1 files changed, 157 insertions, 0 deletions
diff --git a/llama.cpp/examples/lookup/lookup-stats.cpp b/llama.cpp/examples/lookup/lookup-stats.cpp
new file mode 100644
index 0000000..ae28b2e
--- /dev/null
+++ b/llama.cpp/examples/lookup/lookup-stats.cpp
@@ -0,0 +1,157 @@
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "ngram-cache.h"
+#include "llama.h"
+#include "ggml.h"
+
+#include <cstdint>
+#include <cstdio>
+#include <cinttypes>
+#include <fstream>
+#include <string>
+#include <vector>
+
+int main(int argc, char ** argv){
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) {
+        return 1;
+    }
+
+    common_init();
+
+    const int n_draft = params.speculative.n_max;
+
+    // init llama.cpp
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // load the model
+    auto llama_init = common_init_from_params(params);
+
+    llama_context * ctx = llama_init->context();
+
+    // tokenize the prompt
+    std::vector<llama_token> inp;
+    inp = common_tokenize(ctx, params.prompt, true, true);
+
+    common_ngram_cache ngram_cache_context;
+    common_ngram_cache ngram_cache_dynamic;
+    common_ngram_cache ngram_cache_static;
+
+    int64_t t_draft_flat_us = 0;
+    int64_t t_draft_us = 0;
+
+    {
+        const int64_t t_start_draft_us = ggml_time_us();
+
+        if (!params.speculative.lookup_cache_static.empty()) {
+            try {
+                ngram_cache_static = common_ngram_cache_load(params.speculative.lookup_cache_static);
+            } catch (std::ifstream::failure const &) {
+                LOG_ERR("failed to open static lookup cache: %s", params.speculative.lookup_cache_static.c_str());
+                exit(1);
+            }
+        }
+
+        if (!params.speculative.lookup_cache_dynamic.empty()) {
+            try {
+                ngram_cache_dynamic = common_ngram_cache_load(params.speculative.lookup_cache_dynamic);
+            } catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
+        }
+
+        t_draft_flat_us += ggml_time_us() - t_start_draft_us;
+    }
+
+    const int n_input = inp.size();
+    const int n_ctx = llama_n_ctx(ctx);
+
+    int n_drafted = 0;
+    int n_accept  = 0;
+
+    const int64_t t_start_ms = ggml_time_ms();
+
+    // Iterate over input tokens in chunks of size n_ctx.
+    // Each chunk is treated as if a sequential generation but with pre-determined tokens to ensure reproducibility.
+    for (int i_start = 0; i_start + n_ctx < n_input; i_start += n_ctx) {
+        const std::vector<llama_token> inp_slice(inp.begin() + i_start, inp.begin() + i_start + n_ctx);
+        std::vector<llama_token> pseudo_output;
+        pseudo_output.push_back(inp_slice[0]);
+
+        while ((int) pseudo_output.size() < n_ctx) {
+            // Simulate drafting and decoding from draft:
+            std::vector<llama_token> draft;
+            draft.push_back(pseudo_output.back());
+
+            {
+                const int64_t t_start_draft_us = ggml_time_us();
+                common_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
+                t_draft_us += ggml_time_us() - t_start_draft_us;
+            }
+
+            n_drafted += draft.size() - 1;
+
+            for (size_t j = 1; j < draft.size() && (int) pseudo_output.size() < n_ctx; ++j) {
+                const llama_token ground_truth = inp_slice[pseudo_output.size()];
+                const llama_token drafted = draft[j];
+
+                if (ground_truth != drafted) {
+                    break;
+                }
+
+                ++n_accept;
+                pseudo_output.push_back(ground_truth);
+
+                {
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
+            }
+
+            // After each simulated batch decoding simulate the sampling of a single token:
+            if ((int) pseudo_output.size() < n_ctx) {
+                pseudo_output.push_back(inp_slice[pseudo_output.size()]);
+                {
+                    const int64_t t_start_draft_us = ggml_time_us();
+                    common_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
+                    t_draft_us += ggml_time_us() - t_start_draft_us;
+                }
+            }
+
+            draft.erase(draft.begin());
+
+        }
+        if (i_start > 0 && i_start / 100000 != (i_start - n_ctx) / 100000) {
+            const int64_t t_now_ms = ggml_time_ms();
+            const int64_t eta_ms   = (n_input - i_start) * (t_now_ms - t_start_ms) / i_start;
+            const int64_t eta_min  = eta_ms / (60*1000);
+            const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
+
+            LOG_INF("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s);
+        }
+
+        // After each chunk, update the dynamic ngram cache with the context ngram cache:
+        common_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
+        ngram_cache_context.clear();
+    }
+
+    LOG("\n");
+
+    LOG_INF("\n");
+    LOG_INF("n_draft      = %d\n", n_draft);
+    LOG_INF("n_predict    = %d\n", n_input - n_input % n_ctx);
+    LOG_INF("n_drafted    = %d\n", n_drafted);
+    LOG_INF("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
+    LOG_INF("t_draft      = %.2f ms, %.2f us per token, %.2f tokens per second\n",
+            t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
+    LOG_INF("n_accept     = %d\n", n_accept);
+    LOG_INF("accept       = %.3f%%\n", 100.0f * n_accept / n_drafted);
+
+    llama_backend_free();
+
+    LOG("\n\n");
+
+    return 0;
+}