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Merge remote-tracking branch 'origin/master' into json-bounds2

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Olivier Chafik 2024-06-08 20:05:17 +01:00
commit a786c0381b
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2
README.md
View file

@ -598,7 +598,7 @@ Building the program with BLAS support may lead to some performance improvements
To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives.
Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives.
It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
```bash

2
common/CMakeLists.txt
View file

@ -84,4 +84,4 @@ endif ()
target_include_directories(${TARGET} PUBLIC .)
target_compile_features(${TARGET} PUBLIC cxx_std_11)
target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama)
target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)

94
common/common.cpp
View file

@ -273,6 +273,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
} catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
params = params_org;
return false;
}
@ -408,6 +409,20 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
}
return true;
}
if (arg == "--in-file") {
if (++i >= argc) {
invalid_param = true;
return true;
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
invalid_param = true;
return true;
}
params.in_files.push_back(argv[i]);
return true;
}
if (arg == "-n" || arg == "--predict" || arg == "--n-predict") {
if (++i >= argc) {
invalid_param = true;
@ -1081,7 +1096,15 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
return true;
}
if (arg == "-v" || arg == "--verbose") {
params.verbose = true;
params.verbosity = 1;
return true;
}
if (arg == "--verbosity") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.verbosity = std::stoi(argv[i]);
return true;
}
if (arg == "--verbose-prompt") {
@ -1391,6 +1414,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.timeout_write = std::stoi(argv[i]);
return true;
}
if (arg == "--threads-http") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.n_threads_http = std::stoi(argv[i]);
return true;
}
if (arg == "-spf" || arg == "--system-prompt-file") {
if (++i >= argc) {
invalid_param = true;
@ -1460,6 +1491,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.chat_template = argv[i];
return true;
}
if (arg == "--slot-prompt-similarity" || arg == "-sps") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.slot_prompt_similarity = std::stof(argv[i]);
return true;
}
if (arg == "-pps") {
params.is_pp_shared = true;
return true;
@ -1537,6 +1576,46 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.i_pos = std::stoi(argv[i]);
return true;
}
if (arg == "-o" || arg == "--output" || arg == "--output-file") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.out_file = argv[i];
return true;
}
if (arg == "-ofreq" || arg == "--output-frequency") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.n_out_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--save-frequency") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.n_save_freq = std::stoi(argv[i]);
return true;
}
if (arg == "--process-output") {
params.process_output = true;
return true;
}
if (arg == "--no-ppl") {
params.compute_ppl = false;
return true;
}
if (arg == "--chunk" || arg == "--from-chunk") {
if (++i >= argc) {
invalid_param = true;
return true;
}
params.i_chunk = std::stoi(argv[i]);
return true;
}
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
if (log_param_single_parse(argv[i])) {
@ -1612,6 +1691,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", "-h, --help, --usage", "print usage and exit" });
options.push_back({ "*", " --version", "show version and build info" });
options.push_back({ "*", "-v, --verbose", "print verbose information" });
options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity });
options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" });
options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" });
options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" });
@ -1637,6 +1717,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" });
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: '%s')", params.prompt.c_str() });
options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" });
options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" });
options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" });
options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" });
options.push_back({ "*", " --no-escape", "do not process escape sequences" });
@ -1804,6 +1885,14 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk });
options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos });
options.push_back({ "imatrix" });
options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() });
options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq });
options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq });
options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" });
options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" });
options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk });
options.push_back({ "bench" });
options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" });
options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" });
@ -1820,6 +1909,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" });
options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" });
options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read });
options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http });
options.push_back({ "server", " --system-prompt-file FNAME",
"set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" });
options.push_back({ "server", " --log-format {text,json}",
@ -1831,6 +1921,8 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
"set custom jinja chat template (default: template taken from model's metadata)\n"
"only commonly used templates are accepted:\n"
"https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" });
options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY",
"how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity });
#ifndef LOG_DISABLE_LOGS
options.push_back({ "logging" });

101
common/common.h
View file

@ -56,43 +56,42 @@ struct gpt_params {
uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed
int32_t n_threads = cpu_get_num_math();
int32_t n_threads_draft = -1;
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 5; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
int32_t n_beams = 0; // if non-zero then use beam search of given width.
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
int32_t n_threads_draft = -1;
int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads)
int32_t n_threads_batch_draft = -1;
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 0; // context size
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_draft = 5; // number of tokens to draft during speculative decoding
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
int32_t n_sequences = 1; // number of sequences to decode
float p_split = 0.1f; // speculative decoding split probability
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
int32_t n_beams = 0; // if non-zero then use beam search of given width.
int32_t grp_attn_n = 1; // group-attention factor
int32_t grp_attn_w = 512; // group-attention width
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
float rope_freq_base = 0.0f; // RoPE base frequency
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor
float yarn_beta_fast = 32.0f; // YaRN low correction dim
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
float yarn_beta_slow = 1.0f; // YaRN high correction dim
int32_t yarn_orig_ctx = 0; // YaRN original context length
float defrag_thold = -1.0f; // KV cache defragmentation threshold
std::string rpc_servers = ""; // comma separated list of RPC servers
ggml_backend_sched_eval_callback cb_eval = nullptr;
void * cb_eval_user_data = nullptr;
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
@ -114,7 +113,9 @@ struct gpt_params {
std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding
std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
std::string logits_file = ""; // file for saving *all* logits
std::string rpc_servers = ""; // comma separated list of RPC servers
std::vector<std::string> in_files; // all input files
std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
std::vector<llama_model_kv_override> kv_overrides;
@ -124,23 +125,24 @@ struct gpt_params {
std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
int32_t verbosity = 0;
int32_t control_vector_layer_start = -1; // layer range for control vector
int32_t control_vector_layer_end = -1; // layer range for control vector
int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
// (which is more convenient to use for plotting)
//
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
// (which is more convenient to use for plotting)
//
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
size_t winogrande_tasks= 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
bool kl_divergence = false; // compute KL divergence
bool kl_divergence = false; // compute KL divergence
bool usage = false; // print usage
bool use_color = false; // use color to distinguish generations and inputs
@ -163,7 +165,6 @@ struct gpt_params {
bool logits_all = false; // return logits for all tokens in the batch
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool verbose = false;
bool verbose_prompt = false; // print prompt tokens before generation
bool display_prompt = true; // print prompt before generation
bool infill = false; // use infill mode
@ -180,10 +181,10 @@ struct gpt_params {
std::vector<std::string> image; // path to image file(s)
// server params
int32_t port = 8080;
int32_t timeout_read = 600;
int32_t timeout_write = timeout_read;
int32_t n_threads_http = -1;
int32_t port = 8080; // server listens on this network port
int32_t timeout_read = 600; // http read timeout in seconds
int32_t timeout_write = timeout_read; // http write timeout in seconds
int32_t n_threads_http = -1; // number of threads to process HTTP requests
std::string hostname = "127.0.0.1";
std::string public_path = "";
@ -202,6 +203,8 @@ struct gpt_params {
std::string slot_save_path;
float slot_prompt_similarity = 0.5f;
// batched-bench params
bool is_pp_shared = false;
@ -219,6 +222,16 @@ struct gpt_params {
// passkey params
int32_t n_junk = 250; // number of times to repeat the junk text
int32_t i_pos = -1; // position of the passkey in the junk text
// imatrix params
std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations
int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations
int32_t i_chunk = 0; // start processing from this chunk
bool process_output = false; // collect data for the output tensor
bool compute_ppl = true; // whether to compute perplexity
};
void gpt_params_handle_model_default(gpt_params & params);

11
common/grammar-parser.cpp
View file

@ -266,6 +266,10 @@ namespace grammar_parser {
throw std::runtime_error(std::string("expecting ')' at ") + pos);
}
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '.') { // any char
last_sym_start = out_elements.size();
out_elements.push_back({LLAMA_GRETYPE_CHAR_ANY, 0});
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '*') {
pos = parse_space(pos + 1, is_nested);
handle_repetitions(0, -1);
@ -401,6 +405,7 @@ namespace grammar_parser {
case LLAMA_GRETYPE_CHAR_NOT: return true;
case LLAMA_GRETYPE_CHAR_ALT: return true;
case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;
case LLAMA_GRETYPE_CHAR_ANY: return true;
default: return false;
}
}
@ -415,6 +420,7 @@ namespace grammar_parser {
case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break;
case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break;
case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "CHAR_ANY"); break;
}
switch (elem.type) {
case LLAMA_GRETYPE_END:
@ -426,6 +432,7 @@ namespace grammar_parser {
case LLAMA_GRETYPE_CHAR_NOT:
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
case LLAMA_GRETYPE_CHAR_ALT:
case LLAMA_GRETYPE_CHAR_ANY:
fprintf(file, "(\"");
print_grammar_char(file, elem.value);
fprintf(file, "\") ");
@ -483,11 +490,15 @@ namespace grammar_parser {
}
print_grammar_char(file, elem.value);
break;
case LLAMA_GRETYPE_CHAR_ANY:
fprintf(file, ".");
break;
}
if (is_char_element(elem)) {
switch (rule[i + 1].type) {
case LLAMA_GRETYPE_CHAR_ALT:
case LLAMA_GRETYPE_CHAR_RNG_UPPER:
case LLAMA_GRETYPE_CHAR_ANY:
break;
default:
fprintf(file, "] ");

6
examples/gguf-split/gguf-split.cpp
View file

@ -61,10 +61,10 @@ 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 = (size_t)n * 1024 * 1024; // megabytes
n_bytes = (size_t)n * 1000 * 1000; // megabytes
} else if (str.back() == 'G') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes
n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes
} else {
throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
}
@ -284,7 +284,7 @@ struct split_strategy {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
total_size += ggml_nbytes(t);
}
total_size = total_size / 1024 / 1024; // convert to megabytes
total_size = total_size / 1000 / 1000; // convert to megabytes
printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
i_split++;
}

11
examples/imatrix/README.md
View file

@ -6,16 +6,19 @@ More information is available here: https://github.com/ggerganov/llama.cpp/pull/
## Usage
```
./imatrix -m <some_fp_model> -f <some_training_data> [-o <output_file>] [--verbosity <verbosity_level>]
[-ofreq num_chunks] [-ow <0 or 1>] [other common params]
./imatrix \
-m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \
[--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \
[--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]
```
Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
The parameters in square brackets are optional and have the following meaning:
* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
* `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
* `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
* `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never)
* `--process-output` specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
For faster computation, make sure to use GPU offloading via the `-ngl` argument

249
examples/imatrix/imatrix.cpp
View file

@ -17,39 +17,37 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static void print_usage(int argc, char ** argv, const gpt_params & params) {
gpt_params_print_usage(argc, argv, params);
LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
" [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
" [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
LOG_TEE("\n");
}
struct Stats {
std::vector<float> values;
std::vector<int> counts;
int ncall = 0;
};
struct StatParams {
std::string dataset;
std::string ofile = "imatrix.dat";
int n_output_frequency = 10;
int verbosity = 1;
int keep_every = 0;
bool collect_output_weight = false;
};
class IMatrixCollector {
public:
IMatrixCollector() = default;
void set_parameters(StatParams&& params) { m_params = std::move(params); }
void set_params(gpt_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix() const;
bool load_imatrix(const char * file_name, bool add);
static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
void save_imatrix(int ncall = -1) const;
bool load_imatrix(const char * file_name);
private:
std::unordered_map<std::string, Stats> m_stats;
StatParams m_params;
gpt_params m_params;
std::mutex m_mutex;
int m_last_call = 0;
std::vector<float> m_src1_data;
std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
//
void save_imatrix(const char * file_name, const char * dataset) const;
void keep_imatrix(int ncall) const;
};
// remove any prefix and suffixes from the name
@ -85,7 +83,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
if (t->op != GGML_OP_MUL_MAT) return false;
// why are small batches ignored (<16 tokens)?
if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false;
if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false;
return true;
}
@ -153,21 +151,25 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[e_start + j] += x[j]*x[j];
e.counts[e_start + j]++;
if (!std::isfinite(e.values[e_start + j])) {
fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
exit(1);
}
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_output_frequency == 0) {
if (m_last_call % m_params.n_out_freq == 0) {
save_imatrix();
}
if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
keep_imatrix(m_last_call);
if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
}
}
}
} else {
auto& e = m_stats[wname];
auto & e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.counts.resize(src1->ne[0], 0);
@ -185,15 +187,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.counts[j]++;
if (!std::isfinite(e.values[j])) {
fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str());
exit(1);
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_output_frequency == 0) {
if (m_last_call % m_params.n_out_freq == 0) {
save_imatrix();
}
if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
keep_imatrix(m_last_call);
if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
}
}
}
@ -201,19 +207,17 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
return true;
}
void IMatrixCollector::save_imatrix() const {
save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str());
}
void IMatrixCollector::save_imatrix(int ncall) const {
auto fname = m_params.out_file;
if (fname.empty()) {
fname = "imatrix.dat";
}
void IMatrixCollector::keep_imatrix(int ncall) const {
auto file_name = m_params.ofile;
if (file_name.empty()) file_name = "imatrix.dat";
file_name += ".at_";
file_name += std::to_string(ncall);
save_imatrix(file_name.c_str(), m_params.dataset.c_str());
}
if (ncall > 0) {
fname += ".at_";
fname += std::to_string(ncall);
}
void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const {
std::ofstream out(fname, std::ios::binary);
int n_entries = m_stats.size();
out.write((const char *) &n_entries, sizeof(n_entries));
@ -236,26 +240,28 @@ void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) co
// Write the number of call the matrix was computed with
out.write((const char *) &m_last_call, sizeof(m_last_call));
// Write the dataset name at the end of the file to later on specify it in quantize
int n_dataset = strlen(dataset);
out.write((const char *) &n_dataset, sizeof(n_dataset));
out.write(dataset, n_dataset);
// Write the input filename at the end of the file to later on specify it in quantize
{
int len = m_params.prompt_file.size();
out.write((const char *) &len, sizeof(len));
out.write(m_params.prompt_file.c_str(), len);
}
if (m_params.verbosity > 0) {
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname);
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
}
}
bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
std::ifstream in(imatrix_file, std::ios::binary);
bool IMatrixCollector::load_imatrix(const char * fname) {
std::ifstream in(fname, std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__,imatrix_file);
printf("%s: failed to open %s\n",__func__, fname);
return false;
}
int n_entries;
in.read((char*)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, imatrix_file);
printf("%s: no data in file %s\n", __func__, fname);
return false;
}
for (int i = 0; i < n_entries; ++i) {
@ -263,23 +269,22 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
std::vector<char> name_as_vec(len+1);
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
printf("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
return false;
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto& e = imatrix_data[std::move(name)];
auto & e = m_stats[std::move(name)];
int ncall;
in.read((char*)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n",__func__,i);
imatrix_data = {};
m_stats = {};
return false;
}
// When re-called from load_imatrix() with add set, this will already be created.
if (e.values.empty()) {
e.values.resize(nval, 0);
e.counts.resize(nval, 0);
@ -289,7 +294,7 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
in.read((char*)tmp.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n",__func__,i);
imatrix_data = {};
m_stats = {};
return false;
}
@ -304,13 +309,6 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma
return true;
}
bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
if (!add) {
m_stats.clear();
}
return load_imatrix(file_name, m_stats);
}
static IMatrixCollector g_collector;
static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
@ -324,7 +322,7 @@ struct results_log_softmax {
float prob;
};
static std::vector<float> softmax(const std::vector<float>& logits) {
static std::vector<float> softmax(const std::vector<float> & logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) {
@ -358,8 +356,7 @@ static results_log_softmax log_softmax(int n_vocab, const float * logits, int to
static void process_logits(
int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
double & nll, double & nll2, float * logit_history, float * prob_history
) {
double & nll, double & nll2, float * logit_history, float * prob_history) {
std::mutex mutex;
int counter = 0;
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
@ -391,8 +388,7 @@ static void process_logits(
}
}
static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
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);
@ -405,13 +401,13 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
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());
if (from_chunk > 0) {
if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
if (params.i_chunk > 0) {
if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) {
fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk);
return false;
}
fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx);
tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx);
}
if (int(tokens.size()) < 2*n_ctx) {
@ -424,7 +420,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
std::vector<float> logit_history;
std::vector<float> prob_history;
if (compute_ppl) {
if (params.compute_ppl) {
logit_history.resize(tokens.size());
prob_history.resize(tokens.size());
}
@ -446,7 +442,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
std::vector<float> logits;
if (compute_ppl && num_batches > 1) {
if (params.compute_ppl && num_batches > 1) {
logits.reserve((size_t)n_ctx * n_vocab);
}
@ -482,7 +478,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
// restore the original token in case it was set to BOS
tokens[batch_start] = token_org;
if (compute_ppl && num_batches > 1) {
if (params.compute_ppl && num_batches > 1) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
}
@ -501,7 +497,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
}
if (compute_ppl) {
if (params.compute_ppl) {
const int first = n_ctx/2;
const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
@ -516,7 +512,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
}
printf("\n");
if (compute_ppl) {
if (params.compute_ppl) {
nll2 /= count;
nll /= count;
const double ppl = exp(nll);
@ -533,109 +529,32 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
}
int main(int argc, char ** argv) {
StatParams sparams;
std::string prev_result_file;
std::string combine_files;
bool compute_ppl = true;
int from_chunk = 0;
std::vector<char*> args;
args.push_back(argv[0]);
int iarg = 1;
for (; iarg < argc-1; ++iarg) {
std::string arg{argv[iarg]};
if (arg == "-o" || arg == "--output-file") {
sparams.ofile = argv[++iarg];
}
else if (arg == "-ofreq" || arg == "--output-frequency") {
sparams.n_output_frequency = std::stoi(argv[++iarg]);
}
else if (arg == "-ow" || arg == "--output-weight") {
sparams.collect_output_weight = std::stoi(argv[++iarg]);
}
else if (arg == "--verbosity") {
sparams.verbosity = std::stoi(argv[++iarg]);
} else if (arg == "--no-ppl") {
compute_ppl = false;
} else if (arg == "--keep-imatrix") {
sparams.keep_every = std::stoi(argv[++iarg]);
} else if (arg == "--continue-from") {
prev_result_file = argv[++iarg];
} else if (arg == "--combine") {
combine_files = argv[++iarg];
}
else if (arg == "--from-chunk") {
from_chunk = std::stoi(argv[++iarg]);
} else {
args.push_back(argv[iarg]);
}
}
if (iarg < argc) {
std::string arg{argv[iarg]};
if (arg == "--no-ppl") {
compute_ppl = false;
} else {
args.push_back(argv[iarg]);
}
}
gpt_params params;
params.n_batch = 512;
params.n_ctx = 512;
params.logits_all = true;
params.verbosity = 1;
if (!gpt_params_parse(argc, argv, params)) {
gpt_params_print_usage(argc, argv, params);
print_usage(argc, argv, params);
return 1;
}
params.logits_all = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
print_build_info();
g_collector.set_params(params);
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
sparams.dataset = params.prompt_file;
g_collector.set_parameters(std::move(sparams));
if (!combine_files.empty()) {
std::vector<std::string> files;
size_t pos = 0;
while (true) {
auto new_pos = combine_files.find(',', pos);
if (new_pos != std::string::npos) {
files.emplace_back(combine_files.substr(pos, new_pos - pos));
pos = new_pos + 1;
} else {
files.emplace_back(combine_files.substr(pos));
break;
}
}
if (files.size() < 2) {
fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
for (const auto & in_file : params.in_files) {
printf("%s : loading imatrix from '%s'\n", __func__, in_file.c_str());
if (!g_collector.load_imatrix(in_file.c_str())) {
fprintf(stderr, "%s : failed to load %s\n", __func__, in_file.c_str());
return 1;
}
printf("Combining the following %d files\n", int(files.size()));
for (auto& file : files) {
printf(" %s\n", file.c_str());
if (!g_collector.load_imatrix(file.c_str(), true)) {
fprintf(stderr, "Failed to load %s\n", file.c_str());
return 1;
}
}
}
if (params.in_files.size() > 1) {
printf("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
g_collector.save_imatrix();
return 0;
}
if (!prev_result_file.empty()) {
if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
return 1;
}
}
llama_backend_init();
@ -650,6 +569,7 @@ int main(int argc, char ** argv) {
// 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__);
@ -668,8 +588,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
}
bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
if (!OK) {
if (!compute_imatrix(ctx, params)) {
return 1;
}

2
examples/server/README.md
View file

@ -279,7 +279,7 @@ node index.js
`id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1`
`cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. Default: `false`
`cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false`
`system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)

142
examples/server/server.cpp
View file

@ -647,6 +647,9 @@ struct server_context {
server_metrics metrics;
// Necessary similarity of prompt for slot selection
float slot_prompt_similarity = 0.0f;
~server_context() {
if (ctx) {
llama_free(ctx);
@ -795,24 +798,88 @@ struct server_context {
return prompt_tokens;
}
server_slot * get_slot(int id) {
int64_t t_last = ggml_time_us();
server_slot * last_used = nullptr;
server_slot * get_slot_by_id(int id) {
for (server_slot & slot : slots) {
if (slot.id == id && slot.available()) {
if (slot.id == id) {
return &slot;
}
// among all available slots, find the one that has been least recently used
if (slot.available() && slot.t_last_used < t_last) {
last_used = &slot;
t_last = slot.t_last_used;
}
}
return last_used;
return nullptr;
}
server_slot * get_available_slot(const std::string & prompt) {
server_slot * ret = nullptr;
// find the slot that has at least n% prompt similarity
if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty()) {
int max_lcp_len = 0;
float similarity = 0;
for (server_slot & slot : slots) {
// skip the slot if it is not available
if (!slot.available()) {
continue;
}
// skip the slot if it does not contains prompt
if (!slot.prompt.is_string()) {
continue;
}
// current slot's prompt
std::string slot_prompt = slot.prompt.get<std::string>();
// length of the current slot's prompt
int slot_prompt_len = slot_prompt.size();
// length of the Longest Common Prefix between the current slot's prompt and the input prompt
int lcp_len = common_part(slot_prompt, prompt);
// fraction of the common substring length compared to the current slot's prompt length
similarity = static_cast<float>(lcp_len) / slot_prompt_len;
// select the current slot if the criteria match
if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) {
max_lcp_len = lcp_len;
ret = &slot;
}
}
if (ret != nullptr) {
LOG_VERBOSE("selected slot by lcp similarity", {
{"id_slot", ret->id},
{"max_lcp_len", max_lcp_len},
{"similarity", similarity},
});
}
}
// find the slot that has been least recently used
if (ret == nullptr) {
int64_t t_last = ggml_time_us();
for (server_slot & slot : slots) {
// skip the slot if it is not available
if (!slot.available()) {
continue;
}
// select the current slot if the criteria match
if (slot.t_last_used < t_last) {
t_last = slot.t_last_used;
ret = &slot;
}
}
if (ret != nullptr) {
LOG_VERBOSE("selected slot by lru", {
{"id_slot", ret->id},
{"t_last", t_last},
});
}
}
return ret;
}
bool launch_slot_with_task(server_slot & slot, const server_task & task) {
@ -888,7 +955,7 @@ struct server_context {
slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix);
// get prompt
{
if (!task.infill) {
const auto & prompt = data.find("prompt");
if (prompt == data.end()) {
send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST);
@ -1515,13 +1582,29 @@ struct server_context {
switch (task.type) {
case SERVER_TASK_TYPE_COMPLETION:
{
server_slot * slot = get_slot(json_value(task.data, "id_slot", -1));
int id_slot = json_value(task.data, "id_slot", -1);
std::string prompt = json_value(task.data, "prompt", std::string());
server_slot * slot;
if (id_slot != -1) {
slot = get_slot_by_id(id_slot);
} else {
slot = get_available_slot(prompt);
}
if (slot == nullptr) {
// if no slot is available, we defer this task for processing later
LOG_VERBOSE("no slot is available", {{"id_task", task.id}});
queue_tasks.defer(task);
break;
}
if (!slot->available()) {
// if requested slot is unavailable, we defer this task for processing later
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
queue_tasks.defer(task);
break;
}
if (task.data.contains("system_prompt")) {
std::string sys_prompt = json_value(task.data, "system_prompt", std::string());
@ -1638,11 +1721,17 @@ struct server_context {
case SERVER_TASK_TYPE_SLOT_SAVE:
{
int id_slot = task.data.at("id_slot");
server_slot * slot = get_slot(id_slot);
server_slot * slot = get_slot_by_id(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
break;
}
if (!slot->available()) {
// if requested slot is unavailable, we defer this task for processing later
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
queue_tasks.defer(task);
break;
}
const size_t token_count = slot->cache_tokens.size();
const int64_t t_start = ggml_time_us();
@ -1673,11 +1762,17 @@ struct server_context {
case SERVER_TASK_TYPE_SLOT_RESTORE:
{
int id_slot = task.data.at("id_slot");
server_slot * slot = get_slot(id_slot);
server_slot * slot = get_slot_by_id(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
break;
}
if (!slot->available()) {
// if requested slot is unavailable, we defer this task for processing later
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
queue_tasks.defer(task);
break;
}
const int64_t t_start = ggml_time_us();
@ -1715,11 +1810,17 @@ struct server_context {
case SERVER_TASK_TYPE_SLOT_ERASE:
{
int id_slot = task.data.at("id_slot");
server_slot * slot = get_slot(id_slot);
server_slot * slot = get_slot_by_id(id_slot);
if (slot == nullptr) {
send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST);
break;
}
if (!slot->available()) {
// if requested slot is unavailable, we defer this task for processing later
LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}});
queue_tasks.defer(task);
break;
}
// Erase token cache
const size_t n_erased = slot->cache_tokens.size();
@ -2360,7 +2461,7 @@ int main(int argc, char ** argv) {
// TODO: not great to use extern vars
server_log_json = params.log_json;
server_verbose = params.verbose;
server_verbose = params.verbosity > 0;
// struct that contains llama context and inference
server_context ctx_server;
@ -2467,6 +2568,9 @@ int main(int argc, char ** argv) {
log_data["api_key"] = "api_key: " + std::to_string(params.api_keys.size()) + " keys loaded";
}
// Necessary similarity of prompt for slot selection
ctx_server.slot_prompt_similarity = params.slot_prompt_similarity;
// load the model
if (!ctx_server.load_model(params)) {
state.store(SERVER_STATE_ERROR);

7
examples/server/utils.hpp
View file

@ -253,6 +253,13 @@ static size_t common_part(const std::vector<llama_token> & a, const std::vector<
return i;
}
static size_t common_part(const std::string & a, const std::string & b) {
size_t i;
for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
return i;
}
static bool ends_with(const std::string & str, const std::string & suffix) {
return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}

1
ggml-sycl.cpp
View file

@ -9108,6 +9108,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const
// find the sum of exps in the block
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
item_ct1.barrier(sycl::access::fence_space::local_space);
if (warp_id == 0) {
buf[lane_id] = 0.f;
}

128
ggml-vulkan.cpp
View file

@ -345,15 +345,12 @@ struct vk_context {
};
struct ggml_tensor_extra_gpu {
bool ready;
size_t ctx_idx;
vk_buffer_ref buffer_gpu;
uint64_t offset;
void reset() {
ready = false;
ctx_idx = 0;
buffer_gpu.reset();
offset = 0;
@ -2949,7 +2946,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
const uint64_t d_sz = sizeof(float) * d_ne;
vk_buffer d_D = extra->buffer_gpu.lock();
const uint64_t d_buf_offset = extra->offset;
const uint64_t d_buf_offset = extra->offset + dst->view_offs;
GGML_ASSERT(d_D != nullptr);
GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03);
vk_buffer d_X;
@ -2958,12 +2955,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su
uint64_t y_buf_offset = 0;
if (!src0_uma) {
d_Qx = extra_src0->buffer_gpu.lock();
qx_buf_offset = extra_src0->offset;
qx_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_Qx != nullptr);
}
if (!src1_uma) {
d_Qy = extra_src1->buffer_gpu.lock();
qy_buf_offset = extra_src1->offset;
qy_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Qy != nullptr);
}
if (qx_needs_dequant) {
@ -3114,7 +3111,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
const uint64_t d_sz = sizeof(float) * d_ne;
vk_buffer d_D = extra->buffer_gpu.lock();
const uint64_t d_buf_offset = extra->offset;
const uint64_t d_buf_offset = extra->offset + dst->view_offs;
GGML_ASSERT(d_D != nullptr);
vk_buffer d_X;
uint64_t x_buf_offset = 0;
@ -3122,12 +3119,12 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context
uint64_t y_buf_offset = 0;
if(!src0_uma) {
d_Qx = extra_src0->buffer_gpu.lock();
qx_buf_offset = extra_src0->offset;
qx_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_Qx != nullptr);
}
if(!src1_uma) {
d_Qy = extra_src1->buffer_gpu.lock();
qy_buf_offset = extra_src1->offset;
qy_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Qy != nullptr);
}
if (qx_needs_dequant) {
@ -3246,14 +3243,14 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
const uint64_t d_sz = sizeof(float) * d_ne;
vk_buffer d_D = extra->buffer_gpu.lock();
const uint64_t d_buf_offset = extra->offset;
const uint64_t d_buf_offset = extra->offset + dst->view_offs;
GGML_ASSERT(d_D != nullptr);
vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
const uint64_t qx_buf_offset = extra_src0->offset;
const uint64_t qx_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_Qx != nullptr);
if (!src1_uma) {
d_Qy = extra_src1->buffer_gpu.lock();
qy_buf_offset = extra_src1->offset;
qy_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Qx != nullptr);
}
@ -3323,14 +3320,14 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
const uint64_t d_sz = sizeof(float) * d_ne;
vk_buffer d_D = extra->buffer_gpu.lock();
const uint64_t d_buf_offset = extra->offset;
const uint64_t d_buf_offset = extra->offset + dst->view_offs;
GGML_ASSERT(d_D != nullptr);
vk_buffer d_Qx = extra_src0->buffer_gpu.lock();
const uint64_t qx_buf_offset = extra_src0->offset;
const uint64_t qx_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_Qx != nullptr);
if (!src1_uma) {
d_Qy = extra_src1->buffer_gpu.lock();
qy_buf_offset = extra_src1->offset;
qy_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Qx != nullptr);
}
@ -3459,7 +3456,7 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context *
const uint64_t d_sz = sizeof(float) * d_ne;
vk_buffer d_D = extra->buffer_gpu.lock();
const uint64_t d_buf_offset = extra->offset;
const uint64_t d_buf_offset = extra->offset + dst->view_offs;
GGML_ASSERT(d_D != nullptr);
vk_buffer d_X;
uint64_t x_buf_offset = 0;
@ -3467,17 +3464,17 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context *
uint64_t y_buf_offset = 0;
if (!src0_uma) {
d_Qx = extra_src0->buffer_gpu.lock();
qx_buf_offset = extra_src0->offset;
qx_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_Qx != nullptr);
}
if (!src1_uma) {
d_Qy = extra_src1->buffer_gpu.lock();
qy_buf_offset = extra_src1->offset;
qy_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Qy != nullptr);
}
if (!ids_uma) {
d_ids = extra_ids->buffer_gpu.lock();
ids_buf_offset = extra_ids->offset;
ids_buf_offset = extra_ids->offset + ids->view_offs;
GGML_ASSERT(d_ids != nullptr);
}
if (qx_needs_dequant) {
@ -3636,7 +3633,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
const uint64_t d_sz = sizeof(float) * d_ne;
vk_buffer d_D = extra->buffer_gpu.lock();
const uint64_t d_buf_offset = extra->offset;
const uint64_t d_buf_offset = extra->offset + dst->view_offs;
GGML_ASSERT(d_D != nullptr);
vk_buffer d_X;
uint64_t x_buf_offset = 0;
@ -3644,17 +3641,17 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
uint64_t y_buf_offset = 0;
if(!src0_uma) {
d_Qx = extra_src0->buffer_gpu.lock();
qx_buf_offset = extra_src0->offset;
qx_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_Qx != nullptr);
}
if(!src1_uma) {
d_Qy = extra_src1->buffer_gpu.lock();
qy_buf_offset = extra_src1->offset;
qy_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Qy != nullptr);
}
if(!ids_uma) {
d_ids = extra_ids->buffer_gpu.lock();
ids_buf_offset = extra_ids->offset;
ids_buf_offset = extra_ids->offset + ids->view_offs;
GGML_ASSERT(d_ids != nullptr);
}
if (qx_needs_dequant) {
@ -3769,9 +3766,9 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx
ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra;
const vk_buffer src_buf = extra_src0->buffer_gpu.lock();
const uint64_t src_offset = extra_src0->offset;
const uint64_t src_offset = extra_src0->offset + src0->view_offs;
vk_buffer dst_buf = extra->buffer_gpu.lock();
const uint64_t dst_offset = extra->offset;
const uint64_t dst_offset = extra->offset + dst->view_offs;
std::vector<vk::BufferCopy> copies;
@ -4062,21 +4059,21 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c
}
GGML_ASSERT(d_D != nullptr);
uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
uint64_t d_buf_offset = ((extra->offset + dst->view_offs) / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT
if(!src0_uma) {
d_X = extra_src0->buffer_gpu.lock();
x_buf_offset = extra_src0->offset;
x_buf_offset = extra_src0->offset + src0->view_offs;
GGML_ASSERT(d_X != nullptr);
}
if (use_src1 && !src1_uma) {
d_Y = extra_src1->buffer_gpu.lock();
y_buf_offset = extra_src1->offset;
y_buf_offset = extra_src1->offset + src1->view_offs;
GGML_ASSERT(d_Y != nullptr);
}
if (use_src2 && !src2_uma) {
d_Z = extra_src2->buffer_gpu.lock();
z_buf_offset = extra_src2->offset;
z_buf_offset = extra_src2->offset + src2->view_offs;
GGML_ASSERT(d_Z != nullptr);
}
@ -4336,7 +4333,7 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra;
const uint32_t src0_type_size = ggml_type_size(src0->type);
const uint32_t dst_type_size = ggml_type_size(dst->type);
const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
const uint32_t d_offset = ((extra->offset + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size;
ggml_vk_op_f32<vk_op_unary_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, {
(uint32_t)ggml_nelements(src0),
@ -5569,6 +5566,13 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
const ggml_tensor * src2 = node->src[2];
switch (node->op) {
// Return on empty ops to avoid generating a compute_ctx and setting exit_tensor
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_NONE:
return;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(node)) {
case GGML_UNARY_OP_SILU:
@ -5590,10 +5594,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_CPY:
case GGML_OP_CONT:
case GGML_OP_DUP:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_DIAG_MASK_INF:
@ -5601,7 +5601,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_ROPE:
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_NONE:
case GGML_OP_ARGSORT:
case GGML_OP_SUM_ROWS:
break;
@ -5654,12 +5653,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_DUP:
ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node);
break;
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
case GGML_OP_TRANSPOSE:
case GGML_OP_NONE:
break;
case GGML_OP_NORM:
ggml_vk_norm(ctx, ctx->compute_ctx, src0, node);
@ -5712,7 +5705,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
return;
}
extra->ready = true;
extra->ctx_idx = ctx->compute_ctx->idx;
#ifdef GGML_VULKAN_CHECK_RESULTS
@ -5796,8 +5788,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
ggml_vk_check_results_0(ctx, params, tensor);
#endif
GGML_ASSERT(extra->ready);
vk_context& subctx = ctx->gc.contexts[extra->ctx_idx];
// Only run if ctx hasn't been submitted yet
@ -5822,8 +5812,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_
subctx.out_memcpys.clear();
}
extra->ready = false;
return true;
}
@ -5943,7 +5931,9 @@ struct ggml_backend_vk_buffer_context {
~ggml_backend_vk_buffer_context() {
ggml_vk_destroy_buffer(dev_buffer);
delete[] temp_tensor_extras;
if (temp_tensor_extras != nullptr) {
delete[] temp_tensor_extras;
}
}
ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() {
@ -5990,18 +5980,16 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b
#endif
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra();
if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) {
if (tensor->view_src != nullptr) {
GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft);
ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra;
extra->buffer_gpu = extra_view->buffer_gpu;
extra->offset = extra_view->offset + tensor->view_offs;
GGML_ASSERT(tensor->view_src->extra != nullptr);
tensor->extra = tensor->view_src->extra;
} else {
ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra();
extra->buffer_gpu = ctx->dev_buffer;
extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base;
tensor->extra = extra;
}
tensor->extra = extra;
}
GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
@ -6014,7 +6002,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu
vk_buffer buf = extra->buffer_gpu.lock();
ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size);
ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + tensor->view_offs + offset, data, size);
}
GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
@ -6027,7 +6015,7 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu
vk_buffer buf = extra->buffer_gpu.lock();
ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size);
ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + tensor->view_offs + offset, data, size);
}
GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
@ -6038,7 +6026,7 @@ GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t bu
vk_buffer src_buf = src_extra->buffer_gpu.lock();
vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
ggml_vk_buffer_copy(dst_buf, dst_extra->offset + dst->view_offs, src_buf, src_extra->offset + src->view_offs, ggml_nbytes(src));
return true;
}
@ -6264,7 +6252,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g
vk_buffer buf = extra->buffer_gpu.lock();
ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size);
}
GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
@ -6284,7 +6272,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c
vk_buffer buf = extra->buffer_gpu.lock();
ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size);
ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size);
}
GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) {
@ -6305,7 +6293,7 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c
vk_buffer src_buf = src_extra->buffer_gpu.lock();
vk_buffer dst_buf = dst_extra->buffer_gpu.lock();
ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src));
ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset + dst->view_offs, src_buf, src_extra->offset + src->view_offs, ggml_nbytes(src));
return true;
}
@ -6478,11 +6466,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
// return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
// } break;
case GGML_OP_ROPE:
{
const int mode = ((const int32_t *) op->op_params)[2];
return true;
} break;
return true;
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
@ -6725,7 +6709,7 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
}
std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl;
@ -6809,7 +6793,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
} else if (ggml_backend_buffer_is_vk(src0->buffer)) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra;
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
uint64_t offset = extra->offset;
uint64_t offset = extra->offset + src0->view_offs;
if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) {
for (int i3 = 0; i3 < src0->ne[3]; i3++) {
for (int i2 = 0; i2 < src0->ne[2]; i2++) {
@ -6851,7 +6835,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
} else if (ggml_backend_buffer_is_vk(src1->buffer)) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra;
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
uint64_t offset = extra->offset;
uint64_t offset = extra->offset + src1->view_offs;
if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) {
for (int i3 = 0; i3 < src1->ne[3]; i3++) {
for (int i2 = 0; i2 < src1->ne[2]; i2++) {
@ -6909,7 +6893,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_
} else if (ggml_backend_buffer_is_vk(src2->buffer)) {
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra;
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
uint64_t offset = extra->offset;
uint64_t offset = extra->offset + src2->view_offs;
if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) {
for (int i3 = 0; i3 < src2->ne[3]; i3++) {
for (int i2 = 0; i2 < src2->ne[2]; i2++) {
@ -7092,11 +7076,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_
ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra;
vk_buffer buffer_gpu = extra->buffer_gpu.lock();
if (extra->offset + tensor_size >= buffer_gpu->size) {
tensor_size = buffer_gpu->size - (extra->offset);
if (extra->offset + tensor->view_offs + tensor_size >= buffer_gpu->size) {
tensor_size = buffer_gpu->size - (extra->offset + tensor->view_offs);
}
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size);
ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size);
}
float first_error_result = -1.0f;

20
llama.cpp
View file

@ -13640,7 +13640,7 @@ static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
const uint32_t chr) {
bool found = false;
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
@ -13649,6 +13649,10 @@ static std::pair<bool, const llama_grammar_element *> llama_grammar_match_char(
// inclusive range, e.g. [a-z]
found = found || (pos->value <= chr && chr <= pos[1].value);
pos += 2;
} else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
// Any character matches "."
found = true;
pos += 1;
} else {
// exact char match, e.g. [a] or "a"
found = found || pos->value == chr;
@ -13666,7 +13670,7 @@ static bool llama_grammar_match_partial_char(
const llama_grammar_element * pos,
const llama_partial_utf8 partial_utf8) {
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
uint32_t partial_value = partial_utf8.value;
@ -13696,6 +13700,9 @@ static bool llama_grammar_match_partial_char(
return is_positive_char;
}
pos += 2;
} else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
// Any character matches "."
return true;
} else {
// exact char match, e.g. [a] or "a"
if (low <= pos->value && pos->value <= high) {
@ -13756,6 +13763,7 @@ static void llama_grammar_advance_stack(
}
case LLAMA_GRETYPE_CHAR:
case LLAMA_GRETYPE_CHAR_NOT:
case LLAMA_GRETYPE_CHAR_ANY:
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);
@ -15229,6 +15237,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
if (imatrix_data) {
LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
qs.has_imatrix = true;
// check imatrix for nans or infs
for (const auto & kv : *imatrix_data) {
for (float f : kv.second) {
if (!std::isfinite(f)) {
throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
}
}
}
}
}

3
llama.h
View file

@ -365,6 +365,9 @@ extern "C" {
// modifies a preceding LLAMA_GRETYPE_CHAR or
// LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
LLAMA_GRETYPE_CHAR_ALT = 6,
// any character (.)
LLAMA_GRETYPE_CHAR_ANY = 7,
};
typedef struct llama_grammar_element {

28
tests/test-grammar-integration.cpp
View file

@ -285,6 +285,33 @@ static void test_complex_grammar() {
);
}
static void test_special_chars() {
// A collection of tests to exercise special characters such as "."
test_grammar(
"special characters",
// Grammar
R"""(
root ::= ... "abc" ...
)""",
// Passing strings
{
"abcabcabc",
"aaaabcccc",
// NOTE: Also ensures that multi-byte characters still count as a single character
"🔵🟠✅abc❌🟠🔵"
},
// Failing strings
{
"aaabcccc",
"aaaaabcccc",
"aaaabccc",
"aaaabccccc",
"🔵🟠✅❌abc❌✅🟠🔵"
"🔵🟠abc🟠🔵"
}
);
}
static void test_quantifiers() {
// A collection of tests to exercise * + and ? quantifiers
@ -525,6 +552,7 @@ int main() {
fprintf(stdout, "Running grammar integration tests...\n");
test_simple_grammar();
test_complex_grammar();
test_special_chars();
test_quantifiers();
test_failure_missing_root();
test_failure_missing_reference();