{
  "cases": [
    {
      "name": "relu_top1_no_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "default activation_type (relu), one expert per token",
        "notes": "Exercises the schema's default activation with no optional biases or FC3 input."
      },
      "attrs": { "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "gelu_top1_fc1_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "activation_type gelu with an FC1 bias",
        "notes": "Tanh-approximate GELU after the FC1 projection and its per-expert bias."
      },
      "attrs": { "k": 1, "activation_type": "gelu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "silu_topk2_fc2_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "activation_type silu at k = 2 with an FC2 bias",
        "notes": "The FC2 bias belongs to the expert, so at k = 2 each selected expert contributes its own bias scaled by its own routing weight. Adding it once to the routed sum instead would be wrong by the difference of the two experts' biases."
      },
      "attrs": { "k": 2, "activation_type": "silu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "silu_gate_fc3_no_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "activation_type silu with FC3 as a multiplicative linear projection",
        "notes": "The pinned CUDA provider's separate FC3 gated-MLP form computes the FC3 projection times SiLU of the FC1 projection."
      },
      "attrs": { "k": 1, "activation_type": "silu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "silu_gate_fc3_every_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "all three optional biases present at once",
        "notes": "The SiLU gated path applies FC1, FC2, and FC3 biases in one invocation."
      },
      "attrs": { "k": 2, "activation_type": "silu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.23, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "swiglu_fusion2_top1",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "swiglu_fusion 2 (fused, concatenated halves)",
        "notes": "One fused FC1 whose row block holds the gate half then the linear half. inter_size is the FC1 row count halved, which is what fusion_size 2 means in the schema's shape formula."
      },
      "attrs": {
        "k": 1,
        "activation_type": "swiglu",
        "swiglu_fusion": 2,
        "normalize_routing_weights": 1,
        "activation_alpha": 1.702,
        "activation_beta": 0.05
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.33, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.41, "cosStep": 0.23, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 6, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "swiglu_fusion1_interleaved_topk2",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "swiglu_fusion 1 (fused, interleaved rows)",
        "notes": "The same fused FC1 as fusion 2 but with the two operands interleaved: lane i reads rows 2i and 2i+1 rather than i and inter+i. Reading the concatenated layout here still lands on the right gate row for lane 0 and on the wrong linear row for it, and on the wrong row for both operands of every later lane."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "swiglu_fusion": 1,
        "normalize_routing_weights": 1,
        "activation_alpha": 1.702,
        "activation_beta": 0.05
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [4, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.33, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [4, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.41, "cosStep": 0.23, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 6, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 6],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [4, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "swiglu_fusion0_separate_fc3",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "swiglu_fusion 0 (two unfused GEMMs)",
        "notes": "With the GEMMs unfused the SwiGLU operands come from separate FC1 and FC3 tensors, so fusion_size is 1 and inter_size is the full FC1 row count. FC3 is required in this form and rejected in the fused ones."
      },
      "attrs": {
        "k": 1,
        "activation_type": "swiglu",
        "swiglu_fusion": 0,
        "normalize_routing_weights": 0,
        "activation_alpha": 1.702,
        "activation_beta": 0.05
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "swiglu_fusion2_limit_clamped",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "swiglu_limit clamping both operands",
        "notes": "swiglu_limit clamps the gate operand from above and the linear operand into [-limit, limit] before the product. The limit is small enough here that it actually binds; when the attribute is absent no clamp is applied at all."
      },
      "attrs": {
        "k": 1,
        "activation_type": "swiglu",
        "swiglu_fusion": 2,
        "normalize_routing_weights": 1,
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_limit": 0.35
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.33, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.41, "cosStep": 0.23, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 6, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "rank3_batched_relu",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "3D input (batch, sequence, hidden)",
        "notes": "num_tokens is the product of every leading dimension, so a [1, 3, 4] input routes exactly like the [3, 4] case while the output keeps the 3D shape."
      },
      "attrs": { "k": 1, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [1, 3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [1, 3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "topk_equals_expert_count",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "k equal to the expert count",
        "notes": "Every expert is selected for every token, which is the upper bound the contract allows and the case where the router's taken-exclusion has to visit every index without repeating one."
      },
      "attrs": { "k": 3, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.17, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.19, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "empty_tokens_zero_dim",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "zero-token input",
        "notes": "A zero-length token axis: every stage dispatches nothing and the output is empty. The route scratch is still allocated at one element so the binding stays well-formed."
      },
      "attrs": { "k": 1, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": { "dtype": "float32", "shape": [0, 4], "data": { "kind": "values", "values": [] } },
        "routerT": { "dtype": "float32", "shape": [0, 3], "data": { "kind": "values", "values": [] } },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [0, 4], "tolerance": 0 } }
    },
    {
      "name": "gelu_fc1_bias_fc2_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "Uses FC1 and FC2 biases without an FC3 projection, exercising GELU on the FC1 projection before the biased FC2 projection."
      },
      "attrs": { "k": 1, "activation_type": "gelu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.19, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.21, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_silu_gate_fc3_fc2_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "This optional-input layout uses a 128-wide reduction so the cooperative GEMV schedule exercises SiLU with a separate, unbiased FC3 projection and FC2 bias."
      },
      "attrs": { "k": 2, "activation_type": "silu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.21, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.23, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_silu_gate_fc3_bias_only",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "This optional-input layout supplies enough routed slots for the expert-grouped schedule to exercise SiLU with a separately biased FC3 projection and no FC2 bias."
      },
      "attrs": { "k": 1, "activation_type": "silu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.25, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.23, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "silu_gate_fc3_bias_fc2_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "Uses the gated SiLU branch with an FC3 bias and an FC2 bias while omitting the FC1 bias; routing weights are normalized across the selected experts."
      },
      "attrs": { "k": 2, "activation_type": "silu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.25, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.27, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.23, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "silu_fc1_bias_gate_fc3",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "Uses the gated SiLU branch with an FC1 bias, an unbiased FC3 projection, and no FC2 bias."
      },
      "attrs": { "k": 1, "activation_type": "silu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.27, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.29, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "silu_fc1_bias_gate_fc3_fc2_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "Uses normalized top-2 routing with the gated SiLU branch, an FC1 bias, an unbiased FC3 projection, and an FC2 bias."
      },
      "attrs": { "k": 2, "activation_type": "silu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.31, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.17, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "ort_softmax_tie_break_top1",
      "provenance": {
        "source": "onnxruntime/contrib_ops/cpu/moe/moe_cpu.cc",
        "test": "full-softmax routing and equal-probability pair ordering",
        "notes": "Equal zero logits become probabilities [0.5, 0.5], with ties selecting the higher expert index. That expert emits 6, and its non-normalized route probability scales the result to exactly 3. Also exercises the defaults k=1, activation_type=relu, and normalize_routing_weights=0."
      },
      "inputs": {
        "inputT": { "dtype": "float32", "shape": [1, 1], "data": { "kind": "values", "values": [1.0] } },
        "routerT": { "dtype": "float32", "shape": [1, 2], "data": { "kind": "values", "values": [0.0, 0.0] } },
        "fc1T": { "dtype": "float32", "shape": [2, 1, 1], "data": { "kind": "values", "values": [1.0, 2.0] } },
        "fc2T": { "dtype": "float32", "shape": [2, 1, 1], "data": { "kind": "values", "values": [1.0, 3.0] } }
      },
      "outputs": {
        "outputT": {
          "dtype": "float32",
          "shape": [1, 1],
          "tolerance": 0,
          "data": { "kind": "values", "values": [3.0] }
        }
      }
    },
    {
      "name": "silu_fc1_bias_gate_fc3_bias",
      "provenance": {
        "source": "onnxruntime/docs/ContribOperators.md#com.microsoft.MoE",
        "test": "com.microsoft.MoE optional-input combination",
        "notes": "Uses the gated SiLU branch with FC1 and FC3 biases and no FC2 bias."
      },
      "attrs": { "k": 1, "activation_type": "silu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.33, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.13, "scale": 0.25 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.23, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1plain_fc3none_fc2plain_identity",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with plain fc1, no fc3, plain fc2, and identity activation."
      },
      "attrs": { "activation_type": "identity", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.31, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.26, "cosStep": 0.24, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.36, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.09, "cosStep": 0.28, "scale": 0.12 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1plain_fc3none_fc2bias_swiglu2",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with plain fc1, no fc3, biased fc2, and SwiGLU formula 2."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 2,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.083, "cosStep": 0.299, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.273, "cosStep": 0.229, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 256, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.123, "cosStep": 0.349, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.103, "cosStep": 0.269, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.233, "cosStep": 0.319, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1bias_fc3none_fc2plain_swiglu1",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with biased fc1, no fc3, plain fc2, and SwiGLU formula 1."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 1, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.096, "cosStep": 0.288, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.286, "cosStep": 0.218, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 256, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.136, "cosStep": 0.338, "scale": 0.12 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 256],
          "data": { "kind": "fillFloat32", "sinStep": 0.186, "cosStep": 0.418, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.116, "cosStep": 0.258, "scale": 0.12 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1bias_fc3none_fc2bias_swiglu2",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with biased fc1, no fc3, biased fc2, and SwiGLU formula 2."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 2, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.109, "cosStep": 0.277, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.299, "cosStep": 0.207, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 256, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.149, "cosStep": 0.327, "scale": 0.12 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 256],
          "data": { "kind": "fillFloat32", "sinStep": 0.199, "cosStep": 0.407, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.129, "cosStep": 0.247, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.259, "cosStep": 0.297, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1plain_fc3plain_fc2plain_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with plain fc1, plain fc3, plain fc2, and formula 0."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.122, "cosStep": 0.266, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.312, "cosStep": 0.196, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.162, "cosStep": 0.316, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.142, "cosStep": 0.236, "scale": 0.12 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.182, "cosStep": 0.356, "scale": 0.12 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1plain_fc3plain_fc2bias_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with plain fc1, plain fc3, biased fc2, and formula 0."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.135, "cosStep": 0.255, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.325, "cosStep": 0.185, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.175, "cosStep": 0.305, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.155, "cosStep": 0.225, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.285, "cosStep": 0.275, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.195, "cosStep": 0.345, "scale": 0.12 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1plain_fc3biased_fc2plain_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with plain fc1, biased fc3, plain fc2, and formula 0."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.148, "cosStep": 0.244, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.338, "cosStep": 0.174, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.188, "cosStep": 0.294, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.168, "cosStep": 0.214, "scale": 0.12 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.208, "cosStep": 0.334, "scale": 0.12 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.258, "cosStep": 0.194, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1plain_fc3biased_fc2bias_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with plain fc1, biased fc3, biased fc2, and formula 0."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.161, "cosStep": 0.233, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.351, "cosStep": 0.163, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.201, "cosStep": 0.283, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.181, "cosStep": 0.203, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.311, "cosStep": 0.253, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.221, "cosStep": 0.323, "scale": 0.12 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.271, "cosStep": 0.183, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1bias_fc3plain_fc2plain_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with biased fc1, plain fc3, plain fc2, and formula 0."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.174, "cosStep": 0.222, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.364, "cosStep": 0.152, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.214, "cosStep": 0.272, "scale": 0.12 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.264, "cosStep": 0.352, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.194, "cosStep": 0.192, "scale": 0.12 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.234, "cosStep": 0.312, "scale": 0.12 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1bias_fc3plain_fc2bias_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with biased fc1, plain fc3, biased fc2, and formula 0."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.187, "cosStep": 0.211, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.377, "cosStep": 0.141, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.227, "cosStep": 0.261, "scale": 0.12 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.277, "cosStep": 0.341, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.207, "cosStep": 0.181, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.337, "cosStep": 0.231, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.247, "cosStep": 0.301, "scale": 0.12 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1bias_fc3biased_fc2plain_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with biased fc1, biased fc3, plain fc2, and formula 0."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.2, "cosStep": 0.2, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.39, "cosStep": 0.13, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.24, "cosStep": 0.25, "scale": 0.12 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.33, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.22, "cosStep": 0.17, "scale": 0.12 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.26, "cosStep": 0.29, "scale": 0.12 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.15, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_fc1bias_fc3biased_fc2bias_swiglu0",
      "provenance": {
        "notes": "A 128-element reduction on both projections exercises the cooperative schedule with biased fc1, biased fc3, biased fc2, and formula 0."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.213, "cosStep": 0.189, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.403, "cosStep": 0.119, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.253, "cosStep": 0.239, "scale": 0.12 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.303, "cosStep": 0.319, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.233, "cosStep": 0.159, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.363, "cosStep": 0.209, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.273, "cosStep": 0.279, "scale": 0.12 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.323, "cosStep": 0.139, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "identity_activation_top1",
      "provenance": {
        "notes": "With `activation_type=identity`, the FC1 projection passes through unchanged before the output projection."
      },
      "attrs": { "activation_type": "identity" },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [3, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [3, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.17, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 5, 4],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 4, 5],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.18 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [3, 4], "tolerance": 0.00002 } }
    },
    {
      "name": "deep_reduction_gelu_fc2bias",
      "provenance": {
        "notes": "GELU over a 128-long reduction makes the cooperative schedule exercise its tanh-approximation activation path."
      },
      "attrs": { "k": 2, "activation_type": "gelu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.17, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [2, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.43, "cosStep": 0.11, "scale": 0.6 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.27, "cosStep": 0.19, "scale": 0.12 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.25, "cosStep": 0.33, "scale": 0.12 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.38, "cosStep": 0.21, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1plain_fc3none_fc2plain_relu",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "k": 2, "activation_type": "relu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.09, "cosStep": 0.29, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.3, "cosStep": 0.24, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.12, "cosStep": 0.36, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.14, "cosStep": 0.26, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1plain_fc3none_fc2bias_swiglu2",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 2,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.107, "cosStep": 0.281, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.317, "cosStep": 0.231, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 64, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.137, "cosStep": 0.351, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.157, "cosStep": 0.251, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.277, "cosStep": 0.321, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1bias_fc3none_fc2plain_swiglu1",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 1, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.124, "cosStep": 0.272, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.334, "cosStep": 0.222, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 64, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.154, "cosStep": 0.342, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.234, "cosStep": 0.422, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.174, "cosStep": 0.242, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1bias_fc3none_fc2bias_swiglu2",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 2, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.141, "cosStep": 0.263, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.351, "cosStep": 0.213, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 64, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.171, "cosStep": 0.333, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.251, "cosStep": 0.413, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.191, "cosStep": 0.233, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.311, "cosStep": 0.303, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_partial_tiles_gelu",
      "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": "gelu", "normalize_routing_weights": 1, "k": 2 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.173, "cosStep": 0.229, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [32, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.347, "cosStep": 0.163, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.199, "cosStep": 0.271, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.251, "cosStep": 0.413, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.241, "cosStep": 0.179, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.311, "cosStep": 0.303, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [32, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1plain_fc3plain_fc2plain_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.158, "cosStep": 0.254, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.368, "cosStep": 0.204, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.188, "cosStep": 0.324, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.208, "cosStep": 0.224, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.228, "cosStep": 0.364, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1plain_fc3plain_fc2bias_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.175, "cosStep": 0.245, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.385, "cosStep": 0.195, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.205, "cosStep": 0.315, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.225, "cosStep": 0.215, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.345, "cosStep": 0.285, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.245, "cosStep": 0.355, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1plain_fc3biased_fc2plain_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.192, "cosStep": 0.236, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.402, "cosStep": 0.186, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.222, "cosStep": 0.306, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.242, "cosStep": 0.206, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.262, "cosStep": 0.346, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.322, "cosStep": 0.176, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1plain_fc3biased_fc2bias_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.209, "cosStep": 0.227, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.419, "cosStep": 0.177, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.239, "cosStep": 0.297, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.259, "cosStep": 0.197, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.379, "cosStep": 0.267, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.279, "cosStep": 0.337, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.339, "cosStep": 0.167, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1bias_fc3plain_fc2plain_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.226, "cosStep": 0.218, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.436, "cosStep": 0.168, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.256, "cosStep": 0.288, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.336, "cosStep": 0.368, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.276, "cosStep": 0.188, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.296, "cosStep": 0.328, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1bias_fc3plain_fc2bias_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.243, "cosStep": 0.209, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.453, "cosStep": 0.159, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.273, "cosStep": 0.279, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.353, "cosStep": 0.359, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.293, "cosStep": 0.179, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.413, "cosStep": 0.249, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.313, "cosStep": 0.319, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1bias_fc3biased_fc2plain_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.26, "cosStep": 0.2, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.47, "cosStep": 0.15, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.27, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.35, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.17, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.33, "cosStep": 0.31, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.39, "cosStep": 0.14, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_fc1bias_fc3biased_fc2bias_swiglu0",
      "provenance": {
        "notes": "Enough routed slots per expert to fill grouped tiles, so the expert-grouped schedule reuses one staged weight tile across a tile of slots."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.277, "cosStep": 0.191, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.487, "cosStep": 0.141, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.307, "cosStep": 0.261, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.387, "cosStep": 0.341, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.327, "cosStep": 0.161, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.447, "cosStep": 0.231, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 32, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.347, "cosStep": 0.301, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 32],
          "data": { "kind": "fillFloat32", "sinStep": 0.407, "cosStep": 0.131, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 32], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_identity_no_fc3",
      "provenance": {
        "notes": "The smallest default-tile prefill that meets the grouped schedule's routed-slot threshold, and exercises identity activation without FC3 after expert grouping."
      },
      "attrs": { "k": 2, "activation_type": "identity", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 16],
          "data": { "kind": "fillFloat32", "sinStep": 0.181, "cosStep": 0.217, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.391, "cosStep": 0.127, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 16, 16],
          "data": { "kind": "fillFloat32", "sinStep": 0.211, "cosStep": 0.287, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 16, 16],
          "data": { "kind": "fillFloat32", "sinStep": 0.231, "cosStep": 0.187, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 16], "tolerance": 0.00002 } }
    },
    {
      "name": "grouped_prefill_unaligned_hidden_inter",
      "provenance": {
        "notes": "Hidden 34 and intermediate 30 are not divisible by four, so grouped staging uses scalar activation and weight reads. The gated FC3 stream adds a second scalar weight stream, and every K tile is partial against the 16-wide tile."
      },
      "attrs": { "activation_type": "silu", "normalize_routing_weights": 1, "k": 2 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 34],
          "data": { "kind": "fillFloat32", "sinStep": 0.163, "cosStep": 0.239, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.337, "cosStep": 0.173, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 30, 34],
          "data": { "kind": "fillFloat32", "sinStep": 0.209, "cosStep": 0.281, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 30],
          "data": { "kind": "fillFloat32", "sinStep": 0.261, "cosStep": 0.423, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 34, 30],
          "data": { "kind": "fillFloat32", "sinStep": 0.251, "cosStep": 0.189, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 34],
          "data": { "kind": "fillFloat32", "sinStep": 0.321, "cosStep": 0.313, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 30, 34],
          "data": { "kind": "fillFloat32", "sinStep": 0.227, "cosStep": 0.197, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 30],
          "data": { "kind": "fillFloat32", "sinStep": 0.283, "cosStep": 0.359, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 34], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_silu_gate_fc3_bias_only",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_silu_gate_fc3_bias_only. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "k": 1, "activation_type": "silu", "normalize_routing_weights": 0 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.23, "cosStep": 0.31, "scale": 0.5 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.25, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.11, "cosStep": 0.23, "scale": 0.18 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.07, "cosStep": 0.41, "scale": 0.16 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.13, "cosStep": 0.29, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.23, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1plain_fc3none_fc2plain_relu",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1plain_fc3none_fc2plain_relu. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "k": 2, "activation_type": "relu", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.09, "cosStep": 0.29, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.3, "cosStep": 0.24, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 64, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.12, "cosStep": 0.36, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.14, "cosStep": 0.26, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1plain_fc3none_fc2bias_swiglu2",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1plain_fc3none_fc2bias_swiglu2. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 2,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.107, "cosStep": 0.281, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.317, "cosStep": 0.231, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 192, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.137, "cosStep": 0.351, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.157, "cosStep": 0.251, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.277, "cosStep": 0.321, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1bias_fc3none_fc2plain_swiglu1",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1bias_fc3none_fc2plain_swiglu1. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 1, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 24, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.124, "cosStep": 0.272, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.334, "cosStep": 0.222, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 192, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.154, "cosStep": 0.342, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 192],
          "data": { "kind": "fillFloat32", "sinStep": 0.234, "cosStep": 0.422, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.174, "cosStep": 0.242, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 24, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1bias_fc3none_fc2bias_swiglu2",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1bias_fc3none_fc2bias_swiglu2. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 2, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.141, "cosStep": 0.263, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.351, "cosStep": 0.213, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 192, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.171, "cosStep": 0.333, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 192],
          "data": { "kind": "fillFloat32", "sinStep": 0.251, "cosStep": 0.413, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.191, "cosStep": 0.233, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.311, "cosStep": 0.303, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_partial_tiles_gelu",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_partial_tiles_gelu. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "activation_type": "gelu", "normalize_routing_weights": 1, "k": 2 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [32, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.173, "cosStep": 0.229, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [32, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.347, "cosStep": 0.163, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 64, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.199, "cosStep": 0.271, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.251, "cosStep": 0.413, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.241, "cosStep": 0.179, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.311, "cosStep": 0.303, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [32, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1plain_fc3plain_fc2plain_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1plain_fc3plain_fc2plain_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 48, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.158, "cosStep": 0.254, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.368, "cosStep": 0.204, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.188, "cosStep": 0.324, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.208, "cosStep": 0.224, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.228, "cosStep": 0.364, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 48, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1plain_fc3plain_fc2bias_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1plain_fc3plain_fc2bias_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.175, "cosStep": 0.245, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.385, "cosStep": 0.195, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.205, "cosStep": 0.315, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.225, "cosStep": 0.215, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.345, "cosStep": 0.285, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.245, "cosStep": 0.355, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1plain_fc3biased_fc2plain_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1plain_fc3biased_fc2plain_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.192, "cosStep": 0.236, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.402, "cosStep": 0.186, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.222, "cosStep": 0.306, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.242, "cosStep": 0.206, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.262, "cosStep": 0.346, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.322, "cosStep": 0.176, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1plain_fc3biased_fc2bias_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1plain_fc3biased_fc2bias_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 24, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.209, "cosStep": 0.227, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.419, "cosStep": 0.177, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.239, "cosStep": 0.297, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.259, "cosStep": 0.197, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.379, "cosStep": 0.267, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.279, "cosStep": 0.337, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.339, "cosStep": 0.167, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 24, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1bias_fc3plain_fc2plain_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1bias_fc3plain_fc2plain_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": {
        "k": 2,
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.226, "cosStep": 0.218, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.436, "cosStep": 0.168, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.256, "cosStep": 0.288, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.336, "cosStep": 0.368, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.276, "cosStep": 0.188, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.296, "cosStep": 0.328, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1bias_fc3plain_fc2bias_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1bias_fc3plain_fc2bias_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.243, "cosStep": 0.209, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.453, "cosStep": 0.159, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.273, "cosStep": 0.279, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.353, "cosStep": 0.359, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.293, "cosStep": 0.179, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.413, "cosStep": 0.249, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.313, "cosStep": 0.319, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1bias_fc3biased_fc2plain_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1bias_fc3biased_fc2plain_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "k": 2, "activation_type": "swiglu", "swiglu_fusion": 0, "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 24, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.26, "cosStep": 0.2, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.47, "cosStep": 0.15, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.29, "cosStep": 0.27, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.37, "cosStep": 0.35, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.31, "cosStep": 0.17, "scale": 0.15 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.33, "cosStep": 0.31, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.39, "cosStep": 0.14, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 24, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_fc1bias_fc3biased_fc2bias_swiglu0",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_fc1bias_fc3biased_fc2bias_swiglu0. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": {
        "activation_type": "swiglu",
        "activation_alpha": 1.702,
        "activation_beta": 0.05,
        "swiglu_fusion": 0,
        "normalize_routing_weights": 1
      },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.277, "cosStep": 0.191, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.487, "cosStep": 0.141, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.307, "cosStep": 0.261, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.387, "cosStep": 0.341, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.327, "cosStep": 0.161, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.447, "cosStep": 0.231, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.347, "cosStep": 0.301, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.407, "cosStep": 0.131, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [96, 64], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_identity_no_fc3",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_identity_no_fc3. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "k": 2, "activation_type": "identity", "normalize_routing_weights": 1 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [48, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.181, "cosStep": 0.217, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [48, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.391, "cosStep": 0.127, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 64, 128],
          "data": { "kind": "fillFloat32", "sinStep": 0.211, "cosStep": 0.287, "scale": 0.15 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 128, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.231, "cosStep": 0.187, "scale": 0.15 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [48, 128], "tolerance": 0.00002 } }
    },
    {
      "name": "matrix_unaligned_hidden_inter",
      "provenance": {
        "notes": "Direct-weight matrix sibling of grouped_prefill_unaligned_hidden_inter. Preserves the activation, biases, input recipes, and tolerance; varies aligned reduction extents and rank while routed expert slices may end in partial row tiles."
      },
      "attrs": { "activation_type": "silu", "normalize_routing_weights": 1, "k": 2 },
      "inputs": {
        "inputT": {
          "dtype": "float32",
          "shape": [2, 48, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.163, "cosStep": 0.239, "scale": 0.4 }
        },
        "routerT": {
          "dtype": "float32",
          "shape": [96, 3],
          "data": { "kind": "fillFloat32", "sinStep": 0.337, "cosStep": 0.173, "scale": 0.7 }
        },
        "fc1T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.209, "cosStep": 0.281, "scale": 0.15 }
        },
        "fc1BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.261, "cosStep": 0.423, "scale": 0.2 }
        },
        "fc2T": {
          "dtype": "float32",
          "shape": [3, 64, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.251, "cosStep": 0.189, "scale": 0.15 }
        },
        "fc2BiasT": {
          "dtype": "float32",
          "shape": [3, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.321, "cosStep": 0.313, "scale": 0.2 }
        },
        "fc3T": {
          "dtype": "float32",
          "shape": [3, 96, 64],
          "data": { "kind": "fillFloat32", "sinStep": 0.227, "cosStep": 0.197, "scale": 0.15 }
        },
        "fc3BiasT": {
          "dtype": "float32",
          "shape": [3, 96],
          "data": { "kind": "fillFloat32", "sinStep": 0.283, "cosStep": 0.359, "scale": 0.2 }
        }
      },
      "outputs": { "outputT": { "dtype": "float32", "shape": [2, 48, 64], "tolerance": 0.00002 } }
    }
  ]
}