Engage!

3 files changed, 294 insertions, 0 deletions

diff --git a/llama.cpp/examples/passkey/CMakeLists.txt b/llama.cpp/examples/passkey/CMakeLists.txt
new file mode 100644
index 0000000..9bc5110
--- /dev/null
+++ b/llama.cpp/examples/passkey/CMakeLists.txt
@@ -0,0 +1,5 @@
+set(TARGET llama-passkey)
+add_executable(${TARGET} passkey.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
diff --git a/llama.cpp/examples/passkey/README.md b/llama.cpp/examples/passkey/README.md
new file mode 100644
index 0000000..cbaf28f
--- /dev/null
+++ b/llama.cpp/examples/passkey/README.md
@@ -0,0 +1,15 @@
+# llama.cpp/example/passkey
+
+A passkey retrieval task is an evaluation method used to measure a language
+models ability to recall information from long contexts.
+
+See the following PRs for more info:
+
+- https://github.com/ggml-org/llama.cpp/pull/3856
+- https://github.com/ggml-org/llama.cpp/pull/4810
+
+### Usage
+
+```bash
+llama-passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250
+```
diff --git a/llama.cpp/examples/passkey/passkey.cpp b/llama.cpp/examples/passkey/passkey.cpp
new file mode 100644
index 0000000..8a4faa3
--- /dev/null
+++ b/llama.cpp/examples/passkey/passkey.cpp
@@ -0,0 +1,274 @@
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+#include <algorithm>
+
+static void print_usage(int, char ** argv) {
+    LOG("\nexample usage:\n");
+    LOG("\n    %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]);
+    LOG("\n");
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.n_junk = 250;
+    params.n_keep = 32;
+    params.i_pos  = -1;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_PASSKEY, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    int n_junk = params.n_junk;
+    int n_keep = params.n_keep;
+    int n_grp  = params.grp_attn_n;
+    int i_pos  = params.i_pos;
+
+    if (i_pos == -1) {
+        i_pos = rand() % n_junk;
+    }
+
+    const std::string prompt_prefix = "There is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.";
+    const std::string prompt_suffix = " What is the pass key? The pass key is";
+
+    // generate junk text
+    params.prompt = prompt_prefix;
+
+    const int passkey = rand() % 50000 + 1;
+
+    for (int i = 0; i < n_junk; i++) {
+        if (i % n_junk == i_pos) {
+            params.prompt += " The pass key is " + std::to_string(passkey) + ". Remember it. " + std::to_string(passkey) + " is the pass key.";
+        }
+
+        params.prompt += " The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.";
+    }
+
+    params.prompt += prompt_suffix;
+
+    // init LLM
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // initialize the model
+
+    llama_model_params model_params = common_model_params_to_llama(params);
+
+    llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
+
+    if (model == NULL) {
+        LOG_ERR("%s: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    // initialize the context
+
+    llama_context_params ctx_params = common_context_params_to_llama(params);
+
+    ctx_params.n_ctx = llama_model_n_ctx_train(model)*n_grp + n_keep;
+
+    GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp");
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
+    if (ctx == NULL) {
+        LOG_ERR("%s: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    auto sparams = llama_sampler_chain_default_params();
+
+    llama_sampler * smpl = llama_sampler_chain_init(sparams);
+
+    llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
+
+    // tokenize the prompt
+    std::vector<llama_token> tokens_list;
+    tokens_list = common_tokenize(ctx, params.prompt, true);
+
+    // tokenize the prefix and use it as a sink
+    const int n_tokens_prefix = common_tokenize(ctx, prompt_prefix, true).size();
+
+    const int n_tokens_all = tokens_list.size();
+
+    // we leave a margin of 16 tokens for the generated text - it should contain just the passkey
+    const int n_predict = 16;
+
+    // total length of the sequences including the prompt
+    const int n_len = n_tokens_all + n_predict;
+
+    const int n_ctx       = llama_n_ctx(ctx) - n_keep;
+    const int n_kv_req    = llama_n_ctx(ctx);
+    const int n_batch     = ctx_params.n_batch;
+    const int n_batch_grp = ctx_params.n_batch/n_grp;
+
+    LOG_INF("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d, n_grp = %d, n_batch = %d, n_junk = %d, i_pos = %d\n", __func__, n_len, n_ctx, n_kv_req, n_grp, n_batch, n_junk, i_pos);
+
+    // print the prompt token-by-token
+
+    LOG_INF("\n");
+    LOG_INF("prefix tokens: %d\n", n_tokens_prefix);
+    LOG_INF("prompt tokens: %d\n", n_tokens_all);
+    //LOG_INF("prompt: %s\n", params.prompt.c_str());
+
+    llama_batch batch = llama_batch_init(params.n_batch, 0, 1);
+
+    int n_past = 0;
+
+    auto * mem = llama_get_memory(ctx);
+
+    // fill the KV cache
+    for (int i = 0; i < n_ctx; i += n_batch) {
+        if (i > 0 && n_grp > 1) {
+            // if SelfExtend is enabled, we compress the position from the last batch by a factor of n_grp
+            const int ib = i/n_batch - 1;
+            const int bd = n_batch_grp*(n_grp - 1);
+
+            llama_memory_seq_add(mem, 0, n_past - n_batch,         n_past,         ib*bd);
+            llama_memory_seq_div(mem, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp);
+
+            n_past = llama_memory_seq_pos_max(mem, 0) + 1;
+        }
+
+        common_batch_clear(batch);
+
+        for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
+            common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
+        }
+
+        if (i + n_batch >= n_tokens_all) {
+            batch.logits[batch.n_tokens - 1] = true;
+        }
+
+        if (llama_decode(ctx, batch) != 0) {
+            LOG_INF("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+
+        LOG_INF("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
+
+        if (i + n_batch >= n_tokens_all) {
+            break;
+        }
+    }
+
+    for (int i = n_ctx; i < n_tokens_all; i += n_batch) {
+        const int n_discard = n_batch;
+
+        LOG_INF("%s: shifting KV cache with %d\n", __func__, n_discard);
+
+        llama_memory_seq_rm (mem, 0, n_keep            , n_keep + n_discard);
+        llama_memory_seq_add(mem, 0, n_keep + n_discard, n_ctx,  -n_discard);
+
+        n_past = llama_memory_seq_pos_max(mem, 0) + 1;
+
+        common_batch_clear(batch);
+
+        for (int j = 0; j < n_batch && i + j < n_tokens_all; j++) {
+            common_batch_add(batch, tokens_list[i + j], n_past++, { 0 }, false);
+        }
+
+        if (i + n_batch >= n_tokens_all) {
+            batch.logits[batch.n_tokens - 1] = true;
+        }
+
+        if (llama_decode(ctx, batch) != 0) {
+            LOG_ERR("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+
+        LOG_INF("%s: processed: [%6d, %6d)\n", __func__, i, std::min(i + n_batch, n_tokens_all));
+    }
+
+    {
+        const int n_discard = n_past - n_ctx + n_predict;
+
+        if (n_discard > 0) {
+            LOG_INF("%s: shifting KV cache with %d to free space for the answer\n", __func__, n_discard);
+
+            llama_memory_seq_rm (mem, 0, n_keep            , n_keep + n_discard);
+            llama_memory_seq_add(mem, 0, n_keep + n_discard, n_ctx,  -n_discard);
+
+            n_past = llama_memory_seq_pos_max(mem, 0) + 1;
+        }
+    }
+
+    LOG_INF("\n");
+    LOG_INF("%s: passkey = %d, inserted at position %d / %d (token pos: ~%d)\n", __func__, passkey, i_pos, n_junk, (i_pos * n_tokens_all) / n_junk);
+    LOG_INF("\n");
+
+    // main loop
+
+    int n_cur    = n_tokens_all;
+    int n_decode = 0;
+
+    LOG_INF("%s", prompt_suffix.c_str());
+
+    const auto t_main_start = ggml_time_us();
+
+    while (n_cur <= n_len) {
+        // sample the next token
+        {
+            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, batch.n_tokens - 1);
+
+            // is it an end of generation?
+            if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
+                LOG("\n");
+
+                break;
+            }
+
+            LOG("%s", common_token_to_piece(ctx, new_token_id).c_str());
+
+            n_decode += 1;
+
+            // prepare the next batch
+            common_batch_clear(batch);
+
+            // push this new token for next evaluation
+            common_batch_add(batch, new_token_id, n_past++, { 0 }, true);
+        }
+
+        n_cur += 1;
+
+        // evaluate the current batch with the transformer model
+        if (llama_decode(ctx, batch)) {
+            LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
+            return 1;
+        }
+    }
+
+    LOG("\n");
+
+    const auto t_main_end = ggml_time_us();
+
+    LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
+            __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
+
+    LOG("\n");
+    llama_perf_context_print(ctx);
+
+    LOG("\n");
+
+    llama_sampler_free(smpl);
+
+    llama_batch_free(batch);
+
+    llama_free(ctx);
+    llama_model_free(model);
+
+    llama_backend_free();
+
+    return 0;
+}