diff --git a/examples/talk-llama/llama.cpp b/examples/talk-llama/llama.cpp index 7af38718..d28382f7 100644 --- a/examples/talk-llama/llama.cpp +++ b/examples/talk-llama/llama.cpp @@ -1393,6 +1393,9 @@ struct llama_cparams { bool mul_mat_q; bool offload_kqv; + + ggml_backend_sched_eval_callback cb_eval; + void * cb_eval_user_data; }; struct llama_layer { @@ -6254,6 +6257,7 @@ static int llama_decode_internal( //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head); ggml_backend_sched_reset(lctx.sched); + ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); ggml_cgraph * gf = llama_build_graph(lctx, batch); @@ -7898,39 +7902,59 @@ static void llama_log_softmax(float * array, size_t size) { } } +void llama_sample_apply_guidance( + struct llama_context * ctx, + float * logits, + float * logits_guidance, + float scale) { + GGML_ASSERT(ctx); + + const auto t_start_sample_us = ggml_time_us(); + const auto n_vocab = llama_n_vocab(llama_get_model(ctx)); + + llama_log_softmax(logits, n_vocab); + llama_log_softmax(logits_guidance, n_vocab); + + for (int i = 0; i < n_vocab; ++i) { + auto & l = logits[i]; + const auto & g = logits_guidance[i]; + + l = scale * (l - g) + g; + } + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; +} + void llama_sample_classifier_free_guidance( struct llama_context * ctx, llama_token_data_array * candidates, struct llama_context * guidance_ctx, float scale) { - int64_t t_start_sample_us = ggml_time_us(); - GGML_ASSERT(ctx); + int64_t t_start_sample_us; - auto n_vocab = llama_n_vocab(llama_get_model(ctx)); + t_start_sample_us = ggml_time_us(); + const size_t n_vocab = llama_n_vocab(llama_get_model(ctx)); - GGML_ASSERT(n_vocab == (int)candidates->size); + GGML_ASSERT(n_vocab == candidates->size); GGML_ASSERT(!candidates->sorted); - std::vector logits_base; - logits_base.reserve(candidates->size); - for (size_t i = 0; i < candidates->size; ++i) { - logits_base.push_back(candidates->data[i].logit); - } - llama_log_softmax(logits_base.data(), candidates->size); - - float* logits_guidance = llama_get_logits(guidance_ctx); - llama_log_softmax(logits_guidance, n_vocab); - - for (int i = 0; i < n_vocab; ++i) { - float logit_guidance = logits_guidance[i]; - float logit_base = logits_base[i]; - candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance; + std::vector logits_base(n_vocab); + for (size_t i = 0; i < n_vocab; ++i) { + logits_base[i] = candidates->data[i].logit; } - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + float * logits_guidance = llama_get_logits(guidance_ctx); + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + llama_sample_apply_guidance(ctx, logits_base.data(), logits_guidance, scale); + t_start_sample_us = ggml_time_us(); + + for (size_t i = 0; i < n_vocab; ++i) { + candidates->data[i].logit = logits_base[i]; } + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; } llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) { @@ -8354,6 +8378,8 @@ struct quantize_state_internal { int n_k_quantized = 0; int n_fallback = 0; + bool has_imatrix = false; + quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params) : model(model) , params(params) @@ -8455,7 +8481,12 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } else if (name == "token_embd.weight") new_type = GGML_TYPE_Q2_K; } else if (name.find("attn_v.weight") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) { + new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) { + new_type = GGML_TYPE_Q4_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } @@ -8526,6 +8557,13 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) { new_type = GGML_TYPE_Q5_K; } + else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0) + && qs.has_imatrix && i_layer < n_layer/8) { + // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0. + // We only do it when an imatrix is provided because a) we want to make sure that one can always get the + // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix. + new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1; + } ++qs.i_feed_forward_w2; } else if (name.find("attn_output.weight") != std::string::npos) { if (arch != LLM_ARCH_FALCON) { @@ -8559,7 +8597,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty //} bool convert_incompatible_tensor = false; if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || - new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) { + new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || + new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -8571,6 +8610,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } if (convert_incompatible_tensor) { switch (new_type) { + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break; case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break; case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; @@ -8646,6 +8687,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s imatrix_data = static_cast>*>(params->imatrix); if (imatrix_data) { LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size())); + qs.has_imatrix = true; } } @@ -8705,8 +8747,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // placeholder for the meta data ::zeros(fout, meta_size); - std::set used_iq2; - for (int i = 0; i < ml.n_tensors; ++i) { struct ggml_tensor * tensor = ml.get_tensor_meta(i); @@ -8759,11 +8799,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else { const size_t nelements = ggml_nelements(tensor); - if ((new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_XS) && used_iq2.find(new_type) == used_iq2.end()) { - ggml_init_iq2_quantization(new_type); - used_iq2.insert(new_type); - } - const float * imatrix = nullptr; if (imatrix_data) { auto it = imatrix_data->find(tensor->name); @@ -8889,10 +8924,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s fout.close(); - for (auto type : used_iq2) { - ggml_deinit_iq2_quantization(type); - } - gguf_free(ctx_out); LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); @@ -9238,6 +9269,8 @@ struct llama_context_params llama_context_default_params() { /*.yarn_beta_fast =*/ 32.0f, /*.yarn_beta_slow =*/ 1.0f, /*.yarn_orig_ctx =*/ 0, + /*.cb_eval =*/ nullptr, + /*.cb_eval_user_data =*/ nullptr, /*.type_k =*/ GGML_TYPE_F16, /*.type_v =*/ GGML_TYPE_F16, /*.mul_mat_q =*/ true, @@ -9298,6 +9331,7 @@ void llama_backend_free(void) { #ifdef GGML_USE_MPI ggml_mpi_backend_free(); #endif + ggml_quantize_free(); } int64_t llama_time_us(void) { @@ -9378,6 +9412,9 @@ struct llama_context * llama_new_context_with_model( hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx : hparams.n_ctx_train; + cparams.cb_eval = params.cb_eval; + cparams.cb_eval_user_data = params.cb_eval_user_data; + auto rope_scaling_type = params.rope_scaling_type; if (rope_scaling_type == LLAMA_ROPE_SCALING_UNSPECIFIED) { rope_scaling_type = hparams.rope_scaling_type_train; diff --git a/examples/talk-llama/llama.h b/examples/talk-llama/llama.h index 79c8335b..e268d7a1 100644 --- a/examples/talk-llama/llama.h +++ b/examples/talk-llama/llama.h @@ -2,6 +2,7 @@ #define LLAMA_H #include "ggml.h" +#include "ggml-backend.h" #ifdef GGML_USE_CUBLAS #include "ggml-cuda.h" #define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES @@ -231,6 +232,9 @@ extern "C" { float yarn_beta_slow; // YaRN high correction dim uint32_t yarn_orig_ctx; // YaRN original context size + ggml_backend_sched_eval_callback cb_eval; + void * cb_eval_user_data; + enum ggml_type type_k; // data type for K cache enum ggml_type type_v; // data type for V cache @@ -714,14 +718,21 @@ extern "C" { float penalty_present); /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806 - /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted. - /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. - /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. - LLAMA_API void llama_sample_classifier_free_guidance( + /// @param logits Logits extracted from the original generation context. + /// @param logits_guidance Logits extracted from a separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context. + /// @param scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance. + LLAMA_API void llama_sample_apply_guidance( + struct llama_context * ctx, + float * logits, + float * logits_guidance, + float scale); + + LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance( struct llama_context * ctx, llama_token_data_array * candidates, struct llama_context * guidance_ctx, - float scale); + float scale), + "use llama_sample_apply_guidance() instead"); /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. LLAMA_API void llama_sample_softmax(