diff options
Diffstat (limited to 'examples/embedding/embedding.cpp')
| -rw-r--r-- | examples/embedding/embedding.cpp | 115 |
1 files changed, 82 insertions, 33 deletions
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index b4b73c01..1466e5b2 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -7,13 +7,19 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static std::vector<std::string> split_lines(const std::string & s) { - std::string line; +static std::vector<std::string> split_lines(const std::string & s, const std::string & separator = "\n") { std::vector<std::string> lines; - std::stringstream ss(s); - while (std::getline(ss, line)) { - lines.push_back(line); + size_t start = 0; + size_t end = s.find(separator); + + while (end != std::string::npos) { + lines.push_back(s.substr(start, end - start)); + start = end + separator.length(); + end = s.find(separator, start); } + + lines.push_back(s.substr(start)); // Add the last part + return lines; } @@ -24,7 +30,7 @@ static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & toke } } -static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) { +static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd, int embd_norm) { // clear previous kv_cache values (irrelevant for embeddings) llama_kv_cache_clear(ctx); @@ -44,13 +50,7 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu GGML_ASSERT(embd != NULL && "failed to get sequence embeddings"); float * out = output + batch.seq_id[i][0] * n_embd; - //TODO: I would also add a parameter here to enable normalization or not. - /*fprintf(stdout, "unnormalized_embedding:"); - for (int hh = 0; hh < n_embd; hh++) { - fprintf(stdout, "%9.6f ", embd[hh]); - } - fprintf(stdout, "\n");*/ - llama_embd_normalize(embd, out, n_embd); + llama_embd_normalize(embd, out, n_embd, embd_norm); } } @@ -110,7 +110,7 @@ int main(int argc, char ** argv) { } // split the prompt into lines - std::vector<std::string> prompts = split_lines(params.prompt); + std::vector<std::string> prompts = split_lines(params.prompt, params.embd_sep); // max batch size const uint64_t n_batch = params.n_batch; @@ -170,7 +170,7 @@ int main(int argc, char ** argv) { // encode if at capacity if (batch.n_tokens + n_toks > n_batch) { float * out = emb + p * n_embd; - batch_decode(ctx, batch, out, s, n_embd); + batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); llama_batch_clear(batch); p += s; s = 0; @@ -183,29 +183,78 @@ int main(int argc, char ** argv) { // final batch float * out = emb + p * n_embd; - batch_decode(ctx, batch, out, s, n_embd); - - // print the first part of the embeddings or for a single prompt, the full embedding - fprintf(stdout, "\n"); - for (int j = 0; j < n_prompts; j++) { - fprintf(stdout, "embedding %d: ", j); - for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) { - fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); - } - fprintf(stdout, "\n"); - } + batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize); - // print cosine similarity matrix - if (n_prompts > 1) { + if (params.embd_out.empty()) { + // print the first part of the embeddings or for a single prompt, the full embedding 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); + for (int j = 0; j < n_prompts; j++) { + fprintf(stdout, "embedding %d: ", j); + for (int i = 0; i < (n_prompts > 1 ? std::min(16, n_embd) : n_embd); i++) { + if (params.embd_normalize == 0) { + fprintf(stdout, "%6.0f ", emb[j * n_embd + i]); + } else { + fprintf(stdout, "%9.6f ", emb[j * n_embd + i]); + } + } + fprintf(stdout, "\n"); + } + + // print cosine similarity matrix + if (n_prompts > 1) { + fprintf(stdout, "\n"); + printf("cosine similarity matrix:\n\n"); + for (int i = 0; i < n_prompts; i++) { + fprintf(stdout, "%6.6s ", prompts[i].c_str()); } fprintf(stdout, "\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, "%1.10s", prompts[i].c_str()); + fprintf(stdout, "\n"); + } + } + } + + if (params.embd_out == "json" || params.embd_out == "json+" || params.embd_out == "array") { + const bool notArray = params.embd_out != "array"; + + fprintf(stdout, notArray ? "{\n \"object\": \"list\",\n \"data\": [\n" : "["); + for (int j = 0;;) { // at least one iteration (one prompt) + if (notArray) fprintf(stdout, " {\n \"object\": \"embedding\",\n \"index\": %d,\n \"embedding\": ",j); + fprintf(stdout, "["); + for (int i = 0;;) { // at least one iteration (n_embd > 0) + fprintf(stdout, params.embd_normalize == 0 ? "%1.0f" : "%1.7f", emb[j * n_embd + i]); + i++; + if (i < n_embd) fprintf(stdout, ","); else break; + } + fprintf(stdout, notArray ? "]\n }" : "]"); + j++; + if (j < n_prompts) fprintf(stdout, notArray ? ",\n" : ","); else break; } + fprintf(stdout, notArray ? "\n ]" : "]\n"); + + if (params.embd_out == "json+" && n_prompts > 1) { + fprintf(stdout, ",\n \"cosineSimilarity\": [\n"); + for (int i = 0;;) { // at least two iteration (n_prompts > 1) + fprintf(stdout, " ["); + for (int j = 0;;) { // at least two iteration (n_prompts > 1) + float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd); + fprintf(stdout, "%6.2f", sim); + j++; + if (j < n_prompts) fprintf(stdout, ", "); else break; + } + fprintf(stdout, " ]"); + i++; + if (i < n_prompts) fprintf(stdout, ",\n"); else break; + } + fprintf(stdout, "\n ]"); + } + + if (notArray) fprintf(stdout, "\n}\n"); } // clean up |