mirror of
https://github.com/ggerganov/llama.cpp.git
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2b3389677a
* ggml : unify rope norm/neox (CPU) * ggml : fix compile warning * ggml : remove GLM rope mode ggml-ci * metal : better rope implementation ggml-ci * cuda : better rope implementation ggml-ci * naming : n_orig_ctx -> n_ctx_orig ggml-ci * dev : add reminders to update backends ggml-ci * vulkan : fix ggml_rope_ext() usage * cuda : fix array size + indents ggml-ci
68 lines
2.2 KiB
Plaintext
68 lines
2.2 KiB
Plaintext
#include "common.comp"
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// TODO: use a local size of 32 or more (Metal uses 1024)
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layout(local_size_x = 1) in;
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layout (push_constant) uniform parameter {
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uint inAOff;
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uint inBOff;
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uint outOff;
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int n_dims;
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int mode;
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int n_ctx_orig;
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float freq_base;
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float freq_scale;
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float ext_factor;
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float attn_factor;
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float beta_fast;
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float beta_slow;
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uint nb00;
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uint nb01;
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uint nb02;
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uint nb03;
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int ne0;
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uint nb0;
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uint nb1;
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uint nb2;
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uint nb3;
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} pcs;
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float rope_yarn_ramp(const float low, const float high, const float i0) {
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const float y = (i0 / 2 - low) / max(0.001f, high - low);
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return 1.0f - min(1.0f, max(0.0f, y));
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}
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// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
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// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
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void rope_yarn(
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float theta_extrap, float freq_scale, float corr_dims[2], float i0, float ext_factor, float mscale,
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out float cos_theta, out float sin_theta
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) {
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// Get n-d rotational scaling corrected for extrapolation
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float theta_interp = freq_scale * theta_extrap;
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float theta = theta_interp;
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if (ext_factor != 0.0f) {
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float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
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theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
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// Get n-d magnitude scaling corrected for interpolation
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mscale *= 1.0f + 0.1f * log(1.0f / freq_scale);
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}
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cos_theta = cos(theta) * mscale;
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sin_theta = sin(theta) * mscale;
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}
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// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get
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// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))`
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float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) {
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return n_dims * log(n_ctx_orig / (n_rot * TWOPI_F)) / (2 * log(base));
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}
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void rope_yarn_corr_dims(
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int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, out float dims[2]
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) {
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// start and end correction dims
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dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base)));
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dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base)));
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}
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