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embedding : print cosine similarity (#899)

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Georgi Gerganov 2024-03-14 10:12:29 +02:00
parent 19885d205e
commit 0fd6c1f015
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4 changed files with 36 additions and 25 deletions
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13
common/common.cpp
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@ -1877,3 +1877,16 @@ void llama_embd_normalize(const float * inp, float * out, int n) {
}
}
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n){
double sum = 0.0;
double sum1 = 0.0;
double sum2 = 0.0;
for (int i = 0; i < n; i++) {
sum += embd1[i] * embd2[i];
sum1 += embd1[i] * embd1[i];
sum2 += embd2[i] * embd2[i];
}
return sum / (sqrt(sum1) * sqrt(sum2));
}

1
common/common.h
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@ -268,3 +268,4 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40
void llama_embd_normalize(const float * inp, float * out, int n);
float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);

21
examples/embedding/embedding.cpp
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@ -168,14 +168,25 @@ int main(int argc, char ** argv) {
batch_decode(ctx, batch, out, s, n_embd);
// print first 3 embeddings
fprintf(stdout, "\n");
for (int j = 0; j < std::min(3, n_prompts); j++) {
fprintf(stderr, "embedding %d: ", j);
for (int i = 0; i < n_embd; i++) {
fprintf(stderr, "%f ", emb[j * n_embd + i]);
fprintf(stdout, "embedding %d: ", j);
for (int i = 0; i < std::min(16, n_embd); i++) {
fprintf(stdout, "%f ", emb[j * n_embd + i]);
}
fprintf(stderr, "\n\n");
fprintf(stdout, "\n");
}
// print cosine similarity matrix
fprintf(stdout, "\n");
printf("cosine similarity matrix:\n\n");
for (int i = 0; i < n_prompts; i++) {
for (int j = 0; j < n_prompts; j++) {
float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
fprintf(stdout, "%6.2f ", sim);
}
fprintf(stdout, "\n");
}
fprintf(stderr, "\n");
// clean up
llama_print_timings(ctx);

26
examples/gritlm/gritlm.cpp
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@ -6,22 +6,6 @@
// #define GRIT_DEBUG
static float dot_product(const std::vector<float> & v1, const std::vector<float> & v2) {
float dot = 0.0f;
for (uint64_t i = 0; i < v1.size(); ++i) {
dot += v1[i] * v2[i];
}
return dot;
}
static float norm(const std::vector<float> & v) {
return std::sqrt(dot_product(v, v));
}
static float cosine_similarity(const std::vector<float> & v1, const std::vector<float> & v2) {
return dot_product(v1, v2) / (norm(v1) * norm(v2));
}
static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
std::vector<std::vector<float>> result;
@ -203,10 +187,12 @@ int main(int argc, char * argv[]) {
const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
const std::vector<std::vector<float>> q_rep = encode(ctx, queries, gritlm_instruction(instruction));
const float cosine_sim_q0_d0 = cosine_similarity(q_rep[0], d_rep[0]);
const float cosine_sim_q0_d1 = cosine_similarity(q_rep[0], d_rep[1]);
const float cosine_sim_q1_d0 = cosine_similarity(q_rep[1], d_rep[0]);
const float cosine_sim_q1_d1 = cosine_similarity(q_rep[1], d_rep[1]);
const int n_embd = llama_n_embd(mdl);
const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);