refactor: unify extension API to blhd layout and is_causal

- Rename ops.py to attention_ops.py
- Remove layout/scale params: fixed blhd, auto scale
- Replace causal_offset with is_causal bool
- Move SDPA fallback to backend, ops only calls CUDA kernels
- Update __init__.py exports

astrai/extension/__init__.py

@@ -4,27 +4,39 @@ Public API:
     - ``attn_decode`` — single-query decode attention
     - ``attn_prefill`` — multi-query prefill attention
     - ``attn_paged_decode`` — paged decode attention (direct page-table access)
+    - ``AttentionBackend`` — ABC for attention computation strategies
+    - ``TorchNativeBackend`` — default SDPA backend with KV cache I/O
 
-Interface (shared by all wrappers):
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
-    causal_offset: -1 = non-causal; >=0 = absolute position of first Q token
+(blhd). Scale is always ``1/sqrt(head_dim)``.
-    mask:       2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True = keep)
-    scale:      0.0 = auto (1/sqrt(head_dim)); >0 = explicit
-    layout:     "bhld" (default) or "blhd"
-
-Causal and mask can coexist — both are applied simultaneously.
 
 Each wrapper dispatches to its compiled CUDA kernel (``astrai.extension.attn_*``)
 when available, otherwise falls back to ``torch.nn.functional.scaled_dot_product_attention``.
 """
 
+from astrai.extension.attention_backend import (
+    ATTN_BACKEND,
+    AttentionBackend,
+    TorchNativeBackend,
+    attn_backend,
+    get_backend,
+)
+from astrai.extension.attention_ops import (
+    attn_decode,
+    attn_paged_decode,
+    attn_prefill,
+)
 from astrai.extension.loader import KERNEL_NAMES, is_available
-from astrai.extension.ops import attention, attn_decode, attn_paged_decode, attn_prefill
 
 __all__ = [
+    "ATTN_BACKEND",
+    "AttentionBackend",
+    "TorchNativeBackend",
+    "attn_backend",
+    "get_backend",
     "attn_decode",
     "attn_paged_decode",
     "attn_prefill",
-    "attention",
     "is_available",
     "KERNEL_NAMES",
 ]

astrai/extension/attention_backend.py

@@ -24,15 +24,13 @@ Thread-safe via ``contextvars`` — each scheduler thread gets its own
 active backend. ``get_backend()`` returns the active one, falling back
 to a process-wide ``TorchNativeBackend`` singleton.
 
-Contract (q/k/v are always ``[batch, seq_len, n_heads, head_dim]``):
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
-    q: [batch, q_len, n_heads, head_dim]
+(blhd). The backend returns ``[batch, seq_len, n_heads * head_dim]``.
-    k: [batch, q_len, n_kv_heads, head_dim]
-    v: [batch, q_len, n_kv_heads, head_dim]
-    -> returns [batch, q_len, n_heads * head_dim]
 """
 
 import contextvars
 import enum
+import math
 from abc import ABC, abstractmethod
 from contextlib import contextmanager
 from typing import Optional, Union
@@ -143,10 +141,8 @@ class AttentionBackend(ABC):
         v: Tensor,
         kv_cache: Optional[KVCache],
         layer_id: int,
-        n_rep: int,
         attn_mask: Optional[Tensor] = None,
         is_causal: bool = False,
-        scale: Optional[float] = None,
     ) -> Tensor:
         """Dispatch to decode or extend based on q_len.
 
@@ -156,21 +152,17 @@ class AttentionBackend(ABC):
             v: [batch, q_len, n_kv_heads, head_dim]
             kv_cache: cache dataclass, or None for training (no cache).
             layer_id: transformer layer index for buffer access.
-            n_rep: Q-heads // KV-heads (1 when heads match, e.g. MLA).
             attn_mask: pre-built attention mask compatible with SDPA.
             is_causal: whether to apply causal masking.
-            scale: explicit softmax scale; None = 1/sqrt(head_dim).
 
         Returns:
             [batch, q_len, n_heads * head_dim]
         """
         if kv_cache is not None and q.size(1) == 1:
             return self.forward_decode(
-                q, k, v, kv_cache, layer_id, n_rep, attn_mask, is_causal, scale
+                q, k, v, kv_cache, layer_id, attn_mask, is_causal
-            )
-        return self.forward_extend(
-            q, k, v, kv_cache, layer_id, n_rep, attn_mask, is_causal, scale
             )
+        return self.forward_extend(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
 
     @abstractmethod
     def forward_decode(
@@ -180,10 +172,8 @@ class AttentionBackend(ABC):
         v: Tensor,
         kv_cache: Optional[KVCache],
         layer_id: int,
-        n_rep: int,
         attn_mask: Optional[Tensor] = None,
         is_causal: bool = False,
-        scale: Optional[float] = None,
     ) -> Tensor:
         """Single-token decode with KV cache."""
 
@@ -195,10 +185,8 @@ class AttentionBackend(ABC):
         v: Tensor,
         kv_cache: Optional[KVCache],
         layer_id: int,
-        n_rep: int,
         attn_mask: Optional[Tensor] = None,
         is_causal: bool = False,
-        scale: Optional[float] = None,
     ) -> Tensor:
         """Multi-token prefill or training forward."""
 
@@ -221,14 +209,10 @@ class TorchNativeBackend(AttentionBackend):
         v: Tensor,
         kv_cache: Optional[KVCache],
         layer_id: int,
-        n_rep: int,
         attn_mask: Optional[Tensor] = None,
         is_causal: bool = False,
-        scale: Optional[float] = None,
     ) -> Tensor:
-        return self._forward(
+        return self._forward(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
-            q, k, v, kv_cache, layer_id, n_rep, attn_mask, is_causal, scale
-        )
 
     def forward_extend(
         self,
@@ -237,14 +221,10 @@ class TorchNativeBackend(AttentionBackend):
         v: Tensor,
         kv_cache: Optional[KVCache],
         layer_id: int,
-        n_rep: int,
         attn_mask: Optional[Tensor] = None,
         is_causal: bool = False,
-        scale: Optional[float] = None,
     ) -> Tensor:
-        return self._forward(
+        return self._forward(q, k, v, kv_cache, layer_id, attn_mask, is_causal)
-            q, k, v, kv_cache, layer_id, n_rep, attn_mask, is_causal, scale
-        )
 
     def _forward(
         self,
@@ -253,10 +233,8 @@ class TorchNativeBackend(AttentionBackend):
         v: Tensor,
         kv_cache: Optional[KVCache],
         layer_id: int,
-        n_rep: int,
         attn_mask: Optional[Tensor] = None,
         is_causal: bool = False,
-        scale: Optional[float] = None,
     ) -> Tensor:
         if kv_cache is not None:
             kv_cache.k_buffer[layer_id, kv_cache.out_cache_loc] = k
@@ -272,6 +250,7 @@ class TorchNativeBackend(AttentionBackend):
             k = kv_cache.k_buffer[layer_id, indices]
             v = kv_cache.v_buffer[layer_id, indices]
 
+        n_rep = q.size(2) // k.size(2)
         if n_rep > 1:
             k = repeat_kv(k, n_rep)
             v = repeat_kv(v, n_rep)
@@ -280,11 +259,7 @@ class TorchNativeBackend(AttentionBackend):
         k = k.permute(0, 2, 1, 3)
         v = v.permute(0, 2, 1, 3)
 
-        sdpa_kwargs: dict = {"is_causal": is_causal}
+        out = F.scaled_dot_product_attention(q, k, v, attn_mask, is_causal=is_causal)
-        if scale is not None:
-            sdpa_kwargs["scale"] = scale
-
-        out = F.scaled_dot_product_attention(q, k, v, attn_mask, **sdpa_kwargs)
         out = out.permute(0, 2, 1, 3).contiguous().flatten(2)
         return out
 

astrai/extension/attention_ops.py

@@ -0,0 +1,117 @@
+"""Attention kernel wrapper functions — one entry point per compiled kernel.
+
+Each wrapper calls its CUDA kernel directly. If the kernel is not
+available, raises ``RuntimeError``. Fallback to torch SDPA is the
+responsibility of the attention backend, not this module.
+
+Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
+(blhd). Scale is always ``1/sqrt(head_dim)``.
+
+Interface (all functions):
+    is_causal: True = causal mask; False = non-causal
+    mask:      2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True=keep)
+"""
+
+import torch
+
+from astrai.extension.loader import _available, _modules
+
+
+def _check_available(name: str):
+    if not _available.get(name):
+        raise RuntimeError(
+            f"CUDA kernel '{name}' is not available. "
+            f"Build with CSRC_KERNELS=true or use a torch-native backend."
+        )
+
+
+def attn_decode(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None = None,
+    is_causal: bool = False,
+) -> torch.Tensor:
+    """GQA decode attention (q_len == 1).
+
+    Args:
+        q: [batch, 1, n_heads, head_dim] (blhd, bf16)
+        k: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        v: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        mask: 2D [batch, kv_len] or 3D [batch, 1, kv_len] (bool, True=keep)
+        is_causal: apply causal mask
+
+    Returns:
+        [batch, 1, n_heads, head_dim] (blhd, bf16)
+    """
+    _check_available("attn_decode")
+    causal_offset = (k.size(1) - 1) if is_causal else -1
+    return _modules["attn_decode"].attn_decode(
+        q, k, v, mask=mask, causal_offset=causal_offset, layout=1
+    )
+
+
+def attn_prefill(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    mask: torch.Tensor | None = None,
+    is_causal: bool = False,
+) -> torch.Tensor:
+    """GQA prefill attention (q_len > 1).
+
+    Args:
+        q: [batch, q_len, n_heads, head_dim] (blhd, bf16)
+        k: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        v: [batch, kv_len, n_kv_heads, head_dim] (blhd, bf16)
+        mask: 2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool, True=keep)
+        is_causal: apply causal mask
+
+    Returns:
+        [batch, q_len, n_heads, head_dim] (blhd, bf16)
+    """
+    _check_available("attn_prefill")
+    causal_offset = (k.size(1) - q.size(1)) if is_causal else -1
+    return _modules["attn_prefill"].attn_prefill(
+        q, k, v, mask=mask, causal_offset=causal_offset, layout=1
+    )
+
+
+def attn_paged_decode(
+    q: torch.Tensor,
+    page_table: torch.Tensor,
+    k_cache: torch.Tensor,
+    v_cache: torch.Tensor,
+    page_size: int,
+    kv_len: int,
+    mask: torch.Tensor | None = None,
+    is_causal: bool = False,
+) -> torch.Tensor:
+    """Paged GQA decode attention (q_len == 1, direct page-table access).
+
+    Args:
+        q: [batch, 1, n_heads, head_dim] (blhd, bf16)
+        page_table: [batch, max_pages] (int64)
+        k_cache: [n_pages, page_size, n_kv_heads, head_dim] (bf16)
+        v_cache: same as k_cache
+        page_size: tokens per page
+        kv_len: actual sequence length per request
+        mask: 2D [batch, kv_len] or 3D [batch, 1, kv_len] (bool, True=keep)
+        is_causal: apply causal mask
+
+    Returns:
+        [batch, 1, n_heads, head_dim] (blhd, bf16)
+    """
+    _check_available("attn_paged_decode")
+    causal_offset = (kv_len - 1) if is_causal else -1
+    return _modules["attn_paged_decode"].attn_paged_decode(
+        q,
+        page_table,
+        k_cache,
+        v_cache,
+        page_size,
+        kv_len,
+        mask=mask,
+        causal_offset=causal_offset,
+        layout=1,
+    )

astrai/extension/ops.py

@@ -1,298 +0,0 @@
-"""GQA attention wrapper functions — one entry point per compiled kernel.
-
-Each wrapper dispatches to its CUDA kernel (loaded in ``loader.py``) when
-available, otherwise falls back to ``torch`` SDPA.
-
-Interface (all functions):
-    causal_offset: -1 = non-causal; >=0 = absolute position of first Q token
-    mask:       2D [batch, kv_len] or 3D [batch, q_len, kv_len] (bool)
-    scale:      0.0 = auto (1/sqrt(head_dim)); >0 = explicit
-    layout:     "bhld" (default) or "blhd"
-
-Add new kernel wrappers here; split into per-variant files only if this file
-grows large.
-"""
-
-import math
-
-import torch
-import torch.nn.functional as F
-
-from astrai.extension.loader import _available, _modules
-
-_LAYOUT_CODES: dict[str, int] = {"bhld": 0, "blhd": 1}
-
-
-def _parse_layout(layout: str | int) -> int:
-    if isinstance(layout, int):
-        return layout
-    code = _LAYOUT_CODES.get(layout.lower())
-    if code is None:
-        raise ValueError(
-            f"unknown layout '{layout}', expected one of {list(_LAYOUT_CODES)}"
-        )
-    return code
-
-
-def _to_bhld(t: torch.Tensor, layout: int) -> torch.Tensor:
-    """Normalize to b h l d view. Zero-copy transpose if layout==1 (b l h d)."""
-    if layout == 1:
-        return t.transpose(1, 2)
-    return t
-
-
-def _expand_kv_heads(
-    k: torch.Tensor, v: torch.Tensor, q_head: int
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """Expand K/V heads to match Q heads for GQA fallback."""
-    kv_head = k.size(1)
-    if kv_head == q_head:
-        return k, v
-    group = q_head // kv_head
-    k = k.repeat_interleave(group, dim=1)
-    v = v.repeat_interleave(group, dim=1)
-    return k, v
-
-
-def _build_attn_mask(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    mask: torch.Tensor | None,
-    causal_offset: int,
-    scale: float,
-) -> tuple[torch.Tensor | None, float]:
-    """Build SDPA-compatible attn_mask + resolved scale.
-
-    q and k must already be in b h l d layout.
-    Causal and mask can coexist: causal sets -inf above the diagonal, mask
-    sets -inf for padded positions. Both are OR'd into a single bool mask.
-    """
-    q_len = q.size(2)
-    kv_len = k.size(2)
-    head_dim = q.size(3)
-    resolved_scale = scale if scale and scale > 0 else 1.0 / math.sqrt(head_dim)
-
-    attn_mask = None
-
-    if mask is not None:
-        if mask.dim() == 2:
-            # [batch, kv_len] → [batch, 1, 1, kv_len]
-            attn_mask = mask[:, None, None, :]
-        elif mask.dim() == 3:
-            # [batch, q_len, kv_len] → [batch, 1, q_len, kv_len]
-            attn_mask = mask[:, None, :, :]
-        else:
-            raise ValueError(f"mask must be 2D or 3D, got {mask.dim()}D")
-
-    if causal_offset >= 0:
-        batch = q.size(0)
-        # q row i attends to kv cols 0..(causal_offset + i)
-        q_idx = torch.arange(q_len, device=q.device).unsqueeze(1)  # [q_len, 1]
-        kv_idx = torch.arange(kv_len, device=q.device).unsqueeze(0)  # [1, kv_len]
-        causal_bool = kv_idx > (causal_offset + q_idx)  # True = masked out
-        causal_mask = causal_bool.unsqueeze(0).expand(
-            batch, -1, -1
-        )  # [batch, q_len, kv_len]
-        causal_mask = causal_mask[:, None, :, :]  # [batch, 1, q_len, kv_len]
-
-        if attn_mask is not None:
-            attn_mask = attn_mask | causal_mask
-        else:
-            attn_mask = causal_mask
-
-    return attn_mask, resolved_scale
-
-
-def _torch_fallback(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    mask: torch.Tensor | None,
-    causal_offset: int,
-    scale: float,
-    q_layout: int,
-    kv_layout: int | None = None,
-) -> torch.Tensor:
-    """Reference attention via ``scaled_dot_product_attention``.
-
-    q_layout / kv_layout: 0 = b h l d, 1 = b l h d.
-    If kv_layout is None, uses q_layout (Q and K/V share the same layout).
-    """
-    if kv_layout is None:
-        kv_layout = q_layout
-    q = _to_bhld(q, q_layout)
-    k = _to_bhld(k, kv_layout)
-    v = _to_bhld(v, kv_layout)
-    k, v = _expand_kv_heads(k, v, q.size(1))
-    attn_mask, resolved_scale = _build_attn_mask(q, k, mask, causal_offset, scale)
-    out = F.scaled_dot_product_attention(
-        q, k, v, attn_mask=attn_mask, is_causal=False, scale=resolved_scale
-    )
-    # Restore Q's original layout
-    if q_layout == 1:
-        out = out.transpose(1, 2)
-    return out
-
-
-def _gather_kv_from_pages(
-    page_table: torch.Tensor,
-    k_cache: torch.Tensor,
-    v_cache: torch.Tensor,
-    page_size: int,
-    kv_len: int,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """Gather contiguous K/V from paged cache for torch SDPA fallback.
-
-    Shapes:
-        page_table : [batch, max_pages] (int64)
-        k_cache    : [n_pages, page_size, n_kv_heads, head_dim]
-        v_cache    : same as k_cache
-    Returns:
-        k, v : [batch, kv_len, n_kv_heads, head_dim]  (b l h d)
-    """
-    batch, max_pages = page_table.shape
-    _, ps, n_kv_heads, head_dim = k_cache.shape
-    if ps != page_size:
-        raise ValueError(f"k_cache page_size mismatch: {ps} vs {page_size}")
-
-    # Vectorized gather: build physical page + offset indices, then advanced-index
-    positions = torch.arange(kv_len, device=page_table.device)
-    logical_pages = positions // page_size  # [kv_len]
-    page_offsets = positions % page_size  # [kv_len]
-
-    phys_pages = page_table[:, logical_pages]  # [batch, kv_len]
-    # k_cache[phys_pages, page_offsets] → [batch, kv_len, n_kv_heads, head_dim] (b l h d)
-    k = k_cache[phys_pages, page_offsets]
-    v = v_cache[phys_pages, page_offsets]
-    return k, v
-
-
-def attn_decode(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    mask: torch.Tensor | None = None,
-    causal_offset: int = -1,
-    scale: float = 0.0,
-    layout: str = "bhld",
-) -> torch.Tensor:
-    li = _parse_layout(layout)
-    if _available["attn_decode"]:
-        return _modules["attn_decode"].attn_decode(
-            q,
-            k,
-            v,
-            mask=mask,
-            causal_offset=causal_offset,
-            scale=scale,
-            layout=li,
-        )
-    return _torch_fallback(q, k, v, mask, causal_offset, scale, q_layout=li)
-
-
-def attn_prefill(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    mask: torch.Tensor | None = None,
-    causal_offset: int = -1,
-    scale: float = 0.0,
-    layout: str = "bhld",
-) -> torch.Tensor:
-    li = _parse_layout(layout)
-    if _available["attn_prefill"]:
-        return _modules["attn_prefill"].attn_prefill(
-            q,
-            k,
-            v,
-            mask=mask,
-            causal_offset=causal_offset,
-            scale=scale,
-            layout=li,
-        )
-    return _torch_fallback(q, k, v, mask, causal_offset, scale, q_layout=li)
-
-
-def attn_paged_decode(
-    q: torch.Tensor,
-    page_table: torch.Tensor,
-    k_cache: torch.Tensor,
-    v_cache: torch.Tensor,
-    page_size: int,
-    kv_len: int,
-    mask: torch.Tensor | None = None,
-    causal_offset: int = -1,
-    scale: float = 0.0,
-    layout: str = "bhld",
-) -> torch.Tensor:
-    li = _parse_layout(layout)
-    if _available["attn_paged_decode"]:
-        return _modules["attn_paged_decode"].attn_paged_decode(
-            q,
-            page_table,
-            k_cache,
-            v_cache,
-            page_size,
-            kv_len,
-            mask=mask,
-            causal_offset=causal_offset,
-            scale=scale,
-            layout=li,
-        )
-    # Gathered K/V are always b l h d
-    k, v = _gather_kv_from_pages(page_table, k_cache, v_cache, page_size, kv_len)
-    return _torch_fallback(
-        q, k, v, mask, causal_offset, scale, q_layout=li, kv_layout=1
-    )
-
-
-def attention(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    mask: torch.Tensor | None = None,
-    causal_offset: int = -1,
-    scale: float = 0.0,
-    layout: str = "bhld",
-) -> torch.Tensor:
-    """Dispatch to decode or prefill attention based on the query length.
-
-    A query length of one is the decode case; longer queries use prefill.
-    The paged-cache decode path cannot be selected here because its page-table
-    arguments are not part of this interface.
-    """
-    li = _parse_layout(layout)
-
-    if q.ndim not in (2, 3, 4) or k.ndim != q.ndim or v.ndim != q.ndim:
-        raise ValueError(
-            "q, k, and v must all have the same rank in {2, 3, 4}, "
-            f"got {q.ndim}D, {k.ndim}D, {v.ndim}D"
-        )
-    if k.shape != v.shape:
-        raise ValueError(
-            f"k and v must have the same shape, got {k.shape} and {v.shape}"
-        )
-
-    original_ndim = q.ndim
-    if original_ndim == 2:
-        # [L, D] -> [1, 1, L, D] or [1, L, 1, D]
-        q = q.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
-        k = k.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
-        v = v.unsqueeze(0).unsqueeze(1 if li == 0 else 2)
-    elif original_ndim == 3:
-        # [B, L, D] -> single-head 4D input.
-        q = q.unsqueeze(1 if li == 0 else 2)
-        k = k.unsqueeze(1 if li == 0 else 2)
-        v = v.unsqueeze(1 if li == 0 else 2)
-
-    q_len = q.size(2 if li == 0 else 1)
-    if q_len == 1:
-        out = attn_decode(q, k, v, mask, causal_offset, scale, layout)
-    else:
-        out = attn_prefill(q, k, v, mask, causal_offset, scale, layout)
-
-    if original_ndim == 2:
-        return out.squeeze(0).squeeze(0 if li == 0 else 1)
-    if original_ndim == 3:
-        return out.squeeze(1 if li == 0 else 2)
-    return out