llama : save and restore kv cache for single seq id (#6341)

* llama : save and restore kv cache for single seq id

* remove trailing whitespace

* respond error in case there's no space in the kv cache

* add kv seq save restore to test case

* add --slot-save-path arg to enable save restore and restrict save location

* Returning 0 for some cases, instead of asserting.

* cleanup error cases

* rename sequence state functions

* rename state get set functions

* add previous function names back in with DEPRECATED notice

* update doc

* adjust endpoints to preferred style

* fix restoring zero cell count

* handle seq rm return value

* unused param

* keep in the size check

* fix return types

* add server test case for slot save restore

* cleanup

* add cake

* cleanup style

* add special

* removing a whole sequence never fails

* move sequence state file functionality from server to llama to match session api and add version tags

* catch exceptions on save as well

* error log messages

* check types for stricter restore

* update server doc

* readme : update API changes date

* strict filename validation

* move include, reject bom as well

* also reject empty filename

* reject whitespace and trailing dot

---------

Co-authored-by: Martin Evans <martindevans@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

README.md

@@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
 
 ### Recent API changes
 
+- [2024 Apr 4] State and session file functions reorganized under `llama_state_*` https://github.com/ggerganov/llama.cpp/pull/6341
 - [2024 Mar 26] Logits and embeddings API updated for compactness https://github.com/ggerganov/llama.cpp/pull/6122
 - [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
 - [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328

common/common.cpp

@@ -16,6 +16,7 @@
 #include <unordered_set>
 #include <vector>
 #include <cinttypes>
+#include <codecvt>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -27,7 +28,6 @@
 #ifndef NOMINMAX
 #   define NOMINMAX
 #endif
-#include <codecvt>
 #include <locale>
 #include <windows.h>
 #include <fcntl.h>
@@ -1500,6 +1500,77 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
     GGML_UNREACHABLE();
 }
 
+// Validate if a filename is safe to use
+// To validate a full path, split the path by the OS-specific path separator, and validate each part with this function
+bool validate_file_name(const std::string & filename) {
+    if (!filename.length()) {
+        // Empty filename invalid
+        return false;
+    }
+    if (filename.length() > 255) {
+        // Limit at common largest possible filename on Linux filesystems
+        // to avoid unnecessary further validation
+        // (On systems with smaller limits it will be caught by the OS)
+        return false;
+    }
+
+    std::u32string filename_utf32;
+    try {
+        std::wstring_convert<std::codecvt_utf8<char32_t>, char32_t> converter;
+        filename_utf32 = converter.from_bytes(filename);
+
+        // If the reverse conversion mismatches, it means overlong UTF-8 sequences were used,
+        // or invalid encodings were encountered. Reject such attempts
+        std::string filename_reencoded = converter.to_bytes(filename_utf32);
+        if (filename_reencoded != filename) {
+            return false;
+        }
+    } catch (const std::exception &) {
+        return false;
+    }
+
+    // Check for forbidden codepoints:
+    // - Control characters
+    // - Unicode equivalents of illegal characters
+    // - UTF-16 surrogate pairs
+    // - UTF-8 replacement character
+    // - Byte order mark (BOM)
+    // - Illegal characters: / \ : * ? " < > |
+    for (char32_t c : filename_utf32) {
+        if (c <= 0x1F // Control characters (C0)
+            || c == 0x7F // Control characters (DEL)
+            || (c >= 0x80 && c <= 0x9F) // Control characters (C1)
+            || c == 0xFF0E // Fullwidth Full Stop (period equivalent)
+            || c == 0x2215 // Division Slash (forward slash equivalent)
+            || c == 0x2216 // Set Minus (backslash equivalent)
+            || (c >= 0xD800 && c <= 0xDFFF) // UTF-16 surrogate pairs
+            || c == 0xFFFD // Replacement Character (UTF-8)
+            || c == 0xFEFF // Byte Order Mark (BOM)
+            || c == '/' || c == '\\' || c == ':' || c == '*' // Illegal characters
+            || c == '?' || c == '"' || c == '<' || c == '>' || c == '|') {
+            return false;
+        }
+    }
+
+    // Reject any leading or trailing ' ', or any trailing '.', these are stripped on Windows and will cause a different filename
+    // Unicode and other whitespace is not affected, only 0x20 space
+    if (filename.front() == ' ' || filename.back() == ' ' || filename.back() == '.') {
+        return false;
+    }
+
+    // Reject any ".." (currently stricter than necessary, it should be fine to just check for == ".." instead)
+    if (filename.find("..") != std::string::npos) {
+        return false;
+    }
+
+    // Reject "."
+    if (filename == ".") {
+        return false;
+    }
+
+    return true;
+}
+
 //
 // String utils
 //

common/common.h

@@ -179,6 +179,8 @@ std::string gpt_random_prompt(std::mt19937 & rng);
 
 void process_escapes(std::string& input);
 
+bool validate_file_name(const std::string & filename);
+
 //
 // String utils
 //

examples/main/main.cpp

@@ -235,7 +235,7 @@ int main(int argc, char ** argv) {
             // The file exists and is not empty
             session_tokens.resize(n_ctx);
             size_t n_token_count_out = 0;
-            if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
+            if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
                 LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
                 return 1;
             }
@@ -693,7 +693,7 @@ int main(int argc, char ** argv) {
             // optionally save the session on first sample (for faster prompt loading next time)
             if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
                 need_to_save_session = false;
-                llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+                llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
 
                 LOG("saved session to %s\n", path_session.c_str());
             }
@@ -935,7 +935,7 @@ int main(int argc, char ** argv) {
 
     if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
         LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
-        llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+        llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
     }
 
     llama_print_timings(ctx);

examples/save-load-state/save-load-state.cpp

@@ -24,6 +24,7 @@ int main(int argc, char ** argv) {
 
     std::string result0;
     std::string result1;
+    std::string result2;
 
     // init
     llama_model * model;
@@ -44,8 +45,8 @@ int main(int argc, char ** argv) {
 
     // save state (rng, logits, embedding and kv_cache) to file
     {
-        std::vector<uint8_t> state_mem(llama_get_state_size(ctx));
+        std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
-        const size_t written = llama_copy_state_data(ctx, state_mem.data());
+        const size_t written = llama_state_get_data(ctx, state_mem.data());
 
         FILE *fp_write = fopen("dump_state.bin", "wb");
         fwrite(state_mem.data(), 1, written, fp_write);
@@ -97,13 +98,13 @@ int main(int argc, char ** argv) {
 
     // load state (rng, logits, embedding and kv_cache) from file
     {
-        std::vector<uint8_t> state_mem(llama_get_state_size(ctx2));
+        std::vector<uint8_t> state_mem(llama_state_get_size(ctx2));
 
         FILE * fp_read = fopen("dump_state.bin", "rb");
         const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
         fclose(fp_read);
 
-        if (read != llama_set_state_data(ctx2, state_mem.data())) {
+        if (read != llama_state_set_data(ctx2, state_mem.data())) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
             llama_free(ctx2);
             llama_free_model(model);
@@ -141,16 +142,104 @@ int main(int argc, char ** argv) {
         n_past += 1;
     }
 
-    printf("\n");
+    printf("\n\n");
 
     llama_free(ctx2);
-    llama_free_model(model);
 
     if (result0 != result1) {
         fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
         return 1;
     }
 
+    // make new context
+    auto* ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
+
+    printf("\nsingle seq run: %s", params.prompt.c_str());
+
+    // load state (rng, logits, embedding and kv_cache) from file
+    {
+        std::vector<uint8_t> state_mem(llama_state_get_size(ctx3));
+
+        FILE * fp_read = fopen("dump_state.bin", "rb");
+        const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
+        fclose(fp_read);
+
+        if (read != llama_state_set_data(ctx3, state_mem.data())) {
+            fprintf(stderr, "\n%s : failed to read state\n", __func__);
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+
+        fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
+    }
+
+    // restore state (last tokens)
+    n_past = n_past_saved;
+
+    // save seq 0 and load into seq 1
+    {
+        // save kv of seq 0
+        std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
+        const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), 0);
+        if (ncopy != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
+
+        // erase whole kv
+        llama_kv_cache_clear(ctx3);
+        fprintf(stderr, "%s : kv cache cleared\n", __func__);
+
+        // restore kv into seq 1
+        const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), 1);
+        if (nset != seq_store.size()) {
+            fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+        fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
+    }
+
+    // third run with seq 1 instead of 0
+    for (auto i = 0; i < params.n_predict; i++) {
+        auto * logits = llama_get_logits(ctx3);
+        auto n_vocab = llama_n_vocab(model);
+        std::vector<llama_token_data> candidates;
+        candidates.reserve(n_vocab);
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        }
+        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+        auto next_token = llama_sample_token(ctx3, &candidates_p);
+        auto next_token_str = llama_token_to_piece(ctx3, next_token);
+
+        printf("%s", next_token_str.c_str());
+        result2 += next_token_str;
+
+        if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
+            fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
+            llama_free(ctx3);
+            llama_free_model(model);
+            return 1;
+        }
+        n_past += 1;
+    }
+
+    printf("\n");
+
+    llama_free(ctx3);
+    llama_free_model(model);
+
+    if (result0 != result2) {
+        fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
+        return 1;
+    }
+
     fprintf(stderr, "\n%s : success\n", __func__);
 
     return 0;

examples/server/README.md

@@ -57,6 +57,7 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
 - `-n N, --n-predict N`: Set the maximum tokens to predict. Default: `-1`
 - `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
 - `--metrics`: enable prometheus `/metrics` compatible endpoint. Default: disabled
+- `--slot-save-path PATH`: Specifies the path where the state of slots (the prompt cache) can be stored. If not provided, the slot management endpoints will be disabled.
 - `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name.  Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
 - `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled
 - `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json`
@@ -517,6 +518,57 @@ Available metrics:
 - `llamacpp:requests_processing`: Number of requests processing.
 - `llamacpp:requests_deferred`: Number of requests deferred.
 
+- **POST** `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
+
+    *Options:*
+
+    `filename`: Name of the file to save the slot's prompt cache. The file will be saved in the directory specified by the `--slot-save-path` server parameter.
+
+### Result JSON
+
+```json
+{
+    "id_slot": 0,
+    "filename": "slot_save_file.bin",
+    "n_saved": 1745,
+    "n_written": 14309796,
+    "timings": {
+        "save_ms": 49.865
+    }
+}
+```
+
+- **POST** `/slots/{id_slot}?action=restore`: Restore the prompt cache of the specified slot from a file.
+
+    *Options:*
+
+    `filename`: Name of the file to restore the slot's prompt cache from. The file should be located in the directory specified by the `--slot-save-path` server parameter.
+
+### Result JSON
+
+```json
+{
+    "id_slot": 0,
+    "filename": "slot_save_file.bin",
+    "n_restored": 1745,
+    "n_read": 14309796,
+    "timings": {
+        "restore_ms": 42.937
+    }
+}
+```
+
+- **POST** `/slots/{id_slot}?action=erase`: Erase the prompt cache of the specified slot.
+
+### Result JSON
+
+```json
+{
+    "id_slot": 0,
+    "n_erased": 1745
+}
+```
+
 ## More examples
 
 ### Change system prompt on runtime

examples/server/server.cpp

@@ -61,7 +61,10 @@ enum server_task_type {
     SERVER_TASK_TYPE_COMPLETION,
     SERVER_TASK_TYPE_CANCEL,
     SERVER_TASK_TYPE_NEXT_RESPONSE,
-    SERVER_TASK_TYPE_METRICS
+    SERVER_TASK_TYPE_METRICS,
+    SERVER_TASK_TYPE_SLOT_SAVE,
+    SERVER_TASK_TYPE_SLOT_RESTORE,
+    SERVER_TASK_TYPE_SLOT_ERASE,
 };
 
 struct server_task {
@@ -128,6 +131,7 @@ struct server_params {
 
     bool slots_endpoint   = true;
     bool metrics_endpoint = false;
+    std::string slot_save_path;
 };
 
 struct server_slot {
@@ -1612,6 +1616,107 @@ struct server_context {
                     }
                     queue_results.send(res);
                 } break;
+            case SERVER_TASK_TYPE_SLOT_SAVE:
+                {
+                    int id_slot = task.data["id_slot"];
+                    server_slot * slot = get_slot(id_slot);
+                    if (slot == nullptr) {
+                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+
+                    const size_t token_count = slot->cache_tokens.size();
+                    const int64_t t_start = ggml_time_us();
+
+                    std::string filename = task.data["filename"];
+                    std::string filepath = task.data["filepath"];
+
+                    const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count);
+
+                    const int64_t t_end = ggml_time_us();
+                    const double t_save_ms = (t_end - t_start) / 1000.0;
+
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json {
+                        { "id_slot",   id_slot },
+                        { "filename",  filename },
+                        { "n_saved",   token_count }, // tokens saved
+                        { "n_written", nwrite },      // bytes written
+                        { "timings", {
+                            { "save_ms", t_save_ms }
+                        } }
+                    };
+                    queue_results.send(result);
+                } break;
+            case SERVER_TASK_TYPE_SLOT_RESTORE:
+                {
+                    int id_slot = task.data["id_slot"];
+                    server_slot * slot = get_slot(id_slot);
+                    if (slot == nullptr) {
+                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+
+                    const int64_t t_start = ggml_time_us();
+
+                    std::string filename = task.data["filename"];
+                    std::string filepath = task.data["filepath"];
+
+                    slot->cache_tokens.resize(slot->n_ctx);
+                    size_t token_count = 0;
+                    size_t nread = llama_state_seq_load_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), slot->cache_tokens.size(), &token_count);
+                    if (nread == 0) {
+                        slot->cache_tokens.resize(0);
+                        send_error(task, "Unable to restore slot, no available space in KV cache or invalid slot save file", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+                    slot->cache_tokens.resize(token_count);
+
+                    const int64_t t_end = ggml_time_us();
+                    const double t_restore_ms = (t_end - t_start) / 1000.0;
+
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json {
+                        { "id_slot",    id_slot },
+                        { "filename",   filename },
+                        { "n_restored", token_count }, // tokens restored
+                        { "n_read",     nread },       // bytes read
+                        { "timings", {
+                            { "restore_ms", t_restore_ms }
+                        } }
+                    };
+                    queue_results.send(result);
+                } break;
+            case SERVER_TASK_TYPE_SLOT_ERASE:
+                {
+                    int id_slot = task.data["id_slot"];
+                    server_slot * slot = get_slot(id_slot);
+                    if (slot == nullptr) {
+                        send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
+                        break;
+                    }
+
+                    // Erase token cache
+                    const size_t n_erased = slot->cache_tokens.size();
+                    llama_kv_cache_seq_rm(ctx, slot->id + 1, -1, -1);
+                    slot->cache_tokens.clear();
+
+                    server_task_result result;
+                    result.id = task.id;
+                    result.stop = true;
+                    result.error = false;
+                    result.data = json {
+                        { "id_slot",  id_slot },
+                        { "n_erased", n_erased }
+                    };
+                    queue_results.send(result);
+                } break;
         }
     }
 
@@ -2249,6 +2354,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
     printf("  --log-disable             disables logging to a file.\n");
     printf("  --slots-endpoint-disable  disables slots monitoring endpoint.\n");
     printf("  --metrics                 enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled");
+    printf("  --slot-save-path PATH     path to save slot kv cache (default: disabled)\n");
     printf("\n");
     printf("  -n, --n-predict           maximum tokens to predict (default: %d)\n", params.n_predict);
     printf("  --override-kv KEY=TYPE:VALUE\n");
@@ -2657,6 +2763,16 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
             sparams.slots_endpoint = false;
         } else if (arg == "--metrics") {
             sparams.metrics_endpoint = true;
+        } else if (arg == "--slot-save-path") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            sparams.slot_save_path = argv[i];
+            // if doesn't end with DIRECTORY_SEPARATOR, add it
+            if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) {
+                sparams.slot_save_path += DIRECTORY_SEPARATOR;
+            }
         } else if (arg == "--chat-template") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -3159,6 +3275,112 @@ int main(int argc, char ** argv) {
         res.status = 200; // HTTP OK
     };
 
+    const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
+        json request_data = json::parse(req.body);
+        std::string filename = request_data["filename"];
+        if (!validate_file_name(filename)) {
+            res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+        std::string filepath = sparams.slot_save_path + filename;
+
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SLOT_SAVE;
+        task.data = {
+            { "id_slot", id_slot },
+            { "filename", filename },
+            { "filepath", filepath }
+        };
+
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        if (result.error) {
+            res_error(res, result.data);
+        } else {
+            res.set_content(result.data.dump(), "application/json");
+        }
+    };
+
+    const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) {
+        json request_data = json::parse(req.body);
+        std::string filename = request_data["filename"];
+        if (!validate_file_name(filename)) {
+            res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+        std::string filepath = sparams.slot_save_path + filename;
+
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SLOT_RESTORE;
+        task.data = {
+            { "id_slot", id_slot },
+            { "filename", filename },
+            { "filepath", filepath }
+        };
+
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        if (result.error) {
+            res_error(res, result.data);
+        } else {
+            res.set_content(result.data.dump(), "application/json");
+        }
+    };
+
+    const auto handle_slots_erase = [&ctx_server, &res_error](const httplib::Request & /* req */, httplib::Response & res, int id_slot) {
+        server_task task;
+        task.type = SERVER_TASK_TYPE_SLOT_ERASE;
+        task.data = {
+            { "id_slot", id_slot },
+        };
+
+        const int id_task = ctx_server.queue_tasks.post(task);
+        ctx_server.queue_results.add_waiting_task_id(id_task);
+
+        server_task_result result = ctx_server.queue_results.recv(id_task);
+        ctx_server.queue_results.remove_waiting_task_id(id_task);
+
+        if (result.error) {
+            res_error(res, result.data);
+        } else {
+            res.set_content(result.data.dump(), "application/json");
+        }
+    };
+
+    const auto handle_slots_action = [&res_error, &handle_slots_save, &handle_slots_restore, &handle_slots_erase](const httplib::Request & req, httplib::Response & res) {
+        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
+
+        std::string id_slot_str = req.path_params.at("id_slot");
+        int id_slot;
+
+        try {
+            id_slot = std::stoi(id_slot_str);
+        } catch (const std::exception &) {
+            res_error(res, format_error_response("Invalid slot ID", ERROR_TYPE_INVALID_REQUEST));
+            return;
+        }
+
+        std::string action = req.get_param_value("action");
+
+        if (action == "save") {
+            handle_slots_save(req, res, id_slot);
+        } else if (action == "restore") {
+            handle_slots_restore(req, res, id_slot);
+        } else if (action == "erase") {
+            handle_slots_erase(req, res, id_slot);
+        } else {
+            res_error(res, format_error_response("Invalid action", ERROR_TYPE_INVALID_REQUEST));
+        }
+    };
+
     const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) {
         res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
         json data = {
@@ -3521,6 +3743,10 @@ int main(int argc, char ** argv) {
     svr->Post("/v1/embeddings",       handle_embeddings);
     svr->Post("/tokenize",            handle_tokenize);
     svr->Post("/detokenize",          handle_detokenize);
+    if (!sparams.slot_save_path.empty()) {
+        // only enable slot endpoints if slot_save_path is set
+        svr->Post("/slots/:id_slot",  handle_slots_action);
+    }
 
     //
     // Start the server

examples/server/tests/features/slotsave.feature

@@ -0,0 +1,58 @@
+@llama.cpp
+@slotsave
+Feature: llama.cpp server slot management
+
+  Background: Server startup
+    Given a server listening on localhost:8080
+    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   prompt caching is enabled
+    And   2 slots
+    And   . as slot save path
+    And   2048 KV cache size
+    And   42 as server seed
+    And   24 max tokens to predict
+    Then  the server is starting
+    Then  the server is healthy
+
+  Scenario: Save and Restore Slot
+    # First prompt in slot 1 should be fully processed
+    Given a user prompt "What is the capital of France?"
+    And   using slot id 1
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   22 prompt tokens are processed
+    When  the slot 1 is saved with filename "slot1.bin"
+    Then  the server responds with status code 200
+    # Since we have cache, this should only process the last tokens
+    Given a user prompt "What is the capital of Germany?"
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Thank|special)
+    And   7 prompt tokens are processed
+    # Loading the original cache into slot 0,
+    # we should only be processing 1 prompt token and get the same output
+    When  the slot 0 is restored with filename "slot1.bin"
+    Then  the server responds with status code 200
+    Given a user prompt "What is the capital of France?"
+    And   using slot id 0
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   1 prompt tokens are processed
+    # For verification that slot 1 was not corrupted during slot 0 load, same thing
+    Given a user prompt "What is the capital of Germany?"
+    And   using slot id 1
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Thank|special)
+    And   1 prompt tokens are processed
+
+  Scenario: Erase Slot
+    Given a user prompt "What is the capital of France?"
+    And   using slot id 1
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   22 prompt tokens are processed
+    When  the slot 1 is erased
+    Then  the server responds with status code 200
+    Given a user prompt "What is the capital of France?"
+    And   a completion request with no api error
+    Then  24 tokens are predicted matching (Lily|cake)
+    And   22 prompt tokens are processed

examples/server/tests/features/steps/steps.py

@@ -49,6 +49,9 @@ def step_server_config(context, server_fqdn, server_port):
     context.n_predict = None
     context.n_prompts = 0
     context.n_server_predict = None
+    context.slot_save_path = None
+    context.id_slot = None
+    context.cache_prompt = None
     context.n_slots = None
     context.prompt_prefix = None
     context.prompt_suffix = None
@@ -119,6 +122,21 @@ def step_server_n_predict(context, n_predict):
     context.n_server_predict = n_predict
 
 
+@step('{slot_save_path} as slot save path')
+def step_slot_save_path(context, slot_save_path):
+    context.slot_save_path = slot_save_path
+
+
+@step('using slot id {id_slot:d}')
+def step_id_slot(context, id_slot):
+    context.id_slot = id_slot
+
+
+@step('prompt caching is enabled')
+def step_enable_prompt_cache(context):
+    context.cache_prompt = True
+
+
 @step('continuous batching')
 def step_server_continuous_batching(context):
     context.server_continuous_batching = True
@@ -212,6 +230,8 @@ async def step_request_completion(context, api_error):
                                           context.base_url,
                                           debug=context.debug,
                                           n_predict=context.n_predict,
+                                          cache_prompt=context.cache_prompt,
+                                          id_slot=context.id_slot,
                                           seed=await completions_seed(context),
                                           expect_api_error=expect_api_error,
                                           user_api_key=context.user_api_key)
@@ -711,12 +731,48 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
     await asyncio.sleep(0.1)
 
 
+@step('the slot {slot_id:d} is saved with filename "{filename}"')
+@async_run_until_complete
+async def step_save_slot(context, slot_id, filename):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/slots/{slot_id}?action=save',
+                                json={"filename": filename},
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+
+
+@step('the slot {slot_id:d} is restored with filename "{filename}"')
+@async_run_until_complete
+async def step_restore_slot(context, slot_id, filename):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/slots/{slot_id}?action=restore',
+                                json={"filename": filename},
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+
+
+@step('the slot {slot_id:d} is erased')
+@async_run_until_complete
+async def step_erase_slot(context, slot_id):
+    async with aiohttp.ClientSession() as session:
+        async with session.post(f'{context.base_url}/slots/{slot_id}?action=erase',
+                                headers={"Content-Type": "application/json"}) as response:
+            context.response = response
+
+
+@step('the server responds with status code {status_code:d}')
+def step_server_responds_with_status_code(context, status_code):
+    assert context.response.status == status_code
+
+
 async def request_completion(prompt,
                              base_url,
                              debug=False,
                              prompt_prefix=None,
                              prompt_suffix=None,
                              n_predict=None,
+                             cache_prompt=False,
+                             id_slot=None,
                              seed=None,
                              expect_api_error=None,
                              user_api_key=None):
@@ -738,6 +794,8 @@ async def request_completion(prompt,
                                     "prompt": prompt,
                                     "input_suffix": prompt_suffix,
                                     "n_predict": n_predict if n_predict is not None else -1,
+                                    "cache_prompt": cache_prompt,
+                                    "id_slot": id_slot,
                                     "seed": seed if seed is not None else 42
                                 },
                                 headers=headers,
@@ -1104,6 +1162,8 @@ def start_server_background(context):
         server_args.extend(['--parallel', context.n_slots])
     if context.n_server_predict:
         server_args.extend(['--n-predict', context.n_server_predict])
+    if context.slot_save_path:
+        server_args.extend(['--slot-save-path', context.slot_save_path])
     if context.server_api_key:
         server_args.extend(['--api-key', context.server_api_key])
     if context.n_ga:

llama.cpp

@@ -14907,9 +14907,33 @@ void llama_kv_cache_update(struct llama_context * ctx) {
     llama_kv_cache_update_internal(*ctx);
 }
 
+// deprecated
+size_t llama_get_state_size(const struct llama_context * ctx) {
+    return llama_state_get_size(ctx);
+}
+
+// deprecated
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+    return llama_state_get_data(ctx, dst);
+}
+
+// deprecated
+size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
+    return llama_state_set_data(ctx, src);
+}
+
+// deprecated
+bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+}
+
+// deprecated
+bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    return llama_state_save_file(ctx, path_session, tokens, n_token_count);
+}
 
 // Returns the *maximum* size of the state
-size_t llama_get_state_size(const struct llama_context * ctx) {
+size_t llama_state_get_size(const struct llama_context * ctx) {
     const auto & cparams = ctx->cparams;
     const auto & hparams = ctx->model.hparams;
 
@@ -14997,15 +15021,15 @@ struct llama_data_file_context : llama_data_context {
  * file context:
  * llama_file file("/path", "wb");
  * llama_data_file_context data_ctx(&file);
- * llama_copy_state_data(ctx, &data_ctx);
+ * llama_state_get_data(ctx, &data_ctx);
  *
  * buffer context:
  * std::vector<uint8_t> buf(max_size, 0);
  * llama_data_buffer_context data_ctx(&buf.data());
- * llama_copy_state_data(ctx, &data_ctx);
+ * llama_state_get_data(ctx, &data_ctx);
  *
 */
-static void llama_copy_state_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
+static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
     // copy rng
     {
         std::ostringstream rng_ss;
@@ -15149,15 +15173,15 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
     }
 }
 
-size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) {
     llama_data_buffer_context data_ctx(dst);
-    llama_copy_state_data_internal(ctx, &data_ctx);
+    llama_state_get_data_internal(ctx, &data_ctx);
 
     return data_ctx.get_size_written();
 }
 
 // Sets the state reading from the specified source address
-size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
+size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
     const uint8_t * inp = src;
 
     // set rng
@@ -15309,14 +15333,14 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
     }
 
     const size_t nread    = inp - src;
-    const size_t max_size = llama_get_state_size(ctx);
+    const size_t max_size = llama_state_get_size(ctx);
 
     GGML_ASSERT(nread <= max_size);
 
     return nread;
 }
 
-static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
     llama_file file(path_session, "rb");
 
     // sanity checks
@@ -15354,7 +15378,7 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
     // restore the context state
     {
         const size_t n_state_size_cur = file.size - file.tell();
-        const size_t n_state_size_max = llama_get_state_size(ctx);
+        const size_t n_state_size_max = llama_state_get_size(ctx);
 
         if (n_state_size_cur > n_state_size_max) {
             LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
@@ -15364,22 +15388,22 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c
         std::vector<uint8_t> state_data(n_state_size_max);
         file.read_raw(state_data.data(), n_state_size_cur);
 
-        llama_set_state_data(ctx, state_data.data());
+        llama_state_set_data(ctx, state_data.data());
     }
 
     return true;
 }
 
-bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
     try {
-        return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+        return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
     } catch (const std::exception & err) {
         LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
         return false;
     }
 }
 
-bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
     llama_file file(path_session, "wb");
 
     file.write_u32(LLAMA_SESSION_MAGIC);
@@ -15393,11 +15417,420 @@ bool llama_save_session_file(struct llama_context * ctx, const char * path_sessi
 
     // save the context state using stream saving
     llama_data_file_context data_ctx(&file);
-    llama_copy_state_data_internal(ctx, &data_ctx);
+    llama_state_get_data_internal(ctx, &data_ctx);
 
     return true;
 }
 
+bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error saving session file: %s\n", err.what());
+        return false;
+    }
+}
+
+size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) {
+    // save the size of size_t as a uint32_t for safety check
+    const size_t size_t_size_size = sizeof(uint32_t);
+
+    // other values
+    const size_t s_cell_count_size = sizeof(uint32_t);
+    const size_t s_layer_count_size = sizeof(uint32_t);
+    const size_t n_embd_v_gqa_size = sizeof(uint32_t);
+
+    size_t s_cell_count = 0;
+    size_t s_cell_data_size = 0;
+    const auto & kv_self = ctx->kv_self;
+    const auto & hparams = ctx->model.hparams;
+
+    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+
+    for (uint32_t i = 0; i < kv_self.size; ++i) {
+        const auto & cell = kv_self.cells[i];
+        if (cell.seq_id.count(seq_id) > 0) {
+            ++s_cell_count;
+            s_cell_data_size += sizeof(llama_pos);
+        }
+    }
+
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // types of keys and values
+        s_cell_data_size += sizeof(int32_t) * 2;
+        // k_size_row and v_size_el values of layer
+        s_cell_data_size += sizeof(size_t) * 2;
+
+        // keys
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        s_cell_data_size += k_size_row * s_cell_count;
+
+        // values (transposed)
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        s_cell_data_size += v_size_el * s_cell_count * n_embd_v_gqa;
+    }
+
+    const size_t s_total = (
+        size_t_size_size +
+        s_cell_count_size +
+        s_layer_count_size +
+        n_embd_v_gqa_size +
+        s_cell_data_size
+        );
+
+    return s_total;
+}
+
+static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) {
+    const auto & kv_self = ctx->kv_self;
+    GGML_ASSERT(!kv_self.recurrent); // not implemented
+
+    // Save the size of size_t as a uint32_t for safety check
+    const uint32_t size_t_size = sizeof(size_t);
+    data_ctx.write(&size_t_size, sizeof(size_t_size));
+
+    std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+    uint32_t cell_count = 0;
+
+    // Count the number of cells with the specified seq_id
+    // Find all the ranges of cells with this seq id
+    {
+        uint32_t cell_range_begin = kv_self.size;
+        for (uint32_t i = 0; i < kv_self.size; ++i) {
+            const auto & cell = kv_self.cells[i];
+            if (cell.has_seq_id(seq_id)) {
+                ++cell_count;
+                if (cell_range_begin == kv_self.size) {
+                    cell_range_begin = i;
+                }
+            }
+            else {
+                if (cell_range_begin != kv_self.size) {
+                    cell_ranges.push_back({ cell_range_begin, i });
+                    cell_range_begin = kv_self.size;
+                }
+            }
+        }
+        if (cell_range_begin != kv_self.size) {
+            cell_ranges.push_back({ cell_range_begin, kv_self.size });
+        }
+
+        // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
+        uint32_t cell_count_check = 0;
+        for (const auto & range : cell_ranges) {
+            cell_count_check += range.second - range.first;
+        }
+        GGML_ASSERT(cell_count == cell_count_check);
+    }
+
+    // Write the cell count
+    data_ctx.write(&cell_count, sizeof(cell_count));
+
+    const auto & hparams = ctx->model.hparams;
+    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+
+    // Write the layer count
+    data_ctx.write(&n_layer, sizeof(n_layer));
+
+    // Write n_embd_v_gqa
+    data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
+
+    // Iterate the ranges and write all the pos (this is the token position in the prompt)
+    for (const auto & range : cell_ranges) {
+        for (uint32_t i = range.first; i < range.second; ++i) {
+            const auto & cell = kv_self.cells[i];
+            data_ctx.write(&cell.pos, sizeof(cell.pos));
+        }
+    }
+
+    // Iterate and write all the keys first, each row is a cell
+    // Get whole range at a time
+    std::vector<uint8_t> tmp_buf;
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // Write key type
+        const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+        data_ctx.write(&k_type_i, sizeof(k_type_i));
+
+        // Write row size of key
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        data_ctx.write(&k_size_row, sizeof(k_size_row));
+
+        // Read each range of cells of k_size length each into tmp_buf and write out
+        for (const auto & range : cell_ranges) {
+            const size_t range_size = range.second - range.first;
+            tmp_buf.resize(range_size * k_size_row);
+            ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), range.first * k_size_row, range_size * k_size_row);
+            data_ctx.write(tmp_buf.data(), tmp_buf.size());
+        }
+    }
+
+    // For the values, they are transposed, so we also need the element size and get the element ranges from each row
+    const uint32_t kv_size = kv_self.size;
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // Write value type
+        const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+        data_ctx.write(&v_type_i, sizeof(v_type_i));
+
+        // Write element size
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        data_ctx.write(&v_size_el, sizeof(v_size_el));
+
+        // For each row, we get the element values of each cell
+        for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+            // Read each range of cells of v_size_el length each into tmp_buf and write out
+            for (const auto & range : cell_ranges) {
+                const size_t range_size = range.second - range.first;
+                const size_t src_offset = (range.first + j * kv_size) * v_size_el;
+                tmp_buf.resize(range_size * v_size_el);
+                ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size());
+                data_ctx.write(tmp_buf.data(), tmp_buf.size());
+            }
+        }
+    }
+
+    return data_ctx.get_size_written();
+}
+
+size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_seq_id seq_id) {
+    llama_data_buffer_context data_ctx(dst);
+    return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+}
+
+size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) {
+    auto & kv_self = ctx->kv_self;
+    GGML_ASSERT(!kv_self.recurrent); // not implemented
+
+    // Wipe the slot
+    llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+
+    const uint8_t * inp = src;
+
+    // Read size of size_t
+    uint32_t size_t_size;
+    memcpy(&size_t_size, inp, sizeof(size_t_size));
+    inp += sizeof(size_t_size);
+    if (size_t_size != sizeof(size_t)) {
+        LLAMA_LOG_ERROR("%s: size_t size mismatch\n", __func__);
+        return 0;
+    }
+
+    // Read the cell count
+    uint32_t cell_count;
+    memcpy(&cell_count, inp, sizeof(cell_count));
+    inp += sizeof(cell_count);
+
+    // Read the layer count
+    uint32_t n_layer_ref;
+    memcpy(&n_layer_ref, inp, sizeof(n_layer_ref));
+    inp += sizeof(n_layer_ref);
+
+    // Read n_embd_v_gqa
+    uint32_t n_embd_v_gqa_ref;
+    memcpy(&n_embd_v_gqa_ref, inp, sizeof(n_embd_v_gqa_ref));
+    inp += sizeof(n_embd_v_gqa_ref);
+
+    // Sanity check model compatibility
+    const auto & hparams = ctx->model.hparams;
+    const uint32_t n_layer = hparams.n_layer;
+    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s();
+    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa() + hparams.n_embd_v_s();
+    if (n_layer != n_layer_ref) {
+        LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref);
+        return 0;
+    }
+    if (n_embd_v_gqa != n_embd_v_gqa_ref) {
+        LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref);
+        return 0;
+    }
+
+    // Allocate the new cells for the slot
+    if (cell_count) {
+        llama_batch batch = llama_batch_init(cell_count, 0, 1);
+        batch.n_tokens = cell_count;
+        for (uint32_t i = 0; i < cell_count; ++i) {
+            llama_pos pos;
+            memcpy(&pos, inp, sizeof(pos));
+            inp += sizeof(pos);
+
+            batch.pos[i] = pos;
+            batch.n_seq_id[i] = 1;
+            batch.seq_id[i][0] = dest_seq_id;
+        }
+        if (!llama_kv_cache_find_slot(kv_self, batch)) {
+            llama_batch_free(batch);
+            LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+            return 0;
+        }
+
+        // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
+        // Assume that this is one contiguous block of cells
+        GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
+        GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
+        GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
+        GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
+        GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
+
+        // Cleanup
+        llama_batch_free(batch);
+    }
+
+    const uint32_t kv_size = kv_self.size;
+    const uint32_t kv_head = kv_self.head;
+
+    // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // Read type of key
+        int32_t k_type_i_ref;
+        memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref));
+        inp += sizeof(k_type_i_ref);
+        const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+        if (k_type_i != k_type_i_ref) {
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+            LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
+            return 0;
+        }
+
+        // Read row size of key
+        size_t k_size_row_ref;
+        memcpy(&k_size_row_ref, inp, sizeof(k_size_row_ref));
+        inp += sizeof(k_size_row_ref);
+        const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+        if (k_size_row != k_size_row_ref) {
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+            LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, k_size_row_ref, il);
+            return 0;
+        }
+
+        if (cell_count) {
+            // Read and set the keys for the whole cell range
+            ggml_backend_tensor_set(kv_self.k_l[il], inp, kv_head * k_size_row, cell_count * k_size_row);
+            inp += cell_count * k_size_row;
+        }
+    }
+
+    // For each layer, read the values for each cell (transposed)
+    for (int il = 0; il < (int)n_layer; ++il) {
+        // Read type of value
+        int32_t v_type_i_ref;
+        memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
+        inp += sizeof(v_type_i_ref);
+        const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+        if (v_type_i != v_type_i_ref) {
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+            LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+            return 0;
+        }
+
+        // Read element size of value
+        size_t v_size_el_ref;
+        memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref));
+        inp += sizeof(v_size_el_ref);
+        const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+        if (v_size_el != v_size_el_ref) {
+            llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+            LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il);
+            return 0;
+        }
+
+        if (cell_count) {
+            // For each row in the transposed matrix, read the values for the whole cell range
+            for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                const size_t dst_offset = (kv_head + j * kv_size) * v_size_el;
+                ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el);
+                inp += cell_count * v_size_el;
+            }
+        }
+    }
+
+    const size_t nread = inp - src;
+    return nread;
+}
+
+static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+    llama_file file(filepath, "wb");
+
+    file.write_u32(LLAMA_STATE_SEQ_MAGIC);
+    file.write_u32(LLAMA_STATE_SEQ_VERSION);
+
+    // save the prompt
+    file.write_u32((uint32_t)n_token_count);
+    file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+    // save the context state using stream saving
+    llama_data_file_context data_ctx(&file);
+    llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+
+    const size_t res = file.tell();
+    GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written());
+    return res;
+}
+
+static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    llama_file file(filepath, "rb");
+
+    // version checks
+    {
+        const uint32_t magic   = file.read_u32();
+        const uint32_t version = file.read_u32();
+
+        if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
+            LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
+            return 0;
+        }
+    }
+
+    // load the prompt
+    {
+        const uint32_t n_token_count = file.read_u32();
+
+        if (n_token_count > n_token_capacity) {
+            LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+            return 0;
+        }
+
+        file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+        *n_token_count_out = n_token_count;
+    }
+
+    // restore the context state
+    {
+        const size_t state_size = file.size - file.tell();
+        std::vector<uint8_t> state_data(state_size);
+        file.read_raw(state_data.data(), state_size);
+        const size_t nread = llama_state_seq_set_data(ctx, state_data.data(), dest_seq_id);
+        if (!nread) {
+            LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
+            return 0;
+        }
+        GGML_ASSERT(nread <= state_size);
+        GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
+    }
+
+    return file.tell();
+}
+
+size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+    try {
+        return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error saving sequence state file: %s\n", err.what());
+        return 0;
+    }
+}
+
+size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+    try {
+        return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
+    } catch (const std::exception & err) {
+        LLAMA_LOG_ERROR("error loading sequence state file: %s\n", err.what());
+        return 0;
+    }
+}
+
 void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
     ctx->cparams.n_threads       = n_threads;
     ctx->cparams.n_threads_batch = n_threads_batch;

llama.h

@@ -37,10 +37,14 @@
 
 #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
 #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
+#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
 
 #define LLAMA_SESSION_MAGIC   LLAMA_FILE_MAGIC_GGSN
 #define LLAMA_SESSION_VERSION 5
 
+#define LLAMA_STATE_SEQ_MAGIC   LLAMA_FILE_MAGIC_GGSQ
+#define LLAMA_STATE_SEQ_VERSION 1
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -523,6 +527,7 @@ extern "C" {
             struct llama_context * ctx);
 
     // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
+    // Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
     // seq_id < 0 : match any sequence
     // p0 < 0     : [0,  p1]
     // p1 < 0     : [p0, inf)
@@ -594,34 +599,92 @@ extern "C" {
 
     // Returns the maximum size in bytes of the state (rng, logits, embedding
     // and kv_cache) - will often be smaller after compacting tokens
-    LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx);
+    LLAMA_API size_t llama_state_get_size(const struct llama_context * ctx);
+    LLAMA_API DEPRECATED(size_t llama_get_state_size(const struct llama_context * ctx),
+        "use llama_state_get_size instead");
 
     // Copies the state to the specified destination address.
     // Destination needs to have allocated enough memory.
     // Returns the number of bytes copied
-    LLAMA_API size_t llama_copy_state_data(
+    LLAMA_API size_t llama_state_get_data(
             struct llama_context * ctx,
                          uint8_t * dst);
+    LLAMA_API DEPRECATED(size_t llama_copy_state_data(
+            struct llama_context * ctx,
+                         uint8_t * dst),
+        "use llama_state_get_data instead");
 
     // Set the state reading from the specified address
     // Returns the number of bytes read
-    LLAMA_API size_t llama_set_state_data(
+    LLAMA_API size_t llama_state_set_data(
             struct llama_context * ctx,
                    const uint8_t * src);
+    LLAMA_API DEPRECATED(size_t llama_set_state_data(
+            struct llama_context * ctx,
+                   const uint8_t * src),
+        "use llama_state_set_data instead");
 
     // Save/load session file
-    LLAMA_API bool llama_load_session_file(
+    LLAMA_API bool llama_state_load_file(
             struct llama_context * ctx,
                       const char * path_session,
                      llama_token * tokens_out,
                           size_t   n_token_capacity,
                           size_t * n_token_count_out);
+    LLAMA_API DEPRECATED(bool llama_load_session_file(
+            struct llama_context * ctx,
+                      const char * path_session,
+                     llama_token * tokens_out,
+                          size_t   n_token_capacity,
+                          size_t * n_token_count_out),
+        "use llama_state_load_file instead");
 
-    LLAMA_API bool llama_save_session_file(
+    LLAMA_API bool llama_state_save_file(
             struct llama_context * ctx,
                       const char * path_session,
                const llama_token * tokens,
                           size_t   n_token_count);
+    LLAMA_API DEPRECATED(bool llama_save_session_file(
+            struct llama_context * ctx,
+                      const char * path_session,
+               const llama_token * tokens,
+                          size_t   n_token_count),
+        "use llama_state_save_file instead");
+
+    // Get the exact size needed to copy the KV cache of a single sequence
+    LLAMA_API size_t llama_state_seq_get_size(
+            struct llama_context * ctx,
+                    llama_seq_id   seq_id);
+
+    // Copy the KV cache of a single sequence into the specified buffer
+    LLAMA_API size_t llama_state_seq_get_data(
+            struct llama_context * ctx,
+                         uint8_t * dst,
+                    llama_seq_id   seq_id);
+
+    // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence
+    // Returns:
+    //  - Positive: Ok
+    //  - Zero: Failed to load
+    LLAMA_API size_t llama_state_seq_set_data(
+            struct llama_context * ctx,
+                   const uint8_t * src,
+                    llama_seq_id   dest_seq_id);
+
+    LLAMA_API size_t llama_state_seq_save_file(
+            struct llama_context * ctx,
+                      const char * filepath,
+                    llama_seq_id   seq_id,
+               const llama_token * tokens,
+                          size_t   n_token_count);
+
+    LLAMA_API size_t llama_state_seq_load_file(
+            struct llama_context * ctx,
+                      const char * filepath,
+                    llama_seq_id   dest_seq_id,
+                     llama_token * tokens_out,
+                          size_t   n_token_capacity,
+                          size_t * n_token_count_out);
 
     //
     // Decoding