DeepSeek-V3.2-Exp · Generate RoPE coefficients

Generate RoPE coefficients

no parametersprecompute_freqs_cis(args)

Construct YaRN-extended rotations for the 64-feature rotary parts.

{\htmlData{light=model-rotations frequencies}{{\htmlClass{vl-stream-context}{R_{t,j}}}}}=e^{\mathrm i t\omega_j}

in: $p$ [T positions]
out: $R$ [T positions, 32 pairs]

Precomputed once, at construction, for all max_seq_len positions, as complex numbers (model.py 887, 399–401).

Explanation

$t$ is the position and $j<32$ the pair.

The base frequencies are $\theta^{-2j/64}$ with $\theta=10,000$ . Because the maximum length (16,384) exceeds the original 4,096, YaRN divides the low frequencies by 40 and blends smoothly into the unchanged high frequencies between the correction bounds set by $\beta_{\text{fast}}=32$ and $\beta_{\text{slow}}=1$ .

Codemodel.py 56–402

model.py 334–339

    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

model.py 393–402

    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

model.py 56

    max_seq_len: int = 4096 * 4

model.py 81–86

    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.

Sources2

Direct evidence

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

Read code excerpt

    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

Direct evidence

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

Read code excerpt

    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.