ViperEkura
3406157431
- keep training attention on dense 4d tensors - use packed 3d tensors with KV cache for inference - extend CUDA rotary embedding to packed 3d inputs - adapt torch, CUDA and FlashAttention backend dispatch
40 lines
1.2 KiB
Python
40 lines
1.2 KiB
Python
"""Rotary embedding CUDA kernel wrapper.
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Calls the compiled CUDA kernel directly. If the kernel is not available,
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raises ``RuntimeError``. Fallback to torch complex multiply is the
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responsibility of ``astrai.extension.rotary_backend.apply_rotary_emb``.
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Layout: x is packed [tokens, n_heads, head_dim] or dense
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[batch, seq_len, n_heads, head_dim]. ``freqs_cis`` has matching token axes.
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"""
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import torch
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from astrai.extension.loader import _available, _modules
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def _check_available():
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if not _available.get("rotary_emb"):
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raise RuntimeError(
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"CUDA kernel 'rotary_emb' is not available. "
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"Build with CSRC_KERNELS=true or use the torch fallback."
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)
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def rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
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"""Fused rotary embedding kernel.
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Args:
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x: packed 3D or dense 4D bf16 tensor.
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freqs_cis: matching token axes followed by [head_dim/2, 2].
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Returns:
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Tensor with the same shape as ``x``.
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"""
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_check_available()
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if not x.is_contiguous():
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x = x.contiguous()
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if not freqs_cis.is_contiguous():
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freqs_cis = freqs_cis.contiguous()
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return _modules["rotary_emb"].rotary_emb(x, freqs_cis)
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