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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..8e376826f5a5cf7085a8d2fcf8afe60551c2a799
--- /dev/null
+++ b/README.md
@@ -0,0 +1,16 @@
+---
+tags:
+  - kernels
+---
+
+RWKV kernel for transformers
+### Performance
+
+<img class="dark:hidden border border-gray-200 dark:border-gray-700 rounded-lg" src="media/benches_light_animation.svg" />
+<img class="hidden dark:block border border-gray-200 dark:border-gray-700 rounded-lg" src="media/benches_dark_animation.svg" />
+
+<img class="dark:hidden border border-gray-200 dark:border-gray-700 rounded-lg" src="media/benches_light_latency.svg" />
+<img class="hidden dark:block border border-gray-200 dark:border-gray-700 rounded-lg" src="media/benches_dark_latency.svg" />
+
+<img class="dark:hidden border border-gray-200 dark:border-gray-700 rounded-lg" src="media/benches_light_throughput.svg" />
+<img class="hidden dark:block border border-gray-200 dark:border-gray-700 rounded-lg" src="media/benches_dark_throughput.svg" />
diff --git a/benchmarks/benchmark.py b/benchmarks/benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..d997b615e88d1ff417b3b1c159014806440034de
--- /dev/null
+++ b/benchmarks/benchmark.py
@@ -0,0 +1,81 @@
+import torch
+
+from kernels.benchmark import Benchmark
+
+
+def rwkv_wkv_reference(
+    w: torch.Tensor, u: torch.Tensor, k: torch.Tensor, v: torch.Tensor
+) -> torch.Tensor:
+    B, T, C = k.shape
+    device = k.device
+    dtype = k.dtype
+
+    y = torch.zeros(B, T, C, device=device, dtype=dtype)
+
+    # State: accumulated numerator, denominator, and max exponent
+    aa = torch.zeros(B, C, device=device, dtype=torch.float32)
+    bb = torch.zeros(B, C, device=device, dtype=torch.float32)
+    pp = torch.full((B, C), -1e38, device=device, dtype=torch.float32)
+
+    w = w.float()
+    u = u.float()
+
+    for t in range(T):
+        kt = k[:, t, :].float()  # [B, C]
+        vt = v[:, t, :].float()  # [B, C]
+
+        # Output computation
+        ww = u + kt
+        p = torch.maximum(pp, ww)
+        e1 = torch.exp(pp - p)
+        e2 = torch.exp(ww - p)
+        y[:, t, :] = ((e1 * aa + e2 * vt) / (e1 * bb + e2)).to(dtype)
+
+        # State update (note: w + pp, not pp - w)
+        ww = w + pp
+        p = torch.maximum(ww, kt)
+        e1 = torch.exp(ww - p)
+        e2 = torch.exp(kt - p)
+        aa = e1 * aa + e2 * vt
+        bb = e1 * bb + e2
+        pp = p
+
+    return y
+
+
+class RwkvBenchmark(Benchmark):
+    seed: int = 42
+
+    def setup(self):
+        B, T, C = 2, 64, 256
+
+        self.w = torch.randn(
+            C, device=self.device, dtype=torch.float32
+        ).abs()  # Decay should be positive
+        self.u = torch.randn(C, device=self.device, dtype=torch.float32)
+        self.k = torch.randn(B, T, C, device=self.device, dtype=torch.float32) * 0.1
+        self.v = torch.randn(B, T, C, device=self.device, dtype=torch.float32) * 0.1
+        self.out = torch.zeros(B, T, C, device=self.device, dtype=torch.float32)
+
+    def benchmark_base(self):
+        self.out.zero_()
+        self.kernel.forward(self.w, self.u, self.k, self.v, self.out)
+
+    def verify_base(self) -> torch.Tensor:
+        return rwkv_wkv_reference(self.w, self.u, self.k, self.v)
+
+    def setup_large(self):
+        B, T, C = 8, 256, 512
+
+        self.w = torch.randn(C, device=self.device, dtype=torch.float32).abs()
+        self.u = torch.randn(C, device=self.device, dtype=torch.float32)
+        self.k = torch.randn(B, T, C, device=self.device, dtype=torch.float32) * 0.1
+        self.v = torch.randn(B, T, C, device=self.device, dtype=torch.float32) * 0.1
+        self.out = torch.zeros(B, T, C, device=self.device, dtype=torch.float32)
+
+    def benchmark_large(self):
+        self.out.zero_()
+        self.kernel.forward(self.w, self.u, self.k, self.v, self.out)
+
+    def verify_large(self) -> torch.Tensor:
+        return rwkv_wkv_reference(self.w, self.u, self.k, self.v)
diff --git a/build.toml b/build.toml
new file mode 100644
index 0000000000000000000000000000000000000000..212d2cdef56c5edd6e5a38954dba357f181e69f8
--- /dev/null
+++ b/build.toml
@@ -0,0 +1,31 @@
+[general]
+name = "rwkv"
+universal = false
+
+[torch]
+src = [
+  "torch-ext/torch_binding.cpp",
+]
+
+[kernel.rwkv]
+depends = ["torch"]
+backend = "cuda"
+cuda-capabilities = [
+    "8.0",
+    "8.9",
+    "9.0",
+    "10.0",
+    "12.0",
+]
+include = ["."]
+src = [
+    "rwkv/wkv_cuda.cu",
+    "rwkv/wkv_cuda_bf16.cu",
+]
+cuda-flags = [
+        "-res-usage",
+        "--use_fast_math",
+        "-O3",
+        "--extra-device-vectorization",
+        "-DTmax=1024",
+]
diff --git a/build/torch210-cu128-x86_64-windows/__init__.py b/build/torch210-cu128-x86_64-windows/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfd6a665271bbf1428c46acfb331ac4ff0258f5e
--- /dev/null
+++ b/build/torch210-cu128-x86_64-windows/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cu128-x86_64-windows/_ops.py b/build/torch210-cu128-x86_64-windows/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..d42b492624cadfc963e93ba90b8a1cdecc4b0999
--- /dev/null
+++ b/build/torch210-cu128-x86_64-windows/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_38ccc47
+ops = torch.ops._rwkv_cuda_38ccc47
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_38ccc47::{op_name}"
diff --git a/build/torch210-cu128-x86_64-windows/_rwkv_cuda_38ccc47.pyd b/build/torch210-cu128-x86_64-windows/_rwkv_cuda_38ccc47.pyd
new file mode 100644
index 0000000000000000000000000000000000000000..3ae6662c009f80685af977e90beb43c793b78c61
--- /dev/null
+++ b/build/torch210-cu128-x86_64-windows/_rwkv_cuda_38ccc47.pyd
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e29ecb0b1d346a26decac4671633cf56fbc0b12df10027b1cca06a41f48976a7
+size 423424
diff --git a/build/torch210-cu128-x86_64-windows/metadata.json b/build/torch210-cu128-x86_64-windows/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..a1693688376cea25e6195d1985d05148d8e8d6fe
--- /dev/null
+++ b/build/torch210-cu128-x86_64-windows/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cu128-x86_64-windows/rwkv/__init__.py b/build/torch210-cu128-x86_64-windows/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc434ef44e63409acb52a8f3fff54a4adc46ed6a
--- /dev/null
+++ b/build/torch210-cu128-x86_64-windows/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch210-cxx11-cu126-aarch64-linux/__init__.py b/build/torch210-cxx11-cu126-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch210-cxx11-cu126-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cxx11-cu126-aarch64-linux/_ops.py b/build/torch210-cxx11-cu126-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch210-cxx11-cu126-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch210-cxx11-cu126-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch210-cxx11-cu126-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..03c28f3b8000c9ad78d6c224fe17e1a5089de3c1
--- /dev/null
+++ b/build/torch210-cxx11-cu126-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6eedf562b917d7e6865bbeb76296d462c5dd519365ab0454bc31558788c889c3
+size 2232072
diff --git a/build/torch210-cxx11-cu126-aarch64-linux/metadata.json b/build/torch210-cxx11-cu126-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..724a12daaf58a25e6b5fc3e62fc3b76e4b54ccd9
--- /dev/null
+++ b/build/torch210-cxx11-cu126-aarch64-linux/metadata.json
@@ -0,0 +1,13 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cxx11-cu126-aarch64-linux/rwkv/__init__.py b/build/torch210-cxx11-cu126-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch210-cxx11-cu126-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch210-cxx11-cu126-x86_64-linux/__init__.py b/build/torch210-cxx11-cu126-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch210-cxx11-cu126-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cxx11-cu126-x86_64-linux/_ops.py b/build/torch210-cxx11-cu126-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch210-cxx11-cu126-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch210-cxx11-cu126-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch210-cxx11-cu126-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..47529547732afe5c9cf217545484fbbf8b9d06b8
--- /dev/null
+++ b/build/torch210-cxx11-cu126-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef57a3b7b3028cf74874ad5f99fd237a942d20322b6c47e824cdcde75a612ac7
+size 2116408
diff --git a/build/torch210-cxx11-cu126-x86_64-linux/metadata.json b/build/torch210-cxx11-cu126-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..724a12daaf58a25e6b5fc3e62fc3b76e4b54ccd9
--- /dev/null
+++ b/build/torch210-cxx11-cu126-x86_64-linux/metadata.json
@@ -0,0 +1,13 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cxx11-cu126-x86_64-linux/rwkv/__init__.py b/build/torch210-cxx11-cu126-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch210-cxx11-cu126-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch210-cxx11-cu128-aarch64-linux/__init__.py b/build/torch210-cxx11-cu128-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch210-cxx11-cu128-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cxx11-cu128-aarch64-linux/_ops.py b/build/torch210-cxx11-cu128-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch210-cxx11-cu128-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch210-cxx11-cu128-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch210-cxx11-cu128-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..c9b63aa134adf3218288823bacb450b9e5bb4114
--- /dev/null
+++ b/build/torch210-cxx11-cu128-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7baac5a668392dd6029e0b87b9d54f163e0c41ca6052a35a5d03116773784114
+size 2428792
diff --git a/build/torch210-cxx11-cu128-aarch64-linux/metadata.json b/build/torch210-cxx11-cu128-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch210-cxx11-cu128-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cxx11-cu128-aarch64-linux/rwkv/__init__.py b/build/torch210-cxx11-cu128-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch210-cxx11-cu128-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch210-cxx11-cu128-x86_64-linux/__init__.py b/build/torch210-cxx11-cu128-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch210-cxx11-cu128-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cxx11-cu128-x86_64-linux/_ops.py b/build/torch210-cxx11-cu128-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch210-cxx11-cu128-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch210-cxx11-cu128-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch210-cxx11-cu128-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..9a02a6025049256b06b95909cb4734b5d53cc744
--- /dev/null
+++ b/build/torch210-cxx11-cu128-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb33f213f9412b8120ed540d92ef1e9c70ed35590feff5d53113140ce5298ee0
+size 2318768
diff --git a/build/torch210-cxx11-cu128-x86_64-linux/metadata.json b/build/torch210-cxx11-cu128-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch210-cxx11-cu128-x86_64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cxx11-cu128-x86_64-linux/rwkv/__init__.py b/build/torch210-cxx11-cu128-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch210-cxx11-cu128-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch210-cxx11-cu130-aarch64-linux/__init__.py b/build/torch210-cxx11-cu130-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch210-cxx11-cu130-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cxx11-cu130-aarch64-linux/_ops.py b/build/torch210-cxx11-cu130-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch210-cxx11-cu130-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch210-cxx11-cu130-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch210-cxx11-cu130-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..018b21e75e17a5a78cd972bf46f8484e6accd848
--- /dev/null
+++ b/build/torch210-cxx11-cu130-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a90cc433fa1453bb04a4993f2af0485266c227d0ece13808be55d3a7f280f4f
+size 2432776
diff --git a/build/torch210-cxx11-cu130-aarch64-linux/metadata.json b/build/torch210-cxx11-cu130-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch210-cxx11-cu130-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cxx11-cu130-aarch64-linux/rwkv/__init__.py b/build/torch210-cxx11-cu130-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch210-cxx11-cu130-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch210-cxx11-cu130-x86_64-linux/__init__.py b/build/torch210-cxx11-cu130-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch210-cxx11-cu130-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch210-cxx11-cu130-x86_64-linux/_ops.py b/build/torch210-cxx11-cu130-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch210-cxx11-cu130-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch210-cxx11-cu130-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch210-cxx11-cu130-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..94af622484b088865eb866947b7bfbd73ad56149
--- /dev/null
+++ b/build/torch210-cxx11-cu130-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:024c9733ab553416a3165fabcfd1f8957e99b2c256c3fc5f51d1e3032200ff96
+size 2348248
diff --git a/build/torch210-cxx11-cu130-x86_64-linux/metadata.json b/build/torch210-cxx11-cu130-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch210-cxx11-cu130-x86_64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch210-cxx11-cu130-x86_64-linux/rwkv/__init__.py b/build/torch210-cxx11-cu130-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch210-cxx11-cu130-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch211-cxx11-cu126-aarch64-linux/__init__.py b/build/torch211-cxx11-cu126-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch211-cxx11-cu126-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch211-cxx11-cu126-aarch64-linux/_ops.py b/build/torch211-cxx11-cu126-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch211-cxx11-cu126-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch211-cxx11-cu126-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch211-cxx11-cu126-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..b30b2056e569db65fccc0195498ab93179539ef0
--- /dev/null
+++ b/build/torch211-cxx11-cu126-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f97c2ec3958d76db37b74a83093e2f127317b51c94d089bc0ac710499fbdc49a
+size 2232072
diff --git a/build/torch211-cxx11-cu126-aarch64-linux/metadata.json b/build/torch211-cxx11-cu126-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..724a12daaf58a25e6b5fc3e62fc3b76e4b54ccd9
--- /dev/null
+++ b/build/torch211-cxx11-cu126-aarch64-linux/metadata.json
@@ -0,0 +1,13 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch211-cxx11-cu126-aarch64-linux/rwkv/__init__.py b/build/torch211-cxx11-cu126-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch211-cxx11-cu126-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch211-cxx11-cu126-x86_64-linux/__init__.py b/build/torch211-cxx11-cu126-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch211-cxx11-cu126-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch211-cxx11-cu126-x86_64-linux/_ops.py b/build/torch211-cxx11-cu126-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch211-cxx11-cu126-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch211-cxx11-cu126-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch211-cxx11-cu126-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..f9bd0e3c7d67e019fcca722f0d1f3706e040a58a
--- /dev/null
+++ b/build/torch211-cxx11-cu126-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a55661a0185ca9375700bbd80fe85272180c0f081e115d45ef53b471f55fc1e9
+size 2116408
diff --git a/build/torch211-cxx11-cu126-x86_64-linux/metadata.json b/build/torch211-cxx11-cu126-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..724a12daaf58a25e6b5fc3e62fc3b76e4b54ccd9
--- /dev/null
+++ b/build/torch211-cxx11-cu126-x86_64-linux/metadata.json
@@ -0,0 +1,13 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch211-cxx11-cu126-x86_64-linux/rwkv/__init__.py b/build/torch211-cxx11-cu126-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch211-cxx11-cu126-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch211-cxx11-cu128-aarch64-linux/__init__.py b/build/torch211-cxx11-cu128-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch211-cxx11-cu128-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch211-cxx11-cu128-aarch64-linux/_ops.py b/build/torch211-cxx11-cu128-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch211-cxx11-cu128-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch211-cxx11-cu128-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch211-cxx11-cu128-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..0b930c093e6f03c71f6d22f4f1bfb95e64aaeeb4
--- /dev/null
+++ b/build/torch211-cxx11-cu128-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01ce97da4775c7e455e8002a81050197073a3455e1465674e0f08f41eee082fe
+size 2428792
diff --git a/build/torch211-cxx11-cu128-aarch64-linux/metadata.json b/build/torch211-cxx11-cu128-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch211-cxx11-cu128-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch211-cxx11-cu128-aarch64-linux/rwkv/__init__.py b/build/torch211-cxx11-cu128-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch211-cxx11-cu128-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch211-cxx11-cu128-x86_64-linux/__init__.py b/build/torch211-cxx11-cu128-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch211-cxx11-cu128-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch211-cxx11-cu128-x86_64-linux/_ops.py b/build/torch211-cxx11-cu128-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch211-cxx11-cu128-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch211-cxx11-cu128-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch211-cxx11-cu128-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..fa4f44f581e035f206ca3b10b7567118736def1f
--- /dev/null
+++ b/build/torch211-cxx11-cu128-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ba2b12cfcc7f264fcb312f71be6f98763a697e1870300120fc4294a7715d5de
+size 2318768
diff --git a/build/torch211-cxx11-cu128-x86_64-linux/metadata.json b/build/torch211-cxx11-cu128-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch211-cxx11-cu128-x86_64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch211-cxx11-cu128-x86_64-linux/rwkv/__init__.py b/build/torch211-cxx11-cu128-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch211-cxx11-cu128-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch211-cxx11-cu130-aarch64-linux/__init__.py b/build/torch211-cxx11-cu130-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch211-cxx11-cu130-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch211-cxx11-cu130-aarch64-linux/_ops.py b/build/torch211-cxx11-cu130-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch211-cxx11-cu130-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch211-cxx11-cu130-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch211-cxx11-cu130-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..0b55948d42d16a03617534637377957b2be5d966
--- /dev/null
+++ b/build/torch211-cxx11-cu130-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3095e84d107fbbe7425a85b2f9cde793d0fef4c25afea4f7f42ff16974268106
+size 2432776
diff --git a/build/torch211-cxx11-cu130-aarch64-linux/metadata.json b/build/torch211-cxx11-cu130-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch211-cxx11-cu130-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch211-cxx11-cu130-aarch64-linux/rwkv/__init__.py b/build/torch211-cxx11-cu130-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch211-cxx11-cu130-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch211-cxx11-cu130-x86_64-linux/__init__.py b/build/torch211-cxx11-cu130-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch211-cxx11-cu130-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch211-cxx11-cu130-x86_64-linux/_ops.py b/build/torch211-cxx11-cu130-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch211-cxx11-cu130-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch211-cxx11-cu130-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch211-cxx11-cu130-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..8ae414352979a91551bbb10891b2a223333279d6
--- /dev/null
+++ b/build/torch211-cxx11-cu130-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:582930c14cdc9d0f9a5b83ad198433bbf0d53e2a3b904a63d788f4918487f347
+size 2348248
diff --git a/build/torch211-cxx11-cu130-x86_64-linux/metadata.json b/build/torch211-cxx11-cu130-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch211-cxx11-cu130-x86_64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch211-cxx11-cu130-x86_64-linux/rwkv/__init__.py b/build/torch211-cxx11-cu130-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch211-cxx11-cu130-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__init__.py b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..c881de2e0f54ba27fce13d55f35013e2b90dea0b
Binary files /dev/null and b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc differ
diff --git a/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..49bded237b33ffeb440b398ca5da96497d92364c
Binary files /dev/null and b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc differ
diff --git a/build/torch27-cxx11-cu118-x86_64-linux/rwkv/_ops.py b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..c54b92a73953ef7168a8a45b1d3caa2e64acc4cb
--- /dev/null
+++ b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_0aa7cb0
+ops = torch.ops._rwkv_0aa7cb0
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_0aa7cb0::{op_name}"
\ No newline at end of file
diff --git a/build/torch27-cxx11-cu118-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..af67a351934b49c158ae3e5d7a4fa465ace19ab1
--- /dev/null
+++ b/build/torch27-cxx11-cu118-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a561edd91db3da01aad4f484eca06f44e98843f859317aeaa9a3df85339ce850
+size 2065400
diff --git a/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__init__.py b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..7dee5151cb6937785bd5a63c6a8f02f9bb3d351c
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diff --git a/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..3bad2999c65772f5fd868bf6af0fc88b592fa665
Binary files /dev/null and b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc differ
diff --git a/build/torch27-cxx11-cu126-x86_64-linux/rwkv/_ops.py b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..c54b92a73953ef7168a8a45b1d3caa2e64acc4cb
--- /dev/null
+++ b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_0aa7cb0
+ops = torch.ops._rwkv_0aa7cb0
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_0aa7cb0::{op_name}"
\ No newline at end of file
diff --git a/build/torch27-cxx11-cu126-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..fdf4405e0825430c46f683f9a763da3a39d96873
--- /dev/null
+++ b/build/torch27-cxx11-cu126-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b115095e24c64d21eecefdc0ed4d9c13ea9485930f8f56cc99874762c501f278
+size 2106408
diff --git a/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__init__.py b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__pycache__/__init__.cpython-313.pyc
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index 0000000000000000000000000000000000000000..382a6ba0237ee4e4fe73cbb610a447987d9005e7
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diff --git a/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/__pycache__/_ops.cpython-313.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..0fdac79c69dd79159f35740b56bde8c18f82a375
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diff --git a/build/torch27-cxx11-cu128-x86_64-linux/rwkv/_ops.py b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..c54b92a73953ef7168a8a45b1d3caa2e64acc4cb
--- /dev/null
+++ b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_0aa7cb0
+ops = torch.ops._rwkv_0aa7cb0
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_0aa7cb0::{op_name}"
\ No newline at end of file
diff --git a/build/torch27-cxx11-cu128-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..df248c48b997c805b9ec6368141f1d027a9c641b
--- /dev/null
+++ b/build/torch27-cxx11-cu128-x86_64-linux/rwkv/_rwkv_0aa7cb0.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19029bb516a8cf3428f4e47a2b5f26f091d169206e51df7c4e89880553a15df7
+size 2308848
diff --git a/build/torch28-cxx11-cu126-x86_64-linux/__init__.py b/build/torch28-cxx11-cu126-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch28-cxx11-cu126-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu126-x86_64-linux/_ops.py b/build/torch28-cxx11-cu126-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada2ab024f61c605a79ddc243163cfb3b8a39ef7
--- /dev/null
+++ b/build/torch28-cxx11-cu126-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_44f2fa4
+ops = torch.ops._rwkv_44f2fa4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_44f2fa4::{op_name}"
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu126-x86_64-linux/_rwkv_44f2fa4.abi3.so b/build/torch28-cxx11-cu126-x86_64-linux/_rwkv_44f2fa4.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..4cc06763b7d171a586c906cf1e6bb55cba0e4709
--- /dev/null
+++ b/build/torch28-cxx11-cu126-x86_64-linux/_rwkv_44f2fa4.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0d383c1186dde17bb427291205ae83bdb2e49abf15b4eb482151aa0940e51d4d
+size 2111504
diff --git a/build/torch28-cxx11-cu126-x86_64-linux/metadata.json b/build/torch28-cxx11-cu126-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5deed9898dce769f4cc73913d3530b92a0bd8
--- /dev/null
+++ b/build/torch28-cxx11-cu126-x86_64-linux/metadata.json
@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu126-x86_64-linux/rwkv/__init__.py b/build/torch28-cxx11-cu126-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch28-cxx11-cu126-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch28-cxx11-cu128-x86_64-linux/__init__.py b/build/torch28-cxx11-cu128-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch28-cxx11-cu128-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu128-x86_64-linux/_ops.py b/build/torch28-cxx11-cu128-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada2ab024f61c605a79ddc243163cfb3b8a39ef7
--- /dev/null
+++ b/build/torch28-cxx11-cu128-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_44f2fa4
+ops = torch.ops._rwkv_44f2fa4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_44f2fa4::{op_name}"
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu128-x86_64-linux/_rwkv_44f2fa4.abi3.so b/build/torch28-cxx11-cu128-x86_64-linux/_rwkv_44f2fa4.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..026e5fa245ea37eee14a4233f948fd28abe48747
--- /dev/null
+++ b/build/torch28-cxx11-cu128-x86_64-linux/_rwkv_44f2fa4.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fb0335b36148895aac63e1822e14c4be054dd842dbdddcfab668b65b2335ff2
+size 2318032
diff --git a/build/torch28-cxx11-cu128-x86_64-linux/metadata.json b/build/torch28-cxx11-cu128-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5deed9898dce769f4cc73913d3530b92a0bd8
--- /dev/null
+++ b/build/torch28-cxx11-cu128-x86_64-linux/metadata.json
@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu128-x86_64-linux/rwkv/__init__.py b/build/torch28-cxx11-cu128-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch28-cxx11-cu128-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch28-cxx11-cu129-x86_64-linux/__init__.py b/build/torch28-cxx11-cu129-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch28-cxx11-cu129-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu129-x86_64-linux/_ops.py b/build/torch28-cxx11-cu129-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada2ab024f61c605a79ddc243163cfb3b8a39ef7
--- /dev/null
+++ b/build/torch28-cxx11-cu129-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_44f2fa4
+ops = torch.ops._rwkv_44f2fa4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_44f2fa4::{op_name}"
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu129-x86_64-linux/_rwkv_44f2fa4.abi3.so b/build/torch28-cxx11-cu129-x86_64-linux/_rwkv_44f2fa4.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..62492882cb398511bfca1748b134d07316503937
--- /dev/null
+++ b/build/torch28-cxx11-cu129-x86_64-linux/_rwkv_44f2fa4.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2178eb41ec85f3861d61391de45ec47c3127490fbd902f1e2167683514cf867b
+size 2335432
diff --git a/build/torch28-cxx11-cu129-x86_64-linux/metadata.json b/build/torch28-cxx11-cu129-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5deed9898dce769f4cc73913d3530b92a0bd8
--- /dev/null
+++ b/build/torch28-cxx11-cu129-x86_64-linux/metadata.json
@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}
\ No newline at end of file
diff --git a/build/torch28-cxx11-cu129-x86_64-linux/rwkv/__init__.py b/build/torch28-cxx11-cu129-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch28-cxx11-cu129-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu126-aarch64-linux/__init__.py b/build/torch29-cxx11-cu126-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu126-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu126-aarch64-linux/_ops.py b/build/torch29-cxx11-cu126-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dffb89844d9c7644252ba9bc5b0635b106174cd
--- /dev/null
+++ b/build/torch29-cxx11-cu126-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_efd954c
+ops = torch.ops._rwkv_cuda_efd954c
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_efd954c::{op_name}"
diff --git a/build/torch29-cxx11-cu126-aarch64-linux/_rwkv_cuda_efd954c.abi3.so b/build/torch29-cxx11-cu126-aarch64-linux/_rwkv_cuda_efd954c.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..894d88388663606729b454e35fd4a17f9de139dc
--- /dev/null
+++ b/build/torch29-cxx11-cu126-aarch64-linux/_rwkv_cuda_efd954c.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6d39263049bd9e5823007907a9b2f366226c9dacc5a6157f68f8e317bd1108c
+size 2230968
diff --git a/build/torch29-cxx11-cu126-aarch64-linux/metadata.json b/build/torch29-cxx11-cu126-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..724a12daaf58a25e6b5fc3e62fc3b76e4b54ccd9
--- /dev/null
+++ b/build/torch29-cxx11-cu126-aarch64-linux/metadata.json
@@ -0,0 +1,13 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch29-cxx11-cu126-aarch64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu126-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch29-cxx11-cu126-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu126-x86_64-linux/__init__.py b/build/torch29-cxx11-cu126-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu126-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu126-x86_64-linux/_ops.py b/build/torch29-cxx11-cu126-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada2ab024f61c605a79ddc243163cfb3b8a39ef7
--- /dev/null
+++ b/build/torch29-cxx11-cu126-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_44f2fa4
+ops = torch.ops._rwkv_44f2fa4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_44f2fa4::{op_name}"
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu126-x86_64-linux/_rwkv_44f2fa4.abi3.so b/build/torch29-cxx11-cu126-x86_64-linux/_rwkv_44f2fa4.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..87947ce206483ed63225e5ac32379232817c05b3
--- /dev/null
+++ b/build/torch29-cxx11-cu126-x86_64-linux/_rwkv_44f2fa4.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64e542cd9e7f9a7ea5bf22a91a27fb34f6352b7fbbc39463f2f5d183f6386d05
+size 2111480
diff --git a/build/torch29-cxx11-cu126-x86_64-linux/metadata.json b/build/torch29-cxx11-cu126-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5deed9898dce769f4cc73913d3530b92a0bd8
--- /dev/null
+++ b/build/torch29-cxx11-cu126-x86_64-linux/metadata.json
@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu126-x86_64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu126-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch29-cxx11-cu126-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu128-aarch64-linux/__init__.py b/build/torch29-cxx11-cu128-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu128-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu128-aarch64-linux/_ops.py b/build/torch29-cxx11-cu128-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dffb89844d9c7644252ba9bc5b0635b106174cd
--- /dev/null
+++ b/build/torch29-cxx11-cu128-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_efd954c
+ops = torch.ops._rwkv_cuda_efd954c
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_efd954c::{op_name}"
diff --git a/build/torch29-cxx11-cu128-aarch64-linux/_rwkv_cuda_efd954c.abi3.so b/build/torch29-cxx11-cu128-aarch64-linux/_rwkv_cuda_efd954c.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..87d47bd77178df8cfa9089434aa9eca8f59562de
--- /dev/null
+++ b/build/torch29-cxx11-cu128-aarch64-linux/_rwkv_cuda_efd954c.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1cde967371ab4341efc9a43eead44a82fe2e80099f170e0f4781f96f8d68961
+size 2427688
diff --git a/build/torch29-cxx11-cu128-aarch64-linux/metadata.json b/build/torch29-cxx11-cu128-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch29-cxx11-cu128-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch29-cxx11-cu128-aarch64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu128-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch29-cxx11-cu128-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu128-x86_64-linux/__init__.py b/build/torch29-cxx11-cu128-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu128-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu128-x86_64-linux/_ops.py b/build/torch29-cxx11-cu128-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada2ab024f61c605a79ddc243163cfb3b8a39ef7
--- /dev/null
+++ b/build/torch29-cxx11-cu128-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_44f2fa4
+ops = torch.ops._rwkv_44f2fa4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_44f2fa4::{op_name}"
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu128-x86_64-linux/_rwkv_44f2fa4.abi3.so b/build/torch29-cxx11-cu128-x86_64-linux/_rwkv_44f2fa4.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..602898b1e3cb5930b41c1772b31a77942b830400
--- /dev/null
+++ b/build/torch29-cxx11-cu128-x86_64-linux/_rwkv_44f2fa4.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e37b16a151528c1ad0eecfeb1c3296ac45e6856d3b5b08f7acc68499e3ceca4c
+size 2318000
diff --git a/build/torch29-cxx11-cu128-x86_64-linux/metadata.json b/build/torch29-cxx11-cu128-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5deed9898dce769f4cc73913d3530b92a0bd8
--- /dev/null
+++ b/build/torch29-cxx11-cu128-x86_64-linux/metadata.json
@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu128-x86_64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu128-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch29-cxx11-cu128-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu129-aarch64-linux/__init__.py b/build/torch29-cxx11-cu129-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu129-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu129-aarch64-linux/_ops.py b/build/torch29-cxx11-cu129-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch29-cxx11-cu129-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch29-cxx11-cu129-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch29-cxx11-cu129-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..07ad4b66966eb57d772dffb889dafeabb11fa217
--- /dev/null
+++ b/build/torch29-cxx11-cu129-aarch64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03745749fe48ca7bda892fa48ee9ca71897488a03287aa18dcd103b1baf63c85
+size 2429112
diff --git a/build/torch29-cxx11-cu129-aarch64-linux/metadata.json b/build/torch29-cxx11-cu129-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch29-cxx11-cu129-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch29-cxx11-cu129-aarch64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu129-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch29-cxx11-cu129-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu129-x86_64-linux/__init__.py b/build/torch29-cxx11-cu129-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu129-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu129-x86_64-linux/_ops.py b/build/torch29-cxx11-cu129-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..6985878866ce4c1c622d1d977162df0e6c564cee
--- /dev/null
+++ b/build/torch29-cxx11-cu129-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_5849bdb
+ops = torch.ops._rwkv_cuda_5849bdb
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_5849bdb::{op_name}"
diff --git a/build/torch29-cxx11-cu129-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so b/build/torch29-cxx11-cu129-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..d3c16bd6d3b3f3eec3b740fe15241cc7faba92d9
--- /dev/null
+++ b/build/torch29-cxx11-cu129-x86_64-linux/_rwkv_cuda_5849bdb.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e124cba0e78954c0aa034427c32ff799bd43f5050eeaceca2c8989841b45495
+size 2335432
diff --git a/build/torch29-cxx11-cu129-x86_64-linux/metadata.json b/build/torch29-cxx11-cu129-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch29-cxx11-cu129-x86_64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch29-cxx11-cu129-x86_64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu129-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9b2672c1cd85b74c1b3ded0fc0b2100e1aeac23
--- /dev/null
+++ b/build/torch29-cxx11-cu129-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import importlib.util
+import sys
+from pathlib import Path
+from types import ModuleType
+
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu130-aarch64-linux/__init__.py b/build/torch29-cxx11-cu130-aarch64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu130-aarch64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu130-aarch64-linux/_ops.py b/build/torch29-cxx11-cu130-aarch64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..5dffb89844d9c7644252ba9bc5b0635b106174cd
--- /dev/null
+++ b/build/torch29-cxx11-cu130-aarch64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_cuda_efd954c
+ops = torch.ops._rwkv_cuda_efd954c
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_cuda_efd954c::{op_name}"
diff --git a/build/torch29-cxx11-cu130-aarch64-linux/_rwkv_cuda_efd954c.abi3.so b/build/torch29-cxx11-cu130-aarch64-linux/_rwkv_cuda_efd954c.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..8325463d49dec8d7492c677dbe0863e4056c206f
--- /dev/null
+++ b/build/torch29-cxx11-cu130-aarch64-linux/_rwkv_cuda_efd954c.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab7e3bbeaf2bb811191f7dd30fa68f7c5c772423020b9fe994d31273bf77dc4a
+size 2431672
diff --git a/build/torch29-cxx11-cu130-aarch64-linux/metadata.json b/build/torch29-cxx11-cu130-aarch64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..890b88c81c1da1a10714a26cdca182aa27c63f6f
--- /dev/null
+++ b/build/torch29-cxx11-cu130-aarch64-linux/metadata.json
@@ -0,0 +1,15 @@
+{
+  "version": 1,
+  "license": "Apache-2.0",
+  "python-depends": [],
+  "backend": {
+    "type": "cuda",
+    "archs": [
+      "10.0",
+      "12.0",
+      "8.0",
+      "8.9",
+      "9.0"
+    ]
+  }
+}
diff --git a/build/torch29-cxx11-cu130-aarch64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu130-aarch64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch29-cxx11-cu130-aarch64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/build/torch29-cxx11-cu130-x86_64-linux/__init__.py b/build/torch29-cxx11-cu130-x86_64-linux/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/build/torch29-cxx11-cu130-x86_64-linux/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu130-x86_64-linux/_ops.py b/build/torch29-cxx11-cu130-x86_64-linux/_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..ada2ab024f61c605a79ddc243163cfb3b8a39ef7
--- /dev/null
+++ b/build/torch29-cxx11-cu130-x86_64-linux/_ops.py
@@ -0,0 +1,9 @@
+import torch
+from . import _rwkv_44f2fa4
+ops = torch.ops._rwkv_44f2fa4
+
+def add_op_namespace_prefix(op_name: str):
+    """
+    Prefix op by namespace.
+    """
+    return f"_rwkv_44f2fa4::{op_name}"
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu130-x86_64-linux/_rwkv_44f2fa4.abi3.so b/build/torch29-cxx11-cu130-x86_64-linux/_rwkv_44f2fa4.abi3.so
new file mode 100644
index 0000000000000000000000000000000000000000..6844b7973cff37a87fa06f89130dba801198b046
--- /dev/null
+++ b/build/torch29-cxx11-cu130-x86_64-linux/_rwkv_44f2fa4.abi3.so
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66177f2e067a2624a79e80664f3d87547c1a5916a91711f792495c4a77f5b70f
+size 2343392
diff --git a/build/torch29-cxx11-cu130-x86_64-linux/metadata.json b/build/torch29-cxx11-cu130-x86_64-linux/metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..9cf5deed9898dce769f4cc73913d3530b92a0bd8
--- /dev/null
+++ b/build/torch29-cxx11-cu130-x86_64-linux/metadata.json
@@ -0,0 +1,4 @@
+{
+  "version": 1,
+  "python-depends": []
+}
\ No newline at end of file
diff --git a/build/torch29-cxx11-cu130-x86_64-linux/rwkv/__init__.py b/build/torch29-cxx11-cu130-x86_64-linux/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..03dbc1afe1cf156661a2b1b22003cd5f599a0309
--- /dev/null
+++ b/build/torch29-cxx11-cu130-x86_64-linux/rwkv/__init__.py
@@ -0,0 +1,26 @@
+import ctypes
+import sys
+
+import importlib
+from pathlib import Path
+from types import ModuleType
+
+def _import_from_path(file_path: Path) -> ModuleType:
+    # We cannot use the module name as-is, after adding it to `sys.modules`,
+    # it would also be used for other imports. So, we make a module name that
+    # depends on the path for it to be unique using the hex-encoded hash of
+    # the path.
+    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
+    module_name = path_hash
+    spec = importlib.util.spec_from_file_location(module_name, file_path)
+    if spec is None:
+        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
+    module = importlib.util.module_from_spec(spec)
+    if module is None:
+        raise ImportError(f"Cannot load module {module_name} from spec")
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)  # type: ignore
+    return module
+
+
+globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))
diff --git a/flake.lock b/flake.lock
new file mode 100644
index 0000000000000000000000000000000000000000..85b5d60a855bf4c19555cc9b8de8ca88d6fd3ae9
--- /dev/null
+++ b/flake.lock
@@ -0,0 +1,168 @@
+{
+  "nodes": {
+    "flake-compat": {
+      "locked": {
+        "lastModified": 1747046372,
+        "narHash": "sha256-CIVLLkVgvHYbgI2UpXvIIBJ12HWgX+fjA8Xf8PUmqCY=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "9100a0f413b0c601e0533d1d94ffd501ce2e7885",
+        "type": "github"
+      },
+      "original": {
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "type": "github"
+      }
+    },
+    "flake-compat_2": {
+      "locked": {
+        "lastModified": 1747046372,
+        "narHash": "sha256-CIVLLkVgvHYbgI2UpXvIIBJ12HWgX+fjA8Xf8PUmqCY=",
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "rev": "9100a0f413b0c601e0533d1d94ffd501ce2e7885",
+        "type": "github"
+      },
+      "original": {
+        "owner": "edolstra",
+        "repo": "flake-compat",
+        "type": "github"
+      }
+    },
+    "flake-utils": {
+      "inputs": {
+        "systems": "systems"
+      },
+      "locked": {
+        "lastModified": 1731533236,
+        "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "flake-utils_2": {
+      "inputs": {
+        "systems": "systems_2"
+      },
+      "locked": {
+        "lastModified": 1731533236,
+        "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "hf-nix": {
+      "inputs": {
+        "flake-compat": "flake-compat_2",
+        "flake-utils": "flake-utils_2",
+        "nixpkgs": "nixpkgs"
+      },
+      "locked": {
+        "lastModified": 1759851564,
+        "narHash": "sha256-Xybkhm0FM/VzlZ5WndTYq/X/9MAeddd4EQ2Vz8GdkOA=",
+        "owner": "huggingface",
+        "repo": "hf-nix",
+        "rev": "351655d9f124805ed7c1193aa61550ce245f4570",
+        "type": "github"
+      },
+      "original": {
+        "owner": "huggingface",
+        "repo": "hf-nix",
+        "type": "github"
+      }
+    },
+    "kernel-builder": {
+      "inputs": {
+        "flake-compat": "flake-compat",
+        "flake-utils": "flake-utils",
+        "hf-nix": "hf-nix",
+        "nixpkgs": [
+          "kernel-builder",
+          "hf-nix",
+          "nixpkgs"
+        ]
+      },
+      "locked": {
+        "lastModified": 1760035358,
+        "narHash": "sha256-N5vmCrgwcIluPclf/hmnofLK77EJJYh5PR8SRvw++es=",
+        "owner": "huggingface",
+        "repo": "kernel-builder",
+        "rev": "a48cbd19ae7e425dfc1865188ef06dac43ab9244",
+        "type": "github"
+      },
+      "original": {
+        "owner": "huggingface",
+        "repo": "kernel-builder",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1755963616,
+        "narHash": "sha256-6yD0ww/S8n+U2uPYcJZ3DRURP8Kx036GRpR2uPNZroE=",
+        "owner": "nixos",
+        "repo": "nixpkgs",
+        "rev": "73e96df7cff5783f45e21342a75a1540c4eddce4",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nixos",
+        "ref": "nixos-unstable-small",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "kernel-builder": "kernel-builder"
+      }
+    },
+    "systems": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    },
+    "systems_2": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}
diff --git a/flake.nix b/flake.nix
new file mode 100644
index 0000000000000000000000000000000000000000..f4954b5ca991ee9ab5893205c29997dd1c1112c2
--- /dev/null
+++ b/flake.nix
@@ -0,0 +1,17 @@
+{
+  description = "Flake for rwkv kernels";
+
+  inputs = {
+    kernel-builder.url = "github:huggingface/kernel-builder";
+  };
+
+  outputs =
+    {
+      self,
+      kernel-builder,
+    }:
+    kernel-builder.lib.genFlakeOutputs {
+      path = ./.;
+      rev = self.shortRev or self.dirtyShortRev or self.lastModifiedDate;
+    };
+}
diff --git a/media/benches_dark_animation.svg b/media/benches_dark_animation.svg
new file mode 100644
index 0000000000000000000000000000000000000000..5c6d7df0404cd13941099d4dbdac79a6f9a49cae
--- /dev/null
+++ b/media/benches_dark_animation.svg
@@ -0,0 +1,33 @@
+<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 800 220" style="background:#101623;font-family:DejaVu Sans Mono,monospace">
+<text x="10" y="25" font-size="14" font-weight="bold" fill="#E6EDF3">kernels-community/rwkv vs Torch - Relative Speed</text>
+<text x="790" y="25" font-size="10" fill="#6B7280" text-anchor="end">PyTorch 2.11.0+cu130 · CPU</text>
+<rect x="180" y="60" width="470" height="30" rx="4" fill="#30363D" stroke="#484F58"/>
+<text x="170" y="79" font-size="10" fill="#E6EDF3" text-anchor="end">RwkvBenchmark.base</text>
+<text x="660" y="79" font-size="10" font-weight="bold" fill="#E6EDF3">455.24x</text>
+<circle cx="188" cy="69" r="8" fill="#FF9D00" stroke="white" stroke-width="1.5">
+  <animate attributeName="cx" values="188;642;188" dur="0.008786638942920385s" repeatCount="indefinite" calcMode="spline" keySplines="0.5 0 0.5 1;0.5 0 0.5 1"/>
+</circle>
+<circle cx="188" cy="81" r="8" fill="#6B7280" stroke="white" stroke-width="1.5">
+  <animate attributeName="cx" values="188;642;188" dur="4.0s" repeatCount="indefinite" calcMode="spline" keySplines="0.5 0 0.5 1;0.5 0 0.5 1"/>
+</circle>
+<rect x="180" y="110" width="470" height="30" rx="4" fill="#30363D" stroke="#484F58"/>
+<text x="170" y="129" font-size="10" fill="#E6EDF3" text-anchor="end">RwkvBenchmark.large</text>
+<text x="660" y="129" font-size="10" font-weight="bold" fill="#E6EDF3">1137.57x</text>
+<circle cx="188" cy="119" r="8" fill="#FF9D00" stroke="white" stroke-width="1.5">
+  <animate attributeName="cx" values="188;642;188" dur="0.0035162582771217925s" repeatCount="indefinite" calcMode="spline" keySplines="0.5 0 0.5 1;0.5 0 0.5 1"/>
+</circle>
+<circle cx="188" cy="131" r="8" fill="#6B7280" stroke="white" stroke-width="1.5">
+  <animate attributeName="cx" values="188;642;188" dur="4.0s" repeatCount="indefinite" calcMode="spline" keySplines="0.5 0 0.5 1;0.5 0 0.5 1"/>
+</circle>
+<circle cx="650" cy="200" r="6" fill="#FF9D00" stroke="white"/>
+<text x="662" y="204" font-size="9" fill="#E6EDF3">Kernel</text>
+<circle cx="730" cy="200" r="6" fill="#6B7280" stroke="white"/>
+<text x="742" y="204" font-size="9" fill="#E6EDF3">Torch (ref)</text>
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diff --git a/rwkv/wkv_cuda.cu b/rwkv/wkv_cuda.cu
new file mode 100644
index 0000000000000000000000000000000000000000..571d5a8a8307e95aac689eb3c9333d1ad350c7de
--- /dev/null
+++ b/rwkv/wkv_cuda.cu
@@ -0,0 +1,187 @@
+#include <stdio.h>
+#include <assert.h>
+
+#define MIN_VALUE (-1e38)
+
+template <typename F>
+__global__ void kernel_forward(
+    const int B, const int T, const int C, const F *__restrict__ const _w, const F *__restrict__ const _u,
+    const F *__restrict__ const _k, const F *__restrict__ const _v, F *__restrict__ const _y
+) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    F u = _u[_c];
+    F w = _w[_c];
+    const F *__restrict__ const k = _k + _offset;
+    const F *__restrict__ const v = _v + _offset;
+    F *__restrict__ const y = _y + _offset;
+
+    // aa and bb are running sums divided by exp(pp) (to avoid overflow)
+    F aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+
+        F ww = u + kk;
+        F p = max(pp, ww);
+        F e1 = exp(pp - p);
+        F e2 = exp(ww - p);
+        y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2);
+        
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+}
+
+template <typename F>
+__global__ void kernel_forward_with_state(
+    const int B, const int T, const int C, const F *__restrict__ const _w, const F *__restrict__ const _u,
+    const F *__restrict__ const _k, const F *__restrict__ const _v, F *__restrict__ const _y, F *__restrict__ const _s
+) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset_s = _b * C * 3 + _c * 3;
+    const int _offset = _b * T * C + _c;
+
+    F u = _u[_c];
+    F w = _w[_c];
+    const F *__restrict__ const k = _k + _offset;
+    const F *__restrict__ const v = _v + _offset;
+    F *__restrict__ const y = _y + _offset;
+    F *__restrict__ const s = _s + _offset_s;
+
+    // aa and bb are running sums divided by exp(pp) (to avoid overflow)
+    F aa = s[0], bb = s[1], pp = s[2];
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+
+        F ww = u + kk;
+        F p = max(pp, ww);
+        F e1 = exp(pp - p);
+        F e2 = exp(ww - p);
+        y[ii] = (e1 * aa + e2 * vv) / (e1 * bb + e2);
+        
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    s[0] = aa;
+    s[1] = bb;
+    s[2] = pp;
+}
+
+template <typename F>
+__global__ void kernel_backward(
+    const int B, const int T, const int C, const F *__restrict__ const _w, const F *__restrict__ const _u,
+    const F *__restrict__ const _k, const F *__restrict__ const _v, const F *__restrict__ const _y,
+    const F *__restrict__ const _gy, F *__restrict__ const _gw, F *__restrict__ const _gu, F *__restrict__ const _gk,
+    F *__restrict__ const _gv
+) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    F u = _u[_c];
+    F w = _w[_c];
+    const F *__restrict__ const k = _k + _offset;
+    const F *__restrict__ const v = _v + _offset;
+    const F *__restrict__ const y = _y + _offset;
+    const F *__restrict__ const gy = _gy + _offset;
+    F *__restrict__ const gk = _gk + _offset;
+    F *__restrict__ const gv = _gv + _offset;
+
+    F q[Tmax], r[Tmax];
+
+    F gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+        const F yy = y[ii];
+
+        F ww = u + kk;
+        F p = max(pp, ww);
+        F e1 = exp(pp - p);
+        F e2 = exp(ww - p);
+        const F qq = gy[ii] / (e1 * bb + e2);
+        gw += (ga - gb * yy) * e1 * qq;
+        gu += (vv - yy) * e2 * qq;
+        q[i] = qq;
+        r[i] = ww - p;
+
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        ga = e1 * (aa + ga);
+        gb = e1 * (bb + gb);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    const int _offsetBC = _b * C + _c;
+    _gw[_offsetBC] = gw * _w[_c]; // multiply by w because of w -> -exp(w) in python forward()
+    _gu[_offsetBC] = gu;
+
+    aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = T - 1; i >= 0; i--) {
+        const int ii = i * C;
+        const F kk = k[ii];
+        const F vv = v[ii];
+        const F yy = y[ii];
+        const F qq = q[i];
+        const F rr = r[i];
+
+        F e1 = qq * exp(rr);
+        F e2 = exp(kk + pp);
+        gk[ii] = e1 * (vv - yy) + e2 * (aa * vv + bb);
+        gv[ii] = e1 + e2 * aa;
+
+        const F ww = w + pp;
+        const F www = rr - u - kk;
+        const F p = max(ww, www);
+        e1 = exp(ww - p);
+        e2 = qq * exp(www - p);
+        aa = e1 * aa + e2;
+        bb = e1 * bb - e2 * yy;
+        pp = p;
+    }
+}
+
+void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_forward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
+}
+
+void cuda_forward_with_state(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *s) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_forward_with_state<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, s);
+}
+
+void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_backward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv);
+}
diff --git a/rwkv/wkv_cuda_bf16.cu b/rwkv/wkv_cuda_bf16.cu
new file mode 100644
index 0000000000000000000000000000000000000000..042cb4aba1db98be5916aea1de86a7fed0b6510d
--- /dev/null
+++ b/rwkv/wkv_cuda_bf16.cu
@@ -0,0 +1,186 @@
+#include <stdio.h>
+#include <assert.h>
+#include "ATen/ATen.h"
+#define MIN_VALUE (-1e38)
+typedef at::BFloat16 bf16;
+
+__global__ void kernel_forward_bf16(
+    const int B, const int T, const int C, const float *__restrict__ const _w, const bf16 *__restrict__ const _u,
+    const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v, bf16 *__restrict__ const _y
+) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    float u = float(_u[_c]);
+    float w = _w[_c];
+    const bf16 *__restrict__ const k = _k + _offset;
+    const bf16 *__restrict__ const v = _v + _offset;
+    bf16 *__restrict__ const y = _y + _offset;
+
+    // aa and bb are running sums divided by exp(pp) (to avoid overflow)
+    float aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+
+        float ww = u + kk;
+        float p = max(pp, ww);
+        float e1 = exp(pp - p);
+        float e2 = exp(ww - p);
+        y[ii] = bf16((e1 * aa + e2 * vv) / (e1 * bb + e2));
+        
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+}
+
+__global__ void kernel_forward_with_state_bf16(
+    const int B, const int T, const int C, const float *__restrict__ const _w, const bf16 *__restrict__ const _u,
+    const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v, bf16 *__restrict__ const _y,
+    float *__restrict__ const _s
+) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset_s = _b * C * 3 + _c * 3;
+    const int _offset = _b * T * C + _c;
+
+    float u = float(_u[_c]);
+    float w = _w[_c];
+    const bf16 *__restrict__ const k = _k + _offset;
+    const bf16 *__restrict__ const v = _v + _offset;
+    bf16 *__restrict__ const y = _y + _offset;
+    float *__restrict__ const s = _s + _offset_s;
+
+    // aa and bb are running sums divided by exp(pp) (to avoid overflow)
+    float aa = s[0], bb = s[1], pp = s[2];
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+
+        float ww = u + kk;
+        float p = max(pp, ww);
+        float e1 = exp(pp - p);
+        float e2 = exp(ww - p);
+        y[ii] = bf16(e1 * aa + e2 * vv) / (e1 * bb + e2);
+        
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    s[0] = aa;
+    s[1] = bb;
+    s[2] = pp;
+}
+
+__global__ void kernel_backward_bf16(
+    const int B, const int T, const int C, const float *__restrict__ const _w, const bf16 *__restrict__ const _u,
+    const bf16 *__restrict__ const _k, const bf16 *__restrict__ const _v, const bf16 *__restrict__ const _y,
+    const bf16 *__restrict__ const _gy, bf16 *__restrict__ const _gw, bf16 *__restrict__ const _gu,
+    bf16 *__restrict__ const _gk, bf16 *__restrict__ const _gv
+) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    const int _b = idx / C;
+    const int _c = idx % C;
+    const int _offset = _b * T * C + _c;
+
+    float u = float(_u[_c]);
+    float w = _w[_c];
+    const bf16 *__restrict__ const k = _k + _offset;
+    const bf16 *__restrict__ const v = _v + _offset;
+    const bf16 *__restrict__ const y = _y + _offset;
+    const bf16 *__restrict__ const gy = _gy + _offset;
+    bf16 *__restrict__ const gk = _gk + _offset;
+    bf16 *__restrict__ const gv = _gv + _offset;
+
+    float q[Tmax], r[Tmax];
+
+    float gw = 0, gu = 0, aa = 0, bb = 0, ga = 0, gb = 0, pp = MIN_VALUE;
+    for (int i = 0; i < T; i++) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+        const float yy = float(y[ii]);
+
+        float ww = u + kk;
+        float p = max(pp, ww);
+        float e1 = exp(pp - p);
+        float e2 = exp(ww - p);
+        const float qq = float(gy[ii]) / (e1 * bb + e2);
+        gw += (ga - gb * yy) * e1 * qq;
+        gu += (vv - yy) * e2 * qq;
+        q[i] = qq;
+        r[i] = ww - p;
+
+        ww = w + pp;
+        p = max(ww, kk);
+        e1 = exp(ww - p);
+        e2 = exp(kk - p);
+        ga = e1 * (aa + ga);
+        gb = e1 * (bb + gb);
+        aa = e1 * aa + e2 * vv;
+        bb = e1 * bb + e2;
+        pp = p;
+    }
+    const int _offsetBC = _b * C + _c;
+    _gw[_offsetBC] = bf16(gw * _w[_c]); // multiply by w because of w -> -exp(w) in python forward()
+    _gu[_offsetBC] = bf16(gu);
+
+    aa = 0, bb = 0, pp = MIN_VALUE;
+    for (int i = T - 1; i >= 0; i--) {
+        const int ii = i * C;
+        const float kk = float(k[ii]);
+        const float vv = float(v[ii]);
+        const float yy = float(y[ii]);
+        const float qq = q[i];
+        const float rr = r[i];
+
+        float e1 = qq * exp(rr);
+        float e2 = exp(kk + pp);
+        gk[ii] = bf16(e1 * (vv - yy) + e2 * (aa * vv + bb));
+        gv[ii] = bf16(e1 + e2 * aa);
+
+        const float ww = w + pp;
+        const float www = rr - u - kk;
+        const float p = max(ww, www);
+        e1 = exp(ww - p);
+        e2 = qq * exp(www - p);
+        aa = e1 * aa + e2;
+        bb = e1 * bb - e2 * yy;
+        pp = p;
+    }
+}
+
+void cuda_forward_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_forward_bf16<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
+}
+
+void cuda_forward_with_state_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, float *s) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_forward_with_state_bf16<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, s);
+}
+
+void cuda_backward_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, bf16 *gy, bf16 *gw, bf16 *gu, bf16 *gk, bf16 *gv) {
+    dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
+    assert(B * C % threadsPerBlock.x == 0);
+    dim3 numBlocks(B * C / threadsPerBlock.x);
+    kernel_backward_bf16<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y, gy, gw, gu, gk, gv);
+}
diff --git a/torch-ext/rwkv/__init__.py b/torch-ext/rwkv/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ea22dbc160d2b75b32c3c8c5d882d25c77b494
--- /dev/null
+++ b/torch-ext/rwkv/__init__.py
@@ -0,0 +1,170 @@
+from ._ops import ops
+from typing import Tuple, Any
+
+# Use a broad Tensor alias to avoid importing torch at import time.
+from torch import Tensor
+
+def forward(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (float32).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward(w, u, k, v, y)
+
+
+def forward_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor) -> None:
+    """RWKV WKV forward pass (bfloat16 inputs/outputs, float32 ``w``).
+
+    Runs the CUDA kernel and writes the result into ``y`` in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y))
+    ops.forward_bf16(w, u, k, v, y)
+
+
+def forward_with_state(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (float32).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.float32``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state(w, u, k, v, y, s)
+
+
+def forward_with_state_bf16(w: Tensor, u: Tensor, k: Tensor, v: Tensor, y: Tensor, s: Tensor) -> None:
+    """RWKV WKV forward pass with persistent state (bfloat16 inputs/outputs, float32 ``w`` and ``s``).
+
+    Runs the CUDA kernel using and updating state ``s`` and writes the result into ``y``.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u: Input tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        k: Key tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        v: Value tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        y: Output tensor, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+        s: Stateful tensor, shape ``[B, C]``, dtype ``torch.float32`` (updated in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B`` and ``C``; ``y`` shares ``[B, T, C]`` with inputs.
+    """
+    _validate_device_match((w, u, k, v, y, s))
+    ops.forward_with_state_bf16(w, u, k, v, y, s)
+
+
+def backward(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (float32).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.float32``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.float32`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.float32`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def backward_bf16(
+    w: Tensor,
+    u: Tensor,
+    k: Tensor,
+    v: Tensor,
+    y: Tensor,
+    gy: Tensor,
+    gw: Tensor,
+    gu: Tensor,
+    gk: Tensor,
+    gv: Tensor,
+) -> None:
+    """RWKV WKV backward pass (bfloat16 inputs/outputs/gradients, float32 ``w``).
+
+    Writes gradients into the provided tensors in-place.
+
+    Args:
+        w: Decay weights, shape ``[C]``, dtype ``torch.float32``.
+        u, k, v, y: Forward-pass tensors, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gy: Gradient of ``y``, shape ``[B, T, C]``, dtype ``torch.bfloat16``.
+        gw: Gradient for ``w``, shape ``[C]``, dtype ``torch.bfloat16`` (written in-place).
+        gu, gk, gv: Gradients for ``u``, ``k``, ``v`` respectively, shape ``[B, T, C]``, dtype ``torch.bfloat16`` (written in-place).
+
+    Notes:
+        - All tensors must be on the same CUDA device.
+        - Shapes must agree on ``B``, ``T`` and ``C``.
+    """
+    _validate_device_match((w, u, k, v, y, gy, gw, gu, gk, gv))
+    ops.backward_bf16(w, u, k, v, y, gy, gw, gu, gk, gv)
+
+
+def _validate_device_match(tensors: Tuple[Tensor, ...]) -> None:
+    """Minimal runtime validation that all tensors live on the same CUDA device."""
+    if not tensors:
+        return
+    device = tensors[0].device
+    if not device.type == "cuda":
+        raise RuntimeError("RWKV CUDA ops require CUDA tensors")
+    for t in tensors[1:]:
+        if t.device != device:
+            raise RuntimeError("All tensors must be on the same CUDA device")
+
+
+__all__ = [
+    "forward",
+    "forward_bf16",
+    "forward_with_state",
+    "forward_with_state_bf16",
+    "backward",
+    "backward_bf16",
+]
\ No newline at end of file
diff --git a/torch-ext/torch_binding.cpp b/torch-ext/torch_binding.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..c42395d0aa9bb14566a58d73f959c9803fc73cde
--- /dev/null
+++ b/torch-ext/torch_binding.cpp
@@ -0,0 +1,74 @@
+#include <torch/all.h>
+#include "ATen/ATen.h"
+#include <torch/library.h>
+
+#include "registration.h"
+
+typedef at::BFloat16 bf16;
+
+void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y);
+void cuda_forward_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y);
+void cuda_forward_with_state(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *s);
+void cuda_forward_with_state_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, float *s);
+void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv);
+void cuda_backward_bf16(int B, int T, int C, float *w, bf16 *u, bf16 *k, bf16 *v, bf16 *y, bf16 *gy, bf16 *gw, bf16 *gu, bf16 *gk, bf16 *gv);
+
+void forward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) {
+    const int B = k.size(0);
+    const int T = k.size(1);
+    const int C = k.size(2);
+    cuda_forward(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>());
+}
+void forward_bf16(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) {
+    const int B = k.size(0);
+    const int T = k.size(1);
+    const int C = k.size(2);
+    cuda_forward_bf16(B, T, C, w.data_ptr<float>(), u.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), y.data_ptr<bf16>());
+}
+void forward_with_state(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &s) {
+    const int B = k.size(0);
+    const int T = k.size(1);
+    const int C = k.size(2);
+    cuda_forward_with_state(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>(), s.data_ptr<float>());
+}
+void forward_with_state_bf16(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &s) {
+    const int B = k.size(0);
+    const int T = k.size(1);
+    const int C = k.size(2);
+    cuda_forward_with_state_bf16(B, T, C, w.data_ptr<float>(), u.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), y.data_ptr<bf16>(), s.data_ptr<float>());
+}
+void backward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) {
+    const int B = k.size(0);
+    const int T = k.size(1);
+    const int C = k.size(2);
+    cuda_backward(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>(), gy.data_ptr<float>(), gw.data_ptr<float>(), gu.data_ptr<float>(), gk.data_ptr<float>(), gv.data_ptr<float>());
+}
+void backward_bf16(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) {
+    const int B = k.size(0);
+    const int T = k.size(1);
+    const int C = k.size(2);
+    cuda_backward_bf16(B, T, C, w.data_ptr<float>(), u.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), y.data_ptr<bf16>(),
+        gy.data_ptr<bf16>(), gw.data_ptr<bf16>(), gu.data_ptr<bf16>(), gk.data_ptr<bf16>(), gv.data_ptr<bf16>());
+}
+
+TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
+    ops.def("forward", forward);
+    ops.impl("forward", torch::kCUDA, &forward);
+
+    ops.def("forward_bf16", forward_bf16);
+    ops.impl("forward_bf16", torch::kCUDA, &forward_bf16);
+
+    ops.def("forward_with_state", forward_with_state);
+    ops.impl("forward_with_state", torch::kCUDA, &forward_with_state);
+
+    ops.def("forward_with_state_bf16", forward_with_state_bf16);
+    ops.impl("forward_with_state_bf16", torch::kCUDA, &forward_with_state_bf16);
+
+    ops.def("backward", backward);
+    ops.impl("backward", torch::kCUDA, &backward);
+
+    ops.def("backward_bf16", backward_bf16);
+    ops.impl("backward_bf16", torch::kCUDA, &backward_bf16);
+}
+
+REGISTER_EXTENSION(TORCH_EXTENSION_NAME)
\ No newline at end of file