
 "notes": "Enough routed slots per expert fill grouped tiles, allowing each dequantized weight tile to serve several slots. The tolerance covers f32 reassociation in the tiled reduction while remaining far below a 10% error on outputs near 0.4."
 },
 "attrs": {
 "activation_type": "swiglu",
 "block_size": 32,
 "k": 2,
 "normalize_routing_weights": 1,
 "swiglu_fusion": 1,
 "weights_prepacked": 0,
 "expert_weight_bits": 8
 },
 "inputs": {
 "inputT": {
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}