feat(ws1): Add PyTorch RoPE reference operator

docs/operators/README.md

@@ -20,5 +20,6 @@ Every operator page should include:
 
 - [Fused LogP](fused-logp.md)
 - [GRPO Loss](grpo-loss.md)
+- [RoPE](rope.md)
 - [Sampling](sampling.md)
 - [Operator Doc Template](../contributing/operator-doc-template.md)

docs/operators/rope.md

@@ -0,0 +1,98 @@
+# RoPE
+
+RoPE applies rotary position embeddings to per-head query or key tensors. The
+current implementation is a pure PyTorch reference operator for Issue #108
+ground-truth validation; it is not a fused CUDA or Triton kernel.
+
+This page documents the PyTorch baseline version.
+
+## Entry Point
+
+```python
+from rl_engine.kernels.registry import kernel_registry
+
+rope = kernel_registry.get_op("rope")
+output = rope.forward(x, positions, theta=1_000_000.0)
+reference = rope.forward_fp32(x, positions, theta=1_000_000.0)
+```
+
+The operator can also be imported directly:
+
+```python
+from rl_engine.kernels.ops.pytorch.rotary_embedding import NativeRoPEOp
+
+rope = NativeRoPEOp()
+```
+
+## Backend
+
+| Backend | Wrapper | Native symbol | Notes |
+| --- | --- | --- | --- |
+| PyTorch native | `NativeRoPEOp` | None | Reference baseline for Qwen3-style RoPE. |
+
+`kernel_registry.get_op("rope")` dispatches to the PyTorch native backend on CPU,
+CUDA, and ROCm. CUDA/Triton fused RoPE kernels should compare against this reference.
+
+## Tensor Contract
+
+| Argument | Shape | Dtype | Requirements |
+| --- | --- | --- | --- |
+| `x` | `[B, H, S, D]` | `float32`, `bfloat16`, or `float16` | Query or key tensor; Qwen3 uses `D=128`. |
+| `positions` | `[S]` or `[B, S]` | Integer | Absolute token positions. |
+| `theta` | scalar | float | Defaults to `1_000_000.0` for Qwen3. |
+| Output | `[B, H, S, D]` | See below | Same shape as `x`. |
+
+`forward(...)` returns the input dtype. `forward_fp32(...)` computes and returns
+`float32` and is the gold-standard reference path.
+
+## Reference Semantics
+
+The implementation uses the Hugging Face rotate-half convention, pairing dimensions
+`(i, i + D/2)` rather than adjacent dimensions.
+
+```python
+half = x.shape[-1] // 2
+inv_freq = 1.0 / (theta ** (torch.arange(0, half, dtype=torch.float32) / half))
+freqs = positions.float()[..., None] * inv_freq
+cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1)
+sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1)
+
+a, b = x.float()[..., :half], x.float()[..., half:]
+rotated = torch.cat([-b, a], dim=-1)
+out = x.float() * cos + rotated * sin
+```
+
+For Qwen3-8B validation, RoPE is applied after QK-Norm and before attention:
+
+```text
+RMSNorm(q), RMSNorm(k) -> RoPE(theta=1e6) -> attention
+```
+
+## Accuracy
+
+RoPE is categorized as an `elementwise` operator in the numerical contract.
+Expected comparison behavior:
+
+| Path | Expected dtype | Purpose |
+| --- | --- | --- |
+| `forward` | Same as `x.dtype` | Candidate dtype behavior. |
+| `forward_fp32` | `torch.float32` | Deterministic reference output. |
+
+Batch invariance is expected to be bitwise: applying RoPE to a full batch and then
+slicing a row must match applying RoPE to that row alone.
+
+## Tests
+
+```bash
+python -m pytest tests/test_rope.py -q
+```
+
+The test covers shape, dtype behavior, HF rotate-half equivalence, `positions`
+as `[S]` and `[B, S]`, batch invariance, and Qwen3 query/key head shapes.
+
+## Implementation Files
+
+- `rl_engine/kernels/ops/pytorch/rotary_embedding/rope.py`
+- `rl_engine/kernels/ops/pytorch/rotary_embedding/__init__.py`
+- `rl_engine/kernels/registry.py`
+- `tests/test_rope.py`

rl_engine/kernels/ops/pytorch/rotary_embedding/__init__.py

@@ -0,0 +1,6 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from rl_engine.kernels.ops.pytorch.rotary_embedding.rope import NativeRoPEOp
+
+__all__ = ["NativeRoPEOp"]

rl_engine/kernels/ops/pytorch/rotary_embedding/rope.py

@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) 2026 RL-Kernel Contributors
+
+from __future__ import annotations
+
+import torch
+from torch import Tensor
+
+
+class NativeRoPEOp:
+    """Pure PyTorch reference RoPE — GPT-NeoX style (HF rotate-half).
+
+    Qwen3-8B defaults: theta=1e6, head_dim=128, full-dimension rotation (half=64).
+    Dimension pairing: (i, i+half) — NOT adjacent (i, i+1).
+    cos/sin are computed internally in fp32 from positions and theta — no external
+    cos/sin cache is accepted or returned.
+    """
+
+    op_class = "elementwise"
+
+    def __init__(self) -> None:
+        pass
+
+    def __call__(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
+        return self.forward(x, positions, theta=theta)
+
+    def forward(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
+        """Apply RoPE in input dtype. Cos/sin always computed in fp32."""
+        cos, sin = self._compute_cos_sin(x, positions, theta=theta)
+        xf = x
+        x1, x2 = xf[..., : xf.shape[-1] // 2], xf[..., xf.shape[-1] // 2 :]
+        rotated = torch.cat([-x2, x1], dim=-1)
+        out = xf * cos + rotated * sin
+        return out.to(dtype=x.dtype)
+
+    def forward_fp32(self, x: Tensor, positions: Tensor, *, theta: float = 1_000_000.0) -> Tensor:
+        """fp32 gold standard: internal computation and output are fp32."""
+        cos, sin = self._compute_cos_sin(x, positions, theta=theta)
+        xf = x.float()
+        x1, x2 = xf[..., : xf.shape[-1] // 2], xf[..., xf.shape[-1] // 2 :]
+        rotated = torch.cat([-x2, x1], dim=-1)
+        out = xf * cos + rotated * sin
+        return out
+
+    # ------------------------------------------------------------------ #
+    # Helpers
+    # ------------------------------------------------------------------ #
+
+    @staticmethod
+    def _compute_cos_sin(x: Tensor, positions: Tensor, *, theta: float) -> tuple[Tensor, Tensor]:
+        """Compute cos/sin tables in fp32 from positions and theta.
+
+        Args:
+            x: [..., D] — only x.shape[-1] (head_dim) is used.
+            positions: [S] or [B, S] int64 — absolute token positions.
+            theta: RoPE base frequency (Qwen3 = 1e6).
+
+        Returns:
+            cos, sin: broadcastable to x shape, fp32.
+        """
+        D = x.shape[-1]
+        half = D // 2
+
+        # inv_freq[i] = 1 / (theta^(2i/D)) = 1 / (theta^(i/half))
+        # shape: [half]
+        inv_freq = 1.0 / (
+            theta ** (torch.arange(0, half, dtype=torch.float32, device=x.device) / half)
+        )
+
+        # positions: [S] -> [S, 1] or [B, S] -> [B, S, 1]
+        pos_float = positions.float().unsqueeze(-1)
+
+        # freqs: [S, half] or [B, S, half]
+        freqs = pos_float * inv_freq
+
+        # Duplicate to full dim: [S, D] or [B, S, D]
+        emb = torch.cat([freqs, freqs], dim=-1)
+
+        cos = emb.cos()
+        sin = emb.sin()
+
+        # Reshape for broadcasting with x: [B, H, S, D]
+        if positions.dim() == 1:
+            # positions [S] -> cos/sin [1, 1, S, D]
+            cos = cos.unsqueeze(0).unsqueeze(0)
+            sin = sin.unsqueeze(0).unsqueeze(0)
+        else:
+            # positions [B, S] -> cos/sin [B, 1, S, D]
+            cos = cos.unsqueeze(1)
+            sin = sin.unsqueeze(1)
+
+        return cos, sin

rl_engine/kernels/registry.py

@@ -40,6 +40,7 @@ class OpBackend(Enum, metaclass=_KernelEnumMeta):
     # Generic fallback
     TRITON_GENERIC = "rl_engine.kernels.ops.triton.generic.TritonOp"
     PYTORCH_NATIVE = "rl_engine.kernels.ops.pytorch.loss.logp.NativeLogpOp"
+    PYTORCH_NATIVE_ROPE = "rl_engine.kernels.ops.pytorch.rotary_embedding.rope.NativeRoPEOp"
 
 
 class KernelRegistry:
@@ -75,16 +76,19 @@ def __init__(self):
                 "attn": [OpBackend.FLASH_ATTN, OpBackend.TRITON_GENERIC, OpBackend.PYTORCH_NATIVE],
                 "grpo_loss": [OpBackend.TRITON_GRPO_LOSS, OpBackend.PYTORCH_GRPO_LOSS],
                 # Default dispatch logic for new operators
+                "rope": [OpBackend.PYTORCH_NATIVE_ROPE],
             },
             "rocm": {
                 "logp": [OpBackend.ROCM_AITER, OpBackend.TRITON_GENERIC, OpBackend.PYTORCH_NATIVE],
                 "attn": [OpBackend.TRITON_GENERIC, OpBackend.PYTORCH_NATIVE],
                 "grpo_loss": [OpBackend.TRITON_GRPO_LOSS, OpBackend.PYTORCH_GRPO_LOSS],
+                "rope": [OpBackend.PYTORCH_NATIVE_ROPE],
             },
             "cpu": {
                 "logp": [OpBackend.PYTORCH_NATIVE],
                 "attn": [OpBackend.PYTORCH_NATIVE],
                 "grpo_loss": [OpBackend.PYTORCH_GRPO_LOSS],
+                "rope": [OpBackend.PYTORCH_NATIVE_ROPE],
             },
         }
         logger.info(f"KernelRegistry initialized for {device_ctx.device_type}")