mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-10 17:14:36 +00:00
Merge branch 'upstream' into concedo_experimental
# Conflicts: # .github/workflows/build.yml # .github/workflows/docker.yml # Makefile # README-sycl.md # README.md # ci/run.sh # ggml-cuda.cu # ggml.c # grammars/README.md # scripts/get-wikitext-2.sh # scripts/hf.sh # scripts/sync-ggml.last # tests/test-backend-ops.cpp # tests/test-grammar-integration.cpp # tests/test-json-schema-to-grammar.cpp
This commit is contained in:
Concedo
commit
9a25d77cc1
58 changed files with 6529 additions and 6121 deletions
1
.gitignore
vendored
1
.gitignore
vendored
|
|
@ -32,6 +32,7 @@ models-mnt
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|||
/convert-llama2c-to-ggml
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/embd-input-test
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/embedding
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||||
/eval-callback
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/gguf
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/gguf-llama-simple
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/gritlm
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|
|
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|
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@ -49,11 +49,11 @@ If you intend to run multiple models in parallel with shared memory, it is your
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|
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1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
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1. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
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2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
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1. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
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3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
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1. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
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4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
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## Reporting a vulnerability
|
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|
|
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@ -1746,6 +1746,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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cparams.yarn_orig_ctx = params.yarn_orig_ctx;
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cparams.pooling_type = params.pooling_type;
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cparams.defrag_thold = params.defrag_thold;
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cparams.cb_eval = params.cb_eval;
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cparams.cb_eval_user_data = params.cb_eval_user_data;
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cparams.offload_kqv = !params.no_kv_offload;
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cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
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@ -2193,7 +2195,7 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
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}
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{
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if (params.warmup) {
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LOG("warming up the model with an empty run\n");
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std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
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@ -2213,23 +2215,23 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
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std::vector<llama_token> llama_tokenize(
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const struct llama_context * ctx,
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const std::string & text,
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bool add_bos,
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bool special) {
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return llama_tokenize(llama_get_model(ctx), text, add_bos, special);
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bool add_special,
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bool parse_special) {
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return llama_tokenize(llama_get_model(ctx), text, add_special, parse_special);
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}
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std::vector<llama_token> llama_tokenize(
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const struct llama_model * model,
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const std::string & text,
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bool add_bos,
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bool special) {
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bool add_special,
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bool parse_special) {
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// upper limit for the number of tokens
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int n_tokens = text.length() + add_bos;
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int n_tokens = text.length() + 2 * add_special;
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std::vector<llama_token> result(n_tokens);
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n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
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n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
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if (n_tokens < 0) {
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result.resize(-n_tokens);
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int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
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int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
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GGML_ASSERT(check == -n_tokens);
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} else {
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result.resize(n_tokens);
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|
|
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|
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@ -76,6 +76,9 @@ struct gpt_params {
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int32_t yarn_orig_ctx = 0; // YaRN original context length
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float defrag_thold = -1.0f; // KV cache defragmentation threshold
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ggml_backend_sched_eval_callback cb_eval = nullptr;
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void * cb_eval_user_data = nullptr;
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ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
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llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
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@ -172,6 +175,7 @@ struct gpt_params {
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bool infill = false; // use infill mode
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bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
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bool no_kv_offload = false; // disable KV offloading
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bool warmup = true; // warmup run
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|
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std::string cache_type_k = "f16"; // KV cache data type for the K
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std::string cache_type_v = "f16"; // KV cache data type for the V
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|
|
@ -239,14 +243,14 @@ void llama_batch_add(
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std::vector<llama_token> llama_tokenize(
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const struct llama_context * ctx,
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const std::string & text,
|
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bool add_bos,
|
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bool special = false);
|
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bool add_special,
|
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bool parse_special = false);
|
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|
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std::vector<llama_token> llama_tokenize(
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const struct llama_model * model,
|
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const std::string & text,
|
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bool add_bos,
|
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bool special = false);
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bool add_special,
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bool parse_special = false);
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|
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// tokenizes a token into a piece
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// should work similar to Python's `tokenizer.id_to_piece`
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|
|
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|
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@ -11,35 +11,101 @@
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|
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using json = nlohmann::ordered_json;
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|
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template <typename Iterator>
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static std::string join(Iterator begin, Iterator end, const std::string & separator);
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|
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static std::string repeat(const std::string & str, size_t n);
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|
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static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) {
|
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if (separator_rule.empty()) {
|
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if (min_items == 0 && max_items == 1) {
|
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return item_rule + "?";
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} else if (min_items == 1 && max_items == std::numeric_limits<int>::max()) {
|
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return item_rule + "+";
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}
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}
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|
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std::string result;
|
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if (min_items > 0) {
|
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if (item_rule_is_literal && separator_rule.empty()) {
|
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result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\"";
|
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} else {
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std::vector<std::string> items(min_items, item_rule);
|
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result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " ");
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}
|
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}
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std::function<std::string(int, bool)> opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string {
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auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule;
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|
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if (up_to_n == 0) {
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return "";
|
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} else if (up_to_n == 1) {
|
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return "(" + content + ")?";
|
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} else if (!separator_rule.empty() && !prefix_with_sep) {
|
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return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?";
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} else {
|
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std::string res = repeat("(" + content + " ", up_to_n);
|
||||
// strip trailing space
|
||||
res = res.substr(0, res.length() - 1);
|
||||
res += repeat(")?", up_to_n);
|
||||
return res;
|
||||
}
|
||||
};
|
||||
|
||||
if (min_items > 0 && max_items != min_items) {
|
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result += " ";
|
||||
}
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|
||||
if (max_items != std::numeric_limits<int>::max()) {
|
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result += opt_repetitions(max_items - min_items, min_items > 0);
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} else {
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std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")";
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if (min_items == 0 && !separator_rule.empty()) {
|
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result = "(" + item_rule + " " + item_operator + "*)?";
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} else {
|
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result += item_operator + "*";
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}
|
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}
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return result;
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||||
}
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|
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const std::string SPACE_RULE = "\" \"?";
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|
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std::unordered_map<std::string, std::string> PRIMITIVE_RULES = {
|
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{"boolean", "(\"true\" | \"false\") space"},
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{"number", "(\"-\"? ([0-9] | [1-9] [0-9]*)) (\".\" [0-9]+)? ([eE] [-+]? [0-9]+)? space"},
|
||||
{"integer", "(\"-\"? ([0-9] | [1-9] [0-9]*)) space"},
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||||
{"value", "object | array | string | number | boolean"},
|
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{"object", "\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space"},
|
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{"array", "\"[\" space ( value (\",\" space value)* )? \"]\" space"},
|
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{"uuid", "\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
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"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
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"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
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"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
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"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space"},
|
||||
{"string", " \"\\\"\" (\n"
|
||||
" [^\"\\\\] |\n"
|
||||
" \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])\n"
|
||||
" )* \"\\\"\" space"},
|
||||
{"null", "\"null\" space"}
|
||||
struct BuiltinRule {
|
||||
std::string content;
|
||||
std::vector<std::string> deps;
|
||||
};
|
||||
std::vector<std::string> OBJECT_RULE_NAMES = {"object", "array", "string", "number", "boolean", "null", "value"};
|
||||
|
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std::unordered_map<std::string, std::string> DATE_RULES = {
|
||||
{"date", "[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )"},
|
||||
{"time", "([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )"},
|
||||
{"date-time", "date \"T\" time"},
|
||||
{"date-string", "\"\\\"\" date \"\\\"\" space"},
|
||||
{"time-string", "\"\\\"\" time \"\\\"\" space"},
|
||||
{"date-time-string", "\"\\\"\" date-time \"\\\"\" space"}
|
||||
const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15);
|
||||
|
||||
std::unordered_map<std::string, BuiltinRule> PRIMITIVE_RULES = {
|
||||
{"boolean", {"(\"true\" | \"false\") space", {}}},
|
||||
{"decimal-part", {"[0-9] " + _up_to_15_digits, {}}},
|
||||
{"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}},
|
||||
{"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}},
|
||||
{"integer", {"(\"-\"? integral-part) space", {"integral-part"}}},
|
||||
{"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}},
|
||||
{"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}},
|
||||
{"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}},
|
||||
{"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
||||
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
||||
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
||||
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] "
|
||||
"\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}},
|
||||
{"char", {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}},
|
||||
{"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}},
|
||||
{"null", {"\"null\" space", {}}},
|
||||
};
|
||||
|
||||
std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
|
||||
{"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}},
|
||||
{"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}},
|
||||
{"date-time", {"date \"T\" time", {"date", "time"}}},
|
||||
{"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}},
|
||||
{"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}},
|
||||
{"date-time-string", {"\"\\\"\" date-time \"\\\"\" space", {"date-time"}}}
|
||||
};
|
||||
|
||||
static bool is_reserved_name(const std::string & name) {
|
||||
|
|
@ -47,7 +113,7 @@ static bool is_reserved_name(const std::string & name) {
|
|||
if (RESERVED_NAMES.empty()) {
|
||||
RESERVED_NAMES.insert("root");
|
||||
for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
|
||||
for (const auto &p : DATE_RULES) RESERVED_NAMES.insert(p.first);
|
||||
for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first);
|
||||
}
|
||||
return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
|
||||
}
|
||||
|
|
@ -192,7 +258,7 @@ private:
|
|||
if (_dotall) {
|
||||
rule = "[\\U00000000-\\U0010FFFF]";
|
||||
} else {
|
||||
rule = "[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]";
|
||||
rule = "[^\\x0A\\x0D]";
|
||||
}
|
||||
return _add_rule("dot", rule);
|
||||
};
|
||||
|
|
@ -308,13 +374,6 @@ private:
|
|||
auto &sub = last.first;
|
||||
auto sub_is_literal = last.second;
|
||||
|
||||
if (min_times == 0 && max_times == std::numeric_limits<int>::max()) {
|
||||
sub += "*";
|
||||
} else if (min_times == 0 && max_times == 1) {
|
||||
sub += "?";
|
||||
} else if (min_times == 1 && max_times == std::numeric_limits<int>::max()) {
|
||||
sub += "+";
|
||||
} else {
|
||||
if (!sub_is_literal) {
|
||||
std::string & sub_id = sub_rule_ids[sub];
|
||||
if (sub_id.empty()) {
|
||||
|
|
@ -322,33 +381,14 @@ private:
|
|||
}
|
||||
sub = sub_id;
|
||||
}
|
||||
std::string result;
|
||||
if (sub_is_literal && min_times > 0) {
|
||||
result = "\"" + repeat(sub.substr(1, sub.length() - 2), min_times) + "\"";
|
||||
} else {
|
||||
for (int j = 0; j < min_times; j++) {
|
||||
if (j > 0) {
|
||||
result += " ";
|
||||
}
|
||||
result += sub;
|
||||
}
|
||||
}
|
||||
if (min_times > 0 && min_times < max_times) {
|
||||
result += " ";
|
||||
}
|
||||
if (max_times == std::numeric_limits<int>::max()) {
|
||||
result += sub + "*";
|
||||
} else {
|
||||
for (int j = min_times; j < max_times; j++) {
|
||||
if (j > min_times) {
|
||||
result += " ";
|
||||
}
|
||||
result += sub + "?";
|
||||
}
|
||||
}
|
||||
seq.back().first = result;
|
||||
seq.back().first = build_repetition(
|
||||
sub_is_literal ? "\"" + sub + "\"" : sub,
|
||||
min_times,
|
||||
max_times,
|
||||
"",
|
||||
sub_is_literal
|
||||
);
|
||||
seq.back().second = false;
|
||||
}
|
||||
} else {
|
||||
std::string literal;
|
||||
auto is_non_literal = [&](char c) {
|
||||
|
|
@ -424,7 +464,7 @@ private:
|
|||
if (additional_properties.is_object() || (additional_properties.is_boolean() && additional_properties.get<bool>())) {
|
||||
std::string sub_name = name + (name.empty() ? "" : "-") + "additional";
|
||||
std::string value_rule = visit(additional_properties.is_object() ? additional_properties : json::object(), sub_name + "-value");
|
||||
std::string kv_rule = _add_rule(sub_name + "-kv", _add_rule("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
|
||||
std::string kv_rule = _add_rule(sub_name + "-kv", _add_primitive("string", PRIMITIVE_RULES.at("string")) + " \":\" space " + value_rule);
|
||||
prop_kv_rule_names["*"] = kv_rule;
|
||||
optional_props.push_back("*");
|
||||
}
|
||||
|
|
@ -486,6 +526,25 @@ private:
|
|||
return rule;
|
||||
}
|
||||
|
||||
std::string _add_primitive(const std::string & name, const BuiltinRule & rule) {
|
||||
auto n = _add_rule(name, rule.content);
|
||||
for (const auto & dep : rule.deps) {
|
||||
BuiltinRule dep_rule;
|
||||
auto it = PRIMITIVE_RULES.find(dep);
|
||||
if (it == PRIMITIVE_RULES.end()) {
|
||||
it = STRING_FORMAT_RULES.find(dep);
|
||||
if (it == STRING_FORMAT_RULES.end()) {
|
||||
_errors.push_back("Rule " + dep + " not known");
|
||||
continue;
|
||||
}
|
||||
}
|
||||
if (_rules.find(dep) == _rules.end()) {
|
||||
_add_primitive(dep, it->second);
|
||||
}
|
||||
}
|
||||
return n;
|
||||
}
|
||||
|
||||
public:
|
||||
SchemaConverter(
|
||||
const std::function<json(const std::string &)> & fetch_json,
|
||||
|
|
@ -647,49 +706,33 @@ public:
|
|||
return _add_rule(rule_name, rule);
|
||||
} else {
|
||||
std::string item_rule_name = visit(items, name + (name.empty() ? "" : "-") + "item");
|
||||
std::string list_item_operator = "( \",\" space " + item_rule_name + " )";
|
||||
std::string successive_items;
|
||||
int min_items = schema.contains("minItems") ? schema["minItems"].get<int>() : 0;
|
||||
json max_items_json = schema.contains("maxItems") ? schema["maxItems"] : json();
|
||||
int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : -1;
|
||||
if (min_items > 0) {
|
||||
successive_items += repeat(list_item_operator, min_items - 1);
|
||||
min_items--;
|
||||
}
|
||||
if (max_items >= 0 && max_items > min_items) {
|
||||
successive_items += repeat(list_item_operator + "?", max_items - min_items - 1);
|
||||
} else {
|
||||
successive_items += list_item_operator + "*";
|
||||
}
|
||||
std::string rule;
|
||||
if (min_items == 0) {
|
||||
rule = "\"[\" space ( " + item_rule_name + " " + successive_items + " )? \"]\" space";
|
||||
} else {
|
||||
rule = "\"[\" space " + item_rule_name + " " + successive_items + " \"]\" space";
|
||||
}
|
||||
return _add_rule(rule_name, rule);
|
||||
int max_items = max_items_json.is_number_integer() ? max_items_json.get<int>() : std::numeric_limits<int>::max();
|
||||
|
||||
return _add_rule(rule_name, "\"[\" space " + build_repetition(item_rule_name, min_items, max_items, "\",\" space") + " \"]\" space");
|
||||
}
|
||||
} else if ((schema_type.is_null() || schema_type == "string") && schema.contains("pattern")) {
|
||||
return _visit_pattern(schema["pattern"], rule_name);
|
||||
} else if ((schema_type.is_null() || schema_type == "string") && std::regex_match(schema_format, std::regex("^uuid[1-5]?$"))) {
|
||||
return _add_rule(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
|
||||
} else if ((schema_type.is_null() || schema_type == "string") && DATE_RULES.find(schema_format) != DATE_RULES.end()) {
|
||||
for (const auto & kv : DATE_RULES) {
|
||||
_add_rule(kv.first, kv.second);
|
||||
}
|
||||
return schema_format + "-string";
|
||||
return _add_primitive(rule_name == "root" ? "root" : schema_format, PRIMITIVE_RULES.at("uuid"));
|
||||
} else if ((schema_type.is_null() || schema_type == "string") && STRING_FORMAT_RULES.find(schema_format + "-string") != STRING_FORMAT_RULES.end()) {
|
||||
auto prim_name = schema_format + "-string";
|
||||
return _add_rule(rule_name, _add_primitive(prim_name, STRING_FORMAT_RULES.at(prim_name)));
|
||||
} else if (schema_type == "string" && (schema.contains("minLength") || schema.contains("maxLength"))) {
|
||||
std::string char_rule = _add_primitive("char", PRIMITIVE_RULES.at("char"));
|
||||
int min_len = schema.contains("minLength") ? schema["minLength"].get<int>() : 0;
|
||||
int max_len = schema.contains("maxLength") ? schema["maxLength"].get<int>() : std::numeric_limits<int>::max();
|
||||
return _add_rule(rule_name, "\"\\\"\" " + build_repetition(char_rule, min_len, max_len) + " \"\\\"\" space");
|
||||
} else if (schema.empty() || schema_type == "object") {
|
||||
for (const auto & n : OBJECT_RULE_NAMES) {
|
||||
_add_rule(n, PRIMITIVE_RULES.at(n));
|
||||
}
|
||||
return _add_rule(rule_name, "object");
|
||||
return _add_rule(rule_name, _add_primitive("object", PRIMITIVE_RULES.at("object")));
|
||||
} else {
|
||||
if (!schema_type.is_string() || PRIMITIVE_RULES.find(schema_type.get<std::string>()) == PRIMITIVE_RULES.end()) {
|
||||
_errors.push_back("Unrecognized schema: " + schema.dump());
|
||||
return "";
|
||||
}
|
||||
// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
|
||||
return _add_rule(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
|
||||
return _add_primitive(rule_name == "root" ? "root" : schema_type.get<std::string>(), PRIMITIVE_RULES.at(schema_type.get<std::string>()));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -43,17 +43,18 @@ AnyModel = TypeVar("AnyModel", bound="type[Model]")
|
|||
class Model(ABC):
|
||||
_model_classes: dict[str, type[Model]] = {}
|
||||
|
||||
def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
|
||||
def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool):
|
||||
self.dir_model = dir_model
|
||||
self.ftype = ftype
|
||||
self.fname_out = fname_out
|
||||
self.is_big_endian = is_big_endian
|
||||
self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
|
||||
self.use_temp_file = use_temp_file
|
||||
self.is_safetensors = self._is_model_safetensors()
|
||||
self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
|
||||
self.part_names = self._get_part_names()
|
||||
self.hparams = Model.load_hparams(self.dir_model)
|
||||
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
|
||||
self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file)
|
||||
self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
|
||||
|
||||
@property
|
||||
|
|
@ -227,15 +228,14 @@ class Model(ABC):
|
|||
return ("pytorch_model.bin",)
|
||||
return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
|
||||
|
||||
def _set_vocab_gpt2(self):
|
||||
dir_model = self.dir_model
|
||||
hparams = self.hparams
|
||||
# used for GPT-2 BPE and WordPiece vocabs
|
||||
def get_basic_vocab(self) -> tuple[list[str], list[int]]:
|
||||
tokens: list[str] = []
|
||||
toktypes: list[int] = []
|
||||
|
||||
from transformers import AutoTokenizer
|
||||
tokenizer = AutoTokenizer.from_pretrained(dir_model)
|
||||
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
|
||||
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
|
||||
vocab_size = self.hparams.get("vocab_size", len(tokenizer.vocab))
|
||||
assert max(tokenizer.vocab.values()) < vocab_size
|
||||
|
||||
reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()}
|
||||
|
|
@ -255,11 +255,15 @@ class Model(ABC):
|
|||
tokens.append(reverse_vocab[i])
|
||||
toktypes.append(gguf.TokenType.NORMAL)
|
||||
|
||||
return tokens, toktypes
|
||||
|
||||
def _set_vocab_gpt2(self) -> None:
|
||||
tokens, toktypes = self.get_basic_vocab()
|
||||
self.gguf_writer.add_tokenizer_model("gpt2")
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
|
||||
special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
|
||||
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
|
||||
special_vocab.add_to_gguf(self.gguf_writer)
|
||||
|
||||
def _set_vocab_qwen(self):
|
||||
|
|
@ -1424,6 +1428,102 @@ class GrokModel(Model):
|
|||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
@Model.register("DbrxForCausalLM")
|
||||
class DbrxModel(Model):
|
||||
model_arch = gguf.MODEL_ARCH.DBRX
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
ffn_config = self.hparams["ffn_config"]
|
||||
attn_config = self.hparams["attn_config"]
|
||||
self.gguf_writer.add_name(self.hparams["model_type"])
|
||||
self.gguf_writer.add_block_count(self.hparams["n_layers"])
|
||||
|
||||
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
|
||||
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
|
||||
self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
|
||||
|
||||
self.gguf_writer.add_head_count(self.hparams["n_heads"])
|
||||
self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
|
||||
|
||||
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
|
||||
|
||||
self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
|
||||
self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
|
||||
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
|
||||
|
||||
self.gguf_writer.add_layer_norm_eps(1e-5)
|
||||
|
||||
self.gguf_writer.add_file_type(self.ftype)
|
||||
print(f"gguf: file type = {self.ftype}")
|
||||
|
||||
def write_tensors(self):
|
||||
block_count = self.hparams.get("n_layers")
|
||||
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
|
||||
for name, data_torch in self.get_tensors():
|
||||
n_expert = self.hparams["ffn_config"]["moe_num_experts"]
|
||||
n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
|
||||
n_embd = self.hparams["d_model"]
|
||||
|
||||
# Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
|
||||
# original implementation expects (n_expert, n_ff, n_embd) for all experts weights
|
||||
# But llama.cpp moe graph works differently
|
||||
# AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
|
||||
# so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
|
||||
exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
|
||||
"ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert}
|
||||
"ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
|
||||
experts = False
|
||||
for exp_tensor_name in exp_tensor_names.keys():
|
||||
if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
|
||||
experts = True
|
||||
data_torch = data_torch.view(n_expert, n_ff, n_embd)
|
||||
if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
|
||||
data_torch = data_torch.permute(*permute_tensor)
|
||||
break
|
||||
|
||||
old_dtype = data_torch.dtype
|
||||
|
||||
# convert any unsupported data types to float32
|
||||
if data_torch.dtype not in (torch.float16, torch.float32):
|
||||
data_torch = data_torch.to(torch.float32)
|
||||
|
||||
data = data_torch.squeeze().numpy()
|
||||
|
||||
# map tensor names
|
||||
# In MoE models the ffn tensors are typically most of the model weights,
|
||||
# and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
|
||||
# Every other model has the weight names ending in .weight,
|
||||
# let's assume that is the convention which is not the case for dbrx:
|
||||
# https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
|
||||
new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
|
||||
if new_name is None:
|
||||
print(f"Can not map tensor {name!r}")
|
||||
sys.exit()
|
||||
|
||||
n_dims = len(data.shape)
|
||||
data_dtype = data.dtype
|
||||
|
||||
# Most of the codebase that takes in 1D tensors only handles F32 tensors
|
||||
# and most of the outputs tensors are F32.
|
||||
if data_dtype != np.float32 and n_dims == 1:
|
||||
print(f"Can not map tensor {name!r}: all 1D tensors must be F32")
|
||||
sys.exit()
|
||||
|
||||
# if f32 desired, convert any float16 to float32
|
||||
if self.ftype == 0 and data_dtype == np.float16:
|
||||
data = data.astype(np.float32)
|
||||
|
||||
# if f16 desired, convert any float32 2-dim weight tensors to float16
|
||||
if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
|
||||
|
||||
self.gguf_writer.add_tensor(new_name, data)
|
||||
|
||||
|
||||
@Model.register("MiniCPMForCausalLM")
|
||||
class MiniCPMModel(Model):
|
||||
model_arch = gguf.MODEL_ARCH.MINICPM
|
||||
|
|
@ -2043,34 +2143,25 @@ class BertModel(Model):
|
|||
self.gguf_writer.add_pooling_type(pooling_type)
|
||||
|
||||
def set_vocab(self):
|
||||
# use huggingface vocab to get all tokens
|
||||
vocab = LlamaHfVocab(self.dir_model, ignore_nonllama=True)
|
||||
tokens, scores, toktypes = zip(*vocab.all_tokens())
|
||||
assert len(tokens) == vocab.vocab_size
|
||||
self.vocab_size = vocab.vocab_size
|
||||
tokens, toktypes = self.get_basic_vocab()
|
||||
self.vocab_size = len(tokens)
|
||||
|
||||
# we need this to validate the size of the token_type embeddings
|
||||
# though currently we are passing all zeros to the token_type embeddings
|
||||
n_token_types = len(set(toktypes))
|
||||
self.gguf_writer.add_token_type_count(n_token_types)
|
||||
self.gguf_writer.add_token_type_count(2) # "Sequence A" or "Sequence B"
|
||||
|
||||
# convert to phantom space vocab
|
||||
def phantom(tok, typ):
|
||||
if tok.startswith(b"[") and tok.endswith(b"]"):
|
||||
def phantom(tok):
|
||||
if tok.startswith("[") and tok.endswith("]"):
|
||||
return tok
|
||||
if tok.startswith(b"##"):
|
||||
if tok.startswith("##"):
|
||||
return tok[2:]
|
||||
return b"\xe2\x96\x81" + tok
|
||||
tokens = tuple(phantom(t, y) for t, y in zip(tokens, toktypes))
|
||||
|
||||
# set up bos and eos tokens (cls and sep)
|
||||
self.gguf_writer.add_bos_token_id(vocab.tokenizer.cls_token_id)
|
||||
self.gguf_writer.add_eos_token_id(vocab.tokenizer.sep_token_id)
|
||||
return "\u2581" + tok
|
||||
tokens = list(map(phantom, tokens))
|
||||
|
||||
# add vocab to gguf
|
||||
self.gguf_writer.add_tokenizer_model("bert")
|
||||
self.gguf_writer.add_token_list(tokens)
|
||||
self.gguf_writer.add_token_scores(scores)
|
||||
self.gguf_writer.add_token_types(toktypes)
|
||||
|
||||
# handle special tokens
|
||||
|
|
@ -2142,16 +2233,6 @@ class NomicBertModel(BertModel):
|
|||
super().set_gguf_parameters()
|
||||
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
|
||||
|
||||
def get_tensors(self):
|
||||
assert self.vocab_size is not None
|
||||
for name, data in super().get_tensors():
|
||||
# Nomic Embed's token embeddings tensor is padded, but llama.cpp wants tensor sizes to match exactly.
|
||||
if name == 'embeddings.word_embeddings.weight' and data.shape[1] != self.vocab_size:
|
||||
rounded_vocab_size = (self.vocab_size + 63) // 64 * 64
|
||||
assert data.shape == (rounded_vocab_size, self.hparams["n_embd"])
|
||||
data = data[:self.vocab_size, :]
|
||||
yield name, data
|
||||
|
||||
|
||||
@Model.register("GemmaForCausalLM")
|
||||
class GemmaModel(Model):
|
||||
|
|
@ -2327,7 +2408,8 @@ class MambaModel(Model):
|
|||
data = data.astype(np.float32)
|
||||
|
||||
# if f16 desired, convert big float32 2-dim weight tensors to float16
|
||||
if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
|
||||
new_weight_name = new_name[:-len(".weight")] if new_name.endswith(".weight") else ""
|
||||
if self.ftype == 1 and data_dtype == np.float32 and new_weight_name.endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
|
||||
data = data.astype(np.float16)
|
||||
|
||||
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
|
||||
|
|
@ -2378,6 +2460,7 @@ def parse_args() -> argparse.Namespace:
|
|||
"model", type=Path,
|
||||
help="directory containing model file",
|
||||
)
|
||||
parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)")
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
|
@ -2421,7 +2504,7 @@ def main() -> None:
|
|||
|
||||
with torch.inference_mode():
|
||||
model_class = Model.from_model_architecture(hparams["architectures"][0])
|
||||
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)
|
||||
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file)
|
||||
|
||||
print("Set model parameters")
|
||||
model_instance.set_gguf_parameters()
|
||||
|
|
|
|||
|
|
@ -1,4 +1,6 @@
|
|||
#!/usr/bin/env python3
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
|
|
|
|||
23
convert.py
23
convert.py
|
|
@ -33,7 +33,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
|
|||
import gguf
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from typing import TypeAlias
|
||||
from typing_extensions import Self, TypeAlias
|
||||
|
||||
if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'):
|
||||
faulthandler.register(signal.SIGUSR1)
|
||||
|
|
@ -517,7 +517,7 @@ class LlamaHfVocab(Vocab):
|
|||
tokenizer_model = "llama"
|
||||
name = "hfft"
|
||||
|
||||
def __init__(self, base_path: Path, ignore_nonllama: bool = False):
|
||||
def __init__(self, base_path: Path):
|
||||
fname_tokenizer = base_path / FAST_TOKENIZER_FILE
|
||||
# if this fails, FileNotFoundError propagates to caller
|
||||
with open(fname_tokenizer, encoding='utf-8') as f:
|
||||
|
|
@ -525,9 +525,7 @@ class LlamaHfVocab(Vocab):
|
|||
|
||||
# pre-check so we know if we need transformers
|
||||
tokenizer_model: dict[str, Any] = tokenizer_json['model']
|
||||
if ignore_nonllama:
|
||||
pass # workaround incorrect use of this class for WordPiece
|
||||
elif (
|
||||
if (
|
||||
tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
|
||||
or tokenizer_json['decoder']['type'] != 'Sequence'
|
||||
):
|
||||
|
|
@ -647,16 +645,17 @@ def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
|
|||
|
||||
|
||||
class Tensor(ABC):
|
||||
ndarray: NDArray
|
||||
data_type: DataType
|
||||
|
||||
@abstractmethod
|
||||
def astype(self, data_type: DataType) -> Tensor: ...
|
||||
def astype(self, data_type: DataType) -> Self: ...
|
||||
@abstractmethod
|
||||
def permute(self, n_head: int, n_head_kv: int) -> Tensor: ...
|
||||
def permute(self, n_head: int, n_head_kv: int) -> Self: ...
|
||||
@abstractmethod
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ...
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> Self: ...
|
||||
@abstractmethod
|
||||
def part(self, n_part: int) -> UnquantizedTensor: ...
|
||||
def part(self, n_part: int) -> Self: ...
|
||||
@abstractmethod
|
||||
def to_ggml(self) -> GGMLCompatibleTensor: ...
|
||||
|
||||
|
|
@ -673,13 +672,13 @@ class UnquantizedTensor(Tensor):
|
|||
self.ndarray = ndarray
|
||||
self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
|
||||
|
||||
def astype(self, data_type: DataType) -> Tensor:
|
||||
def astype(self, data_type: DataType) -> UnquantizedTensor:
|
||||
dtype = data_type.dtype
|
||||
if self.data_type == DT_BF16:
|
||||
self.ndarray = bf16_to_fp32(self.ndarray)
|
||||
return UnquantizedTensor(self.ndarray.astype(dtype))
|
||||
|
||||
def to_ggml(self) -> UnquantizedTensor:
|
||||
def to_ggml(self) -> Self:
|
||||
return self
|
||||
|
||||
def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor:
|
||||
|
|
@ -1351,7 +1350,7 @@ def load_some_model(path: Path) -> ModelPlus:
|
|||
# Be extra-friendly and accept either a file or a directory:
|
||||
if path.is_dir():
|
||||
# Check if it's a set of safetensors files first
|
||||
globs = ["model-00001-of-*.safetensors", "model.safetensors"]
|
||||
globs = ["model-00001-of-*.safetensors", "model.safetensors", "consolidated.safetensors"]
|
||||
files = [file for glob in globs for file in path.glob(glob)]
|
||||
if not files:
|
||||
# Try the PyTorch patterns too, with lower priority
|
||||
|
|
|
|||
119
docs/HOWTO-add-model.md
Normal file
119
docs/HOWTO-add-model.md
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
## Add a new model architecture to `llama.cpp`
|
||||
|
||||
Adding a model requires few steps:
|
||||
|
||||
1. Convert the model to GGUF
|
||||
2. Define the model architecture in `llama.cpp`
|
||||
3. Build the GGML graph implementation
|
||||
|
||||
After following these steps, you can open PR.
|
||||
|
||||
Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
|
||||
- [main](../examples/main)
|
||||
- [imatrix](../examples/imatrix)
|
||||
- [quantize](../examples/quantize)
|
||||
- [server](../examples/server)
|
||||
|
||||
### 1. Convert the model to GGUF
|
||||
|
||||
This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
|
||||
Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py).
|
||||
|
||||
The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
|
||||
|
||||
The required steps to implement for an HF model are:
|
||||
|
||||
1. Define the model `Model.register` annotation in a new `Model` subclass, example:
|
||||
|
||||
```python
|
||||
@Model.register("MyModelForCausalLM")
|
||||
class MyModel(Model):
|
||||
model_arch = gguf.MODEL_ARCH.GROK
|
||||
```
|
||||
|
||||
2. Define the layout of the GGUF tensors in [constants.py](../gguf-py/gguf/constants.py)
|
||||
|
||||
Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
|
||||
|
||||
Example for `falcon` model:
|
||||
```python
|
||||
MODEL_ARCH.FALCON: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_NORM_2,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
]
|
||||
```
|
||||
|
||||
3. Map the original tensor names to the standardize equivalent in GGUF
|
||||
|
||||
As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
|
||||
|
||||
Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](../gguf-py/gguf/tensor_mapping.py) file.
|
||||
|
||||
If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
|
||||
|
||||
Example for the normalization tensor in attention layers:
|
||||
|
||||
```python
|
||||
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
||||
# Attention norm
|
||||
MODEL_TENSOR.ATTN_NORM: (
|
||||
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
|
||||
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
|
||||
"transformer.blocks.{bid}.norm_1", # mpt
|
||||
...
|
||||
)
|
||||
}
|
||||
```
|
||||
|
||||
`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
|
||||
|
||||
Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
|
||||
- `Model#set_gguf_parameters`
|
||||
- `Model#set_vocab`
|
||||
- `Model#write_tensors`
|
||||
|
||||
NOTE: Tensor names must end with `.weight` suffix, that is the convention and several tools like `quantize` expect this to proceed the weights.
|
||||
|
||||
### 2. Define the model architecture in `llama.cpp`
|
||||
|
||||
The model params and tensors layout must be defined in `llama.cpp`:
|
||||
1. Define a new `llm_arch`
|
||||
2. Define the tensors layout in `LLM_TENSOR_NAMES`
|
||||
3. Add any non standard metadata in `llm_load_hparams`
|
||||
4. Create the tensors for inference in `llm_load_tensors`
|
||||
5. If the model has a RoPE operation, add the rope type in `llama_rope_type`
|
||||
|
||||
NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
|
||||
|
||||
### 3. Build the GGML graph implementation
|
||||
|
||||
This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`.
|
||||
|
||||
Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`.
|
||||
|
||||
When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR.
|
||||
|
||||
Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback).
|
||||
|
||||
## GGUF specification
|
||||
|
||||
https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
|
||||
|
||||
## Resources
|
||||
|
||||
- YaRN RoPE scaling https://github.com/ggerganov/llama.cpp/pull/2268
|
||||
- support Baichuan serial models https://github.com/ggerganov/llama.cpp/pull/3009
|
||||
- support attention bias https://github.com/ggerganov/llama.cpp/pull/4283
|
||||
- Mixtral support https://github.com/ggerganov/llama.cpp/pull/4406
|
||||
- BERT embeddings https://github.com/ggerganov/llama.cpp/pull/5423
|
||||
- Grok-1 support https://github.com/ggerganov/llama.cpp/pull/6204
|
||||
- Command R Plus support https://github.com/ggerganov/llama.cpp/pull/6491
|
||||
- support arch DBRX https://github.com/ggerganov/llama.cpp/pull/6515
|
||||
- How to convert HuggingFace model to GGUF format https://github.com/ggerganov/llama.cpp/discussions/2948
|
||||
|
|
@ -19,6 +19,7 @@ else()
|
|||
add_subdirectory(benchmark)
|
||||
add_subdirectory(convert-llama2c-to-ggml)
|
||||
add_subdirectory(embedding)
|
||||
add_subdirectory(eval-callback)
|
||||
add_subdirectory(finetune)
|
||||
add_subdirectory(gritlm)
|
||||
add_subdirectory(gguf-split)
|
||||
|
|
|
|||
|
|
@ -124,10 +124,10 @@ int main(int argc, char ** argv) {
|
|||
inputs.push_back(inp);
|
||||
}
|
||||
|
||||
// add eos if not present
|
||||
// add SEP if not present
|
||||
for (auto & inp : inputs) {
|
||||
if (inp.empty() || inp.back() != llama_token_eos(model)) {
|
||||
inp.push_back(llama_token_eos(model));
|
||||
if (inp.empty() || inp.back() != llama_token_sep(model)) {
|
||||
inp.push_back(llama_token_sep(model));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
9
examples/eval-callback/CMakeLists.txt
Normal file
9
examples/eval-callback/CMakeLists.txt
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
set(TARGET eval-callback)
|
||||
add_executable(${TARGET} eval-callback.cpp)
|
||||
install(TARGETS ${TARGET} RUNTIME)
|
||||
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
|
||||
target_compile_features(${TARGET} PRIVATE cxx_std_11)
|
||||
|
||||
set(TEST_TARGET test-eval-callback)
|
||||
add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0)
|
||||
set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl)
|
||||
95
examples/eval-callback/README.md
Normal file
95
examples/eval-callback/README.md
Normal file
|
|
@ -0,0 +1,95 @@
|
|||
# llama.cpp/examples/eval-callback
|
||||
|
||||
A simple example which demonstrates how to use callback during the inference.
|
||||
It simply prints to the console all operations and tensor data.
|
||||
|
||||
Usage:
|
||||
|
||||
```shell
|
||||
eval-callback \
|
||||
--hf-repo ggml-org/models \
|
||||
--hf-file phi-2/ggml-model-q4_0.gguf \
|
||||
--model phi-2-q4_0.gguf \
|
||||
--prompt hello \
|
||||
--seed 42 \
|
||||
-ngl 33
|
||||
```
|
||||
|
||||
Will print:
|
||||
|
||||
```shell
|
||||
llm_load_tensors: offloaded 33/33 layers to GPU
|
||||
...
|
||||
llama_new_context_with_model: n_ctx = 512
|
||||
...
|
||||
llama_new_context_with_model: CUDA0 compute buffer size = 105.00 MiB
|
||||
llama_new_context_with_model: CUDA_Host compute buffer size = 6.01 MiB
|
||||
llama_new_context_with_model: graph nodes = 1225
|
||||
llama_new_context_with_model: graph splits = 2
|
||||
ggml_debug: inp_embd = (f32) GET_ROWS(token_embd.weight{2560, 51200, 1, 1}, inp_tokens{1, 1, 1, 1}}) = {2560, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -0.0181, 0.0272, 0.0272, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: norm-0 = (f32) NORM(CUDA0#inp_embd#0{2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -0.6989, 1.0636, 1.0636, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: norm_w-0 = (f32) MUL(norm-0{2560, 1, 1, 1}, blk.0.attn_norm.weight{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -0.1800, 0.2817, 0.2632, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: attn_norm-0 = (f32) ADD(norm_w-0{2560, 1, 1, 1}, blk.0.attn_norm.bias{2560, 1, 1, 1}}) = {2560, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -0.1863, 0.2970, 0.2604, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: wqkv-0 = (f32) MUL_MAT(blk.0.attn_qkv.weight{2560, 7680, 1, 1}, attn_norm-0{2560, 1, 1, 1}}) = {7680, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -1.1238, 1.2876, -1.8086, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: bqkv-0 = (f32) ADD(wqkv-0{7680, 1, 1, 1}, blk.0.attn_qkv.bias{7680, 1, 1, 1}}) = {7680, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -1.1135, 1.4604, -1.9226, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: bqkv-0 (view) = (f32) VIEW(bqkv-0{7680, 1, 1, 1}, }) = {2560, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -1.1135, 1.4604, -1.9226, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: Qcur-0 = (f32) CONT(bqkv-0 (view){2560, 1, 1, 1}, }) = {2560, 1, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -1.1135, 1.4604, -1.9226, ...],
|
||||
],
|
||||
]
|
||||
ggml_debug: Qcur-0 (reshaped) = (f32) RESHAPE(Qcur-0{2560, 1, 1, 1}, }) = {80, 32, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -1.1135, 1.4604, -1.9226, ...],
|
||||
[ -0.3608, 0.5076, -1.8866, ...],
|
||||
[ 1.7643, 0.0273, -2.1065, ...],
|
||||
...
|
||||
],
|
||||
]
|
||||
ggml_debug: Qcur-0 = (f32) ROPE(Qcur-0 (reshaped){80, 32, 1, 1}, CUDA0#inp_pos#0{1, 1, 1, 1}}) = {80, 32, 1, 1}
|
||||
[
|
||||
[
|
||||
[ -1.1135, 1.4604, -1.9226, ...],
|
||||
[ -0.3608, 0.5076, -1.8866, ...],
|
||||
[ 1.7643, 0.0273, -2.1065, ...],
|
||||
...
|
||||
],
|
||||
]
|
||||
```
|
||||
195
examples/eval-callback/eval-callback.cpp
Normal file
195
examples/eval-callback/eval-callback.cpp
Normal file
|
|
@ -0,0 +1,195 @@
|
|||
#include "common.h"
|
||||
#include "llama.h"
|
||||
#include "ggml.h"
|
||||
|
||||
#include <cstdio>
|
||||
#include <random>
|
||||
#include <string>
|
||||
#include <tuple>
|
||||
#include <vector>
|
||||
|
||||
/**
|
||||
* This the arbitrary data which will be passed to each callback.
|
||||
* Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
|
||||
*/
|
||||
struct callback_data {
|
||||
std::vector<uint8_t> data;
|
||||
};
|
||||
|
||||
static std::string ggml_ne_string(const ggml_tensor * t) {
|
||||
std::string str;
|
||||
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
|
||||
str += std::to_string(t->ne[i]);
|
||||
if (i + 1 < GGML_MAX_DIMS) {
|
||||
str += ", ";
|
||||
}
|
||||
}
|
||||
return str;
|
||||
}
|
||||
|
||||
static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
|
||||
GGML_ASSERT(n > 0);
|
||||
float sum = 0;
|
||||
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
|
||||
printf(" [\n");
|
||||
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
|
||||
if (i2 == n && ne[2] > 2*n) {
|
||||
printf(" ..., \n");
|
||||
i2 = ne[2] - n;
|
||||
}
|
||||
printf(" [\n");
|
||||
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
|
||||
if (i1 == n && ne[1] > 2*n) {
|
||||
printf(" ..., \n");
|
||||
i1 = ne[1] - n;
|
||||
}
|
||||
printf(" [");
|
||||
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
|
||||
if (i0 == n && ne[0] > 2*n) {
|
||||
printf("..., ");
|
||||
i0 = ne[0] - n;
|
||||
}
|
||||
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
|
||||
float v;
|
||||
if (type == GGML_TYPE_F16) {
|
||||
v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i);
|
||||
} else if (type == GGML_TYPE_F32) {
|
||||
v = *(float *) data + i;
|
||||
} else if (type == GGML_TYPE_I32) {
|
||||
v = (float) *(int32_t *) data + i;
|
||||
} else if (type == GGML_TYPE_I16) {
|
||||
v = (float) *(int16_t *) data + i;
|
||||
} else if (type == GGML_TYPE_I8) {
|
||||
v = (float) *(int8_t *) data + i;
|
||||
} else {
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
printf("%12.4f", v);
|
||||
sum += v;
|
||||
if (i0 < ne[0] - 1) printf(", ");
|
||||
}
|
||||
printf("],\n");
|
||||
}
|
||||
printf(" ],\n");
|
||||
}
|
||||
printf(" ]\n");
|
||||
printf(" sum = %f\n", sum);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* GGML operations callback during the graph execution.
|
||||
*
|
||||
* @param t current tensor
|
||||
* @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
|
||||
* if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
|
||||
* see ggml_backend_sched_eval_callback
|
||||
* @param user_data user data to pass at each call back
|
||||
* @return true to receive data or continue the graph, false otherwise
|
||||
*/
|
||||
static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
|
||||
auto * cb_data = (callback_data *) user_data;
|
||||
|
||||
const struct ggml_tensor * src0 = t->src[0];
|
||||
const struct ggml_tensor * src1 = t->src[1];
|
||||
|
||||
if (ask) {
|
||||
return true; // Always retrieve data
|
||||
}
|
||||
|
||||
char src1_str[128] = {0};
|
||||
if (src1) {
|
||||
sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
|
||||
}
|
||||
|
||||
printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
|
||||
t->name, ggml_type_name(t->type), ggml_op_desc(t),
|
||||
src0->name, ggml_ne_string(src0).c_str(),
|
||||
src1 ? src1_str : "",
|
||||
ggml_ne_string(t).c_str());
|
||||
|
||||
|
||||
// copy the data from the GPU memory if needed
|
||||
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
|
||||
|
||||
if (!is_host) {
|
||||
auto n_bytes = ggml_nbytes(t);
|
||||
cb_data->data.resize(n_bytes);
|
||||
ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
|
||||
}
|
||||
|
||||
if (!ggml_is_quantized(t->type)) {
|
||||
uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
|
||||
ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static bool run(llama_context * ctx, const gpt_params & params) {
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
|
||||
|
||||
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
|
||||
callback_data cb_data;
|
||||
|
||||
gpt_params params;
|
||||
if (!gpt_params_parse(argc, argv, params)) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
print_build_info();
|
||||
|
||||
std::mt19937 rng(params.seed);
|
||||
if (params.random_prompt) {
|
||||
params.prompt = gpt_random_prompt(rng);
|
||||
}
|
||||
|
||||
llama_backend_init();
|
||||
llama_numa_init(params.numa);
|
||||
|
||||
// pass the callback to the backend scheduler
|
||||
// it will be executed for each node during the graph computation
|
||||
params.cb_eval = ggml_debug;
|
||||
params.cb_eval_user_data = &cb_data;
|
||||
params.warmup = false;
|
||||
|
||||
// init
|
||||
llama_model * model;
|
||||
llama_context * ctx;
|
||||
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
||||
if (model == nullptr || ctx == nullptr) {
|
||||
fprintf(stderr, "%s : failed to init\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
// print system information
|
||||
{
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "%s\n", get_system_info(params).c_str());
|
||||
}
|
||||
|
||||
bool OK = run(ctx, params);
|
||||
if (!OK) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_print_timings(ctx);
|
||||
|
||||
llama_free(ctx);
|
||||
llama_free_model(model);
|
||||
|
||||
llama_backend_free();
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
@ -17,7 +17,7 @@ static bool llama_sample_grammar_string(struct llama_grammar * grammar, const st
|
|||
size_t pos = 0;
|
||||
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
|
||||
auto prev_stacks = grammar->stacks;
|
||||
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
|
||||
llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
|
||||
if (grammar->stacks.empty()) {
|
||||
error_pos = pos;
|
||||
error_msg = "Unexpected character '" + unicode_cpt_to_utf8(*it) + "'";
|
||||
|
|
|
|||
|
|
@ -5,5 +5,6 @@ CLI to split / merge GGUF files.
|
|||
**Command line options:**
|
||||
|
||||
- `--split`: split GGUF to multiple GGUF, default operation.
|
||||
- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`.
|
||||
- `--split-max-tensors`: maximum tensors in each split: default(128)
|
||||
- `--merge`: merge multiple GGUF to a single GGUF.
|
||||
|
|
|
|||
|
|
@ -60,10 +60,10 @@ static size_t split_str_to_n_bytes(std::string str) {
|
|||
int n;
|
||||
if (str.back() == 'M') {
|
||||
sscanf(str.c_str(), "%d", &n);
|
||||
n_bytes = n * 1024 * 1024; // megabytes
|
||||
n_bytes = (size_t)n * 1024 * 1024; // megabytes
|
||||
} else if (str.back() == 'G') {
|
||||
sscanf(str.c_str(), "%d", &n);
|
||||
n_bytes = n * 1024 * 1024 * 1024; // gigabytes
|
||||
n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes
|
||||
} else {
|
||||
throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
|
||||
}
|
||||
|
|
|
|||
89
examples/gguf-split/tests.sh
Normal file
89
examples/gguf-split/tests.sh
Normal file
|
|
@ -0,0 +1,89 @@
|
|||
#!/bin/bash
|
||||
|
||||
set -eu
|
||||
|
||||
if [ $# -lt 1 ]
|
||||
then
|
||||
echo "usage: $0 path_to_build_binary [path_to_temp_folder]"
|
||||
echo "example: $0 ../../build/bin ../../tmp"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
if [ $# -gt 1 ]
|
||||
then
|
||||
TMP_DIR=$2
|
||||
else
|
||||
TMP_DIR=/tmp
|
||||
fi
|
||||
|
||||
set -x
|
||||
|
||||
SPLIT=$1/gguf-split
|
||||
MAIN=$1/main
|
||||
WORK_PATH=$TMP_DIR/gguf-split
|
||||
CUR_DIR=$(pwd)
|
||||
|
||||
mkdir -p "$WORK_PATH"
|
||||
|
||||
# Clean up in case of previously failed test
|
||||
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
|
||||
|
||||
# 1. Get a model
|
||||
(
|
||||
cd $WORK_PATH
|
||||
"$CUR_DIR"/../../scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
|
||||
)
|
||||
echo PASS
|
||||
|
||||
# 2. Split with max tensors strategy
|
||||
$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
|
||||
echo PASS
|
||||
echo
|
||||
|
||||
# 2b. Test the sharded model is loading properly
|
||||
$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32
|
||||
echo PASS
|
||||
echo
|
||||
|
||||
# 3. Merge
|
||||
$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf
|
||||
echo PASS
|
||||
echo
|
||||
|
||||
# 3b. Test the merged model is loading properly
|
||||
$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32
|
||||
echo PASS
|
||||
echo
|
||||
|
||||
# 4. Split with no tensor in metadata
|
||||
#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
|
||||
#echo PASS
|
||||
#echo
|
||||
|
||||
# 4b. Test the sharded model is loading properly
|
||||
#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32
|
||||
#echo PASS
|
||||
#echo
|
||||
|
||||
# 5. Merge
|
||||
#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf
|
||||
#echo PASS
|
||||
#echo
|
||||
|
||||
# 5b. Test the merged model is loading properly
|
||||
#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32
|
||||
#echo PASS
|
||||
#echo
|
||||
|
||||
# 6. Split with size strategy
|
||||
$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G
|
||||
echo PASS
|
||||
echo
|
||||
|
||||
# 6b. Test the sharded model is loading properly
|
||||
$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32
|
||||
echo PASS
|
||||
echo
|
||||
|
||||
# Clean up
|
||||
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
|
||||
|
|
@ -142,7 +142,7 @@ static bool gguf_ex_read_0(const std::string & fname) {
|
|||
}
|
||||
|
||||
// read and create ggml_context containing the tensors and their data
|
||||
static bool gguf_ex_read_1(const std::string & fname) {
|
||||
static bool gguf_ex_read_1(const std::string & fname, bool check_data) {
|
||||
struct ggml_context * ctx_data = NULL;
|
||||
|
||||
struct gguf_init_params params = {
|
||||
|
|
@ -206,7 +206,7 @@ static bool gguf_ex_read_1(const std::string & fname) {
|
|||
printf("\n\n");
|
||||
|
||||
// check data
|
||||
{
|
||||
if (check_data) {
|
||||
const float * data = (const float *) cur->data;
|
||||
for (int j = 0; j < ggml_nelements(cur); ++j) {
|
||||
if (data[j] != 100 + i) {
|
||||
|
|
@ -229,9 +229,16 @@ static bool gguf_ex_read_1(const std::string & fname) {
|
|||
|
||||
int main(int argc, char ** argv) {
|
||||
if (argc < 3) {
|
||||
printf("usage: %s data.gguf r|w\n", argv[0]);
|
||||
printf("usage: %s data.gguf r|w [n]\n", argv[0]);
|
||||
printf("r: read data.gguf file\n");
|
||||
printf("w: write data.gguf file\n");
|
||||
printf("n: no check of tensor data\n");
|
||||
return -1;
|
||||
}
|
||||
bool check_data = true;
|
||||
if (argc == 4) {
|
||||
check_data = false;
|
||||
}
|
||||
|
||||
const std::string fname(argv[1]);
|
||||
const std::string mode (argv[2]);
|
||||
|
|
@ -242,7 +249,7 @@ int main(int argc, char ** argv) {
|
|||
GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file");
|
||||
} else if (mode == "r") {
|
||||
GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file");
|
||||
GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file");
|
||||
GGML_ASSERT(gguf_ex_read_1(fname, check_data) && "failed to read gguf file");
|
||||
}
|
||||
|
||||
return 0;
|
||||
|
|
|
|||
|
|
@ -108,9 +108,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
|
|||
|
||||
// the top-k selected expert ids are stored in the ids tensor
|
||||
// for simplicity, always copy ids to host, because it is small
|
||||
// take into account that ids is not contiguous!
|
||||
GGML_ASSERT(ids->ne[1] == src1->ne[1]);
|
||||
GGML_ASSERT(n_as*ggml_nrows(ids)*sizeof(int) == GGML_PAD(ggml_nbytes(ids), n_as*sizeof(int)));
|
||||
m_ids.resize(ggml_nbytes(ids)/sizeof(int));
|
||||
ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
|
||||
|
||||
|
|
@ -350,12 +348,13 @@ static void process_logits(
|
|||
static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
|
||||
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
|
||||
const int n_ctx = llama_n_ctx(ctx);
|
||||
|
||||
auto tim1 = std::chrono::high_resolution_clock::now();
|
||||
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
|
||||
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
|
||||
|
||||
auto tim2 = std::chrono::high_resolution_clock::now();
|
||||
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
|
||||
|
|
@ -597,24 +596,18 @@ int main(int argc, char ** argv) {
|
|||
llama_backend_init();
|
||||
llama_numa_init(params.numa);
|
||||
|
||||
llama_model_params mparams = llama_model_params_from_gpt_params(params);
|
||||
|
||||
llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
|
||||
if (model == NULL) {
|
||||
fprintf(stderr, "%s: error: unable to load model\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
llama_context_params cparams = llama_context_params_from_gpt_params(params);
|
||||
|
||||
// pass the callback to the backend scheduler
|
||||
// it will be executed for each node during the graph computation
|
||||
cparams.cb_eval = ik_collect_imatrix;
|
||||
cparams.cb_eval_user_data = NULL;
|
||||
params.cb_eval = ik_collect_imatrix;
|
||||
params.cb_eval_user_data = NULL;
|
||||
params.warmup = false;
|
||||
|
||||
llama_context * ctx = llama_new_context_with_model(model, cparams);
|
||||
if (ctx == NULL) {
|
||||
fprintf(stderr, "%s: error: unable to create context\n", __func__);
|
||||
// init
|
||||
llama_model * model;
|
||||
llama_context * ctx;
|
||||
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
||||
if (model == nullptr || ctx == nullptr) {
|
||||
fprintf(stderr, "%s : failed to init\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -36,6 +36,11 @@ The `infill` program offers a seamless way to interact with LLaMA models, allowi
|
|||
|
||||
### Example
|
||||
|
||||
Download a model that supports infill, for example CodeLlama:
|
||||
```console
|
||||
scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.gguf --outdir models
|
||||
```
|
||||
|
||||
```bash
|
||||
./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n "
|
||||
```
|
||||
|
|
|
|||
|
|
@ -240,6 +240,7 @@ int main(int argc, char ** argv) {
|
|||
LOG_TEE("%s\n", get_system_info(params).c_str());
|
||||
}
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
GGML_ASSERT(llama_add_eos_token(model) != 1);
|
||||
LOG("add_bos: %d\n", add_bos);
|
||||
|
||||
bool suff_rm_leading_spc = params.escape;
|
||||
|
|
@ -280,10 +281,10 @@ int main(int argc, char ** argv) {
|
|||
if (ctx_guidance) {
|
||||
LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
|
||||
|
||||
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);
|
||||
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true);
|
||||
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
|
||||
|
||||
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
|
||||
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
|
||||
LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
|
||||
|
||||
original_prompt_len = original_inp.size();
|
||||
|
|
|
|||
|
|
@ -6,37 +6,94 @@ import re
|
|||
import sys
|
||||
from typing import Any, Dict, List, Set, Tuple, Union
|
||||
|
||||
def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False):
|
||||
if not separator_rule:
|
||||
if min_items == 0 and max_items == 1:
|
||||
return f'{item_rule}?'
|
||||
elif min_items == 1 and max_items is None:
|
||||
return f'{item_rule}+'
|
||||
|
||||
result = ''
|
||||
|
||||
if min_items > 0:
|
||||
if item_rule_is_literal and separator_rule is None:
|
||||
result = '"' + (item_rule[1:-1] * min_items) + '"'
|
||||
else:
|
||||
result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items)
|
||||
|
||||
def opt_repetitions(up_to_n, prefix_with_sep=False):
|
||||
'''
|
||||
- n=4, no sep: '(a (a (a (a)?)?)?)?'
|
||||
- n=4, sep=',', prefix: '("," a ("," a ("," a ("," a)?)?)?)?'
|
||||
- n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?'
|
||||
'''
|
||||
|
||||
content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule
|
||||
if up_to_n == 0:
|
||||
return ''
|
||||
elif up_to_n == 1:
|
||||
return f'({content})?'
|
||||
elif separator_rule and not prefix_with_sep:
|
||||
return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?'
|
||||
else:
|
||||
return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n)
|
||||
|
||||
if min_items > 0 and max_items != min_items:
|
||||
result += ' '
|
||||
|
||||
if max_items is not None:
|
||||
result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0)
|
||||
else:
|
||||
item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})'
|
||||
|
||||
if min_items == 0 and separator_rule:
|
||||
result = f'({item_rule} {item_operator}*)?'
|
||||
else:
|
||||
result += f'{item_operator}*'
|
||||
|
||||
return result
|
||||
|
||||
|
||||
class BuiltinRule:
|
||||
def __init__(self, content: str, deps: list = None):
|
||||
self.content = content
|
||||
self.deps = deps or []
|
||||
|
||||
_up_to_15_digits = _build_repetition('[0-9]', 0, 15)
|
||||
|
||||
# whitespace is constrained to a single space char to prevent model "running away" in
|
||||
# whitespace. Also maybe improves generation quality?
|
||||
SPACE_RULE = '" "?'
|
||||
|
||||
PRIMITIVE_RULES = {
|
||||
'boolean': '("true" | "false") space',
|
||||
'number': '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
|
||||
'integer': '("-"? ([0-9] | [1-9] [0-9]*)) space',
|
||||
'value' : 'object | array | string | number | boolean',
|
||||
'object' : '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
|
||||
'array' : '"[" space ( value ("," space value)* )? "]" space',
|
||||
'uuid' : '"\\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + ' "\\"" space',
|
||||
'string': r''' "\"" (
|
||||
[^"\\] |
|
||||
"\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
|
||||
)* "\"" space''',
|
||||
'null': '"null" space',
|
||||
'boolean' : BuiltinRule('("true" | "false") space', []),
|
||||
'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []),
|
||||
'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []),
|
||||
'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
|
||||
'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']),
|
||||
'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
|
||||
'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
|
||||
'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
|
||||
'uuid' : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []),
|
||||
'char' : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []),
|
||||
'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']),
|
||||
'null' : BuiltinRule('"null" space', []),
|
||||
}
|
||||
OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value']
|
||||
|
||||
# TODO: support "uri", "email" string formats
|
||||
DATE_RULES = {
|
||||
'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )',
|
||||
'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
|
||||
'date-time': 'date "T" time',
|
||||
'date-string': '"\\"" date "\\"" space',
|
||||
'time-string': '"\\"" time "\\"" space',
|
||||
'date-time-string': '"\\"" date-time "\\"" space',
|
||||
STRING_FORMAT_RULES = {
|
||||
'date' : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
|
||||
'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
|
||||
'date-time' : BuiltinRule('date "T" time', ['date', 'time']),
|
||||
'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']),
|
||||
'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']),
|
||||
'date-time-string': BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
|
||||
}
|
||||
|
||||
RESERVED_NAMES = set(["root", *PRIMITIVE_RULES.keys(), *DATE_RULES.keys()])
|
||||
DOTALL = '[\\U00000000-\\U0010FFFF]'
|
||||
DOT = '[^\\x0A\\x0D]'
|
||||
|
||||
RESERVED_NAMES = set(["root", "dot", *PRIMITIVE_RULES.keys(), *STRING_FORMAT_RULES.keys()])
|
||||
|
||||
INVALID_RULE_CHARS_RE = re.compile(r'[^a-zA-Z0-9-]+')
|
||||
GRAMMAR_LITERAL_ESCAPE_RE = re.compile(r'[\r\n"]')
|
||||
|
|
@ -46,8 +103,6 @@ GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']'
|
|||
NON_LITERAL_SET = set('|.()[]{}*+?')
|
||||
ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = set('[]()|{}*+?')
|
||||
|
||||
DATE_PATTERN = '[0-9]{4}-(0[1-9]|1[0-2])-([0-2][0-9]|3[0-1])'
|
||||
TIME_PATTERN = '([01][0-9]|2[0-3])(:[0-5][0-9]){2}(\\.[0-9]{1,3})?(Z|[+-](([01][0-9]|2[0-3]):[0-5][0-9]))' # Cap millisecond precision w/ 3 digits
|
||||
|
||||
class SchemaConverter:
|
||||
def __init__(self, *, prop_order, allow_fetch, dotall, raw_pattern):
|
||||
|
|
@ -55,7 +110,9 @@ class SchemaConverter:
|
|||
self._allow_fetch = allow_fetch
|
||||
self._dotall = dotall
|
||||
self._raw_pattern = raw_pattern
|
||||
self._rules = {'space': SPACE_RULE}
|
||||
self._rules = {
|
||||
'space': SPACE_RULE,
|
||||
}
|
||||
self._refs = {}
|
||||
self._refs_being_resolved = set()
|
||||
|
||||
|
|
@ -65,6 +122,29 @@ class SchemaConverter:
|
|||
)
|
||||
return f'"{escaped}"'
|
||||
|
||||
def not_literal(self, literal: str, dotall: bool = True, maybe_escaped_underscores = False) -> str:
|
||||
'''
|
||||
not_literal('a') -> '[^a]'
|
||||
not_literal('abc') -> '([^a] | "a" ([^b] | "b" ([^c])?)?)?'
|
||||
'''
|
||||
assert len(literal) > 0, 'Empty literal not supported'
|
||||
def recurse(i: int):
|
||||
c = literal[i]
|
||||
if maybe_escaped_underscores and c == '_':
|
||||
yield f'[^{c}\\\\]'
|
||||
yield ' | '
|
||||
yield f'"\\\\"? "{c}"'
|
||||
else:
|
||||
yield f'[^{c}]'
|
||||
if i < len(literal) - 1:
|
||||
yield ' | '
|
||||
yield self._format_literal(c)
|
||||
yield ' ('
|
||||
yield from recurse(i + 1)
|
||||
yield ')?'
|
||||
|
||||
return ''.join(('(', *recurse(0), ')'))
|
||||
|
||||
def _add_rule(self, name, rule):
|
||||
esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
|
||||
if esc_name not in self._rules or self._rules[esc_name] == rule:
|
||||
|
|
@ -169,10 +249,10 @@ class SchemaConverter:
|
|||
|
||||
def get_dot():
|
||||
if self._dotall:
|
||||
rule = '[\\U00000000-\\U0010FFFF]'
|
||||
rule = DOTALL
|
||||
else:
|
||||
# Accept any character... except \n and \r line break chars (\x0A and \xOD)
|
||||
rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]'
|
||||
rule = DOT
|
||||
return self._add_rule(f'dot', rule)
|
||||
|
||||
def join_seq():
|
||||
|
|
@ -246,13 +326,6 @@ class SchemaConverter:
|
|||
|
||||
(sub, sub_is_literal) = seq[-1]
|
||||
|
||||
if min_times == 0 and max_times is None:
|
||||
seq[-1] = (f'{sub}*', False)
|
||||
elif min_times == 0 and max_times == 1:
|
||||
seq[-1] = (f'{sub}?', False)
|
||||
elif min_times == 1 and max_times is None:
|
||||
seq[-1] = (f'{sub}+', False)
|
||||
else:
|
||||
if not sub_is_literal:
|
||||
id = sub_rule_ids.get(sub)
|
||||
if id is None:
|
||||
|
|
@ -260,12 +333,7 @@ class SchemaConverter:
|
|||
sub_rule_ids[sub] = id
|
||||
sub = id
|
||||
|
||||
seq[-1] = (
|
||||
' '.join(
|
||||
([f'"{sub[1:-1] * min_times}"'] if sub_is_literal else [sub] * min_times) +
|
||||
([f'{sub}?'] * (max_times - min_times) if max_times is not None else [f'{sub}*'])),
|
||||
False
|
||||
)
|
||||
seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False)
|
||||
else:
|
||||
literal = ''
|
||||
while i < length:
|
||||
|
|
@ -373,49 +441,47 @@ class SchemaConverter:
|
|||
' "]" space')
|
||||
else:
|
||||
item_rule_name = self.visit(items, f'{name}{"-" if name else ""}item')
|
||||
list_item_operator = f'( "," space {item_rule_name} )'
|
||||
successive_items = ""
|
||||
min_items = schema.get("minItems", 0)
|
||||
max_items = schema.get("maxItems")
|
||||
if min_items > 0:
|
||||
successive_items = list_item_operator * (min_items - 1)
|
||||
min_items -= 1
|
||||
if max_items is not None and max_items > min_items:
|
||||
successive_items += (list_item_operator + "?") * (max_items - min_items - 1)
|
||||
else:
|
||||
successive_items += list_item_operator + "*"
|
||||
if min_items == 0:
|
||||
rule = f'"[" space ( {item_rule_name} {successive_items} )? "]" space'
|
||||
else:
|
||||
rule = f'"[" space {item_rule_name} {successive_items} "]" space'
|
||||
return self._add_rule(rule_name, rule)
|
||||
return self._add_rule(rule_name, '"[" space ' + _build_repetition(item_rule_name, min_items, max_items, separator_rule='"," space') + ' "]" space')
|
||||
|
||||
elif schema_type in (None, 'string') and 'pattern' in schema:
|
||||
return self._visit_pattern(schema['pattern'], rule_name)
|
||||
|
||||
elif schema_type in (None, 'string') and re.match(r'^uuid[1-5]?$', schema_format or ''):
|
||||
return self._add_rule(
|
||||
return self._add_primitive(
|
||||
'root' if rule_name == 'root' else schema_format,
|
||||
PRIMITIVE_RULES['uuid']
|
||||
)
|
||||
|
||||
elif schema_type in (None, 'string') and schema_format in DATE_RULES:
|
||||
for t, r in DATE_RULES.items():
|
||||
self._add_rule(t, r)
|
||||
return schema_format + '-string'
|
||||
elif schema_type in (None, 'string') and f'{schema_format}-string' in STRING_FORMAT_RULES:
|
||||
prim_name = f'{schema_format}-string'
|
||||
return self._add_rule(rule_name, self._add_primitive(prim_name, STRING_FORMAT_RULES[prim_name]))
|
||||
|
||||
elif schema_type == 'string' and ('minLength' in schema or 'maxLength' in schema):
|
||||
char_rule = self._add_primitive('char', PRIMITIVE_RULES['char'])
|
||||
min_len = schema.get('minLength', 0)
|
||||
max_len = schema.get('maxLength')
|
||||
|
||||
return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
|
||||
|
||||
elif (schema_type == 'object') or (len(schema) == 0):
|
||||
for n in OBJECT_RULE_NAMES:
|
||||
self._add_rule(n, PRIMITIVE_RULES[n])
|
||||
return self._add_rule(rule_name, 'object')
|
||||
return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object']))
|
||||
|
||||
else:
|
||||
assert schema_type in PRIMITIVE_RULES, f'Unrecognized schema: {schema}'
|
||||
# TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
|
||||
return self._add_rule(
|
||||
'root' if rule_name == 'root' else schema_type,
|
||||
PRIMITIVE_RULES[schema_type]
|
||||
)
|
||||
return self._add_primitive('root' if rule_name == 'root' else schema_type, PRIMITIVE_RULES[schema_type])
|
||||
|
||||
def _add_primitive(self, name: str, rule: BuiltinRule):
|
||||
n = self._add_rule(name, rule.content)
|
||||
|
||||
for dep in rule.deps:
|
||||
dep_rule = PRIMITIVE_RULES.get(dep) or STRING_FORMAT_RULES.get(dep)
|
||||
assert dep_rule, f'Rule {dep} not known'
|
||||
if dep not in self._rules:
|
||||
self._add_primitive(dep, dep_rule)
|
||||
return n
|
||||
|
||||
def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]):
|
||||
prop_order = self._prop_order
|
||||
|
|
@ -437,7 +503,7 @@ class SchemaConverter:
|
|||
value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value')
|
||||
prop_kv_rule_names["*"] = self._add_rule(
|
||||
f'{sub_name}-kv',
|
||||
self._add_rule('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
|
||||
self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
|
||||
)
|
||||
optional_props.append("*")
|
||||
|
||||
|
|
@ -22,7 +22,7 @@ After building, run: `./llava-cli` to see the usage. For example:
|
|||
|
||||
## Model conversion
|
||||
|
||||
- Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
|
||||
1. Clone `mobileVLM-1.7B` and `clip-vit-large-patch14-336` locally:
|
||||
|
||||
```sh
|
||||
git clone https://huggingface.co/mtgv/MobileVLM-1.7B
|
||||
|
|
|
|||
|
|
@ -24,7 +24,7 @@ After building, run: `./llava-cli` to see the usage. For example:
|
|||
|
||||
## LLaVA 1.5
|
||||
|
||||
- Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
|
||||
1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
|
||||
|
||||
```sh
|
||||
git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
|
||||
|
|
|
|||
|
|
@ -146,7 +146,6 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
int n_past = 0;
|
||||
|
||||
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama));
|
||||
|
||||
std::string system_prompt, user_prompt;
|
||||
size_t image_pos = prompt.find("<image>");
|
||||
|
|
@ -180,7 +179,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
}
|
||||
}
|
||||
|
||||
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos);
|
||||
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, true);
|
||||
llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past);
|
||||
eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false);
|
||||
|
||||
|
|
|
|||
|
|
@ -64,13 +64,10 @@ int main(int argc, char ** argv) {
|
|||
std::tie(model, ctx) = llama_init_from_gpt_params(params);
|
||||
|
||||
// Tokenize the prompt
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
LOG("add_bos tgt: %d\n", add_bos);
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
std::vector<llama_token> all;
|
||||
|
||||
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
inp = ::llama_tokenize(ctx, params.prompt, true, true);
|
||||
all = inp;
|
||||
|
||||
const int max_context_size = llama_n_ctx(ctx);
|
||||
|
|
|
|||
|
|
@ -28,10 +28,8 @@ int main(int argc, char ** argv){
|
|||
GGML_ASSERT(model != nullptr);
|
||||
|
||||
// tokenize the prompt
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
inp = ::llama_tokenize(ctx, params.prompt, true, true);
|
||||
fprintf(stderr, "%s: tokenization done\n", __func__);
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -34,11 +34,8 @@ int main(int argc, char ** argv){
|
|||
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
|
||||
|
||||
// tokenize the prompt
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
LOG("add_bos tgt: %d\n", add_bos);
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
inp = ::llama_tokenize(ctx, params.prompt, true, true);
|
||||
|
||||
llama_ngram_cache ngram_cache_context;
|
||||
llama_ngram_cache ngram_cache_dynamic;
|
||||
|
|
|
|||
|
|
@ -42,11 +42,8 @@ int main(int argc, char ** argv){
|
|||
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
|
||||
|
||||
// tokenize the prompt
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
LOG("add_bos tgt: %d\n", add_bos);
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
inp = ::llama_tokenize(ctx, params.prompt, true, true);
|
||||
|
||||
llama_ngram_cache ngram_cache_context;
|
||||
llama_ngram_cache ngram_cache_dynamic;
|
||||
|
|
|
|||
|
|
@ -310,7 +310,7 @@ These options help improve the performance and memory usage of the LLaMA models.
|
|||
|
||||
### Quantization
|
||||
|
||||
For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run).
|
||||
For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize).
|
||||
|
||||
## Additional Options
|
||||
|
||||
|
|
|
|||
|
|
@ -247,6 +247,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
GGML_ASSERT(llama_add_eos_token(model) != 1);
|
||||
LOG("add_bos: %d\n", add_bos);
|
||||
|
||||
std::vector<llama_token> embd_inp;
|
||||
|
|
@ -256,7 +257,7 @@ int main(int argc, char ** argv) {
|
|||
if (params.chatml) {
|
||||
params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
|
||||
}
|
||||
embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
|
||||
} else {
|
||||
LOG("use session tokens\n");
|
||||
embd_inp = session_tokens;
|
||||
|
|
@ -278,10 +279,10 @@ int main(int argc, char ** argv) {
|
|||
if (ctx_guidance) {
|
||||
LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
|
||||
|
||||
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
|
||||
guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true, true);
|
||||
LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
|
||||
|
||||
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
|
||||
std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true, true);
|
||||
LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
|
||||
|
||||
original_prompt_len = original_inp.size();
|
||||
|
|
@ -340,14 +341,14 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
// prefix & suffix for instruct mode
|
||||
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
|
||||
const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true, true);
|
||||
const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true);
|
||||
|
||||
LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
|
||||
LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
|
||||
|
||||
// chatml prefix & suffix
|
||||
const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
|
||||
const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true);
|
||||
const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
|
||||
|
||||
LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
|
||||
|
|
|
|||
|
|
@ -3,19 +3,18 @@
|
|||
TODO
|
||||
|
||||
## Llama 2 70B Scorechart
|
||||
Quantization | Model size (GiB) | Perplexity | Delta to fp16
|
||||
-- | -- | -- | --
|
||||
Q4_0 | 36.20 | 3.5550 | 3.61%
|
||||
Q4_1 | 40.20 | 3.5125 | 2.37%
|
||||
Q5_0 | 44.20 | 3.4744 | 1.26%
|
||||
Q2_K | 27.27 | 3.7339 | 8.82%
|
||||
Q3_K_S | 27.86 | 3.7019 | 7.89%
|
||||
Q3_K_M | 30.83 | 3.5932 | 4.72%
|
||||
Q3_K_L | 33.67 | 3.5617 | 3.80%
|
||||
Q4_K_S | 36.39 | 3.4852 | 1.57%
|
||||
Q4_K_M | 38.54 | 3.4725 | 1.20%
|
||||
Q5_K_S | 44.20 | 3.4483 | 0.50%
|
||||
Q5_K_M | 45.41 | 3.4451 | 0.40%
|
||||
Q6_K | 52.70 | 3.4367 | 0.16%
|
||||
fp16 | 128.5 | 3.4313 | -
|
||||
|
||||
| Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
|
||||
|--------------|------------------|------------|---------------|
|
||||
| Q4_0 | 36.20 | 3.5550 | 3.61% |
|
||||
| Q4_1 | 40.20 | 3.5125 | 2.37% |
|
||||
| Q5_0 | 44.20 | 3.4744 | 1.26% |
|
||||
| Q2_K | 27.27 | 3.7339 | 8.82% |
|
||||
| Q3_K_S | 27.86 | 3.7019 | 7.89% |
|
||||
| Q3_K_M | 30.83 | 3.5932 | 4.72% |
|
||||
| Q3_K_L | 33.67 | 3.5617 | 3.80% |
|
||||
| Q4_K_S | 36.39 | 3.4852 | 1.57% |
|
||||
| Q4_K_M | 38.54 | 3.4725 | 1.20% |
|
||||
| Q5_K_S | 44.20 | 3.4483 | 0.50% |
|
||||
| Q5_K_M | 45.41 | 3.4451 | 0.40% |
|
||||
| Q6_K | 52.70 | 3.4367 | 0.16% |
|
||||
| fp16 | 128.5 | 3.4313 | - |
|
||||
|
|
|
|||
|
|
@ -316,10 +316,11 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
|
|||
// BOS tokens will be added for each chunk before eval
|
||||
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
|
||||
|
||||
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
|
||||
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
|
||||
|
||||
const int n_ctx = llama_n_ctx(ctx);
|
||||
|
||||
|
|
@ -455,6 +456,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
|
|||
// BOS tokens will be added for each chunk before eval
|
||||
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
|
||||
|
||||
std::ofstream logits_stream;
|
||||
if (!params.logits_file.empty()) {
|
||||
|
|
@ -471,7 +473,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
|
|||
auto tim1 = std::chrono::high_resolution_clock::now();
|
||||
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
|
||||
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
|
||||
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
|
||||
|
||||
auto tim2 = std::chrono::high_resolution_clock::now();
|
||||
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
|
||||
|
|
@ -772,9 +774,6 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
|
|||
const bool is_spm = llama_vocab_type(llama_get_model(ctx)) == LLAMA_VOCAB_TYPE_SPM;
|
||||
fprintf(stderr, "================================= is_spm = %d\n", is_spm);
|
||||
|
||||
// This is needed as usual for LLaMA models
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
|
||||
// The tasks should be randomized so the score stabilizes quickly.
|
||||
bool randomize_tasks = true;
|
||||
|
||||
|
|
@ -819,7 +818,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
|
|||
hs_cur.gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
|
||||
for (size_t j = 0; j < 4; j++) {
|
||||
hs_cur.ending[j] = prompt_lines[idx*6+2+j];
|
||||
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], add_bos);
|
||||
hs_cur.seq_tokens[j] = ::llama_tokenize(ctx, hs_cur.context + " " + hs_cur.ending[j], true);
|
||||
}
|
||||
|
||||
// determine the common prefix of the endings
|
||||
|
|
@ -838,7 +837,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
|
|||
hs_cur.seq_tokens[2].size() - hs_cur.common_prefix +
|
||||
hs_cur.seq_tokens[3].size() - hs_cur.common_prefix;
|
||||
|
||||
//GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, add_bos).size());
|
||||
//GGML_ASSERT(hs_cur.common_prefix >= ::llama_tokenize(ctx, hs_cur.context, true).size());
|
||||
|
||||
// Delete the selected random example from the prompt
|
||||
if (randomize_tasks) {
|
||||
|
|
@ -1111,12 +1110,9 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
|
|||
|
||||
fprintf(stderr, "%s : tokenizing selected tasks\n", __func__);
|
||||
|
||||
// This is needed as usual for LLaMA models
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
|
||||
for (auto & task : data) {
|
||||
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, add_bos);
|
||||
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, add_bos);
|
||||
task.seq_tokens[0] = ::llama_tokenize(ctx, task.first + task.choices[0] + task.second, true);
|
||||
task.seq_tokens[1] = ::llama_tokenize(ctx, task.first + task.choices[1] + task.second, true);
|
||||
|
||||
task.common_prefix = 0;
|
||||
for (size_t k = 0; k < task.seq_tokens[0].size(); k++) {
|
||||
|
|
@ -1131,8 +1127,8 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
|
|||
task.seq_tokens[0].size() - task.common_prefix +
|
||||
task.seq_tokens[1].size() - task.common_prefix;
|
||||
|
||||
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], add_bos).size();
|
||||
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], add_bos).size();
|
||||
task.n_base1 = ::llama_tokenize(ctx, task.first + task.choices[0], true).size();
|
||||
task.n_base2 = ::llama_tokenize(ctx, task.first + task.choices[1], true).size();
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s : calculating winogrande score over selected tasks.\n", __func__);
|
||||
|
|
@ -1323,7 +1319,7 @@ struct multiple_choice_task {
|
|||
std::vector<float> log_probs;
|
||||
};
|
||||
|
||||
static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos, multiple_choice_task& task, bool log_error) {
|
||||
static bool multiple_choice_prepare_one_task(llama_context * ctx, multiple_choice_task& task, bool log_error) {
|
||||
if (task.question.empty() || task.mc1.answers.empty()) {
|
||||
if (log_error) {
|
||||
printf("%s: found bad task with empty question and/or answers\n", __func__);
|
||||
|
|
@ -1338,7 +1334,7 @@ static bool multiple_choice_prepare_one_task(llama_context * ctx, bool add_bos,
|
|||
}
|
||||
return false;
|
||||
}
|
||||
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, add_bos));
|
||||
task.seq_tokens.emplace_back(::llama_tokenize(ctx, task.question + " " + answer, true));
|
||||
}
|
||||
auto min_len = task.seq_tokens.front().size();
|
||||
for (auto& seq : task.seq_tokens) {
|
||||
|
|
@ -1437,9 +1433,6 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
|
|||
n_task = params.multiple_choice_tasks;
|
||||
}
|
||||
|
||||
// This is needed as usual for LLaMA models
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
|
||||
printf("%s: preparing task data", __func__);
|
||||
fflush(stdout);
|
||||
if (n_task > 500) {
|
||||
|
|
@ -1447,7 +1440,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
|
|||
fflush(stdout);
|
||||
std::atomic<int> counter(0);
|
||||
std::atomic<int> n_bad(0);
|
||||
auto prepare = [&counter, &n_bad, &tasks, ctx, add_bos] () {
|
||||
auto prepare = [&counter, &n_bad, &tasks, ctx] () {
|
||||
int num_tasks = tasks.size();
|
||||
int n_bad_local = 0;
|
||||
while (true) {
|
||||
|
|
@ -1458,7 +1451,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
|
|||
}
|
||||
int last = std::min(first + K_TOKEN_CHUNK, num_tasks);
|
||||
for (int i = first; i < last; ++i) {
|
||||
if (!multiple_choice_prepare_one_task(ctx, add_bos, tasks[i], false)) ++n_bad_local;
|
||||
if (!multiple_choice_prepare_one_task(ctx, tasks[i], false)) ++n_bad_local;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
|
@ -1480,7 +1473,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
|
|||
int i_task = 0;
|
||||
for (auto& task : tasks) {
|
||||
++i_task;
|
||||
if (!multiple_choice_prepare_one_task(ctx, add_bos, task, true)) {
|
||||
if (!multiple_choice_prepare_one_task(ctx, task, true)) {
|
||||
return;
|
||||
}
|
||||
if (i_task%n_dot == 0) {
|
||||
|
|
@ -1716,6 +1709,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
|
|||
const int num_batches = (n_ctx + n_batch - 1)/n_batch;
|
||||
const int nv = 2*((n_vocab + 1)/2) + 4;
|
||||
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
||||
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
|
||||
|
||||
std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
|
||||
std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
|
||||
|
|
|
|||
|
|
@ -4,17 +4,17 @@ TODO
|
|||
|
||||
## Llama 2 7B
|
||||
|
||||
Quantization | Bits per Weight (BPW)
|
||||
-- | --
|
||||
Q2_K | 3.35
|
||||
Q3_K_S | 3.50
|
||||
Q3_K_M | 3.91
|
||||
Q3_K_L | 4.27
|
||||
Q4_K_S | 4.58
|
||||
Q4_K_M | 4.84
|
||||
Q5_K_S | 5.52
|
||||
Q5_K_M | 5.68
|
||||
Q6_K | 6.56
|
||||
| Quantization | Bits per Weight (BPW) |
|
||||
|--------------|-----------------------|
|
||||
| Q2_K | 3.35 |
|
||||
| Q3_K_S | 3.50 |
|
||||
| Q3_K_M | 3.91 |
|
||||
| Q3_K_L | 4.27 |
|
||||
| Q4_K_S | 4.58 |
|
||||
| Q4_K_M | 4.84 |
|
||||
| Q5_K_S | 5.52 |
|
||||
| Q5_K_M | 5.68 |
|
||||
| Q6_K | 6.56 |
|
||||
|
||||
## Llama 2 13B
|
||||
Quantization | Bits per Weight (BPW)
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ print(subprocess.check_output(
|
|||
"python",
|
||||
os.path.join(
|
||||
os.path.dirname(os.path.realpath(__file__)),
|
||||
"json-schema-to-grammar.py"),
|
||||
"json_schema_to_grammar.py"),
|
||||
*rest,
|
||||
"-",
|
||||
"--raw-pattern",
|
||||
|
|
|
|||
|
|
@ -11,6 +11,7 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
|
|||
* Continuous batching
|
||||
* Multimodal (wip)
|
||||
* Monitoring endpoints
|
||||
* Schema-constrained JSON response format
|
||||
|
||||
The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggerganov/llama.cpp/issues/4216).
|
||||
|
||||
|
|
@ -250,6 +251,8 @@ node index.js
|
|||
|
||||
`grammar`: Set grammar for grammar-based sampling. Default: no grammar
|
||||
|
||||
`json_schema`: Set a JSON schema for grammar-based sampling (e.g. `{"items": {"type": "string"}, "minItems": 10, "maxItems": 100}` of a list of strings, or `{}` for any JSON). See [tests](../../tests/test-json-schema-to-grammar.cpp) for supported features. Default: no JSON schema.
|
||||
|
||||
`seed`: Set the random number generator (RNG) seed. Default: `-1`, which is a random seed.
|
||||
|
||||
`ignore_eos`: Ignore end of stream token and continue generating. Default: `false`
|
||||
|
|
@ -365,6 +368,8 @@ Notice that each `probs` is an array of length `n_probs`.
|
|||
|
||||
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such as `mirostat` are supported.
|
||||
|
||||
The `response_format` parameter supports both plain JSON output (e.g. `{"type": "json_object"}`) and schema-constrained JSON (e.g. `{"type": "json_object", "schema": {"type": "string", "minLength": 10, "maxLength": 100}}`), similar to other OpenAI-inspired API providers.
|
||||
|
||||
*Examples:*
|
||||
|
||||
You can use either Python `openai` library with appropriate checkpoints:
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
|
|
@ -51,6 +51,26 @@
|
|||
margin-bottom: 0.5em;
|
||||
}
|
||||
|
||||
button, input, textarea, .button, a.button, select {
|
||||
color: #666;
|
||||
border: 1px solid #ddd;
|
||||
border-radius: 4px;
|
||||
line-height: 1.5em;
|
||||
padding: 0.25em 0.25em;
|
||||
text-decoration: none;
|
||||
font-size: 1.1rem;
|
||||
}
|
||||
|
||||
button {
|
||||
border: 1px solid #2a8aad;
|
||||
background: #3584e4;
|
||||
font-weight: normal;
|
||||
color: #fff;
|
||||
}
|
||||
button:disabled {
|
||||
background: #9cbce5;
|
||||
}
|
||||
|
||||
#write form {
|
||||
margin: 1em 0 0 0;
|
||||
display: flex;
|
||||
|
|
@ -567,7 +587,7 @@
|
|||
runCompletion();
|
||||
}
|
||||
return html`
|
||||
<div>
|
||||
<div class="right">
|
||||
<button onclick=${submit} type="button" disabled=${generating.value}>Start</button>
|
||||
<button onclick=${stop} disabled=${!generating.value}>Stop</button>
|
||||
<button onclick=${reset}>Reset</button>
|
||||
|
|
|
|||
|
|
@ -1,33 +1,95 @@
|
|||
// WARNING: This file was ported from json-schema-to-grammar.py, please fix bugs / add features there first.
|
||||
// WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
|
||||
const SPACE_RULE = '" "?';
|
||||
|
||||
const PRIMITIVE_RULES = {
|
||||
boolean: '("true" | "false") space',
|
||||
number: '("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space',
|
||||
integer: '("-"? ([0-9] | [1-9] [0-9]*)) space',
|
||||
value: 'object | array | string | number | boolean',
|
||||
object: '"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space',
|
||||
array: '"[" space ( value ("," space value)* )? "]" space',
|
||||
uuid: '"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space',
|
||||
string: ` "\\"" (
|
||||
[^"\\\\] |
|
||||
"\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])
|
||||
)* "\\"" space`,
|
||||
null: '"null" space',
|
||||
function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
|
||||
const separatorRule = opts.separatorRule ?? '';
|
||||
const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false
|
||||
|
||||
if (separatorRule === '') {
|
||||
if (minItems === 0 && maxItems === 1) {
|
||||
return `${itemRule}?`;
|
||||
} else if (minItems === 1 && maxItems === undefined) {
|
||||
return `${itemRule}+`;
|
||||
}
|
||||
}
|
||||
|
||||
let result = '';
|
||||
if (minItems > 0) {
|
||||
if (itemRuleIsLiteral && separatorRule === '') {
|
||||
result = `"${itemRule.slice(1, -1).repeat(minItems)}"`;
|
||||
} else {
|
||||
result = Array.from({ length: minItems }, () => itemRule)
|
||||
.join(separatorRule !== '' ? ` ${separatorRule} ` : ' ');
|
||||
}
|
||||
}
|
||||
|
||||
const optRepetitions = (upToN, prefixWithSep=false) => {
|
||||
const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule;
|
||||
if (upToN === 0) {
|
||||
return '';
|
||||
} else if (upToN === 1) {
|
||||
return `(${content})?`;
|
||||
} else if (separatorRule !== '' && !prefixWithSep) {
|
||||
return `(${content} ${optRepetitions(upToN - 1, true)})?`;
|
||||
} else {
|
||||
return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join('');
|
||||
}
|
||||
};
|
||||
|
||||
if (minItems > 0 && maxItems !== minItems) {
|
||||
result += ' ';
|
||||
}
|
||||
|
||||
if (maxItems !== undefined) {
|
||||
result += optRepetitions(maxItems - minItems, minItems > 0);
|
||||
} else {
|
||||
const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`;
|
||||
|
||||
if (minItems === 0 && separatorRule !== '') {
|
||||
result = `(${itemRule} ${itemOperator}*)?`;
|
||||
} else {
|
||||
result += `${itemOperator}*`;
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
class BuiltinRule {
|
||||
constructor(content, deps) {
|
||||
this.content = content;
|
||||
this.deps = deps || [];
|
||||
}
|
||||
}
|
||||
|
||||
const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15);
|
||||
|
||||
const PRIMITIVE_RULES = {
|
||||
boolean : new BuiltinRule('("true" | "false") space', []),
|
||||
'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []),
|
||||
'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []),
|
||||
number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
|
||||
integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']),
|
||||
value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
|
||||
object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
|
||||
array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
|
||||
uuid : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []),
|
||||
char : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []),
|
||||
string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
|
||||
null : new BuiltinRule('"null" space', []),
|
||||
};
|
||||
const OBJECT_RULE_NAMES = ['object', 'array', 'string', 'number', 'boolean', 'null', 'value'];
|
||||
|
||||
// TODO: support "uri", "email" string formats
|
||||
const DATE_RULES = {
|
||||
'date' : '[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )',
|
||||
'time' : '([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )',
|
||||
'date-time': 'date "T" time',
|
||||
'date-string': '"\\"" date "\\"" space',
|
||||
'time-string': '"\\"" time "\\"" space',
|
||||
'date-time-string': '"\\"" date-time "\\"" space',
|
||||
};
|
||||
const STRING_FORMAT_RULES = {
|
||||
'date' : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
|
||||
'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
|
||||
'date-time' : new BuiltinRule('date "T" time', ['date', 'time']),
|
||||
'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']),
|
||||
'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']),
|
||||
'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
|
||||
}
|
||||
|
||||
const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...DATE_RULES};
|
||||
const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES};
|
||||
|
||||
const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
|
||||
const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
|
||||
|
|
@ -158,7 +220,7 @@ export class SchemaConverter {
|
|||
rule = '[\\U00000000-\\U0010FFFF]';
|
||||
} else {
|
||||
// Accept any character... except \n and \r line break chars (\x0A and \xOD)
|
||||
rule = '[\\U00000000-\\x09\\x0B\\x0C\\x0E-\\U0010FFFF]';
|
||||
rule = '[^\\x0A\\x0D]';
|
||||
}
|
||||
return this._addRule('dot', rule);
|
||||
};
|
||||
|
|
@ -259,13 +321,6 @@ export class SchemaConverter {
|
|||
|
||||
let [sub, subIsLiteral] = seq[seq.length - 1];
|
||||
|
||||
if (minTimes === 0 && maxTimes === Infinity) {
|
||||
seq[seq.length - 1] = [`${sub}*`, false];
|
||||
} else if (minTimes === 0 && maxTimes === 1) {
|
||||
seq[seq.length - 1] = [`${sub}?`, false];
|
||||
} else if (minTimes === 1 && maxTimes === Infinity) {
|
||||
seq[seq.length - 1] = [`${sub}+`, false];
|
||||
} else {
|
||||
if (!subIsLiteral) {
|
||||
let id = subRuleIds[sub];
|
||||
if (id === undefined) {
|
||||
|
|
@ -275,10 +330,10 @@ export class SchemaConverter {
|
|||
sub = id;
|
||||
}
|
||||
|
||||
const repeatedSub = Array.from({ length: minTimes }, () => subIsLiteral ? `"${sub.slice(1, -1).repeat(minTimes)}"` : sub);
|
||||
const optionalSub = maxTimes !== undefined ? Array.from({ length: maxTimes - minTimes }, () => `${sub}?`) : [`${sub}*`];
|
||||
seq[seq.length - 1] = [repeatedSub.concat(optionalSub).join(' '), false];
|
||||
}
|
||||
seq[seq.length - 1] = [
|
||||
_buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}),
|
||||
false
|
||||
];
|
||||
} else {
|
||||
let literal = '';
|
||||
while (i < length) {
|
||||
|
|
@ -394,49 +449,50 @@ export class SchemaConverter {
|
|||
);
|
||||
} else {
|
||||
const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`);
|
||||
const listItemOperator = `( "," space ${itemRuleName} )`;
|
||||
let successiveItems = '';
|
||||
let minItems = schema.minItems || 0;
|
||||
const minItems = schema.minItems || 0;
|
||||
const maxItems = schema.maxItems;
|
||||
if (minItems > 0) {
|
||||
successiveItems = listItemOperator.repeat(minItems - 1);
|
||||
minItems--;
|
||||
}
|
||||
if (maxItems !== undefined && maxItems > minItems) {
|
||||
successiveItems += `${listItemOperator}?`.repeat(maxItems - minItems - 1);
|
||||
} else {
|
||||
successiveItems += `${listItemOperator}*`;
|
||||
}
|
||||
const rule = minItems === 0
|
||||
? `"[" space ( ${itemRuleName} ${successiveItems} )? "]" space`
|
||||
: `"[" space ${itemRuleName} ${successiveItems} "]" space`;
|
||||
return this._addRule(ruleName, rule);
|
||||
return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space');
|
||||
}
|
||||
} else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) {
|
||||
return this._visitPattern(schema.pattern, ruleName);
|
||||
} else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) {
|
||||
return this._addRule(
|
||||
return this._addPrimitive(
|
||||
ruleName === 'root' ? 'root' : schemaFormat,
|
||||
PRIMITIVE_RULES['uuid'])
|
||||
} else if ((schemaType === undefined || schemaType === 'string') && schema.format in DATE_RULES) {
|
||||
for (const [t, r] of Object.entries(DATE_RULES)) {
|
||||
this._addRule(t, r);
|
||||
}
|
||||
return schemaFormat + '-string';
|
||||
PRIMITIVE_RULES['uuid']
|
||||
);
|
||||
} else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) {
|
||||
const primName = `${schema.format}-string`
|
||||
return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName]));
|
||||
} else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) {
|
||||
const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
|
||||
const minLen = schema.minLength || 0;
|
||||
const maxLen = schema.maxLength;
|
||||
return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space');
|
||||
} else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
|
||||
for (const n of OBJECT_RULE_NAMES) {
|
||||
this._addRule(n, PRIMITIVE_RULES[n]);
|
||||
}
|
||||
return this._addRule(ruleName, 'object');
|
||||
return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object']));
|
||||
} else {
|
||||
if (!(schemaType in PRIMITIVE_RULES)) {
|
||||
throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`);
|
||||
}
|
||||
// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
|
||||
return this._addRule(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
|
||||
return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
|
||||
}
|
||||
}
|
||||
|
||||
_addPrimitive(name, rule) {
|
||||
let n = this._addRule(name, rule.content);
|
||||
for (const dep of rule.deps) {
|
||||
const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep];
|
||||
if (!depRule) {
|
||||
throw new Error(`Rule ${dep} not known`);
|
||||
}
|
||||
if (!(dep in this._rules)) {
|
||||
this._addPrimitive(dep, depRule);
|
||||
}
|
||||
}
|
||||
return n;
|
||||
}
|
||||
|
||||
_buildObjectRule(properties, required, name, additionalProperties) {
|
||||
const propOrder = this._propOrder;
|
||||
// sort by position in prop_order (if specified) then by original order
|
||||
|
|
@ -462,7 +518,7 @@ export class SchemaConverter {
|
|||
const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`);
|
||||
propKvRuleNames['*'] = this._addRule(
|
||||
`${subName}-kv`,
|
||||
`${this._addRule('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
|
||||
`${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
|
||||
optionalProps.push('*');
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -690,6 +690,7 @@ struct server_context {
|
|||
n_ctx = llama_n_ctx(ctx);
|
||||
|
||||
add_bos_token = llama_should_add_bos_token(model);
|
||||
GGML_ASSERT(llama_add_eos_token(model) != 1);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
|
@ -759,7 +760,7 @@ struct server_context {
|
|||
metrics.init();
|
||||
}
|
||||
|
||||
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const {
|
||||
std::vector<llama_token> tokenize(const json & json_prompt, bool add_special) const {
|
||||
// TODO: currently, we tokenize using special tokens by default
|
||||
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
|
||||
// but it's better compared to completely ignoring ChatML and other chat templates
|
||||
|
|
@ -777,7 +778,7 @@ struct server_context {
|
|||
|
||||
std::vector<llama_token> p;
|
||||
if (first) {
|
||||
p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
|
||||
p = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
|
||||
first = false;
|
||||
} else {
|
||||
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
|
||||
|
|
@ -794,7 +795,7 @@ struct server_context {
|
|||
}
|
||||
} else {
|
||||
auto s = json_prompt.template get<std::string>();
|
||||
prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
|
||||
prompt_tokens = ::llama_tokenize(ctx, s, add_special, TMP_FORCE_SPECIAL);
|
||||
}
|
||||
|
||||
return prompt_tokens;
|
||||
|
|
@ -859,7 +860,7 @@ struct server_context {
|
|||
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
|
||||
|
||||
// process "json_schema" and "grammar"
|
||||
if (data.contains("json_schema") && data.contains("grammar")) {
|
||||
if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) {
|
||||
send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
|
||||
return false;
|
||||
} else if (data.contains("json_schema") && !data.contains("grammar")) {
|
||||
|
|
@ -1059,7 +1060,7 @@ struct server_context {
|
|||
system_tokens.clear();
|
||||
|
||||
if (!system_prompt.empty()) {
|
||||
system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);
|
||||
system_tokens = ::llama_tokenize(ctx, system_prompt, true);
|
||||
|
||||
llama_batch_clear(batch);
|
||||
|
||||
|
|
@ -1083,7 +1084,7 @@ struct server_context {
|
|||
};
|
||||
|
||||
if (llama_decode(ctx, batch_view) != 0) {
|
||||
LOG_TEE("%s: llama_decode() failed\n", __func__);
|
||||
LOG_ERROR("llama_decode() failed", {});
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
|
@ -1281,7 +1282,11 @@ struct server_context {
|
|||
}
|
||||
|
||||
void send_error(const int id_task, const int id_multi, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
|
||||
LOG_TEE("task %i - error: %s\n", id_task, error.c_str());
|
||||
LOG_ERROR("task error", {
|
||||
{"id_multi", id_multi},
|
||||
{"id_task", id_task},
|
||||
{"error", error},
|
||||
});
|
||||
|
||||
server_task_result res;
|
||||
res.id = id_task;
|
||||
|
|
@ -1915,7 +1920,7 @@ struct server_context {
|
|||
prefix_tokens.push_back(llama_token_middle(model));
|
||||
prompt_tokens = prefix_tokens;
|
||||
} else {
|
||||
prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token); // add BOS if there isn't system prompt
|
||||
prompt_tokens = tokenize(slot.prompt, system_prompt.empty()); // add BOS if there isn't system prompt
|
||||
}
|
||||
|
||||
slot.n_past = 0;
|
||||
|
|
@ -2186,7 +2191,11 @@ struct server_context {
|
|||
if (ret != 0) {
|
||||
if (n_batch == 1 || ret < 0) {
|
||||
// if you get here, it means the KV cache is full - try increasing it via the context size
|
||||
LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
|
||||
LOG_ERROR("failed to decode the batch: KV cache is full - try increasing it via the context size", {
|
||||
{"i", i},
|
||||
{"n_batch", ret},
|
||||
{"ret", ret},
|
||||
});
|
||||
for (auto & slot : slots) {
|
||||
slot.state = SLOT_STATE_PROCESSING;
|
||||
slot.command = SLOT_COMMAND_NONE;
|
||||
|
|
@ -2196,12 +2205,16 @@ struct server_context {
|
|||
break; // break loop of n_batch
|
||||
}
|
||||
|
||||
LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);
|
||||
|
||||
// retry with half the batch size to try to find a free slot in the KV cache
|
||||
n_batch /= 2;
|
||||
i -= n_batch;
|
||||
|
||||
LOG_WARNING("failed to find free space in the KV cache, retrying with smaller batch size - try increasing it via the context size or enable defragmentation", {
|
||||
{"i", i},
|
||||
{"n_batch", n_batch},
|
||||
{"ret", ret},
|
||||
});
|
||||
|
||||
continue; // continue loop of n_batch
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -78,6 +78,28 @@ int main(int argc, char ** argv) {
|
|||
params.n_threads_batch = params.n_threads_batch_draft;
|
||||
std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params);
|
||||
|
||||
const bool vocab_type_tgt = llama_vocab_type(model_tgt);
|
||||
LOG("vocab_type tgt: %d\n", vocab_type_tgt);
|
||||
|
||||
const bool vocab_type_dft = llama_vocab_type(model_dft);
|
||||
LOG("vocab_type dft: %d\n", vocab_type_dft);
|
||||
|
||||
if (vocab_type_tgt != vocab_type_dft) {
|
||||
fprintf(stderr, "%s: error: draft model vocab type must match target model to use speculation but ", __func__);
|
||||
fprintf(stderr, "vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
|
||||
return 1;
|
||||
}
|
||||
|
||||
if (
|
||||
llama_add_bos_token(model_tgt) != llama_add_bos_token(model_dft) ||
|
||||
llama_add_eos_token(model_tgt) != llama_add_eos_token(model_dft) ||
|
||||
llama_token_bos(model_tgt) != llama_token_bos(model_dft) ||
|
||||
llama_token_eos(model_tgt) != llama_token_eos(model_dft)
|
||||
) {
|
||||
fprintf(stderr, "%s: error: draft model special tokens must match target model to use speculation\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
{
|
||||
const int n_vocab_tgt = llama_n_vocab(model_tgt);
|
||||
const int n_vocab_dft = llama_n_vocab(model_dft);
|
||||
|
|
@ -107,20 +129,8 @@ int main(int argc, char ** argv) {
|
|||
|
||||
|
||||
// Tokenize the prompt
|
||||
const bool add_bos_tgt = llama_should_add_bos_token(model_tgt);
|
||||
LOG("add_bos tgt: %d\n", add_bos_tgt);
|
||||
|
||||
const bool add_bos_dft = llama_should_add_bos_token(model_dft);
|
||||
LOG("add_bos dft: %d\n", add_bos_dft);
|
||||
|
||||
if (add_bos_tgt != add_bos_dft) {
|
||||
fprintf(stderr, "%s: error: draft model add_bos must match target model to use speculation but ", __func__);
|
||||
fprintf(stderr, "add_bos_dft = %d while add_bos_tgt = %d\n", add_bos_dft, add_bos_tgt);
|
||||
return 1;
|
||||
}
|
||||
|
||||
std::vector<llama_token> inp;
|
||||
inp = ::llama_tokenize(ctx_tgt, params.prompt, add_bos_tgt, true);
|
||||
inp = ::llama_tokenize(ctx_tgt, params.prompt, true, true);
|
||||
|
||||
const int max_context_size = llama_n_ctx(ctx_tgt);
|
||||
const int max_tokens_list_size = max_context_size - 4;
|
||||
|
|
|
|||
|
|
@ -20,4 +20,4 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
|||
#cmake --build . --config Release --target llama-bench
|
||||
|
||||
#build all binary
|
||||
cmake --build . --config Release -v
|
||||
cmake --build . --config Release -j -v
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ if [ $# -gt 0 ]; then
|
|||
GGML_SYCL_SINGLE_GPU=1
|
||||
else
|
||||
GGML_SYCL_DEVICE=0
|
||||
GGML_SYCL_SINGLE_GPU=0
|
||||
fi
|
||||
|
||||
#export GGML_SYCL_DEBUG=1
|
||||
|
|
|
|||
|
|
@ -26,11 +26,9 @@ int main(int argc, char ** argv) {
|
|||
llama_context_params ctx_params = llama_context_default_params();
|
||||
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
|
||||
|
||||
const bool add_bos = llama_should_add_bos_token(model);
|
||||
|
||||
std::vector<llama_token> tokens;
|
||||
|
||||
tokens = ::llama_tokenize(model, prompt, add_bos, true);
|
||||
tokens = ::llama_tokenize(model, prompt, true, true);
|
||||
|
||||
for (int i = 0; i < (int) tokens.size(); i++) {
|
||||
if (printing_ids) {
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
#!/bin/bash
|
||||
#
|
||||
# ./examples/ts-type-to-grammar.sh "{a:string,b:string,c?:string}"
|
||||
# python examples/json-schema-to-grammar.py https://json.schemastore.org/tsconfig.json
|
||||
# python examples/json_schema_to_grammar.py https://json.schemastore.org/tsconfig.json
|
||||
#
|
||||
set -euo pipefail
|
||||
|
||||
|
|
@ -25,4 +25,4 @@ npx ts-json-schema-generator --unstable --no-top-ref --path "$DTS_FILE" --type M
|
|||
# https://github.com/YousefED/typescript-json-schema
|
||||
# npx typescript-json-schema --defaultProps --required "$DTS_FILE" MyType | tee "$SCHEMA_FILE" >&2
|
||||
|
||||
./examples/json-schema-to-grammar.py "$SCHEMA_FILE"
|
||||
./examples/json_schema_to_grammar.py "$SCHEMA_FILE"
|
||||
|
|
|
|||
|
|
@ -1948,7 +1948,7 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
|
|||
} else if (!split && !use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) {
|
||||
// KQV single-batch
|
||||
ggml_cuda_mul_mat_vec_nc(ctx, src0, src1, dst);
|
||||
} else if (!split && use_tensor_cores && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
|
||||
} else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16 || use_tensor_cores) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) {
|
||||
// KQ + KQV multi-batch
|
||||
ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst);
|
||||
} else if (use_dequantize_mul_mat_vec) {
|
||||
|
|
|
|||
27
ggml-metal.m
27
ggml-metal.m
|
|
@ -37,6 +37,7 @@ enum ggml_metal_kernel_type {
|
|||
GGML_METAL_KERNEL_TYPE_DIV_ROW,
|
||||
GGML_METAL_KERNEL_TYPE_SCALE,
|
||||
GGML_METAL_KERNEL_TYPE_SCALE_4,
|
||||
GGML_METAL_KERNEL_TYPE_CLAMP,
|
||||
GGML_METAL_KERNEL_TYPE_TANH,
|
||||
GGML_METAL_KERNEL_TYPE_RELU,
|
||||
GGML_METAL_KERNEL_TYPE_GELU,
|
||||
|
|
@ -468,6 +469,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
|
|||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true);
|
||||
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true);
|
||||
|
|
@ -713,6 +715,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
|
|||
case GGML_OP_MUL:
|
||||
case GGML_OP_DIV:
|
||||
case GGML_OP_SCALE:
|
||||
case GGML_OP_CLAMP:
|
||||
case GGML_OP_SQR:
|
||||
case GGML_OP_SUM_ROWS:
|
||||
return true;
|
||||
|
|
@ -1152,6 +1155,25 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&scale length:sizeof(scale) atIndex:2];
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_CLAMP:
|
||||
{
|
||||
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline;
|
||||
|
||||
float min;
|
||||
float max;
|
||||
memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float));
|
||||
memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float));
|
||||
|
||||
[encoder setComputePipelineState:pipeline];
|
||||
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
|
||||
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
|
||||
[encoder setBytes:&min length:sizeof(min) atIndex:2];
|
||||
[encoder setBytes:&max length:sizeof(max) atIndex:3];
|
||||
|
||||
const int64_t n = ggml_nelements(dst);
|
||||
|
||||
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
|
||||
} break;
|
||||
case GGML_OP_UNARY:
|
||||
|
|
@ -1926,7 +1948,12 @@ static enum ggml_status ggml_metal_graph_compute(
|
|||
{
|
||||
nth0 = 4;
|
||||
nth1 = 16;
|
||||
#if QK_K == 64
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline;
|
||||
#else
|
||||
pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline;
|
||||
#endif
|
||||
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
|
|
|
|||
1332
ggml-metal.metal
1332
ggml-metal.metal
File diff suppressed because it is too large
Load diff
|
|
@ -3154,7 +3154,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)(
|
|||
#define SYCL_SCALE_BLOCK_SIZE 256
|
||||
#define SYCL_CLAMP_BLOCK_SIZE 256
|
||||
#define SYCL_ROPE_BLOCK_SIZE 256
|
||||
#define SYCL_SOFT_MAX_BLOCK_SIZE 1024
|
||||
#define SYCL_ALIBI_BLOCK_SIZE 32
|
||||
#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32
|
||||
#define SYCL_QUANTIZE_BLOCK_SIZE 256
|
||||
|
|
@ -13080,11 +13079,13 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float *
|
|||
const int nrows_y, const float scale, const float max_bias,
|
||||
dpct::queue_ptr stream) {
|
||||
int nth = WARP_SIZE;
|
||||
while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2;
|
||||
int max_block_size = g_work_group_size;
|
||||
while (nth < ncols_x && nth < max_block_size) nth *= 2;
|
||||
if (nth>max_block_size) nth = max_block_size;
|
||||
|
||||
const sycl::range<3> block_dims(1, 1, nth);
|
||||
const sycl::range<3> block_nums(1, 1, nrows_x);
|
||||
const size_t n_local_scratch = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE);
|
||||
static_assert(SYCL_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted.");
|
||||
|
||||
const uint32_t n_head_kv = nrows_x/nrows_y;
|
||||
const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
|
||||
|
|
@ -13094,6 +13095,12 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float *
|
|||
|
||||
const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>();
|
||||
if (n_local_scratch*sizeof(float) < local_mem_size) {
|
||||
if (ncols_x > max_block_size) {
|
||||
soft_max_f32_submitter<true, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale,
|
||||
max_bias, m0, m1, n_head_log2, block_nums,
|
||||
block_dims, n_local_scratch, stream);
|
||||
return;
|
||||
}
|
||||
switch (ncols_x) {
|
||||
case 32:
|
||||
soft_max_f32_submitter<true, 32, 32>(x, mask, pos, dst, ncols_x, nrows_y, scale,
|
||||
|
|
@ -16814,11 +16821,13 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
|
|||
const dpct::queue_ptr stream = g_syclStreams[ctx->device][0];
|
||||
SYCL_CHECK(
|
||||
CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
|
||||
|
||||
char* host_buf = (char*)malloc(size);
|
||||
memcpy(host_buf, data, size);
|
||||
SYCL_CHECK(
|
||||
CHECK_TRY_ERROR((*stream)
|
||||
.memcpy((char *)tensor->data + offset, data, size)
|
||||
.memcpy((char *)tensor->data + offset, host_buf, size)
|
||||
.wait()));
|
||||
free(host_buf);
|
||||
}
|
||||
catch (sycl::exception const &exc) {
|
||||
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
|
||||
|
|
|
|||
66
ggml.c
66
ggml.c
|
|
@ -11013,7 +11013,6 @@ static void ggml_compute_forward_mul_mat_id(
|
|||
}
|
||||
|
||||
// initialize matrix_row_counts
|
||||
GGML_ASSERT(wdata == wdata_src1_end);
|
||||
memset(matrix_row_counts, 0, n_as*sizeof(int64_t));
|
||||
|
||||
// group rows by src0 matrix
|
||||
|
|
@ -20565,6 +20564,32 @@ static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset)
|
|||
return ok;
|
||||
}
|
||||
|
||||
static void gguf_free_kv(struct gguf_kv * kv) {
|
||||
if (kv->key.data) {
|
||||
GGML_FREE(kv->key.data);
|
||||
}
|
||||
|
||||
if (kv->type == GGUF_TYPE_STRING) {
|
||||
if (kv->value.str.data) {
|
||||
GGML_FREE(kv->value.str.data);
|
||||
}
|
||||
}
|
||||
|
||||
if (kv->type == GGUF_TYPE_ARRAY) {
|
||||
if (kv->value.arr.data) {
|
||||
if (kv->value.arr.type == GGUF_TYPE_STRING) {
|
||||
for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
|
||||
struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
|
||||
if (str->data) {
|
||||
GGML_FREE(str->data);
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_FREE(kv->value.arr.data);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
struct gguf_context * gguf_init_empty(void) {
|
||||
struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
|
||||
|
||||
|
|
@ -20944,31 +20969,7 @@ void gguf_free(struct gguf_context * ctx) {
|
|||
if (ctx->kv) {
|
||||
// free string memory - not great..
|
||||
for (uint64_t i = 0; i < ctx->header.n_kv; ++i) {
|
||||
struct gguf_kv * kv = &ctx->kv[i];
|
||||
|
||||
if (kv->key.data) {
|
||||
GGML_FREE(kv->key.data);
|
||||
}
|
||||
|
||||
if (kv->type == GGUF_TYPE_STRING) {
|
||||
if (kv->value.str.data) {
|
||||
GGML_FREE(kv->value.str.data);
|
||||
}
|
||||
}
|
||||
|
||||
if (kv->type == GGUF_TYPE_ARRAY) {
|
||||
if (kv->value.arr.data) {
|
||||
if (kv->value.arr.type == GGUF_TYPE_STRING) {
|
||||
for (uint64_t j = 0; j < kv->value.arr.n; ++j) {
|
||||
struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j];
|
||||
if (str->data) {
|
||||
GGML_FREE(str->data);
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_FREE(kv->value.arr.data);
|
||||
}
|
||||
}
|
||||
gguf_free_kv(&ctx->kv[i]);
|
||||
}
|
||||
|
||||
GGML_FREE(ctx->kv);
|
||||
|
|
@ -21193,6 +21194,19 @@ static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) {
|
|||
return n_kv;
|
||||
}
|
||||
|
||||
void gguf_remove_key(struct gguf_context * ctx, const char * key) {
|
||||
const int idx = gguf_find_key(ctx, key);
|
||||
if (idx >= 0) {
|
||||
const int n_kv = gguf_get_n_kv(ctx);
|
||||
gguf_free_kv(&ctx->kv[idx]);
|
||||
for (int i = idx; i < n_kv-1; ++i) {
|
||||
ctx->kv[i] = ctx->kv[i+1];
|
||||
}
|
||||
ctx->kv = realloc(ctx->kv, (n_kv - 1) * sizeof(struct gguf_kv));
|
||||
ctx->header.n_kv--;
|
||||
}
|
||||
}
|
||||
|
||||
void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) {
|
||||
const int idx = gguf_get_or_add_key(ctx, key);
|
||||
|
||||
|
|
|
|||
3
ggml.h
3
ggml.h
|
|
@ -2296,6 +2296,9 @@ extern "C" {
|
|||
GGML_API char * gguf_get_tensor_name (const struct gguf_context * ctx, int i);
|
||||
GGML_API enum ggml_type gguf_get_tensor_type (const struct gguf_context * ctx, int i);
|
||||
|
||||
// removes key if it exists
|
||||
GGML_API void gguf_remove_key(struct gguf_context * ctx, const char * key);
|
||||
|
||||
// overrides existing values or adds a new one
|
||||
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
|
||||
GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
|
||||
|
|
|
|||
|
|
@ -126,6 +126,7 @@ class MODEL_ARCH(IntEnum):
|
|||
MAMBA = auto()
|
||||
XVERSE = auto()
|
||||
COMMAND_R = auto()
|
||||
DBRX = auto()
|
||||
|
||||
|
||||
class MODEL_TENSOR(IntEnum):
|
||||
|
|
@ -195,6 +196,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.MAMBA: "mamba",
|
||||
MODEL_ARCH.XVERSE: "xverse",
|
||||
MODEL_ARCH.COMMAND_R: "command-r",
|
||||
MODEL_ARCH.DBRX: "dbrx",
|
||||
}
|
||||
|
||||
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
||||
|
|
@ -642,6 +644,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.ATTN_K_NORM,
|
||||
MODEL_TENSOR.ATTN_Q_NORM,
|
||||
],
|
||||
MODEL_ARCH.DBRX: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.ATTN_OUT_NORM,
|
||||
MODEL_TENSOR.FFN_GATE_INP,
|
||||
MODEL_TENSOR.FFN_GATE_EXP,
|
||||
MODEL_TENSOR.FFN_DOWN_EXP,
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
],
|
||||
# TODO
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ class TensorNameMap:
|
|||
# Token embeddings
|
||||
MODEL_TENSOR.TOKEN_EMBD: (
|
||||
"gpt_neox.embed_in", # gptneox
|
||||
"transformer.wte", # gpt2 gpt-j mpt refact qwen
|
||||
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx
|
||||
"transformer.word_embeddings", # falcon
|
||||
"word_embeddings", # bloom
|
||||
"model.embed_tokens", # llama-hf
|
||||
|
|
@ -48,7 +48,7 @@ class TensorNameMap:
|
|||
# Output
|
||||
MODEL_TENSOR.OUTPUT: (
|
||||
"embed_out", # gptneox
|
||||
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba
|
||||
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
|
||||
"output", # llama-pth bloom internlm2
|
||||
"word_embeddings_for_head", # persimmon
|
||||
"lm_head.linear", # phi2
|
||||
|
|
@ -60,7 +60,7 @@ class TensorNameMap:
|
|||
"transformer.ln_f", # gpt2 gpt-j falcon
|
||||
"model.norm", # llama-hf baichuan internlm2
|
||||
"norm", # llama-pth
|
||||
"transformer.norm_f", # mpt
|
||||
"transformer.norm_f", # mpt dbrx
|
||||
"ln_f", # refact bloom qwen gpt2
|
||||
"language_model.encoder.final_layernorm", # persimmon
|
||||
"model.final_layernorm", # persimmon
|
||||
|
|
@ -96,6 +96,7 @@ class TensorNameMap:
|
|||
"model.layers.{bid}.norm", # mamba-qbert
|
||||
"backbone.layers.{bid}.norm", # mamba
|
||||
"transformer.decoder_layer.{bid}.rms_norm", # Grok
|
||||
"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
|
||||
),
|
||||
|
||||
# Attention norm 2
|
||||
|
|
@ -108,6 +109,7 @@ class TensorNameMap:
|
|||
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
|
||||
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
|
||||
"transformer.blocks.{bid}.attn.Wqkv", # mpt
|
||||
"transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx
|
||||
"transformer.h.{bid}.self_attention.query_key_value", # falcon
|
||||
"h.{bid}.self_attention.query_key_value", # bloom
|
||||
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
|
||||
|
|
@ -168,7 +170,8 @@ class TensorNameMap:
|
|||
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
|
||||
"model.layers.{bid}.attention.wo", # internlm2
|
||||
"encoder.layers.{bid}.attn.out_proj", # nomic-bert
|
||||
"transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok
|
||||
"transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok
|
||||
"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
|
||||
),
|
||||
|
||||
# Attention output norm
|
||||
|
|
@ -176,6 +179,7 @@ class TensorNameMap:
|
|||
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
|
||||
"encoder.layers.{bid}.norm1", # nomic-bert
|
||||
"transformer.decoder_layer.{bid}.rms_norm_1", # Grok
|
||||
"transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx
|
||||
),
|
||||
|
||||
# Rotary embeddings
|
||||
|
|
@ -204,7 +208,8 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.FFN_GATE_INP: (
|
||||
"layers.{bid}.feed_forward.gate", # mixtral
|
||||
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
|
||||
"transformer.decoder_layer.{bid}.router" # Grok
|
||||
"transformer.decoder_layer.{bid}.router", # Grok
|
||||
"transformer.blocks.{bid}.ffn.router.layer", # dbrx
|
||||
),
|
||||
|
||||
# Feed-forward up
|
||||
|
|
@ -233,6 +238,7 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.FFN_UP_EXP: (
|
||||
"layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
|
||||
"transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
|
||||
"transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
|
||||
),
|
||||
|
||||
# AWQ-activation gate
|
||||
|
|
@ -252,7 +258,8 @@ class TensorNameMap:
|
|||
|
||||
MODEL_TENSOR.FFN_GATE_EXP: (
|
||||
"layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
|
||||
"transformer.decoder_layer.{bid}.moe.linear" # Grok (merged)
|
||||
"transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
|
||||
"transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
|
||||
),
|
||||
|
||||
# Feed-forward down
|
||||
|
|
@ -280,6 +287,7 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.FFN_DOWN_EXP: (
|
||||
"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
|
||||
"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
|
||||
"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ATTN_Q_NORM: (
|
||||
|
|
|
|||
|
|
@ -594,7 +594,8 @@ static void grammar_accept_token(FileFormat file_format, int32_t n_vocab, struct
|
|||
const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
|
||||
const auto & code_points = decoded.first;
|
||||
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
|
||||
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
|
||||
auto prev_stacks = grammar->stacks;
|
||||
llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks);
|
||||
}
|
||||
grammar->partial_utf8 = decoded.second;
|
||||
GGML_ASSERT(!grammar->stacks.empty());
|
||||
|
|
|
|||
481
llama.cpp
481
llama.cpp
|
|
@ -108,7 +108,7 @@
|
|||
#endif
|
||||
|
||||
#define LLAMA_MAX_NODES 8192
|
||||
#define LLAMA_MAX_EXPERTS 8
|
||||
#define LLAMA_MAX_EXPERTS 16
|
||||
|
||||
|
||||
//
|
||||
|
|
@ -242,6 +242,7 @@ enum llm_arch {
|
|||
LLM_ARCH_MAMBA,
|
||||
LLM_ARCH_XVERSE,
|
||||
LLM_ARCH_COMMAND_R,
|
||||
LLM_ARCH_DBRX,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -274,6 +275,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_MAMBA, "mamba" },
|
||||
{ LLM_ARCH_XVERSE, "xverse" },
|
||||
{ LLM_ARCH_COMMAND_R, "command-r" },
|
||||
{ LLM_ARCH_DBRX, "dbrx" },
|
||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||
};
|
||||
|
||||
|
|
@ -340,6 +342,8 @@ enum llm_kv {
|
|||
LLM_KV_TOKENIZER_UNK_ID,
|
||||
LLM_KV_TOKENIZER_SEP_ID,
|
||||
LLM_KV_TOKENIZER_PAD_ID,
|
||||
LLM_KV_TOKENIZER_CLS_ID,
|
||||
LLM_KV_TOKENIZER_MASK_ID,
|
||||
LLM_KV_TOKENIZER_ADD_BOS,
|
||||
LLM_KV_TOKENIZER_ADD_EOS,
|
||||
LLM_KV_TOKENIZER_ADD_PREFIX,
|
||||
|
|
@ -410,6 +414,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
|||
{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
|
||||
{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
|
||||
{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
|
||||
{ LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
|
||||
{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
|
||||
{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
|
||||
{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
|
||||
{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
|
||||
|
|
@ -952,6 +958,22 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
|
|||
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_DBRX,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||
{ LLM_TENSOR_OUTPUT, "output" },
|
||||
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
|
||||
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
|
||||
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
|
||||
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
|
||||
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
|
||||
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
|
||||
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
|
||||
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_UNKNOWN,
|
||||
{
|
||||
|
|
@ -1660,17 +1682,17 @@ static size_t llama_get_device_memory(int device) {
|
|||
#if defined(GGML_USE_CUDA)
|
||||
size_t total;
|
||||
size_t free;
|
||||
ggml_backend_cuda_get_device_memory(device, &total, &free);
|
||||
ggml_backend_cuda_get_device_memory(device, &free, &total);
|
||||
return free;
|
||||
#elif defined(GGML_USE_SYCL)
|
||||
size_t total;
|
||||
size_t free;
|
||||
ggml_backend_sycl_get_device_memory(device, &total, &free);
|
||||
ggml_backend_sycl_get_device_memory(device, &free, &total);
|
||||
return free;
|
||||
#elif defined(GGML_USE_VULKAN)
|
||||
size_t total;
|
||||
size_t free;
|
||||
ggml_backend_vk_get_device_memory(device, &total, &free);
|
||||
ggml_backend_vk_get_device_memory(device, &free, &total);
|
||||
return free;
|
||||
#else
|
||||
return 1;
|
||||
|
|
@ -1727,6 +1749,9 @@ enum e_model {
|
|||
MODEL_MEDIUM,
|
||||
MODEL_LARGE,
|
||||
MODEL_XL,
|
||||
MODEL_8x7B,
|
||||
MODEL_8x22B,
|
||||
MODEL_16x12B,
|
||||
};
|
||||
|
||||
static const size_t kiB = 1024;
|
||||
|
|
@ -2049,6 +2074,8 @@ struct llama_vocab {
|
|||
id special_unk_id = 0;
|
||||
id special_sep_id = -1;
|
||||
id special_pad_id = -1;
|
||||
id special_cls_id = -1;
|
||||
id special_mask_id = -1;
|
||||
|
||||
int special_add_bos = -1; // -1 unknown, 1 add, 0 don't add.
|
||||
int special_add_eos = -1; // -1 unknown, 1 add, 0 don't add.
|
||||
|
|
@ -3596,6 +3623,9 @@ static const char * llama_model_type_name(e_model type) {
|
|||
case MODEL_MEDIUM: return "0.4B";
|
||||
case MODEL_LARGE: return "0.8B";
|
||||
case MODEL_XL: return "1.5B";
|
||||
case MODEL_8x7B: return "8x7B";
|
||||
case MODEL_8x22B: return "8x22B";
|
||||
case MODEL_16x12B: return "16x12B";
|
||||
default: return "?B";
|
||||
}
|
||||
}
|
||||
|
|
@ -3710,6 +3740,13 @@ static void llm_load_hparams(
|
|||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
|
||||
if (hparams.n_expert == 8) {
|
||||
switch (hparams.n_layer) {
|
||||
case 32: model.type = e_model::MODEL_8x7B; break;
|
||||
case 56: model.type = e_model::MODEL_8x22B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} else {
|
||||
switch (hparams.n_layer) {
|
||||
case 22: model.type = e_model::MODEL_1B; break;
|
||||
case 26: model.type = e_model::MODEL_3B; break;
|
||||
|
|
@ -3720,6 +3757,7 @@ static void llm_load_hparams(
|
|||
case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_MINICPM:
|
||||
{
|
||||
|
|
@ -4009,6 +4047,16 @@ static void llm_load_hparams(
|
|||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_DBRX:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
||||
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 40: model.type = e_model::MODEL_16x12B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
default: (void)0;
|
||||
}
|
||||
|
||||
|
|
@ -4022,7 +4070,9 @@ static void llm_load_hparams(
|
|||
}
|
||||
|
||||
// TODO: This should probably be in llama.h
|
||||
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special = false);
|
||||
static std::vector<llama_vocab::id> llama_tokenize_internal(
|
||||
const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special = false
|
||||
);
|
||||
static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch);
|
||||
static bool OldBPETokenizerMode = false;
|
||||
|
||||
|
|
@ -4050,6 +4100,8 @@ static void llm_load_vocab(
|
|||
vocab.special_unk_id = -1;
|
||||
vocab.special_sep_id = -1;
|
||||
vocab.special_pad_id = -1;
|
||||
vocab.special_cls_id = -1;
|
||||
vocab.special_mask_id = -1;
|
||||
vocab.linefeed_id = -1;
|
||||
|
||||
return;
|
||||
|
|
@ -4062,6 +4114,8 @@ static void llm_load_vocab(
|
|||
vocab.special_unk_id = 0;
|
||||
vocab.special_sep_id = -1;
|
||||
vocab.special_pad_id = -1;
|
||||
vocab.special_cls_id = -1;
|
||||
vocab.special_mask_id = -1;
|
||||
|
||||
const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str());
|
||||
if (add_space_prefix_keyidx != -1) {
|
||||
|
|
@ -4110,15 +4164,19 @@ static void llm_load_vocab(
|
|||
vocab.special_unk_id = -1;
|
||||
vocab.special_sep_id = -1;
|
||||
vocab.special_pad_id = -1;
|
||||
vocab.special_cls_id = -1;
|
||||
vocab.special_mask_id = -1;
|
||||
} else if (tokenizer_name == "bert") {
|
||||
vocab.type = LLAMA_VOCAB_TYPE_WPM;
|
||||
|
||||
// default special tokens
|
||||
vocab.special_bos_id = 101;
|
||||
vocab.special_eos_id = 102;
|
||||
vocab.special_bos_id = -1;
|
||||
vocab.special_eos_id = -1;
|
||||
vocab.special_unk_id = 100;
|
||||
vocab.special_sep_id = -1;
|
||||
vocab.special_pad_id = -1;
|
||||
vocab.special_sep_id = 102;
|
||||
vocab.special_pad_id = 0;
|
||||
vocab.special_cls_id = 101;
|
||||
vocab.special_mask_id = 103;
|
||||
vocab.add_space_prefix = false;
|
||||
} else {
|
||||
LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str());
|
||||
|
|
@ -4195,6 +4253,8 @@ static void llm_load_vocab(
|
|||
{ LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
|
||||
{ LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
|
||||
{ LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
|
||||
{ LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id },
|
||||
{ LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id },
|
||||
};
|
||||
for (const auto & it : special_token_types) {
|
||||
const std::string & key = kv(std::get<0>(it));
|
||||
|
|
@ -4391,6 +4451,8 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
|
||||
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
|
||||
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
|
||||
if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
|
||||
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
|
||||
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
|
||||
}
|
||||
|
||||
|
|
@ -4709,6 +4771,39 @@ static bool llm_load_tensors(
|
|||
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_DBRX:
|
||||
{
|
||||
if (n_expert == 0) {
|
||||
throw std::runtime_error("DBRX model cannot have zero experts");
|
||||
}
|
||||
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
||||
// output
|
||||
{
|
||||
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
||||
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
ggml_context * ctx_layer = ctx_for_layer(i);
|
||||
ggml_context * ctx_split = ctx_for_layer_split(i);
|
||||
|
||||
auto & layer = model.layers[i];
|
||||
|
||||
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
||||
|
||||
layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
||||
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
||||
|
||||
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
||||
|
||||
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
||||
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
||||
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
|
||||
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_BAICHUAN:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
|
|
@ -6471,6 +6566,40 @@ struct llm_build_context {
|
|||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx0, cur, layer_dir);
|
||||
}
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
// REVIEW: will be replaced by https://github.com/ggerganov/llama.cpp/pull/6505
|
||||
ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, int il) {
|
||||
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
|
||||
cb(logits, "ffn_moe_logits", il);
|
||||
|
||||
|
|
@ -6502,13 +6631,25 @@ struct llm_build_context {
|
|||
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
|
||||
cb(cur_up, "ffn_moe_up", il);
|
||||
|
||||
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
|
||||
cb(cur_gate, "ffn_moe_gate", il);
|
||||
ggml_tensor * gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
|
||||
cb(gate, "ffn_moe_gate", il);
|
||||
|
||||
cur_gate = ggml_silu(ctx0, cur_gate);
|
||||
cb(cur_gate, "ffn_moe_silu", il);
|
||||
switch (type_op) {
|
||||
case LLM_FFN_SILU:
|
||||
{
|
||||
gate = ggml_silu(ctx0, gate);
|
||||
cb(gate, "ffn_moe_silu", il);
|
||||
} break;
|
||||
case LLM_FFN_GELU:
|
||||
{
|
||||
gate = ggml_gelu(ctx0, gate);
|
||||
cb(gate, "ffn_moe_gelu", il);
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
|
||||
cur_expert = ggml_mul(ctx0, cur_up, gate);
|
||||
cb(cur_expert, "ffn_moe_gate_par", il);
|
||||
|
||||
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
|
||||
|
|
@ -6526,36 +6667,7 @@ struct llm_build_context {
|
|||
}
|
||||
}
|
||||
|
||||
cur = moe_out;
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx0, cur, layer_dir);
|
||||
}
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
return moe_out;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_baichuan() {
|
||||
|
|
@ -7005,63 +7117,7 @@ struct llm_build_context {
|
|||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
|
||||
cb(logits, "ffn_moe_logits", il);
|
||||
|
||||
ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
|
||||
cb(probs, "ffn_moe_probs", il);
|
||||
|
||||
// select experts
|
||||
ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
|
||||
cb(selected_experts->src[0], "ffn_moe_argsort", il);
|
||||
|
||||
ggml_tensor * weights = ggml_get_rows(ctx0,
|
||||
ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
|
||||
cb(weights, "ffn_moe_weights", il);
|
||||
|
||||
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
|
||||
|
||||
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
|
||||
cb(weights_sum, "ffn_moe_weights_sum", il);
|
||||
|
||||
weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
|
||||
cb(weights, "ffn_moe_weights_norm", il);
|
||||
|
||||
// compute expert outputs
|
||||
ggml_tensor * moe_out = nullptr;
|
||||
|
||||
for (int i = 0; i < n_expert_used; ++i) {
|
||||
ggml_tensor * cur_expert;
|
||||
|
||||
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
|
||||
cb(cur_up, "ffn_moe_up", il);
|
||||
|
||||
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
|
||||
cb(cur_gate, "ffn_moe_gate", il);
|
||||
|
||||
//GeLU
|
||||
cur_gate = ggml_gelu(ctx0, cur_gate);
|
||||
cb(cur_gate, "ffn_moe_gelu", il);
|
||||
|
||||
cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
|
||||
cb(cur_expert, "ffn_moe_gate_par", il);
|
||||
|
||||
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
|
||||
cb(cur_expert, "ffn_moe_down", il);
|
||||
|
||||
cur_expert = ggml_mul(ctx0, cur_expert,
|
||||
ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
|
||||
cb(cur_expert, "ffn_moe_weighted", il);
|
||||
|
||||
if (i == 0) {
|
||||
moe_out = cur_expert;
|
||||
} else {
|
||||
moe_out = ggml_add(ctx0, moe_out, cur_expert);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
}
|
||||
}
|
||||
|
||||
cur = moe_out;
|
||||
cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, il);
|
||||
|
||||
// Grok
|
||||
// if layer_out_norm is present then apply it before adding the input
|
||||
|
|
@ -7109,6 +7165,126 @@ struct llm_build_context {
|
|||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_dbrx() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
// mutable variable, needed during the last layer of the computation to skip unused tokens
|
||||
int32_t n_tokens = this->n_tokens;
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
struct ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
cur = llm_build_norm(ctx0, inpL, hparams,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
struct ggml_tensor * Qcur = nullptr;
|
||||
struct ggml_tensor * Kcur = nullptr;
|
||||
struct ggml_tensor * Vcur = nullptr;
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
|
||||
cb(cur, "wqkv", il);
|
||||
|
||||
cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
|
||||
cb(cur, "wqkv_clamped", il);
|
||||
|
||||
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
|
||||
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
|
||||
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_rope_custom(
|
||||
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
||||
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
|
||||
model.layers[il].wo, NULL,
|
||||
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
n_tokens = n_outputs;
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
// MoE branch
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].attn_out_norm, NULL,
|
||||
LLM_NORM, cb, il);
|
||||
cb(cur, "attn_out_norm", il);
|
||||
|
||||
cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
|
||||
if (layer_dir != nullptr) {
|
||||
cur = ggml_add(ctx0, cur, layer_dir);
|
||||
}
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_starcoder() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
|
||||
|
||||
|
|
@ -9823,6 +9999,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_command_r();
|
||||
} break;
|
||||
case LLM_ARCH_DBRX:
|
||||
{
|
||||
result = llm.build_dbrx();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
|
|
@ -11380,7 +11560,7 @@ struct llm_tokenizer_bpe {
|
|||
add_new_bigram(bigram.left, left_symbol.next); // right side of current symbol
|
||||
}
|
||||
|
||||
// add the fnished tokens to the final list keeping correct order for next and prev
|
||||
// add the finished tokens to the final list keeping correct order for next and prev
|
||||
for (auto & sym : symbols) {
|
||||
if (sym.n > 0) {
|
||||
sym.prev = final_prev_index;
|
||||
|
|
@ -11649,9 +11829,6 @@ struct llm_tokenizer_wpm {
|
|||
output.push_back(vocab.special_unk_id);
|
||||
}
|
||||
}
|
||||
|
||||
// append eos token
|
||||
output.push_back(vocab.special_eos_id);
|
||||
}
|
||||
|
||||
std::vector<std::string> preprocess(const std::string & text) {
|
||||
|
|
@ -11856,30 +12033,28 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
|
|||
}
|
||||
}
|
||||
|
||||
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos, bool special) {
|
||||
static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool add_special, bool parse_special) {
|
||||
std::vector<llama_vocab::id> output;
|
||||
|
||||
// OG tokenizer behavior:
|
||||
//
|
||||
// tokenizer.encode('', add_bos=True) returns [1]
|
||||
// tokenizer.encode('', add_bos=False) returns []
|
||||
|
||||
if (bos && vocab.special_bos_id != -1) {
|
||||
output.push_back(vocab.special_bos_id);
|
||||
}
|
||||
|
||||
if (raw_text.empty()) {
|
||||
return output;
|
||||
}
|
||||
|
||||
std::forward_list<fragment_buffer_variant> fragment_buffer;
|
||||
fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
|
||||
|
||||
if (special) tokenizer_st_partition(vocab, fragment_buffer);
|
||||
if (!raw_text.empty()) {
|
||||
fragment_buffer.emplace_front(raw_text, 0, raw_text.length());
|
||||
if (parse_special) tokenizer_st_partition(vocab, fragment_buffer);
|
||||
}
|
||||
|
||||
switch (vocab.type) {
|
||||
case LLAMA_VOCAB_TYPE_SPM:
|
||||
{
|
||||
// OG tokenizer behavior:
|
||||
//
|
||||
// tokenizer.encode('', add_special_tokens=True) returns [1]
|
||||
// tokenizer.encode('', add_special_tokens=False) returns []
|
||||
|
||||
if (add_special && vocab.special_add_bos != 0) {
|
||||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||||
output.push_back(vocab.special_bos_id);
|
||||
}
|
||||
|
||||
for (const auto & fragment : fragment_buffer) {
|
||||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||||
// without adding this leading whitespace, we do not get the same results as the original tokenizer
|
||||
|
|
@ -11905,9 +12080,19 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
|
|||
output.push_back(fragment.token);
|
||||
}
|
||||
}
|
||||
|
||||
if (add_special && vocab.special_add_eos == 1) {
|
||||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||||
output.push_back(vocab.special_eos_id);
|
||||
}
|
||||
} break;
|
||||
case LLAMA_VOCAB_TYPE_BPE:
|
||||
{
|
||||
if (add_special && vocab.special_add_bos == 1) {
|
||||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||||
output.push_back(vocab.special_bos_id);
|
||||
}
|
||||
|
||||
for (const auto & fragment : fragment_buffer) {
|
||||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||||
|
|
@ -11931,9 +12116,16 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
|
|||
output.push_back(fragment.token);
|
||||
}
|
||||
}
|
||||
|
||||
GGML_ASSERT(vocab.special_add_eos != 1);
|
||||
} break;
|
||||
case LLAMA_VOCAB_TYPE_WPM:
|
||||
{
|
||||
if (add_special) {
|
||||
GGML_ASSERT(vocab.special_cls_id != -1);
|
||||
output.push_back(vocab.special_cls_id);
|
||||
}
|
||||
|
||||
for (const auto & fragment : fragment_buffer) {
|
||||
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
|
||||
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
|
||||
|
|
@ -11947,6 +12139,11 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
|
|||
output.push_back(fragment.token);
|
||||
}
|
||||
}
|
||||
|
||||
if (add_special) {
|
||||
GGML_ASSERT(vocab.special_sep_id != -1);
|
||||
output.push_back(vocab.special_sep_id);
|
||||
}
|
||||
} break;
|
||||
case LLAMA_VOCAB_TYPE_NONE:
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -12113,7 +12310,9 @@ static void llama_grammar_advance_stack(
|
|||
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
|
||||
|
||||
if (stack.empty()) {
|
||||
if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
|
||||
new_stacks.emplace_back(stack);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
|
|
@ -12150,7 +12349,10 @@ static void llama_grammar_advance_stack(
|
|||
}
|
||||
case LLAMA_GRETYPE_CHAR:
|
||||
case LLAMA_GRETYPE_CHAR_NOT:
|
||||
if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
|
||||
// only add the stack if it's not a duplicate of one we already have
|
||||
new_stacks.emplace_back(stack);
|
||||
}
|
||||
break;
|
||||
default:
|
||||
// end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range
|
||||
|
|
@ -12164,12 +12366,13 @@ static void llama_grammar_advance_stack(
|
|||
// be positioned at a character range (see `llama_grammar_advance_stack`), and
|
||||
// produces the N possible stacks if the given char is accepted at those
|
||||
// positions
|
||||
std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
|
||||
void llama_grammar_accept(
|
||||
const std::vector<std::vector<llama_grammar_element>> & rules,
|
||||
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
||||
const uint32_t chr) {
|
||||
const uint32_t chr,
|
||||
std::vector<std::vector<const llama_grammar_element *>> & new_stacks) {
|
||||
|
||||
std::vector<std::vector<const llama_grammar_element *>> new_stacks;
|
||||
new_stacks.clear();
|
||||
|
||||
for (const auto & stack : stacks) {
|
||||
if (stack.empty()) {
|
||||
|
|
@ -12188,8 +12391,6 @@ std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
|
|||
llama_grammar_advance_stack(rules, new_stack, new_stacks);
|
||||
}
|
||||
}
|
||||
|
||||
return new_stacks;
|
||||
}
|
||||
|
||||
static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates(
|
||||
|
|
@ -12203,6 +12404,7 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
|
|||
const std::vector<llama_grammar_candidate> & candidates) {
|
||||
|
||||
std::vector<llama_grammar_candidate> rejects;
|
||||
rejects.reserve(candidates.size());
|
||||
|
||||
if (stack.empty()) {
|
||||
for (const auto & tok : candidates) {
|
||||
|
|
@ -12216,6 +12418,8 @@ static std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_
|
|||
const llama_grammar_element * stack_pos = stack.back();
|
||||
|
||||
std::vector<llama_grammar_candidate> next_candidates;
|
||||
next_candidates.reserve(candidates.size());
|
||||
|
||||
for (const auto & tok : candidates) {
|
||||
if (*tok.code_points == 0) {
|
||||
// reached end of full codepoints in token, reject iff it ended in a partial sequence
|
||||
|
|
@ -13037,8 +13241,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
|
|||
// Note terminating 0 in decoded string
|
||||
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
|
||||
const auto & code_points = decoded.first;
|
||||
std::vector<std::vector<const llama_grammar_element *>> tmp_new_stacks;
|
||||
for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
|
||||
grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it);
|
||||
llama_grammar_accept(grammar->rules, grammar->stacks, *it, tmp_new_stacks);
|
||||
grammar->stacks = tmp_new_stacks;
|
||||
}
|
||||
grammar->partial_utf8 = decoded.second;
|
||||
GGML_ASSERT(!grammar->stacks.empty());
|
||||
|
|
@ -13792,6 +13998,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
gguf_set_kv (ctx_out, ml.meta);
|
||||
gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION);
|
||||
gguf_set_val_u32(ctx_out, "general.file_type", ftype);
|
||||
// Remove split metadata
|
||||
gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
|
||||
gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
|
||||
gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
|
||||
|
||||
if (params->kv_overrides) {
|
||||
const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
|
||||
|
|
@ -14881,6 +15091,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||
// the pairs of head values are offset by n_rot/2
|
||||
case LLM_ARCH_FALCON:
|
||||
case LLM_ARCH_GROK:
|
||||
case LLM_ARCH_DBRX:
|
||||
case LLM_ARCH_PERSIMMON:
|
||||
case LLM_ARCH_BERT:
|
||||
case LLM_ARCH_NOMIC_BERT:
|
||||
|
|
@ -16420,6 +16631,14 @@ llama_token llama_token_eos(const struct llama_model * model) {
|
|||
return model->vocab.special_eos_id;
|
||||
}
|
||||
|
||||
llama_token llama_token_cls(const struct llama_model * model) {
|
||||
return model->vocab.special_cls_id;
|
||||
}
|
||||
|
||||
llama_token llama_token_sep(const struct llama_model * model) {
|
||||
return model->vocab.special_sep_id;
|
||||
}
|
||||
|
||||
llama_token llama_token_nl(const struct llama_model * model) {
|
||||
return model->vocab.linefeed_id;
|
||||
}
|
||||
|
|
@ -16454,9 +16673,9 @@ int32_t llama_tokenize(
|
|||
int32_t text_len,
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens_max,
|
||||
bool add_bos,
|
||||
bool special) {
|
||||
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_bos, special);
|
||||
bool add_special,
|
||||
bool parse_special) {
|
||||
auto res = llama_tokenize_internal(model->vocab, std::string(text, text_len), add_special, parse_special);
|
||||
|
||||
if (n_tokens_max < (int) res.size()) {
|
||||
// LLAMA_LOG_ERROR("%s: too many tokens\n", __func__);
|
||||
|
|
|
|||
15
llama.h
15
llama.h
|
|
@ -788,6 +788,8 @@ extern "C" {
|
|||
// Special tokens
|
||||
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
|
||||
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
|
||||
LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
|
||||
LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
|
||||
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
|
||||
|
||||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||||
|
|
@ -810,16 +812,16 @@ extern "C" {
|
|||
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
|
||||
/// @return Returns the number of tokens on success, no more than n_tokens_max
|
||||
/// @return Returns a negative number on failure - the number of tokens that would have been returned
|
||||
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
|
||||
/// Does not insert a leading space.
|
||||
/// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
|
||||
/// as plaintext. Does not insert a leading space.
|
||||
LLAMA_API int32_t llama_tokenize(
|
||||
const struct llama_model * model,
|
||||
const char * text,
|
||||
int32_t text_len,
|
||||
llama_token * tokens,
|
||||
int32_t n_tokens_max,
|
||||
bool add_bos,
|
||||
bool special);
|
||||
bool add_special,
|
||||
bool parse_special);
|
||||
|
||||
// Token Id -> Piece.
|
||||
// Uses the vocabulary in the provided context.
|
||||
|
|
@ -1099,10 +1101,11 @@ const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal
|
|||
struct llama_context * ctx
|
||||
);
|
||||
|
||||
std::vector<std::vector<const llama_grammar_element *>> llama_grammar_accept(
|
||||
void llama_grammar_accept(
|
||||
const std::vector<std::vector<llama_grammar_element>> & rules,
|
||||
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
|
||||
const uint32_t chr);
|
||||
const uint32_t chr,
|
||||
std::vector<std::vector<const llama_grammar_element *>> & new_stacks);
|
||||
|
||||
std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
|
||||
const std::string & src,
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue