Sync from upstream llama.cpp repository

examples/debug/CMakeLists.txt

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+set(TARGET llama-debug)
+add_executable(${TARGET} debug.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)

examples/debug/README.md

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+# llama.cpp/examples/debug
+
+This is a utility intended to help debug a model by registering a callback that
+logs GGML operations and tensor data. It can also store the generated logits or
+embeddings as well as the prompt and token ids for comparision with the original
+model.
+
+### Usage
+
+```shell
+llama-debug \
+  --hf-repo ggml-org/models \
+  --hf-file phi-2/ggml-model-q4_0.gguf \
+  --model phi-2-q4_0.gguf \
+  --prompt hello \
+  --save-logits \
+  --verbose
+```
+The tensor data is logged as debug and required the --verbose flag. The reason
+for this is that while useful for a model with many layers there can be a lot of
+output. You can filter the tensor names using the `--tensor-filter` option.
+
+A recommended approach is to first run without `--verbose` and see if the
+generated logits/embeddings are close to the original model. If they are not,
+then it might be required to inspect tensor by tensor and in that case it is
+useful to enable the `--verbose` flag along with `--tensor-filter` to focus on
+specific tensors.
+
+### Options
+This example supports all standard `llama.cpp` options and also accepts the
+following options:
+```console
+$ llama-debug --help
+...
+
+----- example-specific params -----
+
+--save-logits                           save final logits to files for verification (default: false)
+--logits-output-dir PATH                directory for saving logits output files (default: data)
+--tensor-filter REGEX                   filter tensor names for debug output (regex pattern, can be specified multiple times)
+```
+
+### Output Files
+
+When `--save-logits` is enabled, the following files are created in the output
+directory:
+
+* `llamacpp-<model>[-embeddings].bin`        - Binary output (logits or embeddings)
+* `llamacpp-<model>[-embeddings].txt`        - Text output (logits or embeddings, one per line)
+* `llamacpp-<model>[-embeddings]-prompt.txt` - Prompt text and token IDs
+* `llamacpp-<model>[-embeddings]-tokens.bin` - Binary token IDs for programmatic comparison
+
+These files can be compared against the original model's output to verify the
+converted model.

examples/debug/debug.cpp

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+#include "debug.h"
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+#include "llama.h"
+
+#include <cstdlib>
+#include <string>
+#include <vector>
+#include <filesystem>
+#include <fstream>
+#include <regex>
+
+static void print_usage(int /*argc*/, char ** argv) {
+    const std::string usage_template = R"(
+        example usage:
+
+          Print tensors:
+
+          {prog} -m model.gguf -p "Hello my name is" --verbose
+
+          The tensors to be printed can be filtered with --tensor-filter option.
+
+          Save logits/embeddings:
+
+          {prog} -m model.gguf -p "Hello my name is" --save-logits
+
+          Add --embedding to save embeddings)" "\n";
+
+    // Fix the source code indentation above that is introduced by the raw string literal.
+    std::string usage = std::regex_replace(usage_template, std::regex("\\n {8}"), "\n");
+    usage = std::regex_replace(usage, std::regex("\\{prog\\}"), argv[0]);
+    LOG("%s\n", usage.c_str());
+}
+
+static bool has_pooling(llama_context * ctx) {
+    switch (llama_pooling_type(ctx)) {
+        case LLAMA_POOLING_TYPE_NONE:
+        case LLAMA_POOLING_TYPE_UNSPECIFIED:
+            return false;
+        default:
+            return true;
+    }
+}
+
+struct output_data {
+    float *                  data_ptr    = nullptr;
+    int                      data_size   = 0;
+    std::string              type_suffix;
+    std::vector<float>       embd_norm;
+    std::string              prompt;
+    std::vector<llama_token> tokens;
+
+    output_data(llama_context * ctx, const llama_model * model, const common_params & params) {
+        const llama_vocab * vocab = llama_model_get_vocab(model);
+        const bool add_bos = llama_vocab_get_add_bos(vocab);
+
+        tokens = common_tokenize(ctx, params.prompt, add_bos);
+        prompt = params.prompt;
+
+        if (params.embedding) {
+            const int n_embd       = llama_model_n_embd_out(model);
+            const bool pooling     = has_pooling(ctx);
+            const int n_embd_count = pooling ? 1 : tokens.size();
+            const int n_floats     = n_embd * n_embd_count;
+
+            float * embd_raw = pooling ? llama_get_embeddings_seq(ctx, 0) : llama_get_embeddings(ctx);
+            if (embd_raw == nullptr) {
+                throw std::runtime_error("failed to get embeddings from the model");
+            }
+
+            LOG_DBG("pooling_enabled: %s\n", pooling ? "true" : "false");
+            LOG_DBG("n_embd: %d\n", n_embd);
+            LOG_DBG("n_floats: %d\n", n_floats);
+            LOG_DBG("n_embd_count: %d\n", n_embd_count);
+
+            data_ptr    = embd_raw;
+            data_size   = n_floats;
+            type_suffix = "-embeddings";
+
+            if (params.embd_normalize >= 0) {
+                embd_norm.resize(n_floats);
+                for (int i = 0; i < n_embd_count; i++) {
+                    common_embd_normalize(embd_raw+i*n_embd, embd_norm.data()+i*n_embd, n_embd, params.embd_normalize);
+                }
+                data_ptr = embd_norm.data();
+            }
+        } else {
+            const float * logits = llama_get_logits_ith(ctx, tokens.size() - 1);
+            const int n_logits = llama_vocab_n_tokens(vocab);
+
+            data_ptr = const_cast<float*>(logits);
+            data_size = n_logits;
+            type_suffix = "";
+        }
+    }
+};
+
+static void save_output_data(const output_data & output, const std::string & model_name, const std::string & output_dir) {
+    std::filesystem::create_directory(output_dir);
+    auto base_path = std::filesystem::path{output_dir} / ("llamacpp-" + model_name + output.type_suffix);
+
+    // Save logits/embeddings to binary file.
+    {
+        std::filesystem::path filepath{base_path.string() + ".bin"};
+        std::ofstream file{filepath, std::ios::binary};
+        if (!file) {
+            throw std::runtime_error("failed to open binary output file: " + filepath.string());
+        }
+        file.write(reinterpret_cast<const char*>(output.data_ptr), output.data_size * sizeof(float));
+        LOG("Data saved to %s\n", filepath.c_str());
+    }
+
+    // Save logits/embeddings to text file.
+    {
+        std::filesystem::path filepath{base_path.string() + ".txt"};
+        std::ofstream file{filepath};
+        if (!file) {
+            throw std::runtime_error("failed to open text output file: " + filepath.string());
+        }
+        for (int i = 0; i < output.data_size; i++) {
+            file << i << ": " << output.data_ptr[i] << '\n';
+        }
+        LOG("Data saved to %s\n", filepath.c_str());
+    }
+
+    // Save prompt and tokens to text file.
+    {
+        std::filesystem::path filepath{base_path.string() + "-prompt.txt"};
+        std::ofstream file{filepath};
+        if (!file) {
+            throw std::runtime_error("failed to open prompt output file: " + filepath.string());
+        }
+
+        file << "prompt: " << output.prompt << '\n';
+        file << "n_tokens: " << output.tokens.size() << '\n';
+
+        file << "token ids: ";
+        for (size_t i = 0; i < output.tokens.size(); i++) {
+            file << output.tokens[i];
+            if (i + 1 < output.tokens.size()) {
+                file << ", ";
+            }
+        }
+        file << '\n';
+        LOG("Prompt saved to %s\n", filepath.c_str());
+    }
+
+    // Save token ids to binary file.
+    {
+        std::filesystem::path filepath{base_path.string() + "-tokens.bin"};
+        std::ofstream file{filepath, std::ios::binary};
+        if (!file) {
+            throw std::runtime_error("failed to open tokens binary file: " + filepath.string());
+        }
+        file.write(reinterpret_cast<const char*>(output.tokens.data()), output.tokens.size() * sizeof(llama_token));
+        LOG("Tokens saved to %s\n", filepath.c_str());
+    }
+
+}
+
+static void print_tokenized_prompt(llama_context * ctx, const std::vector<llama_token> & tokens, const std::string & prompt) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    LOG("Model add_bos: %s\n", llama_vocab_get_add_bos(vocab) ? "true" : "false");
+    LOG("Input prompt: \"%s\"\n", prompt.c_str());
+    LOG("Token ids (%zu):\n", tokens.size());
+
+    for (auto id : tokens) {
+        std::string piece(128, '\0');
+        int n = llama_token_to_piece(vocab, id, piece.data(), piece.size(), 0, true);
+        if (n < 0) {
+            LOG_ERR("failed to convert token %d to piece\n", id);
+            continue;
+        }
+        piece.resize(n);
+        LOG("%s(%d) ", piece.c_str(), id);
+    }
+    LOG("\n");
+}
+
+static bool run(llama_context * ctx, const common_params & params) {
+    const llama_model * model = llama_get_model(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab);
+
+    std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
+
+    if (tokens.empty()) {
+        LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);
+        return false;
+    }
+
+    if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
+        LOG_ERR("%s : failed to eval\n", __func__);
+        return false;
+    }
+
+    print_tokenized_prompt(ctx, tokens, params.prompt);
+
+    if (params.save_logits) {
+        output_data output {ctx, model, params};
+        std::filesystem::path model_path{params.model.path};
+        std::string model_name{model_path.stem().string()};
+        save_output_data(output, model_name, params.logits_output_dir);
+    }
+
+    return true;
+}
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DEBUG, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    base_callback_data cb_data(params, params.tensor_filter);
+
+    auto llama_init = common_init_from_params(params);
+
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
+
+    if (model == nullptr || ctx == nullptr) {
+        LOG_ERR("%s : failed to init\n", __func__);
+        return 1;
+    }
+
+    {
+        LOG_INF("\n");
+        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+        LOG_INF("\n");
+    }
+
+    if (!run(ctx, params)) {
+        return 1;
+    }
+
+    LOG("\n");
+    llama_perf_context_print(ctx);
+
+    llama_backend_free();
+
+    return 0;
+}