refactor: unify operator selection behind generic dispatch
- add astrai/extension/dispatch.py: per-family decision tables over composable Specs with explicit-strict / implicit-loose resolution, ASTR_OPS env overrides, profile presets, and explain traces - make the axis schema family-owned: register_family takes an axes extractor that snapshots whatever decision axes that family needs from the call, and the core only supplies the axis() predicate vocabulary plus a tensor_axes helper - drop the central CallContext dataclass; resolve and explain take the raw call arguments, so unregistered handles are probed through supports_call on the same args - migrate attention and rotary onto family-owned axes with behavior-preserving specs and spec-vs-supports_call mirror tests - replace the non-ASCII member-of glyph in spec descriptions with plain ASCII " in "
This commit is contained in:
@@ -27,6 +27,22 @@ from astrai.extension.backend import (
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attn_backend,
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get_backend,
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)
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from astrai.extension.dispatch import (
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Axes,
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ExplicitSelectionError,
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ImplRecord,
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Resolution,
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Spec,
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axis,
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explain,
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explain_plan,
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op_backend,
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register_env_alias,
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register_family,
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resolve,
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resolve_plan,
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tensor_axes,
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)
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from astrai.extension.loader import KERNEL_NAMES, is_available
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from astrai.extension.ops import (
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TensorLayout,
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@@ -52,4 +68,18 @@ __all__ = [
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"is_available",
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"KERNEL_NAMES",
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"apply_rotary_emb",
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"Axes",
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"ExplicitSelectionError",
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"ImplRecord",
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"Resolution",
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"Spec",
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"axis",
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"explain",
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"explain_plan",
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"op_backend",
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"register_env_alias",
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"register_family",
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"resolve",
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"resolve_plan",
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"tensor_axes",
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]
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@@ -21,10 +21,15 @@ Usage — mirroring ``torch.nn.attention.sdpa_kernel``:
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...
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Thread-safe via ``contextvars`` — each scheduler thread gets its own
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active backend. Backend resolution follows a strict precedence:
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active backend. Backend resolution is a thin facade over the generic
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operator dispatcher (``astrai.extension.dispatch``): the three backends
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are registered as the "attention" family and the decision table lives in
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``_attention_records``. Resolution follows a strict precedence:
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1. explicit ``attn_backend(...)`` context (wins over everything),
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2. the process-wide ``ASTR_BACKEND`` environment override,
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2. the process-wide ``ASTR_BACKEND`` environment override
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(or an ``ASTR_OPS`` ``attention=`` entry, which wins over the legacy
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variable),
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3. an implicit default picked from the available backends
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(cuda > flash > torch).
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@@ -40,12 +45,9 @@ Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
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(blhd). The backend returns ``[batch, seq_len, n_heads * head_dim]``.
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"""
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import contextvars
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import enum
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import functools
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import logging
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import os
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import threading
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from abc import ABC, abstractmethod
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from contextlib import contextmanager
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from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
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@@ -54,6 +56,22 @@ import torch
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import torch.nn.functional as F
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from torch import Tensor
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from astrai.extension.dispatch import (
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Axes,
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ImplRecord,
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Spec,
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axis,
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env_selection,
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get_override,
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register_env_alias,
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register_family,
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reset_override,
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set_override,
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tensor_axes,
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)
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from astrai.extension.dispatch import (
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resolve as _dispatch_resolve,
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)
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from astrai.extension.loader import is_available
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from astrai.extension.ops.attention import (
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attn_paged_decode,
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@@ -72,15 +90,6 @@ if TYPE_CHECKING:
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logger = logging.getLogger(__name__)
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_default_backend_lock = threading.Lock()
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_env_backend_name: Optional[str] = None
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_env_backend: Optional["AttentionBackend"] = None
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_current_backend: contextvars.ContextVar[Optional["AttentionBackend"]] = (
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contextvars.ContextVar("attn_backend", default=None)
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)
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# Backends are stateless — one canonical instance per class, created lazily
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# and reused everywhere (resolution, fallback, context managers).
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_singletons: Dict[type, "AttentionBackend"] = {}
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@@ -157,28 +166,6 @@ def _resolve_default_backend() -> "AttentionBackend":
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return _priority_backends()[0]
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def _environment_backend() -> Optional["AttentionBackend"]:
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"""Resolve the process-wide ``ASTR_BACKEND`` override, if configured."""
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global _env_backend, _env_backend_name
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name = os.environ.get("ASTR_BACKEND", "").strip().lower()
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if not name:
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return None
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if name != _env_backend_name:
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with _default_backend_lock:
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if name != _env_backend_name:
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try:
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_env_backend = _resolve_backend(name)
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except (ValueError, RuntimeError):
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_env_backend = None
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logger.warning(
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"ASTR_BACKEND=%r is not a registered attention backend; "
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"falling back to default resolution",
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name,
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)
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_env_backend_name = name
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return _env_backend
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def _resolve_backend(
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backend: Optional[Union[str, ATTN_BACKEND, "AttentionBackend", type]] = None,
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) -> "AttentionBackend":
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@@ -204,18 +191,44 @@ def _resolve_backend(
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return _resolve_default_backend()
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_ENV_WARNED: set = set()
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def _environment_backend() -> Optional["AttentionBackend"]:
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"""Resolve the process-wide env override (``ASTR_OPS`` or the legacy
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``ASTR_BACKEND``) to a backend instance, if it names a registered one.
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Invalid names warn once and are ignored, falling back to default
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resolution — the override is soft, never fatal.
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"""
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name = env_selection("attention")
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if name is None:
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return None
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try:
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return _resolve_backend(name)
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except (ValueError, RuntimeError):
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message = (
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f"ASTR_BACKEND/ASTR_OPS value {name!r} is not a registered "
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f"attention backend; falling back to default resolution"
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)
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if message not in _ENV_WARNED:
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_ENV_WARNED.add(message)
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logger.warning(message)
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return None
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def get_backend(
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use_default: bool = True,
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) -> Optional["AttentionBackend"]:
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"""Resolve the active backend: explicit context > env > default.
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An ``attn_backend(...)`` context is the caller's explicit choice and
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always wins. ``ASTR_BACKEND`` is a process-wide override consulted
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only when no context is set. Pass ``use_default=False`` at request
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submission to retain only an environment override or the caller's
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:func:`attn_backend` value.
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always wins. ``ASTR_BACKEND`` (or ``ASTR_OPS``) is a process-wide
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override consulted only when no context is set. Pass
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``use_default=False`` at request submission to retain only an
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environment override or the caller's :func:`attn_backend` value.
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"""
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context_backend = _current_backend.get()
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context_backend = get_override("attention")
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if context_backend is not None:
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return context_backend
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env_backend = _environment_backend()
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@@ -241,11 +254,11 @@ def attn_backend(backend: Union[str, ATTN_BACKEND, "AttentionBackend", type]):
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...
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"""
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instance = _resolve_backend(backend)
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token = _current_backend.set(instance)
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token = set_override("attention", instance)
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try:
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yield instance
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finally:
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_current_backend.reset(token)
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reset_override(token)
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def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
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@@ -260,6 +273,24 @@ def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
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)
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def _axes(
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q: Tensor,
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kv_cache: Optional["KVCache"],
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attn_mask: Optional[Tensor],
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is_causal: bool,
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fwd: Optional[str],
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) -> Axes:
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"""Snapshot the axes the attention decision table depends on."""
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return tensor_axes(
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q,
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fwd=fwd,
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ndim=q.dim(),
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head_dim=q.size(-1) if q.dim() >= 1 else None,
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has_cache=kv_cache is not None,
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has_mask=attn_mask is not None,
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)
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def attention(
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q: Tensor,
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k: Tensor,
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@@ -300,37 +331,13 @@ def attention(
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Returns:
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[batch, q_len, n_heads * head_dim]
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"""
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if backend is not None:
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selected = _resolve_backend(backend)
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explicit = True
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else:
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context_backend = _current_backend.get()
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explicit = context_backend is not None
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# Resolve through the same chain as inference: explicit context >
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# ASTR_BACKEND env > default. Training calls (fwd=None, no cache)
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# land on the CUDA backend and fall back by capability below —
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# flash when it can handle the call, else torch SDPA.
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selected = get_backend()
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assert selected is not None
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if not selected.supports_call(q, kv_cache, attn_mask, is_causal, fwd):
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if explicit:
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raise RuntimeError(
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f"Explicitly-set backend {type(selected).__name__} cannot "
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f"handle this attention call (shape={q.shape}, "
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f"dtype={q.dtype}, kv_cache={'none' if kv_cache is None else 'present'}, "
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f"attn_mask={'none' if attn_mask is None else 'present'}). "
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f"Remove the attn_backend() context or switch to a compatible backend."
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)
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selected = next(
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(
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candidate
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for candidate in _priority_backends()
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if candidate.supports_call(q, kv_cache, attn_mask, is_causal, fwd)
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),
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_instance(TorchNativeBackend),
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)
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return selected.forward(q, k, v, kv_cache, layer_id, attn_mask, is_causal, fwd)
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explicit = _resolve_backend(backend) if backend is not None else None
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resolution = _dispatch_resolve(
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"attention", q, kv_cache, attn_mask, is_causal, fwd, explicit=explicit
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)
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return resolution.record.obj.forward(
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q, k, v, kv_cache, layer_id, attn_mask, is_causal, fwd
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)
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class AttentionBackend(ABC):
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@@ -362,11 +369,11 @@ class AttentionBackend(ABC):
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"""
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def __enter__(self) -> "AttentionBackend":
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self._token = _current_backend.set(self)
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self._token = set_override("attention", self)
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return self
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def __exit__(self, *exc) -> None:
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_current_backend.reset(self._token)
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reset_override(self._token)
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@classmethod
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@abstractmethod
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@@ -817,3 +824,78 @@ class FlashAttnBackend(AttentionBackend):
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causal=True,
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)
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return out
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# Family registration over the generic dispatcher: the "attention" decision
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# table. The Specs mirror each backend's ``supports_call`` exactly (a unit
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# test asserts they never drift). The provider is re-evaluated per
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# resolution, so monkeypatching ``flash_attn_available`` (plus clearing
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# ``_priority_backends``) is honored, as before.
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_CLASS_TO_NAME: Dict[type, str] = {
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CudaBackend: ATTN_BACKEND.CUDA.value,
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FlashAttnBackend: ATTN_BACKEND.FLASH.value,
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TorchNativeBackend: ATTN_BACKEND.TORCH_NATIVE.value,
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}
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_SPEC_CUDA = (
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axis("fwd").in_("prefill", "decode")
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& axis("has_cache").truthy()
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& axis("ndim").eq(3)
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& axis("dtype").in_(torch.bfloat16)
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& axis("head_dim").in_(*CudaBackend.HEAD_DIMS)
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& Spec.of(
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lambda ax: is_available(f"attn_paged_{ax.get('fwd')}"), "paged kernels loaded"
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)
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)
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_SPEC_FLASH = (
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axis("dtype").in_(torch.float16, torch.bfloat16)
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& Spec.of(lambda ax: flash_attn_available(), "flash-attn available")
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& (
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(
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axis("fwd").not_none()
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& axis("ndim").eq(3)
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& Spec.of(
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lambda ax: (
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_flash_attn is not None
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and hasattr(_flash_attn, "flash_attn_varlen_func")
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),
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"varlen api present",
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)
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)
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| (axis("fwd").none() & axis("has_mask").falsy())
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)
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)
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def _attention_records() -> list:
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specs = {
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CudaBackend: _SPEC_CUDA,
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FlashAttnBackend: _SPEC_FLASH,
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TorchNativeBackend: Spec.always(),
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}
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return [
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ImplRecord(
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family="attention",
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name=_CLASS_TO_NAME[type(backend)],
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obj=backend,
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spec=specs[type(backend)],
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priority=position,
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)
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for position, backend in enumerate(_priority_backends())
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]
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def _reference_record() -> ImplRecord:
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return ImplRecord(
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family="attention",
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name=ATTN_BACKEND.TORCH_NATIVE.value,
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obj=_instance(TorchNativeBackend),
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spec=Spec.always(),
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priority=999,
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)
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register_family("attention", _axes, _attention_records, _reference_record)
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register_env_alias("attention", "ASTR_BACKEND")
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@@ -1,7 +1,9 @@
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"""Rotary embedding with auto-dispatch to CUDA kernel.
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"""Rotary embedding dispatch (family "rotary").
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Single entry point ``apply_rotary_emb(x, freqs_cis)`` — uses the fused
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CUDA kernel when available, falls back to torch complex multiply otherwise.
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Registered rows: the fused CUDA kernel (bf16 CUDA, inference-only) and the
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torch complex-multiply fallback (autograd-safe). Selection runs through
|
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the generic dispatcher, so ``op_backend(rotary=...)`` and
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``ASTR_OPS=rotary=torch`` work exactly like for attention.
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Layout: x is [batch, seq_len, n_heads, head_dim] (bf16).
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freqs_cis is [batch, seq_len, dim/2, 2] (f32) — [cos, sin] pairs.
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@@ -10,16 +12,22 @@ freqs_cis is [batch, seq_len, dim/2, 2] (f32) — [cos, sin] pairs.
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import torch
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from torch import Tensor
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|
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from astrai.extension.dispatch import (
|
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ImplRecord,
|
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Spec,
|
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axis,
|
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register_family,
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resolve,
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tensor_axes,
|
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)
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from astrai.extension.loader import is_available
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from astrai.extension.ops.rotary import rotary_emb as _cuda_rotary
|
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_cache = {"available": None}
|
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|
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|
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def _cuda_available() -> bool:
|
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if _cache["available"] is None:
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_cache["available"] = is_available("rotary_emb")
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return _cache["available"]
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_SPEC_CUDA = (
|
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axis("device_cuda").truthy()
|
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& axis("dtype").in_(torch.bfloat16)
|
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& axis("grad_enabled").eq(False)
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)
|
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|
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|
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def _torch_apply(x: Tensor, freqs_cis: Tensor) -> Tensor:
|
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@@ -33,6 +41,34 @@ def _torch_apply(x: Tensor, freqs_cis: Tensor) -> Tensor:
|
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return x_out.to(dtype)
|
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|
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|
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def _rotary_records() -> list:
|
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return [
|
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ImplRecord(
|
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family="rotary",
|
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name="cuda",
|
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obj=_cuda_rotary,
|
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spec=_SPEC_CUDA,
|
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available=lambda: is_available("rotary_emb"),
|
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priority=0,
|
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),
|
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ImplRecord(
|
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family="rotary",
|
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name="torch",
|
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obj=_torch_apply,
|
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spec=Spec.always(),
|
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priority=99,
|
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),
|
||||
]
|
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|
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|
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register_family(
|
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"rotary",
|
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lambda x, freqs_cis: tensor_axes(x),
|
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_rotary_records,
|
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lambda: _rotary_records()[-1],
|
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)
|
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|
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|
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def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
|
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"""Apply rotary embedding to x.
|
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|
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@@ -43,11 +79,4 @@ def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
|
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Returns:
|
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[batch, seq_len, n_heads, head_dim] (bf16)
|
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"""
|
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if (
|
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_cuda_available()
|
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and not torch.is_grad_enabled()
|
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and x.is_cuda
|
||||
and x.dtype == torch.bfloat16
|
||||
):
|
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return _cuda_rotary(x, freqs_cis)
|
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return _torch_apply(x, freqs_cis)
|
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return resolve("rotary", x, freqs_cis).record.obj(x, freqs_cis)
|
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|
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@@ -0,0 +1,400 @@
|
||||
"""Operator dispatch: one selection mechanism for all op families.
|
||||
|
||||
A family registers three things with the core: an ``axes`` extractor whose
|
||||
signature mirrors the op call and snapshots whatever decision axes *that
|
||||
family* needs, an ordered list of ``ImplRecord`` rows (name, impl object,
|
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capability ``Spec``, machine-level ``available``), and a fallback record.
|
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The core defines no axes itself — each ``Spec`` predicates over the axes
|
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dict produced by the family's own extractor. Resolution: explicit/context
|
||||
selection (strict — raises when incapable) > ``ASTR_OPS`` env entry (soft —
|
||||
falls through) > first capable row > family fallback. The rows are the
|
||||
family's decision table, printable via ``explain``.
|
||||
|
||||
Records flagged ``faithful=False`` change numerics (e.g. fp8) and are only
|
||||
reachable through an explicit selection, never the implicit chain.
|
||||
"""
|
||||
|
||||
import contextvars
|
||||
import logging
|
||||
import os
|
||||
import threading
|
||||
from contextlib import contextmanager
|
||||
from dataclasses import dataclass
|
||||
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple
|
||||
|
||||
import torch
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
Axes = Mapping[str, Any]
|
||||
Call = Tuple[Tuple, Dict[str, Any]]
|
||||
|
||||
|
||||
def _fmt(value: Any) -> str:
|
||||
return str(value)
|
||||
|
||||
|
||||
class Spec:
|
||||
"""Composable, self-describing predicate over a family's axes dict."""
|
||||
|
||||
__slots__ = ("_fn", "_desc")
|
||||
|
||||
def __init__(self, fn: Callable[[Axes], bool], desc: str):
|
||||
self._fn = fn
|
||||
self._desc = desc
|
||||
|
||||
def matches(self, ax: Axes) -> bool:
|
||||
return bool(self._fn(ax))
|
||||
|
||||
@property
|
||||
def description(self) -> str:
|
||||
return self._desc
|
||||
|
||||
def __and__(self, other: "Spec") -> "Spec":
|
||||
return Spec(
|
||||
lambda ax: self._fn(ax) and other._fn(ax),
|
||||
f"({self._desc} and {other._desc})",
|
||||
)
|
||||
|
||||
def __or__(self, other: "Spec") -> "Spec":
|
||||
return Spec(
|
||||
lambda ax: self._fn(ax) or other._fn(ax),
|
||||
f"({self._desc} or {other._desc})",
|
||||
)
|
||||
|
||||
def __invert__(self) -> "Spec":
|
||||
return Spec(lambda ax: not self._fn(ax), f"not({self._desc})")
|
||||
|
||||
@classmethod
|
||||
def always(cls) -> "Spec":
|
||||
return cls(lambda ax: True, "always")
|
||||
|
||||
@classmethod
|
||||
def of(cls, fn: Callable[[Axes], bool], desc: str) -> "Spec":
|
||||
return cls(fn, desc)
|
||||
|
||||
|
||||
class Axis:
|
||||
"""Named-axis predicate builder: ``axis("dtype").in_(torch.bfloat16)``.
|
||||
|
||||
Axis names belong to each family; the core never defines or inspects
|
||||
them beyond the predicate the builder closes over.
|
||||
"""
|
||||
|
||||
__slots__ = ("_name",)
|
||||
|
||||
def __init__(self, name: str):
|
||||
self._name = name
|
||||
|
||||
def in_(self, *values: Any) -> Spec:
|
||||
rendered = ", ".join(_fmt(v) for v in values)
|
||||
return Spec(
|
||||
lambda ax: ax.get(self._name) in values,
|
||||
f"{self._name} in {{{rendered}}}",
|
||||
)
|
||||
|
||||
def eq(self, value: Any) -> Spec:
|
||||
return Spec(
|
||||
lambda ax: ax.get(self._name) == value, f"{self._name}=={_fmt(value)}"
|
||||
)
|
||||
|
||||
def is_(self, value: Any) -> Spec:
|
||||
return Spec(
|
||||
lambda ax: ax.get(self._name) is value, f"{self._name} is {_fmt(value)}"
|
||||
)
|
||||
|
||||
def none(self) -> Spec:
|
||||
return Spec(lambda ax: ax.get(self._name) is None, f"{self._name} is None")
|
||||
|
||||
def not_none(self) -> Spec:
|
||||
return Spec(
|
||||
lambda ax: ax.get(self._name) is not None, f"{self._name} is not None"
|
||||
)
|
||||
|
||||
def truthy(self) -> Spec:
|
||||
return Spec(lambda ax: bool(ax.get(self._name)), self._name)
|
||||
|
||||
def falsy(self) -> Spec:
|
||||
return Spec(lambda ax: not ax.get(self._name), f"!{self._name}")
|
||||
|
||||
|
||||
def axis(name: str) -> Axis:
|
||||
"""Entry point for named-axis predicates; see ``Axis``."""
|
||||
return Axis(name)
|
||||
|
||||
|
||||
def tensor_axes(x: torch.Tensor, **extra: Any) -> Dict[str, Any]:
|
||||
"""Tensor-derived axes shared by most families; opt-in, extendable."""
|
||||
return {
|
||||
"dtype": x.dtype,
|
||||
"device_cuda": x.is_cuda,
|
||||
"grad_enabled": torch.is_grad_enabled(),
|
||||
**extra,
|
||||
}
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class ImplRecord:
|
||||
"""One decision-table row: an implementation plus its capability."""
|
||||
|
||||
family: str
|
||||
name: str
|
||||
obj: Any
|
||||
spec: Spec
|
||||
available: Callable[[], bool] = lambda: True
|
||||
priority: int = 100
|
||||
faithful: bool = True
|
||||
|
||||
|
||||
@dataclass
|
||||
class OpFamily:
|
||||
name: str
|
||||
axes: Callable[..., Axes]
|
||||
provider: Callable[[], List[ImplRecord]]
|
||||
fallback: Callable[[], ImplRecord]
|
||||
|
||||
|
||||
_FAMILIES: Dict[str, OpFamily] = {}
|
||||
_ENV_ALIASES: Dict[str, str] = {}
|
||||
|
||||
_current_overrides: contextvars.ContextVar[Dict[str, Any]] = contextvars.ContextVar(
|
||||
"astrai_op_overrides", default={}
|
||||
)
|
||||
|
||||
_env_lock = threading.Lock()
|
||||
_env_cache: Dict[tuple, Optional[Dict[str, str]]] = {}
|
||||
_warned: set = set()
|
||||
|
||||
|
||||
def register_family(
|
||||
name: str,
|
||||
axes: Callable[..., Axes],
|
||||
provider: Callable[[], List[ImplRecord]],
|
||||
fallback: Callable[[], ImplRecord],
|
||||
) -> None:
|
||||
"""Register (or replace) a family; ``provider`` is re-evaluated per
|
||||
resolution so availability changes (tests, late imports) are honored.
|
||||
``axes`` mirrors the op call signature and snapshots that family's
|
||||
decision axes; unregistered handles are probed through the same args.
|
||||
"""
|
||||
_FAMILIES[name] = OpFamily(name, axes, provider, fallback)
|
||||
|
||||
|
||||
def register_env_alias(family: str, varname: str) -> None:
|
||||
"""Legacy single-value env var for a family (e.g. attention →
|
||||
ASTR_BACKEND); an ASTR_OPS entry wins when both are set."""
|
||||
_ENV_ALIASES[family] = varname
|
||||
|
||||
|
||||
def _family(name: str) -> OpFamily:
|
||||
fam = _FAMILIES.get(name)
|
||||
if fam is None:
|
||||
raise KeyError(f"no operator family registered under {name!r}")
|
||||
return fam
|
||||
|
||||
|
||||
def _warn_once(message: str) -> None:
|
||||
if message not in _warned:
|
||||
_warned.add(message)
|
||||
logger.warning(message)
|
||||
|
||||
|
||||
def set_override(family: str, handle: Any) -> contextvars.Token:
|
||||
overrides = dict(_current_overrides.get())
|
||||
overrides[family] = handle
|
||||
return _current_overrides.set(overrides)
|
||||
|
||||
|
||||
def reset_override(token: contextvars.Token) -> None:
|
||||
_current_overrides.reset(token)
|
||||
|
||||
|
||||
def get_override(family: str) -> Optional[Any]:
|
||||
return _current_overrides.get().get(family)
|
||||
|
||||
|
||||
@contextmanager
|
||||
def op_backend(**handles: Any):
|
||||
"""Select implementations per family for the enclosed scope::
|
||||
|
||||
with op_backend(attention="torch_native", rotary="torch"):
|
||||
engine.generate(...)
|
||||
|
||||
String handles are validated eagerly against the family's currently
|
||||
available implementations; object handles pass through unchecked.
|
||||
"""
|
||||
for family, handle in handles.items():
|
||||
if isinstance(handle, str):
|
||||
fam = _FAMILIES.get(family)
|
||||
if fam is None:
|
||||
raise ValueError(f"unknown operator family {family!r}")
|
||||
if _record_for_handle(fam, handle) is None:
|
||||
raise ValueError(f"Unknown {family} implementation: {handle!r}")
|
||||
tokens = [set_override(f, h) for f, h in handles.items()]
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
for token in reversed(tokens):
|
||||
reset_override(token)
|
||||
|
||||
|
||||
def env_overrides() -> Dict[str, str]:
|
||||
"""Merged ASTR_OPS + legacy-alias selections (family or "profile").
|
||||
|
||||
Cached per distinct env content; unknown families / malformed entries
|
||||
warn once and are dropped (soft override, never fatal).
|
||||
"""
|
||||
with _env_lock:
|
||||
merged: Dict[str, str] = {}
|
||||
raw = os.environ.get("ASTR_OPS", "").strip()
|
||||
if raw:
|
||||
key = ("ASTR_OPS", raw)
|
||||
if key not in _env_cache:
|
||||
parsed: Dict[str, str] = {}
|
||||
for item in raw.split(","):
|
||||
key_part, sep, value = item.strip().partition("=")
|
||||
key_part, value = key_part.strip(), value.strip()
|
||||
if not sep or not key_part or not value:
|
||||
_warn_once(f"ASTR_OPS: ignoring malformed entry {item!r}")
|
||||
continue
|
||||
parsed[key_part] = value
|
||||
_env_cache[key] = parsed or None
|
||||
merged.update(_env_cache[key] or {})
|
||||
for fam, varname in _ENV_ALIASES.items():
|
||||
raw = os.environ.get(varname, "").strip()
|
||||
if raw:
|
||||
key = (varname, raw)
|
||||
if key not in _env_cache:
|
||||
_env_cache[key] = {fam: raw.lower()}
|
||||
merged.setdefault(fam, _env_cache[key][fam])
|
||||
for fam in [f for f in merged if f not in _FAMILIES and f != "profile"]:
|
||||
_warn_once(f"ASTR_OPS: unknown operator family {fam!r}; dropping it")
|
||||
merged.pop(fam)
|
||||
return merged
|
||||
|
||||
|
||||
def env_selection(family: str) -> Optional[str]:
|
||||
return env_overrides().get(family)
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class Resolution:
|
||||
record: ImplRecord
|
||||
origin: str
|
||||
|
||||
|
||||
class ExplicitSelectionError(RuntimeError):
|
||||
"""An explicitly selected implementation cannot handle the call."""
|
||||
|
||||
|
||||
def _record_for_handle(fam: OpFamily, handle: Any) -> Optional[ImplRecord]:
|
||||
records = sorted(fam.provider(), key=lambda r: r.priority)
|
||||
if isinstance(handle, str):
|
||||
return next((r for r in records if r.name == handle), None)
|
||||
return next((r for r in records if r.obj is handle), None)
|
||||
|
||||
|
||||
def _adhoc_record(family: str, handle: Any, args: Tuple, kwargs: Dict) -> ImplRecord:
|
||||
"""Wrap an unregistered object; capability probes its own method on
|
||||
the original call arguments."""
|
||||
supports = getattr(handle, "supports_call", None)
|
||||
if supports is not None:
|
||||
spec = Spec.of(
|
||||
lambda ax: bool(supports(*args, **kwargs)),
|
||||
f"{type(handle).__name__}.supports_call",
|
||||
)
|
||||
else:
|
||||
spec = Spec.always()
|
||||
return ImplRecord(family, type(handle).__name__, handle, spec)
|
||||
|
||||
|
||||
def resolve(
|
||||
family: str, *args: Any, explicit: Optional[Any] = None, **kwargs: Any
|
||||
) -> Resolution:
|
||||
"""Resolve one family for one call (explicit-strict / implicit-loose).
|
||||
|
||||
``args``/``kwargs`` mirror the op call: the family's ``axes`` extractor
|
||||
snapshots the decision axes from them, and unregistered handles are
|
||||
probed through their own ``supports_call`` with the same arguments.
|
||||
"""
|
||||
fam = _family(family)
|
||||
ax = fam.axes(*args, **kwargs)
|
||||
|
||||
handle: Optional[Any] = None
|
||||
origin = "chain"
|
||||
if explicit is not None:
|
||||
handle, origin = explicit, "explicit"
|
||||
elif get_override(family) is not None:
|
||||
handle, origin = get_override(family), "context"
|
||||
else:
|
||||
env_name = env_selection(family)
|
||||
if env_name is not None:
|
||||
handle, origin = env_name, "env"
|
||||
|
||||
if handle is not None:
|
||||
record = _record_for_handle(fam, handle)
|
||||
if record is None and not isinstance(handle, str):
|
||||
record = _adhoc_record(family, handle, args, kwargs)
|
||||
if record is None:
|
||||
if origin in ("explicit", "context"):
|
||||
raise ValueError(f"Unknown {family} implementation: {handle!r}")
|
||||
_warn_once(f"ASTR_OPS: {family}={handle!r} is not registered; ignoring")
|
||||
else:
|
||||
if record.available() and record.spec.matches(ax):
|
||||
return Resolution(record, origin)
|
||||
if origin in ("explicit", "context"):
|
||||
raise ExplicitSelectionError(
|
||||
f"Explicitly-set backend {type(record.obj).__name__} cannot "
|
||||
f"handle this {family} call; required: {record.spec.description}"
|
||||
)
|
||||
|
||||
if handle is None and env_overrides().get("profile") == "reference":
|
||||
return Resolution(fam.fallback(), "profile")
|
||||
|
||||
for record in sorted(fam.provider(), key=lambda r: r.priority):
|
||||
if record.available() and record.faithful and record.spec.matches(ax):
|
||||
return Resolution(record, "chain")
|
||||
return Resolution(fam.fallback(), "fallback")
|
||||
|
||||
|
||||
def resolve_plan(calls: Mapping[str, Call]) -> Dict[str, Resolution]:
|
||||
"""Resolve several families at once (one decision snapshot)."""
|
||||
return {
|
||||
family: resolve(family, *args, **kwargs)
|
||||
for family, (args, kwargs) in calls.items()
|
||||
}
|
||||
|
||||
|
||||
def _describe_axes(ax: Axes) -> str:
|
||||
return " ".join(f"{key}={ax[key]}" for key in sorted(ax))
|
||||
|
||||
|
||||
def explain(
|
||||
family: str, *args: Any, explicit: Optional[Any] = None, **kwargs: Any
|
||||
) -> str:
|
||||
"""Human-readable decision trace for one family call."""
|
||||
fam = _family(family)
|
||||
ax = fam.axes(*args, **kwargs)
|
||||
records = sorted(fam.provider(), key=lambda r: r.priority)
|
||||
lines = [f"[{family}] {_describe_axes(ax)}"]
|
||||
for record in records:
|
||||
if not record.available():
|
||||
lines.append(f" {record.name}: SKIP unavailable")
|
||||
elif not record.faithful:
|
||||
lines.append(f" {record.name}: SKIP not faithful (explicit-only)")
|
||||
elif record.spec.matches(ax):
|
||||
lines.append(f" {record.name}: MATCH ({record.spec.description})")
|
||||
else:
|
||||
lines.append(f" {record.name}: reject ({record.spec.description})")
|
||||
try:
|
||||
resolution = resolve(family, *args, explicit=explicit, **kwargs)
|
||||
lines.append(f" => {resolution.record.name} (origin={resolution.origin})")
|
||||
except (ExplicitSelectionError, ValueError) as exc:
|
||||
lines.append(f" => ERROR: {exc}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def explain_plan(calls: Mapping[str, Call]) -> str:
|
||||
return "\n".join(
|
||||
explain(family, *args, **kwargs) for family, (args, kwargs) in calls.items()
|
||||
)
|
||||
@@ -0,0 +1,283 @@
|
||||
"""Tests for the generic operator dispatcher (Spec / decision tables)."""
|
||||
|
||||
import importlib
|
||||
from types import SimpleNamespace
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
import astrai.extension.dispatch as dispatch
|
||||
from astrai.extension import (
|
||||
ATTN_BACKEND,
|
||||
ExplicitSelectionError,
|
||||
ImplRecord,
|
||||
Spec,
|
||||
axis,
|
||||
explain,
|
||||
op_backend,
|
||||
resolve,
|
||||
resolve_plan,
|
||||
)
|
||||
from astrai.extension.backend import apply_rotary_emb
|
||||
|
||||
attn_mod = importlib.import_module("astrai.extension.backend.attention")
|
||||
rotary_mod = importlib.import_module("astrai.extension.backend.rotary")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def toy_family():
|
||||
"""A toy family: alpha (restricted), beta, and an unfaithful fast row."""
|
||||
calls = []
|
||||
|
||||
def records():
|
||||
return [
|
||||
ImplRecord(
|
||||
"toy",
|
||||
"alpha",
|
||||
"alpha-obj",
|
||||
axis("dtype").in_(torch.bfloat16),
|
||||
priority=0,
|
||||
),
|
||||
ImplRecord(
|
||||
"toy",
|
||||
"beta",
|
||||
"beta-obj",
|
||||
Spec.always(),
|
||||
priority=10,
|
||||
),
|
||||
ImplRecord(
|
||||
"toy",
|
||||
"fp8",
|
||||
"fp8-obj",
|
||||
Spec.always(),
|
||||
priority=1,
|
||||
faithful=False,
|
||||
),
|
||||
]
|
||||
|
||||
dispatch.register_family(
|
||||
"toy",
|
||||
lambda **kw: kw,
|
||||
records,
|
||||
lambda: ImplRecord("toy", "beta", "beta-obj", Spec.always()),
|
||||
)
|
||||
yield calls
|
||||
dispatch._FAMILIES.pop("toy", None)
|
||||
|
||||
|
||||
def test_spec_composition_and_description():
|
||||
spec = axis("dtype").in_(torch.bfloat16) & axis("grad_enabled").eq(False)
|
||||
assert spec.matches({"dtype": torch.bfloat16, "grad_enabled": False})
|
||||
assert not spec.matches({"dtype": torch.bfloat16, "grad_enabled": True})
|
||||
assert "dtype" in spec.description and "grad_enabled" in spec.description
|
||||
|
||||
either = axis("fwd").none() | axis("has_cache").truthy()
|
||||
assert either.matches({"fwd": None})
|
||||
assert either.matches({"fwd": "decode", "has_cache": True})
|
||||
assert not either.matches({"fwd": "decode"})
|
||||
|
||||
assert (~axis("fwd").none()).matches({"fwd": "decode"})
|
||||
|
||||
|
||||
def test_chain_returns_first_capable(toy_family):
|
||||
assert resolve("toy", dtype=torch.bfloat16).record.obj == "alpha-obj"
|
||||
assert resolve("toy", dtype=torch.float32).record.obj == "beta-obj"
|
||||
|
||||
|
||||
def test_unfaithful_rows_are_chain_invisible(toy_family):
|
||||
resolution = resolve("toy", dtype=torch.float32)
|
||||
assert resolution.record.obj == "beta-obj"
|
||||
with op_backend(toy="fp8"):
|
||||
assert resolve("toy", dtype=torch.float32).record.obj == "fp8-obj"
|
||||
|
||||
|
||||
def test_explicit_selection_is_strict(toy_family):
|
||||
with pytest.raises(ExplicitSelectionError):
|
||||
resolve("toy", dtype=torch.float32, explicit="alpha")
|
||||
assert resolve("toy", dtype=torch.bfloat16, explicit="alpha").origin == "explicit"
|
||||
|
||||
|
||||
def test_context_selection_is_strict(toy_family):
|
||||
with op_backend(toy="alpha"):
|
||||
with pytest.raises(ExplicitSelectionError):
|
||||
resolve("toy", dtype=torch.float32)
|
||||
assert resolve("toy", dtype=torch.bfloat16).origin == "context"
|
||||
|
||||
|
||||
def test_unknown_explicit_name_raises(toy_family):
|
||||
with pytest.raises(ValueError, match="Unknown toy implementation"):
|
||||
resolve("toy", explicit="nope")
|
||||
with pytest.raises(ValueError, match="Unknown toy implementation"):
|
||||
with op_backend(toy="nope"):
|
||||
pass
|
||||
|
||||
|
||||
class _Probe:
|
||||
def __init__(self, capable):
|
||||
self.capable = capable
|
||||
self.probed = 0
|
||||
|
||||
def supports_call(self, *args, **kwargs):
|
||||
self.probed += 1
|
||||
return self.capable
|
||||
|
||||
|
||||
def test_adhoc_instance_probed_via_supports_call(toy_family):
|
||||
probe = _Probe(capable=True)
|
||||
resolution = resolve("toy", dtype=torch.float32, explicit=probe)
|
||||
assert resolution.record.obj is probe and probe.probed == 1
|
||||
|
||||
incapable = _Probe(capable=False)
|
||||
with pytest.raises(ExplicitSelectionError):
|
||||
resolve("toy", dtype=torch.float32, explicit=incapable)
|
||||
|
||||
|
||||
def test_nested_op_backend_scopes(toy_family):
|
||||
with op_backend(toy="alpha"):
|
||||
with op_backend(toy="beta"):
|
||||
assert resolve("toy", dtype=torch.float32).origin == "context"
|
||||
assert resolve("toy", dtype=torch.bfloat16).origin == "context"
|
||||
|
||||
|
||||
def test_env_entry_is_soft(toy_family, monkeypatch):
|
||||
monkeypatch.setenv("ASTR_OPS", "toy=alpha")
|
||||
resolution = resolve("toy", dtype=torch.float32)
|
||||
assert resolution.record.obj == "beta-obj" and resolution.origin == "chain"
|
||||
assert resolve("toy", dtype=torch.bfloat16).origin == "env"
|
||||
|
||||
|
||||
def test_env_unknown_impl_ignored(toy_family, monkeypatch):
|
||||
monkeypatch.setenv("ASTR_OPS", "toy=missing")
|
||||
assert resolve("toy", dtype=torch.float32).record.obj == "beta-obj"
|
||||
|
||||
|
||||
def test_env_profile_reference(toy_family, monkeypatch):
|
||||
monkeypatch.setenv("ASTR_OPS", "profile=reference")
|
||||
resolution = resolve("toy", dtype=torch.bfloat16)
|
||||
assert resolution.origin == "profile"
|
||||
monkeypatch.setenv("ASTR_OPS", "toy=alpha,profile=reference")
|
||||
assert resolve("toy", dtype=torch.bfloat16).origin == "env"
|
||||
|
||||
|
||||
def test_legacy_env_alias(monkeypatch):
|
||||
monkeypatch.setenv("ASTR_BACKEND", "torch_native")
|
||||
assert dispatch.env_selection("attention") == "torch_native"
|
||||
monkeypatch.setenv("ASTR_OPS", "attention=cuda")
|
||||
assert dispatch.env_selection("attention") == "cuda"
|
||||
|
||||
|
||||
def test_context_beats_env(toy_family, monkeypatch):
|
||||
monkeypatch.setenv("ASTR_OPS", "toy=alpha")
|
||||
with op_backend(toy="beta"):
|
||||
assert resolve("toy", dtype=torch.float32).origin == "context"
|
||||
|
||||
|
||||
def test_resolve_plan_snapshots_families(toy_family):
|
||||
plan = resolve_plan(
|
||||
{
|
||||
"toy": ((), {"dtype": torch.bfloat16}),
|
||||
"rotary": (
|
||||
(
|
||||
SimpleNamespace(dtype=torch.bfloat16, is_cuda=True),
|
||||
SimpleNamespace(),
|
||||
),
|
||||
{},
|
||||
),
|
||||
}
|
||||
)
|
||||
assert plan["toy"].record.obj == "alpha-obj"
|
||||
assert plan["rotary"].record.name in ("cuda", "torch")
|
||||
|
||||
|
||||
def test_explain_shows_rejection_reasons(toy_family):
|
||||
text = explain("toy", dtype=torch.float32)
|
||||
assert "alpha: reject" in text and "beta: MATCH" in text
|
||||
assert "=> beta" in text
|
||||
|
||||
|
||||
def test_explain_reports_strict_error(toy_family):
|
||||
text = explain("toy", dtype=torch.float32, explicit="alpha")
|
||||
assert "ERROR" in text
|
||||
|
||||
|
||||
_DUMMY_CACHE = object()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float32])
|
||||
@pytest.mark.parametrize("head_dim", [64, 96])
|
||||
@pytest.mark.parametrize(
|
||||
"fwd,has_cache,ndim,has_mask",
|
||||
[
|
||||
("decode", True, 3, False),
|
||||
("prefill", True, 3, False),
|
||||
(None, False, 4, False),
|
||||
(None, False, 4, True),
|
||||
],
|
||||
)
|
||||
def test_attention_specs_mirror_supports_call(
|
||||
dtype, head_dim, fwd, has_cache, ndim, has_mask
|
||||
):
|
||||
shape = {3: (1, 2, head_dim), 4: (1, 2, 4, head_dim)}[ndim]
|
||||
q = torch.zeros(shape, dtype=dtype)
|
||||
mask = torch.zeros(1, 1, 2, 2, dtype=torch.bool) if has_mask else None
|
||||
cache = _DUMMY_CACHE if has_cache else None
|
||||
ax = attn_mod._axes(q, cache, mask, False, fwd)
|
||||
|
||||
cuda = attn_mod._instance(attn_mod.CudaBackend)
|
||||
assert attn_mod._SPEC_CUDA.matches(ax) == cuda.supports_call(
|
||||
q, cache, mask, False, fwd
|
||||
)
|
||||
|
||||
flash = attn_mod._instance(attn_mod.FlashAttnBackend)
|
||||
assert attn_mod._SPEC_FLASH.matches(ax) == flash.supports_call(
|
||||
q, cache, mask, False, fwd
|
||||
)
|
||||
|
||||
|
||||
def test_attention_resolution_matches_legacy_semantics():
|
||||
q = torch.zeros(1, 2, 4, 8, dtype=torch.float32)
|
||||
resolution = resolve("attention", q, None, None, True, "prefill")
|
||||
assert resolution.record.name == ATTN_BACKEND.TORCH_NATIVE.value
|
||||
|
||||
|
||||
@pytest.mark.skipif(
|
||||
not torch.cuda.is_available(), reason="rotary CUDA path needs a GPU"
|
||||
)
|
||||
class TestRotaryDispatch:
|
||||
def _input(self):
|
||||
torch.manual_seed(0)
|
||||
x = torch.randn(1, 5, 3, 16, device="cuda", dtype=torch.bfloat16)
|
||||
freqs = torch.randn(1, 5, 8, 2, device="cuda", dtype=torch.float32)
|
||||
return x, freqs
|
||||
|
||||
def test_cuda_row_selected_under_inference_mode(self):
|
||||
from astrai.extension.loader import is_available
|
||||
|
||||
x, freqs = self._input()
|
||||
with torch.inference_mode():
|
||||
resolution = resolve("rotary", x, freqs)
|
||||
expected = "cuda" if is_available("rotary_emb") else "torch"
|
||||
assert resolution.record.name == expected
|
||||
|
||||
def test_grad_falls_back_to_torch(self):
|
||||
x, freqs = self._input()
|
||||
assert resolve("rotary", x, freqs).record.name == "torch"
|
||||
|
||||
def test_context_switch_to_torch(self):
|
||||
x, freqs = self._input()
|
||||
with torch.inference_mode():
|
||||
with op_backend(rotary="torch"):
|
||||
out = apply_rotary_emb(x, freqs)
|
||||
assert out.shape == x.shape and out.dtype == torch.bfloat16
|
||||
|
||||
def test_cuda_matches_torch_numerics(self):
|
||||
from astrai.extension.loader import is_available
|
||||
|
||||
if not is_available("rotary_emb"):
|
||||
pytest.skip("rotary kernel not built")
|
||||
x, freqs = self._input()
|
||||
with torch.inference_mode():
|
||||
fast = apply_rotary_emb(x, freqs)
|
||||
slow = rotary_mod._torch_apply
|
||||
ref = slow(x, freqs)
|
||||
assert torch.allclose(fast.float(), ref.float(), atol=2e-2, rtol=1e-2)
|
||||
Reference in New Issue
Block a user