ViperEkura
7540acb43e
- replace the per-shape auto tables in the linear backend with an M-banded rule (M in [2,4] on compute capability 8.0+) that measured at the HBM bandwidth floor across every family, and fold the capability check into the capable guard - drop the unreachable swiglu auto shape-table machinery so both backends share one env-mode ladder via the new dispatch.env_mode helper - add __all__ across extension modules, name the rotary registration records, and unify typing to the typing-module style - rewrite test_linear_dispatch.py around behavioral routing assertions and document the M-banded policy in the developer docs - Benchmark: L20 SM89, Python dispatch overhead 2.9us to 1.5us, auto now covers every projection shape at M in [2,4].
97 lines
2.3 KiB
Python
97 lines
2.3 KiB
Python
"""CUDA kernel wrappers, operator dispatch, and backend selection.
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Public API:
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- ``attention``, ``linear``, ``swiglu``, ``apply_rotary_emb`` — op
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families with safe torch fallbacks (see ``astrai.extension.backend``)
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- ``attn_decode`` / ``attn_prefill`` / ``attn_paged_decode`` /
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``attn_paged_prefill`` — direct attention kernel wrappers
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- ``bf16_gemv`` / ``bf16_swiglu`` — directly callable linear/MLP kernels
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- ``AttentionBackend`` / ``TorchNativeBackend`` / ``CudaBackend`` /
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``FlashAttnBackend`` — attention backend strategies
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- ``resolve`` / ``explain`` / ``op_backend`` / ``env_mode`` — the shared
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operator dispatcher (see ``astrai.extension.dispatch``)
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Layout convention: all q/k/v are ``[batch, seq_len, n_heads, head_dim]``
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(blhd). Scale is always ``1/sqrt(head_dim)``. Wrapper functions call their
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compiled CUDA kernels directly; fallback is the backend's responsibility.
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"""
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from astrai.extension.backend import (
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ATTN_BACKEND,
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AttentionBackend,
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AttentionBackendFactory,
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CudaBackend,
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FlashAttnBackend,
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TorchNativeBackend,
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apply_rotary_emb,
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attention,
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attn_backend,
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get_backend,
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linear,
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swiglu,
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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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env_mode,
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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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attn_decode,
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attn_paged_decode,
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attn_prefill,
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bf16_gemv,
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bf16_swiglu,
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)
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__all__ = [
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"ATTN_BACKEND",
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"AttentionBackend",
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"AttentionBackendFactory",
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"CudaBackend",
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"TorchNativeBackend",
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"FlashAttnBackend",
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"TensorLayout",
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"attention",
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"attn_backend",
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"get_backend",
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"linear",
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"swiglu",
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"attn_decode",
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"attn_paged_decode",
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"attn_prefill",
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"bf16_gemv",
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"bf16_swiglu",
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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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"env_mode",
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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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