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https://github.com/ggerganov/llama.cpp.git
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49c03c79cd
* remove completions file * fix inverted vector * add mean method * code style * remove inverted pca hotfix
49 lines
1.5 KiB
C++
49 lines
1.5 KiB
C++
#include "common.h"
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#include "llama.h"
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#include "ggml.h"
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#include <string>
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#include <vector>
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#include <math.h>
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namespace mean {
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static void run(
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const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_embd, n_samples]
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const std::vector<struct ggml_tensor *> & v_output) {
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printf("%s: Running mean...\n", __func__);
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for (size_t il = 0; il < v_input.size(); ++il) {
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// prepare output vector
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struct ggml_tensor * ctrl_out = v_output[il];
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ggml_format_name(ctrl_out, "direction.%ld", il+1);
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// calculate mean vector
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struct ggml_tensor * t_layer = v_input[il];
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GGML_ASSERT(t_layer->ne[0] == ctrl_out->ne[0]); // == n_embd
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for (int ic = 0; ic < t_layer->ne[0]; ic++) {
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float f = 0.0;
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for (int ir = 0; ir < t_layer->ne[1]; ir++) {
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f += ggml_get_f32_nd(t_layer, ic, ir, 0, 0);
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}
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f /= t_layer->ne[1];
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ggml_set_f32_1d(ctrl_out, ic, f);
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}
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// normalize output vector
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float norm = 0.0;
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for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
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float f = ggml_get_f32_1d(ctrl_out, i);
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norm += f*f;
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}
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norm = sqrt(norm);
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for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
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float f = ggml_get_f32_1d(ctrl_out, i);
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ggml_set_f32_1d(ctrl_out, i, f / norm);
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}
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printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size());
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}
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}
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}
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