diff --git a/common/arg.cpp b/common/arg.cpp index ba184c700..f71b8189f 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -990,10 +990,6 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context params.tensor_buft_overrides.push_back({nullptr, nullptr}); } - if (params.reranking && params.embedding) { - throw std::invalid_argument("error: either --embedding or --reranking can be specified, but not both"); - } - if (!params.chat_template.empty() && !common_chat_verify_template(params.chat_template, params.use_jinja)) { throw std::runtime_error(string_format( "error: the supplied chat template is not supported: %s%s\n", @@ -2749,9 +2745,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS")); add_opt(common_arg( {"--reranking", "--rerank"}, - string_format("enable reranking endpoint on server (default: %s)", params.reranking ? "enabled" : "disabled"), + string_format("enable reranking endpoint on server (default: %s)", "disabled"), [](common_params & params) { - params.reranking = true; + params.embedding = true; + params.pooling_type = LLAMA_POOLING_TYPE_RANK; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_RERANKING")); add_opt(common_arg( diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp index 65b664cb3..18a30e49a 100644 --- a/common/chat-parser.cpp +++ b/common/chat-parser.cpp @@ -49,6 +49,7 @@ bool common_chat_msg_parser::add_tool_call(const std::string & name, const std:: // LOG_DBG("Tool call arguments:\n\traw: %s\n\tresult: %s\n", arguments.c_str(), tool_call.arguments.c_str()); result_.tool_calls.emplace_back(tool_call); + return true; } bool common_chat_msg_parser::add_tool_call(const json & tool_call) { @@ -378,3 +379,7 @@ std::optional common_chat_msg_parse /* .is_partial = */ found_healing_marker, }; } + +void common_chat_msg_parser::clear_tools() { + result_.tool_calls.clear(); +} diff --git a/common/chat-parser.h b/common/chat-parser.h index 7ee355056..0e64c341a 100644 --- a/common/chat-parser.h +++ b/common/chat-parser.h @@ -115,4 +115,6 @@ class common_chat_msg_parser { const std::vector> & args_paths = {}, const std::vector> & content_paths = {} ); + + void clear_tools(); }; diff --git a/common/chat.cpp b/common/chat.cpp index 846d7ab45..478b59d6b 100644 --- a/common/chat.cpp +++ b/common/chat.cpp @@ -1838,7 +1838,7 @@ static common_chat_params common_chat_templates_apply_legacy( if (res < 0) { // if the custom "tmpl" is not supported, we throw an error // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template() - throw std::runtime_error("this custom template is not supported"); + throw std::runtime_error("this custom template is not supported, try using --jinja"); } // if it turns out that our buffer is too small, we resize it @@ -1921,7 +1921,9 @@ common_chat_msg common_chat_parse(const std::string & input, bool is_partial, co } catch (const common_chat_msg_partial_exception & ex) { LOG_DBG("Partial parse: %s\n", ex.what()); if (!is_partial) { - throw std::runtime_error(ex.what()); + builder.clear_tools(); + builder.move_to(0); + common_chat_parse_content_only(builder); } } auto msg = builder.result(); diff --git a/common/common.cpp b/common/common.cpp index 844a2eb58..8850cb795 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -775,6 +775,9 @@ bool fs_validate_filename(const std::string & filename) { return true; } +#include + + // returns true if successful, false otherwise bool fs_create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -792,9 +795,16 @@ bool fs_create_directory_with_parents(const std::string & path) { // process path from front to back, procedurally creating directories while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { const std::wstring subpath = wpath.substr(0, pos_slash); - const wchar_t * test = subpath.c_str(); - const bool success = CreateDirectoryW(test, NULL); + pos_slash += 1; + + // skip the drive letter, in some systems it can return an access denied error + if (subpath.length() == 2 && subpath[1] == ':') { + continue; + } + + const bool success = CreateDirectoryW(subpath.c_str(), NULL); + if (!success) { const DWORD error = GetLastError(); @@ -808,8 +818,6 @@ bool fs_create_directory_with_parents(const std::string & path) { return false; } } - - pos_slash += 1; } return true; @@ -905,34 +913,6 @@ struct common_init_result common_init_from_params(common_params & params) { const llama_vocab * vocab = llama_model_get_vocab(model); - if (params.reranking) { - bool ok = true; - - if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__); - ok = false; - } - - bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL; - bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL; - - if (!has_eos && !has_sep) { - LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__); - ok = false; - } else if (!has_eos) { - LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__); - } else if (!has_sep) { - LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__); - ok = false; - } - - if (!ok) { - llama_model_free(model); - - return iparams; - } - } - auto cparams = common_context_params_to_llama(params); llama_context * lctx = llama_init_from_model(model, cparams); @@ -974,6 +954,35 @@ struct common_init_result common_init_from_params(common_params & params) { } } + if (llama_pooling_type(lctx) == LLAMA_POOLING_TYPE_RANK) { + bool ok = true; + + if (llama_vocab_bos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have a BOS token, reranking will not work\n", __func__); + ok = false; + } + + bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL; + bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL; + + if (!has_eos && !has_sep) { + LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__); + ok = false; + } else if (!has_eos) { + LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__); + } else if (!has_sep) { + LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__); + ok = false; + } + + if (!ok) { + llama_free(lctx); + llama_model_free(model); + + return iparams; + } + } + // load and optionally apply lora adapters for (auto & la : params.lora_adapters) { llama_adapter_lora_ptr lora; @@ -1151,11 +1160,6 @@ struct llama_context_params common_context_params_to_llama(const common_params & cparams.op_offload = !params.no_op_offload; cparams.swa_full = params.swa_full; - if (params.reranking) { - cparams.embeddings = true; - cparams.pooling_type = LLAMA_POOLING_TYPE_RANK; - } - cparams.type_k = params.cache_type_k; cparams.type_v = params.cache_type_v; diff --git a/common/common.h b/common/common.h index c1795831a..fc61e6c38 100644 --- a/common/common.h +++ b/common/common.h @@ -351,7 +351,6 @@ struct common_params { int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm) std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix std::string embd_sep = "\n"; // separator of embeddings - bool reranking = false; // enable reranking support on server // server params int32_t port = 8080; // server listens on this network port diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 173a103ba..58e455ae6 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -519,7 +519,7 @@ class TextModel(ModelBase): def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.block_count) - if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions"], optional=True)) is not None: + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") @@ -2020,6 +2020,20 @@ class LlamaModel(TextModel): raise ValueError(f"Unprocessed experts: {experts}") +@ModelBase.register("ArceeForCausalLM") +class ArceeModel(LlamaModel): + model_arch = gguf.MODEL_ARCH.ARCEE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self._try_set_pooling_type() + rope_scaling = self.hparams.get("rope_scaling") or {} + if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) + + @ModelBase.register( "LlavaForConditionalGeneration", # pixtral "Mistral3ForConditionalGeneration", # mistral small 3.1 @@ -4062,6 +4076,34 @@ class NomicBertModel(BertModel): raise ValueError(f"unknown tokenizer: {toktyp}") +@ModelBase.register("NeoBERT", "NeoBERTLMHead", "NeoBERTForSequenceClassification") +class NeoBert(BertModel): + model_arch = gguf.MODEL_ARCH.NEO_BERT + + def set_gguf_parameters(self): + super().set_gguf_parameters() + + # NeoBERT uses 2/3 of the intermediate size as feed forward length + self.gguf_writer.add_feed_forward_length(int(2 * self.hparams["intermediate_size"] / 3)) + self.gguf_writer.add_rope_freq_base(10000.0) # default value for NeoBERT + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + + f_rms_eps = self.hparams.get("norm_eps", 1e-6) # default value for NeoBERT + self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) + logger.info(f"gguf: rms norm epsilon = {f_rms_eps}") + + self.gguf_writer.add_pooling_type(gguf.PoolingType.CLS) # https://huggingface.co/chandar-lab/NeoBERT#how-to-use + + def modify_tensors(self, data_torch, name, bid): + if name.startswith("decoder."): + return [] + + if name.startswith("model."): + name = name[6:] + + return super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification") class XLMRobertaModel(BertModel): model_arch = gguf.MODEL_ARCH.BERT @@ -5262,6 +5304,34 @@ class DeepseekV2Model(TextModel): raise ValueError(f"Unprocessed experts: {experts}") +@ModelBase.register("Dots1ForCausalLM") +class Dots1Model(Qwen2MoeModel): + model_arch = gguf.MODEL_ARCH.DOTS1 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.hparams["num_experts"] = self.hparams["n_routed_experts"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_leading_dense_block_count(self.hparams["first_k_dense_replace"]) + self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"]) + self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"]) + + if self.hparams["scoring_func"] == "noaux_tc": + self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) + else: + raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}") + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + if name.endswith("e_score_correction_bias"): + name = name.replace("e_score_correction_bias", "e_score_correction.bias") + if "shared_experts" in name: + return [(self.map_tensor_name(name), data_torch)] + return super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("PLMForCausalLM") class PLMModel(TextModel): model_arch = gguf.MODEL_ARCH.PLM diff --git a/ggml/src/ggml-cpu/arch-fallback.h b/ggml/src/ggml-cpu/arch-fallback.h new file mode 100644 index 000000000..10e534251 --- /dev/null +++ b/ggml/src/ggml-cpu/arch-fallback.h @@ -0,0 +1,184 @@ +#pragma once + +// Rename `_generic` functions if no native implementation is available. +// This effectively selects the generic implementation. + +#if defined(GGML_CPU_GENERIC) +// quants.c +#define quantize_row_q8_0_generic quantize_row_q8_0 +#define quantize_row_q8_1_generic quantize_row_q8_1 +#define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0 +#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1 +#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0 +#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1 +#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0 +#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K +#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K +#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K +#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K +#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K +#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K +#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K +#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K +#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K +#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K +#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K +#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K +#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K +#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K +#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0 +#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0 +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64) +// repack.cpp +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64) +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#elif defined(__POWERPC__) || defined(__powerpc__) +// ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679 +// quants.c +#define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K +#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K +#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0 +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#elif defined(__loongarch64) +// quants.c +#define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K +#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K +#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0 +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#elif defined(__riscv) +// quants.c +#define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K +#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K +#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K +#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K +#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K +#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K +#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K +#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K +#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K +#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0 +#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#elif defined(__s390x__) +// quants.c +#define quantize_row_q8_K_generic quantize_row_q8_K +#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0 +#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1 +#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K +#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K +#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K +#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K +#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K +#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K +#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K +#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K +#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K +#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0 +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#elif defined(__wasm__) +// quants.c +#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1 +#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K +#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K +#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K +#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K +#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K +#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K +#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K +#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K +#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K +#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0 +#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K +// repack.cpp +#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 +#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 +#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 +#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 +#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 +#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0 +#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K +#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 +#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 +#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 +#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0 +#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K +#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 +#endif diff --git a/ggml/src/ggml-cpu/ggml-cpu-impl.h b/ggml/src/ggml-cpu/ggml-cpu-impl.h index 69415daa8..bbd93c0ef 100644 --- a/ggml/src/ggml-cpu/ggml-cpu-impl.h +++ b/ggml/src/ggml-cpu/ggml-cpu-impl.h @@ -503,31 +503,9 @@ static __m256 __lasx_xvreplfr2vr_s(const float val) { // TODO: move to ggml-threading void ggml_barrier(struct ggml_threadpool * tp); +void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value); +int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value); + #ifdef __cplusplus } #endif - -#define GGML_DO_PRAGMA_(x) _Pragma (#x) -#define GGML_DO_PRAGMA(x) GGML_DO_PRAGMA_(x) -#if defined(GGML_CPU_GENERIC) || defined(__HIPCC__) -// Note for Apple targets: -// - clang: aliases are not supported on darwin -// - all native kernels need to be implemented in both x86 and arm files -// - on iOS, tvOS, and visionOS, if cmake cannot determine the target architecture, all `_generic` names are replaced by defines -# define GGML_WEAK_ALIAS(name, alias) -#elif defined(__GNUC__) -// GCC/Clang on *nix -# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(weak name = alias) // NOLINT -#elif defined(_MSC_VER) && defined(_WIN64) -// MSVC -// Note: C name mangling varies across different calling conventions -// see https://learn.microsoft.com/en-us/cpp/build/reference/decorated-names?view=msvc-170 -# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(comment(linker, "/alternatename:" #name "=" #alias)) -#elif defined(_MSC_VER) && defined(WIN32) -// ref: https://github.com/ggml-org/whisper.cpp/pull/3239#issuecomment-2958224591 -# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(comment(linker, "/alternatename:_" #name "=_" #alias)) -#else -# error "Unsupported compiler for GGML_WEAK_ALIAS" -#endif - -#define GGML_CPU_NATIVE_IMPL(name) GGML_WEAK_ALIAS(name, name ## _generic) diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c index 2c93b4b31..5e94f7f43 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.c +++ b/ggml/src/ggml-cpu/ggml-cpu.c @@ -563,6 +563,14 @@ void ggml_barrier(struct ggml_threadpool * tp) { #endif } +void ggml_threadpool_chunk_set(struct ggml_threadpool * tp, int value) { + atomic_store_explicit(&tp->current_chunk, value, memory_order_relaxed); +} + +int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value) { + return atomic_fetch_add_explicit(&tp->current_chunk, value, memory_order_relaxed); +} + #if defined(__gnu_linux__) && !defined(__BIONIC__) static cpu_set_t ggml_get_numa_affinity(void) { cpu_set_t cpuset; diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index 1d46158f9..1c545f803 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -53,7 +53,6 @@ #include "ggml-cpu-impl.h" #include "ggml-quants.h" -#include #include #include @@ -394,8 +393,6 @@ class tinyBLAS { template NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) { - static std::atomic current_chunk; - GGML_ASSERT(m % (RM * BM) == 0); const int64_t ytiles = m / (RM * BM); const int64_t xtiles = (n + RN -1) / RN; @@ -410,7 +407,7 @@ class tinyBLAS { if (params->ith == 0) { GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles); // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. - std::atomic_store_explicit(¤t_chunk, (int64_t)params->nth, std::memory_order_relaxed); + ggml_threadpool_chunk_set(params->threadpool, params->nth); } ggml_barrier(params->threadpool); @@ -439,8 +436,7 @@ class tinyBLAS { GGML_ASSERT(jj == jj2); } - // next step. - job = std::atomic_fetch_add_explicit(¤t_chunk, (int64_t)1, std::memory_order_relaxed); + job = ggml_threadpool_chunk_add(params->threadpool, 1); } ggml_barrier(params->threadpool); diff --git a/ggml/src/ggml-cpu/quants.c b/ggml/src/ggml-cpu/quants.c index 1ca9c50e7..d2e705f28 100644 --- a/ggml/src/ggml-cpu/quants.c +++ b/ggml/src/ggml-cpu/quants.c @@ -5,6 +5,8 @@ #include "ggml-quants.h" #include "quants.h" +#include "arch-fallback.h" + #include #include #include @@ -38,12 +40,10 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in void quantize_row_q8_0_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) { quantize_row_q8_0_ref(x, y, k); } -GGML_CPU_NATIVE_IMPL(quantize_row_q8_0) void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) { quantize_row_q8_1_ref(x, y, k); } -GGML_CPU_NATIVE_IMPL(quantize_row_q8_1) // // 2-6 bit quantization in super-blocks @@ -104,7 +104,6 @@ void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, void quantize_row_q8_K_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) { quantize_row_q8_K_ref(x, y, k); } -GGML_CPU_NATIVE_IMPL(quantize_row_q8_K) //===================================== Dot products ================================= @@ -143,7 +142,6 @@ void ggml_vec_dot_q4_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_0_q8_0) // TODO: add WASM SIMD void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { @@ -181,7 +179,6 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_1_q8_1) void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { const int qk = QK8_0; @@ -225,7 +222,6 @@ void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_0_q8_0) void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { const int qk = QK8_1; @@ -269,7 +265,6 @@ void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_1_q8_1) void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { const int qk = QK8_0; @@ -300,7 +295,6 @@ void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q8_0_q8_0) void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(nrc == 1); @@ -353,7 +347,6 @@ void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_tq1_0_q8_K) void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(nrc == 1); @@ -386,7 +379,6 @@ void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_tq2_0_q8_K) void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(nrc == 1); @@ -439,7 +431,6 @@ void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c } *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q2_K_q8_K) void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -519,7 +510,6 @@ void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c for (int l = 0; l < 8; ++l) sumf += sums[l]; *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q3_K_q8_K) void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -595,7 +585,6 @@ void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c for (int l = 0; l < 8; ++l) sumf += sums[l]; *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_K_q8_K) void ggml_vec_dot_q5_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -676,7 +665,6 @@ void ggml_vec_dot_q5_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c for (int l = 0; l < 8; ++l) sumf += sums[l]; *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_K_q8_K) void ggml_vec_dot_q6_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -732,7 +720,6 @@ void ggml_vec_dot_q6_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c for (int l = 0; l < 8; ++l) sumf += sums[l]; *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q6_K_q8_K) void ggml_vec_dot_iq2_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -775,7 +762,6 @@ void ggml_vec_dot_iq2_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs } *s = 0.125f * sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_xxs_q8_K) void ggml_vec_dot_iq2_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -826,7 +812,6 @@ void ggml_vec_dot_iq2_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } *s = 0.125f * sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_xs_q8_K) void ggml_vec_dot_iq2_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -879,7 +864,6 @@ void ggml_vec_dot_iq2_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, *s = 0.125f * sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_s_q8_K) void ggml_vec_dot_iq3_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -924,7 +908,6 @@ void ggml_vec_dot_iq3_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs } *s = 0.25f * sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq3_xxs_q8_K) void ggml_vec_dot_iq3_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -981,7 +964,6 @@ void ggml_vec_dot_iq3_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq3_s_q8_K) void ggml_vec_dot_iq1_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -1025,7 +1007,6 @@ void ggml_vec_dot_iq1_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq1_s_q8_K) void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(n % QK_K == 0); @@ -1087,7 +1068,6 @@ void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq1_m_q8_K) void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(nrc == 1); @@ -1117,7 +1097,6 @@ void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, } *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq4_nl_q8_0) void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) { assert(nrc == 1); @@ -1164,7 +1143,6 @@ void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } *s = sumf; } -GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq4_xs_q8_K) // ============================ 4-bit non-linear quants diff --git a/ggml/src/ggml-cpu/quants.h b/ggml/src/ggml-cpu/quants.h index d729e07d6..dc4342c87 100644 --- a/ggml/src/ggml-cpu/quants.h +++ b/ggml/src/ggml-cpu/quants.h @@ -84,33 +84,6 @@ void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc); -#if defined(GGML_CPU_GENERIC) -#define quantize_row_q8_0_generic quantize_row_q8_0 -#define quantize_row_q8_1_generic quantize_row_q8_1 -#define quantize_row_q8_K_generic quantize_row_q8_K -#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0 -#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1 -#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0 -#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1 -#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0 -#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K -#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K -#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K -#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K -#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K -#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K -#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K -#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K -#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K -#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K -#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K -#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K -#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K -#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K -#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0 -#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K -#endif - #ifdef __cplusplus } #endif diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp index 9d744f012..762039b39 100644 --- a/ggml/src/ggml-cpu/repack.cpp +++ b/ggml/src/ggml-cpu/repack.cpp @@ -8,6 +8,8 @@ #include "ggml-cpu-impl.h" #include "traits.h" +#include "arch-fallback.h" + #include #include #include @@ -83,7 +85,6 @@ void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GG } } } -GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_0_4x4) void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK8_0 == 32); @@ -122,7 +123,6 @@ void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GG } } } -GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_0_4x8) void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK_K == 256); @@ -174,7 +174,6 @@ void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GG } } } -GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_K_4x8) } // extern "C" @@ -244,7 +243,6 @@ void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; } } -GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_4x4_q8_0) void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -289,7 +287,6 @@ void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j]; } } -GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_4x8_q8_0) void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -336,7 +333,6 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, } } } -GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_8x8_q8_0) void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK_K; @@ -415,7 +411,6 @@ void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } } } -GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_K_8x8_q8_K) void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -462,7 +457,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs } } } -GGML_CPU_NATIVE_IMPL(ggml_gemv_iq4_nl_4x4_q8_0) void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -519,7 +513,6 @@ void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, } } } -GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_4x4_q8_0) void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -574,7 +567,6 @@ void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, } } } -GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_4x8_q8_0) void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -629,7 +621,6 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, } } } -GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_8x8_q8_0) void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK_K; @@ -719,7 +710,6 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, } } } -GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_K_8x8_q8_K) void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) { const int qk = QK8_0; @@ -776,7 +766,6 @@ void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs } } } -GGML_CPU_NATIVE_IMPL(ggml_gemm_iq4_nl_4x4_q8_0) } // extern "C" diff --git a/ggml/src/ggml-cpu/repack.h b/ggml/src/ggml-cpu/repack.h index 8ee6e92ea..4421e5f8e 100644 --- a/ggml/src/ggml-cpu/repack.h +++ b/ggml/src/ggml-cpu/repack.h @@ -64,10 +64,6 @@ static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wro extern "C" { #endif -// Workaround for clang: -// clang++ complains: ``error: call to 'ggml_gemm_q4_0_4x4_q8_0' is ambiguous'' -// repro: https://godbolt.org/z/oKdeWKonM (ICE), https://godbolt.org/z/1szq6P36v (ambiguous call) -#if defined(GGML_CPU_CLANG_WORKAROUND) || !(defined(__GNUC__) && defined(__clang__)) || defined(__HIPCC__) void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); @@ -81,7 +77,6 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -#endif // !defined(__clang__) // Native implementations void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k); @@ -98,22 +93,6 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc); -#if defined(GGML_CPU_GENERIC) -#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4 -#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8 -#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8 -#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0 -#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0 -#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0 -#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K -#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0 -#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0 -#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0 -#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0 -#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K -#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0 -#endif - #if defined(__cplusplus) } // extern "C" #endif diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh index a1999a08d..516b23a78 100644 --- a/ggml/src/ggml-cuda/common.cuh +++ b/ggml/src/ggml-cuda/common.cuh @@ -211,9 +211,9 @@ typedef float2 dfloat2; #define FP16_MMA_AVAILABLE #endif // !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA -#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4)) +#if defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4))) #define FP16_MMA_AVAILABLE -#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || defined(RDNA4)) +#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4))) #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING #define NEW_MMA_AVAILABLE @@ -266,11 +266,11 @@ static bool cp_async_available(const int cc) { } static constexpr __device__ int ggml_cuda_get_physical_warp_size() { -#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) - return __AMDGCN_WAVEFRONT_SIZE; +#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__)) + return 64; #else return 32; -#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) +#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__) && (defined(__GFX9__) || defined(__GFX8__)) } [[noreturn]] diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu index b583556a3..96c7264ce 100644 --- a/ggml/src/ggml-cuda/ggml-cuda.cu +++ b/ggml/src/ggml-cuda/ggml-cuda.cu @@ -2669,7 +2669,9 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft) || (integrated && ggml_backend_buft_is_cuda_host(node->src[j]->buffer->buft))); } } -#endif +#else + GGML_UNUSED(integrated); +#endif // NDEBUG bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); if (!ok) { diff --git a/ggml/src/ggml-cuda/ssm-scan.cu b/ggml/src/ggml-cuda/ssm-scan.cu index 37ee208c0..2d34b8360 100644 --- a/ggml/src/ggml-cuda/ssm-scan.cu +++ b/ggml/src/ggml-cuda/ssm-scan.cu @@ -10,6 +10,8 @@ __global__ void __launch_bounds__(splitD, 2) float * __restrict__ dst, const int64_t L) { GGML_UNUSED(src1_nb0); GGML_UNUSED(src2_nb0); + + constexpr int warp_size = ggml_cuda_get_physical_warp_size(); const int bidx = blockIdx.x; // split along B const int bidy = blockIdx.y; // split along D const int tid = threadIdx.x; @@ -44,16 +46,16 @@ __global__ void __launch_bounds__(splitD, 2) if (N == 16) { #pragma unroll for (size_t i = 0; i < splitD / 4; i += 2) { - float value = A_block[(wid * warpSize + i) * stride_A + wtid]; + float value = A_block[(wid * warp_size + i) * stride_A + wtid]; // todo: bank conflict // I am always confused with how to use the swizzling method to solve // bank conflit. Hoping somebody can tell me. - smem_A[(wid * warpSize + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value; + smem_A[(wid * warp_size + i) * stride_sA + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value; } #pragma unroll for (size_t i = 0; i < splitD / 4; i += 2) { - float value = s0_block[(wid * warpSize + i) * stride_s0 + wtid]; - smem_s0[(wid * warpSize + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value; + float value = s0_block[(wid * warp_size + i) * stride_s0 + wtid]; + smem_s0[(wid * warp_size + i) * stride_ss0 + wtid + ((wtid / 16) > 0 ? 1 : 0)] = value; } } diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 70068398f..1b4a4e97c 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -184,6 +184,11 @@ struct vk_command_pool { vk_queue *q; }; +// Prevent simultaneous submissions to the same queue. +// This could be per vk_queue if we stopped having two vk_queue structures +// sharing the same vk::Queue. +static std::mutex queue_mutex; + struct vk_queue { uint32_t queue_family_index; vk::Queue queue; @@ -1282,6 +1287,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_device& device, vk_command static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { if (ctx->seqs.empty()) { if (fence) { + std::lock_guard guard(queue_mutex); ctx->p->q->queue.submit({}, fence); } return; @@ -1351,6 +1357,7 @@ static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { } } + std::lock_guard guard(queue_mutex); ctx->p->q->queue.submit(submit_infos, fence); ctx->seqs.clear(); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 3ee2b2064..834a1d5e1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -291,6 +291,7 @@ class MODEL_ARCH(IntEnum): BERT = auto() NOMIC_BERT = auto() NOMIC_BERT_MOE = auto() + NEO_BERT = auto() JINA_BERT_V2 = auto() BLOOM = auto() STABLELM = auto() @@ -343,6 +344,8 @@ class MODEL_ARCH(IntEnum): WAVTOKENIZER_DEC = auto() PLM = auto() BAILINGMOE = auto() + DOTS1 = auto() + ARCEE = auto() class VISION_PROJECTOR_TYPE(IntEnum): @@ -571,6 +574,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", MODEL_ARCH.NOMIC_BERT_MOE: "nomic-bert-moe", + MODEL_ARCH.NEO_BERT: "neo-bert", MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", @@ -623,6 +627,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec", MODEL_ARCH.PLM: "plm", MODEL_ARCH.BAILINGMOE: "bailingmoe", + MODEL_ARCH.DOTS1: "dots1", + MODEL_ARCH.ARCEE: "arcee", } VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { @@ -1077,6 +1083,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP_EXP, MODEL_TENSOR.LAYER_OUT_NORM, ], + MODEL_ARCH.NEO_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + MODEL_TENSOR.CLS, + MODEL_TENSOR.CLS_OUT, + ], MODEL_ARCH.JINA_BERT_V2: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, @@ -2044,6 +2062,45 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_SHEXP, MODEL_TENSOR.FFN_UP_SHEXP, ], + MODEL_ARCH.DOTS1: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.ARCEE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index adc673e38..54ca0c33f 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -271,7 +271,7 @@ class GGUFWriter: def add_key_value(self, key: str, val: Any, vtype: GGUFValueType, sub_type: GGUFValueType | None = None) -> None: if any(key in kv_data for kv_data in self.kv_data): - raise ValueError(f'Duplicated key name {key!r}') + logger.warning(f'Duplicated key name {key!r}, overwriting it with new value {val!r} of type {vtype.name}') self.kv_data[0][key] = GGUFValue(value=val, type=vtype, sub_type=sub_type) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 439fc1afe..79f044d2a 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -31,6 +31,7 @@ class TensorNameMap: "model.embeddings", # rwkv7 "model.word_embeddings", # bailingmoe "language_model.model.embed_tokens", # llama4 + "encoder", # neobert ), # Token type embeddings @@ -134,6 +135,7 @@ class TensorNameMap: "rwkv.blocks.{bid}.ln1", # rwkv6 "model.layers.{bid}.ln1", # rwkv7 "model.layers.{bid}.input_layernorm", # llama4 + "transformer_encoder.{bid}.attention_norm", # neobert ), # Attention norm 2 @@ -161,6 +163,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.qkv_proj", # phi3 "encoder.layers.{bid}.self_attention.query_key_value", # chatglm "transformer.layers.{bid}.attn.qkv_proj", # openelm + "transformer_encoder.{bid}.qkv", # neobert ), # Attention query @@ -236,6 +239,7 @@ class TensorNameMap: "transformer.layers.{bid}.attn.out_proj", # openelm "transformer.h.{bid}.attn.attention.out_proj", # exaone "model.layers.{bid}.self_attn.o_proj", # llama4 + "transformer_encoder.{bid}.wo", # neobert ), # Attention output norm @@ -276,6 +280,7 @@ class TensorNameMap: "encoder.layers.{bid}.post_attention_layernorm", # chatglm "transformer.layers.{bid}.ffn_norm", # openelm "model.layers.{bid}.post_attention_layernorm", # llama4 + "transformer_encoder.{bid}.ffn_norm", # neobert ), # Post feed-forward norm @@ -305,7 +310,7 @@ class TensorNameMap: ), MODEL_TENSOR.FFN_EXP_PROBS_B: ( - "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 + "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1 ), # Feed-forward up @@ -340,6 +345,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm "transformer.h.{bid}.mlp.c_fc_1", # exaone "model.layers.{bid}.feed_forward.up_proj", # llama4 + "transformer_encoder.{bid}.ffn.w12", # neobert ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -422,6 +428,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm "model.layers.h.{bid}.mlp.c_proj", # exaone "model.layers.{bid}.feed_forward.down_proj", # llama4 + "transformer_encoder.{bid}.ffn.w3", # neobert ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -832,12 +839,14 @@ class TensorNameMap: # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg MODEL_TENSOR.ENC_OUTPUT_NORM: ( "encoder.final_layer_norm", # t5 + "layer_norm", # neobert ), MODEL_TENSOR.CLS: ( "classifier", # jina "classifier.dense", # roberta "pre_classifier", # distillbert + "dense", # neobert ), MODEL_TENSOR.CLS_OUT: ( diff --git a/include/llama.h b/include/llama.h index 499eaac1f..c7cb21e8d 100644 --- a/include/llama.h +++ b/include/llama.h @@ -246,18 +246,21 @@ extern "C" { typedef bool (*llama_progress_callback)(float progress, void * user_data); - // Input data for llama_decode + // Input data for llama_encode/llama_decode // A llama_batch object can contain input about one or many sequences // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens // // - token : the token ids of the input (used when embd is NULL) // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - pos : the positions of the respective token in the sequence - // (if set to NULL, the token position will be tracked automatically by llama_decode) + // (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode) // - seq_id : the sequence to which the respective token belongs // (if set to NULL, the sequence ID will be assumed to be 0) // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output - // (if set to NULL, only the logits for last token will be returned) + // (if set to NULL: + // - if embeddings: all tokens are output + // - if not: only the last token is output + // ) // typedef struct llama_batch { int32_t n_tokens; @@ -265,8 +268,8 @@ extern "C" { llama_token * token; float * embd; llama_pos * pos; - int32_t * n_seq_id; // TODO: remove, should belong to only 1 sequence - llama_seq_id ** seq_id; // TODO: become llama_seq_id * seq_id; + int32_t * n_seq_id; + llama_seq_id ** seq_id; int8_t * logits; // TODO: rename this to "output" } llama_batch; @@ -964,8 +967,8 @@ extern "C" { // Get the number of threads used for prompt and batch processing (multiple token). LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx); - // Set whether the model is in embeddings mode or not - // If true, embeddings will be returned but logits will not + // Set whether the context outputs embeddings or not + // TODO: rename to avoid confusion with llama_get_embeddings() LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings); // Set whether to use causal attention or not diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index 43fa60a80..de8d289cf 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -20,6 +20,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_BERT, "bert" }, { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, { LLM_ARCH_NOMIC_BERT_MOE, "nomic-bert-moe" }, + { LLM_ARCH_NEO_BERT, "neo-bert" }, { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, { LLM_ARCH_BLOOM, "bloom" }, { LLM_ARCH_STABLELM, "stablelm" }, @@ -72,6 +73,8 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" }, { LLM_ARCH_PLM, "plm" }, { LLM_ARCH_BAILINGMOE, "bailingmoe" }, + { LLM_ARCH_DOTS1, "dots1" }, + { LLM_ARCH_ARCEE, "arcee" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -243,6 +246,24 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_ARCEE, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_LLAMA4, { @@ -494,6 +515,21 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_NEO_BERT, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" }, + { LLM_TENSOR_CLS, "cls" }, + { LLM_TENSOR_CLS_OUT, "cls.output" }, + }, + }, { LLM_ARCH_JINA_BERT_V2, { @@ -1555,6 +1591,34 @@ static const std::map> LLM_TENSOR_N { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, }, }, + { + LLM_ARCH_DOTS1, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" }, + } + }, { LLM_ARCH_UNKNOWN, { diff --git a/src/llama-arch.h b/src/llama-arch.h index f3825528a..3e8a61da3 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -24,6 +24,7 @@ enum llm_arch { LLM_ARCH_BERT, LLM_ARCH_NOMIC_BERT, LLM_ARCH_NOMIC_BERT_MOE, + LLM_ARCH_NEO_BERT, LLM_ARCH_JINA_BERT_V2, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, @@ -76,6 +77,8 @@ enum llm_arch { LLM_ARCH_WAVTOKENIZER_DEC, LLM_ARCH_PLM, LLM_ARCH_BAILINGMOE, + LLM_ARCH_DOTS1, + LLM_ARCH_ARCEE, LLM_ARCH_UNKNOWN, }; diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp index bdbf76626..8b6d14fe8 100644 --- a/src/llama-batch.cpp +++ b/src/llama-batch.cpp @@ -3,6 +3,7 @@ #include "llama-impl.h" #include "llama-cparams.h" #include "llama-vocab.h" +#include "llama-memory.h" #include #include @@ -287,21 +288,28 @@ llama_sbatch::llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple llama_batch_allocr::llama_batch_allocr() { const char * LLAMA_BATCH_DEBUG = getenv("LLAMA_BATCH_DEBUG"); debug = LLAMA_BATCH_DEBUG ? atoi(LLAMA_BATCH_DEBUG) : 0; + + seq_pos.resize(LLAMA_MAX_SEQ); + seq_cpl.resize(LLAMA_MAX_SEQ); + for (auto & cur : seq_cpl) { + cur.resize(LLAMA_MAX_SEQ); + } } -bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab & vocab, llama_pos p0) { +bool llama_batch_allocr::init( + const llama_batch & batch_inp, + const llama_vocab & vocab, + const llama_memory_i * memory, + bool embd_all) { clear(); batch = batch_inp; GGML_ASSERT(batch.n_tokens > 0); - if (!batch.pos) { - if (batch.seq_id) { - LLAMA_LOG_ERROR("%s: pos == NULL, but seq_id != NULL\n", __func__); - return false; - } - } + // + // validate input batch + // if (batch.token) { for (int32_t i = 0; i < batch.n_tokens; ++i) { @@ -315,22 +323,17 @@ bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab & if (batch.seq_id) { for (int32_t i = 0; i < batch.n_tokens; ++i) { for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { - if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= LLAMA_MAX_PARALLEL_SEQUENCES)) { - LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], LLAMA_MAX_PARALLEL_SEQUENCES); + if (batch.seq_id && (batch.seq_id[i][s] < 0 || batch.seq_id[i][s] >= LLAMA_MAX_SEQ)) { + LLAMA_LOG_ERROR("%s: invalid seq_id[%d][%d] = %d > %d\n", __func__, i, s, batch.seq_id[i][s], LLAMA_MAX_SEQ); return false; } } } } - if (!batch.pos) { - assert(p0 >= 0); - pos.resize(batch.n_tokens); - for (int32_t i = 0; i < batch.n_tokens; i++) { - pos[i] = p0 + i; - } - batch.pos = pos.data(); - } + // + // auto-generate missing fields + // if (!batch.n_seq_id) { n_seq_id.resize(batch.n_tokens); @@ -349,20 +352,90 @@ bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab & batch.seq_id = seq_id.data(); } - if (!batch.logits) { - // by default return the output only for the last token - output.resize(batch.n_tokens); - output[output.size() - 1] = true; - batch.logits = output.data(); + if (!batch.pos) { + pos.resize(batch.n_tokens); + + // initialize the starting position for each sequence based on the positions in the memory + llama_pos p0[LLAMA_MAX_SEQ]; + for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + if (!memory) { + p0[s] = 0; + } else { + p0[s] = memory->seq_pos_max(s) + 1; + } + } + + for (int32_t i = 0; i < batch.n_tokens; i++) { + const llama_seq_id seq_id = batch.seq_id[i][0]; + + pos[i] = p0[seq_id]; + + for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { + p0[batch.seq_id[i][s]] = pos[i] + 1; + } + } + + batch.pos = pos.data(); } + if (!batch.logits) { + if (embd_all) { + // return the output for all tokens + output.resize(batch.n_tokens, true); + } else { + // return the output only for the last token + output.resize(batch.n_tokens, false); + output[output.size() - 1] = true; + } + + batch.logits = output.data(); + } else if (embd_all) { + bool warn = false; + + for (int32_t i = 0; i < batch.n_tokens; ++i) { + if (batch.logits[i] == 0) { + warn = true; + } + } + + if (warn) { + LLAMA_LOG_WARN("%s: embeddings required but some input tokens were not marked as outputs -> overriding\n", __func__); + + output.resize(batch.n_tokens, true); + batch.logits = output.data(); + } + } + + // + // compute stats + // + for (int32_t i = 0; i < batch.n_tokens; ++i) { n_outputs += batch.logits[i] != 0; } + // determine coupled sequences + // these are pairs of sequences that have at least one token in the input batch that is assigned to both of them + for (int32_t i = 0; i < batch.n_tokens; ++i) { + for (int32_t s = 0; s < batch.n_seq_id[i]; ++s) { + seq_pos[batch.seq_id[i][s]].insert(batch.pos[i]); + + if (s > 0) { + const llama_seq_id s0 = batch.seq_id[i][0]; + const llama_seq_id s1 = batch.seq_id[i][s]; + + // mark that sequence s1 is coupled to s0 + seq_cpl[s1][s0] = true; + + // note: the other way around is not necessary for now + //seq_cpl[s0][s1] = true; + } + } + } + if (debug > 0) { - LLAMA_LOG_DEBUG("%s: input batch info (p0 = %d):\n", __func__, p0); - LLAMA_LOG_DEBUG("%s: n_tokens = %d\n", __func__, batch.n_tokens); + LLAMA_LOG_DEBUG("%s: input batch info:\n", __func__); + LLAMA_LOG_DEBUG("%s: n_tokens = %d\n", __func__, batch.n_tokens); LLAMA_LOG_DEBUG("%s: token = %p\n", __func__, (void *) batch.token); LLAMA_LOG_DEBUG("%s: embd = %p\n", __func__, (void *) batch.embd); LLAMA_LOG_DEBUG("%s: pos = %p\n", __func__, (void *) batch.pos); @@ -404,6 +477,58 @@ bool llama_batch_allocr::init(const llama_batch & batch_inp, const llama_vocab & batch.pos[i], batch.n_seq_id[i], ss.str().c_str(), batch.logits[i]); } LLAMA_LOG_DEBUG("%s: ]\n", __func__); + + LLAMA_LOG_DEBUG("%s: seq = [\n", __func__); + for (int s0 = 0; s0 < (int) seq_pos.size(); ++s0) { + if (seq_pos[s0].empty()) { + continue; + } + + std::stringstream ss; + for (int s1 = 0; s1 < (int) seq_cpl[s0].size(); ++s1) { + if (seq_cpl[s0][s1]) { + ss << s1 << " "; + } + } + + LLAMA_LOG_DEBUG("%s: %4d: pos = [%4d, %4d], cpl = %s\n", + __func__, s0, seq_pos_min(s0), seq_pos_max(s0), ss.str().empty() ? "-" : ss.str().c_str()); + } + LLAMA_LOG_DEBUG("%s: ]\n", __func__); + } + } + + // + // consistency checks + // + + for (int32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { + if (seq_pos[s].empty()) { + continue; + } + + if (memory && seq_pos_min(s) != memory->seq_pos_max(s) + 1) { + LLAMA_LOG_ERROR("%s: sequence %d does not start from the last position stored in the memory\n", __func__, s); + return false; + } + + if (seq_pos_max(s) - seq_pos_min(s) + 1 > (int) seq_pos[s].size()) { + LLAMA_LOG_ERROR("%s: sequence %d positions are not continuous\n", __func__, s); + return false; + } + } + + if (memory) { + for (int32_t s0 = 0; s0 < LLAMA_MAX_SEQ; ++s0) { + for (int32_t s1 = 0; s1 < LLAMA_MAX_SEQ; ++s1) { + if (seq_cpl[s0][s1]) { + if (memory->seq_pos_min(s0) != memory->seq_pos_min(s1) || + memory->seq_pos_max(s0) != memory->seq_pos_max(s1)) { + LLAMA_LOG_ERROR("%s: sequence %d is coupled to %d in the input batch, but have divereged\n", __func__, s0, s1); + return false; + } + } + } } } @@ -418,6 +543,14 @@ uint32_t llama_batch_allocr::get_n_outputs() const { return n_outputs; } +llama_pos llama_batch_allocr::seq_pos_min(llama_seq_id seq_id) const { + return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].begin(); +} + +llama_pos llama_batch_allocr::seq_pos_max(llama_seq_id seq_id) const { + return seq_pos[seq_id].empty() ? -1 : *seq_pos[seq_id].rbegin(); +} + void llama_batch_allocr::clear() { n_outputs = 0; @@ -426,6 +559,14 @@ void llama_batch_allocr::clear() { n_seq_id.clear(); seq_id.clear(); output.clear(); + + for (auto & cur : seq_pos) { + cur.clear(); + } + + for (auto & cur : seq_cpl) { + std::fill(cur.begin(), cur.end(), false); + } } // diff --git a/src/llama-batch.h b/src/llama-batch.h index 1e0be8ac2..a555c1572 100644 --- a/src/llama-batch.h +++ b/src/llama-batch.h @@ -4,6 +4,7 @@ #include #include +#include // very similar to llama_batch, // but has more metadata about sequences @@ -77,18 +78,26 @@ struct llama_sbatch { llama_sbatch(const llama_batch & batch, size_t n_embd, bool simple_split = false); }; -// temporary allocate memory for the input batch if needed +// a helper for sanitizing and fulfilling a batch class llama_batch_allocr { public: llama_batch_allocr(); - // optionally fulfill the batch returned by llama_batch_get_one - bool init(const llama_batch & batch_inp, const llama_vocab & vocab, llama_pos p0); + // sanitize and auto-gen missing data in the input batch + // memory is optional. if provided will be used to check for sequence continuity and to determine the positions + bool init( + const llama_batch & batch_inp, + const llama_vocab & vocab, + const llama_memory_i * memory, + bool embd_all); const llama_batch & get_batch() const; uint32_t get_n_outputs() const; + llama_pos seq_pos_min(llama_seq_id seq_id) const; + llama_pos seq_pos_max(llama_seq_id seq_id) const; + private: void clear(); @@ -103,5 +112,8 @@ private: std::vector seq_id; std::vector output; + std::vector> seq_pos; // seq_pos[s]: the set of positions in sequence s + std::vector> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1 + int debug; }; diff --git a/src/llama-chat.cpp b/src/llama-chat.cpp index d12743e6b..bc4fa05a7 100644 --- a/src/llama-chat.cpp +++ b/src/llama-chat.cpp @@ -183,6 +183,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) { return LLM_CHAT_TEMPLATE_BAILING; } else if (tmpl_contains("<|header_start|>") && tmpl_contains("<|header_end|>")) { return LLM_CHAT_TEMPLATE_LLAMA4; + } else if (tmpl_contains("<|endofuserprompt|>")) { + return LLM_CHAT_TEMPLATE_DOTS1; } return LLM_CHAT_TEMPLATE_UNKNOWN; } @@ -643,6 +645,21 @@ int32_t llm_chat_apply_template( if (add_ass) { ss << "Assistant:"; } + } else if (tmpl == LLM_CHAT_TEMPLATE_DOTS1) { + // dots.llm1.inst (DOTS1) + for (auto message : chat) { + std::string role(message->role); + if (role == "system") { + ss << "<|system|>" << message->content << "<|endofsystem|>"; + } else if (role == "user") { + ss << "<|userprompt|>" << message->content << "<|endofuserprompt|>"; + } else { + ss << "<|response|>" << message->content << "<|endofresponse|>"; + } + } + if (add_ass) { + ss << "<|response|>"; + } } else { // template not supported return -1; diff --git a/src/llama-chat.h b/src/llama-chat.h index db24ade21..38800010a 100644 --- a/src/llama-chat.h +++ b/src/llama-chat.h @@ -43,6 +43,7 @@ enum llm_chat_template { LLM_CHAT_TEMPLATE_BAILING, LLM_CHAT_TEMPLATE_LLAMA4, LLM_CHAT_TEMPLATE_SMOLVLM, + LLM_CHAT_TEMPLATE_DOTS1, LLM_CHAT_TEMPLATE_UNKNOWN, }; diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 1c0de1cc0..87b5f96bf 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -29,8 +29,8 @@ llama_context::llama_context( const auto & hparams = model.hparams; cparams.n_seq_max = std::max(1u, params.n_seq_max); - if (cparams.n_seq_max > LLAMA_MAX_PARALLEL_SEQUENCES) { - throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_PARALLEL_SEQUENCES)); + if (cparams.n_seq_max > LLAMA_MAX_SEQ) { + throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_SEQ)); } cparams.n_threads = params.n_threads; @@ -727,9 +727,8 @@ int llama_context::encode(const llama_batch & batch_inp) { return -1; } - // temporary allocate memory for the input batch if needed // note: during encode, we always pass the full sequence starting from pos = 0 - if (!batch_allocr->init(batch_inp, model.vocab, batch_inp.pos ? -1 : 0)) { + if (!batch_allocr->init(batch_inp, model.vocab, nullptr, true)) { LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__); return -1; } @@ -895,8 +894,10 @@ int llama_context::decode(const llama_batch & batch_inp) { return -1; } - // temporary allocate memory for the input batch if needed - if (!batch_allocr->init(batch_inp, model.vocab, batch_inp.pos ? -1 : memory->seq_pos_max(0) + 1)) { + // when computing embeddings, all tokens are output + const bool embd_all = cparams.embeddings; + + if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) { LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__); return -1; } @@ -913,12 +914,9 @@ int llama_context::decode(const llama_batch & batch_inp) { GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT - // this indicates we are doing pooled embedding - const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE; - const uint32_t n_outputs_all = batch_allocr->get_n_outputs(); - if (embd_pooled) { + if (embd_all) { // require that all tokens are output if (n_outputs_all != n_tokens_all) { LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n", @@ -947,7 +945,7 @@ int llama_context::decode(const llama_batch & batch_inp) { llama_memory_state_ptr mstate; while (true) { - mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled); + mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all); if (!mstate) { return -2; } @@ -1025,8 +1023,8 @@ int llama_context::decode(const llama_batch & batch_inp) { if (!res) { // the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache - llama_pos pos_min[LLAMA_MAX_PARALLEL_SEQUENCES]; - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + llama_pos pos_min[LLAMA_MAX_SEQ]; + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { pos_min[s] = std::numeric_limits::max(); } @@ -1037,7 +1035,7 @@ int llama_context::decode(const llama_batch & batch_inp) { pos_min[seq_id] = std::min(pos_min[seq_id], ubatch.pos[i]); } - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (pos_min[s] == std::numeric_limits::max()) { continue; } @@ -1060,7 +1058,7 @@ int llama_context::decode(const llama_batch & batch_inp) { // ggml_graph_dump_dot(gf, NULL, "llama.dot"); //} - auto * t_logits = cparams.embeddings ? nullptr : res->get_logits(); + auto * t_logits = res->get_logits(); auto * t_embd = cparams.embeddings ? res->get_embd() : nullptr; if (t_embd && res->get_embd_pooled()) { @@ -1224,9 +1222,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) { const auto n_vocab = vocab.n_tokens(); const auto n_embd = hparams.n_embd; - // TODO: use a per-batch flag for logits presence instead - bool has_logits = !cparams.embeddings; - bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE); + bool has_logits = true; + bool has_embd = cparams.embeddings; // TODO: hacky enc-dec support if (model.arch == LLM_ARCH_T5) { @@ -2046,14 +2043,11 @@ void llama_context::opt_epoch_iter( n_queued_tokens += n_tokens_all; - // this indicates we are doing pooled embedding - const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE; - embd_seq.clear(); uint32_t n_outputs_all = n_tokens_all; - auto mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled); + auto mstate = memory->init_batch(batch, cparams.n_ubatch, true); if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) { LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__); break; diff --git a/src/llama-cparams.cpp b/src/llama-cparams.cpp index f7b36590f..a3e7a37ee 100644 --- a/src/llama-cparams.cpp +++ b/src/llama-cparams.cpp @@ -1,5 +1,5 @@ #include "llama-cparams.h" size_t llama_max_parallel_sequences(void) { - return LLAMA_MAX_PARALLEL_SEQUENCES; + return LLAMA_MAX_SEQ; } diff --git a/src/llama-cparams.h b/src/llama-cparams.h index 2871031ef..118615d5b 100644 --- a/src/llama-cparams.h +++ b/src/llama-cparams.h @@ -4,7 +4,7 @@ #include -#define LLAMA_MAX_PARALLEL_SEQUENCES 64 +#define LLAMA_MAX_SEQ 64 struct llama_cparams { uint32_t n_ctx; // context size used during inference diff --git a/src/llama-kv-cache-recurrent.cpp b/src/llama-kv-cache-recurrent.cpp index d2593621e..c7ac0c8ad 100644 --- a/src/llama-kv-cache-recurrent.cpp +++ b/src/llama-kv-cache-recurrent.cpp @@ -359,9 +359,7 @@ llama_pos llama_kv_cache_recurrent::seq_pos_max(llama_seq_id seq_id) const { return result; } -llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled) { - GGML_UNUSED(embd_pooled); - +llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) { auto sbatch = llama_sbatch(batch, hparams.n_embd, false); std::vector ubatches; @@ -369,8 +367,8 @@ llama_memory_state_ptr llama_kv_cache_recurrent::init_batch(const llama_batch & while (sbatch.n_tokens > 0) { llama_ubatch ubatch; - if (embd_pooled) { - // Pooled embeddings cannot be split across ubatches (yet) + if (embd_all) { + // if all tokens are output, split by sequence ubatch = sbatch.split_seq(n_ubatch); } else { ubatch = sbatch.split_equal(n_ubatch); diff --git a/src/llama-kv-cache-recurrent.h b/src/llama-kv-cache-recurrent.h index d7c02ea87..f9b01a651 100644 --- a/src/llama-kv-cache-recurrent.h +++ b/src/llama-kv-cache-recurrent.h @@ -32,7 +32,7 @@ public: llama_memory_state_ptr init_batch( const llama_batch & batch, uint32_t n_ubatch, - bool embd_pooled) override; + bool embd_all) override; llama_memory_state_ptr init_full() override; diff --git a/src/llama-kv-cache-unified-iswa.cpp b/src/llama-kv-cache-unified-iswa.cpp index 9814f7663..a4a4c2b1b 100644 --- a/src/llama-kv-cache-unified-iswa.cpp +++ b/src/llama-kv-cache-unified-iswa.cpp @@ -95,8 +95,8 @@ llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const { return kv_swa->seq_pos_max(seq_id); } -llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled) { - GGML_UNUSED(embd_pooled); +llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_all) { + GGML_UNUSED(embd_all); // first try simple split do { diff --git a/src/llama-kv-cache-unified-iswa.h b/src/llama-kv-cache-unified-iswa.h index d114c7378..6e941e1a4 100644 --- a/src/llama-kv-cache-unified-iswa.h +++ b/src/llama-kv-cache-unified-iswa.h @@ -34,7 +34,7 @@ public: llama_memory_state_ptr init_batch( const llama_batch & batch, uint32_t n_ubatch, - bool embd_pooled) override; + bool embd_all) override; llama_memory_state_ptr init_full() override; diff --git a/src/llama-kv-cache-unified.cpp b/src/llama-kv-cache-unified.cpp index 7e227df1f..4d331b5bf 100644 --- a/src/llama-kv-cache-unified.cpp +++ b/src/llama-kv-cache-unified.cpp @@ -310,8 +310,8 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const { llama_memory_state_ptr llama_kv_cache_unified::init_batch( const llama_batch & batch, uint32_t n_ubatch, - bool embd_pooled) { - GGML_UNUSED(embd_pooled); + bool embd_all) { + GGML_UNUSED(embd_all); do { auto sbatch = llama_sbatch(batch, hparams.n_embd, true); @@ -572,7 +572,7 @@ int32_t llama_kv_cache_unified::find_slot(const llama_ubatch & ubatch) const { LLAMA_LOG_DEBUG("\n%s\n", ss.c_str()); } - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (cells.seq_pos_min(s) < 0) { continue; } @@ -652,8 +652,8 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch // keep track of the max sequence position that we would overwrite with this ubatch // for non-SWA cache, this would be always empty - llama_seq_id seq_pos_max_rm[LLAMA_MAX_PARALLEL_SEQUENCES]; - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + llama_seq_id seq_pos_max_rm[LLAMA_MAX_SEQ]; + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { seq_pos_max_rm[s] = -1; } @@ -684,7 +684,7 @@ void llama_kv_cache_unified::apply_ubatch(uint32_t head_cur, const llama_ubatch // note: we want to preserve the invariant that all positions between [pos_min, pos_max] for each sequence // will be present in the cache. so we have to purge any position which is less than those we would overwrite // ref: https://github.com/ggml-org/llama.cpp/pull/13746#issuecomment-2916057092 - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (seq_pos_max_rm[s] == -1) { continue; } @@ -1739,7 +1739,7 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state( llama_context * lctx, bool do_shift, defrag_info dinfo) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), lctx(lctx), do_shift(do_shift), dinfo(std::move(dinfo)) { - if (!do_shift && dinfo.empty()) { + if (!do_shift && this->dinfo.empty()) { status = LLAMA_MEMORY_STATUS_NO_UPDATE; } } diff --git a/src/llama-kv-cache-unified.h b/src/llama-kv-cache-unified.h index d6dcd19f2..d96571d95 100644 --- a/src/llama-kv-cache-unified.h +++ b/src/llama-kv-cache-unified.h @@ -59,7 +59,7 @@ public: llama_memory_state_ptr init_batch( const llama_batch & batch, uint32_t n_ubatch, - bool embd_pooled) override; + bool embd_all) override; llama_memory_state_ptr init_full() override; diff --git a/src/llama-kv-cells.h b/src/llama-kv-cells.h index acf30aebe..1d4e70f4d 100644 --- a/src/llama-kv-cells.h +++ b/src/llama-kv-cells.h @@ -23,7 +23,7 @@ public: used.clear(); - for (uint32_t s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (uint32_t s = 0; s < LLAMA_MAX_SEQ; ++s) { seq_pos[s].clear(); } } @@ -240,7 +240,7 @@ public: llama_seq_id seq_get(uint32_t i) const { assert(seq[i].count() == 1); - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (seq[i].test(s)) { return s; } @@ -253,7 +253,7 @@ public: // return -1 if the sequence is not present llama_pos seq_pos_min(llama_seq_id seq_id) const { assert(seq_id >= 0); - assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES); + assert(seq_id < LLAMA_MAX_SEQ); if (seq_pos[seq_id].empty()) { return -1; @@ -266,7 +266,7 @@ public: // return -1 if the sequence is not present llama_pos seq_pos_max(llama_seq_id seq_id) const { assert(seq_id >= 0); - assert(seq_id < LLAMA_MAX_PARALLEL_SEQUENCES); + assert(seq_id < LLAMA_MAX_SEQ); if (seq_pos[seq_id].empty()) { return -1; @@ -384,20 +384,20 @@ private: // std::vector shift; - using bits_t = std::bitset; + using bits_t = std::bitset; // the bitset seq[i] tells us which sequences are currently occupying the i-th cell std::vector seq; // the set seq_pos[s] tells us which positions are currently present for sequence s // this way seq_pos[s].begin() and seq_pos[s].rbegin() give us the min/max positions currently in the cache - std::set seq_pos[LLAMA_MAX_PARALLEL_SEQUENCES]; + std::set seq_pos[LLAMA_MAX_SEQ]; // helper functions for updating `seq_pos`, once cell at a time: // remove cell i void seq_pos_rm(uint32_t i) { - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (seq[i].test(s)) { seq_pos[s].erase(pos[i]); } @@ -406,7 +406,7 @@ private: // add cell i void seq_pos_add(uint32_t i) { - for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) { + for (int s = 0; s < LLAMA_MAX_SEQ; ++s) { if (seq[i].test(s)) { seq_pos[s].insert(pos[i]); } diff --git a/src/llama-memory.h b/src/llama-memory.h index 42e226dc0..24668f861 100644 --- a/src/llama-memory.h +++ b/src/llama-memory.h @@ -73,7 +73,7 @@ struct llama_memory_i { virtual llama_memory_state_ptr init_batch( const llama_batch & batch, uint32_t n_ubatch, - bool embd_pooled) = 0; + bool embd_all) = 0; // simulate full cache, used for allocating worst-case compute buffers virtual llama_memory_state_ptr init_full() = 0; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 2bfdd81e7..6adc4f2a8 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -85,6 +85,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_40B: return "40B"; case LLM_TYPE_65B: return "65B"; case LLM_TYPE_70B: return "70B"; + case LLM_TYPE_142B: return "142B"; case LLM_TYPE_236B: return "236B"; case LLM_TYPE_290B: return "290B"; case LLM_TYPE_314B: return "314B"; @@ -603,6 +604,16 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.use_kq_norm = false; } } break; + case LLM_ARCH_ARCEE: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + // Arcee uses the same structure as Llama + switch (hparams.n_layer) { + case 36: type = LLM_TYPE_4B; break; + default: type = LLM_TYPE_UNKNOWN; + } + } break; case LLM_ARCH_DECI: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -743,6 +754,16 @@ void llama_model::load_hparams(llama_model_loader & ml) { } } } break; + case LLM_ARCH_NEO_BERT: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + + if (hparams.n_layer == 28) { + type = LLM_TYPE_250M; + } + } break; case LLM_ARCH_BLOOM: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -1449,6 +1470,20 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: type = LLM_TYPE_UNKNOWN; } } break; + case LLM_ARCH_DOTS1: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false); + ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false); + switch (hparams.n_layer) { + case 62: type = LLM_TYPE_142B; break; + default: type = LLM_TYPE_UNKNOWN; + } + } break; default: throw std::runtime_error("unsupported model architecture"); } @@ -2283,6 +2318,32 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0); } } break; + case LLM_ARCH_NEO_BERT: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED); + cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, TENSOR_NOT_REQUIRED); + + cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED); + cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {hparams.n_cls_out}, TENSOR_NOT_REQUIRED); + + output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff*2}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + } + } break; case LLM_ARCH_JINA_BERT_V2: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings @@ -4219,6 +4280,89 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); } } break; + case LLM_ARCH_DOTS1: + { + const int64_t n_ff_exp = hparams.n_ff_exp; + const int64_t n_expert_shared = hparams.n_expert_shared; + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0); + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + if (i < (int) hparams.n_layer_dense_lead) { + layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } else { + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED); + + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0"); + } + + // MoE branch + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0); + + // Shared expert branch + layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0); + layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0); + } + } + } break; + case LLM_ARCH_ARCEE: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + // output + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED); + + // if output is NULL, init from the input tok embed + if (output == NULL) { + output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); + } + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0); + layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0); + layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0); + + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); + + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0); + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6174,6 +6318,117 @@ struct llm_build_bert : public llm_graph_context { } }; +struct llm_build_neo_bert : public llm_graph_context { + llm_build_neo_bert(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + ggml_tensor * cur; + ggml_tensor * inpL; + ggml_tensor * inp_pos = build_inp_pos(); + + // construct input embeddings (token, type, position) + inpL = build_inp_embd(model.tok_embd); + cb(inpL, "inp_embd", -1); + + auto * inp_attn = build_attn_inp_no_cache(); + + // iterate layers + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * cur = inpL; + + ggml_tensor * Qcur; + ggml_tensor * Kcur; + ggml_tensor * Vcur; + + // pre-norm + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + + // self-attention + cur = build_lora_mm(model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + // RoPE + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, nullptr, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(cur, "kqv_out", il); + + if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) { + // skip computing output for unused tokens + ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + // re-add the layer input + cur = ggml_add(ctx0, cur, inpL); + + ggml_tensor * ffn_inp = cur; + cb(ffn_inp, "ffn_inp", il); + + // pre-norm + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + // feed-forward network + cur = build_ffn(cur, + model.layers[il].ffn_up, + NULL, NULL, NULL, NULL, NULL, + model.layers[il].ffn_down, + NULL, NULL, NULL, + LLM_FFN_SWIGLU, LLM_FFN_SEQ, il); + + // attentions bypass the intermediate layer + cur = ggml_add(ctx0, cur, ffn_inp); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, + model.output_norm_enc, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_embd", -1); + res->t_embd = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + struct llm_build_bloom : public llm_graph_context { llm_build_bloom(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; @@ -13294,6 +13549,291 @@ struct llm_build_bailingmoe : public llm_graph_context { } }; +struct llm_build_dots1 : public llm_graph_context { + llm_build_dots1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv_unified(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self_attention + { + // compute Q and K and RoPE them + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il); + cb(Kcur, "Kcur_normed", il); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, model.layers[il].bo, + Qcur, Kcur, Vcur, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // MoE branch + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + model.layers[il].ffn_gate, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + ggml_tensor * moe_out = + build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, hparams.expert_weights_norm, + true, hparams.expert_weights_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "ffn_moe_out", il); + + { + ggml_tensor * ffn_shexp = build_ffn(cur, + model.layers[il].ffn_up_shexp, NULL, NULL, + model.layers[il].ffn_gate_shexp, NULL, NULL, + model.layers[il].ffn_down_shexp, NULL, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + } + + cur = ggml_add(ctx0, cur, ffn_inp); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + +struct llm_build_arcee : public llm_graph_context { + llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v; + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + + // inp_pos - contains the positions + ggml_tensor * inp_pos = build_inp_pos(); + + auto * inp_attn = build_attn_inp_kv_unified(); + + const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale; + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + // norm + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // rope freq factors for llama3; may return nullptr for llama2 and other models + ggml_tensor * rope_factors = model.get_rope_factors(cparams, il); + + // compute Q and K and RoPE them + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); + + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, + n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, gf, + model.layers[il].wo, model.layers[il].bo, + Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + // ARCEE uses relu^2 instead of silu + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + NULL, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "ffn_out", il); + + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM_RMS, -1); + + cb(cur, "result_norm", -1); + res->t_embd = cur; + + // lm_head + cur = build_lora_mm(model.output, cur); + + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); + } +}; + llama_memory_i * llama_model::create_memory(const llama_memory_params & params, llama_cparams & cparams) const { llama_memory_i * res; @@ -13302,6 +13842,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_NOMIC_BERT_MOE: + case LLM_ARCH_NEO_BERT: case LLM_ARCH_WAVTOKENIZER_DEC: { res = nullptr; @@ -13410,6 +13951,10 @@ llm_graph_result_ptr llama_model::build_graph( { llm = std::make_unique(*this, params, gf); } break; + case LLM_ARCH_NEO_BERT: + { + llm = std::make_unique(*this, params, gf); + } break; case LLM_ARCH_BLOOM: { llm = std::make_unique(*this, params, gf); @@ -13632,6 +14177,14 @@ llm_graph_result_ptr llama_model::build_graph( { llm = std::make_unique(*this, params, gf); } break; + case LLM_ARCH_DOTS1: + { + llm = std::make_unique(*this, params, gf); + } break; + case LLM_ARCH_ARCEE: + { + llm = std::make_unique(*this, params, gf); + } break; default: GGML_ABORT("fatal error"); } @@ -13781,6 +14334,8 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_GRANITE_MOE: case LLM_ARCH_CHAMELEON: case LLM_ARCH_BAILINGMOE: + case LLM_ARCH_NEO_BERT: + case LLM_ARCH_ARCEE: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 @@ -13814,6 +14369,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_NEMOTRON: case LLM_ARCH_EXAONE: case LLM_ARCH_MINICPM3: + case LLM_ARCH_DOTS1: return LLAMA_ROPE_TYPE_NEOX; case LLM_ARCH_QWEN2VL: diff --git a/src/llama-model.h b/src/llama-model.h index 18b714620..06e6c6879 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -73,6 +73,7 @@ enum llm_type { LLM_TYPE_40B, LLM_TYPE_65B, LLM_TYPE_70B, + LLM_TYPE_142B, LLM_TYPE_236B, LLM_TYPE_290B, LLM_TYPE_314B, diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index a5ad0cd97..5a92e6bd0 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -588,7 +588,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) { gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) { - gguf_set_val_i32(ctx_out.get(), o.key, o.val_i64); + // Setting type to UINT32. See https://github.com/ggml-org/llama.cpp/pull/14182 for context + gguf_set_val_u32(ctx_out.get(), o.key, (uint32_t)abs(o.val_i64)); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) { gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool); } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) { diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 0837e48a6..0b1fd5bf3 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -2226,6 +2226,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { || t.first == "<|eom_id|>" || t.first == "" || t.first == "_" + || t.first == "<|end_of_text|>" ) { special_eog_ids.insert(t.second); if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) { diff --git a/src/llama.cpp b/src/llama.cpp index 9285cde1d..95dd59de0 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -228,14 +228,18 @@ static struct llama_model * llama_model_load_from_file_impl( // if using single GPU mode, remove all except the main GPU if (params.split_mode == LLAMA_SPLIT_MODE_NONE) { - if (params.main_gpu < 0 || params.main_gpu >= (int)model->devices.size()) { - LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %d)\n", __func__, params.main_gpu, (int)model->devices.size()); - llama_model_free(model); - return nullptr; + if (params.main_gpu < 0) { + model->devices.clear(); + } else { + if (params.main_gpu >= (int)model->devices.size()) { + LLAMA_LOG_ERROR("%s: invalid value for main_gpu: %d (available devices: %zu)\n", __func__, params.main_gpu, model->devices.size()); + llama_model_free(model); + return nullptr; + } + ggml_backend_dev_t main_gpu = model->devices[params.main_gpu]; + model->devices.clear(); + model->devices.push_back(main_gpu); } - ggml_backend_dev_t main_gpu = model->devices[params.main_gpu]; - model->devices.clear(); - model->devices.push_back(main_gpu); } for (auto * dev : model->devices) { diff --git a/tests/test-thread-safety.cpp b/tests/test-thread-safety.cpp new file mode 100644 index 000000000..d525b7430 --- /dev/null +++ b/tests/test-thread-safety.cpp @@ -0,0 +1,152 @@ +// thread safety test +// - Loads a copy of the same model on each GPU, plus a copy on the CPU +// - Creates n_parallel (--parallel) contexts per model +// - Runs inference in parallel on each context + +#include +#include +#include +#include "llama.h" +#include "arg.h" +#include "common.h" +#include "log.h" +#include "sampling.h" + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + + llama_backend_init(); + llama_numa_init(params.numa); + + LOG_INF("%s\n", common_params_get_system_info(params).c_str()); + + //llama_log_set([](ggml_log_level level, const char * text, void * /*user_data*/) { + // if (level == GGML_LOG_LEVEL_ERROR) { + // common_log_add(common_log_main(), level, "%s", text); + // } + //}, NULL); + + auto cparams = common_context_params_to_llama(params); + + int dev_count = ggml_backend_dev_count(); + int gpu_dev_count = 0; + for (int i = 0; i < dev_count; ++i) { + auto * dev = ggml_backend_dev_get(i); + if (dev && ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) { + gpu_dev_count++; + } + } + const int num_models = gpu_dev_count + 1 + 1; // GPUs + 1 CPU model + 1 layer split + //const int num_models = std::max(1, gpu_dev_count); + const int num_contexts = std::max(1, params.n_parallel); + + std::vector models; + std::vector threads; + std::atomic failed = false; + + for (int m = 0; m < num_models; ++m) { + auto mparams = common_model_params_to_llama(params); + + if (m < gpu_dev_count) { + mparams.split_mode = LLAMA_SPLIT_MODE_NONE; + mparams.main_gpu = m; + } else if (m == gpu_dev_count) { + mparams.split_mode = LLAMA_SPLIT_MODE_NONE; + mparams.main_gpu = -1; // CPU model + } else { + mparams.split_mode = LLAMA_SPLIT_MODE_LAYER;; + } + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); + if (model == NULL) { + LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str()); + return 1; + } + + models.emplace_back(model); + } + + for (int m = 0; m < num_models; ++m) { + auto * model = models[m].get(); + for (int c = 0; c < num_contexts; ++c) { + threads.emplace_back([&, m, c, model]() { + LOG_INF("Creating context %d/%d for model %d/%d\n", c + 1, num_contexts, m + 1, num_models); + + llama_context_ptr ctx { llama_init_from_model(model, cparams) }; + if (ctx == NULL) { + LOG_ERR("failed to create context\n"); + failed.store(true); + return; + } + + std::unique_ptr sampler { common_sampler_init(model, params.sampling), common_sampler_free }; + if (sampler == NULL) { + LOG_ERR("failed to create sampler\n"); + failed.store(true); + return; + } + + llama_batch batch = {}; + { + auto prompt = common_tokenize(ctx.get(), params.prompt, true); + if (prompt.empty()) { + LOG_ERR("failed to tokenize prompt\n"); + failed.store(true); + return; + } + batch = llama_batch_get_one(prompt.data(), prompt.size()); + if (llama_decode(ctx.get(), batch)) { + LOG_ERR("failed to decode prompt\n"); + failed.store(true); + return; + } + } + + const auto * vocab = llama_model_get_vocab(model); + std::string result = params.prompt; + + for (int i = 0; i < params.n_predict; i++) { + llama_token token; + if (batch.n_tokens > 0) { + token = common_sampler_sample(sampler.get(), ctx.get(), batch.n_tokens - 1); + } else { + token = llama_vocab_bos(vocab); + } + + result += common_token_to_piece(ctx.get(), token); + + if (llama_vocab_is_eog(vocab, token)) { + break; + } + + batch = llama_batch_get_one(&token, 1); + if (llama_decode(ctx.get(), batch)) { + LOG_ERR("Model %d/%d, Context %d/%d: failed to decode\n", m + 1, num_models, c + 1, num_contexts); + failed.store(true); + return; + } + } + + LOG_INF("Model %d/%d, Context %d/%d: %s\n\n", m + 1, num_models, c + 1, num_contexts, result.c_str()); + }); + } + } + + for (auto & thread : threads) { + thread.join(); + } + + if (failed) { + LOG_ERR("One or more threads failed.\n"); + return 1; + } + + LOG_INF("All threads finished without errors.\n"); + return 0; +} diff --git a/tools/server/server.cpp b/tools/server/server.cpp index b439d8b19..721d09182 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -88,6 +88,26 @@ enum error_type { ERROR_TYPE_NOT_SUPPORTED, // custom error }; +static bool server_task_type_need_embd(server_task_type task_type) { + switch (task_type) { + case SERVER_TASK_TYPE_EMBEDDING: + case SERVER_TASK_TYPE_RERANK: + return true; + default: + return false; + } +} + +static bool server_task_type_need_logits(server_task_type task_type) { + switch (task_type) { + case SERVER_TASK_TYPE_COMPLETION: + case SERVER_TASK_TYPE_INFILL: + return true; + default: + return false; + } +} + struct slot_params { bool stream = true; bool cache_prompt = true; // remember the prompt to avoid reprocessing all prompt @@ -1330,13 +1350,24 @@ struct server_slot { n_draft_accepted = 0; } - bool is_non_causal() const { - return task_type == SERVER_TASK_TYPE_EMBEDDING || task_type == SERVER_TASK_TYPE_RERANK; + bool need_embd() const { + return server_task_type_need_embd(task_type); + } + + bool need_logits() const { + return server_task_type_need_logits(task_type); + } + + // if the context does not have a memory module then all embeddings have to be computed within a single ubatch + // also we cannot split if the pooling would require any past tokens + bool can_split() const { + return + !need_embd() || + (llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST); } bool can_batch_with(server_slot & other_slot) const { - return is_non_causal() == other_slot.is_non_causal() - && are_lora_equal(lora, other_slot.lora); + return task_type == other_slot.task_type && are_lora_equal(lora, other_slot.lora); } bool has_budget(const common_params & global_params) { @@ -1480,7 +1511,6 @@ struct server_slot { {"n_ctx", n_ctx}, {"speculative", can_speculate()}, {"is_processing", is_processing()}, - {"non_causal", is_non_causal()}, {"params", params.to_json()}, {"prompt", prompt_tokens.detokenize(ctx, true)}, {"next_token", @@ -2730,6 +2760,7 @@ struct server_context { queue_tasks.defer(std::move(task)); break; } + if (slot->is_processing()) { // if requested slot is unavailable, we defer this task for processing later SRV_DBG("requested slot is unavailable, defer task, id_task = %d\n", task.id); @@ -3092,7 +3123,14 @@ struct server_context { continue; } - if (slot.is_non_causal()) { + // TODO: support memory-less logits computation + if (slot.need_logits() && !llama_get_memory(ctx)) { + slot.release(); + send_error(slot, "the current context does not logits computation. skipping", ERROR_TYPE_SERVER); + continue; + } + + if (!slot.can_split()) { if (slot.n_prompt_tokens > n_ubatch) { slot.release(); send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); @@ -3227,8 +3265,7 @@ struct server_context { } if (slot.n_past == slot.n_prompt_tokens && slot.n_past > 0) { - // we have to evaluate at least 1 token to generate logits. - SLT_WRN(slot, "need to evaluate at least 1 token to generate logits, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens); + SLT_WRN(slot, "need to evaluate at least 1 token for each active slot, n_past = %d, n_prompt_tokens = %d\n", slot.n_past, slot.n_prompt_tokens); slot.n_past--; } @@ -3236,8 +3273,7 @@ struct server_context { slot.n_prompt_tokens_processed = 0; } - // non-causal tasks require to fit the entire prompt in the physical batch - if (slot.is_non_causal()) { + if (!slot.can_split()) { // cannot fit the prompt in the current batch - will try next iter if (batch.n_tokens + slot.n_prompt_tokens > n_batch) { continue; @@ -3259,8 +3295,7 @@ struct server_context { slot.cache_tokens.keep_first(slot.n_past); // check if we should process the image - if (slot.n_past < slot.n_prompt_tokens - && slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) { + if (slot.n_past < slot.n_prompt_tokens && slot.prompt_tokens[slot.n_past] == LLAMA_TOKEN_NULL) { // process the image int32_t new_n_past; int32_t res = slot.prompt_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past); @@ -3291,8 +3326,8 @@ struct server_context { break; // end of text chunk } - // without pooling, we want to output the embeddings for all the tokens in the batch - const bool need_embd = slot.task_type == SERVER_TASK_TYPE_EMBEDDING && llama_pooling_type(slot.ctx) == LLAMA_POOLING_TYPE_NONE; + // embedding requires all tokens in the batch to be output + const bool need_embd = server_task_type_need_embd(slot.task_type); common_batch_add(batch, cur_tok, slot.n_past, { slot.id }, need_embd); slot.cache_tokens.push_back(cur_tok); @@ -3346,17 +3381,15 @@ struct server_context { SRV_DBG("decoding batch, n_tokens = %d\n", batch.n_tokens); if (slot_batched) { - // make sure we're in the right embedding mode - llama_set_embeddings(ctx, slot_batched->is_non_causal()); // apply lora, only need to do it once per batch common_set_adapter_lora(ctx, slot_batched->lora); - } - const bool do_encode = (params_base.embedding || params_base.reranking); + llama_set_embeddings(ctx, slot_batched->need_embd()); + } // pad the batch so that batch.n_tokens >= n_slots // TODO: temporary workaround for https://github.com/ggml-org/llama.cpp/issues/13689 - if (do_encode) { + if (slot_batched->need_embd()) { const int n_slots = slots.size(); if (batch.n_tokens < n_slots) { @@ -3378,8 +3411,11 @@ struct server_context { SRV_WRN("adding %d dummy tokens to the batch, seq_id = %d\n", n_add, seq_id); for (int j = 0; j < n_add; ++j) { - common_batch_add(batch, 0, j, { seq_id }, false); + common_batch_add(batch, 0, j, { seq_id }, true); } + + slots[seq_id].cache_tokens.clear(); + llama_memory_seq_rm(llama_get_memory(ctx), seq_id, -1, -1); } } @@ -4174,11 +4210,6 @@ int main(int argc, char ** argv) { oaicompat_type oaicompat) -> void { GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL); - if (ctx_server.params_base.embedding) { - res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); - return; - } - auto completion_id = gen_chatcmplid(); std::unordered_set task_ids; try { @@ -4433,12 +4464,8 @@ int main(int argc, char ** argv) { OAICOMPAT_TYPE_NONE); // infill is not OAI compatible }; - const auto handle_chat_completions = [&ctx_server, &res_error, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, &handle_completions_impl](const httplib::Request & req, httplib::Response & res) { LOG_DBG("request: %s\n", req.body.c_str()); - if (ctx_server.params_base.embedding) { - res_error(res, format_error_response("This server does not support completions. Start it without `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); - return; - } auto body = json::parse(req.body); std::vector files; @@ -4566,13 +4593,18 @@ int main(int argc, char ** argv) { }; const auto handle_embeddings_impl = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res, oaicompat_type oaicompat) { - const json body = json::parse(req.body); + if (!ctx_server.params_base.embedding) { + res_error(res, format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); + return; + } if (oaicompat != OAICOMPAT_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { res_error(res, format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST)); return; } + const json body = json::parse(req.body); + // for the shape of input/content, see tokenize_input_prompts() json prompt; if (body.count("input") != 0) { @@ -4662,8 +4694,8 @@ int main(int argc, char ** argv) { }; const auto handle_rerank = [&ctx_server, &res_error, &res_ok](const httplib::Request & req, httplib::Response & res) { - if (!ctx_server.params_base.reranking || ctx_server.params_base.embedding) { - res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking` and without `--embedding`", ERROR_TYPE_NOT_SUPPORTED)); + if (!ctx_server.params_base.embedding || ctx_server.params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) { + res_error(res, format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -4878,7 +4910,9 @@ int main(int argc, char ** argv) { }; bool was_bound = false; + bool is_sock = false; if (string_ends_with(std::string(params.hostname), ".sock")) { + is_sock = true; LOG_INF("%s: setting address family to AF_UNIX\n", __func__); svr->set_address_family(AF_UNIX); // bind_to_port requires a second arg, any value other than 0 should @@ -4956,7 +4990,9 @@ int main(int argc, char ** argv) { SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif - LOG_INF("%s: server is listening on http://%s:%d - starting the main loop\n", __func__, params.hostname.c_str(), params.port); + LOG_INF("%s: server is listening on %s - starting the main loop\n", __func__, + is_sock ? string_format("unix://%s", params.hostname.c_str()).c_str() : + string_format("http://%s:%d", params.hostname.c_str(), params.port).c_str()); // this call blocks the main thread until queue_tasks.terminate() is called ctx_server.queue_tasks.start_loop();