mirror of
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Merge commit '017cc5f446' into concedo_experimental
# Conflicts: # .github/ISSUE_TEMPLATE/010-bug-compilation.yml # .github/ISSUE_TEMPLATE/019-bug-misc.yml # CODEOWNERS # examples/batched-bench/batched-bench.cpp # examples/batched/batched.cpp # examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp # examples/gritlm/gritlm.cpp # examples/llama-bench/llama-bench.cpp # examples/passkey/passkey.cpp # examples/quantize-stats/quantize-stats.cpp # examples/run/run.cpp # examples/simple-chat/simple-chat.cpp # examples/simple/simple.cpp # examples/tokenize/tokenize.cpp # ggml/CMakeLists.txt # ggml/src/ggml-metal/CMakeLists.txt # ggml/src/ggml-vulkan/CMakeLists.txt # scripts/sync-ggml.last # src/llama.cpp # tests/test-autorelease.cpp # tests/test-model-load-cancel.cpp # tests/test-tokenizer-0.cpp # tests/test-tokenizer-1-bpe.cpp # tests/test-tokenizer-1-spm.cpp
This commit is contained in:
Concedo
commit
dcfa1eca4e
45 changed files with 806 additions and 229 deletions
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@ -48,7 +48,7 @@ KoboldCpp can now also be run on Novita AI, a newer alternative GPU cloud provid
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## Obtaining a GGUF model
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- KoboldCpp uses GGUF models. They are not included with KoboldCpp, but you can download GGUF files from other places such as [TheBloke's Huggingface](https://huggingface.co/TheBloke). Search for "GGUF" on huggingface.co for plenty of compatible models in the `.gguf` format.
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- For beginners, we recommend the models [Airoboros Mistral](https://huggingface.co/TheBloke/airoboros-mistral2.2-7B-GGUF/resolve/main/airoboros-mistral2.2-7b.Q4_K_S.gguf) or [Tiefighter 13B](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf) (larger model).
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- For beginners, we recommend the models [BookAdventures 8B](https://huggingface.co/KoboldAI/Llama-3.1-8B-BookAdventures-GGUF/resolve/main/Llama-3.1-8B-BookAdventures.Q4_K_S.gguf) or [Tiefighter 13B](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf) (larger model).
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- [Alternatively, you can download the tools to convert models to the GGUF format yourself here](https://kcpptools.concedo.workers.dev). Run `convert-hf-to-gguf.py` to convert them, then `quantize_gguf.exe` to quantize the result.
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- Other models for Whisper (speech recognition), Image Generation or Image Recognition [can be found on the Wiki](https://github.com/LostRuins/koboldcpp/wiki#what-models-does-koboldcpp-support-what-architectures-are-supported)
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@ -48,7 +48,7 @@
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"source": [
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"#@title <b>v-- Enter your model below and then click this to start Koboldcpp</b>\n",
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"\n",
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"Model = \"https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf\" #@param [\"https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/LLaMA2-13B-Estopia-GGUF/resolve/main/LLaMA2-13B-Estopia.Q4_K_S.gguf\",\"https://huggingface.co/mradermacher/Fimbulvetr-11B-v2-GGUF/resolve/main/Fimbulvetr-11B-v2.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/resolve/main/mythomax-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/ReMM-SLERP-L2-13B-GGUF/resolve/main/remm-slerp-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/Xwin-LM-13B-v0.2-GGUF/resolve/main/xwin-lm-13b-v0.2.Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/mini-magnum-12b-v1.1-GGUF/resolve/main/mini-magnum-12b-v1.1.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/Stheno-L2-13B-GGUF/resolve/main/stheno-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-Kimiko-v2-13B-GGUF/resolve/main/mythomax-l2-kimiko-v2-13b.Q4_K_M.gguf\",\"https://huggingface.co/bartowski/Rocinante-12B-v1.1-GGUF/resolve/main/Rocinante-12B-v1.1-Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MistRP-Airoboros-7B-GGUF/resolve/main/mistrp-airoboros-7b.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/airoboros-mistral2.2-7B-GGUF/resolve/main/airoboros-mistral2.2-7b.Q4_K_S.gguf\",\"https://huggingface.co/concedo/KobbleTinyV2-1.1B-GGUF/resolve/main/KobbleTiny-Q4_K.gguf\",\"https://huggingface.co/grimjim/kukulemon-7B-GGUF/resolve/main/kukulemon-7B.Q8_0.gguf\",\"https://huggingface.co/mradermacher/LemonKunoichiWizardV3-GGUF/resolve/main/LemonKunoichiWizardV3.Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Kunoichi-DPO-v2-7B-GGUF-Imatrix/resolve/main/Kunoichi-DPO-v2-7B-Q4_K_M-imatrix.gguf\",\"https://huggingface.co/mradermacher/L3-8B-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Stheno-v3.2.i1-Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix/resolve/main/v2-Llama-3-Lumimaid-8B-v0.1-OAS-Q4_K_M-imat.gguf\",\"https://huggingface.co/bartowski/NeuralDaredevil-8B-abliterated-GGUF/resolve/main/NeuralDaredevil-8B-abliterated-Q4_K_M.gguf\",\"https://huggingface.co/bartowski/L3-8B-Lunaris-v1-GGUF/resolve/main/L3-8B-Lunaris-v1-Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.Q4_K_M.gguf\"]{allow-input: true}\n",
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"Model = \"https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf\" #@param [\"https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter-GGUF/resolve/main/LLaMA2-13B-Tiefighter.Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/LLaMA2-13B-Estopia-GGUF/resolve/main/LLaMA2-13B-Estopia.Q4_K_S.gguf\",\"https://huggingface.co/mradermacher/Fimbulvetr-11B-v2-GGUF/resolve/main/Fimbulvetr-11B-v2.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-13B-GGUF/resolve/main/mythomax-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/ReMM-SLERP-L2-13B-GGUF/resolve/main/remm-slerp-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/Xwin-LM-13B-v0.2-GGUF/resolve/main/xwin-lm-13b-v0.2.Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/mini-magnum-12b-v1.1-GGUF/resolve/main/mini-magnum-12b-v1.1.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/Stheno-L2-13B-GGUF/resolve/main/stheno-l2-13b.Q4_K_M.gguf\",\"https://huggingface.co/TheBloke/MythoMax-L2-Kimiko-v2-13B-GGUF/resolve/main/mythomax-l2-kimiko-v2-13b.Q4_K_M.gguf\",\"https://huggingface.co/bartowski/Rocinante-12B-v1.1-GGUF/resolve/main/Rocinante-12B-v1.1-Q4_K_S.gguf\",\"https://huggingface.co/KoboldAI/Llama-3.1-8B-BookAdventures-GGUF/resolve/main/Llama-3.1-8B-BookAdventures.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/MistRP-Airoboros-7B-GGUF/resolve/main/mistrp-airoboros-7b.Q4_K_S.gguf\",\"https://huggingface.co/TheBloke/airoboros-mistral2.2-7B-GGUF/resolve/main/airoboros-mistral2.2-7b.Q4_K_S.gguf\",\"https://huggingface.co/concedo/KobbleTinyV2-1.1B-GGUF/resolve/main/KobbleTiny-Q4_K.gguf\",\"https://huggingface.co/grimjim/kukulemon-7B-GGUF/resolve/main/kukulemon-7B.Q8_0.gguf\",\"https://huggingface.co/mradermacher/LemonKunoichiWizardV3-GGUF/resolve/main/LemonKunoichiWizardV3.Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Kunoichi-DPO-v2-7B-GGUF-Imatrix/resolve/main/Kunoichi-DPO-v2-7B-Q4_K_M-imatrix.gguf\",\"https://huggingface.co/mradermacher/L3-8B-Stheno-v3.2-i1-GGUF/resolve/main/L3-8B-Stheno-v3.2.i1-Q4_K_M.gguf\",\"https://huggingface.co/Lewdiculous/Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix/resolve/main/v2-Llama-3-Lumimaid-8B-v0.1-OAS-Q4_K_M-imat.gguf\",\"https://huggingface.co/bartowski/NeuralDaredevil-8B-abliterated-GGUF/resolve/main/NeuralDaredevil-8B-abliterated-Q4_K_M.gguf\",\"https://huggingface.co/bartowski/L3-8B-Lunaris-v1-GGUF/resolve/main/L3-8B-Lunaris-v1-Q4_K_M.gguf\",\"https://huggingface.co/mradermacher/L3-Umbral-Mind-RP-v2.0-8B-GGUF/resolve/main/L3-Umbral-Mind-RP-v2.0-8B.Q4_K_M.gguf\"]{allow-input: true}\n",
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"Layers = 99 #@param [99]{allow-input: true}\n",
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"ContextSize = 4096 #@param [4096,8192] {allow-input: true}\n",
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"FlashAttention = True #@param {type:\"boolean\"}\n",
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@ -848,7 +848,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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} else if (!params.model_url.empty()) {
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model = common_load_model_from_url(params.model_url, params.model, params.hf_token, mparams);
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} else {
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model = llama_load_model_from_file(params.model.c_str(), mparams);
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model = llama_model_load_from_file(params.model.c_str(), mparams);
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}
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if (model == NULL) {
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@ -875,7 +875,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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}
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if (!ok) {
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llama_free_model(model);
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llama_model_free(model);
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return iparams;
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}
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@ -886,7 +886,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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llama_context * lctx = llama_new_context_with_model(model, cparams);
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if (lctx == NULL) {
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LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.c_str());
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llama_free_model(model);
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llama_model_free(model);
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return iparams;
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}
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@ -902,7 +902,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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const auto cvec = common_control_vector_load(params.control_vectors);
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if (cvec.n_embd == -1) {
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llama_free(lctx);
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llama_free_model(model);
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llama_model_free(model);
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return iparams;
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}
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@ -915,7 +915,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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params.control_vector_layer_end);
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if (err) {
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llama_free(lctx);
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llama_free_model(model);
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llama_model_free(model);
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return iparams;
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}
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@ -928,7 +928,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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if (lora == nullptr) {
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LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
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llama_free(lctx);
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llama_free_model(model);
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llama_model_free(model);
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return iparams;
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}
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@ -984,7 +984,7 @@ struct common_init_result common_init_from_params(common_params & params) {
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if (llama_model_has_encoder(model)) {
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llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
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llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
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if (decoder_start_token_id == -1) {
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if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
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decoder_start_token_id = bos;
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}
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tmp.clear();
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@ -1413,7 +1413,7 @@ struct llama_model * common_load_model_from_url(
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}
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}
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return llama_load_model_from_file(local_path.c_str(), params);
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return llama_model_load_from_file(local_path.c_str(), params);
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}
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struct llama_model * common_load_model_from_hf(
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@ -65,13 +65,13 @@ constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
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static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
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common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
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if (part_static_it == nc_static.end()) {
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return -1;
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return LLAMA_TOKEN_NULL;
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}
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const common_ngram_cache_part part_static = part_static_it->second;
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int max_count_static = 0;
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int sum_count_static = 0;
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llama_token max_token = -1;
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llama_token max_token = LLAMA_TOKEN_NULL;
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for (std::pair<llama_token, int> token_count_static : part_static) {
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const llama_token token = token_count_static.first;
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@ -85,10 +85,10 @@ static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram
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}
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if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
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return -1;
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return LLAMA_TOKEN_NULL;
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}
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if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
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return -1;
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return LLAMA_TOKEN_NULL;
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}
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return max_token;
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}
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@ -98,9 +98,9 @@ static llama_token try_draft(
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common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
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const int * min_sample_size, const int * min_percent) {
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llama_token drafted_token = -1;
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llama_token drafted_token = LLAMA_TOKEN_NULL;
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for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
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for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) {
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const common_ngram ngram_primary = ngrams_primary[i];
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common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
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@ -112,7 +112,7 @@ static llama_token try_draft(
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int max_count_primary = 0;
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int max_count_static = 0;
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int sum_count_primary = 0;
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llama_token max_token = -1;
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llama_token max_token = LLAMA_TOKEN_NULL;
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for (std::pair<llama_token, int> token_count_primary : part_primary) {
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const llama_token token = token_count_primary.first;
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@ -154,7 +154,7 @@ void common_ngram_cache_draft(
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}
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while ((int) draft.size()-1 < n_draft) {
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llama_token drafted_token = -1;
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llama_token drafted_token = LLAMA_TOKEN_NULL;
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const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
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common_ngram ngram_static;
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@ -177,17 +177,17 @@ void common_ngram_cache_draft(
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}
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ngrams_cd.push_back(ngram_cd);
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}
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if (drafted_token == -1) {
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if (drafted_token == LLAMA_TOKEN_NULL) {
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drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
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}
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if (drafted_token == -1) {
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if (drafted_token == LLAMA_TOKEN_NULL) {
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drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
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}
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if (drafted_token == -1) {
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if (drafted_token == LLAMA_TOKEN_NULL) {
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drafted_token = try_draft(nc_static, ngram_static);
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}
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if (drafted_token == -1) {
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if (drafted_token == LLAMA_TOKEN_NULL) {
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break;
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}
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@ -17,13 +17,13 @@ struct common_ngram {
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common_ngram() {
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for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
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tokens[i] = -1;
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tokens[i] = LLAMA_TOKEN_NULL;
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}
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}
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common_ngram(const llama_token * input, const int ngram_size) {
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for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
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tokens[i] = i < ngram_size ? input[i] : -1;
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tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL;
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}
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}
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@ -687,6 +687,9 @@ class Model:
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if chkhsh == "d4c8f286ea6b520b3d495c4455483cfa2302c0cfcd4be05d781b6a8a0a7cdaf1":
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# ref: https://huggingface.co/Infinigence/Megrez-3B-Instruct
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res = "megrez"
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if chkhsh == "877081d19cf6996e2c4ff0e1236341e9b7bde288f5311a56a937f0afbbb3aeb5":
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# ref: https://huggingface.co/deepseek-ai/DeepSeek-V3
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res = "deepseek-v3"
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if res is None:
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logger.warning("\n")
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@ -3373,6 +3376,24 @@ class CommandR2Model(Model):
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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@Model.register("Cohere2ForCausalLM")
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class Cohere2Model(Model):
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model_arch = gguf.MODEL_ARCH.COHERE2
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
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self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
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self.gguf_writer.add_vocab_size(self.hparams["vocab_size"])
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rotary_pct = self.hparams["rotary_pct"]
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||||
hidden_size = self.hparams["hidden_size"]
|
||||
num_attention_heads = self.hparams["num_attention_heads"]
|
||||
self.gguf_writer.add_rope_dimension_count(int(rotary_pct * (hidden_size // num_attention_heads)))
|
||||
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
|
||||
|
||||
|
||||
@Model.register("OlmoForCausalLM")
|
||||
@Model.register("OLMoForCausalLM")
|
||||
class OlmoModel(Model):
|
||||
|
|
@ -3831,6 +3852,7 @@ class DeepseekModel(Model):
|
|||
|
||||
|
||||
@Model.register("DeepseekV2ForCausalLM")
|
||||
@Model.register("DeepseekV3ForCausalLM")
|
||||
class DeepseekV2Model(Model):
|
||||
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
|
||||
|
||||
|
|
@ -3852,6 +3874,15 @@ class DeepseekV2Model(Model):
|
|||
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
|
||||
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
|
||||
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
|
||||
self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
|
||||
|
||||
if hparams["scoring_func"] == "sigmoid":
|
||||
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
|
||||
elif hparams["scoring_func"] == "softmax":
|
||||
self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
|
||||
else:
|
||||
raise ValueError(f"Unsupported scoring_func value: {hparams['scoring_func']}")
|
||||
|
||||
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
|
||||
|
||||
if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]:
|
||||
|
|
@ -3864,6 +3895,16 @@ class DeepseekV2Model(Model):
|
|||
_experts: list[dict[str, Tensor]] | None = None
|
||||
|
||||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||||
# rename e_score_correction_bias tensors
|
||||
if name.endswith("e_score_correction_bias"):
|
||||
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
|
||||
|
||||
# skip Multi-Token Prediction (MTP) layers
|
||||
block_count = self.hparams["num_hidden_layers"]
|
||||
match = re.match(r"model.layers.(\d+)", name)
|
||||
if match and int(match.group(1)) >= block_count:
|
||||
return []
|
||||
|
||||
# process the experts separately
|
||||
if name.find("mlp.experts") != -1:
|
||||
n_experts = self.hparams["n_routed_experts"]
|
||||
|
|
|
|||
|
|
@ -107,6 +107,7 @@ models = [
|
|||
{"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
|
||||
{"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
|
||||
{"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
|
||||
{"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -221,7 +221,7 @@ static struct llama_model * llava_init(common_params * params) {
|
|||
|
||||
llama_model_params model_params = common_model_params_to_llama(*params);
|
||||
|
||||
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
||||
llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
|
||||
if (model == NULL) {
|
||||
LOG_ERR("%s: unable to load model\n" , __func__);
|
||||
return NULL;
|
||||
|
|
@ -265,7 +265,7 @@ static void llava_free(struct llava_context * ctx_llava) {
|
|||
}
|
||||
|
||||
llama_free(ctx_llava->ctx_llama);
|
||||
llama_free_model(ctx_llava->model);
|
||||
llama_model_free(ctx_llava->model);
|
||||
llama_backend_free();
|
||||
}
|
||||
|
||||
|
|
@ -323,7 +323,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
llama_free_model(model);
|
||||
llama_model_free(model);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -31,7 +31,7 @@ static struct llama_model * llava_init(common_params * params) {
|
|||
|
||||
llama_model_params model_params = common_model_params_to_llama(*params);
|
||||
|
||||
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
||||
llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
|
||||
if (model == NULL) {
|
||||
LOG_ERR("%s: unable to load model\n" , __func__);
|
||||
return NULL;
|
||||
|
|
@ -75,7 +75,7 @@ static void llava_free(struct llava_context * ctx_llava) {
|
|||
}
|
||||
|
||||
llama_free(ctx_llava->ctx_llama);
|
||||
llama_free_model(ctx_llava->model);
|
||||
llama_model_free(ctx_llava->model);
|
||||
llama_backend_free();
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -310,7 +310,7 @@ static struct llama_model * llava_init(common_params * params) {
|
|||
|
||||
llama_model_params model_params = common_model_params_to_llama(*params);
|
||||
|
||||
llama_model * model = llama_load_model_from_file(params->model.c_str(), model_params);
|
||||
llama_model * model = llama_model_load_from_file(params->model.c_str(), model_params);
|
||||
if (model == NULL) {
|
||||
LOG_ERR("%s: unable to load model\n" , __func__);
|
||||
return NULL;
|
||||
|
|
@ -354,7 +354,7 @@ static void llava_free(struct llava_context * ctx_llava) {
|
|||
}
|
||||
|
||||
llama_free(ctx_llava->ctx_llama);
|
||||
llama_free_model(ctx_llava->model);
|
||||
llama_model_free(ctx_llava->model);
|
||||
llama_backend_free();
|
||||
}
|
||||
|
||||
|
|
@ -575,7 +575,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
}
|
||||
|
||||
llama_free_model(model);
|
||||
llama_model_free(model);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -495,7 +495,7 @@ int main(int argc, char ** argv) {
|
|||
}
|
||||
|
||||
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
|
||||
if (decoder_start_token_id == -1) {
|
||||
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
|
||||
decoder_start_token_id = llama_token_bos(model);
|
||||
}
|
||||
|
||||
|
|
@ -832,7 +832,7 @@ int main(int argc, char ** argv) {
|
|||
// if user stop generation mid-way, we must add EOT to finish model's last response
|
||||
if (need_insert_eot && format_chat) {
|
||||
llama_token eot = llama_token_eot(model);
|
||||
embd_inp.push_back(eot == -1 ? llama_token_eos(model) : eot);
|
||||
embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_token_eos(model) : eot);
|
||||
need_insert_eot = false;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -3797,7 +3797,7 @@ int main(int argc, char ** argv) {
|
|||
data["input_extra"] = input_extra; // default to empty array if it's not exist
|
||||
|
||||
std::string prompt = json_value(data, "prompt", std::string());
|
||||
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, true, true);
|
||||
std::vector<llama_tokens> tokenized_prompts = tokenize_input_prompts(ctx_server.ctx, prompt, false, true);
|
||||
SRV_DBG("creating infill tasks, n_prompts = %d\n", (int) tokenized_prompts.size());
|
||||
data["prompt"] = format_infill(
|
||||
ctx_server.ctx,
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ def test_infill_without_input_extra():
|
|||
"input_suffix": "}\n",
|
||||
})
|
||||
assert res.status_code == 200
|
||||
assert match_regex("(Ann|small|shiny)+", res.body["content"])
|
||||
assert match_regex("(Ann|small|shiny|Daddy)+", res.body["content"])
|
||||
|
||||
|
||||
def test_infill_with_input_extra():
|
||||
|
|
|
|||
|
|
@ -507,7 +507,7 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
|
|||
|
||||
// format incomplete utf-8 multibyte character for output
|
||||
static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
|
||||
std::string out = token == -1 ? "" : common_token_to_piece(ctx, token);
|
||||
std::string out = token == LLAMA_TOKEN_NULL ? "" : common_token_to_piece(ctx, token);
|
||||
|
||||
// if the size is 1 and first bit is 1, meaning it's a partial character
|
||||
// (size > 1 meaning it's already a known token)
|
||||
|
|
|
|||
|
|
@ -770,7 +770,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
|
|||
if (tensor->op != GGML_OP_ROPE && src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
|
||||
int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor);
|
||||
// check if a backend with higher prio wants to offload the op
|
||||
if (src_backend_id == sched->n_backends - 1) {
|
||||
if (src_backend_id == sched->n_backends - 1 && ggml_backend_buffer_is_host(src->buffer)) {
|
||||
for (int b = 0; b < src_backend_id; b++) {
|
||||
if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) {
|
||||
SET_CAUSE(tensor, "1.off");
|
||||
|
|
@ -801,9 +801,12 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
|
|||
for (int i = 0; i < graph->n_nodes; i++) {
|
||||
if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
|
||||
ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id];
|
||||
GGML_LOG_DEBUG("\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend),
|
||||
GGML_LOG_DEBUG("\n## SPLIT #%d: %s # %d inputs", cur_split, ggml_backend_name(split_backend),
|
||||
sched->splits[cur_split].n_inputs);
|
||||
for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
|
||||
if (j == 0) {
|
||||
GGML_LOG_DEBUG(": ");
|
||||
}
|
||||
GGML_LOG_DEBUG("[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name,
|
||||
fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
|
||||
}
|
||||
|
|
|
|||
|
|
@ -4231,6 +4231,8 @@ static ggml_backend_buffer_t ggml_backend_cpu_aarch64_buffer_type_alloc_buffer(g
|
|||
buffer->buft = buft;
|
||||
buffer->iface.init_tensor = ggml_backend_cpu_aarch64_buffer_init_tensor;
|
||||
buffer->iface.set_tensor = ggml_backend_cpu_aarch64_buffer_set_tensor;
|
||||
buffer->iface.get_tensor = nullptr;
|
||||
buffer->iface.cpy_tensor = nullptr;
|
||||
return buffer;
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -680,6 +680,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
|
|||
return dequantize_row_iq3_s_cuda;
|
||||
case GGML_TYPE_F16:
|
||||
return convert_unary_cuda<half>;
|
||||
case GGML_TYPE_BF16:
|
||||
return convert_unary_cuda<nv_bfloat16>;
|
||||
default:
|
||||
return nullptr;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1729,7 +1729,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
|
|||
static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
const bool split = ggml_backend_buft_is_cuda_split(src0->buffer->buft);
|
||||
|
||||
bool use_mul_mat_vec = src0->type == GGML_TYPE_F16
|
||||
bool use_mul_mat_vec = (src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16)
|
||||
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32
|
||||
&& src0->ne[0] % 2 == 0 && src1->ne[1] == 1;
|
||||
bool use_mul_mat_vec_q = ggml_is_quantized(src0->type)
|
||||
|
|
@ -2874,6 +2874,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
|
|||
case GGML_TYPE_IQ3_XXS:
|
||||
case GGML_TYPE_IQ4_NL:
|
||||
case GGML_TYPE_IQ4_XS:
|
||||
case GGML_TYPE_BF16:
|
||||
#ifdef GGML_USE_MUSA
|
||||
if (a->type == GGML_TYPE_Q3_K) {
|
||||
return false;
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
#include "common.cuh"
|
||||
#include "mmv.cuh"
|
||||
|
||||
template <typename type_acc, int block_size>
|
||||
template <typename T, typename type_acc, int block_size>
|
||||
static __global__ void mul_mat_vec(
|
||||
const half * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
|
||||
const T * __restrict__ x, const float * __restrict__ y, float * __restrict__ dst, const int64_t ncols2, const int64_t stride_row,
|
||||
const int64_t channel_ratio, const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst) {
|
||||
const int64_t row = blockIdx.x;
|
||||
const int64_t channel = blockIdx.z;
|
||||
|
|
@ -13,7 +13,6 @@ static __global__ void mul_mat_vec(
|
|||
y += channel *stride_channel_y;
|
||||
dst += channel *stride_channel_dst;
|
||||
|
||||
const half2 * x2 = (const half2 *) x;
|
||||
const float2 * y2 = (const float2 *) y;
|
||||
|
||||
extern __shared__ char data_mmv[];
|
||||
|
|
@ -28,6 +27,9 @@ static __global__ void mul_mat_vec(
|
|||
|
||||
float sumf;
|
||||
|
||||
if constexpr (std::is_same<T, half>::value) {
|
||||
const half2 * x2 = (const half2 *) x;
|
||||
|
||||
if (std::is_same<type_acc, float>::value) {
|
||||
sumf = 0.0f;
|
||||
|
||||
|
|
@ -51,6 +53,19 @@ static __global__ void mul_mat_vec(
|
|||
NO_DEVICE_CODE;
|
||||
#endif // FP16_AVAILABLE
|
||||
}
|
||||
} else if constexpr (std::is_same<T, nv_bfloat16>::value) {
|
||||
const int * x2 = (const int *) x;
|
||||
sumf = 0.0f;
|
||||
|
||||
for (int64_t col2 = tid; col2 < ncols2; col2 += block_size) {
|
||||
const int tmpx = x2[col2];
|
||||
const float2 tmpy = y2[col2];
|
||||
sumf += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[0]) * tmpy.x;
|
||||
sumf += float(reinterpret_cast<const nv_bfloat16 *>(&tmpx)[1]) * tmpy.y;
|
||||
}
|
||||
} else {
|
||||
static_assert(std::is_same<T, void>::value, "unsupported type");
|
||||
}
|
||||
|
||||
sumf = warp_reduce_sum(sumf);
|
||||
|
||||
|
|
@ -71,9 +86,9 @@ static __global__ void mul_mat_vec(
|
|||
dst[row] = sumf;
|
||||
}
|
||||
|
||||
template <typename type_acc>
|
||||
template <typename T, typename type_acc>
|
||||
static void launch_mul_mat_vec_cuda(
|
||||
const half * x, const float * y, float * dst,
|
||||
const T * x, const float * y, float * dst,
|
||||
const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
|
||||
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
|
||||
cudaStream_t stream) {
|
||||
|
|
@ -97,35 +112,35 @@ static void launch_mul_mat_vec_cuda(
|
|||
const dim3 block_dims(block_size_best, 1, 1);
|
||||
switch (block_size_best) {
|
||||
case 32: {
|
||||
mul_mat_vec<type_acc, 32><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 32><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 64: {
|
||||
mul_mat_vec<type_acc, 64><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 64><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 96: {
|
||||
mul_mat_vec<type_acc, 96><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 96><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 128: {
|
||||
mul_mat_vec<type_acc, 128><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 128><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 160: {
|
||||
mul_mat_vec<type_acc, 160><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 160><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 192: {
|
||||
mul_mat_vec<type_acc, 192><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 192><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 224: {
|
||||
mul_mat_vec<type_acc, 224><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 224><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
case 256: {
|
||||
mul_mat_vec<type_acc, 256><<<block_nums, block_dims, smem, stream>>>
|
||||
mul_mat_vec<T, type_acc, 256><<<block_nums, block_dims, smem, stream>>>
|
||||
(x, y, dst, ncols/2, stride_row, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst);
|
||||
} break;
|
||||
default: {
|
||||
|
|
@ -134,25 +149,25 @@ static void launch_mul_mat_vec_cuda(
|
|||
}
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
static void mul_mat_vec_cuda(
|
||||
const half * x, const float * y, float * dst,
|
||||
const T * x, const float * y, float * dst,
|
||||
const int64_t ncols, const int64_t nrows, const int64_t stride_row, const int64_t nchannels_x, const int64_t nchannels_y,
|
||||
const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst,
|
||||
enum ggml_prec prec, cudaStream_t stream) {
|
||||
switch (prec) {
|
||||
case GGML_PREC_DEFAULT: {
|
||||
launch_mul_mat_vec_cuda<half>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
|
||||
launch_mul_mat_vec_cuda<T, half>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
|
||||
stride_channel_x, stride_channel_y, stride_channel_dst, stream);
|
||||
} break;
|
||||
case GGML_PREC_F32: {
|
||||
launch_mul_mat_vec_cuda<float>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
|
||||
launch_mul_mat_vec_cuda<T, float>(x, y, dst, ncols, nrows, stride_row, nchannels_x, nchannels_y,
|
||||
stride_channel_x, stride_channel_y, stride_channel_dst, stream);
|
||||
} break;
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
||||
|
||||
|
|
@ -164,7 +179,6 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
|
|||
const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
|
||||
const enum ggml_prec prec = fast_fp16_available(cc) ? ggml_prec(dst->op_params[0]) : GGML_PREC_F32;
|
||||
|
||||
const half * src0_d = (const half *) src0->data;
|
||||
const float * src1_d = (const float *) src1->data;
|
||||
float * dst_d = (float *) dst->data;
|
||||
|
||||
|
|
@ -181,7 +195,20 @@ void ggml_cuda_mul_mat_vec(ggml_backend_cuda_context & ctx, const ggml_tensor *
|
|||
const int64_t channel_stride_y = src1->nb[2] / ggml_type_size(src1->type);
|
||||
const int64_t channel_stride_dst = dst->nb[2] / ggml_type_size( dst->type);
|
||||
|
||||
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12, channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16: {
|
||||
const half * src0_d = (const half *) src0->data;
|
||||
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
|
||||
channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
|
||||
} break;
|
||||
case GGML_TYPE_BF16: {
|
||||
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0->data;
|
||||
mul_mat_vec_cuda(src0_d, src1_d, dst_d, ne00, ne01, stride_row, ne02, ne12,
|
||||
channel_stride_x, channel_stride_y, channel_stride_dst, prec, ctx.stream());
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
|
||||
}
|
||||
}
|
||||
|
||||
void ggml_cuda_op_mul_mat_vec(
|
||||
|
|
@ -190,7 +217,6 @@ void ggml_cuda_op_mul_mat_vec(
|
|||
const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
|
||||
const int64_t src1_padded_row_size, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(src0->type == GGML_TYPE_F16);
|
||||
GGML_ASSERT(src1->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(dst->type == GGML_TYPE_F32);
|
||||
|
||||
|
|
@ -211,8 +237,20 @@ void ggml_cuda_op_mul_mat_vec(
|
|||
const int64_t channel_stride_y = 0;
|
||||
const int64_t channel_stride_dst = 0;
|
||||
|
||||
mul_mat_vec_cuda((const half *) src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
|
||||
switch (src0->type) {
|
||||
case GGML_TYPE_F16: {
|
||||
const half * src0_d = (const half *) src0_dd_i;
|
||||
mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
|
||||
nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
|
||||
} break;
|
||||
case GGML_TYPE_BF16: {
|
||||
const nv_bfloat16 * src0_d = (const nv_bfloat16 *) src0_dd_i;
|
||||
mul_mat_vec_cuda(src0_d, src1_ddf_i, dst_dd_i, ne00, row_diff, stride_row,
|
||||
nchannels_x, nchannels_y, channel_stride_x, channel_stride_y, channel_stride_dst, prec, stream);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("unsupported type: %s", ggml_type_name(src0->type));
|
||||
}
|
||||
|
||||
GGML_UNUSED(ctx);
|
||||
GGML_UNUSED(src1);
|
||||
|
|
|
|||
1
ggml/src/ggml-cuda/vendors/cuda.h
vendored
1
ggml/src/ggml-cuda/vendors/cuda.h
vendored
|
|
@ -3,6 +3,7 @@
|
|||
#include <cuda_runtime.h>
|
||||
#include <cuda.h>
|
||||
#include <cublas_v2.h>
|
||||
#include <cuda_bf16.h>
|
||||
#include <cuda_fp16.h>
|
||||
|
||||
#if CUDART_VERSION < 11020
|
||||
|
|
|
|||
3
ggml/src/ggml-cuda/vendors/hip.h
vendored
3
ggml/src/ggml-cuda/vendors/hip.h
vendored
|
|
@ -3,6 +3,7 @@
|
|||
#include <hip/hip_runtime.h>
|
||||
#include <hipblas/hipblas.h>
|
||||
#include <hip/hip_fp16.h>
|
||||
#include <hip/hip_bfloat16.h>
|
||||
#ifdef __HIP_PLATFORM_AMD__
|
||||
// for rocblas_initialize()
|
||||
#include "rocblas/rocblas.h"
|
||||
|
|
@ -121,6 +122,8 @@
|
|||
#define __has_builtin(x) 0
|
||||
#endif
|
||||
|
||||
typedef hip_bfloat16 nv_bfloat16;
|
||||
|
||||
typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
|
||||
typedef uint8_t uint8x4_t __attribute__((ext_vector_type(4)));
|
||||
static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
|
||||
|
|
|
|||
3
ggml/src/ggml-cuda/vendors/musa.h
vendored
3
ggml/src/ggml-cuda/vendors/musa.h
vendored
|
|
@ -3,6 +3,7 @@
|
|||
#include <musa_runtime.h>
|
||||
#include <musa.h>
|
||||
#include <mublas.h>
|
||||
#include <musa_bf16.h>
|
||||
#include <musa_fp16.h>
|
||||
#define CUBLAS_COMPUTE_16F CUDA_R_16F
|
||||
#define CUBLAS_COMPUTE_32F CUDA_R_32F
|
||||
|
|
@ -132,3 +133,5 @@
|
|||
#define cudaKernelNodeParams musaKernelNodeParams
|
||||
#define cudaStreamCaptureModeRelaxed musaStreamCaptureModeRelaxed
|
||||
#define cudaStreamEndCapture musaStreamEndCapture
|
||||
|
||||
typedef mt_bfloat16 nv_bfloat16;
|
||||
|
|
|
|||
|
|
@ -27,15 +27,6 @@
|
|||
#endif
|
||||
#include <cstring>
|
||||
|
||||
#define UNUSED GGML_UNUSED
|
||||
|
||||
#define GGML_DEBUG 0
|
||||
#if (GGML_DEBUG >= 1)
|
||||
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
|
||||
#else
|
||||
#define GGML_PRINT_DEBUG(...)
|
||||
#endif
|
||||
|
||||
#ifdef _WIN32
|
||||
typedef SOCKET sockfd_t;
|
||||
using ssize_t = __int64;
|
||||
|
|
@ -93,9 +84,23 @@ enum rpc_cmd {
|
|||
RPC_CMD_COPY_TENSOR,
|
||||
RPC_CMD_GRAPH_COMPUTE,
|
||||
RPC_CMD_GET_DEVICE_MEMORY,
|
||||
RPC_CMD_INIT_TENSOR,
|
||||
RPC_CMD_GET_ALLOC_SIZE,
|
||||
RPC_CMD_COUNT,
|
||||
};
|
||||
|
||||
struct rpc_msg_get_alloc_size_req {
|
||||
rpc_tensor tensor;
|
||||
};
|
||||
|
||||
struct rpc_msg_get_alloc_size_rsp {
|
||||
uint64_t alloc_size;
|
||||
};
|
||||
|
||||
struct rpc_msg_init_tensor_req {
|
||||
rpc_tensor tensor;
|
||||
};
|
||||
|
||||
struct rpc_msg_alloc_buffer_req {
|
||||
uint64_t size;
|
||||
};
|
||||
|
|
@ -397,7 +402,7 @@ static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
|
|||
initialized = true;
|
||||
}
|
||||
#else
|
||||
UNUSED(initialized);
|
||||
GGML_UNUSED(initialized);
|
||||
#endif
|
||||
auto sock = socket_connect(host.c_str(), port);
|
||||
if (sock == nullptr) {
|
||||
|
|
@ -461,10 +466,18 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) {
|
|||
}
|
||||
|
||||
static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) {
|
||||
UNUSED(buffer);
|
||||
if (ggml_is_quantized(tensor->type)) {
|
||||
// TODO: this check is due to MATRIX_ROW_PADDING in CUDA and should be generalized
|
||||
GGML_ASSERT(tensor->ne[0] % 512 == 0 && "unsupported quantized tensor");
|
||||
ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context;
|
||||
|
||||
// CUDA backend on the server pads everything to 512 due to CUDA limitations.
|
||||
// Due to bandwidth constraints, we only call the server init tensor functions if necessary.
|
||||
// In particular, only quantized tensors need padding
|
||||
if (ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr)) {
|
||||
rpc_msg_init_tensor_req request;
|
||||
|
||||
request.tensor = serialize_tensor(tensor);
|
||||
|
||||
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_INIT_TENSOR, &request, sizeof(request), nullptr, 0);
|
||||
GGML_ASSERT(status);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -577,8 +590,23 @@ static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) {
|
|||
}
|
||||
|
||||
static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
|
||||
UNUSED(buft);
|
||||
// See comments in init_tensor.
|
||||
if (ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr)) {
|
||||
ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context;
|
||||
auto sock = get_socket(buft_ctx->endpoint);
|
||||
|
||||
rpc_msg_get_alloc_size_req request;
|
||||
|
||||
request.tensor = serialize_tensor(tensor);
|
||||
|
||||
rpc_msg_get_alloc_size_rsp response;
|
||||
bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALLOC_SIZE, &request, sizeof(request), &response, sizeof(response));
|
||||
GGML_ASSERT(status);
|
||||
|
||||
return response.alloc_size;
|
||||
} else {
|
||||
return ggml_nbytes(tensor);
|
||||
}
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = {
|
||||
|
|
@ -603,7 +631,7 @@ static void ggml_backend_rpc_free(ggml_backend_t backend) {
|
|||
}
|
||||
|
||||
static void ggml_backend_rpc_synchronize(ggml_backend_t backend) {
|
||||
UNUSED(backend);
|
||||
GGML_UNUSED(backend);
|
||||
// this is no-op because we don't have any async operations
|
||||
}
|
||||
|
||||
|
|
@ -757,6 +785,8 @@ public:
|
|||
bool get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response);
|
||||
bool copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_copy_tensor_rsp & response);
|
||||
bool graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph_compute_rsp & response);
|
||||
bool init_tensor(const rpc_msg_init_tensor_req & request);
|
||||
bool get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response);
|
||||
|
||||
private:
|
||||
ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor);
|
||||
|
|
@ -770,6 +800,36 @@ private:
|
|||
std::unordered_set<ggml_backend_buffer_t> buffers;
|
||||
};
|
||||
|
||||
bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response) {
|
||||
ggml_backend_buffer_type_t buft;
|
||||
struct ggml_init_params params {
|
||||
/*.mem_size =*/ ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
|
||||
struct ggml_context * ctx = ggml_init(params);
|
||||
ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
|
||||
|
||||
if (tensor == nullptr) {
|
||||
GGML_LOG_ERROR("Null tensor pointer passed to server get_alloc_size function.\n");
|
||||
ggml_free(ctx);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (tensor->buffer == nullptr) {
|
||||
//No buffer allocated.
|
||||
buft = ggml_backend_get_default_buffer_type(backend);
|
||||
} else {
|
||||
buft = tensor->buffer->buft;
|
||||
}
|
||||
|
||||
response.alloc_size = ggml_backend_buft_get_alloc_size(buft,tensor);
|
||||
|
||||
ggml_free(ctx);
|
||||
return true;
|
||||
}
|
||||
|
||||
void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_alloc_buffer_rsp & response) {
|
||||
ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
|
||||
ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, request.size);
|
||||
|
|
@ -781,7 +841,7 @@ void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_
|
|||
GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size);
|
||||
buffers.insert(buffer);
|
||||
} else {
|
||||
GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size);
|
||||
GGML_LOG_ERROR("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -803,7 +863,7 @@ bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rp
|
|||
GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
|
||||
ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
|
||||
if (buffers.find(buffer) == buffers.end()) {
|
||||
GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
|
||||
GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
|
||||
return false;
|
||||
}
|
||||
void * base = ggml_backend_buffer_get_base(buffer);
|
||||
|
|
@ -815,7 +875,7 @@ bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) {
|
|||
GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
|
||||
ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
|
||||
if (buffers.find(buffer) == buffers.end()) {
|
||||
GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
|
||||
GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
|
||||
return false;
|
||||
}
|
||||
ggml_backend_buffer_free(buffer);
|
||||
|
|
@ -827,7 +887,7 @@ bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) {
|
|||
GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value);
|
||||
ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
|
||||
if (buffers.find(buffer) == buffers.end()) {
|
||||
GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__);
|
||||
GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
|
||||
return false;
|
||||
}
|
||||
ggml_backend_buffer_clear(buffer, request.value);
|
||||
|
|
@ -883,7 +943,7 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
|
|||
struct ggml_context * ctx = ggml_init(params);
|
||||
ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor);
|
||||
if (tensor == nullptr) {
|
||||
GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__);
|
||||
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
|
||||
ggml_free(ctx);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -905,6 +965,40 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
|
|||
return true;
|
||||
}
|
||||
|
||||
bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
|
||||
struct ggml_init_params params {
|
||||
/*.mem_size =*/ ggml_tensor_overhead(),
|
||||
/*.mem_buffer =*/ NULL,
|
||||
/*.no_alloc =*/ true,
|
||||
};
|
||||
struct ggml_context * ctx = ggml_init(params);
|
||||
ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
|
||||
if (tensor == nullptr) {
|
||||
GGML_LOG_ERROR("Null tensor pointer passed to server init_tensor function.\n");
|
||||
ggml_free(ctx);
|
||||
return false;
|
||||
}
|
||||
|
||||
// Call the backend's buffer_init_tensor function
|
||||
ggml_backend_buffer_t buffer = tensor->buffer;
|
||||
if (buffer && buffer->iface.init_tensor) {
|
||||
buffer->iface.init_tensor(buffer, tensor);
|
||||
} else {
|
||||
GGML_LOG_ERROR("Null buffer for tensor passed to init_tensor function\n");
|
||||
}
|
||||
|
||||
if (tensor->extra != nullptr) {
|
||||
// This pointer can either be passed around client/server, or probably better stored server-side and kept track of.
|
||||
// Currently unimplemented.
|
||||
GGML_LOG_ERROR("tensor->extra populated by the backend, this is currently unsupported.\n");
|
||||
ggml_free(ctx);
|
||||
return false;
|
||||
}
|
||||
|
||||
ggml_free(ctx);
|
||||
return true;
|
||||
}
|
||||
|
||||
bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<uint8_t> & response) {
|
||||
struct ggml_init_params params {
|
||||
/*.mem_size =*/ ggml_tensor_overhead(),
|
||||
|
|
@ -914,7 +1008,7 @@ bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<
|
|||
struct ggml_context * ctx = ggml_init(params);
|
||||
ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor);
|
||||
if (tensor == nullptr) {
|
||||
GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__);
|
||||
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
|
||||
ggml_free(ctx);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -948,7 +1042,7 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
|
|||
ggml_tensor * src = deserialize_tensor(ctx, &request.src);
|
||||
ggml_tensor * dst = deserialize_tensor(ctx, &request.dst);
|
||||
if (src == nullptr || dst == nullptr) {
|
||||
GGML_PRINT_DEBUG("[%s] error deserializing tensors\n", __func__);
|
||||
GGML_LOG_ERROR("[%s] error deserializing tensors\n", __func__);
|
||||
ggml_free(ctx);
|
||||
return false;
|
||||
}
|
||||
|
|
@ -1058,6 +1152,18 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
|
|||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_GET_ALLOC_SIZE: {
|
||||
rpc_msg_get_alloc_size_req request;
|
||||
if (!recv_msg(sockfd, &request, sizeof(request))) {
|
||||
return;
|
||||
}
|
||||
rpc_msg_get_alloc_size_rsp response;
|
||||
server.get_alloc_size(request, response);
|
||||
if (!send_msg(sockfd, &response, sizeof(response))) {
|
||||
return;
|
||||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_GET_ALIGNMENT: {
|
||||
if (!recv_msg(sockfd, nullptr, 0)) {
|
||||
return;
|
||||
|
|
@ -1133,6 +1239,19 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre
|
|||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_INIT_TENSOR: {
|
||||
rpc_msg_init_tensor_req request;
|
||||
if (!recv_msg(sockfd, &request,sizeof(request))) {
|
||||
return;
|
||||
}
|
||||
if (!server.init_tensor(request)) {
|
||||
return;
|
||||
}
|
||||
if (!send_msg(sockfd, nullptr, 0)) {
|
||||
return;
|
||||
}
|
||||
break;
|
||||
}
|
||||
case RPC_CMD_GET_TENSOR: {
|
||||
rpc_msg_get_tensor_req request;
|
||||
if (!recv_msg(sockfd, &request, sizeof(request))) {
|
||||
|
|
@ -1257,14 +1376,14 @@ static void ggml_backend_rpc_device_get_memory(ggml_backend_dev_t dev, size_t *
|
|||
|
||||
ggml_backend_rpc_get_device_memory(ctx->endpoint.c_str(), free, total);
|
||||
|
||||
UNUSED(dev);
|
||||
GGML_UNUSED(dev);
|
||||
}
|
||||
|
||||
static enum ggml_backend_dev_type ggml_backend_rpc_device_get_type(ggml_backend_dev_t dev) {
|
||||
// TODO: obtain value from the server
|
||||
return GGML_BACKEND_DEVICE_TYPE_GPU;
|
||||
|
||||
UNUSED(dev);
|
||||
GGML_UNUSED(dev);
|
||||
}
|
||||
|
||||
static void ggml_backend_rpc_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
|
||||
|
|
@ -1285,7 +1404,7 @@ static ggml_backend_t ggml_backend_rpc_device_init(ggml_backend_dev_t dev, const
|
|||
|
||||
return ggml_backend_rpc_init(ctx->endpoint.c_str());
|
||||
|
||||
UNUSED(params);
|
||||
GGML_UNUSED(params);
|
||||
}
|
||||
|
||||
static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_backend_dev_t dev) {
|
||||
|
|
@ -1293,12 +1412,12 @@ static ggml_backend_buffer_type_t ggml_backend_rpc_device_get_buffer_type(ggml_b
|
|||
|
||||
return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str());
|
||||
|
||||
UNUSED(dev);
|
||||
GGML_UNUSED(dev);
|
||||
}
|
||||
|
||||
static bool ggml_backend_rpc_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
|
||||
UNUSED(dev);
|
||||
UNUSED(op);
|
||||
GGML_UNUSED(dev);
|
||||
GGML_UNUSED(op);
|
||||
//TODO: call the remote backend and cache the results
|
||||
return true;
|
||||
}
|
||||
|
|
@ -1335,20 +1454,20 @@ static const struct ggml_backend_device_i ggml_backend_rpc_device_i = {
|
|||
static const char * ggml_backend_rpc_reg_get_name(ggml_backend_reg_t reg) {
|
||||
return "RPC";
|
||||
|
||||
UNUSED(reg);
|
||||
GGML_UNUSED(reg);
|
||||
}
|
||||
|
||||
static size_t ggml_backend_rpc_reg_get_device_count(ggml_backend_reg_t reg) {
|
||||
return 0;
|
||||
|
||||
UNUSED(reg);
|
||||
GGML_UNUSED(reg);
|
||||
}
|
||||
|
||||
static ggml_backend_dev_t ggml_backend_rpc_reg_get_device(ggml_backend_reg_t reg, size_t index) {
|
||||
GGML_ABORT("The RPC backend does not have enumerated devices - use ggml_backend_add_device instead");
|
||||
|
||||
UNUSED(reg);
|
||||
UNUSED(index);
|
||||
GGML_UNUSED(reg);
|
||||
GGML_UNUSED(index);
|
||||
}
|
||||
|
||||
static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const char * name) {
|
||||
|
|
@ -1357,7 +1476,7 @@ static void * ggml_backend_rpc_get_proc_address(ggml_backend_reg_t reg, const ch
|
|||
}
|
||||
return NULL;
|
||||
|
||||
UNUSED(reg);
|
||||
GGML_UNUSED(reg);
|
||||
}
|
||||
|
||||
static const struct ggml_backend_reg_i ggml_backend_rpc_reg_i = {
|
||||
|
|
|
|||
|
|
@ -131,7 +131,7 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, const ggml_tensor* s
|
|||
[=](sycl::nd_item<3> item_ct1) {
|
||||
rwkv_wkv_f32_kernel(
|
||||
B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
|
||||
item_ct1, shared_mem_acc.get_pointer()
|
||||
item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
|
||||
);
|
||||
});
|
||||
});
|
||||
|
|
|
|||
|
|
@ -2040,6 +2040,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
std::cerr << "Done!" << std::endl;
|
||||
}
|
||||
|
||||
static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props);
|
||||
|
||||
static vk_device ggml_vk_get_device(size_t idx) {
|
||||
VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")");
|
||||
|
||||
|
|
@ -2175,9 +2177,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
|
|||
|
||||
device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute;
|
||||
|
||||
if (device->vendor_id == VK_VENDOR_ID_INTEL || (device->vendor_id == VK_VENDOR_ID_AMD && (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource))) {
|
||||
// Intel drivers don't support coopmat properly yet
|
||||
// Only RADV supports coopmat properly on AMD
|
||||
if (!ggml_vk_khr_cooperative_matrix_support(device->properties, driver_props)) {
|
||||
device->coopmat_support = false;
|
||||
}
|
||||
|
||||
|
|
@ -2515,7 +2515,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
|
|||
return vk_instance.devices[idx];
|
||||
}
|
||||
|
||||
|
||||
static void ggml_vk_print_gpu_info(size_t idx) {
|
||||
GGML_ASSERT(idx < vk_instance.device_indices.size());
|
||||
size_t dev_num = vk_instance.device_indices[idx];
|
||||
|
|
@ -2565,9 +2564,7 @@ static void ggml_vk_print_gpu_info(size_t idx) {
|
|||
}
|
||||
}
|
||||
|
||||
if (props2.properties.vendorID == VK_VENDOR_ID_INTEL || (props2.properties.vendorID == VK_VENDOR_ID_AMD && (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource))) {
|
||||
// Intel drivers don't support coopmat properly yet
|
||||
// Only RADV supports coopmat properly on AMD
|
||||
if (!ggml_vk_khr_cooperative_matrix_support(props2.properties, driver_props)) {
|
||||
coopmat_support = false;
|
||||
}
|
||||
|
||||
|
|
@ -8088,6 +8085,25 @@ static bool ggml_vk_instance_portability_enumeration_ext_available(const std::ve
|
|||
UNUSED(instance_extensions);
|
||||
}
|
||||
|
||||
static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props) {
|
||||
switch (props.vendorID) {
|
||||
case VK_VENDOR_ID_INTEL:
|
||||
// Intel drivers don't support coopmat properly yet
|
||||
return false;
|
||||
case VK_VENDOR_ID_AMD:
|
||||
if (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource) {
|
||||
// Workaround for AMD proprietary driver reporting support on all GPUs
|
||||
const std::string name = props.deviceName;
|
||||
return name.rfind("AMD Radeon RX 7", 0) == 0 || name.rfind("AMD Radeon(TM) RX 7", 0) == 0 || // RDNA 3 consumer GPUs
|
||||
name.rfind("AMD Radeon PRO W7", 0) == 0 || name.rfind("AMD Radeon(TM) PRO W7", 0) == 0 || // RDNA 3 workstation GPUs
|
||||
name.rfind("AMD Radeon 7", 0) == 0 || name.rfind("AMD Radeon(TM) 7", 0) == 0; // RDNA 3 APUs
|
||||
}
|
||||
return true;
|
||||
default:
|
||||
return true;
|
||||
}
|
||||
}
|
||||
|
||||
// checks
|
||||
|
||||
#ifdef GGML_VULKAN_CHECK_RESULTS
|
||||
|
|
|
|||
|
|
@ -102,6 +102,8 @@ class Keys:
|
|||
EXPERT_USED_COUNT = "{arch}.expert_used_count"
|
||||
EXPERT_SHARED_COUNT = "{arch}.expert_shared_count"
|
||||
EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
|
||||
EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm"
|
||||
EXPERT_GATING_FUNC = "{arch}.expert_gating_func"
|
||||
POOLING_TYPE = "{arch}.pooling_type"
|
||||
LOGIT_SCALE = "{arch}.logit_scale"
|
||||
DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id"
|
||||
|
|
@ -255,6 +257,7 @@ class MODEL_ARCH(IntEnum):
|
|||
MAMBA = auto()
|
||||
XVERSE = auto()
|
||||
COMMAND_R = auto()
|
||||
COHERE2 = auto()
|
||||
DBRX = auto()
|
||||
OLMO = auto()
|
||||
OLMO2 = auto()
|
||||
|
|
@ -312,6 +315,7 @@ class MODEL_TENSOR(IntEnum):
|
|||
FFN_GATE_SHEXP = auto()
|
||||
FFN_DOWN_SHEXP = auto()
|
||||
FFN_UP_SHEXP = auto()
|
||||
FFN_EXP_PROBS_B = auto()
|
||||
ATTN_Q_NORM = auto()
|
||||
ATTN_K_NORM = auto()
|
||||
LAYER_OUT_NORM = auto()
|
||||
|
|
@ -437,6 +441,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.MAMBA: "mamba",
|
||||
MODEL_ARCH.XVERSE: "xverse",
|
||||
MODEL_ARCH.COMMAND_R: "command-r",
|
||||
MODEL_ARCH.COHERE2: "cohere2",
|
||||
MODEL_ARCH.DBRX: "dbrx",
|
||||
MODEL_ARCH.OLMO: "olmo",
|
||||
MODEL_ARCH.OLMO2: "olmo2",
|
||||
|
|
@ -496,6 +501,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
|
|||
MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps",
|
||||
MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps",
|
||||
MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps",
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B: "blk.{bid}.exp_probs_b",
|
||||
MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm",
|
||||
MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in",
|
||||
MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d",
|
||||
|
|
@ -1136,6 +1142,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.ATTN_K_NORM,
|
||||
MODEL_TENSOR.ATTN_Q_NORM,
|
||||
],
|
||||
MODEL_ARCH.COHERE2: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
MODEL_ARCH.DBRX: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
|
|
@ -1276,6 +1294,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_GATE_SHEXP,
|
||||
MODEL_TENSOR.FFN_DOWN_SHEXP,
|
||||
MODEL_TENSOR.FFN_UP_SHEXP,
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B,
|
||||
],
|
||||
MODEL_ARCH.CHATGLM : [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
|
|
@ -1576,6 +1595,11 @@ class GGMLQuantizationType(IntEnum):
|
|||
TQ2_0 = 35
|
||||
|
||||
|
||||
class ExpertGatingFuncType(IntEnum):
|
||||
SOFTMAX = 1
|
||||
SIGMOID = 2
|
||||
|
||||
|
||||
# TODO: add GGMLFileType from ggml_ftype in ggml.h
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ from .constants import (
|
|||
RopeScalingType,
|
||||
PoolingType,
|
||||
TokenType,
|
||||
ExpertGatingFuncType,
|
||||
)
|
||||
|
||||
from .quants import quant_shape_from_byte_shape
|
||||
|
|
@ -715,6 +716,12 @@ class GGUFWriter:
|
|||
def add_expert_weights_scale(self, value: float) -> None:
|
||||
self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value)
|
||||
|
||||
def add_expert_weights_norm(self, value: bool) -> None:
|
||||
self.add_bool(Keys.LLM.EXPERT_WEIGHTS_NORM.format(arch=self.arch), value)
|
||||
|
||||
def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
|
||||
self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
|
||||
|
||||
def add_swin_norm(self, value: bool) -> None:
|
||||
self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value)
|
||||
|
||||
|
|
|
|||
|
|
@ -276,6 +276,10 @@ class TensorNameMap:
|
|||
"model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe
|
||||
),
|
||||
|
||||
MODEL_TENSOR.FFN_EXP_PROBS_B: (
|
||||
"model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3
|
||||
),
|
||||
|
||||
# Feed-forward up
|
||||
MODEL_TENSOR.FFN_UP: (
|
||||
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@
|
|||
#include "llama.h"
|
||||
|
||||
struct llama_model_deleter {
|
||||
void operator()(llama_model * model) { llama_free_model(model); }
|
||||
void operator()(llama_model * model) { llama_model_free(model); }
|
||||
};
|
||||
|
||||
struct llama_context_deleter {
|
||||
|
|
|
|||
|
|
@ -34,7 +34,6 @@
|
|||
|
||||
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
|
||||
|
||||
// TODO: use everywhere in the implementation
|
||||
#define LLAMA_TOKEN_NULL -1
|
||||
|
||||
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
|
||||
|
|
@ -105,6 +104,7 @@ extern "C" {
|
|||
LLAMA_VOCAB_PRE_TYPE_EXAONE = 25,
|
||||
LLAMA_VOCAB_PRE_TYPE_CHAMELEON = 26,
|
||||
LLAMA_VOCAB_PRE_TYPE_MINERVA = 27,
|
||||
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM = 28,
|
||||
};
|
||||
|
||||
enum llama_rope_type {
|
||||
|
|
@ -413,12 +413,19 @@ extern "C" {
|
|||
// Call once at the end of the program - currently only used for MPI
|
||||
LLAMA_API void llama_backend_free(void);
|
||||
|
||||
LLAMA_API struct llama_model * llama_load_model_from_file(
|
||||
DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file(
|
||||
const char * path_model,
|
||||
struct llama_model_params params),
|
||||
"use llama_model_load_from_file instead");
|
||||
|
||||
LLAMA_API struct llama_model * llama_model_load_from_file(
|
||||
const char * path_model,
|
||||
struct llama_model_params params);
|
||||
|
||||
// TODO: rename to llama_model_free
|
||||
LLAMA_API void llama_free_model(struct llama_model * model);
|
||||
DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model),
|
||||
"use llama_model_free instead");
|
||||
|
||||
LLAMA_API void llama_model_free(struct llama_model * model);
|
||||
|
||||
// TODO: rename to llama_init_from_model
|
||||
LLAMA_API struct llama_context * llama_new_context_with_model(
|
||||
|
|
|
|||
|
|
@ -39,6 +39,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_MAMBA, "mamba" },
|
||||
{ LLM_ARCH_XVERSE, "xverse" },
|
||||
{ LLM_ARCH_COMMAND_R, "command-r" },
|
||||
{ LLM_ARCH_COHERE2, "cohere2" },
|
||||
{ LLM_ARCH_DBRX, "dbrx" },
|
||||
{ LLM_ARCH_OLMO, "olmo" },
|
||||
{ LLM_ARCH_OLMO2, "olmo2" },
|
||||
|
|
@ -91,6 +92,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
|
|||
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
|
||||
{ LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" },
|
||||
{ LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
|
||||
{ LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" },
|
||||
{ LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" },
|
||||
{ LLM_KV_POOLING_TYPE, "%s.pooling_type" },
|
||||
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
|
||||
{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
|
||||
|
|
@ -807,6 +810,21 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
|
|||
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_COHERE2,
|
||||
{
|
||||
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
|
||||
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
|
||||
{ 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_FFN_GATE, "blk.%d.ffn_gate" },
|
||||
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
|
||||
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
|
||||
},
|
||||
},
|
||||
{
|
||||
LLM_ARCH_DBRX,
|
||||
{
|
||||
|
|
@ -968,6 +986,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
|
|||
{ 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" },
|
||||
},
|
||||
},
|
||||
{
|
||||
|
|
@ -1350,6 +1369,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
|
|||
{LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
|
||||
{LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
|
||||
{LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
|
||||
{LLM_TENSOR_FFN_EXP_PROBS_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
|
||||
// this tensor is loaded for T5, but never used
|
||||
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
|
||||
{LLM_TENSOR_CONV1D, {LLM_TENSOR_LAYER_INPUT, GGML_OP_IM2COL}},
|
||||
|
|
|
|||
|
|
@ -43,6 +43,7 @@ enum llm_arch {
|
|||
LLM_ARCH_MAMBA,
|
||||
LLM_ARCH_XVERSE,
|
||||
LLM_ARCH_COMMAND_R,
|
||||
LLM_ARCH_COHERE2,
|
||||
LLM_ARCH_DBRX,
|
||||
LLM_ARCH_OLMO,
|
||||
LLM_ARCH_OLMO2,
|
||||
|
|
@ -95,6 +96,8 @@ enum llm_kv {
|
|||
LLM_KV_EXPERT_USED_COUNT,
|
||||
LLM_KV_EXPERT_SHARED_COUNT,
|
||||
LLM_KV_EXPERT_WEIGHTS_SCALE,
|
||||
LLM_KV_EXPERT_WEIGHTS_NORM,
|
||||
LLM_KV_EXPERT_GATING_FUNC,
|
||||
LLM_KV_POOLING_TYPE,
|
||||
LLM_KV_LOGIT_SCALE,
|
||||
LLM_KV_DECODER_START_TOKEN_ID,
|
||||
|
|
@ -230,6 +233,7 @@ enum llm_tensor {
|
|||
LLM_TENSOR_FFN_DOWN_SHEXP,
|
||||
LLM_TENSOR_FFN_GATE_SHEXP,
|
||||
LLM_TENSOR_FFN_UP_SHEXP,
|
||||
LLM_TENSOR_FFN_EXP_PROBS_B,
|
||||
LLM_TENSOR_ATTN_Q_NORM,
|
||||
LLM_TENSOR_ATTN_K_NORM,
|
||||
LLM_TENSOR_LAYER_OUT_NORM,
|
||||
|
|
|
|||
|
|
@ -45,6 +45,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
|
|||
{ "vicuna-orca", LLM_CHAT_TEMPLATE_VICUNA_ORCA },
|
||||
{ "deepseek", LLM_CHAT_TEMPLATE_DEEPSEEK },
|
||||
{ "deepseek2", LLM_CHAT_TEMPLATE_DEEPSEEK_2 },
|
||||
{ "deepseek3", LLM_CHAT_TEMPLATE_DEEPSEEK_3 },
|
||||
{ "command-r", LLM_CHAT_TEMPLATE_COMMAND_R },
|
||||
{ "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 },
|
||||
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 },
|
||||
|
|
@ -148,6 +149,8 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
|
|||
return LLM_CHAT_TEMPLATE_MINICPM;
|
||||
} else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
|
||||
return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
|
||||
} else if (tmpl_contains(LU8("'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'"))) {
|
||||
return LLM_CHAT_TEMPLATE_DEEPSEEK_3;
|
||||
} else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
|
||||
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
|
||||
// EXAONE-3.0-7.8B-Instruct
|
||||
|
|
@ -453,6 +456,21 @@ int32_t llm_chat_apply_template(
|
|||
if (add_ass) {
|
||||
ss << "Assistant:";
|
||||
}
|
||||
} else if (tmpl == LLM_CHAT_TEMPLATE_DEEPSEEK_3) {
|
||||
// DeepSeek-V3
|
||||
for (auto message : chat) {
|
||||
std::string role(message->role);
|
||||
if (role == "system") {
|
||||
ss << message->content << "\n\n";
|
||||
} else if (role == "user") {
|
||||
ss << LU8("<｜User｜>") << message->content;
|
||||
} else if (role == "assistant") {
|
||||
ss << LU8("<｜Assistant｜>") << message->content << LU8("<｜end▁of▁sentence｜>");
|
||||
}
|
||||
}
|
||||
if (add_ass) {
|
||||
ss << LU8("<｜Assistant｜>");
|
||||
}
|
||||
} else if (tmpl == LLM_CHAT_TEMPLATE_EXAONE_3) {
|
||||
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb
|
||||
// EXAONE-3.0-7.8B-Instruct
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ enum llm_chat_template {
|
|||
LLM_CHAT_TEMPLATE_VICUNA_ORCA,
|
||||
LLM_CHAT_TEMPLATE_DEEPSEEK,
|
||||
LLM_CHAT_TEMPLATE_DEEPSEEK_2,
|
||||
LLM_CHAT_TEMPLATE_DEEPSEEK_3,
|
||||
LLM_CHAT_TEMPLATE_COMMAND_R,
|
||||
LLM_CHAT_TEMPLATE_LLAMA_3,
|
||||
LLM_CHAT_TEMPLATE_CHATGML_3,
|
||||
|
|
|
|||
|
|
@ -6,7 +6,13 @@
|
|||
|
||||
// bump if necessary
|
||||
#define LLAMA_MAX_LAYERS 512
|
||||
#define LLAMA_MAX_EXPERTS 160 // DeepSeekV2
|
||||
#define LLAMA_MAX_EXPERTS 256 // DeepSeekV3
|
||||
|
||||
enum llama_expert_gating_func_type {
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_NONE = 0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX = 1,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID = 2,
|
||||
};
|
||||
|
||||
struct llama_hparams_posnet {
|
||||
uint32_t n_embd;
|
||||
|
|
@ -55,6 +61,8 @@ struct llama_hparams {
|
|||
uint32_t n_norm_groups = 0;
|
||||
|
||||
float expert_weights_scale = 0.0;
|
||||
bool expert_weights_norm = false;
|
||||
uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
|
||||
|
||||
float f_norm_eps;
|
||||
float f_norm_rms_eps;
|
||||
|
|
|
|||
|
|
@ -119,10 +119,10 @@ bool llama_kv_cache_init(
|
|||
|
||||
struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
||||
struct llama_kv_cache & cache,
|
||||
const struct llama_ubatch & batch) {
|
||||
const uint32_t n_tokens = batch.n_tokens;
|
||||
const uint32_t n_seqs = batch.n_seqs;
|
||||
const uint32_t n_seq_tokens = batch.n_seq_tokens;
|
||||
const struct llama_ubatch & ubatch) {
|
||||
const uint32_t n_tokens = ubatch.n_tokens;
|
||||
const uint32_t n_seqs = ubatch.n_seqs;
|
||||
const uint32_t n_seq_tokens = ubatch.n_seq_tokens;
|
||||
|
||||
if (cache.recurrent) {
|
||||
// For recurrent state architectures (like Mamba or RWKV),
|
||||
|
|
@ -130,16 +130,16 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
|||
// A slot should be always be contiguous.
|
||||
|
||||
// can only process batches with an equal number of new tokens in each sequence
|
||||
GGML_ASSERT(batch.equal_seqs);
|
||||
GGML_ASSERT(ubatch.equal_seqs);
|
||||
|
||||
int32_t min = cache.size - 1;
|
||||
int32_t max = 0;
|
||||
|
||||
// everything should fit if all seq_ids are smaller than the max
|
||||
for (uint32_t s = 0; s < n_seqs; ++s) {
|
||||
const uint32_t n_seq_id = batch.n_seq_id[s];
|
||||
const uint32_t n_seq_id = ubatch.n_seq_id[s];
|
||||
for (uint32_t j = 0; j < n_seq_id; ++j) {
|
||||
const llama_seq_id seq_id = batch.seq_id[s][j];
|
||||
const llama_seq_id seq_id = ubatch.seq_id[s][j];
|
||||
|
||||
if (seq_id < 0 || (uint32_t) seq_id >= cache.size) {
|
||||
// too big seq_id
|
||||
|
|
@ -198,7 +198,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
|||
|
||||
// find usable cell range
|
||||
for (uint32_t s = 0; s < n_seqs; ++s) {
|
||||
const llama_seq_id seq_id = batch.seq_id[s][0];
|
||||
const llama_seq_id seq_id = ubatch.seq_id[s][0];
|
||||
llama_kv_cell & seq_meta = cache.cells[seq_id];
|
||||
bool has_cell = false;
|
||||
if (seq_meta.tail >= 0) {
|
||||
|
|
@ -237,7 +237,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
|||
// gather and re-order
|
||||
for (uint32_t s = 0; s < n_seqs; ++s) {
|
||||
int32_t dst_id = s + min;
|
||||
int32_t src_id = cache.cells[batch.seq_id[s][0]].tail;
|
||||
int32_t src_id = cache.cells[ubatch.seq_id[s][0]].tail;
|
||||
if (dst_id != src_id) {
|
||||
llama_kv_cell & dst_cell = cache.cells[dst_id];
|
||||
llama_kv_cell & src_cell = cache.cells[src_id];
|
||||
|
|
@ -258,7 +258,7 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
|||
|
||||
// update the pos of the used seqs
|
||||
for (uint32_t s = 0; s < n_seqs; ++s) {
|
||||
const llama_pos last_pos = batch.pos[n_seq_tokens * s + n_seq_tokens - 1];
|
||||
const llama_pos last_pos = ubatch.pos[n_seq_tokens * s + n_seq_tokens - 1];
|
||||
int32_t cell_id = s + min;
|
||||
llama_kv_cell & cell = cache.cells[cell_id];
|
||||
|
||||
|
|
@ -266,12 +266,12 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
|||
// What should happen when the pos backtracks or skips a value?
|
||||
// Clearing the state mid-batch would require special-casing which isn't done.
|
||||
LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n",
|
||||
__func__, last_pos, cell.pos, batch.seq_id[s][0], n_seq_tokens);
|
||||
__func__, last_pos, cell.pos, ubatch.seq_id[s][0], n_seq_tokens);
|
||||
}
|
||||
cell.pos = last_pos;
|
||||
cell.seq_id.clear();
|
||||
for (int32_t j = 0; j < batch.n_seq_id[s]; ++j) {
|
||||
const llama_seq_id seq_id = batch.seq_id[s][j];
|
||||
for (int32_t j = 0; j < ubatch.n_seq_id[s]; ++j) {
|
||||
const llama_seq_id seq_id = ubatch.seq_id[s][j];
|
||||
cell.seq_id.insert(seq_id);
|
||||
cache.cells[seq_id].tail = cell_id;
|
||||
}
|
||||
|
|
@ -325,10 +325,10 @@ struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
|
|||
for (uint32_t s = 0; s < n_seqs; s++) {
|
||||
for (uint32_t i = 0; i < n_seq_tokens; ++i) {
|
||||
uint32_t k = s*n_seq_tokens + i;
|
||||
cache.cells[cache.head + k].pos = batch.pos[k];
|
||||
cache.cells[cache.head + k].pos = ubatch.pos[k];
|
||||
|
||||
for (int32_t j = 0; j < batch.n_seq_id[s]; j++) {
|
||||
cache.cells[cache.head + k].seq_id.insert(batch.seq_id[s][j]);
|
||||
for (int32_t j = 0; j < ubatch.n_seq_id[s]; j++) {
|
||||
cache.cells[cache.head + k].seq_id.insert(ubatch.seq_id[s][j]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -241,12 +241,16 @@ llama_file::~llama_file() = default;
|
|||
size_t llama_file::tell() const { return pimpl->tell(); }
|
||||
size_t llama_file::size() const { return pimpl->size; }
|
||||
|
||||
int llama_file::fileno() const {
|
||||
int llama_file::file_id() const {
|
||||
#ifdef _WIN32
|
||||
return _fileno(pimpl->fp);
|
||||
#else
|
||||
#if defined(fileno)
|
||||
return fileno(pimpl->fp);
|
||||
#else
|
||||
return ::fileno(pimpl->fp);
|
||||
#endif
|
||||
#endif
|
||||
}
|
||||
|
||||
void llama_file::seek(size_t offset, int whence) const { pimpl->seek(offset, whence); }
|
||||
|
|
@ -265,7 +269,7 @@ struct llama_mmap::impl {
|
|||
|
||||
impl(struct llama_file * file, size_t prefetch, bool numa) {
|
||||
size = file->size();
|
||||
int fd = file->fileno();
|
||||
int fd = file->file_id();
|
||||
int flags = MAP_SHARED;
|
||||
if (numa) { prefetch = 0; }
|
||||
#ifdef __linux__
|
||||
|
|
@ -357,7 +361,7 @@ struct llama_mmap::impl {
|
|||
|
||||
size = file->size();
|
||||
|
||||
HANDLE hFile = (HANDLE) _get_osfhandle(file->fileno());
|
||||
HANDLE hFile = (HANDLE) _get_osfhandle(file->file_id());
|
||||
|
||||
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ struct llama_file {
|
|||
size_t tell() const;
|
||||
size_t size() const;
|
||||
|
||||
int fileno() const;
|
||||
int file_id() const; // fileno overload
|
||||
|
||||
void seek(size_t offset, int whence) const;
|
||||
|
||||
|
|
|
|||
|
|
@ -70,6 +70,7 @@ const char * llm_type_name(llm_type type) {
|
|||
case MODEL_70B: return "70B";
|
||||
case MODEL_236B: return "236B";
|
||||
case MODEL_314B: return "314B";
|
||||
case MODEL_671B: return "671B";
|
||||
case MODEL_SMALL: return "0.1B";
|
||||
case MODEL_MEDIUM: return "0.4B";
|
||||
case MODEL_LARGE: return "0.8B";
|
||||
|
|
@ -129,6 +130,14 @@ static std::string llama_model_ftype_name(llama_ftype ftype) {
|
|||
}
|
||||
}
|
||||
|
||||
static const char * llama_expert_gating_func_name(llama_expert_gating_func_type type) {
|
||||
switch (type) {
|
||||
case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax";
|
||||
case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid";
|
||||
default: return "unknown";
|
||||
}
|
||||
}
|
||||
|
||||
std::string llama_model_arch_name (const llama_model & model) {
|
||||
return llm_arch_name(model.arch);
|
||||
}
|
||||
|
|
@ -793,6 +802,16 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
|
|||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_COHERE2:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
||||
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
||||
switch (hparams.n_layer) {
|
||||
case 32: model.type = e_model::MODEL_8B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_DBRX:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
||||
|
|
@ -930,11 +949,19 @@ void llm_load_hparams(llama_model_loader & ml, llama_model & model) {
|
|||
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);
|
||||
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
|
||||
// for compatibility with existing DeepSeek V2 and V2.5 GGUFs
|
||||
// that have no expert_gating_func model parameter set
|
||||
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
|
||||
}
|
||||
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
|
||||
|
||||
switch (hparams.n_layer) {
|
||||
case 27: model.type = e_model::MODEL_16B; break;
|
||||
case 60: model.type = e_model::MODEL_236B; break;
|
||||
case 61: model.type = e_model::MODEL_671B; break;
|
||||
default: model.type = e_model::MODEL_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
|
|
@ -1266,6 +1293,10 @@ void llm_load_vocab(llama_model_loader & ml, llama_model & model) {
|
|||
tokenizer_pre == "deepseek-coder") {
|
||||
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
|
||||
vocab.tokenizer_clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "deepseek-v3") {
|
||||
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
|
||||
vocab.tokenizer_clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "falcon") {
|
||||
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
|
||||
|
|
@ -1912,24 +1943,24 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
|
||||
|
||||
// special tokens
|
||||
if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
||||
if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
||||
if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
|
||||
if (vocab.special_eom_id != -1) { LLAMA_LOG_INFO( "%s: EOM token = %d '%s'\n", __func__, vocab.special_eom_id, vocab.id_to_token[vocab.special_eom_id].text.c_str() ); }
|
||||
if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
|
||||
if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
|
||||
if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
|
||||
if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
|
||||
if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
|
||||
if (vocab.special_bos_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
|
||||
if (vocab.special_eos_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
|
||||
if (vocab.special_eot_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
|
||||
if (vocab.special_eom_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: EOM token = %d '%s'\n", __func__, vocab.special_eom_id, vocab.id_to_token[vocab.special_eom_id].text.c_str() ); }
|
||||
if (vocab.special_unk_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
|
||||
if (vocab.special_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
|
||||
if (vocab.special_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
|
||||
if (vocab.special_cls_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
|
||||
if (vocab.special_mask_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
|
||||
|
||||
if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
|
||||
if (vocab.linefeed_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
|
||||
|
||||
if (vocab.special_fim_pre_id != -1) { LLAMA_LOG_INFO( "%s: FIM PRE token = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
|
||||
if (vocab.special_fim_suf_id != -1) { LLAMA_LOG_INFO( "%s: FIM SUF token = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
|
||||
if (vocab.special_fim_mid_id != -1) { LLAMA_LOG_INFO( "%s: FIM MID token = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
|
||||
if (vocab.special_fim_pad_id != -1) { LLAMA_LOG_INFO( "%s: FIM PAD token = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
|
||||
if (vocab.special_fim_rep_id != -1) { LLAMA_LOG_INFO( "%s: FIM REP token = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
|
||||
if (vocab.special_fim_sep_id != -1) { LLAMA_LOG_INFO( "%s: FIM SEP token = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
|
||||
if (vocab.special_fim_pre_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PRE token = %d '%s'\n", __func__, vocab.special_fim_pre_id, vocab.id_to_token[vocab.special_fim_pre_id].text.c_str() ); }
|
||||
if (vocab.special_fim_suf_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SUF token = %d '%s'\n", __func__, vocab.special_fim_suf_id, vocab.id_to_token[vocab.special_fim_suf_id].text.c_str() ); }
|
||||
if (vocab.special_fim_mid_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM MID token = %d '%s'\n", __func__, vocab.special_fim_mid_id, vocab.id_to_token[vocab.special_fim_mid_id].text.c_str() ); }
|
||||
if (vocab.special_fim_pad_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM PAD token = %d '%s'\n", __func__, vocab.special_fim_pad_id, vocab.id_to_token[vocab.special_fim_pad_id].text.c_str() ); }
|
||||
if (vocab.special_fim_rep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM REP token = %d '%s'\n", __func__, vocab.special_fim_rep_id, vocab.id_to_token[vocab.special_fim_rep_id].text.c_str() ); }
|
||||
if (vocab.special_fim_sep_id != LLAMA_TOKEN_NULL) { LLAMA_LOG_INFO( "%s: FIM SEP token = %d '%s'\n", __func__, vocab.special_fim_sep_id, vocab.id_to_token[vocab.special_fim_sep_id].text.c_str() ); }
|
||||
|
||||
for (const auto & id : vocab.special_eog_ids) {
|
||||
LLAMA_LOG_INFO( "%s: EOG token = %d '%s'\n", __func__, id, vocab.id_to_token[id].text.c_str() );
|
||||
|
|
@ -1951,6 +1982,8 @@ void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
|
|||
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
||||
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
||||
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
||||
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
|
||||
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((enum llama_expert_gating_func_type) hparams.expert_gating_func));
|
||||
LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
|
||||
}
|
||||
|
||||
|
|
@ -1996,6 +2029,10 @@ struct llama_model_params llama_model_default_params() {
|
|||
}
|
||||
|
||||
void llama_free_model(struct llama_model * model) {
|
||||
llama_model_free(model);
|
||||
}
|
||||
|
||||
void llama_model_free(struct llama_model * model) {
|
||||
delete model;
|
||||
}
|
||||
|
||||
|
|
@ -2051,6 +2088,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
|||
case LLM_ARCH_MINICPM:
|
||||
case LLM_ARCH_XVERSE:
|
||||
case LLM_ARCH_COMMAND_R:
|
||||
case LLM_ARCH_COHERE2:
|
||||
case LLM_ARCH_OLMO:
|
||||
case LLM_ARCH_ARCTIC:
|
||||
case LLM_ARCH_DEEPSEEK:
|
||||
|
|
|
|||
|
|
@ -63,6 +63,7 @@ enum llm_type {
|
|||
MODEL_70B,
|
||||
MODEL_236B,
|
||||
MODEL_314B,
|
||||
MODEL_671B,
|
||||
MODEL_SMALL,
|
||||
MODEL_MEDIUM,
|
||||
MODEL_LARGE,
|
||||
|
|
@ -213,6 +214,7 @@ struct llama_layer {
|
|||
struct ggml_tensor * ffn_down_b = nullptr; // b2
|
||||
struct ggml_tensor * ffn_up_b = nullptr; // b3
|
||||
struct ggml_tensor * ffn_act = nullptr;
|
||||
struct ggml_tensor * ffn_exp_probs_b = nullptr;
|
||||
|
||||
// mamba proj
|
||||
struct ggml_tensor * ssm_in = nullptr;
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ static void zeros(std::ofstream & file, size_t n) {
|
|||
}
|
||||
}
|
||||
|
||||
struct quantize_state_internal {
|
||||
struct quantize_state_impl {
|
||||
const llama_model & model;
|
||||
const llama_model_quantize_params * params;
|
||||
|
||||
|
|
@ -43,13 +43,13 @@ struct quantize_state_internal {
|
|||
// used to figure out if a model shares tok_embd with the output weight
|
||||
bool has_output = false;
|
||||
|
||||
quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
|
||||
quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params)
|
||||
: model(model)
|
||||
, params(params)
|
||||
{}
|
||||
};
|
||||
|
||||
static void llama_tensor_dequantize_internal(
|
||||
static void llama_tensor_dequantize_impl(
|
||||
struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
|
||||
const size_t nelements, const int nthread
|
||||
) {
|
||||
|
|
@ -121,7 +121,7 @@ static void llama_tensor_dequantize_internal(
|
|||
workers.clear();
|
||||
}
|
||||
|
||||
static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
|
||||
static ggml_type llama_tensor_get_type(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
|
||||
const std::string name = ggml_get_name(tensor);
|
||||
|
||||
// TODO: avoid hardcoded tensor names - use the TN_* constants
|
||||
|
|
@ -410,7 +410,7 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
|
|||
return new_type;
|
||||
}
|
||||
|
||||
static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
|
||||
static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
|
||||
if (nthread < 2) {
|
||||
// single-thread
|
||||
size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
|
||||
|
|
@ -464,7 +464,7 @@ static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const floa
|
|||
return new_size;
|
||||
}
|
||||
|
||||
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
|
||||
static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
|
||||
ggml_type default_type;
|
||||
llama_ftype ftype = params->ftype;
|
||||
|
||||
|
|
@ -534,7 +534,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
llm_load_hparams(ml, model);
|
||||
llm_load_stats (ml, model);
|
||||
|
||||
struct quantize_state_internal qs(model, params);
|
||||
struct quantize_state_impl qs(model, params);
|
||||
|
||||
if (params->only_copy) {
|
||||
ftype = model.ftype;
|
||||
|
|
@ -837,7 +837,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
} else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
|
||||
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
|
||||
} else {
|
||||
llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
|
||||
llama_tensor_dequantize_impl(tensor, f32_conv_buf, workers, nelements, nthread);
|
||||
f32_data = (float *) f32_conv_buf.data();
|
||||
}
|
||||
|
||||
|
|
@ -866,7 +866,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
|
|||
void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
|
||||
const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
|
||||
|
||||
new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
|
||||
new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
|
||||
}
|
||||
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
|
||||
}
|
||||
|
|
@ -919,7 +919,7 @@ uint32_t llama_model_quantize(
|
|||
const char * fname_out,
|
||||
const llama_model_quantize_params * params) {
|
||||
try {
|
||||
llama_model_quantize_internal(fname_inp, fname_out, params);
|
||||
llama_model_quantize_impl(fname_inp, fname_out, params);
|
||||
} catch (const std::exception & err) {
|
||||
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
|
||||
return 1;
|
||||
|
|
|
|||
|
|
@ -257,7 +257,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
|
|||
for (int i = 0; i < (int)cur_p->size; ++i) {
|
||||
const float val = cur_p->data[i].logit;
|
||||
int ib = int(bucket_scale * val + bucket_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low);
|
||||
ib = std::max(0, std::min(nbuckets-1, ib));
|
||||
ib = std::max(0, std::min(nbuckets - 1, ib));
|
||||
bucket_idx[i] = ib;
|
||||
++histo[ib];
|
||||
}
|
||||
|
|
@ -280,13 +280,13 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
|
|||
for (int i = 0; i < (int)cur_p->size; ++i) {
|
||||
int j = bucket_idx[i];
|
||||
if (j >= ib) {
|
||||
*bucket_ptrs[nbuckets-1-j]++ = cur_p->data[i];
|
||||
*bucket_ptrs[nbuckets - 1 - j]++ = cur_p->data[i];
|
||||
}
|
||||
}
|
||||
|
||||
ptr = tmp_tokens.data();
|
||||
int ndone = 0;
|
||||
for (int j = nbuckets-1; j > ib; --j) {
|
||||
for (int j = nbuckets - 1; j > ib; --j) {
|
||||
std::sort(ptr, ptr + histo[j], comp);
|
||||
ptr += histo[j];
|
||||
ndone += histo[j];
|
||||
|
|
@ -1832,7 +1832,7 @@ static void llama_sampler_dry_apply(struct llama_sampler * smpl, llama_token_dat
|
|||
ctx->dry_repeat_count[last - k] = std::min(n, rep_limit);
|
||||
if (n > 0) {
|
||||
lt = k;
|
||||
rt = k+n-1;
|
||||
rt = k + n - 1;
|
||||
}
|
||||
} else {
|
||||
// If k is inside the current Z-box, consider two cases.
|
||||
|
|
|
|||
|
|
@ -609,6 +609,13 @@ struct llm_tokenizer_bpe : llm_tokenizer {
|
|||
"\\p{N}+",
|
||||
};
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM:
|
||||
regex_exprs = {
|
||||
"\\p{N}{1,3}",
|
||||
"[一-龥-ゟ゠-ヿ]+",
|
||||
"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
|
||||
};
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
|
||||
regex_exprs = {
|
||||
"[\r\n]",
|
||||
|
|
@ -717,7 +724,7 @@ struct llm_tokenizer_bpe_session {
|
|||
|
||||
bool append_bos(std::vector<llama_vocab::id> & output) const {
|
||||
if (vocab.tokenizer_add_bos) {
|
||||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||||
GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_bos_id);
|
||||
return true;
|
||||
}
|
||||
|
|
@ -726,7 +733,7 @@ struct llm_tokenizer_bpe_session {
|
|||
|
||||
bool append_eos(std::vector<llama_vocab::id> & output) const {
|
||||
if (vocab.tokenizer_add_eos) {
|
||||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||||
GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_eos_id);
|
||||
return true;
|
||||
}
|
||||
|
|
@ -1623,7 +1630,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
|
|||
if (source == 0) {
|
||||
buffer.erase_after(buffer.before_begin());
|
||||
} else {
|
||||
buffer.erase_after(std::next(buffer.begin(), (source-1)));
|
||||
buffer.erase_after(std::next(buffer.begin(), (source - 1)));
|
||||
}
|
||||
|
||||
// repeat for the right side
|
||||
|
|
@ -1637,7 +1644,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
|
|||
if (source == 0) {
|
||||
buffer.erase_after(buffer.before_begin());
|
||||
} else {
|
||||
buffer.erase_after(std::next(buffer.begin(), (source-1)));
|
||||
buffer.erase_after(std::next(buffer.begin(), (source - 1)));
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
|
@ -1675,7 +1682,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
|
|||
bool is_prev_special = true; // prefix with space if first token
|
||||
|
||||
if (add_special && vocab.tokenizer_add_bos) {
|
||||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||||
GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_bos_id);
|
||||
is_prev_special = true;
|
||||
}
|
||||
|
|
@ -1710,7 +1717,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
|
|||
// }
|
||||
|
||||
if (add_special && vocab.tokenizer_add_eos) {
|
||||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||||
GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_eos_id);
|
||||
}
|
||||
} break;
|
||||
|
|
@ -1769,7 +1776,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
|
|||
case LLAMA_VOCAB_TYPE_WPM:
|
||||
{
|
||||
if (add_special) {
|
||||
GGML_ASSERT(vocab.special_cls_id != -1);
|
||||
GGML_ASSERT(vocab.special_cls_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_cls_id);
|
||||
}
|
||||
|
||||
|
|
@ -1789,14 +1796,14 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
|
|||
}
|
||||
|
||||
if (add_special) {
|
||||
GGML_ASSERT(vocab.special_sep_id != -1);
|
||||
GGML_ASSERT(vocab.special_sep_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_sep_id);
|
||||
}
|
||||
} break;
|
||||
case LLAMA_VOCAB_TYPE_UGM:
|
||||
{
|
||||
if (add_special && vocab.tokenizer_add_bos) {
|
||||
GGML_ASSERT(vocab.special_bos_id != -1);
|
||||
GGML_ASSERT(vocab.special_bos_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_bos_id);
|
||||
}
|
||||
llm_tokenizer_ugm_session session(vocab);
|
||||
|
|
@ -1821,7 +1828,7 @@ std::vector<llama_vocab::id> llama_tokenize_internal(
|
|||
//}
|
||||
|
||||
if (add_special && vocab.tokenizer_add_eos) {
|
||||
GGML_ASSERT(vocab.special_eos_id != -1);
|
||||
GGML_ASSERT(vocab.special_eos_id != LLAMA_TOKEN_NULL);
|
||||
output.push_back(vocab.special_eos_id);
|
||||
}
|
||||
} break;
|
||||
|
|
@ -1890,7 +1897,7 @@ llama_token_attr llama_token_get_attr_impl(const struct llama_vocab & vocab, lla
|
|||
}
|
||||
|
||||
bool llama_token_is_eog_impl(const struct llama_vocab & vocab, llama_token token) {
|
||||
return token != -1 && vocab.special_eog_ids.count(token) > 0;
|
||||
return token != LLAMA_TOKEN_NULL && vocab.special_eog_ids.count(token) > 0;
|
||||
}
|
||||
|
||||
bool llama_token_is_control_impl(const struct llama_vocab & vocab, llama_token token) {
|
||||
|
|
@ -2134,7 +2141,7 @@ int32_t llama_detokenize_impl(
|
|||
}
|
||||
|
||||
if (remove_special && vocab.tokenizer_add_eos) {
|
||||
if (n_tokens > 0 && tokens[n_tokens-1] == vocab.special_eos_id) {
|
||||
if (n_tokens > 0 && tokens[n_tokens - 1] == vocab.special_eos_id) {
|
||||
n_tokens--;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
275
src/llama.cpp
275
src/llama.cpp
|
|
@ -23,12 +23,8 @@
|
|||
#include <algorithm>
|
||||
#include <array>
|
||||
#include <cassert>
|
||||
#include <cctype>
|
||||
#include <cfloat>
|
||||
#include <cinttypes>
|
||||
#include <climits>
|
||||
#include <cmath>
|
||||
#include <cstdarg>
|
||||
#include <cstddef>
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
|
|
@ -36,7 +32,6 @@
|
|||
#include <ctime>
|
||||
#include <functional>
|
||||
#include <initializer_list>
|
||||
#include <locale>
|
||||
#include <map>
|
||||
#include <numeric>
|
||||
#include <type_traits>
|
||||
|
|
@ -1655,6 +1650,32 @@ static bool llm_load_tensors(
|
|||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_COHERE2:
|
||||
{
|
||||
model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
|
||||
|
||||
// output
|
||||
model.output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
|
||||
// init output from the input tok embed
|
||||
model.output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
|
||||
llama_model_loader::TENSOR_DUPLICATED);
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = model.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 }, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
break;
|
||||
case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
|
||||
{
|
||||
model.tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
|
@ -1934,6 +1955,7 @@ static bool llm_load_tensors(
|
|||
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}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
||||
if (n_expert == 0) {
|
||||
throw std::runtime_error("n_expert must be > 0");
|
||||
|
|
@ -2617,21 +2639,21 @@ static struct ggml_tensor * llm_build_inp_embd(
|
|||
struct ggml_context * ctx,
|
||||
struct llama_context & lctx,
|
||||
const llama_hparams & hparams,
|
||||
const llama_ubatch & batch,
|
||||
const llama_ubatch & ubatch,
|
||||
struct ggml_tensor * tok_embd,
|
||||
const llm_build_cb & cb) {
|
||||
const int64_t n_embd = hparams.n_embd;
|
||||
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
if (batch.token) {
|
||||
lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
|
||||
if (ubatch.token) {
|
||||
lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ubatch.n_tokens);
|
||||
cb(lctx.inp_tokens, "inp_tokens", -1);
|
||||
ggml_set_input(lctx.inp_tokens);
|
||||
|
||||
inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
|
||||
} else {
|
||||
lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
|
||||
lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, ubatch.n_tokens);
|
||||
inpL = lctx.inp_embd;
|
||||
ggml_set_input(lctx.inp_embd);
|
||||
}
|
||||
|
|
@ -2915,12 +2937,14 @@ static struct ggml_tensor * llm_build_moe_ffn(
|
|||
struct ggml_tensor * up_exps,
|
||||
struct ggml_tensor * gate_exps,
|
||||
struct ggml_tensor * down_exps,
|
||||
struct ggml_tensor * exp_probs_b,
|
||||
int64_t n_expert,
|
||||
int64_t n_expert_used,
|
||||
llm_ffn_op_type type_op,
|
||||
bool norm_w,
|
||||
bool scale_w,
|
||||
float w_scale,
|
||||
llama_expert_gating_func_type gating_op,
|
||||
const llm_build_cb & cb,
|
||||
int il) {
|
||||
int64_t n_embd = cur->ne[0];
|
||||
|
|
@ -2929,11 +2953,31 @@ static struct ggml_tensor * llm_build_moe_ffn(
|
|||
ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens]
|
||||
cb(logits, "ffn_moe_logits", il);
|
||||
|
||||
ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
|
||||
ggml_tensor * probs = nullptr;
|
||||
switch (gating_op) {
|
||||
case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX:
|
||||
{
|
||||
probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
|
||||
} break;
|
||||
case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID:
|
||||
{
|
||||
probs = ggml_sigmoid(ctx, logits); // [n_expert, n_tokens]
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
cb(probs, "ffn_moe_probs", il);
|
||||
|
||||
// add experts selection bias - introduced in DeepSeek V3
|
||||
// leave probs unbiased as it's later used to get expert weights
|
||||
ggml_tensor * selection_probs = probs;
|
||||
if (exp_probs_b != nullptr) {
|
||||
selection_probs = ggml_add(ctx, probs, exp_probs_b);
|
||||
cb(selection_probs, "ffn_moe_probs_biased", il);
|
||||
}
|
||||
|
||||
// select experts
|
||||
ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
|
||||
ggml_tensor * selected_experts = ggml_top_k(ctx, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
|
||||
cb(selected_experts->src[0], "ffn_moe_argsort", il);
|
||||
cb(selected_experts, "ffn_moe_topk", il);
|
||||
|
||||
|
|
@ -3218,7 +3262,7 @@ static struct ggml_tensor * llm_build_copy_mask_state(
|
|||
static struct ggml_tensor * llm_build_mamba(
|
||||
struct ggml_context * ctx,
|
||||
struct llama_context & lctx,
|
||||
const llama_ubatch & batch,
|
||||
const llama_ubatch & ubatch,
|
||||
struct ggml_cgraph * graph,
|
||||
struct ggml_tensor * cur,
|
||||
struct ggml_tensor * state_copy,
|
||||
|
|
@ -3234,17 +3278,17 @@ static struct ggml_tensor * llm_build_mamba(
|
|||
const int64_t d_inner = hparams.ssm_d_inner;
|
||||
const int64_t d_state = hparams.ssm_d_state;
|
||||
const int64_t dt_rank = hparams.ssm_dt_rank;
|
||||
const int64_t n_seqs = batch.n_seqs;
|
||||
const int64_t n_seqs = ubatch.n_seqs;
|
||||
// Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers)
|
||||
const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
|
||||
// Use the same RMS norm as the final layer norm
|
||||
const float norm_rms_eps = hparams.f_norm_rms_eps;
|
||||
|
||||
const int64_t n_seq_tokens = batch.n_seq_tokens;
|
||||
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
|
||||
|
||||
GGML_ASSERT(n_seqs != 0);
|
||||
GGML_ASSERT(batch.equal_seqs);
|
||||
GGML_ASSERT(batch.n_tokens == n_seq_tokens * n_seqs);
|
||||
GGML_ASSERT(ubatch.equal_seqs);
|
||||
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
|
||||
|
||||
struct ggml_tensor * conv_states_all = kv.k_l[il];
|
||||
struct ggml_tensor * ssm_states_all = kv.v_l[il];
|
||||
|
|
@ -4072,9 +4116,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, true,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
}
|
||||
|
|
@ -4724,9 +4770,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_GELU, true,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -4865,9 +4913,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, true,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -6113,9 +6163,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, false,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -7755,6 +7807,137 @@ struct llm_build_context {
|
|||
|
||||
}
|
||||
|
||||
struct ggml_cgraph * build_cohere2() {
|
||||
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
|
||||
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
const float f_logit_scale = hparams.f_logit_scale;
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
|
||||
inpL = llm_build_inp_embd(ctx0, lctx, hparams, ubatch, model.tok_embd, cb);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
|
||||
// cohere2 requires different mask for layers using sliding window (SWA)
|
||||
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
|
||||
struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
|
||||
|
||||
// sliding window switch pattern
|
||||
const int32_t sliding_window_pattern = 4;
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
// three layers sliding window attention (window size 4096) and ROPE
|
||||
// fourth layer uses global attention without positional embeddings
|
||||
const bool is_sliding = il % sliding_window_pattern < (sliding_window_pattern - 1);
|
||||
struct ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;
|
||||
|
||||
// norm
|
||||
cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM, cb, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
struct ggml_tensor * ffn_inp = cur;
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// rope freq factors for 128k context
|
||||
struct ggml_tensor * rope_factors = build_rope_factors(il);
|
||||
|
||||
// compute Q and K and RoPE them
|
||||
struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, 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);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, 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);
|
||||
}
|
||||
|
||||
struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, 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);
|
||||
}
|
||||
|
||||
if (is_sliding) {
|
||||
Qcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), 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);
|
||||
|
||||
Kcur = ggml_rope_ext(ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
|
||||
rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
|
||||
attn_factor, beta_fast, beta_slow);
|
||||
cb(Kcur, "Kcur", il);
|
||||
} else {
|
||||
// For non-sliding layers, just reshape without applying RoPE
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
cb(Kcur, "Kcur", il);
|
||||
}
|
||||
|
||||
cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, Kcur, Vcur, Qcur,
|
||||
KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f / sqrtf(float(n_embd_head)), cb, il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// skip computing output for unused tokens
|
||||
struct 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);
|
||||
ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
|
||||
}
|
||||
|
||||
struct ggml_tensor * attn_out = cur;
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
cur = llm_build_ffn(ctx0, lctx, ffn_inp, 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,
|
||||
cb, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
}
|
||||
|
||||
// add together residual + FFN + self-attention
|
||||
cur = ggml_add(ctx0, cur, inpL);
|
||||
cur = ggml_add(ctx0, cur, attn_out);
|
||||
cur = lctx.cvec.apply_to(ctx0, cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM, cb, -1);
|
||||
cb(cur, "result_norm", -1);
|
||||
|
||||
// lm_head
|
||||
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
|
||||
|
||||
if (f_logit_scale) {
|
||||
cur = ggml_scale(ctx0, cur, f_logit_scale);
|
||||
}
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
// ref: https://allenai.org/olmo
|
||||
// based on the original build_llama() function, changes:
|
||||
// * non-parametric layer norm
|
||||
|
|
@ -8107,9 +8290,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, false,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -8504,9 +8689,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, true,
|
||||
false, 0.0,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(cur, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -8645,9 +8832,11 @@ struct llm_build_context {
|
|||
model.layers[il].ffn_up_exps,
|
||||
model.layers[il].ffn_gate_exps,
|
||||
model.layers[il].ffn_down_exps,
|
||||
nullptr,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, false,
|
||||
false, hparams.expert_weights_scale,
|
||||
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
|
||||
cb, il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -8874,9 +9063,11 @@ struct llm_build_context {
|
|||
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, false,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
true, hparams.expert_weights_scale,
|
||||
(enum llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
cb, il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
||||
|
|
@ -10506,6 +10697,10 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
{
|
||||
result = llm.build_command_r();
|
||||
} break;
|
||||
case LLM_ARCH_COHERE2:
|
||||
{
|
||||
result = llm.build_cohere2();
|
||||
} break;
|
||||
case LLM_ARCH_DBRX:
|
||||
{
|
||||
result = llm.build_dbrx();
|
||||
|
|
@ -10639,7 +10834,7 @@ static enum ggml_status llama_graph_compute(
|
|||
// return positive int on warning
|
||||
// return negative int on error
|
||||
//
|
||||
static int llama_decode_internal(
|
||||
static int llama_decode_impl(
|
||||
llama_context & lctx,
|
||||
llama_batch inp_batch) {
|
||||
|
||||
|
|
@ -10974,7 +11169,7 @@ static int llama_decode_internal(
|
|||
// return positive int on warning
|
||||
// return negative int on error
|
||||
//
|
||||
static int llama_encode_internal(
|
||||
static int llama_encode_impl(
|
||||
llama_context & lctx,
|
||||
llama_batch inp_batch) {
|
||||
|
||||
|
|
@ -11156,7 +11351,7 @@ static int llama_encode_internal(
|
|||
}
|
||||
|
||||
// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
|
||||
static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
|
||||
static void llama_kv_cache_defrag_impl(struct llama_context & lctx) {
|
||||
auto & kv_self = lctx.kv_self;
|
||||
|
||||
const auto & hparams = lctx.model.hparams;
|
||||
|
|
@ -11376,7 +11571,7 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
|
|||
//LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
|
||||
}
|
||||
|
||||
static void llama_kv_cache_update_internal(struct llama_context & lctx) {
|
||||
static void llama_kv_cache_update_impl(struct llama_context & lctx) {
|
||||
bool need_reserve = false;
|
||||
|
||||
if (lctx.kv_self.has_shift) {
|
||||
|
|
@ -11412,7 +11607,7 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) {
|
|||
|
||||
// defragment the KV cache if needed
|
||||
if (lctx.kv_self.do_defrag) {
|
||||
llama_kv_cache_defrag_internal(lctx);
|
||||
llama_kv_cache_defrag_impl(lctx);
|
||||
|
||||
need_reserve = true;
|
||||
|
||||
|
|
@ -11441,13 +11636,7 @@ int32_t llama_lora_adapter_set(
|
|||
struct llama_context * ctx,
|
||||
struct llama_lora_adapter * adapter,
|
||||
float scale) {
|
||||
if (ctx->cparams.flash_attn) {
|
||||
LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
|
||||
return -1;
|
||||
}
|
||||
|
||||
ctx->lora_adapters[adapter] = scale;
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
||||
|
|
@ -11582,6 +11771,12 @@ int64_t llama_time_us(void) {
|
|||
struct llama_model * llama_load_model_from_file(
|
||||
const char * path_model,
|
||||
struct llama_model_params params) {
|
||||
return llama_model_load_from_file(path_model, params);
|
||||
}
|
||||
|
||||
struct llama_model * llama_model_load_from_file(
|
||||
const char * path_model,
|
||||
struct llama_model_params params) {
|
||||
ggml_time_init();
|
||||
|
||||
llama_model * model = new llama_model;
|
||||
|
|
@ -11620,7 +11815,7 @@ struct llama_model * llama_load_model_from_file(
|
|||
ggml_backend_reg_t rpc_reg = ggml_backend_reg_by_name("RPC");
|
||||
if (!rpc_reg) {
|
||||
LLAMA_LOG_ERROR("%s: failed to find RPC backend\n", __func__);
|
||||
llama_free_model(model);
|
||||
llama_model_free(model);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
@ -11628,7 +11823,7 @@ struct llama_model * llama_load_model_from_file(
|
|||
ggml_backend_rpc_add_device_t ggml_backend_rpc_add_device_fn = (ggml_backend_rpc_add_device_t) ggml_backend_reg_get_proc_address(rpc_reg, "ggml_backend_rpc_add_device");
|
||||
if (!ggml_backend_rpc_add_device_fn) {
|
||||
LLAMA_LOG_ERROR("%s: failed to find RPC device add function\n", __func__);
|
||||
llama_free_model(model);
|
||||
llama_model_free(model);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
@ -11638,7 +11833,7 @@ struct llama_model * llama_load_model_from_file(
|
|||
model->devices.push_back(dev);
|
||||
} else {
|
||||
LLAMA_LOG_ERROR("%s: failed to add RPC device for server '%s'\n", __func__, server.c_str());
|
||||
llama_free_model(model);
|
||||
llama_model_free(model);
|
||||
return nullptr;
|
||||
}
|
||||
}
|
||||
|
|
@ -11670,7 +11865,7 @@ struct llama_model * llama_load_model_from_file(
|
|||
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_free_model(model);
|
||||
llama_model_free(model);
|
||||
return nullptr;
|
||||
}
|
||||
ggml_backend_dev_t main_gpu = model->devices[params.main_gpu];
|
||||
|
|
@ -11693,7 +11888,7 @@ struct llama_model * llama_load_model_from_file(
|
|||
LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
|
||||
}
|
||||
|
||||
llama_free_model(model);
|
||||
llama_model_free(model);
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
|
@ -12117,7 +12312,7 @@ void llama_kv_cache_defrag(struct llama_context * ctx) {
|
|||
}
|
||||
|
||||
void llama_kv_cache_update(struct llama_context * ctx) {
|
||||
llama_kv_cache_update_internal(*ctx);
|
||||
llama_kv_cache_update_impl(*ctx);
|
||||
}
|
||||
|
||||
bool llama_kv_cache_can_shift(struct llama_context * ctx) {
|
||||
|
|
@ -12129,7 +12324,7 @@ bool llama_kv_cache_can_shift(struct llama_context * ctx) {
|
|||
int32_t llama_encode(
|
||||
struct llama_context * ctx,
|
||||
struct llama_batch batch) {
|
||||
const int ret = llama_encode_internal(*ctx, batch);
|
||||
const int ret = llama_encode_impl(*ctx, batch);
|
||||
if (ret != 0) {
|
||||
LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
|
||||
}
|
||||
|
|
@ -12140,7 +12335,7 @@ int32_t llama_encode(
|
|||
int32_t llama_decode(
|
||||
struct llama_context * ctx,
|
||||
struct llama_batch batch) {
|
||||
const int ret = llama_decode_internal(*ctx, batch);
|
||||
const int ret = llama_decode_impl(*ctx, batch);
|
||||
if (ret != 0) {
|
||||
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
|
||||
}
|
||||
|
|
@ -12360,16 +12555,16 @@ int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix,
|
|||
return 0;
|
||||
}
|
||||
|
||||
int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
|
||||
int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count) {
|
||||
std::string str_split_path(split_path);
|
||||
char postfix[32];
|
||||
snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
|
||||
std::string str_postfix(postfix);
|
||||
|
||||
// check if dest ends with postfix
|
||||
// check if split_prefix ends with postfix
|
||||
int size_prefix = str_split_path.size() - str_postfix.size();
|
||||
if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
|
||||
snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
|
||||
snprintf(split_prefix, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
|
||||
return size_prefix;
|
||||
}
|
||||
|
||||
|
|
@ -12378,6 +12573,8 @@ int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int
|
|||
|
||||
const char * llama_print_system_info(void) {
|
||||
static std::string s;
|
||||
s.clear(); // Clear the string, since it's static, otherwise it will accumulate data from previous calls.
|
||||
|
||||
|
||||
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
|
||||
auto * reg = ggml_backend_reg_get(i);
|
||||
|
|
|
|||
|
|
@ -675,18 +675,24 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
|
|||
{ "\\p{N}", unicode_cpt_flags::NUMBER },
|
||||
{ "\\p{L}", unicode_cpt_flags::LETTER },
|
||||
{ "\\p{P}", unicode_cpt_flags::PUNCTUATION },
|
||||
{ "\\p{M}", unicode_cpt_flags::ACCENT_MARK },
|
||||
{ "\\p{S}", unicode_cpt_flags::SYMBOL },
|
||||
};
|
||||
|
||||
static const std::map<int, int> k_ucat_cpt = {
|
||||
{ unicode_cpt_flags::NUMBER, 0xD1 },
|
||||
{ unicode_cpt_flags::LETTER, 0xD2 },
|
||||
{ unicode_cpt_flags::PUNCTUATION, 0xD3 },
|
||||
{ unicode_cpt_flags::ACCENT_MARK, 0xD4 },
|
||||
{ unicode_cpt_flags::SYMBOL, 0xD5 },
|
||||
};
|
||||
|
||||
static const std::map<int, std::string> k_ucat_map = {
|
||||
{ unicode_cpt_flags::NUMBER, "\x30-\x39" }, // 0-9
|
||||
{ unicode_cpt_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z
|
||||
{ unicode_cpt_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
|
||||
{ unicode_cpt_flags::ACCENT_MARK, "" }, // no sub-128 codepoints
|
||||
{ unicode_cpt_flags::SYMBOL, "\\\x24\\\x2B\x3C-\x3E\x5E\x60\\\x7C" }, // $+<=>^`|
|
||||
};
|
||||
|
||||
// compute collapsed codepoints only if needed by at least one regex
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue