kernel.py

import functools
import math
import warnings

import torch
import torch.utils.checkpoint
from torch.nn import functional as F

try:
    import config as _alpha_config
except ImportError:
    _alpha_config = None


def _kernel_config_attr(name: str, default):
    
    if _alpha_config is None:
        return default
    if not hasattr(_alpha_config, name):
        warnings.warn(
            f"[kernel.py] config module has no attribute '{name}' -- falling back to "
            f"default {default!r}. If this is unexpected, check for a casing mismatch "
            f"or rename in your config.py.",
            stacklevel=3,
        )
        return default
    return getattr(_alpha_config, name)




class _UnsupportedByBackend(Exception):
    """Raised internally to signal 'this backend can't safely do this' -> fall back."""

_warned: set[str] = set()


def _warn_once(key: str, msg: str) -> None:
    if key not in _warned:
        _warned.add(key)
        warnings.warn(f"[kernel.py] {msg}", stacklevel=3)



_backend_stats: dict[str, int] = {}


@torch.compiler.allow_in_graph
def _record_backend(name: str) -> None:
    _backend_stats[name] = _backend_stats.get(name, 0) + 1


def get_backend_stats() -> dict[str, int]:
    
    return dict(_backend_stats)


def reset_backend_stats() -> None:
    _backend_stats.clear()



@torch.compiler.allow_in_graph
@functools.lru_cache(maxsize=1)
def _flash_attn_available() -> bool:
    try:
        import flash_attn  # noqa: F401
        return True
    except ImportError:
        return False


@torch.compiler.allow_in_graph
@functools.lru_cache(maxsize=1)
def _xformers_available() -> bool:
    try:
        import xformers.ops  # noqa: F401
        return True
    except ImportError:
        return False


@torch.compiler.allow_in_graph
@functools.lru_cache(maxsize=1)
def _tpu_kernel_available() -> bool:
    try:
        from torch_xla.experimental import custom_kernel  # noqa: F401
        return True
    except ImportError:
        return False


@torch.compiler.allow_in_graph
@functools.lru_cache(maxsize=None)
def _detect_backend(device_key: str) -> str:
    
    if device_key.startswith("xla"):
        if _tpu_kernel_available():
            return "tpu"
        _warn_once("tpu_missing", "torch_xla not importable on an XLA device -- using reference attention.")
        return "reference"

    if device_key.startswith("cuda"):
        idx = int(device_key.split(":")[1]) if ":" in device_key else torch.cuda.current_device()
        major, _minor = torch.cuda.get_device_capability(idx)
        if major >= 8 and _flash_attn_available():
            return "flash"
        if _xformers_available():
            return "xformers"
        if major >= 8:
            _warn_once("flash_missing", "Ampere+ GPU detected but `flash_attn` isn't installed, and neither is `xformers` -- using reference attention (slow).")
        else:
            _warn_once("xformers_missing", "Pre-Ampere GPU (e.g. T4) detected and `xformers` isn't installed -- using reference attention (slow, high memory).")
        return "reference"

    return "reference"


def _backend_for(device: torch.device) -> str:
    if device.type == "cuda":
        return _detect_backend(f"cuda:{device.index if device.index is not None else torch.cuda.current_device()}")
    return _detect_backend(device.type)



def _attention_block(q_block, k_block, v_block, q_start, k_start, causal, window_size, dropout_p, training, scale):
    """One tile's worth of causal/windowed attention. Kept as a standalone

    function (not a closure) so torch.utils.checkpoint can call it directly."""
    qb, kb = q_block.shape[2], k_block.shape[2]
    scores = torch.matmul(q_block, k_block.transpose(-2, -1)) * scale

    if causal or window_size is not None:
        q_idx = torch.arange(q_start, q_start + qb, device=q_block.device).view(qb, 1)
        k_idx = torch.arange(k_start, k_start + kb, device=q_block.device).view(1, kb)
        allowed = (k_idx <= q_idx) if causal else torch.ones(qb, kb, dtype=torch.bool, device=q_block.device)
        if window_size is not None:
            allowed = allowed & (q_idx - k_idx < window_size)
        mask = torch.where(allowed, torch.zeros(1, device=q_block.device), torch.full((1,), -1e4, device=q_block.device))
        scores = scores + mask.to(scores.dtype)

    weights = torch.softmax(scores.float(), dim=-1).to(scores.dtype)
    if training and dropout_p > 0:
        weights = torch.nn.functional.dropout(weights, p=dropout_p)
    return torch.matmul(weights, v_block)


@torch.compiler.disable
def _reference_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale, query_block_size=128):
    
    b, hq, t, d = q.shape
    _, hkv, tk, _ = k.shape
    if hkv != hq:
        reps = hq // hkv
        k = k.repeat_interleave(reps, dim=1)
        v = v.repeat_interleave(reps, dim=1)

    scale = softmax_scale if softmax_scale is not None else (1.0 / math.sqrt(d))

    outputs = []
    for q_start in range(0, t, query_block_size):
        q_end = min(q_start + query_block_size, t)
        q_block = q[:, :, q_start:q_end, :]

        k_lo = max(0, q_start - window_size + 1) if window_size is not None else 0
        k_hi = min(q_end, tk) if causal else tk
        k_block = k[:, :, k_lo:k_hi, :]
        v_block = v[:, :, k_lo:k_hi, :]

        
        if q_block.device.type == "xla":
            out_block = _attention_block(
                q_block, k_block, v_block, q_start, k_lo,
                causal, window_size, dropout_p, training, scale,
            )
        else:
            out_block = torch.utils.checkpoint.checkpoint(
                _attention_block, q_block, k_block, v_block, q_start, k_lo,
                causal, window_size, dropout_p, training, scale,
                use_reentrant=False,
            )
        outputs.append(out_block)

    return torch.cat(outputs, dim=2)


try:
    from flash_attention_interface import flash_attn_func as _turing_flash_attn_func
except ImportError:
    _turing_flash_attn_func = None

_TURING_SUPPORTED_HEAD_DIMS = (64, 128)


def _turing_flash_eligible(q: torch.Tensor, k: torch.Tensor, causal: bool,

                            window_size: int | None, dropout_p: float) -> bool:
    if _turing_flash_attn_func is None:
        return False
    if q.device.type != "cuda":
        return False
    major, minor = torch.cuda.get_device_capability(q.device)
    if (major, minor) != (7, 5):
        return False
    if q.shape[-1] not in _TURING_SUPPORTED_HEAD_DIMS:
        return False
    if window_size is not None:
        return False
    if dropout_p > 0.0:
        return False
    return True


def _turing_flash_attention(q, k, v, causal, softmax_scale):
    
    q_ = q.transpose(1, 2)  # (B,H,T,D) -> (B,T,H,D), this repo's expected layout
    k_ = k.transpose(1, 2)
    v_ = v.transpose(1, 2)
    out = _turing_flash_attn_func(q_, k_, v_, softmax_scale=softmax_scale, causal=causal)
    return out.transpose(1, 2)  # back to (B,H,T,D) for this codebase's convention


# ---------------------------------------------------------------------------
# FlexAttention (torch.nn.attention.flex_attention). Config-gated, not
# hardware-gated -- set use_flexattention=True in config.py to opt in.
# Unlike every other backend in this file, FlexAttention is a torch-level
# composition of ops, not a hand-fused CUDA kernel -- its speed comes
# entirely from torch.compile specializing the mask/score-mod into one
# fused kernel at trace time. Eager flex_attention runs at roughly
# reference-attention speed, so (unlike the @torch.compiler.disable
# backends elsewhere in this file) this one is deliberately compiled, once,
# and cached.
# ---------------------------------------------------------------------------

@functools.lru_cache(maxsize=1)
def _flex_attention_available() -> bool:
    try:
        from torch.nn.attention.flex_attention import flex_attention  # noqa: F401
        return True
    except ImportError:
        return False


_compiled_flex_attention = None


def _get_compiled_flex_attention():
    global _compiled_flex_attention
    if _compiled_flex_attention is None:
        from torch.nn.attention.flex_attention import flex_attention
        _compiled_flex_attention = torch.compile(flex_attention, dynamic=False)
    return _compiled_flex_attention

@torch.compiler.disable 
@functools.lru_cache(maxsize=32)
 
def _flex_block_mask(q_len: int, kv_len: int, causal: bool, window_size: "int | None", device_key: str):
    """Build (and cache) a FlexAttention BlockMask for one (shape, mask-kind,

    device) combo. Constructing a block mask isn't free, and with a fixed

    training CONTEXT this pays the cost once instead of every forward call."""
    from torch.nn.attention.flex_attention import create_block_mask

    if window_size is not None:
        def mask_mod(b, h, q_idx, kv_idx):
            return (kv_idx <= q_idx) & (q_idx - kv_idx < window_size)
    elif causal:
        def mask_mod(b, h, q_idx, kv_idx):
            return kv_idx <= q_idx
    else:
        return None  # full bidirectional attention -- no block mask needed

    return create_block_mask(mask_mod, B=None, H=None, Q_LEN=q_len, KV_LEN=kv_len, device=device_key)


def _flex_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
    
    if dropout_p > 0 and training:
        raise _UnsupportedByBackend(
            "FlexAttention path here doesn't wire up an attention-weight "
            "dropout term -- falling back."
        )

    b, hq, t, d = q.shape
    _, hkv, tk, _ = k.shape

    block_mask = _flex_block_mask(t, tk, causal, window_size, str(q.device))
    scale = softmax_scale if softmax_scale is not None else (1.0 / math.sqrt(d))
    flex_fn = _get_compiled_flex_attention()

    # FlexAttention takes (B, H, T, D) natively -- no transpose needed, unlike
    # the flash_attn/xformers paths above which want (B, T, H, D).
    return flex_fn(q, k, v, block_mask=block_mask, scale=scale, enable_gqa=(hkv != hq))


# ---------------------------------------------------------------------------
# Blackwell: FlashAttention-4 (CuTeDSL) with FlashAttention-2 fallback.
#
# FA4 (`pip install flash-attn-4`, imported as `flash_attn.cute`) targets
# Hopper and data-center Blackwell (sm_90 / sm_100 / sm_103 -- H100/B200/B300)
# via warp-specialized MMA instructions. It does NOT run on desktop Blackwell
# (sm_120, e.g. RTX 50-series): sm_120 uses the same register-to-register
# HMMA path NVIDIA GPUs have used since Volta, not the warp-specialized MMA
# FA4's kernel design requires, and this is a physical silicon difference
# (confirmed by multiple independent sm_120 FA4 build attempts as of early
# 2026), not a version-gating issue that a newer release fixes. So this
# block tries FA4 only on the capabilities it can actually run on, and
# every other Blackwell-family GPU (sm_120 included) falls through to the
# existing FlashAttention-2 path below.
# ---------------------------------------------------------------------------

_BLACKWELL_CAPABILITIES = {(9, 0), (10, 0), (10, 3), (12, 0)}  # Hopper + all Blackwell variants
_FA4_CAPABILITIES = {(9, 0), (10, 0), (10, 3)}  # NOT (12, 0) -- see note above

try:
    from flash_attn.cute import flash_attn_func as _fa4_attn_func
except ImportError:
    _fa4_attn_func = None


def _fa4_eligible(q: torch.Tensor, causal: bool, window_size: "int | None", dropout_p: float) -> bool:
    if _fa4_attn_func is None:
        return False
    if q.device.type != "cuda":
        return False
    if torch.cuda.get_device_capability(q.device) not in _FA4_CAPABILITIES:
        return False
    if window_size is not None:  # not confirmed supported by FA4's current (beta) public API
        return False
    if dropout_p > 0.0:
        return False
    return True


def _fa4_attention(q, k, v, causal, softmax_scale):
    # FA4's public surface is still small/beta -- only `causal` is confirmed
    # from the library's own usage example (flash_attn.cute docs show only
    # `flash_attn_func(q, k, v, causal=True)`). Anything beyond that
    # (window_size, dropout) is gated out by _fa4_eligible above rather than
    # guessed at here. Assumes the same (B, T, H, D) layout as FA2/FA3; if
    # that assumption is wrong for a given release the call raises and the
    # dispatcher below falls back to FlashAttention-2 automatically.
    qf = q.transpose(1, 2).contiguous()
    kf = k.transpose(1, 2).contiguous()
    vf = v.transpose(1, 2).contiguous()
    kwargs = {"softmax_scale": softmax_scale} if softmax_scale is not None else {}
    out = _fa4_attn_func(qf, kf, vf, causal=causal, **kwargs)
    return out.transpose(1, 2)


# ---------------------------------------------------------------------------
# FlashAttention-2 (NVIDIA Ampere+)
# ---------------------------------------------------------------------------

def _flash_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
    from flash_attn import flash_attn_func

    # flash_attn wants (B, T, H, D); we work in (B, H, T, D) throughout.
    qf = q.transpose(1, 2).contiguous()
    kf = k.transpose(1, 2).contiguous()
    vf = v.transpose(1, 2).contiguous()

    # window_size=(-1,-1) means "unbounded" (plain causal/full) per flash-attn's
    # own convention; (W-1, 0) means "attend to self + W-1 previous tokens".
    ws = (window_size - 1, 0) if window_size is not None else (-1, -1)

    out = flash_attn_func(
        qf, kf, vf,
        dropout_p=dropout_p if training else 0.0,
        softmax_scale=softmax_scale,
        causal=causal,
        window_size=ws,
    )  # GQA/MQA handled natively by flash_attn_func (kf/vf may have fewer heads than qf)
    return out.transpose(1, 2)  # back to (B, H, T, D)


# ---------------------------------------------------------------------------
# xFormers (NVIDIA pre-Ampere, e.g. T4)
# ---------------------------------------------------------------------------

@torch.compiler.disable
def _xformers_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
    
    import xformers.ops as xops
    from xformers.ops.fmha.attn_bias import LowerTriangularMask, BlockDiagonalCausalMask

    qf = q.transpose(1, 2).contiguous()  # (B, T, H, D)
    kf = k.transpose(1, 2).contiguous()
    vf = v.transpose(1, 2).contiguous()

    b, t, hq, d = qf.shape
    _, tk, hkv, _ = kf.shape
    if hkv != hq:
        
        reps = hq // hkv
        kf = kf.repeat_interleave(reps, dim=2)
        vf = vf.repeat_interleave(reps, dim=2)

    if window_size is not None:
        if not causal:
            raise _UnsupportedByBackend("non-causal windowed attention not implemented for the xFormers path")
        
        bias = BlockDiagonalCausalMask.from_seqlens(
            q_seqlen=[t] * b, kv_seqlen=[tk] * b,
        ).make_local_attention(window_size)
        qf_packed = qf.reshape(1, b * t, hq, d)
        kf_packed = kf.reshape(1, b * tk, hq, d)
        vf_packed = vf.reshape(1, b * tk, hq, d)
        out = xops.memory_efficient_attention(
            qf_packed, kf_packed, vf_packed, attn_bias=bias,
            p=dropout_p if training else 0.0,
            scale=softmax_scale,
        )
        out = out.reshape(b, t, hq, d)
        return out.transpose(1, 2)

    bias = LowerTriangularMask() if causal else None
    out = xops.memory_efficient_attention(
        qf, kf, vf, attn_bias=bias,
        p=dropout_p if training else 0.0,
        scale=softmax_scale,
    )
    return out.transpose(1, 2)


# ---------------------------------------------------------------------------
# TPU (torch_xla)
# ---------------------------------------------------------------------------

@functools.lru_cache(maxsize=1)
def _splash_attention_available() -> bool:
    try:
        from jax.experimental.pallas.ops.tpu.splash_attention import (  # noqa: F401
            splash_attention_kernel,
            splash_attention_mask,
        )
        import torch_xla.core.xla_builder  # noqa: F401
        return True
    except ImportError:
        return False



_splash_disabled_reason: str | None = None


def reset_splash_circuit_breaker() -> None:
    global _splash_disabled_reason
    _splash_disabled_reason = None


def get_splash_disabled_reason() -> str | None:
    
    return _splash_disabled_reason




class _SplashAttentionFn(torch.autograd.Function):
   
    @staticmethod
    def forward(ctx, q, k, v, num_heads, q_len, k_len, scale):
        import torch_xla
        import torch_xla.core.xla_builder as xb

        orig_dtype = q.dtype
        q_b, k_b, v_b = q.to(torch.bfloat16), k.to(torch.bfloat16), v.to(torch.bfloat16)

        def fwd_jax(qj, kj, vj):
            from jax.experimental.pallas.ops.tpu.splash_attention import (
                splash_attention_kernel, splash_attention_mask,
            )
            import jax
            mask = splash_attention_mask.MultiHeadMask(
                masks=[splash_attention_mask.CausalMask(shape=(q_len, k_len)) for _ in range(num_heads)]
            )
            kernel_fn = splash_attention_kernel.make_splash_mha(mask=mask, head_shards=1, q_seq_shards=1)
            return jax.vmap(kernel_fn)(q=qj * scale, k=kj, v=vj)

        out = xb.call_jax(fwd_jax, (q_b, k_b, v_b), {}, "arya_splash_attention_fwd")

        
        torch_xla.sync(reset_scope=False)

        ctx.save_for_backward(q_b, k_b, v_b)
        ctx.num_heads, ctx.q_len, ctx.k_len, ctx.scale, ctx.orig_dtype = num_heads, q_len, k_len, scale, orig_dtype
        return out.to(orig_dtype)

    @staticmethod
    def backward(ctx, grad_output):
        import torch_xla
        import torch_xla.core.xla_builder as xb
        q_b, k_b, v_b = ctx.saved_tensors
        num_heads, q_len, k_len, scale = ctx.num_heads, ctx.q_len, ctx.k_len, ctx.scale
        grad_output_b = grad_output.to(torch.bfloat16)

        def bwd_jax(qj, kj, vj, gj):
            from jax.experimental.pallas.ops.tpu.splash_attention import (
                splash_attention_kernel, splash_attention_mask,
            )
            import jax

            def raw(qj_, kj_, vj_):
                mask = splash_attention_mask.MultiHeadMask(
                    masks=[splash_attention_mask.CausalMask(shape=(q_len, k_len)) for _ in range(num_heads)]
                )
                kernel_fn = splash_attention_kernel.make_splash_mha(mask=mask, head_shards=1, q_seq_shards=1)
                return jax.vmap(kernel_fn)(q=qj_ * scale, k=kj_, v=vj_)

            _, vjp_fn = jax.vjp(raw, qj, kj, vj)
            return vjp_fn(gj)  # (dq, dk, dv) -- call_jax supports PyTree returns

        dq, dk, dv = xb.call_jax(bwd_jax, (q_b, k_b, v_b, grad_output_b), {}, "arya_splash_attention_bwd")
        torch_xla.sync(reset_scope=False)  # same reasoning as forward() -- must fail here, not later, elsewhere
        orig_dtype = ctx.orig_dtype
        return dq.to(orig_dtype), dk.to(orig_dtype), dv.to(orig_dtype), None, None, None, None


def _splash_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
    
    if not causal:
        raise _UnsupportedByBackend("Splash Attention wiring here only supports causal=True -- falling back.")
    if window_size is not None:
        raise _UnsupportedByBackend("Splash Attention wiring here doesn't support window_size -- falling back.")
    if dropout_p > 0 and training:
        raise _UnsupportedByBackend("Splash Attention wiring here doesn't support dropout -- falling back.")

    q_, k_, v_ = q, k, v
    hq, hkv = q.shape[1], k.shape[1]
    if hkv != hq:
        reps = hq // hkv
        k_ = k.repeat_interleave(reps, dim=1)
        v_ = v.repeat_interleave(reps, dim=1)

    num_heads = q_.shape[1]
    q_len, k_len = q_.shape[2], k_.shape[2]
    scale = softmax_scale if softmax_scale is not None else (1.0 / math.sqrt(q_.shape[-1]))

    return _SplashAttentionFn.apply(q_, k_, v_, num_heads, q_len, k_len, scale)


@torch.compiler.disable
def _tpu_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
    
    from torch_xla.experimental.custom_kernel import flash_attention as xla_flash_attention

    global _splash_disabled_reason
    if _splash_disabled_reason is not None:
        _record_backend("splash_circuit_broken")
    elif _splash_attention_available():
        try:
            result = _splash_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
            _record_backend("splash_success")
            return result
        except _UnsupportedByBackend as e:
            
            _record_backend(f"splash_fallback:{type(e).__name__}")
            _warn_once(
                f"splash_fail_{type(e).__name__}",
                f"Splash Attention call failed ({e!r}) -- falling back to flash_attention.",
            )
        except Exception as e:  # noqa: BLE001 -- a REAL execution failure (post-sync) -- trip the breaker
            _record_backend(f"splash_fallback:{type(e).__name__}")
            _splash_disabled_reason = repr(e)
            _warn_once(
                f"splash_fail_{type(e).__name__}",
                f"Splash Attention call failed with a real execution error ({e!r}) -- "
                f"this usually means a jax/jaxlib/libtpu version mismatch in this "
                f"environment (e.g. 'Unsupported version: expected <= N but got M' is "
                f"Mosaic IR version skew between jax and libtpu -- fix by aligning "
                f"`pip install -U \"jax[tpu]\" jaxlib` as a matched pair). Disabling "
                f"Splash Attention for the rest of this run and falling back to "
                f"flash_attention -- call kernel.reset_splash_circuit_breaker() to "
                f"retry after fixing the environment.",
            )
    else:
        _record_backend("splash_unavailable")

    if window_size is not None:
        raise _UnsupportedByBackend(
            "torch_xla's built-in flash_attention wrapper doesn't expose a "
            "sliding-window argument -- falling back to reference."
        )
    if dropout_p > 0 and training:
        raise _UnsupportedByBackend(
            "torch_xla's built-in flash_attention wrapper doesn't take a "
            "dropout argument -- falling back to reference."
        )

    q_, k_, v_ = q, k, v
    hq, hkv = q.shape[1], k.shape[1]
    if hkv != hq:
        reps = hq // hkv
        k_ = k.repeat_interleave(reps, dim=1)
        v_ = v.repeat_interleave(reps, dim=1)

    
    orig_dtype = q_.dtype
    q_b = q_.to(torch.bfloat16)
    k_b = k_.to(torch.bfloat16)
    v_b = v_.to(torch.bfloat16)
    result = xla_flash_attention(q_b, k_b, v_b, causal=causal)
    result = result.to(orig_dtype)
    _record_backend("tpu_flash_success")
    return result





@torch.compiler.disable
def fused_block_sparse_attention(

    q_blocks: torch.Tensor,

    k_sel: torch.Tensor,

    v_sel: torch.Tensor,

    bias: torch.Tensor,

    dropout_p: float = 0.0,

    training: bool = True,

    softmax_scale: float | None = None,

) -> torch.Tensor | None:
    
    if q_blocks.device.type == "cuda" and _xformers_available():
        try:
            return _xformers_block_sparse_attention(q_blocks, k_sel, v_sel, bias, dropout_p, training, softmax_scale)
        except Exception as e:  # noqa: BLE001 -- must never crash training, just fall back
            _warn_once(
                f"block_sparse_xformers_fail_{type(e).__name__}",
                f"xFormers block-sparse attention failed ({e!r}) -- falling back to reference.",
            )
    return None






_ATTENTION_FN: "callable | None" = None  


def _build_attention_fn(device: torch.device):
    """Probe backends in priority order and return a direct callable.

    Called once; result is cached in _ATTENTION_FN."""
    backend = _backend_for(device)

    if device.type == "cuda" and _kernel_config_attr("use_flexattention", False) and _flex_attention_available():
        def _flex_dispatch(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
            try:
                result = _flex_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
                _record_backend("flex_success")
                return result
            except _UnsupportedByBackend as e:
                _record_backend(f"flex_fallback:{type(e).__name__}")
                _warn_once(f"flex_unsupported_{type(e).__name__}",
                           f"FlexAttention can't handle this call ({e!r}) -- falling back to xformers.")
            except Exception as e:  # noqa: BLE001
                _record_backend(f"flex_fallback:{type(e).__name__}")
                _warn_once(f"flex_fail_{type(e).__name__}",
                           f"FlexAttention failed ({e!r}) -- falling back to xformers.")
            return _xformers_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
        return "flex", _flex_dispatch

    if device.type == "cuda" and torch.cuda.get_device_capability(device) in _BLACKWELL_CAPABILITIES:
        def _blackwell_dispatch(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
            if _fa4_eligible(q, causal, window_size, dropout_p if training else 0.0):
                try:
                    result = _fa4_attention(q, k, v, causal, softmax_scale)
                    _record_backend("fa4_success")
                    return result
                except Exception as e:  # noqa: BLE001
                    _record_backend(f"fa4_fallback:{type(e).__name__}")
                    _warn_once(f"fa4_fail_{type(e).__name__}",
                               f"FlashAttention-4 failed ({e!r}) -- falling back to FlashAttention-2.")
            if _flash_attn_available():
                try:
                    result = _flash_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
                    _record_backend("flash_success")
                    return result
                except Exception as e:  # noqa: BLE001
                    _record_backend(f"flash_fallback:{type(e).__name__}")
                    _warn_once(f"flash_fail_{type(e).__name__}",
                               f"FlashAttention-2 failed ({e!r}) -- falling back to xformers.")
            return _xformers_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
        return "blackwell", _blackwell_dispatch

    if (
        device.type == "cuda"
        and _turing_flash_attn_func is not None
        and torch.cuda.get_device_capability(device) == (7, 5)
    ):
        def _turing_dispatch(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
            if _turing_flash_eligible(q, causal, window_size, dropout_p if training else 0.0):
                try:
                    result = _turing_flash_attention(q, k, v, causal, softmax_scale)
                    _record_backend("turing_flash_success")
                    return result
                except Exception as e:  # noqa: BLE001
                    _record_backend(f"turing_flash_fallback:{type(e).__name__}")
                    _warn_once(f"turing_flash_fail_{type(e).__name__}",
                               f"flash-attention-turing failed ({e!r}) -- falling back to xformers.")
            return _xformers_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
        return "turing_flash", _turing_dispatch

    if backend == "flash":
        def _flash_dispatch(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
            try:
                result = _flash_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
                _record_backend("flash_success")
                return result
            except Exception as e:  # noqa: BLE001
                _record_backend(f"flash_fallback:{type(e).__name__}")
                _warn_once(f"flash_fail_{type(e).__name__}", f"flash_attn failed ({e!r}) -- falling back to xformers.")
                return _xformers_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
        return "flash", _flash_dispatch

    if backend == "xformers":
        # Direct call — hits @torch.compiler.disable immediately, no wasted tracing.
        return "xformers", _xformers_attention

    if backend == "tpu":
        def _tpu_dispatch(q, k, v, causal, window_size, dropout_p, training, softmax_scale):
            try:
                result = _tpu_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
                return result
            except Exception as e:  # noqa: BLE001
                _record_backend(f"tpu_fallback:{type(e).__name__}")
                _warn_once(f"tpu_fail_{type(e).__name__}", f"torch_xla flash_attention failed ({e!r}) -- falling back.")
                return _reference_attention(q, k, v, causal, window_size, dropout_p, training, softmax_scale)
        return "tpu", _tpu_dispatch

    return "reference", _reference_attention


def warmup_attention_backend(device: "torch.device | str | None" = None) -> str:
    """Resolve and cache the attention backend for *device*.



    Call this once after model construction (before torch.compile) so that

    fused_attention() contains a single unconditional dispatch with no

    branching inside the compiled graph.



    Returns the resolved backend name string (e.g. 'xformers', 'flash').

    """
    global _ATTENTION_FN
    if isinstance(device, str):
        device = torch.device(device)
    if device is None:
        if torch.cuda.is_available():
            device = torch.device("cuda", torch.cuda.current_device())
        else:
            device = torch.device("cpu")
    name, fn = _build_attention_fn(device)
    _ATTENTION_FN = fn
    return name


def fused_attention(

    q: torch.Tensor,

    k: torch.Tensor,

    v: torch.Tensor,

    causal: bool = True,

    window_size: int | None = None,

    dropout_p: float = 0.0,

    training: bool = True,

    softmax_scale: float | None = None,

) -> torch.Tensor:
    global _ATTENTION_FN
    if _ATTENTION_FN is None:
        # First-call lazy init (warmup_attention_backend() not called yet).
        warmup_attention_backend(q.device)
    return _ATTENTION_FN(q, k, v, causal, window_size, dropout_p, training, softmax_scale)


def print_attention_backend(device: "torch.device | str | None" = None) -> None:
    
    if device is None:
        if torch.cuda.is_available():
            device = torch.device("cuda", torch.cuda.current_device())
        else:
            device = torch.device("cpu")
    elif isinstance(device, str):
        device = torch.device(device)

    dev_str = str(device)
    backend = _backend_for(device)

    lines = [f"[kernel] attention backend  device={dev_str}"]

    if device.type == "cuda":
        major, minor = torch.cuda.get_device_capability(device)
        gpu_name = torch.cuda.get_device_name(device)
        lines.append(f"           gpu             {gpu_name}  (sm_{major}{minor})")

        # Turing flash: only sm_75 + flash_attention_interface installed
        turing_eligible = (
            (major, minor) == (7, 5)
            and _turing_flash_attn_func is not None
        )
        if turing_eligible:
            lines.append("           turing-flash    AVAILABLE (sm_75 + flash_attention_interface)")
        else:
            reason = (
                "sm != 7.5" if (major, minor) != (7, 5)
                else "flash_attention_interface not installed"
            )
            lines.append(f"           turing-flash    not eligible ({reason})")

        # FlexAttention: config-gated (use_flexattention in config.py), not hardware-gated
        flex_cfg_on = _kernel_config_attr("use_flexattention", False)
        flex_active = flex_cfg_on and _flex_attention_available()
        if flex_active:
            lines.append("           flex-attention  ACTIVE (use_flexattention=True in config.py)")
        elif flex_cfg_on:
            lines.append("           flex-attention  requested but not importable (torch < 2.5?)")
        else:
            lines.append("           flex-attention  off (set use_flexattention=True in config.py to enable)")

        # Blackwell family (Hopper sm_90 + all Blackwell variants): FA4 with FA2 fallback
        blackwell_eligible = (major, minor) in _BLACKWELL_CAPABILITIES
        if blackwell_eligible:
            if (major, minor) in _FA4_CAPABILITIES and _fa4_attn_func is not None:
                lines.append(f"           fa4             AVAILABLE (sm_{major}{minor})")
            elif (major, minor) in _FA4_CAPABILITIES:
                lines.append(f"           fa4             not installed (pip install flash-attn-4)")
            else:
                lines.append(f"           fa4             not eligible (sm_120 lacks FA4's required warp-specialized MMA -- uses FlashAttention-2 instead)")

        # flash_attn 2.x
        if _flash_attn_available():
            lines.append(f"           flash_attn      available (Ampere+ path, backend={backend!r})")
        else:
            lines.append("           flash_attn      not installed")

        # xformers
        if _xformers_available():
            lines.append("           xformers        available")
        else:
            lines.append("           xformers        not installed")

        # Summarise what will actually fire first.
        if flex_active:
            active = "flex-attention  (config-enabled, torch.compile'd)"
        elif blackwell_eligible and (major, minor) in _FA4_CAPABILITIES and _fa4_attn_func is not None:
            active = f"fa4  (sm_{major}{minor})"
        elif blackwell_eligible and _flash_attn_available():
            active = f"flash_attn 2.x  (sm_{major}{minor}, FA4 not eligible/installed)"
        elif turing_eligible:
            active = "turing-flash  (sm_75 + flash_attention_interface)"
        elif backend == "flash":
            active = "flash_attn 2.x"
        elif backend == "xformers":
            active = "xformers memory_efficient_attention"
        else:
            active = "reference  (tiled matmul — slow; install flash_attn or xformers)"

        lines.append(f"           >>> active <<<  {active}")

    elif device.type == "xla":
        splash = _splash_attention_available()
        lines.append(f"           splash-attn     {'available' if splash else 'not available'}")
        lines.append(f"           torch_xla flash {'available' if _tpu_kernel_available() else 'not available'}")
        active = "splash-attention" if splash else ("torch_xla flash_attention" if _tpu_kernel_available() else "reference")
        lines.append(f"           >>> active <<<  {active}")

    else:
        lines.append("           >>> active <<<  reference  (CPU — tiled matmul)")

    print("\n".join(lines))