Merge branch 'ggerganov:master' into load-parallel-prompt-file
This commit is contained in:
pudepiedj
GitHub
commit
defffb6055
4 changed files with 58 additions and 97 deletions
6
.github/workflows/build.yml
vendored
6
.github/workflows/build.yml
vendored
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@ -10,10 +10,10 @@ on:
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push:
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branches:
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- master
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paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
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paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift']
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pull_request:
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types: [opened, synchronize, reopened]
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paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
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paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift']
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env:
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BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
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@ -258,7 +258,7 @@ jobs:
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strategy:
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matrix:
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destination: ['platform=macOS,name=Any Mac', 'platform=iOS,name=Any iOS Device', 'platform=tvOS,name=Any tvOS Device']
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destination: ['generic/platform=macOS', 'generic/platform=iOS', 'generic/platform=tvOS']
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steps:
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- name: Clone
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@ -1022,10 +1022,11 @@ llama_token llama_sample_token(
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id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
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} else {
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// Temperature sampling
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llama_sample_top_k (ctx, &cur_p, top_k, 1);
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llama_sample_tail_free (ctx, &cur_p, tfs_z, 1);
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llama_sample_typical (ctx, &cur_p, typical_p, 1);
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llama_sample_top_p (ctx, &cur_p, top_p, 1);
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size_t min_keep = std::max(1, params.n_probs);
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llama_sample_top_k (ctx, &cur_p, top_k, min_keep);
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llama_sample_tail_free (ctx, &cur_p, tfs_z, min_keep);
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llama_sample_typical (ctx, &cur_p, typical_p, min_keep);
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llama_sample_top_p (ctx, &cur_p, top_p, min_keep);
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llama_sample_temp(ctx, &cur_p, temp);
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{
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@ -534,98 +534,20 @@ struct llama_server_context
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return result;
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}
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// out of user input, sample next token
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const float temp = params.temp;
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const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(model) : params.top_k;
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const float top_p = params.top_p;
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const float tfs_z = params.tfs_z;
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const float typical_p = params.typical_p;
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const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
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const float repeat_penalty = params.repeat_penalty;
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const float alpha_presence = params.presence_penalty;
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const float alpha_frequency = params.frequency_penalty;
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const int mirostat = params.mirostat;
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const float mirostat_tau = params.mirostat_tau;
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const float mirostat_eta = params.mirostat_eta;
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const bool penalize_nl = params.penalize_nl;
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const int32_t n_probs = params.n_probs;
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{
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auto *logits = llama_get_logits(ctx);
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auto n_vocab = llama_n_vocab(model);
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// Apply params.logit_bias map
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for (const auto &it : params.logit_bias)
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{
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logits[it.first] += it.second;
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}
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// out of user input, sample next token
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std::vector<llama_token_data> candidates;
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candidates.reserve(n_vocab);
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for (llama_token token_id = 0; token_id < n_vocab; token_id++)
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candidates.reserve(llama_n_vocab(model));
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result.tok = llama_sample_token(ctx, NULL, grammar, params, last_n_tokens, candidates);
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llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
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const int32_t n_probs = params.n_probs;
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if (params.temp <= 0 && n_probs > 0)
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{
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candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
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}
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llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
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// Apply penalties
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float nl_logit = logits[llama_token_nl(ctx)];
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auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
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llama_sample_repetition_penalty(ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, repeat_penalty);
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llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
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last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
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last_n_repeat, alpha_frequency, alpha_presence);
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if (!penalize_nl)
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{
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logits[llama_token_nl(ctx)] = nl_logit;
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}
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if (grammar != nullptr) {
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llama_sample_grammar(ctx, &candidates_p, grammar);
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}
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if (temp <= 0)
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{
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// Greedy sampling
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result.tok = llama_sample_token_greedy(ctx, &candidates_p);
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if (n_probs > 0)
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{
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llama_sample_softmax(ctx, &candidates_p);
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}
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}
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else
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{
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if (mirostat == 1)
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{
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static float mirostat_mu = 2.0f * mirostat_tau;
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const int mirostat_m = 100;
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llama_sample_temp(ctx, &candidates_p, temp);
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result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
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}
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else if (mirostat == 2)
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{
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static float mirostat_mu = 2.0f * mirostat_tau;
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llama_sample_temp(ctx, &candidates_p, temp);
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result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
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}
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else
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{
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// Temperature sampling
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size_t min_keep = std::max(1, n_probs);
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llama_sample_top_k(ctx, &candidates_p, top_k, min_keep);
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llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep);
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llama_sample_typical(ctx, &candidates_p, typical_p, min_keep);
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llama_sample_top_p(ctx, &candidates_p, top_p, min_keep);
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llama_sample_temp(ctx, &candidates_p, temp);
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result.tok = llama_sample_token(ctx, &candidates_p);
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}
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}
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if (grammar != nullptr) {
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llama_grammar_accept_token(ctx, grammar, result.tok);
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// For llama_sample_token_greedy we need to sort candidates
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llama_sample_softmax(ctx, &candidates_p);
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}
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for (size_t i = 0; i < std::min(candidates_p.size, (size_t)n_probs); ++i)
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40
llama.cpp
40
llama.cpp
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@ -125,6 +125,27 @@ static void replace_all(std::string & s, const std::string & search, const std::
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}
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s = std::move(result);
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}
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static bool is_float_close(float a, float b, float abs_tol) {
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// Check for non-negative tolerance
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if (abs_tol < 0.0) {
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throw std::invalid_argument("Tolerance must be non-negative");
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}
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// Exact equality check
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if (a == b) {
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return true;
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}
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// Check for infinities
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if (std::isinf(a) || std::isinf(b)) {
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return false;
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}
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// Regular comparison using the provided absolute tolerance
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return std::fabs(b - a) <= abs_tol;
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}
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#ifdef GGML_USE_CPU_HBM
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#include <hbwmalloc.h>
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#endif
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@ -969,7 +990,24 @@ struct llama_hparams {
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float rope_freq_scale_train;
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bool operator!=(const llama_hparams & other) const {
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return static_cast<bool>(memcmp(this, &other, sizeof(llama_hparams))); // NOLINT
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if (this->vocab_only != other.vocab_only) return true;
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if (this->n_vocab != other.n_vocab) return true;
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if (this->n_ctx_train != other.n_ctx_train) return true;
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if (this->n_embd != other.n_embd) return true;
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if (this->n_head != other.n_head) return true;
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if (this->n_head_kv != other.n_head_kv) return true;
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if (this->n_layer != other.n_layer) return true;
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if (this->n_rot != other.n_rot) return true;
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if (this->n_ff != other.n_ff) return true;
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const float EPSILON = 1e-9;
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if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
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if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
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if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true;
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if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
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return false;
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}
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uint32_t n_gqa() const {
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