mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2025-09-10 17:14:36 +00:00
merged, added ability to render special tokens
This commit is contained in:
Concedo
commit
b4d2031215
37 changed files with 335 additions and 7328 deletions
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@ -109,7 +109,7 @@ int32_t get_num_physical_cores() {
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return n_threads > 0 ? (n_threads <= 4 ? n_threads : n_threads / 2) : 4;
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}
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#if defined(__x86_64__) && defined(__linux__)
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#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
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#include <pthread.h>
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static void cpuid(unsigned leaf, unsigned subleaf,
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@ -163,7 +163,7 @@ static int count_math_cpus(int cpu_count) {
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* Returns number of CPUs on system that are useful for math.
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*/
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int get_math_cpu_count() {
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#if defined(__x86_64__) && defined(__linux__)
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#if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__)
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int cpu_count = sysconf(_SC_NPROCESSORS_ONLN);
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if (cpu_count < 1) {
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return get_num_physical_cores();
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@ -2329,10 +2329,10 @@ std::vector<llama_token> llama_tokenize(
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std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
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std::vector<char> result(8, 0);
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const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), true);
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if (n_tokens < 0) {
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result.resize(-n_tokens);
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int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), true);
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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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@ -1301,15 +1301,23 @@ class LlamaModel(Model):
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try:
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self. _set_vocab_sentencepiece()
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except FileNotFoundError:
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self._set_vocab_llama_hf()
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try:
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self._set_vocab_llama_hf()
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except (FileNotFoundError, TypeError):
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# Llama 3
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self._set_vocab_gpt2()
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
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special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
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special_vocab._set_special_token("prefix", 32007)
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special_vocab._set_special_token("suffix", 32008)
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special_vocab._set_special_token("middle", 32009)
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special_vocab._set_special_token("eot", 32010)
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special_vocab.add_to_gguf(self.gguf_writer)
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# Apply to CodeLlama only (and ignore for Llama 3 with a vocab size of 128256)
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if self.hparams.get("vocab_size", 32000) == 32016:
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special_vocab = gguf.SpecialVocab(
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self.dir_model, load_merges=False,
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special_token_types = ['prefix', 'suffix', 'middle', 'eot']
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)
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special_vocab._set_special_token("prefix", 32007)
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special_vocab._set_special_token("suffix", 32008)
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special_vocab._set_special_token("middle", 32009)
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special_vocab._set_special_token("eot", 32010)
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special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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@ -2194,6 +2202,8 @@ class InternLM2Model(Model):
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old_eos = special_vocab.special_token_ids["eos"]
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if "chat" in os.path.basename(self.dir_model.absolute()):
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# For the chat model, we replace the eos with '<|im_end|>'.
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# TODO: this is a hack, should be fixed
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# https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048
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special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer)
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print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \
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in chat mode so that the conversation can end normally.")
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@ -2429,12 +2439,15 @@ class GemmaModel(Model):
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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# TODO: these special tokens should be exported only for the CodeGemma family
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False,
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special_token_types = ['prefix', 'suffix', 'middle', 'eot'])
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special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot'])
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special_vocab._set_special_token("prefix", 67)
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special_vocab._set_special_token("suffix", 69)
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special_vocab._set_special_token("middle", 68)
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special_vocab._set_special_token("eot", 70)
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special_vocab._set_special_token("fsep", 70)
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special_vocab._set_special_token("eot", 107)
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special_vocab.add_to_gguf(self.gguf_writer)
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def set_gguf_parameters(self):
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@ -2523,28 +2536,34 @@ class MambaModel(Model):
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field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
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self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
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field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
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self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
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field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
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self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
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field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
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self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
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field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
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self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
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field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
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self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
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field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
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self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
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def set_gguf_parameters(self):
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d_model = self.find_hparam(["hidden_size", "d_model"])
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d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
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d_model = self.find_hparam(["hidden_size", "d_model"])
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d_conv = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
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d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
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d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
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d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
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# ceiling division
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# ref: https://stackoverflow.com/a/17511341/22827863
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# ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
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dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
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dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
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rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
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# Fail early for models which don't have a block expansion factor of 2
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@ -525,7 +525,14 @@ class LlamaHfVocab(Vocab):
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# pre-check so we know if we need transformers
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tokenizer_model: dict[str, Any] = tokenizer_json['model']
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if (
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is_llama3 = (
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tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False)
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and not tokenizer_model.get('byte_fallback', True)
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)
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if is_llama3:
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raise TypeError('Llama 3 must be converted with BpeVocab')
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if not is_llama3 and (
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tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False)
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or tokenizer_json['decoder']['type'] != 'Sequence'
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):
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@ -153,7 +153,7 @@ while n_cur <= n_len {
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// const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
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// is it an end of stream? -> mark the stream as finished
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if new_token_id == llama_token_eos(model) || n_cur == n_len {
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if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
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i_batch[i] = -1
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// print("")
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if n_parallel > 1 {
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@ -229,7 +229,7 @@ private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
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private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
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var result = [CChar](repeating: 0, count: 8)
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let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
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let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count), false)
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if nTokens < 0 {
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let actualTokensCount = -Int(nTokens)
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result = .init(repeating: 0, count: actualTokensCount)
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@ -237,7 +237,8 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
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model,
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token,
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&result,
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Int32(result.count)
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Int32(result.count),
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false
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)
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assert(check == actualTokensCount)
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} else {
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@ -191,8 +191,8 @@ int main(int argc, char ** argv) {
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//const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
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// is it an end of stream? -> mark the stream as finished
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if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
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// is it an end of generation? -> mark the stream as finished
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if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
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i_batch[i] = -1;
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LOG_TEE("\n");
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if (n_parallel > 1) {
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@ -47,7 +47,7 @@ struct beam_search_callback_data {
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// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
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// For example, eob can be flagged due to maximum token length, stop words, etc.
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static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
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return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
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return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]);
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}
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// Function matching type llama_beam_search_callback_fn_t.
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@ -587,7 +587,7 @@ int main(int argc, char ** argv) {
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// deal with eot token in infill mode
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if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
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if(is_interacting && !params.interactive_first) {
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if (is_interacting && !params.interactive_first) {
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// print an eot token
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printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
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}
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@ -652,8 +652,8 @@ int main(int argc, char ** argv) {
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// LOG_TEE("took new input\n");
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is_interacting = false;
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}
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// deal with end of text token in interactive mode
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else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
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// deal with end of generation tokens in interactive mode
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else if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
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LOG("found EOS token\n");
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if (params.interactive) {
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@ -732,8 +732,8 @@ int main(int argc, char ** argv) {
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}
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}
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// end of text token
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if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
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// end of generation
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if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !params.interactive) {
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break;
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}
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@ -408,7 +408,7 @@ Java_com_example_llama_Llm_completion_1loop(
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const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
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const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
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if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
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if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
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return env->NewStringUTF("");
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}
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@ -158,7 +158,7 @@ actor LlamaContext {
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new_token_id = llama_sample_token_greedy(context, &candidates_p)
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}
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if new_token_id == llama_token_eos(model) || n_cur == n_len {
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if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
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print("\n")
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let new_token_str = String(cString: temporary_invalid_cchars + [0])
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temporary_invalid_cchars.removeAll()
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@ -322,7 +322,7 @@ actor LlamaContext {
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defer {
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result.deallocate()
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}
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let nTokens = llama_token_to_piece(model, token, result, 8)
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let nTokens = llama_token_to_piece(model, token, result, 8, false)
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if nTokens < 0 {
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let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
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@ -330,7 +330,7 @@ actor LlamaContext {
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defer {
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newResult.deallocate()
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}
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let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
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let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens, false)
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let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
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return Array(bufferPointer)
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} else {
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@ -3,6 +3,7 @@
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// I'll gradually clean and extend it
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// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
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#include "clip.h"
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#include "log.h"
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#include "ggml.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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@ -23,7 +24,6 @@
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#include <cstdlib>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <map>
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#include <regex>
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#include <stdexcept>
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@ -145,7 +145,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
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static int get_key_idx(const gguf_context * ctx, const char * key) {
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int i = gguf_find_key(ctx, key);
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if (i == -1) {
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fprintf(stderr, "key %s not found in file\n", key);
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LOG_TEE("key %s not found in file\n", key);
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throw std::runtime_error(format("Missing required key: %s", key));
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}
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@ -247,7 +247,7 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
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static void print_tensor_info(const ggml_tensor * tensor, const char * prefix = "") {
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size_t tensor_size = ggml_nbytes(tensor);
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printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
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LOG_TEE("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
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prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
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tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
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}
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@ -265,7 +265,7 @@ static projector_type clip_projector_type_from_string(const std::string & name)
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static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::string& filename) {
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std::ofstream file(filename, std::ios::binary);
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if (!file.is_open()) {
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std::cerr << "Failed to open file for writing: " << filename << std::endl;
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LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
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return;
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}
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@ -284,7 +284,7 @@ static void clip_image_write_image_to_ppm(const clip_image_u8& img, const std::s
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static void clip_image_save_to_bmp(const clip_image_u8& img, const std::string& filename) {
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std::ofstream file(filename, std::ios::binary);
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if (!file.is_open()) {
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std::cerr << "Failed to open file for writing: " << filename << std::endl;
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LOG_TEE("Failed to open file for writing: %s\n", filename.c_str());
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return;
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}
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@ -515,7 +515,7 @@ struct clip_ctx {
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static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs) {
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if (!ctx->has_vision_encoder) {
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printf("This gguf file seems to have no vision encoder\n");
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LOG_TEE("This gguf file seems to have no vision encoder\n");
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return nullptr;
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}
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@ -879,21 +879,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
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const int idx_name = gguf_find_key(ctx, KEY_NAME);
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if (idx_name != -1) { // make name optional temporarily as some of the uploaded models missing it due to a bug
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const std::string name = gguf_get_val_str(ctx, idx_name);
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printf("%s: model name: %s\n", __func__, name.c_str());
|
||||
LOG_TEE("%s: model name: %s\n", __func__, name.c_str());
|
||||
}
|
||||
printf("%s: description: %s\n", __func__, description.c_str());
|
||||
printf("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
|
||||
printf("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
||||
printf("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
printf("%s: n_kv: %d\n", __func__, n_kv);
|
||||
printf("%s: ftype: %s\n", __func__, ftype_str.c_str());
|
||||
printf("\n");
|
||||
LOG_TEE("%s: description: %s\n", __func__, description.c_str());
|
||||
LOG_TEE("%s: GGUF version: %d\n", __func__, gguf_get_version(ctx));
|
||||
LOG_TEE("%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
|
||||
LOG_TEE("%s: n_tensors: %d\n", __func__, n_tensors);
|
||||
LOG_TEE("%s: n_kv: %d\n", __func__, n_kv);
|
||||
LOG_TEE("%s: ftype: %s\n", __func__, ftype_str.c_str());
|
||||
LOG_TEE("\n");
|
||||
}
|
||||
const int n_tensors = gguf_get_n_tensors(ctx);
|
||||
|
||||
// kv
|
||||
const int n_kv = gguf_get_n_kv(ctx);
|
||||
printf("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
|
||||
LOG_TEE("%s: loaded meta data with %d key-value pairs and %d tensors from %s\n",
|
||||
__func__, n_kv, n_tensors, fname);
|
||||
{
|
||||
std::map<enum ggml_type, uint32_t> n_type;
|
||||
|
|
@ -904,7 +904,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
n_type[type]++;
|
||||
}
|
||||
|
||||
printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
|
||||
LOG_TEE("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
|
||||
for (int i = 0; i < n_kv; i++) {
|
||||
const char * name = gguf_get_key(ctx, i);
|
||||
const enum gguf_type type = gguf_get_kv_type(ctx, i);
|
||||
|
|
@ -920,7 +920,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
}
|
||||
replace_all(value, "\n", "\\n");
|
||||
|
||||
printf("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
|
||||
LOG_TEE("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
|
||||
}
|
||||
|
||||
// print type counts
|
||||
|
|
@ -929,7 +929,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
continue;
|
||||
}
|
||||
|
||||
printf("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
|
||||
LOG_TEE("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -944,7 +944,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
size_t tensor_size = ggml_nbytes(cur);
|
||||
model_size += tensor_size;
|
||||
if (verbosity >= 3) {
|
||||
printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
|
||||
LOG_TEE("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
|
||||
__func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
|
||||
}
|
||||
}
|
||||
|
|
@ -971,18 +971,18 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
|
||||
#ifdef GGML_USE_CUDA
|
||||
new_clip->backend = ggml_backend_cuda_init(0);
|
||||
printf("%s: CLIP using CUDA backend\n", __func__);
|
||||
LOG_TEE("%s: CLIP using CUDA backend\n", __func__);
|
||||
#endif
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
new_clip->backend = ggml_backend_metal_init();
|
||||
printf("%s: CLIP using Metal backend\n", __func__);
|
||||
LOG_TEE("%s: CLIP using Metal backend\n", __func__);
|
||||
#endif
|
||||
|
||||
|
||||
if (!new_clip->backend) {
|
||||
new_clip->backend = ggml_backend_cpu_init();
|
||||
printf("%s: CLIP using CPU backend\n", __func__);
|
||||
LOG_TEE("%s: CLIP using CPU backend\n", __func__);
|
||||
}
|
||||
|
||||
// model size and capabilities
|
||||
|
|
@ -1006,15 +1006,15 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
new_clip->use_gelu = gguf_get_val_bool(ctx, idx);
|
||||
|
||||
if (verbosity >= 1) {
|
||||
printf("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
|
||||
printf("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
|
||||
printf("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
|
||||
printf("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
|
||||
printf("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: text_encoder: %d\n", __func__, new_clip->has_text_encoder);
|
||||
LOG_TEE("%s: vision_encoder: %d\n", __func__, new_clip->has_vision_encoder);
|
||||
LOG_TEE("%s: llava_projector: %d\n", __func__, new_clip->has_llava_projector);
|
||||
LOG_TEE("%s: model size: %.2f MB\n", __func__, model_size / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: metadata size: %.2f MB\n", __func__, ggml_get_mem_size(meta) / 1024.0 / 1024.0);
|
||||
}
|
||||
}
|
||||
|
||||
printf("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
|
||||
LOG_TEE("%s: params backend buffer size = % 6.2f MB (%i tensors)\n", __func__, model_size / (1024.0 * 1024.0), n_tensors);
|
||||
|
||||
// load tensors
|
||||
{
|
||||
|
|
@ -1027,7 +1027,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
|
||||
new_clip->ctx_data = ggml_init(params);
|
||||
if (!new_clip->ctx_data) {
|
||||
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
||||
LOG_TEE("%s: ggml_init() failed\n", __func__);
|
||||
clip_free(new_clip);
|
||||
gguf_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -1035,7 +1035,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
|
||||
auto fin = std::ifstream(fname, std::ios::binary);
|
||||
if (!fin) {
|
||||
printf("cannot open model file for loading tensors\n");
|
||||
LOG_TEE("cannot open model file for loading tensors\n");
|
||||
clip_free(new_clip);
|
||||
gguf_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -1057,7 +1057,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
const size_t offset = gguf_get_data_offset(ctx) + gguf_get_tensor_offset(ctx, i);
|
||||
fin.seekg(offset, std::ios::beg);
|
||||
if (!fin) {
|
||||
printf("%s: failed to seek for tensor %s\n", __func__, name);
|
||||
LOG_TEE("%s: failed to seek for tensor %s\n", __func__, name);
|
||||
clip_free(new_clip);
|
||||
gguf_free(ctx);
|
||||
return nullptr;
|
||||
|
|
@ -1128,23 +1128,23 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
}
|
||||
|
||||
if (verbosity >= 2) {
|
||||
printf("\n%s: vision model hparams\n", __func__);
|
||||
printf("image_size %d\n", hparams.image_size);
|
||||
printf("patch_size %d\n", hparams.patch_size);
|
||||
printf("v_hidden_size %d\n", hparams.hidden_size);
|
||||
printf("v_n_intermediate %d\n", hparams.n_intermediate);
|
||||
printf("v_projection_dim %d\n", hparams.projection_dim);
|
||||
printf("v_n_head %d\n", hparams.n_head);
|
||||
printf("v_n_layer %d\n", hparams.n_layer);
|
||||
printf("v_eps %f\n", hparams.eps);
|
||||
printf("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
|
||||
printf("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
|
||||
printf("v_image_grid_pinpoints: ");
|
||||
LOG_TEE("\n%s: vision model hparams\n", __func__);
|
||||
LOG_TEE("image_size %d\n", hparams.image_size);
|
||||
LOG_TEE("patch_size %d\n", hparams.patch_size);
|
||||
LOG_TEE("v_hidden_size %d\n", hparams.hidden_size);
|
||||
LOG_TEE("v_n_intermediate %d\n", hparams.n_intermediate);
|
||||
LOG_TEE("v_projection_dim %d\n", hparams.projection_dim);
|
||||
LOG_TEE("v_n_head %d\n", hparams.n_head);
|
||||
LOG_TEE("v_n_layer %d\n", hparams.n_layer);
|
||||
LOG_TEE("v_eps %f\n", hparams.eps);
|
||||
LOG_TEE("v_image_mean %f %f %f\n", new_clip->image_mean[0], new_clip->image_mean[1], new_clip->image_mean[2]);
|
||||
LOG_TEE("v_image_std %f %f %f\n", new_clip->image_std[0], new_clip->image_std[1], new_clip->image_std[2]);
|
||||
LOG_TEE("v_image_grid_pinpoints: ");
|
||||
for (int i = 0; i < 32 && (hparams.image_grid_pinpoints[i] != 0); ++i) {
|
||||
printf("%d ", hparams.image_grid_pinpoints[i]);
|
||||
LOG_TEE("%d ", hparams.image_grid_pinpoints[i]);
|
||||
}
|
||||
printf("\n");
|
||||
printf("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
|
||||
LOG_TEE("\n");
|
||||
LOG_TEE("v_mm_patch_merge_type: %s\n", hparams.mm_patch_merge_type);
|
||||
|
||||
}
|
||||
|
||||
|
|
@ -1155,7 +1155,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight"));
|
||||
vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias"));
|
||||
} catch(const std::exception& e) {
|
||||
fprintf(stderr, "%s: failed to load vision model tensors\n", __func__);
|
||||
LOG_TEE("%s: failed to load vision model tensors\n", __func__);
|
||||
}
|
||||
|
||||
// LLaVA projection
|
||||
|
|
@ -1184,7 +1184,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
} catch (std::runtime_error & e) { }
|
||||
try {
|
||||
vision_model.image_newline = get_tensor(new_clip->ctx_data, TN_IMAGE_NEWLINE);
|
||||
// fprintf(stderr, "%s: image_newline tensor (llava-1.6) found\n", __func__);
|
||||
// LOG_TEE("%s: image_newline tensor (llava-1.6) found\n", __func__);
|
||||
} catch (std::runtime_error & e) { }
|
||||
} else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) {
|
||||
// MobileVLM projection
|
||||
|
|
@ -1264,7 +1264,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
|
|||
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch);
|
||||
ggml_gallocr_reserve(new_clip->compute_alloc, gf);
|
||||
size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
|
||||
printf("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
|
||||
LOG_TEE("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
|
||||
}
|
||||
|
||||
return new_clip;
|
||||
|
|
@ -1304,7 +1304,7 @@ bool clip_image_load_from_file(const char * fname, clip_image_u8 * img) {
|
|||
int nx, ny, nc;
|
||||
auto * data = stbi_load(fname, &nx, &ny, &nc, 3);
|
||||
if (!data) {
|
||||
fprintf(stderr, "%s: failed to load image '%s'\n", __func__, fname);
|
||||
LOG_TEE("%s: failed to load image '%s'\n", __func__, fname);
|
||||
return false;
|
||||
}
|
||||
build_clip_img_from_data(data, nx, ny, img);
|
||||
|
|
@ -1316,7 +1316,7 @@ bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length
|
|||
int nx, ny, nc;
|
||||
auto * data = stbi_load_from_memory(bytes, bytes_length, &nx, &ny, &nc, 3);
|
||||
if (!data) {
|
||||
fprintf(stderr, "%s: failed to decode image bytes\n", __func__);
|
||||
LOG_TEE("%s: failed to decode image bytes\n", __func__);
|
||||
return false;
|
||||
}
|
||||
build_clip_img_from_data(data, nx, ny, img);
|
||||
|
|
@ -1506,7 +1506,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int> & or
|
|||
int downscaled_height = static_cast<int>(original_height * scale);
|
||||
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
|
||||
int wasted_resolution = (width * height) - effective_resolution;
|
||||
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
// LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
|
||||
max_effective_resolution = effective_resolution;
|
||||
min_wasted_resolution = wasted_resolution;
|
||||
|
|
@ -1545,7 +1545,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
|
|||
bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
|
||||
bool pad_to_square = true;
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
}
|
||||
auto & params = ctx->vision_model.hparams;
|
||||
|
|
@ -1622,7 +1622,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
|
|||
}
|
||||
|
||||
for (size_t i = 0; i < patches.size(); i++) {
|
||||
// printf("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
|
||||
// LOG_TEE("patch %d: %d %d\n", i, patches[i]->nx, patches[i]->ny);
|
||||
clip_image_u8_free(patches[i]);
|
||||
}
|
||||
|
||||
|
|
@ -1765,7 +1765,7 @@ int clip_n_patches(const struct clip_ctx * ctx) {
|
|||
|
||||
bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f32 * img, float * vec) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -1777,7 +1777,7 @@ bool clip_image_encode(struct clip_ctx * ctx, const int n_threads, clip_image_f3
|
|||
|
||||
bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_image_f32_batch * imgs, float * vec) {
|
||||
if (!ctx->has_vision_encoder) {
|
||||
printf("This gguf file seems to have no vision encoder\n");
|
||||
LOG_TEE("This gguf file seems to have no vision encoder\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -1939,7 +1939,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
new_type = type;
|
||||
if (new_type >= GGML_TYPE_Q2_K && name.find("embd") != std::string::npos) {
|
||||
new_type = GGML_TYPE_Q8_0; // ggml_get_rows needs non K type
|
||||
// fprintf(stderr, "%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
|
||||
// LOG_TEE("%s: quantizing %s to %s\n", __func__, name.c_str(), ggml_type_name(new_type));
|
||||
}
|
||||
const size_t n_elms = ggml_nelements(cur);
|
||||
float * f32_data;
|
||||
|
|
@ -1958,7 +1958,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
f32_data = (float *)conv_buf.data();
|
||||
break;
|
||||
default:
|
||||
printf("Please use an input file in f32 or f16\n");
|
||||
LOG_TEE("Please use an input file in f32 or f16\n");
|
||||
gguf_free(ctx_out);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -1985,7 +1985,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
fout.put(0);
|
||||
}
|
||||
|
||||
printf("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
|
||||
LOG_TEE("%s: n_dims = %d | quantize=%d | size = %f MB -> %f MB\n", name.c_str(), ggml_n_dims(cur), quantize,
|
||||
orig_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
|
|
@ -2001,8 +2001,8 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
|
|||
gguf_free(ctx_out);
|
||||
|
||||
{
|
||||
printf("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
|
||||
printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: original size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
|
||||
LOG_TEE("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
|
||||
}
|
||||
|
||||
return true;
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
#include "ggml.h"
|
||||
#include "log.h"
|
||||
#include "common.h"
|
||||
#include "clip.h"
|
||||
#include "llava.h"
|
||||
|
|
@ -18,7 +19,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
|
|||
n_eval = n_batch;
|
||||
}
|
||||
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
|
||||
fprintf(stderr, "%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
|
||||
LOG_TEE("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
|
||||
return false;
|
||||
}
|
||||
*n_past += n_eval;
|
||||
|
|
@ -45,7 +46,7 @@ static const char * sample(struct llama_sampling_context * ctx_sampling,
|
|||
const llama_token id = llama_sampling_sample(ctx_sampling, ctx_llama, NULL);
|
||||
llama_sampling_accept(ctx_sampling, ctx_llama, id, true);
|
||||
static std::string ret;
|
||||
if (id == llama_token_eos(llama_get_model(ctx_llama))) {
|
||||
if (llama_token_is_eog(llama_get_model(ctx_llama), id)) {
|
||||
ret = "</s>";
|
||||
} else {
|
||||
ret = llama_token_to_piece(ctx_llama, id);
|
||||
|
|
@ -73,7 +74,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
|
|||
size_t img_base64_str_start, img_base64_str_end;
|
||||
find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
|
||||
if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
|
||||
fprintf(stderr, "%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
|
||||
LOG_TEE("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -87,7 +88,7 @@ static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip
|
|||
|
||||
auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
|
||||
if (!embed) {
|
||||
fprintf(stderr, "%s: could not load image from base64 string.\n", __func__);
|
||||
LOG_TEE("%s: could not load image from base64 string.\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -112,8 +113,8 @@ struct llava_context {
|
|||
};
|
||||
|
||||
static void show_additional_info(int /*argc*/, char ** argv) {
|
||||
fprintf(stderr, "\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
||||
fprintf(stderr, " note: a lower temperature value like 0.1 is recommended for better quality.\n");
|
||||
LOG_TEE("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
|
||||
LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n");
|
||||
}
|
||||
|
||||
static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) {
|
||||
|
|
@ -123,18 +124,18 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para
|
|||
auto prompt = params->prompt;
|
||||
if (prompt_contains_image(prompt)) {
|
||||
if (!params->image.empty()) {
|
||||
fprintf(stderr, "using base64 encoded image instead of command line image path\n");
|
||||
LOG_TEE("using base64 encoded image instead of command line image path\n");
|
||||
}
|
||||
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->n_threads, prompt);
|
||||
if (!embed) {
|
||||
fprintf(stderr, "%s: can't load image from prompt\n", __func__);
|
||||
LOG_TEE("%s: can't load image from prompt\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
params->prompt = remove_image_from_prompt(prompt);
|
||||
} else {
|
||||
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str());
|
||||
if (!embed) {
|
||||
fprintf(stderr, "%s: is %s really an image file?\n", __func__, params->image.c_str());
|
||||
LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str());
|
||||
return NULL;
|
||||
}
|
||||
}
|
||||
|
|
@ -153,18 +154,18 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
|
||||
system_prompt = prompt.substr(0, image_pos);
|
||||
user_prompt = prompt.substr(image_pos + std::string("<image>").length());
|
||||
printf("system_prompt: %s\n", system_prompt.c_str());
|
||||
LOG_TEE("system_prompt: %s\n", system_prompt.c_str());
|
||||
if (params->verbose_prompt) {
|
||||
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
|
||||
for (int i = 0; i < (int) tmp.size(); i++) {
|
||||
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
}
|
||||
}
|
||||
printf("user_prompt: %s\n", user_prompt.c_str());
|
||||
LOG_TEE("user_prompt: %s\n", user_prompt.c_str());
|
||||
if (params->verbose_prompt) {
|
||||
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
|
||||
for (int i = 0; i < (int) tmp.size(); i++) {
|
||||
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
}
|
||||
}
|
||||
} else {
|
||||
|
|
@ -174,7 +175,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
if (params->verbose_prompt) {
|
||||
auto tmp = ::llama_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
|
||||
for (int i = 0; i < (int) tmp.size(); i++) {
|
||||
printf("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -185,7 +186,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_
|
|||
|
||||
// generate the response
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
LOG_TEE("\n");
|
||||
|
||||
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams);
|
||||
std::string response = "";
|
||||
|
|
@ -224,7 +225,7 @@ static struct llava_context * llava_init(gpt_params * params) {
|
|||
|
||||
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
||||
if (model == NULL) {
|
||||
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
|
||||
LOG_TEE("%s: error: unable to load model\n" , __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -234,7 +235,7 @@ static struct llava_context * llava_init(gpt_params * params) {
|
|||
llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
|
||||
|
||||
if (ctx_llama == NULL) {
|
||||
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
|
||||
LOG_TEE("%s: error: failed to create the llama_context\n" , __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -257,6 +258,12 @@ static void llava_free(struct llava_context * ctx_llava) {
|
|||
llama_backend_free();
|
||||
}
|
||||
|
||||
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
|
||||
(void) level;
|
||||
(void) user_data;
|
||||
LOG_TEE("%s", text);
|
||||
}
|
||||
|
||||
int main(int argc, char ** argv) {
|
||||
ggml_time_init();
|
||||
|
||||
|
|
@ -266,6 +273,14 @@ int main(int argc, char ** argv) {
|
|||
show_additional_info(argc, argv);
|
||||
return 1;
|
||||
}
|
||||
|
||||
#ifndef LOG_DISABLE_LOGS
|
||||
log_set_target(log_filename_generator("llava", "log"));
|
||||
LOG_TEE("Log start\n");
|
||||
log_dump_cmdline(argc, argv);
|
||||
llama_log_set(llama_log_callback_logTee, nullptr);
|
||||
#endif // LOG_DISABLE_LOGS
|
||||
|
||||
if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
|
||||
gpt_print_usage(argc, argv, params);
|
||||
show_additional_info(argc, argv);
|
||||
|
|
@ -274,7 +289,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
auto ctx_llava = llava_init(¶ms);
|
||||
if (ctx_llava == NULL) {
|
||||
fprintf(stderr, "%s: error: failed to init llava\n", __func__);
|
||||
LOG_TEE("%s: error: failed to init llava\n", __func__);
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -54,7 +54,7 @@ static std::pair<int, int> select_best_resolution(const std::pair<int, int>& ori
|
|||
int downscaled_height = static_cast<int>(original_height * scale);
|
||||
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
|
||||
int wasted_resolution = (width * height) - effective_resolution;
|
||||
// fprintf(stderr, "resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
// LOG_TEE("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
|
||||
max_effective_resolution = effective_resolution;
|
||||
min_wasted_resolution = wasted_resolution;
|
||||
|
|
@ -154,13 +154,13 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
|
|||
model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]);
|
||||
if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) {
|
||||
if (newline_tmp->buffer == NULL) {
|
||||
printf("newline_tmp tensor buffer is NULL\n");
|
||||
LOG_TEE("newline_tmp tensor buffer is NULL\n");
|
||||
}
|
||||
ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp));
|
||||
} else {
|
||||
model.newline->data = newline_tmp->data;
|
||||
if (model.newline->data == NULL) {
|
||||
printf("newline_tmp tensor data is NULL\n");
|
||||
LOG_TEE("newline_tmp tensor data is NULL\n");
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -224,7 +224,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
img_res_v.size = 0;
|
||||
img_res_v.data = nullptr;
|
||||
if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
|
||||
fprintf(stderr, "%s: unable to preprocess image\n", __func__);
|
||||
LOG_TEE("%s: unable to preprocess image\n", __func__);
|
||||
delete[] img_res_v.data;
|
||||
return false;
|
||||
}
|
||||
|
|
@ -239,7 +239,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[0], image_embd); // image_embd shape is 576 x 4096
|
||||
delete[] img_res_v.data;
|
||||
if (!encoded) {
|
||||
fprintf(stderr, "Unable to encode image\n");
|
||||
LOG_TEE("Unable to encode image\n");
|
||||
|
||||
return false;
|
||||
}
|
||||
|
|
@ -252,12 +252,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
|
||||
const bool encoded = clip_image_encode(ctx_clip, n_threads, &img_res_v.data[i], image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
|
||||
if (!encoded) {
|
||||
fprintf(stderr, "Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
|
||||
LOG_TEE("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) img_res_v.size);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
const int64_t t_img_enc_batch_us = ggml_time_us();
|
||||
printf("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
|
||||
LOG_TEE("%s: %d segments encoded in %8.2f ms\n", __func__, (int)img_res_v.size, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
|
||||
|
||||
const int32_t * image_grid = clip_image_grid(ctx_clip);
|
||||
|
||||
|
|
@ -290,12 +290,12 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
|
|||
// clip_image_save_to_bmp(*tmp, "image_feature.bmp");
|
||||
}
|
||||
|
||||
printf("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
|
||||
LOG_TEE("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
|
||||
|
||||
const int64_t t_img_enc_end_us = ggml_time_us();
|
||||
float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
|
||||
|
||||
printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
|
||||
LOG_TEE("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
|
||||
|
||||
return true;
|
||||
}
|
||||
|
|
@ -305,7 +305,7 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
|
|||
int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
|
||||
auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
|
||||
if (n_image_embd != n_llama_embd) {
|
||||
printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
|
||||
LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
|
|
@ -314,13 +314,13 @@ bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx *
|
|||
bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
|
||||
float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*6); // TODO: base on gridsize/llava model
|
||||
if (!image_embd) {
|
||||
fprintf(stderr, "Unable to allocate memory for image embeddings\n");
|
||||
LOG_TEE("Unable to allocate memory for image embeddings\n");
|
||||
return false;
|
||||
}
|
||||
|
||||
int n_img_pos;
|
||||
if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
|
||||
fprintf(stderr, "%s: cannot encode image, aborting\n", __func__);
|
||||
LOG_TEE("%s: cannot encode image, aborting\n", __func__);
|
||||
free(image_embd);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -340,7 +340,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
|
|||
}
|
||||
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
|
||||
if (llama_decode(ctx_llama, batch)) {
|
||||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||||
LOG_TEE("%s : failed to eval\n", __func__);
|
||||
return false;
|
||||
}
|
||||
*n_past += n_eval;
|
||||
|
|
@ -352,7 +352,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
|
|||
clip_image_u8 * img = clip_image_u8_init();
|
||||
if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
|
||||
clip_image_u8_free(img);
|
||||
fprintf(stderr, "%s: can't load image from bytes, is it a valid image?", __func__);
|
||||
LOG_TEE("%s: can't load image from bytes, is it a valid image?", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -361,7 +361,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
|
|||
bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
|
||||
if (!image_embed_result) {
|
||||
clip_image_u8_free(img);
|
||||
fprintf(stderr, "%s: coulnd't embed the image\n", __func__);
|
||||
LOG_TEE("%s: coulnd't embed the image\n", __func__);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
@ -375,7 +375,7 @@ struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * c
|
|||
static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
|
||||
auto file = fopen(path, "rb");
|
||||
if (file == NULL) {
|
||||
fprintf(stderr, "%s: can't read file %s\n", __func__, path);
|
||||
LOG_TEE("%s: can't read file %s\n", __func__, path);
|
||||
return false;
|
||||
}
|
||||
|
||||
|
|
@ -385,7 +385,7 @@ static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long
|
|||
|
||||
auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
|
||||
if (buffer == NULL) {
|
||||
fprintf(stderr, "%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
|
||||
LOG_TEE("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
|
||||
perror("Memory allocation error");
|
||||
fclose(file);
|
||||
return false;
|
||||
|
|
@ -410,7 +410,7 @@ struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx
|
|||
long image_bytes_length;
|
||||
auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
|
||||
if (!loaded) {
|
||||
fprintf(stderr, "%s: failed to load %s\n", __func__, image_path);
|
||||
LOG_TEE("%s: failed to load %s\n", __func__, image_path);
|
||||
return NULL;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -299,7 +299,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
fflush(stdout);
|
||||
|
||||
if (id == llama_token_eos(model)) {
|
||||
if (llama_token_is_eog(model, id)) {
|
||||
has_eos = true;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -141,7 +141,7 @@ int main(int argc, char ** argv){
|
|||
printf("%s", token_str.c_str());
|
||||
}
|
||||
|
||||
if (id == llama_token_eos(model)) {
|
||||
if (llama_token_is_eog(model, id)) {
|
||||
has_eos = true;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -796,8 +796,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// deal with end of text token in interactive mode
|
||||
if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
|
||||
// deal with end of generation tokens in interactive mode
|
||||
if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) {
|
||||
LOG("found EOS token\n");
|
||||
|
||||
if (params.interactive) {
|
||||
|
|
@ -921,8 +921,8 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
|
||||
// end of generation
|
||||
if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) {
|
||||
LOG_TEE(" [end of text]\n");
|
||||
break;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -361,7 +361,7 @@ int main(int argc, char ** argv) {
|
|||
// client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
|
||||
|
||||
if (client.n_decoded > 2 &&
|
||||
(id == llama_token_eos(model) ||
|
||||
(llama_token_is_eog(model, id) ||
|
||||
(params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
|
||||
client.response.find("User:") != std::string::npos ||
|
||||
client.response.find('\n') != std::string::npos)) {
|
||||
|
|
|
|||
|
|
@ -252,8 +252,8 @@ int main(int argc, char ** argv) {
|
|||
// sample the most likely token
|
||||
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream?
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
// is it an end of generation?
|
||||
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
||||
LOG_TEE("\n");
|
||||
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -1,12 +1,29 @@
|
|||
set(TARGET server)
|
||||
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
|
||||
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
|
||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
|
||||
add_executable(${TARGET}
|
||||
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
|
||||
set(TARGET_SRCS
|
||||
server.cpp
|
||||
utils.hpp
|
||||
httplib.h
|
||||
)
|
||||
set(PUBLIC_ASSETS
|
||||
index.html
|
||||
index.js
|
||||
completion.js
|
||||
json-schema-to-grammar.mjs
|
||||
)
|
||||
foreach(asset ${PUBLIC_ASSETS})
|
||||
set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
|
||||
set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
|
||||
list(APPEND TARGET_SRCS ${output})
|
||||
add_custom_command(
|
||||
DEPENDS "${input}"
|
||||
OUTPUT "${output}"
|
||||
COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
|
||||
)
|
||||
endforeach()
|
||||
add_executable(${TARGET} ${TARGET_SRCS})
|
||||
install(TARGETS ${TARGET} RUNTIME)
|
||||
target_compile_definitions(${TARGET} PRIVATE
|
||||
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
|
||||
|
|
|
|||
|
|
@ -1,496 +0,0 @@
|
|||
unsigned char completion_js[] = {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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|
||||
0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72,
|
||||
0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61,
|
||||
0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67,
|
||||
0x73, 0x3b, 0x0a, 0x7d, 0x0a
|
||||
};
|
||||
unsigned int completion_js_len = 5909;
|
||||
|
|
@ -8,13 +8,3 @@ PUBLIC=$DIR/public
|
|||
echo "download js bundle files"
|
||||
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
|
||||
echo >> $PUBLIC/index.js # add newline
|
||||
|
||||
FILES=$(ls $PUBLIC)
|
||||
|
||||
cd $PUBLIC
|
||||
for FILE in $FILES; do
|
||||
echo "generate $FILE.hpp"
|
||||
|
||||
# use simple flag for old version of xxd
|
||||
xxd -i $FILE > $DIR/$FILE.hpp
|
||||
done
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
File diff suppressed because it is too large
Load diff
|
|
@ -1202,7 +1202,7 @@ struct server_context {
|
|||
});
|
||||
}
|
||||
|
||||
if (result.tok == llama_token_eos(model)) {
|
||||
if (llama_token_is_eog(model, result.tok)) {
|
||||
slot.stopped_eos = true;
|
||||
slot.has_next_token = false;
|
||||
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`.
|
|||
cd ../../..
|
||||
mkdir build
|
||||
cd build
|
||||
cmake ../
|
||||
cmake -DLLAMA_CURL=ON ../
|
||||
cmake --build . --target server
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -381,10 +381,6 @@ static json oaicompat_completion_params_parse(
|
|||
} else {
|
||||
llama_params["stop"] = json_value(body, "stop", json::array());
|
||||
}
|
||||
// Some chat templates don't use EOS token to stop generation
|
||||
// We must add their end sequences to list of stop words
|
||||
llama_params["stop"].push_back("<|im_end|>"); // chatml
|
||||
llama_params["stop"].push_back("<end_of_turn>"); // gemma
|
||||
|
||||
// Handle "response_format" field
|
||||
if (body.contains("response_format")) {
|
||||
|
|
|
|||
|
|
@ -133,8 +133,8 @@ int main(int argc, char ** argv) {
|
|||
// sample the most likely token
|
||||
const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
|
||||
|
||||
// is it an end of stream?
|
||||
if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
|
||||
// is it an end of generation?
|
||||
if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {
|
||||
LOG_TEE("\n");
|
||||
|
||||
break;
|
||||
|
|
|
|||
|
|
@ -362,7 +362,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
if (token_id == llama_token_eos(model_tgt)) {
|
||||
if (llama_token_is_eog(model_tgt, token_id)) {
|
||||
has_eos = true;
|
||||
}
|
||||
++n_predict;
|
||||
|
|
|
|||
3
expose.h
3
expose.h
|
|
@ -81,7 +81,8 @@ struct generation_inputs
|
|||
const float mirostat_tau;
|
||||
const samplers sampler_order[KCPP_SAMPLER_MAX];
|
||||
const int sampler_len;
|
||||
const bool unban_tokens_rt;
|
||||
const bool allow_eos_token;
|
||||
const bool render_special;
|
||||
const char * stop_sequence[stop_token_max];
|
||||
const bool stream_sse;
|
||||
const char * grammar;
|
||||
|
|
|
|||
|
|
@ -822,7 +822,11 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
|
|||
GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
|
||||
switch (op->op) {
|
||||
case GGML_OP_CPY:
|
||||
return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float
|
||||
return
|
||||
op->type != GGML_TYPE_IQ2_XXS &&
|
||||
op->type != GGML_TYPE_IQ2_XS &&
|
||||
op->type != GGML_TYPE_IQ1_S &&
|
||||
op->type != GGML_TYPE_IQ1_M; // missing type_traits.from_float
|
||||
case GGML_OP_MUL_MAT:
|
||||
return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type;
|
||||
default:
|
||||
|
|
|
|||
|
|
@ -872,6 +872,7 @@ GGML_QUANT_SIZES = {
|
|||
GGMLQuantizationType.I32: (1, 4),
|
||||
GGMLQuantizationType.I64: (1, 8),
|
||||
GGMLQuantizationType.F64: (1, 8),
|
||||
GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32),
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -139,7 +139,7 @@ inline bool LogitsDuplicated(std::vector<float> & arr1, std::vector<float> & arr
|
|||
}
|
||||
|
||||
|
||||
static std::string FileFormatTokenizeID(int id, FileFormat file_format)
|
||||
static std::string FileFormatTokenizeID(int id, FileFormat file_format, bool return_special = false)
|
||||
{
|
||||
if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF || file_format == FileFormat::GGJT || file_format == FileFormat::GGJT_2)
|
||||
{
|
||||
|
|
@ -151,7 +151,7 @@ static std::string FileFormatTokenizeID(int id, FileFormat file_format)
|
|||
}
|
||||
else if(file_format == FileFormat::GGUF_GENERIC)
|
||||
{
|
||||
return std::string(llama_token_to_str(llama_ctx_v4, id));
|
||||
return std::string(llama_token_to_piece(llama_ctx_v4, id, return_special));
|
||||
}
|
||||
else
|
||||
{
|
||||
|
|
@ -285,7 +285,7 @@ static std::string get_tok_vec_str(std::vector<int> &embd)
|
|||
std::string tmp = "";
|
||||
for (auto id : embd)
|
||||
{
|
||||
tmp += "'" + FileFormatTokenizeID(id, file_format) + " (" + std::to_string(id) + ")', ";
|
||||
tmp += "'" + FileFormatTokenizeID(id, file_format, true) + " (" + std::to_string(id) + ")', ";
|
||||
}
|
||||
::utreplace(tmp, "\n", "\\n");
|
||||
return tmp;
|
||||
|
|
@ -604,7 +604,7 @@ static void grammar_accept_token(FileFormat file_format, int32_t n_vocab, struct
|
|||
}
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
const std::string piece = FileFormatTokenizeID(token,file_format); //llama_token_to_str(ctx, token);
|
||||
const std::string piece = FileFormatTokenizeID(token,file_format);
|
||||
|
||||
// Note terminating 0 in decoded string
|
||||
const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8);
|
||||
|
|
@ -1984,7 +1984,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
|
|||
printf("\n[First Run] Banning %zu token sequences...",banned_tokens.size());
|
||||
for(int v=0;v<n_vocab;++v)
|
||||
{
|
||||
std::string word = FileFormatTokenizeID(v,file_format);
|
||||
std::string word = FileFormatTokenizeID(v,file_format, true);
|
||||
for(int i=0;i<banned_tokens.size();++i)
|
||||
{
|
||||
if (word.find(banned_tokens[i]) != std::string::npos)
|
||||
|
|
@ -2171,7 +2171,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
|
|||
lowestLogit = LowestLogit(logits);
|
||||
}
|
||||
|
||||
if (!inputs.unban_tokens_rt)
|
||||
if (!inputs.allow_eos_token)
|
||||
{
|
||||
// set the logit of the eos token to very low to avoid sampling it
|
||||
logitsPtr[eosID] = lowestLogit;
|
||||
|
|
@ -2204,7 +2204,7 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
|
|||
|
||||
for (auto id : embd)
|
||||
{
|
||||
std::string tokenizedstr = FileFormatTokenizeID(id, file_format);
|
||||
std::string tokenizedstr = FileFormatTokenizeID(id, file_format, inputs.render_special);
|
||||
if(stream_sse)
|
||||
{
|
||||
generated_tokens.push_back(tokenizedstr);
|
||||
|
|
@ -2229,14 +2229,14 @@ generation_outputs gpttype_generate(const generation_inputs inputs)
|
|||
printf(" ");
|
||||
}
|
||||
firstloop = false;
|
||||
std::string tokenizedstr = FileFormatTokenizeID(pick.id, file_format);
|
||||
std::string tokenizedstr = FileFormatTokenizeID(pick.id, file_format, true);
|
||||
::utreplace(tokenizedstr, "\n", "\\n");
|
||||
printf("(%s %.2f%%)", RemoveBell(tokenizedstr).c_str(), pick.p*100);
|
||||
}
|
||||
printf("]\n");
|
||||
}
|
||||
|
||||
if(inputs.unban_tokens_rt && id==eosID)
|
||||
if(inputs.allow_eos_token && id==eosID)
|
||||
{
|
||||
stopper_unused_tokens = remaining_tokens;
|
||||
if(allow_regular_prints)
|
||||
|
|
|
|||
|
|
@ -193,9 +193,14 @@
|
|||
"description": "KoboldCpp ONLY. If set, takes an array of base64 encoded strings, each one representing an image to be processed.",
|
||||
"type": "array"
|
||||
},
|
||||
"trim_stop": {
|
||||
"trim_stop": {
|
||||
"default": false,
|
||||
"description": "KoboldCpp ONLY. If true, also removes detected stop_sequences from the output and truncates all text after them. Does not work with SSE streaming.",
|
||||
"description": "KoboldCpp ONLY. If true, also removes detected stop_sequences from the output and truncates all text after them.",
|
||||
"type": "boolean"
|
||||
},
|
||||
"render_special": {
|
||||
"default": false,
|
||||
"description": "KoboldCpp ONLY. If true, prints special tokens as text for GGUF models",
|
||||
"type": "boolean"
|
||||
},
|
||||
"logit_bias": {
|
||||
|
|
|
|||
11
koboldcpp.py
11
koboldcpp.py
|
|
@ -81,7 +81,8 @@ class generation_inputs(ctypes.Structure):
|
|||
("mirostat_eta", ctypes.c_float),
|
||||
("sampler_order", ctypes.c_int * sampler_order_max),
|
||||
("sampler_len", ctypes.c_int),
|
||||
("unban_tokens_rt", ctypes.c_bool),
|
||||
("allow_eos_token", ctypes.c_bool),
|
||||
("render_special", ctypes.c_bool),
|
||||
("stop_sequence", ctypes.c_char_p * stop_token_max),
|
||||
("stream_sse", ctypes.c_bool),
|
||||
("grammar", ctypes.c_char_p),
|
||||
|
|
@ -398,7 +399,7 @@ def load_model(model_filename):
|
|||
ret = handle.load_model(inputs)
|
||||
return ret
|
||||
|
||||
def generate(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}):
|
||||
def generate(prompt, memory="", images=[], max_length=32, max_context_length=512, temperature=0.7, top_k=100, top_a=0.0, top_p=0.92, min_p=0.0, typical_p=1.0, tfs=1.0, rep_pen=1.0, rep_pen_range=128, presence_penalty=0.0, mirostat=0, mirostat_tau=5.0, mirostat_eta=0.1, sampler_order=[6,0,1,3,4,2,5], seed=-1, stop_sequence=[], use_default_badwordsids=False, stream_sse=False, grammar='', grammar_retain_state=False, genkey='', trimstop=False, quiet=False, dynatemp_range=0.0, dynatemp_exponent=1.0, smoothing_factor=0.0, logit_biases={}, render_special=False):
|
||||
global maxctx, args, currentusergenkey, totalgens, pendingabortkey
|
||||
inputs = generation_inputs()
|
||||
inputs.prompt = prompt.encode("UTF-8")
|
||||
|
|
@ -436,7 +437,8 @@ def generate(prompt, memory="", images=[], max_length=32, max_context_length=512
|
|||
inputs.smoothing_factor = smoothing_factor
|
||||
inputs.grammar = grammar.encode("UTF-8")
|
||||
inputs.grammar_retain_state = grammar_retain_state
|
||||
inputs.unban_tokens_rt = not use_default_badwordsids
|
||||
inputs.allow_eos_token = not use_default_badwordsids
|
||||
inputs.render_special = render_special
|
||||
if mirostat in (1, 2):
|
||||
inputs.mirostat = mirostat
|
||||
inputs.mirostat_tau = mirostat_tau
|
||||
|
|
@ -806,7 +808,8 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
|
|||
dynatemp_range=genparams.get('dynatemp_range', 0.0),
|
||||
dynatemp_exponent=genparams.get('dynatemp_exponent', 1.0),
|
||||
smoothing_factor=genparams.get('smoothing_factor', 0.0),
|
||||
logit_biases=genparams.get('logit_bias', {})
|
||||
logit_biases=genparams.get('logit_bias', {}),
|
||||
render_special=genparams.get('render_special', False),
|
||||
)
|
||||
|
||||
recvtxt = ""
|
||||
|
|
|
|||
107
llama.cpp
107
llama.cpp
|
|
@ -1626,12 +1626,12 @@ struct llama_mlock {
|
|||
};
|
||||
using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
|
||||
|
||||
static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
|
||||
static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) {
|
||||
std::vector<char> result(8, 0);
|
||||
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
||||
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
||||
if (n_tokens < 0) {
|
||||
result.resize(-n_tokens);
|
||||
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
|
||||
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special);
|
||||
GGML_ASSERT(check == -n_tokens);
|
||||
}
|
||||
else {
|
||||
|
|
@ -2146,7 +2146,7 @@ struct llama_vocab {
|
|||
id special_prefix_id = -1;
|
||||
id special_suffix_id = -1;
|
||||
id special_middle_id = -1;
|
||||
id special_eot_id = -1;
|
||||
id special_eot_id = -1; // TODO: move above after "eos_id", and here add "file separator" token
|
||||
|
||||
bool add_space_prefix = true;
|
||||
int find_bpe_rank(std::string token_left, std::string token_right) const {
|
||||
|
|
@ -3814,7 +3814,7 @@ static void llm_load_hparams(
|
|||
switch (hparams.n_layer) {
|
||||
case 22: model.type = e_model::MODEL_1B; break;
|
||||
case 26: model.type = e_model::MODEL_3B; break;
|
||||
case 32: model.type = e_model::MODEL_7B; break;
|
||||
case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA
|
||||
case 40: model.type = e_model::MODEL_13B; break;
|
||||
case 48: model.type = e_model::MODEL_34B; break;
|
||||
case 60: model.type = e_model::MODEL_30B; break;
|
||||
|
|
@ -4224,7 +4224,10 @@ static void llm_load_vocab(
|
|||
vocab.special_prefix_id = 67;
|
||||
vocab.special_suffix_id = 69;
|
||||
vocab.special_middle_id = 68;
|
||||
vocab.special_eot_id = 70;
|
||||
// TODO: this is not EOT, it is "file separator" token, needs fix
|
||||
// https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
|
||||
//vocab.special_eot_id = 70;
|
||||
vocab.special_eot_id = 107;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -4371,6 +4374,7 @@ static void llm_load_vocab(
|
|||
{ LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
|
||||
{ LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id },
|
||||
};
|
||||
|
||||
for (const auto & it : special_token_types) {
|
||||
const std::string & key = kv(std::get<0>(it));
|
||||
int32_t & id = std::get<1>(it);
|
||||
|
|
@ -4385,7 +4389,6 @@ static void llm_load_vocab(
|
|||
} else {
|
||||
id = new_id;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
// Handle add_bos_token and add_eos_token
|
||||
|
|
@ -4399,6 +4402,27 @@ static void llm_load_vocab(
|
|||
vocab.special_add_eos = int(temp);
|
||||
}
|
||||
}
|
||||
|
||||
// find EOT token: "<|eot_id|>", "<|im_emd|>", "<end_of_turn>", etc.
|
||||
//
|
||||
// TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
|
||||
// for now, we apply this workaround to find the EOT token based on its text
|
||||
if (vocab.special_eot_id == -1) {
|
||||
for (const auto & t : vocab.token_to_id) {
|
||||
if (
|
||||
// TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
|
||||
// need to fix convert script
|
||||
//vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
|
||||
(t.first == "<|eot_id|>" ||
|
||||
t.first == "<|im_emd|>" ||
|
||||
t.first == "<end_of_turn>"
|
||||
)
|
||||
) {
|
||||
vocab.special_eot_id = t.second;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// build special tokens cache
|
||||
|
|
@ -4561,14 +4585,19 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
|
||||
|
||||
// special tokens
|
||||
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
||||
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
||||
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() ); }
|
||||
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
||||
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
||||
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() ); }
|
||||
if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
|
||||
if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
|
||||
if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
|
||||
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
|
||||
}
|
||||
|
||||
// Returns false if cancelled by progress_callback
|
||||
|
|
@ -13583,16 +13612,14 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
|
|||
GGML_ASSERT(ctx);
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
bool allow_eos = false;
|
||||
bool allow_eog = false;
|
||||
for (const auto & stack : grammar->stacks) {
|
||||
if (stack.empty()) {
|
||||
allow_eos = true;
|
||||
allow_eog = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
const llama_token eos = llama_token_eos(&ctx->model);
|
||||
|
||||
std::vector<std::pair<std::vector<uint32_t>, llama_partial_utf8>> candidates_decoded;
|
||||
candidates_decoded.reserve(candidates->size);
|
||||
std::vector<llama_grammar_candidate> candidates_grammar;
|
||||
|
|
@ -13600,9 +13627,10 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
|
|||
|
||||
for (size_t i = 0; i < candidates->size; ++i) {
|
||||
const llama_token id = candidates->data[i].id;
|
||||
const std::string piece = llama_token_to_str(ctx, id);
|
||||
if (id == eos) {
|
||||
if (!allow_eos) {
|
||||
const std::string piece = llama_token_to_piece(ctx, id, false);
|
||||
|
||||
if (llama_token_is_eog(&ctx->model, id)) {
|
||||
if (!allow_eog) {
|
||||
candidates->data[i].logit = -INFINITY;
|
||||
}
|
||||
} else if (piece.empty() || piece[0] == 0) {
|
||||
|
|
@ -13791,7 +13819,7 @@ llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_arra
|
|||
void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token) {
|
||||
const int64_t t_start_sample_us = ggml_time_us();
|
||||
|
||||
if (token == llama_token_eos(&ctx->model)) {
|
||||
if (llama_token_is_eog(&ctx->model, token)) {
|
||||
for (const auto & stack : grammar->stacks) {
|
||||
if (stack.empty()) {
|
||||
return;
|
||||
|
|
@ -13800,7 +13828,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
|
|||
GGML_ASSERT(false);
|
||||
}
|
||||
|
||||
const std::string piece = llama_token_to_str(ctx, token);
|
||||
const std::string piece = llama_token_to_piece(ctx, token, false);
|
||||
|
||||
// Note terminating 0 in decoded string
|
||||
const auto decoded = decode_utf8(piece, grammar->partial_utf8);
|
||||
|
|
@ -17196,6 +17224,13 @@ llama_token_type llama_token_get_type(const struct llama_model * model, llama_to
|
|||
return model->vocab.id_to_token[token].type;
|
||||
}
|
||||
|
||||
bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
|
||||
return token != -1 && (
|
||||
token == llama_token_eos(model) ||
|
||||
token == llama_token_eot(model)
|
||||
);
|
||||
}
|
||||
|
||||
llama_token llama_token_bos(const struct llama_model * model) {
|
||||
return model->vocab.special_bos_id;
|
||||
}
|
||||
|
|
@ -17273,12 +17308,11 @@ static std::string llama_decode_text(const std::string & text) {
|
|||
}
|
||||
|
||||
// does not write null-terminator to buf
|
||||
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length) {
|
||||
int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
|
||||
if(OldBPETokenizerMode)
|
||||
{
|
||||
return llama_token_to_piece_old(model, token, buf, length);
|
||||
}
|
||||
|
||||
if (0 <= token && token < llama_n_vocab(model)) {
|
||||
switch (llama_vocab_get_type(model->vocab)) {
|
||||
case LLAMA_VOCAB_TYPE_WPM:
|
||||
|
|
@ -17293,7 +17327,9 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
|
|||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
} else if (llama_is_user_defined_token(model->vocab, token)) {
|
||||
} else if (
|
||||
(llama_is_user_defined_token(model->vocab, token)) ||
|
||||
(llama_is_control_token (model->vocab, token) && special)) {
|
||||
std::string result = model->vocab.id_to_token[token].text;
|
||||
if (length < (int) result.length()) {
|
||||
return -(int) result.length();
|
||||
|
|
@ -17306,8 +17342,6 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
|
|||
}
|
||||
memcpy(buf, "\xe2\x96\x85", 3);
|
||||
return 3;
|
||||
} else if (llama_is_control_token(model->vocab, token)) {
|
||||
;
|
||||
} else if (llama_is_byte_token(model->vocab, token)) {
|
||||
if (length < 1) {
|
||||
return -1;
|
||||
|
|
@ -17328,15 +17362,15 @@ int32_t llama_token_to_piece(const struct llama_model * model, llama_token token
|
|||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
} else if (llama_is_user_defined_token(model->vocab, token)) {
|
||||
} else if (
|
||||
(llama_is_user_defined_token(model->vocab, token)) ||
|
||||
(llama_is_control_token (model->vocab, token) && special)) {
|
||||
std::string result = model->vocab.id_to_token[token].text;
|
||||
if (length < (int) result.length()) {
|
||||
return -(int) result.length();
|
||||
}
|
||||
memcpy(buf, result.c_str(), result.length());
|
||||
return result.length();
|
||||
} else if (llama_is_control_token(model->vocab, token)) {
|
||||
;
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
|
@ -17534,6 +17568,15 @@ static int32_t llama_chat_apply_template_internal(
|
|||
if (add_ass) {
|
||||
ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
|
||||
}
|
||||
} else if (tmpl == "llama3" || (tmpl.find("<|start_header_id|>") != std::string::npos && tmpl.find("<|end_header_id|>") != std::string::npos)) {
|
||||
// Llama 3
|
||||
for (auto message : chat) {
|
||||
std::string role(message->role);
|
||||
ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
|
||||
}
|
||||
if (add_ass) {
|
||||
ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
|
||||
}
|
||||
} else {
|
||||
// template not supported
|
||||
return -1;
|
||||
|
|
|
|||
9
llama.h
9
llama.h
|
|
@ -785,6 +785,9 @@ extern "C" {
|
|||
|
||||
LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
|
||||
|
||||
// Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
|
||||
LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
|
||||
|
||||
// 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
|
||||
|
|
@ -798,7 +801,7 @@ extern "C" {
|
|||
// Returns -1 if unknown, 1 for true or 0 for false.
|
||||
LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
|
||||
|
||||
// codellama infill tokens
|
||||
// Codellama infill tokens
|
||||
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
|
||||
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
|
||||
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
|
||||
|
|
@ -827,11 +830,13 @@ extern "C" {
|
|||
// Uses the vocabulary in the provided context.
|
||||
// Does not write null terminator to the buffer.
|
||||
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
|
||||
// @param special If true, special tokens are rendered in the output.
|
||||
LLAMA_API int32_t llama_token_to_piece(
|
||||
const struct llama_model * model,
|
||||
llama_token token,
|
||||
char * buf,
|
||||
int32_t length);
|
||||
int32_t length,
|
||||
bool special);
|
||||
|
||||
/// Apply chat template. Inspired by hf apply_chat_template() on python.
|
||||
/// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
|
||||
|
|
|
|||
16
scripts/xxd.cmake
Normal file
16
scripts/xxd.cmake
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
# CMake equivalent of `xxd -i ${INPUT} ${OUTPUT}`
|
||||
# Usage: cmake -DINPUT=examples/server/public/index.html -DOUTPUT=examples/server/index.html.hpp -P scripts/xxd.cmake
|
||||
|
||||
SET(INPUT "" CACHE STRING "Input File")
|
||||
SET(OUTPUT "" CACHE STRING "Output File")
|
||||
|
||||
get_filename_component(filename "${INPUT}" NAME)
|
||||
string(REGEX REPLACE "\\.|-" "_" name "${filename}")
|
||||
|
||||
file(READ "${INPUT}" hex_data HEX)
|
||||
string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," hex_sequence "${hex_data}")
|
||||
|
||||
string(LENGTH ${hex_data} hex_len)
|
||||
math(EXPR len "${hex_len} / 2")
|
||||
|
||||
file(WRITE "${OUTPUT}" "unsigned char ${name}[] = {${hex_sequence}};\nunsigned int ${name}_len = ${len};\n")
|
||||
Loading…
Add table
Add a link
Reference in a new issue