ViperEkura
75411ce0cc
- apply_rotary_emb now checks torch.is_grad_enabled() before dispatching to CUDA kernel - Training (grad enabled) uses torch complex multiply path which supports autograd backward - Inference (inference_mode/no_grad) uses CUDA kernel as before - Without this fix, training backward would crash — the CUDA kernel has no autograd backward()
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
"""Rotary embedding with auto-dispatch to CUDA kernel.
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Single entry point ``apply_rotary_emb(x, cos, sin)`` — uses the fused
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CUDA kernel when available, falls back to torch complex multiply otherwise.
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Layout: x is [batch, seq_len, n_heads, head_dim] (bf16).
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cos/sin are [batch, seq_len, head_dim/2] (f32).
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"""
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import torch
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from torch import Tensor
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from astrai.extension.loader import is_available
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from astrai.extension.rotary_ops import rotary_emb as _cuda_rotary
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_cache = {"available": None}
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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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def _torch_apply(x: Tensor, cos: Tensor, sin: Tensor) -> Tensor:
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dtype = x.dtype
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x_ = x.float().reshape(*x.shape[:-1], -1, 2)
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x_complex = torch.view_as_complex(x_)
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freqs_cis = torch.complex(cos, sin).unsqueeze(2)
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x_rotated = x_complex * freqs_cis
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x_out = torch.view_as_real(x_rotated).flatten(-2)
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return x_out.to(dtype)
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def apply_rotary_emb(x: Tensor, rotary_emb: tuple[Tensor, Tensor]) -> Tensor:
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"""Apply rotary embedding to x.
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Args:
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x: [batch, seq_len, n_heads, head_dim] (bf16)
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rotary_emb: (cos, sin) tuple, each [batch, seq_len, head_dim/2] (f32)
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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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cos, sin = rotary_emb
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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
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and x.dtype == torch.bfloat16
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):
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return _cuda_rotary(x, cos, sin)
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return _torch_apply(x, cos, sin)
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