"""Custom fused Triton RoPE kernel for Qwen3.5/3.6 full-attention layers.

Liger ships a RoPE kernel but its qwen3_5 patcher *refuses* to apply it
(``raise NotImplementedError`` "due to hybrid attention: Gated DeltaNet + Gated
Attention") — the GDN layers don't call ``apply_rotary_pos_emb`` at all, only the
full-attention layers do, and Liger's blanket patch couldn't target just those.

This module sidesteps that by monkeypatching the module-level
``transformers.models.qwen3_5.modeling_qwen3_5.apply_rotary_pos_emb`` function
itself — which ONLY the ``Qwen3_5Attention`` layers call — so the GDN path is
untouched. The HF eager version is heavily unfused: ``rotate_half`` allocates a
full ``cat([-x2, x1])`` tensor and the rotation is ~8 separate elementwise
kernels + intermediates per attention layer. We fuse the whole rotation into one
Triton kernel (forward + backward), eliminating those launches/allocations.

Correctness is gated by a live-GPU numeric self-test (loss + grad vs the eager
reference within tolerance); ANY import/compile/self-test failure leaves the
eager path untouched — correctness over speed. Opt-in via AUTOSLM_ROPE_KERNEL=1.

Semantics matched exactly to modeling_qwen3_5.apply_rotary_pos_emb:
  rotate_half(x) = cat(-x[d/2:], x[:d/2])            # GPT-NeoX / non-interleaved
  q_embed = (q_rot * cos) + (rotate_half(q_rot) * sin); q_pass kept as-is
with rotary_dim = cos.shape[-1] possibly < head_dim (the tail is passed through),
cos/sin of shape [batch, seq, rotary_dim] (broadcast over heads).
"""

from __future__ import annotations

import os

# Populated by install_qwen35_rope/benchmark so the worker can fold the measured speedup
# into metrics.json's `notes` (RunPod doesn't persist worker stdout, but metrics.json is
# always uploaded). Empty {} means the kernel was not engaged this run.
RESULT: dict = {}


def _enabled() -> bool:
    return os.environ.get("AUTOSLM_ROPE_KERNEL", "0").strip().lower() not in (
        "0",
        "false",
        "no",
        "off",
        "none",
        "",
    )


def _build_kernels():
    """Import torch/triton and define the fused RoPE forward+backward kernels + the
    autograd Function. Returns ``apply_fn`` (HF-signature drop-in) or raises on any
    import/compile problem (the caller treats a raise as "keep eager")."""
    import torch
    import triton
    import triton.language as tl

    @triton.jit
    def _rope_fwd_kernel(
        x_ptr, cos_ptr, sin_ptr, out_ptr,
        H_T, T, head_dim, rotary_dim, half,
        x_row_stride, cs_row_stride,
        BLOCK: tl.constexpr,
    ):
        # one program == one [head_dim] vector for a single (batch, head, token)
        pid = tl.program_id(0)
        b = pid // H_T
        t = pid % T
        cs_row = b * T + t

        offs = tl.arange(0, BLOCK)
        mask_half = offs < half
        # the two rotary halves of this row
        x1 = tl.load(x_ptr + pid * x_row_stride + offs, mask=mask_half, other=0.0)
        x2 = tl.load(x_ptr + pid * x_row_stride + half + offs, mask=mask_half, other=0.0)
        cos1 = tl.load(cos_ptr + cs_row * cs_row_stride + offs, mask=mask_half, other=0.0)
        sin1 = tl.load(sin_ptr + cs_row * cs_row_stride + offs, mask=mask_half, other=0.0)
        cos2 = tl.load(cos_ptr + cs_row * cs_row_stride + half + offs, mask=mask_half, other=0.0)
        sin2 = tl.load(sin_ptr + cs_row * cs_row_stride + half + offs, mask=mask_half, other=0.0)
        # rotate_half: out1 = x1*cos1 - x2*sin1 ; out2 = x2*cos2 + x1*sin2
        out1 = x1 * cos1 - x2 * sin1
        out2 = x2 * cos2 + x1 * sin2
        tl.store(out_ptr + pid * x_row_stride + offs, out1, mask=mask_half)
        tl.store(out_ptr + pid * x_row_stride + half + offs, out2, mask=mask_half)
        # pass-through tail [rotary_dim : head_dim]
        if head_dim > rotary_dim:
            poffs = tl.arange(0, BLOCK)
            pmask = poffs < (head_dim - rotary_dim)
            xp = tl.load(x_ptr + pid * x_row_stride + rotary_dim + poffs, mask=pmask, other=0.0)
            tl.store(out_ptr + pid * x_row_stride + rotary_dim + poffs, xp, mask=pmask)

    @triton.jit
    def _rope_bwd_kernel(
        g_ptr, cos_ptr, sin_ptr, dx_ptr,
        H_T, T, head_dim, rotary_dim, half,
        g_row_stride, cs_row_stride,
        BLOCK: tl.constexpr,
    ):
        pid = tl.program_id(0)
        b = pid // H_T
        t = pid % T
        cs_row = b * T + t
        offs = tl.arange(0, BLOCK)
        mask_half = offs < half
        g1 = tl.load(g_ptr + pid * g_row_stride + offs, mask=mask_half, other=0.0)
        g2 = tl.load(g_ptr + pid * g_row_stride + half + offs, mask=mask_half, other=0.0)
        cos1 = tl.load(cos_ptr + cs_row * cs_row_stride + offs, mask=mask_half, other=0.0)
        sin1 = tl.load(sin_ptr + cs_row * cs_row_stride + offs, mask=mask_half, other=0.0)
        cos2 = tl.load(cos_ptr + cs_row * cs_row_stride + half + offs, mask=mask_half, other=0.0)
        sin2 = tl.load(sin_ptr + cs_row * cs_row_stride + half + offs, mask=mask_half, other=0.0)
        # transpose of the forward (orthogonal rotation):
        #   dx1 = g1*cos1 + g2*sin2 ; dx2 = -g1*sin1 + g2*cos2
        dx1 = g1 * cos1 + g2 * sin2
        dx2 = -g1 * sin1 + g2 * cos2
        tl.store(dx_ptr + pid * g_row_stride + offs, dx1, mask=mask_half)
        tl.store(dx_ptr + pid * g_row_stride + half + offs, dx2, mask=mask_half)
        if head_dim > rotary_dim:
            poffs = tl.arange(0, BLOCK)
            pmask = poffs < (head_dim - rotary_dim)
            gp = tl.load(g_ptr + pid * g_row_stride + rotary_dim + poffs, mask=pmask, other=0.0)
            tl.store(dx_ptr + pid * g_row_stride + rotary_dim + poffs, gp, mask=pmask)

    def _next_pow2(n: int) -> int:
        p = 1
        while p < n:
            p <<= 1
        return max(p, 1)

    def _rope_one(x, cos, sin, forward: bool):
        # x: [B, H, T, D] (contiguous), cos/sin: [B, T, rotary_dim]
        B, H, T, D = x.shape
        rotary_dim = cos.shape[-1]
        half = rotary_dim // 2
        x = x.contiguous()
        out = torch.empty_like(x)
        xf = x.view(B * H * T, D)
        of = out.view(B * H * T, D)
        cosf = cos.contiguous().view(B * T, rotary_dim)
        sinf = sin.contiguous().view(B * T, rotary_dim)
        BLOCK = _next_pow2(max(half, D - rotary_dim, 1))
        grid = (B * H * T,)
        kern = _rope_fwd_kernel if forward else _rope_bwd_kernel
        kern[grid](
            xf, cosf, sinf, of,
            H * T, T, D, rotary_dim, half,
            xf.stride(0), cosf.stride(0),
            BLOCK=BLOCK,
        )
        return out

    class _RoPEFunction(torch.autograd.Function):
        @staticmethod
        def forward(ctx, q, k, cos, sin):
            ctx.save_for_backward(cos, sin)
            q_embed = _rope_one(q, cos, sin, forward=True)
            k_embed = _rope_one(k, cos, sin, forward=True)
            return q_embed, k_embed

        @staticmethod
        def backward(ctx, gq, gk):
            cos, sin = ctx.saved_tensors
            dq = _rope_one(gq.contiguous(), cos, sin, forward=False)
            dk = _rope_one(gk.contiguous(), cos, sin, forward=False)
            return dq, dk, None, None

    def apply_fn(q, k, cos, sin, unsqueeze_dim=1):
        # cos/sin arrive as [B, T, rotary_dim]; HF unsqueezes to broadcast over heads.
        # Our kernel broadcasts over heads internally, so use cos/sin as-is ([B,T,rd]).
        # Fall back to eager for any shape we don't handle (interleaved/odd ranks).
        if q.dim() != 4 or cos.dim() != 3 or (cos.shape[-1] % 2) != 0:
            return _eager_apply(q, k, cos, sin, unsqueeze_dim)
        return _RoPEFunction.apply(q, k, cos, sin)

    return apply_fn


def _eager_apply(q, k, cos, sin, unsqueeze_dim=1):
    """The exact HF reference (used as the self-test oracle and the shape fallback)."""
    import torch

    cos_u = cos.unsqueeze(unsqueeze_dim)
    sin_u = sin.unsqueeze(unsqueeze_dim)
    rd = cos_u.shape[-1]
    q_rot, q_pass = q[..., :rd], q[..., rd:]
    k_rot, k_pass = k[..., :rd], k[..., rd:]

    def rh(x):
        x1 = x[..., : x.shape[-1] // 2]
        x2 = x[..., x.shape[-1] // 2 :]
        return torch.cat((-x2, x1), dim=-1)

    q_embed = torch.cat([(q_rot * cos_u) + (rh(q_rot) * sin_u), q_pass], dim=-1)
    k_embed = torch.cat([(k_rot * cos_u) + (rh(k_rot) * sin_u), k_pass], dim=-1)
    return q_embed, k_embed


def self_test(apply_fn, *, head_dim=128, rotary_dim=128, dtype=None) -> bool:
    """Numeric parity of the fused kernel vs eager HF apply_rotary_pos_emb, on the
    live GPU: forward q/k AND backward dq/dk. Returns True iff within tolerance."""
    import torch

    if not torch.cuda.is_available():
        return False
    dtype = dtype or torch.bfloat16
    B, Hh, T = 2, 4, 64
    dev = "cuda"
    torch.manual_seed(0)
    q = torch.randn(B, Hh, T, head_dim, device=dev, dtype=dtype, requires_grad=True)
    k = torch.randn(B, Hh, T, head_dim, device=dev, dtype=dtype, requires_grad=True)
    pos = torch.arange(T, device=dev)
    inv = 1.0 / (10000 ** (torch.arange(0, rotary_dim // 2, device=dev).float() / (rotary_dim // 2)))
    ang = pos[:, None].float() * inv[None, :]
    emb = torch.cat([ang, ang], dim=-1)  # [T, rotary_dim], duplicated halves (standard RoPE)
    cos = emb.cos()[None].expand(B, T, rotary_dim).to(dtype).contiguous()
    sin = emb.sin()[None].expand(B, T, rotary_dim).to(dtype).contiguous()

    qe_ref, ke_ref = _eager_apply(q, k, cos, sin)
    (qe_ref.float().square().mean() + ke_ref.float().square().mean()).backward()
    dq_ref, dk_ref = q.grad.clone(), k.grad.clone()
    q.grad = None
    k.grad = None

    qe, ke = apply_fn(q, k, cos, sin)
    (qe.float().square().mean() + ke.float().square().mean()).backward()
    dq, dk = q.grad.clone(), k.grad.clone()

    def close(a, b, atol=2e-2, rtol=2e-2):
        return torch.allclose(a.float(), b.float(), atol=atol, rtol=rtol)

    ok = (
        close(qe, qe_ref) and close(ke, ke_ref) and close(dq, dq_ref) and close(dk, dk_ref)
    )
    if not ok:
        print(
            "[rope] self-test FAILED "
            f"(fwd_q={close(qe, qe_ref)} fwd_k={close(ke, ke_ref)} "
            f"bwd_q={close(dq, dq_ref)} bwd_k={close(dk, dk_ref)}) -> keeping eager",
            flush=True,
        )
    return ok


def benchmark(apply_fn, *, head_dim=128, rotary_dim=128, n_heads=16, seq=4096, iters=50) -> None:
    """Time eager HF vs the fused kernel (forward+backward) at Qwen-attention shapes,
    on the live GPU; prints the speedup. Diagnostic only — never raises."""
    import torch

    try:
        if not torch.cuda.is_available():
            return
        dev = "cuda"
        dt = torch.bfloat16
        B = 1
        pos = torch.arange(seq, device=dev)
        inv = 1.0 / (10000 ** (torch.arange(0, rotary_dim // 2, device=dev).float() / (rotary_dim // 2)))
        ang = pos[:, None].float() * inv[None, :]
        emb = torch.cat([ang, ang], dim=-1)
        cos = emb.cos()[None].expand(B, seq, rotary_dim).to(dt).contiguous()
        sin = emb.sin()[None].expand(B, seq, rotary_dim).to(dt).contiguous()

        def run(fn):
            q = torch.randn(B, n_heads, seq, head_dim, device=dev, dtype=dt, requires_grad=True)
            k = torch.randn(B, n_heads, seq, head_dim, device=dev, dtype=dt, requires_grad=True)
            qe, ke = fn(q, k, cos, sin)
            (qe.float().square().mean() + ke.float().square().mean()).backward()

        for _ in range(5):  # warmup (Triton JIT + autotune)
            run(_eager_apply)
            run(apply_fn)
        torch.cuda.synchronize()

        def timed(fn):
            s = torch.cuda.Event(enable_timing=True)
            e = torch.cuda.Event(enable_timing=True)
            torch.cuda.synchronize()
            s.record()
            for _ in range(iters):
                run(fn)
            e.record()
            torch.cuda.synchronize()
            return s.elapsed_time(e) / iters  # ms/iter

        t_eager = timed(_eager_apply)
        t_kernel = timed(apply_fn)
        speedup = t_eager / t_kernel if t_kernel > 0 else 0.0
        RESULT.update(
            {
                "head_dim": head_dim,
                "heads": n_heads,
                "seq": seq,
                "eager_ms": round(t_eager, 4),
                "kernel_ms": round(t_kernel, 4),
                "speedup": round(speedup, 3),
            }
        )
        print(
            f"[rope][bench] head_dim={head_dim} heads={n_heads} seq={seq} fwd+bwd: "
            f"eager={t_eager:.3f}ms kernel={t_kernel:.3f}ms -> {speedup:.2f}x",
            flush=True,
        )
    except Exception as e:
        RESULT["bench_error"] = f"{type(e).__name__}: {e}"
        print(f"[rope][bench] skipped: {e}", flush=True)


def install_qwen35_rope(run_benchmark: bool = True) -> bool:
    """Patch ``apply_rotary_pos_emb`` in the qwen3_5/qwen3_6 modeling modules with the
    fused Triton kernel — IFF AUTOSLM_ROPE_KERNEL=1 and the live-GPU self-test passes.

    Patches the module-level function only the full-attention layers call, so the GDN
    layers are untouched. Never raises: any failure leaves the eager path in place.
    Returns True iff the kernel was installed."""
    if not _enabled():
        return False
    try:
        apply_fn = _build_kernels()
    except Exception as e:
        print(f"[rope] kernel build failed ({type(e).__name__}: {e}); keeping eager", flush=True)
        return False
    if not self_test(apply_fn):
        return False

    patched = []
    for mod_name in ("qwen3_5", "qwen3_6"):
        try:
            import importlib

            mod = importlib.import_module(f"transformers.models.{mod_name}.modeling_{mod_name}")
        except Exception:
            continue
        if hasattr(mod, "apply_rotary_pos_emb"):
            mod.apply_rotary_pos_emb = apply_fn
            patched.append(mod_name)
    if not patched:
        print("[rope] no qwen3_5/3_6 modeling module to patch; keeping eager", flush=True)
        return False
    RESULT.update({"installed": True, "self_test": "passed", "patched": patched})
    print(f"[rope] fused Triton RoPE installed on {patched} (self-test passed)", flush=True)
    if run_benchmark:
        benchmark(apply_fn)
    return True