DeepSeek-V3.2-Exp · RoPE coefficients

61 transformer blocks › 58 MoE blocks › Sparse latent attention › Key heads

RoPE coefficients

supplied · no parametersfreqs_cis

Position-dependent rotations supplied by the block.

shape: $R$ [1 positions, 32 pairs]

Sources

Contextual evidence from Generate RoPE coefficients

Rotary coefficients are precomputed once for the maximum sequence length, with YaRN frequency interpolation beyond the original 4,096 positions.

rope · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 334–402

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    dim = args.qk_rope_head_dim
    seqlen = args.max_seq_len
    beta_fast = args.beta_fast
    beta_slow = args.beta_slow
    base = args.rope_theta
    factor = args.rope_factor

… lines 340–392 omitted …

    freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
    if seqlen > args.original_seq_len:
        low, high = find_correction_range(beta_fast, beta_slow, dim, base, args.original_seq_len)
        smooth = 1 - linear_ramp_factor(low, high, dim // 2)
        freqs = freqs / factor * (1 - smooth) + freqs * smooth

    t = torch.arange(seqlen)
    freqs = torch.outer(t, freqs)
    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
    return freqs_cis

Contextual evidence from Generate RoPE coefficients

Reference defaults the configuration does not override: maximum sequence length, YaRN RoPE settings and the softmax scale correction.

model-args · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 56–86

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    max_seq_len: int = 4096 * 4

… lines 57–80 omitted …

    original_seq_len: int = 4096
    rope_theta: float = 10000.0
    rope_factor: float = 40
    beta_fast: int = 32
    beta_slow: int = 1
    mscale: float = 1.

Contextual evidence from Rotate shared key

Rotary embedding multiplies feature pairs by the coefficients; the indexer uses the non-interleaved layout.

apply-rope · deepseek-ai/DeepSeek-V3.2-Exp / inference/model.py
Pinned revision 87e509a2e5a1 ↗ · lines 405–425

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def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor, interleaved: bool = True) -> torch.Tensor:
    """
    Applies rotary positional embeddings to the input tensor.

    Args:
        x (torch.Tensor): Input tensor with positional embeddings to be applied.
        freqs_cis (torch.Tensor): Precomputed complex exponential values for positional embeddings.

    Returns:
        torch.Tensor: Tensor with rotary embeddings applied.
    """
    dtype = x.dtype
    shape = x.shape
    if not interleaved:
        x = x.view(*shape[:-1], 2, -1).transpose(-1, -2).contiguous()
    x = torch.view_as_complex(x.float().view(*shape[:-1], -1, 2))
    freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
    y = torch.view_as_real(x * freqs_cis).flatten(3)
    if not interleaved:
        y = torch.cat([y[..., 0::2], y[..., 1::2]], dim=-1)
    return y.to(dtype)