ViperEkura
6709534d64
- delete csrc/kernels/gemm.cu and swiglu.cu and drop their CMake and setup.py registration - remove the ops wrappers plus backend/linear.py and backend/swiglu.py so Linear and MLP call F.linear directly - drop the four gemm and swiglu kernel test files and prune the stale cuda_kernels.md sections - add csrc/bench benchmarks for the remaining kernels: attention decode prefill paged decode paged prefill versus single-launch SDPA references, rotary versus the torch fallback, fp8 quantize and mm_fp8 versus torch baselines - attention, rotary_emb, and fp8_ops kernels are unchanged
93 lines
2.3 KiB
Python
93 lines
2.3 KiB
Python
"""CUDA kernel wrappers, operator dispatch, and backend selection.
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Public API:
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- ``attention``, ``apply_rotary_emb`` — op families with safe torch
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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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- ``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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Linear projections and dense-MLP SwiGLU run plain torch (``F.linear`` /
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``Linear`` / ``MLP``); the former bf16_gemm / bf16_swiglu kernels and their
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backends were removed.
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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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)
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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_paged_prefill,
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attn_prefill,
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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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"attn_decode",
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"attn_paged_decode",
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"attn_prefill",
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"attn_paged_prefill",
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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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