0z5a
a144d7f306
- add decode-shape benchmark harness - add bf16 GEMV CUDA primitive with head-dim generic kernel - dispatch decode-time linear layers to gemv for M=1 - extend gemv coverage to small decode batches
90 lines
2.1 KiB
Python
90 lines
2.1 KiB
Python
"""CUDA attention kernel wrappers with torch fallback.
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Public API:
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- ``attn_decode`` — single-query decode attention
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- ``attn_prefill`` — multi-query prefill attention
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- ``attn_paged_decode`` — paged decode attention (direct page-table access)
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- ``AttentionBackend`` — ABC for attention computation strategies
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- ``TorchNativeBackend`` — default SDPA backend with KV cache I/O
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- ``CudaBackend`` — CUDA kernel backend with paged decode + prefill
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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)``.
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Each wrapper calls its compiled CUDA kernel directly. Fallback to torch
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SDPA is handled by the attention backend, not the wrapper functions.
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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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)
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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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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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)
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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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"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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"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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