From 147a5216363c5b2edcdeb17f01ef3b28372ec872 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 22 Dec 2025 11:00:37 +0100 Subject: [PATCH 01/25] tool/ex/tests: consistently free ctx, then model (#18168) --- common/common.cpp | 2 ++ src/llama-context.cpp | 31 +++++++++++++-------------- tests/test-grammar-llguidance.cpp | 3 +++ tests/test-tokenizer-0.cpp | 2 +- tests/test-tokenizer-1-bpe.cpp | 2 +- tests/test-tokenizer-1-spm.cpp | 2 +- tools/batched-bench/batched-bench.cpp | 11 ++++++++++ tools/llama-bench/llama-bench.cpp | 14 ++++++++++++ 8 files changed, 48 insertions(+), 19 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index d4e8c7405e..acf2ec841d 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1078,6 +1078,8 @@ struct common_init_result::impl { impl() = default; ~impl() = default; + // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top + llama_model_ptr model; llama_context_ptr context; diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 8786d4ee3e..015ebae71d 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -459,23 +459,22 @@ llama_context::llama_context( } llama_context::~llama_context() { - // FIXME this currently results in a use-after-free bug if the model is freed before the context - // if (!model.hparams.no_alloc) { - // for (size_t i = 0; i < backend_ptrs.size(); ++i) { - // ggml_backend_t backend = backend_ptrs[i]; - // ggml_backend_buffer_type_t buft = backend_buft[i]; + if (!model.hparams.no_alloc) { + for (size_t i = 0; i < backend_ptrs.size(); ++i) { + ggml_backend_t backend = backend_ptrs[i]; + ggml_backend_buffer_type_t buft = backend_buft[i]; - // const size_t size_exp = backend_buf_exp_size[i]; - // const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend); - // if (size_exp == size_act) { - // LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n", - // __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); - // } else { - // LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n", - // __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); - // } - // } - // } + const size_t size_exp = backend_buf_exp_size[i]; + const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend); + if (size_exp == size_act) { + LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n", + __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); + } else { + LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n", + __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); + } + } + } ggml_opt_free(opt_ctx); } diff --git a/tests/test-grammar-llguidance.cpp b/tests/test-grammar-llguidance.cpp index 566b039a07..34746c200c 100644 --- a/tests/test-grammar-llguidance.cpp +++ b/tests/test-grammar-llguidance.cpp @@ -1196,6 +1196,9 @@ int main(int argc, const char ** argv) { test_sampler_chain(); + llama_free(ctx); + llama_model_free(model); + fprintf(stdout, "All tests passed.\n"); return 0; } diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp index 59dda48772..37f8312c46 100644 --- a/tests/test-tokenizer-0.cpp +++ b/tests/test-tokenizer-0.cpp @@ -300,8 +300,8 @@ int main(int argc, char **argv) { fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str()); } - llama_model_free(model); llama_free(ctx); + llama_model_free(model); llama_backend_free(); diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index b183da47f3..505dbfdb93 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -146,8 +146,8 @@ int main(int argc, char **argv) { } } - llama_model_free(model); llama_free(ctx); + llama_model_free(model); llama_backend_free(); diff --git a/tests/test-tokenizer-1-spm.cpp b/tests/test-tokenizer-1-spm.cpp index ba6e94ba8e..8e370d2c7b 100644 --- a/tests/test-tokenizer-1-spm.cpp +++ b/tests/test-tokenizer-1-spm.cpp @@ -116,8 +116,8 @@ int main(int argc, char ** argv) { } } - llama_model_free(model); llama_free(ctx); + llama_model_free(model); llama_backend_free(); diff --git a/tools/batched-bench/batched-bench.cpp b/tools/batched-bench/batched-bench.cpp index 2032a386bb..0f627c5ff6 100644 --- a/tools/batched-bench/batched-bench.cpp +++ b/tools/batched-bench/batched-bench.cpp @@ -55,6 +55,7 @@ int main(int argc, char ** argv) { if (ctx == NULL) { fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + llama_model_free(model); return 1; } @@ -108,6 +109,8 @@ int main(int argc, char ** argv) { if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) { LOG_ERR("%s: llama_decode() failed\n", __func__); + llama_free(ctx); + llama_model_free(model); return 1; } } @@ -147,6 +150,8 @@ int main(int argc, char ** argv) { if (!decode_helper(ctx, batch, ctx_params.n_batch, false)) { LOG_ERR("%s: llama_decode() failed\n", __func__); + llama_free(ctx); + llama_model_free(model); return 1; } @@ -165,6 +170,8 @@ int main(int argc, char ** argv) { common_batch_add(batch, get_token_rand(), pp + 0, { 0 }, true); if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) { LOG_ERR("%s: llama_decode() failed\n", __func__); + llama_free(ctx); + llama_model_free(model); return 1; } llama_memory_seq_rm(mem, 0, pp, -1); @@ -184,6 +191,8 @@ int main(int argc, char ** argv) { if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) { LOG_ERR("%s: llama_decode() failed\n", __func__); + llama_free(ctx); + llama_model_free(model); return 1; } } @@ -200,6 +209,8 @@ int main(int argc, char ** argv) { if (!decode_helper(ctx, batch, ctx_params.n_batch, true)) { LOG_ERR("%s: llama_decode() failed\n", __func__); + llama_free(ctx); + llama_model_free(model); return 1; } } diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp index 0be6ed6948..b431c7f31b 100644 --- a/tools/llama-bench/llama-bench.cpp +++ b/tools/llama-bench/llama-bench.cpp @@ -2102,6 +2102,8 @@ int main(int argc, char ** argv) { struct ggml_threadpool_params tpp = ggml_threadpool_params_default(t.n_threads); if (!parse_cpu_mask(t.cpu_mask, tpp.cpumask)) { fprintf(stderr, "%s: failed to parse cpu-mask: %s\n", __func__, t.cpu_mask.c_str()); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } tpp.strict_cpu = t.cpu_strict; @@ -2111,6 +2113,8 @@ int main(int argc, char ** argv) { struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp); if (!threadpool) { fprintf(stderr, "%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } @@ -2126,6 +2130,8 @@ int main(int argc, char ** argv) { bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); if (!res) { fprintf(stderr, "%s: error: failed to run prompt warmup\n", __func__); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } } @@ -2136,6 +2142,8 @@ int main(int argc, char ** argv) { bool res = test_gen(ctx, 1, t.n_threads); if (!res) { fprintf(stderr, "%s: error: failed to run gen warmup\n", __func__); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } } @@ -2164,6 +2172,8 @@ int main(int argc, char ** argv) { bool res = test_prompt(ctx, t.n_depth, t.n_batch, t.n_threads); if (!res) { fprintf(stderr, "%s: error: failed to run depth\n", __func__); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } @@ -2189,6 +2199,8 @@ int main(int argc, char ** argv) { bool res = test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads); if (!res) { fprintf(stderr, "%s: error: failed to run prompt\n", __func__); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } } @@ -2200,6 +2212,8 @@ int main(int argc, char ** argv) { bool res = test_gen(ctx, t.n_gen, t.n_threads); if (!res) { fprintf(stderr, "%s: error: failed to run gen\n", __func__); + llama_free(ctx); + llama_model_free(lmodel); exit(1); } } From 86af848153294b65442f7bddfa3d24baa5cb0c81 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 22 Dec 2025 12:22:01 +0100 Subject: [PATCH 02/25] server: (docs) remove mention about extra_args (#18262) --- tools/server/README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/tools/server/README.md b/tools/server/README.md index 71f1d4777c..29ce254652 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -1567,7 +1567,6 @@ Load a model Payload: - `model`: name of the model to be loaded. -- `extra_args`: (optional) an array of additional arguments to be passed to the model instance. Note: you must start the server with `--models-allow-extra-args` to enable this feature. ```json { From a28310488c3fc68511a0d404d9836b0eae4e498b Mon Sep 17 00:00:00 2001 From: Aaron Teo Date: Mon, 22 Dec 2025 20:03:49 +0800 Subject: [PATCH 03/25] convert: rework ftype heuristics (#18214) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * convert: rework ftype heuristics Signed-off-by: Aaron Teo convert: fix type-check Signed-off-by: Aaron Teo convert: bring back heuristics comment Signed-off-by: Aaron Teo * convert: revert to using first tensor Signed-off-by: Aaron Teo * convert: rework heuristics logic Signed-off-by: Aaron Teo * convert: rm redundant float32 check Co-authored-by: Sigbjørn Skjæret --------- Signed-off-by: Aaron Teo Co-authored-by: Sigbjørn Skjæret --- convert_hf_to_gguf.py | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 432be59946..22f703e6ad 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -141,16 +141,24 @@ class ModelBase: self.model_name = model_name self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py - # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type + # Apply heuristics to figure out typical tensor encoding based on first tensor's dtype + # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. if self.ftype == gguf.LlamaFileType.GUESSED: - # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. - _, first_tensor = next(self.get_tensors()) - if first_tensor.dtype == torch.float16: - logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})") - self.ftype = gguf.LlamaFileType.MOSTLY_F16 + for _, tensor in self.get_tensors(): + if tensor.dim() < 2: + continue + + if tensor.dtype == torch.bfloat16: + self.ftype = gguf.LlamaFileType.MOSTLY_BF16 + logger.info("heuristics detected bfloat16 tensor dtype, setting --outtype bf16") + break + elif tensor.dtype == torch.float16: + self.ftype = gguf.LlamaFileType.MOSTLY_F16 + logger.info("heuristics detected float16 tensor dtype, setting --outtype f16") + break else: - logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})") - self.ftype = gguf.LlamaFileType.MOSTLY_BF16 + self.ftype = gguf.LlamaFileType.MOSTLY_F16 + logger.info("heuristics unable to detect tensor dtype, defaulting to --outtype f16") self.dequant_model() @@ -10557,8 +10565,8 @@ def parse_args() -> argparse.Namespace: help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", ) parser.add_argument( - "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="f16", - help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", + "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "tq1_0", "tq2_0", "auto"], default="auto", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, tq1_0 or tq2_0 for ternary, and auto for the highest-fidelity 16-bit float type", ) parser.add_argument( "--bigendian", action="store_true", From ee746429821d9e1dd8b386c0f490072e47eea24b Mon Sep 17 00:00:00 2001 From: Mattt Date: Mon, 22 Dec 2025 04:11:46 -0800 Subject: [PATCH 04/25] release: update release workflow to store XCFramework as Zip file (#18284) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update release workflow to store XCFramework as Zip file * Add comments to document Zip file requirement for XCFramework * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- .github/workflows/release.yml | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 11f850511f..4cc2f4665c 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -688,13 +688,15 @@ jobs: - name: Pack artifacts id: pack_artifacts run: | - tar -czvf llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz -C build-apple llama.xcframework + # Zip file is required for Swift Package Manager, which does not support tar.gz for binary targets. + # For more details, see https://developer.apple.com/documentation/xcode/distributing-binary-frameworks-as-swift-packages + zip -r -y llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework - name: Upload artifacts uses: actions/upload-artifact@v4 with: - path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz - name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz + path: llama-${{ steps.tag.outputs.name }}-xcframework.zip + name: llama-${{ steps.tag.outputs.name }}-xcframework.zip openEuler-cann: @@ -863,7 +865,7 @@ jobs: **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz) - - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz) + - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.tar.gz) From 3997c78e339be05d8f64934f0765a6eead413e6e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 22 Dec 2025 13:21:43 +0100 Subject: [PATCH 05/25] server: fix data race in to_json_anthropic (#18283) --- tools/server/server-task.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index 360826062b..487e70b34e 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -1153,7 +1153,7 @@ json server_task_result_rerank::to_json() { json server_task_result_cmpl_partial::to_json_anthropic() { json events = json::array(); bool first = (n_decoded == 1); - static bool text_block_started = false; + bool text_block_started = false; if (first) { text_block_started = false; From 6ce863c8039b2dbfef2a22408f347842d438f03d Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 22 Dec 2025 14:23:34 +0100 Subject: [PATCH 06/25] server: prevent data race from HTTP threads (#18263) * server: prevent data race from HTTP threads * fix params * fix default_generation_settings * nits: make handle_completions_impl looks less strange * stricter const * fix GGML_ASSERT(idx < states.size()) * move index to be managed by server_response_reader * http: make sure req & res lifecycle are tied together * fix compile * fix index handling buggy * fix data race for lora endpoint * nits: fix shadow variable * nits: revert redundant changes * nits: correct naming for json_webui_settings --- tools/cli/cli.cpp | 2 +- tools/server/server-common.cpp | 27 +- tools/server/server-common.h | 5 +- tools/server/server-context.cpp | 573 +++++++++++++++++--------------- tools/server/server-context.h | 62 +++- tools/server/server-http.cpp | 26 +- tools/server/server-queue.cpp | 15 +- tools/server/server-queue.h | 7 +- tools/server/server-task.cpp | 42 ++- tools/server/server-task.h | 63 ++-- tools/server/server.cpp | 3 +- 11 files changed, 459 insertions(+), 366 deletions(-) diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp index 128679d020..2f0ffea1c2 100644 --- a/tools/cli/cli.cpp +++ b/tools/cli/cli.cpp @@ -216,7 +216,7 @@ int main(int argc, char ** argv) { ctx_cli.ctx_server.start_loop(); }); - auto inf = ctx_cli.ctx_server.get_info(); + auto inf = ctx_cli.ctx_server.get_meta(); std::string modalities = "text"; if (inf.has_inp_image) { modalities += ", vision"; diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp index ab6b3aa7ce..b02afaefda 100644 --- a/tools/server/server-common.cpp +++ b/tools/server/server-common.cpp @@ -115,26 +115,14 @@ bool lora_should_clear_cache( !lora_all_alora(next)); } -std::vector parse_lora_request( - const std::vector & lora_base, - const json & data) { - std::vector lora(lora_base); - int max_idx = lora.size(); - - // clear existing value - for (auto & entry : lora) { - entry.scale = 0.0f; - } +std::map parse_lora_request(const json & data) { + std::map lora; // set value for (const auto & entry : data) { int id = json_value(entry, "id", -1); float scale = json_value(entry, "scale", 0.0f); - if (0 <= id && id < max_idx) { - lora[id].scale = scale; - } else { - throw std::runtime_error("invalid adapter id"); - } + lora[id] = scale; } return lora; @@ -1435,7 +1423,7 @@ std::string safe_json_to_str(const json & data) { // TODO: reuse llama_detokenize template -static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { +static std::string tokens_to_str(const llama_vocab * ctx, Iter begin, Iter end) { std::string ret; for (; begin != end; ++begin) { ret += common_token_to_piece(ctx, *begin); @@ -1445,7 +1433,12 @@ static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { } std::string tokens_to_str(llama_context * ctx, const llama_tokens & tokens) { - return tokens_to_str(ctx, tokens.begin(), tokens.end()); + auto model = llama_get_model(ctx); + return tokens_to_str(llama_model_get_vocab(model), tokens.begin(), tokens.end()); +} + +std::string tokens_to_str(const llama_vocab * vocab, const llama_tokens & tokens) { + return tokens_to_str(vocab, tokens.begin(), tokens.end()); } // format incomplete utf-8 multibyte character for output diff --git a/tools/server/server-common.h b/tools/server/server-common.h index 0629bb5edd..152a2a3c46 100644 --- a/tools/server/server-common.h +++ b/tools/server/server-common.h @@ -107,9 +107,7 @@ bool lora_should_clear_cache( const std::vector & current, const std::vector & next); -std::vector parse_lora_request( - const std::vector & lora_base, - const json & data); +std::map parse_lora_request(const json & data); bool are_lora_equal( const std::vector & l1, @@ -325,6 +323,7 @@ std::vector get_token_probabilities(llama_context * ctx, int i std::string safe_json_to_str(const json & data); std::string tokens_to_str(llama_context * ctx, const llama_tokens & tokens); +std::string tokens_to_str(const llama_vocab * vocab, const llama_tokens & tokens); // format incomplete utf-8 multibyte character for output std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token); diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index cde34e6533..a132b87c84 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -507,19 +507,42 @@ struct server_metrics { // struct server_context_impl { + friend struct server_context; + +public: + // only use these pointers outside of this class: + // - when not in sleeping state + // - and, with thread-safe APIs (e.g., tokenizer calls) + llama_model * model = nullptr; + mtmd_context * mctx = nullptr; + const llama_vocab * vocab = nullptr; + + server_queue queue_tasks; + server_response queue_results; + + common_chat_templates_ptr chat_templates; + oaicompat_parser_options oai_parser_opt; + + ~server_context_impl() { + if (!sleeping) { + // destroy() is already called when entering sleeping state + // we don't call it again here to avoid double free + destroy(); + } + } + +private: + // note: accessing these fields outside of this class is not thread-safe + // use server_context methods instead + common_params params_base; // note: keep these alive - they determine the lifetime of the model, context, etc. common_init_result_ptr llama_init; common_init_result_ptr llama_init_dft; - llama_model * model = nullptr; llama_context * ctx = nullptr; - // multimodal - mtmd_context * mctx = nullptr; - - const llama_vocab * vocab = nullptr; bool vocab_dft_compatible = true; llama_model * model_dft = nullptr; @@ -537,35 +560,19 @@ struct server_context_impl { int slots_debug = 0; - server_queue queue_tasks; - server_response queue_results; - std::unique_ptr prompt_cache; server_metrics metrics; - // cached responses for HTTP API (read-only from HTTP threads) - json json_server_props = json::object(); - json json_server_model_meta = json::object(); + json json_webui_settings = json::object(); // Necessary similarity of prompt for slot selection float slot_prompt_similarity = 0.0f; std::string model_name; // name of the loaded model, to be used by API - common_chat_templates_ptr chat_templates; - oaicompat_parser_options oai_parser_opt; - bool sleeping = false; - ~server_context_impl() { - if (!sleeping) { - // destroy() is already called when entering sleeping state - // we don't call it again here to avoid double free - destroy(); - } - } - void destroy() { llama_init.reset(); ctx = nullptr; @@ -871,17 +878,7 @@ struct server_context_impl { metrics.init(); - if (!populate_json_responses()) { - SRV_ERR("%s", "failed to populate JSON responses\n"); - return false; - } - - return true; - } - - bool populate_json_responses() { // populate webui settings - json json_webui_settings = json::object(); { if (!params_base.webui_config_json.empty()) { try { @@ -893,53 +890,6 @@ struct server_context_impl { } } - // populate server properties - { - task_params params; - params.sampling = params_base.sampling; - json default_generation_settings_for_props = json { - {"params", params.to_json(true)}, - {"n_ctx", get_slot_n_ctx()}, - }; - - json_server_props = { - { "default_generation_settings", default_generation_settings_for_props }, - { "total_slots", params_base.n_parallel }, - { "model_alias", model_name }, - { "model_path", params_base.model.path }, - { "modalities", json { - {"vision", oai_parser_opt.allow_image}, - {"audio", oai_parser_opt.allow_audio}, - } }, - { "endpoint_slots", params_base.endpoint_slots }, - { "endpoint_props", params_base.endpoint_props }, - { "endpoint_metrics", params_base.endpoint_metrics }, - { "webui", params_base.webui }, - { "webui_settings", json_webui_settings }, - { "chat_template", common_chat_templates_source(chat_templates.get()) }, - { "bos_token", common_token_to_piece(ctx, llama_vocab_bos(vocab), /* special= */ true)}, - { "eos_token", common_token_to_piece(ctx, llama_vocab_eos(vocab), /* special= */ true)}, - { "build_info", build_info }, - }; - if (params_base.use_jinja) { - if (auto tool_use_src = common_chat_templates_source(chat_templates.get(), "tool_use")) { - json_server_props["chat_template_tool_use"] = tool_use_src; - } - } - } - - // populate model metadata - { - json_server_model_meta = { - {"vocab_type", llama_vocab_type (vocab)}, - {"n_vocab", llama_vocab_n_tokens (vocab)}, - {"n_ctx_train", llama_model_n_ctx_train(model)}, - {"n_embd", llama_model_n_embd (model)}, - {"n_params", llama_model_n_params (model)}, - {"size", llama_model_size (model)}, - }; - } - return true; } @@ -1098,18 +1048,37 @@ struct server_context_impl { return res; } + std::vector construct_lora_list(const std::map & config) { + std::vector output = params_base.lora_adapters; // copy + for (size_t i = 0; i < output.size(); ++i) { + auto it = config.find(i); + if (it != config.end()) { + output[i].scale = it->second; + } else { + output[i].scale = 0.0f; + } + } + return output; + } + bool launch_slot_with_task(server_slot & slot, server_task && task) { slot.reset(); - if (!are_lora_equal(task.params.lora, slot.lora)) { - // if lora has changed, check to see if the cache should be cleared - if (lora_should_clear_cache(slot.lora, task.params.lora)) { - SLT_INF(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), task.params.lora.size()); - slot.prompt.tokens.clear(); - } else { - SLT_INF(slot, "keeping cache for alora. %zu target loras\n", task.params.lora.size()); + // process per-request lora adapters + if (!task.params.lora.empty()) { + auto task_loras = construct_lora_list(task.params.lora); + if (!are_lora_equal(task_loras, slot.lora)) { + // if lora has changed, check to see if the cache should be cleared + if (lora_should_clear_cache(slot.lora, task_loras)) { + SLT_INF(slot, "clearing cache for lora change. %zu loras -> %zu loras\n", slot.lora.size(), task.params.lora.size()); + slot.prompt.tokens.clear(); + } else { + SLT_INF(slot, "keeping cache for alora. %zu target loras\n", task_loras.size()); + } + slot.lora = task_loras; } - slot.lora = task.params.lora; + } else { + slot.lora = params_base.lora_adapters; } // if using alora, make sure it's only a single one requested and active @@ -1839,9 +1808,41 @@ struct server_context_impl { res->n_erased = n_erased; queue_results.send(std::move(res)); } break; + case SERVER_TASK_TYPE_GET_LORA: + { + // TODO @ngxson : make lora_adapters a dedicated member of server_context + auto & loras = params_base.lora_adapters; + auto res = std::make_unique(); + res->id = task.id; + for (size_t i = 0; i < loras.size(); ++i) { + auto & lora = loras[i]; + std::string alora_invocation_string = ""; + const uint64_t n_alora_tokens = llama_adapter_get_alora_n_invocation_tokens(lora.ptr); + llama_tokens alora_invocation_tokens; + if (n_alora_tokens) { + const llama_token * alora_tokens = llama_adapter_get_alora_invocation_tokens(lora.ptr); + for (uint64_t j = 0; j < n_alora_tokens; ++j) { + alora_invocation_string += common_token_to_piece(vocab, alora_tokens[j]); + alora_invocation_tokens.push_back(alora_tokens[j]); + } + } + res->loras.push_back(server_task_result_get_lora::lora{ + lora, + alora_invocation_string, + alora_invocation_tokens, + }); + } + queue_results.send(std::move(res)); + } break; case SERVER_TASK_TYPE_SET_LORA: { - params_base.lora_adapters = std::move(task.set_lora); + auto new_loras = construct_lora_list(task.set_lora); + // logging + for (size_t i = 0; i < new_loras.size(); ++i) { + SRV_INF("set lora adapter idx=%zu scale=%f\n", i, new_loras[i].scale); + } + // TODO @ngxson : make lora_adapters a dedicated member of server_context + params_base.lora_adapters = new_loras; auto res = std::make_unique(); res->id = task.id; queue_results.send(std::move(res)); @@ -2781,12 +2782,34 @@ server_response_reader server_context::get_response_reader() { return impl->get_response_reader(); } -server_context_info server_context::get_info() const { - return server_context_info { - /* build_info */ build_info, - /* model_name */ impl->model_name, - /* has_inp_image */ impl->oai_parser_opt.allow_image, - /* has_inp_audio */ impl->oai_parser_opt.allow_audio, +server_context_meta server_context::get_meta() const { + auto tool_use_src = common_chat_templates_source(impl->chat_templates.get(), "tool_use"); + return server_context_meta { + /* build_info */ build_info, + /* model_name */ impl->model_name, + /* model_path */ impl->params_base.model.path, + /* has_mtmd */ impl->mctx != nullptr, + /* has_inp_image */ impl->oai_parser_opt.allow_image, + /* has_inp_audio */ impl->oai_parser_opt.allow_audio, + /* json_webui_settings */ impl->json_webui_settings, + /* slot_n_ctx */ impl->get_slot_n_ctx(), + /* pooling_type */ llama_pooling_type(impl->ctx), + + /* chat_template */ common_chat_templates_source(impl->chat_templates.get()), + /* chat_template_tool_use */ tool_use_src ? tool_use_src : "", + + /* bos_token_str */ common_token_to_piece(impl->ctx, llama_vocab_bos(impl->vocab), true), + /* eos_token_str */ common_token_to_piece(impl->ctx, llama_vocab_eos(impl->vocab), true), + /* fim_pre_token */ llama_vocab_fim_pre(impl->vocab), + /* fim_sub_token */ llama_vocab_fim_suf(impl->vocab), + /* fim_mid_token */ llama_vocab_fim_mid(impl->vocab), + + /* model_vocab_type */ llama_vocab_type(impl->vocab), + /* model_vocab_n_tokens */ llama_vocab_n_tokens(impl->vocab), + /* model_n_ctx_train */ llama_model_n_ctx_train(impl->model), + /* model_n_embd_inp */ llama_model_n_embd(impl->model), + /* model_n_params */ llama_model_n_params(impl->model), + /* model_size */ llama_model_size(impl->model), }; } @@ -2796,12 +2819,12 @@ server_context_info server_context::get_info() const { // may have bypass_sleep = true if the task does not use ctx_server struct server_res_generator : server_http_res { server_response_reader rd; - server_res_generator(server_context_impl & ctx_server, bool bypass_sleep = false) - : rd(ctx_server.queue_tasks, ctx_server.queue_results, HTTP_POLLING_SECONDS) { + server_res_generator(server_queue & queue_tasks, server_response & queue_results, int sleep_idle_seconds, bool bypass_sleep = false) + : rd(queue_tasks, queue_results, HTTP_POLLING_SECONDS) { // fast path in case sleeping is disabled - bypass_sleep |= ctx_server.params_base.sleep_idle_seconds < 0; + bypass_sleep |= sleep_idle_seconds < 0; if (!bypass_sleep) { - ctx_server.queue_tasks.wait_until_no_sleep(); + queue_tasks.wait_until_no_sleep(); } } void ok(const json & response_data) { @@ -2820,17 +2843,15 @@ struct server_res_generator : server_http_res { // server_routes // -static std::unique_ptr handle_completions_impl( - std::unique_ptr && res_ptr, - server_context_impl & ctx_server, +std::unique_ptr server_routes::handle_completions_impl( + const server_http_req & req, server_task_type type, const json & data, const std::vector & files, - const std::function & should_stop, task_response_type res_type) { GGML_ASSERT(type == SERVER_TASK_TYPE_COMPLETION || type == SERVER_TASK_TYPE_INFILL); - auto res = std::move(res_ptr); + auto res = create_response(); auto completion_id = gen_chatcmplid(); auto & rd = res->rd; @@ -2852,32 +2873,30 @@ static std::unique_ptr handle_completions_impl( inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true); } tasks.reserve(inputs.size()); - int idx = 0; for (size_t i = 0; i < inputs.size(); i++) { server_task task = server_task(type); - task.id = ctx_server.queue_tasks.get_new_id(); - task.index = idx++; + task.id = rd.get_new_id(); task.tokens = std::move(inputs[i]); task.params = server_task::params_from_json_cmpl( - ctx_server.ctx, - ctx_server.params_base, + ctx_server.vocab, + params, + meta->slot_n_ctx, data); task.id_slot = json_value(data, "id_slot", -1); // OAI-compat task.params.res_type = res_type; task.params.oaicompat_cmpl_id = completion_id; - task.params.oaicompat_model = ctx_server.model_name; + task.params.oaicompat_model = meta->model_name; if (task.params.n_cmpl > 1) { task.n_children = task.params.n_cmpl - 1; for (size_t j = 0; j < task.n_children; j++) { server_task child = task.create_child( task.id, - ctx_server.queue_tasks.get_new_id(), - idx++); + rd.get_new_id()); tasks.push_back(std::move(child)); } } @@ -2895,7 +2914,7 @@ static std::unique_ptr handle_completions_impl( if (!stream) { // non-stream, wait for the results - auto all_results = rd.wait_for_all(should_stop); + auto all_results = rd.wait_for_all(req.should_stop); if (all_results.is_terminated) { return res; // connection is closed } else if (all_results.error) { @@ -2927,7 +2946,7 @@ static std::unique_ptr handle_completions_impl( // in streaming mode, the first error must be treated as non-stream response // this is to match the OAI API behavior // ref: https://github.com/ggml-org/llama.cpp/pull/16486#discussion_r2419657309 - server_task_result_ptr first_result = rd.next(should_stop); + server_task_result_ptr first_result = rd.next(req.should_stop); if (first_result == nullptr) { return res; // connection is closed } else if (first_result->is_error()) { @@ -2950,7 +2969,7 @@ static std::unique_ptr handle_completions_impl( } res->status = 200; res->content_type = "text/event-stream"; - res->next = [res_this = res.get(), res_type, &should_stop](std::string & output) -> bool { + res->next = [res_this = res.get(), res_type, &req](std::string & output) -> bool { static auto format_error = [](task_response_type res_type, const json & res_json) { if (res_type == TASK_RESPONSE_TYPE_ANTHROPIC) { return format_anthropic_sse({ @@ -2963,7 +2982,7 @@ static std::unique_ptr handle_completions_impl( }; try { - if (should_stop()) { + if (req.should_stop()) { SRV_DBG("%s", "stopping streaming due to should_stop condition\n"); return false; // should_stop condition met } @@ -2992,7 +3011,7 @@ static std::unique_ptr handle_completions_impl( } // receive subsequent results - auto result = rd.next(should_stop); + auto result = rd.next(req.should_stop); if (result == nullptr) { SRV_DBG("%s", "stopping streaming due to should_stop condition\n"); return false; // should_stop condition met @@ -3033,37 +3052,51 @@ static std::unique_ptr handle_completions_impl( return res; } +std::unique_ptr server_routes::create_response(bool bypass_sleep) { + return std::make_unique(queue_tasks, queue_results, params.sleep_idle_seconds, bypass_sleep); +} + +server_routes::server_routes(const common_params & params, server_context & ctx_server) + : params(params), + ctx_server(*ctx_server.impl), + queue_tasks(ctx_server.impl->queue_tasks), + queue_results(ctx_server.impl->queue_results) { + init_routes(); +} + void server_routes::init_routes() { - // IMPORTANT: all lambda functions must start with std::make_unique + // IMPORTANT: all lambda functions must start with create_response() // this is to ensure that the server_res_generator can handle sleeping case correctly this->get_health = [this](const server_http_req &) { // error and loading states are handled by middleware - auto res = std::make_unique(ctx_server, true); + auto res = create_response(true); + + // this endpoint can be accessed during sleeping + // the next LOC is to avoid someone accidentally use ctx_server + bool server_ctx; // do NOT delete this line + GGML_UNUSED(server_ctx); + res->ok({{"status", "ok"}}); return res; }; - this->get_metrics = [this](const server_http_req &) { - auto res = std::make_unique(ctx_server); + this->get_metrics = [this](const server_http_req & req) { + auto res = create_response(); if (!params.endpoint_metrics) { res->error(format_error_response("This server does not support metrics endpoint. Start it with `--metrics`", ERROR_TYPE_NOT_SUPPORTED)); return res; } // request slots data using task queue - // TODO: use server_response_reader - int task_id = ctx_server.queue_tasks.get_new_id(); { server_task task(SERVER_TASK_TYPE_METRICS); - task.id = task_id; - ctx_server.queue_results.add_waiting_task_id(task_id); - ctx_server.queue_tasks.post(std::move(task), true); // high-priority task + task.id = res->rd.get_new_id(); + res->rd.post_task(std::move(task), true); // high-priority task } // get the result - server_task_result_ptr result = ctx_server.queue_results.recv(task_id); - ctx_server.queue_results.remove_waiting_task_id(task_id); + auto result = res->rd.next(req.should_stop); if (result->is_error()) { res->error(result->to_json()); @@ -3149,24 +3182,21 @@ void server_routes::init_routes() { }; this->get_slots = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); if (!params.endpoint_slots) { res->error(format_error_response("This server does not support slots endpoint. Start it with `--slots`", ERROR_TYPE_NOT_SUPPORTED)); return res; } // request slots data using task queue - int task_id = ctx_server.queue_tasks.get_new_id(); { server_task task(SERVER_TASK_TYPE_METRICS); - task.id = task_id; - ctx_server.queue_results.add_waiting_task_id(task_id); - ctx_server.queue_tasks.post(std::move(task), true); // high-priority task + task.id = res->rd.get_new_id(); + res->rd.post_task(std::move(task), true); // high-priority task } // get the result - server_task_result_ptr result = ctx_server.queue_results.recv(task_id); - ctx_server.queue_results.remove_waiting_task_id(task_id); + auto result = res->rd.next(req.should_stop); if (result->is_error()) { res->error(result->to_json()); @@ -3190,7 +3220,7 @@ void server_routes::init_routes() { }; this->post_slots = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); if (params.slot_save_path.empty()) { res->error(format_error_response("This server does not support slots action. Start it with `--slot-save-path`", ERROR_TYPE_NOT_SUPPORTED)); return res; @@ -3221,15 +3251,51 @@ void server_routes::init_routes() { }; this->get_props = [this](const server_http_req &) { - auto res = std::make_unique(ctx_server, true); - auto props = ctx_server.json_server_props; - props["is_sleeping"] = ctx_server.queue_tasks.is_sleeping(); + auto res = create_response(true); + + // this endpoint can be accessed during sleeping + // the next LOC is to avoid someone accidentally use ctx_server + bool server_ctx; // do NOT delete this line + GGML_UNUSED(server_ctx); + + task_params tparams; + tparams.sampling = params.sampling; + json default_generation_settings_for_props = json { + { "params", tparams.to_json(true) }, + { "n_ctx", meta->slot_n_ctx }, + }; + + json props = { + { "default_generation_settings", default_generation_settings_for_props }, + { "total_slots", params.n_parallel }, + { "model_alias", meta->model_name }, + { "model_path", meta->model_path }, + { "modalities", json { + {"vision", meta->has_inp_image}, + {"audio", meta->has_inp_audio}, + } }, + { "endpoint_slots", params.endpoint_slots }, + { "endpoint_props", params.endpoint_props }, + { "endpoint_metrics", params.endpoint_metrics }, + { "webui", params.webui }, + { "webui_settings", meta->json_webui_settings }, + { "chat_template", meta->chat_template }, + { "bos_token", meta->bos_token_str }, + { "eos_token", meta->eos_token_str }, + { "build_info", meta->build_info }, + { "is_sleeping", queue_tasks.is_sleeping() }, + }; + if (params.use_jinja) { + if (!meta->chat_template_tool_use.empty()) { + props["chat_template_tool_use"] = meta->chat_template_tool_use; + } + } res->ok(props); return res; }; this->post_props = [this](const server_http_req &) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); if (!params.endpoint_props) { res->error(format_error_response("This server does not support changing global properties. Start it with `--props`", ERROR_TYPE_NOT_SUPPORTED)); return res; @@ -3241,20 +3307,16 @@ void server_routes::init_routes() { }; this->get_api_show = [this](const server_http_req &) { - auto res = std::make_unique(ctx_server); - bool has_mtmd = ctx_server.mctx != nullptr; + auto res = create_response(); json data = { - { - "template", common_chat_templates_source(ctx_server.chat_templates.get()), - }, { "model_info", { - { "llama.context_length", ctx_server.get_slot_n_ctx() }, + { "llama.context_length", meta->slot_n_ctx }, } }, {"modelfile", ""}, {"parameters", ""}, - {"template", common_chat_templates_source(ctx_server.chat_templates.get())}, + {"template", meta->chat_template}, {"details", { {"parent_model", ""}, {"format", "gguf"}, @@ -3264,7 +3326,7 @@ void server_routes::init_routes() { {"quantization_level", ""} }}, {"model_info", ""}, - {"capabilities", has_mtmd ? json({"completion","multimodal"}) : json({"completion"})} + {"capabilities", meta->has_mtmd ? json({"completion","multimodal"}) : json({"completion"})} }; res->ok(data); @@ -3272,7 +3334,7 @@ void server_routes::init_routes() { }; this->post_infill = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); // check model compatibility std::string err; if (llama_vocab_fim_pre(ctx_server.vocab) == LLAMA_TOKEN_NULL) { @@ -3333,54 +3395,48 @@ void server_routes::init_routes() { data.at("input_prefix"), data.at("input_suffix"), data.at("input_extra"), - ctx_server.params_base.n_batch, - ctx_server.params_base.n_predict, - ctx_server.get_slot_n_ctx(), - ctx_server.params_base.spm_infill, + params.n_batch, + params.n_predict, + meta->slot_n_ctx, + params.spm_infill, tokenized_prompts[0].get_text_tokens() // TODO: this could maybe be multimodal. ); std::vector files; // dummy return handle_completions_impl( - std::move(res), - ctx_server, + req, SERVER_TASK_TYPE_INFILL, data, files, - req.should_stop, TASK_RESPONSE_TYPE_NONE); // infill is not OAI compatible }; this->post_completions = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); std::vector files; // dummy const json body = json::parse(req.body); return handle_completions_impl( - std::move(res), - ctx_server, + req, SERVER_TASK_TYPE_COMPLETION, body, files, - req.should_stop, TASK_RESPONSE_TYPE_NONE); }; this->post_completions_oai = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); std::vector files; // dummy const json body = json::parse(req.body); return handle_completions_impl( - std::move(res), - ctx_server, + req, SERVER_TASK_TYPE_COMPLETION, body, files, - req.should_stop, TASK_RESPONSE_TYPE_OAI_CMPL); }; this->post_chat_completions = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); std::vector files; json body = json::parse(req.body); json body_parsed = oaicompat_chat_params_parse( @@ -3388,17 +3444,15 @@ void server_routes::init_routes() { ctx_server.oai_parser_opt, files); return handle_completions_impl( - std::move(res), - ctx_server, + req, SERVER_TASK_TYPE_COMPLETION, body_parsed, files, - req.should_stop, TASK_RESPONSE_TYPE_OAI_CHAT); }; this->post_anthropic_messages = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); std::vector files; json body = convert_anthropic_to_oai(json::parse(req.body)); json body_parsed = oaicompat_chat_params_parse( @@ -3406,17 +3460,15 @@ void server_routes::init_routes() { ctx_server.oai_parser_opt, files); return handle_completions_impl( - std::move(res), - ctx_server, + req, SERVER_TASK_TYPE_COMPLETION, body_parsed, files, - req.should_stop, TASK_RESPONSE_TYPE_ANTHROPIC); }; this->post_anthropic_count_tokens = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); std::vector files; json body = convert_anthropic_to_oai(json::parse(req.body)); json body_parsed = oaicompat_chat_params_parse( @@ -3426,14 +3478,13 @@ void server_routes::init_routes() { json prompt = body_parsed.at("prompt"); llama_tokens tokens = tokenize_mixed(ctx_server.vocab, prompt, true, true); - res->ok({{"input_tokens", static_cast(tokens.size())}}); return res; }; // same with handle_chat_completions, but without inference part this->post_apply_template = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); std::vector files; // dummy, unused json body = json::parse(req.body); json data = oaicompat_chat_params_parse( @@ -3444,27 +3495,26 @@ void server_routes::init_routes() { return res; }; - // TODO: this endpoint is unsafe to access during model reloading (i.e. wake up from sleeping) - // how to make it work even during load_model()? this->get_models = [this](const server_http_req &) { - auto res = std::make_unique(ctx_server); - json model_meta = nullptr; - if (is_ready()) { - model_meta = ctx_server.json_server_model_meta; - } - bool has_mtmd = ctx_server.mctx != nullptr; + auto res = create_response(true); + + // this endpoint can be accessed during sleeping + // the next LOC is to avoid someone accidentally use ctx_server + bool server_ctx; // do NOT delete this line + GGML_UNUSED(server_ctx); + json models = { {"models", { { - {"name", ctx_server.model_name}, - {"model", ctx_server.model_name}, + {"name", meta->model_name}, + {"model", meta->model_name}, {"modified_at", ""}, {"size", ""}, {"digest", ""}, // dummy value, llama.cpp does not support managing model file's hash {"type", "model"}, {"description", ""}, {"tags", {""}}, - {"capabilities", has_mtmd ? json({"completion","multimodal"}) : json({"completion"})}, + {"capabilities", meta->has_mtmd ? json({"completion","multimodal"}) : json({"completion"})}, {"parameters", ""}, {"details", { {"parent_model", ""}, @@ -3479,11 +3529,18 @@ void server_routes::init_routes() { {"object", "list"}, {"data", { { - {"id", ctx_server.model_name}, + {"id", meta->model_name}, {"object", "model"}, {"created", std::time(0)}, {"owned_by", "llamacpp"}, - {"meta", model_meta}, + {"meta", { + {"vocab_type", meta->model_vocab_type}, + {"n_vocab", meta->model_vocab_n_tokens}, + {"n_ctx_train", meta->model_n_ctx_train}, + {"n_embd", meta->model_n_embd_inp}, + {"n_params", meta->model_n_params}, + {"size", meta->model_size}, + }}, }, }} }; @@ -3493,7 +3550,7 @@ void server_routes::init_routes() { }; this->post_tokenize = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); const json body = json::parse(req.body); json tokens_response = json::array(); if (body.count("content") != 0) { @@ -3505,7 +3562,7 @@ void server_routes::init_routes() { if (with_pieces) { for (const auto& token : tokens) { - std::string piece = common_token_to_piece(ctx_server.ctx, token); + std::string piece = common_token_to_piece(ctx_server.vocab, token); json piece_json; // Check if the piece is valid UTF-8 @@ -3534,13 +3591,13 @@ void server_routes::init_routes() { }; this->post_detokenize = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); const json body = json::parse(req.body); std::string content; if (body.count("tokens") != 0) { const llama_tokens tokens = body.at("tokens"); - content = tokens_to_str(ctx_server.ctx, tokens); + content = tokens_to_str(ctx_server.vocab, tokens); } res->ok(json{{"content", std::move(content)}}); @@ -3556,8 +3613,8 @@ void server_routes::init_routes() { }; this->post_rerank = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); - if (!ctx_server.params_base.embedding || ctx_server.params_base.pooling_type != LLAMA_POOLING_TYPE_RANK) { + auto res = create_response(); + if (!params.embedding || params.pooling_type != LLAMA_POOLING_TYPE_RANK) { res->error(format_error_response("This server does not support reranking. Start it with `--reranking`", ERROR_TYPE_NOT_SUPPORTED)); return res; } @@ -3592,15 +3649,14 @@ void server_routes::init_routes() { // create and queue the task json responses = json::array(); - server_response_reader rd = ctx_server.get_response_reader(); + auto & rd = res->rd; { std::vector tasks; tasks.reserve(documents.size()); for (size_t i = 0; i < documents.size(); i++) { auto tmp = format_prompt_rerank(ctx_server.model, ctx_server.vocab, ctx_server.mctx, query, documents[i]); server_task task = server_task(SERVER_TASK_TYPE_RERANK); - task.id = ctx_server.queue_tasks.get_new_id(); - task.index = i; + task.id = rd.get_new_id(); task.tokens = std::move(tmp); tasks.push_back(std::move(task)); } @@ -3626,7 +3682,7 @@ void server_routes::init_routes() { // write JSON response json root = format_response_rerank( body, - ctx_server.model_name, + meta->model_name, responses, is_tei_format, documents, @@ -3636,57 +3692,47 @@ void server_routes::init_routes() { return res; }; - this->get_lora_adapters = [this](const server_http_req &) { - auto res = std::make_unique(ctx_server); - json result = json::array(); - const auto & loras = ctx_server.params_base.lora_adapters; - for (size_t i = 0; i < loras.size(); ++i) { - auto & lora = loras[i]; - json entry = { - {"id", i}, - {"path", lora.path}, - {"scale", lora.scale}, - {"task_name", lora.task_name}, - {"prompt_prefix", lora.prompt_prefix}, - }; - std::string alora_invocation_string = ""; - const uint64_t n_alora_tokens = llama_adapter_get_alora_n_invocation_tokens(lora.ptr); - std::vector alora_invocation_tokens; - if (n_alora_tokens) { - const llama_token * alora_tokens = llama_adapter_get_alora_invocation_tokens(lora.ptr); - for (uint64_t i = 0; i < n_alora_tokens; ++i) { - alora_invocation_string += common_token_to_piece(ctx_server.ctx, alora_tokens[i]); - alora_invocation_tokens.push_back(alora_tokens[i]); - } - entry["alora_invocation_string"] = alora_invocation_string; - entry["alora_invocation_tokens"] = alora_invocation_tokens; - } - result.push_back(std::move(entry)); + this->get_lora_adapters = [this](const server_http_req & req) { + auto res = create_response(); + + auto & rd = res->rd; + { + server_task task(SERVER_TASK_TYPE_GET_LORA); + task.id = rd.get_new_id(); + rd.post_task(std::move(task)); } - res->ok(result); + + // get the result + server_task_result_ptr result = rd.next(req.should_stop); + + if (result->is_error()) { + res->error(result->to_json()); + return res; + } + + GGML_ASSERT(dynamic_cast(result.get()) != nullptr); + res->ok(result->to_json()); return res; }; this->post_lora_adapters = [this](const server_http_req & req) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); const json body = json::parse(req.body); if (!body.is_array()) { res->error(format_error_response("Request body must be an array", ERROR_TYPE_INVALID_REQUEST)); return res; } - int task_id = ctx_server.queue_tasks.get_new_id(); + auto & rd = res->rd; { server_task task(SERVER_TASK_TYPE_SET_LORA); - task.id = task_id; - task.set_lora = parse_lora_request(ctx_server.params_base.lora_adapters, body); - ctx_server.queue_results.add_waiting_task_id(task_id); - ctx_server.queue_tasks.post(std::move(task)); + task.id = rd.get_new_id(); + task.set_lora = parse_lora_request(body); + rd.post_task(std::move(task)); } // get the result - server_task_result_ptr result = ctx_server.queue_results.recv(task_id); - ctx_server.queue_results.remove_waiting_task_id(task_id); + server_task_result_ptr result = rd.next(req.should_stop); if (result->is_error()) { res->error(result->to_json()); @@ -3700,7 +3746,7 @@ void server_routes::init_routes() { } std::unique_ptr server_routes::handle_slots_save(const server_http_req & req, int id_slot) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); const json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); if (!fs_validate_filename(filename)) { @@ -3709,21 +3755,17 @@ std::unique_ptr server_routes::handle_slots_save(const ser } std::string filepath = params.slot_save_path + filename; - int task_id = ctx_server.queue_tasks.get_new_id(); + auto & rd = res->rd; { server_task task(SERVER_TASK_TYPE_SLOT_SAVE); - task.id = task_id; + task.id = rd.get_new_id(); task.slot_action.slot_id = id_slot; task.slot_action.filename = filename; task.slot_action.filepath = filepath; - - // TODO: use server_response_reader - ctx_server.queue_results.add_waiting_task_id(task_id); - ctx_server.queue_tasks.post(std::move(task)); + rd.post_task(std::move(task)); } - server_task_result_ptr result = ctx_server.queue_results.recv(task_id); - ctx_server.queue_results.remove_waiting_task_id(task_id); + server_task_result_ptr result = rd.next(req.should_stop); if (result->is_error()) { res->error(result->to_json()); @@ -3735,7 +3777,7 @@ std::unique_ptr server_routes::handle_slots_save(const ser } std::unique_ptr server_routes::handle_slots_restore(const server_http_req & req, int id_slot) { - auto res = std::make_unique(ctx_server); + auto res = create_response(); const json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); if (!fs_validate_filename(filename)) { @@ -3744,21 +3786,17 @@ std::unique_ptr server_routes::handle_slots_restore(const } std::string filepath = params.slot_save_path + filename; - int task_id = ctx_server.queue_tasks.get_new_id(); + auto & rd = res->rd; { server_task task(SERVER_TASK_TYPE_SLOT_RESTORE); - task.id = task_id; + task.id = rd.get_new_id(); task.slot_action.slot_id = id_slot; task.slot_action.filename = filename; task.slot_action.filepath = filepath; - - // TODO: use server_response_reader - ctx_server.queue_results.add_waiting_task_id(task_id); - ctx_server.queue_tasks.post(std::move(task)); + rd.post_task(std::move(task)); } - server_task_result_ptr result = ctx_server.queue_results.recv(task_id); - ctx_server.queue_results.remove_waiting_task_id(task_id); + server_task_result_ptr result = rd.next(req.should_stop); if (result->is_error()) { res->error(result->to_json()); @@ -3770,21 +3808,17 @@ std::unique_ptr server_routes::handle_slots_restore(const return res; } -std::unique_ptr server_routes::handle_slots_erase(const server_http_req &, int id_slot) { - auto res = std::make_unique(ctx_server); - int task_id = ctx_server.queue_tasks.get_new_id(); +std::unique_ptr server_routes::handle_slots_erase(const server_http_req & req, int id_slot) { + auto res = create_response(); + auto & rd = res->rd; { server_task task(SERVER_TASK_TYPE_SLOT_ERASE); - task.id = task_id; + task.id = rd.get_new_id(); task.slot_action.slot_id = id_slot; - - // TODO: use server_response_reader - ctx_server.queue_results.add_waiting_task_id(task_id); - ctx_server.queue_tasks.post(std::move(task)); + rd.post_task(std::move(task)); } - server_task_result_ptr result = ctx_server.queue_results.recv(task_id); - ctx_server.queue_results.remove_waiting_task_id(task_id); + server_task_result_ptr result = rd.next(req.should_stop); if (result->is_error()) { res->error(result->to_json()); @@ -3797,13 +3831,13 @@ std::unique_ptr server_routes::handle_slots_erase(const se } std::unique_ptr server_routes::handle_embeddings_impl(const server_http_req & req, task_response_type res_type) { - auto res = std::make_unique(ctx_server); - if (!ctx_server.params_base.embedding) { + auto res = create_response(); + if (!params.embedding) { res->error(format_error_response("This server does not support embeddings. Start it with `--embeddings`", ERROR_TYPE_NOT_SUPPORTED)); return res; } - if (res_type != TASK_RESPONSE_TYPE_NONE && llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { + if (res_type != TASK_RESPONSE_TYPE_NONE && meta->pooling_type == LLAMA_POOLING_TYPE_NONE) { res->error(format_error_response("Pooling type 'none' is not OAI compatible. Please use a different pooling type", ERROR_TYPE_INVALID_REQUEST)); return res; } @@ -3824,7 +3858,7 @@ std::unique_ptr server_routes::handle_embeddings_impl(cons bool use_base64 = false; if (body.count("encoding_format") != 0) { - const std::string& format = body.at("encoding_format"); + const std::string & format = body.at("encoding_format"); if (format == "base64") { use_base64 = true; } else if (format != "float") { @@ -3845,21 +3879,20 @@ std::unique_ptr server_routes::handle_embeddings_impl(cons int embd_normalize = 2; // default to Euclidean/L2 norm if (body.count("embd_normalize") != 0) { embd_normalize = body.at("embd_normalize"); - if (llama_pooling_type(ctx_server.ctx) == LLAMA_POOLING_TYPE_NONE) { - SRV_DBG("embd_normalize is not supported by pooling type %d, ignoring it\n", llama_pooling_type(ctx_server.ctx)); + if (meta->pooling_type == LLAMA_POOLING_TYPE_NONE) { + SRV_DBG("embd_normalize is not supported by pooling type %d, ignoring it\n", meta->pooling_type); } } // create and queue the task json responses = json::array(); - server_response_reader rd = ctx_server.get_response_reader(); + auto & rd = res->rd; { std::vector tasks; for (size_t i = 0; i < tokenized_prompts.size(); i++) { server_task task = server_task(SERVER_TASK_TYPE_EMBEDDING); - task.id = ctx_server.queue_tasks.get_new_id(); - task.index = i; + task.id = rd.get_new_id(); task.tokens = std::move(tokenized_prompts[i]); // OAI-compat @@ -3889,7 +3922,7 @@ std::unique_ptr server_routes::handle_embeddings_impl(cons // write JSON response json root = res_type == TASK_RESPONSE_TYPE_OAI_EMBD - ? format_embeddings_response_oaicompat(body, ctx_server.model_name, responses, use_base64) + ? format_embeddings_response_oaicompat(body, meta->model_name, responses, use_base64) : json(responses); res->ok(root); return res; diff --git a/tools/server/server-context.h b/tools/server/server-context.h index a56be7b8e7..09bec15ae1 100644 --- a/tools/server/server-context.h +++ b/tools/server/server-context.h @@ -9,11 +9,35 @@ struct server_context_impl; // private implementation -struct server_context_info { +struct server_context_meta { std::string build_info; std::string model_name; + std::string model_path; + bool has_mtmd; bool has_inp_image; bool has_inp_audio; + json json_webui_settings; + int slot_n_ctx; + enum llama_pooling_type pooling_type; + + // chat template + std::string chat_template; + std::string chat_template_tool_use; + + // tokens + std::string bos_token_str; + std::string eos_token_str; + llama_token fim_pre_token; + llama_token fim_sub_token; + llama_token fim_mid_token; + + // model meta + enum llama_vocab_type model_vocab_type; + int32_t model_vocab_n_tokens; + int32_t model_n_ctx_train; + int32_t model_n_embd_inp; + uint64_t model_n_params; + uint64_t model_size; }; struct server_context { @@ -33,14 +57,15 @@ struct server_context { void terminate(); // get the underlaying llama_context, can return nullptr if sleeping + // not thread-safe, should only be used from the main thread llama_context * get_llama_context() const; // get a new response reader, used by CLI application server_response_reader get_response_reader(); - // get server info - // used by CLI application - server_context_info get_info() const; + // get server metadata (read-only), can only be called after load_model() + // not thread-safe, should only be used from the main thread + server_context_meta get_meta() const; }; @@ -48,13 +73,17 @@ struct server_context { struct server_res_generator; struct server_routes { - server_routes(const common_params & params, server_context & ctx_server, std::function is_ready = []() { return true; }) - : params(params), ctx_server(*ctx_server.impl), is_ready(is_ready) { - init_routes(); - } + server_routes(const common_params & params, server_context & ctx_server); void init_routes(); + + // note: this is not thread-safe and can only when ctx_http.is_ready is false + void update_meta(const server_context & ctx_server) { + this->meta = std::make_unique(ctx_server.get_meta()); + } + // handlers using lambda function, so that they can capture `this` without `std::bind` + // they won't be called until ctx_http.is_ready is set to true server_http_context::handler_t get_health; server_http_context::handler_t get_metrics; server_http_context::handler_t get_slots; @@ -78,13 +107,24 @@ struct server_routes { server_http_context::handler_t get_lora_adapters; server_http_context::handler_t post_lora_adapters; private: - // TODO: move these outside of server_routes? + std::unique_ptr handle_completions_impl( + const server_http_req & req, + server_task_type type, + const json & data, + const std::vector & files, + task_response_type res_type); std::unique_ptr handle_slots_save(const server_http_req & req, int id_slot); std::unique_ptr handle_slots_restore(const server_http_req & req, int id_slot); std::unique_ptr handle_slots_erase(const server_http_req &, int id_slot); std::unique_ptr handle_embeddings_impl(const server_http_req & req, task_response_type res_type); + // using unique_ptr to allow late initialization of const + std::unique_ptr meta; + const common_params & params; - server_context_impl & ctx_server; - std::function is_ready; + const server_context_impl & ctx_server; + + server_queue & queue_tasks; + server_response & queue_results; + std::unique_ptr create_response(bool bypass_sleep = false); }; diff --git a/tools/server/server-http.cpp b/tools/server/server-http.cpp index 622505714c..5d67e5722d 100644 --- a/tools/server/server-http.cpp +++ b/tools/server/server-http.cpp @@ -177,12 +177,11 @@ bool server_http_context::init(const common_params & params) { if (!ready) { auto tmp = string_split(req.path, '.'); if (req.path == "/" || tmp.back() == "html") { - res.set_content(reinterpret_cast(loading_html), loading_html_len, "text/html; charset=utf-8"); res.status = 503; - } else if (req.path == "/models" || req.path == "/v1/models" || req.path == "/api/tags") { - // allow the models endpoint to be accessed during loading - return true; + res.set_content(reinterpret_cast(loading_html), loading_html_len, "text/html; charset=utf-8"); } else { + // no endpoints is allowed to be accessed when the server is not ready + // this is to prevent any data races or inconsistent states res.status = 503; res.set_content( safe_json_to_str(json { @@ -334,12 +333,16 @@ static std::map get_headers(const httplib::Request & r return headers; } -static void process_handler_response(server_http_res_ptr & response, httplib::Response & res) { +// using unique_ptr for request to allow safe capturing in lambdas +using server_http_req_ptr = std::unique_ptr; + +static void process_handler_response(server_http_req_ptr && request, server_http_res_ptr & response, httplib::Response & res) { if (response->is_stream()) { res.status = response->status; set_headers(res, response->headers); std::string content_type = response->content_type; // convert to shared_ptr as both chunked_content_provider() and on_complete() need to use it + std::shared_ptr q_ptr = std::move(request); std::shared_ptr r_ptr = std::move(response); const auto chunked_content_provider = [response = r_ptr](size_t, httplib::DataSink & sink) -> bool { std::string chunk; @@ -355,8 +358,9 @@ static void process_handler_response(server_http_res_ptr & response, httplib::Re } return has_next; }; - const auto on_complete = [response = r_ptr](bool) mutable { + const auto on_complete = [request = q_ptr, response = r_ptr](bool) mutable { response.reset(); // trigger the destruction of the response object + request.reset(); // trigger the destruction of the request object }; res.set_chunked_content_provider(content_type, chunked_content_provider, on_complete); } else { @@ -368,27 +372,29 @@ static void process_handler_response(server_http_res_ptr & response, httplib::Re void server_http_context::get(const std::string & path, const server_http_context::handler_t & handler) const { pimpl->srv->Get(path_prefix + path, [handler](const httplib::Request & req, httplib::Response & res) { - server_http_res_ptr response = handler(server_http_req{ + server_http_req_ptr request = std::make_unique(server_http_req{ get_params(req), get_headers(req), req.path, req.body, req.is_connection_closed }); - process_handler_response(response, res); + server_http_res_ptr response = handler(*request); + process_handler_response(std::move(request), response, res); }); } void server_http_context::post(const std::string & path, const server_http_context::handler_t & handler) const { pimpl->srv->Post(path_prefix + path, [handler](const httplib::Request & req, httplib::Response & res) { - server_http_res_ptr response = handler(server_http_req{ + server_http_req_ptr request = std::make_unique(server_http_req{ get_params(req), get_headers(req), req.path, req.body, req.is_connection_closed }); - process_handler_response(response, res); + server_http_res_ptr response = handler(*request); + process_handler_response(std::move(request), response, res); }); } diff --git a/tools/server/server-queue.cpp b/tools/server/server-queue.cpp index 835938bfc2..9a6ba560a3 100644 --- a/tools/server/server-queue.cpp +++ b/tools/server/server-queue.cpp @@ -325,23 +325,25 @@ void server_response::terminate() { // server_response_reader // -void server_response_reader::post_task(server_task && task) { +void server_response_reader::post_task(server_task && task, bool front) { GGML_ASSERT(id_tasks.empty() && "post_task() can only be called once per reader"); + task.index = 0; id_tasks.insert(task.id); states.push_back(task.create_state()); queue_results.add_waiting_task_id(task.id); - queue_tasks.post(std::move(task)); + queue_tasks.post(std::move(task), front); } -void server_response_reader::post_tasks(std::vector && tasks) { +void server_response_reader::post_tasks(std::vector && tasks, bool front) { GGML_ASSERT(id_tasks.empty() && "post_tasks() can only be called once per reader"); id_tasks = server_task::get_list_id(tasks); states.reserve(tasks.size()); for (size_t i = 0; i < tasks.size(); i++) { + tasks[i].index = i; states.push_back(tasks[i].create_state()); } queue_results.add_waiting_tasks(tasks); - queue_tasks.post(std::move(tasks)); + queue_tasks.post(std::move(tasks), front); } bool server_response_reader::has_next() const { @@ -367,7 +369,7 @@ server_task_result_ptr server_response_reader::next(const std::function } if (!states.empty()) { // update the generation state if needed - size_t idx = result->get_index(); + const size_t idx = result->index; GGML_ASSERT(idx < states.size()); result->update(states[idx]); } @@ -383,6 +385,7 @@ server_task_result_ptr server_response_reader::next(const std::function server_response_reader::batch_response server_response_reader::wait_for_all(const std::function & should_stop) { batch_response batch_res; + batch_res.results.clear(); batch_res.results.resize(id_tasks.size()); while (has_next()) { auto res = next(should_stop); @@ -394,7 +397,7 @@ server_response_reader::batch_response server_response_reader::wait_for_all(cons batch_res.error = std::move(res); return batch_res; } - const size_t idx = res->get_index(); + const size_t idx = res->index; GGML_ASSERT(idx < batch_res.results.size() && "index out of range"); GGML_ASSERT(batch_res.results[idx] == nullptr && "duplicate result received"); batch_res.results[idx] = std::move(res); diff --git a/tools/server/server-queue.h b/tools/server/server-queue.h index 8ac37a20f6..3798aa299e 100644 --- a/tools/server/server-queue.h +++ b/tools/server/server-queue.h @@ -5,6 +5,7 @@ #include #include #include +#include #include // struct for managing server tasks @@ -173,8 +174,10 @@ struct server_response_reader { int get_new_id() { return queue_tasks.get_new_id(); } - void post_task(server_task && task); - void post_tasks(std::vector && tasks); + + // if front = true, the task will be posted to the front of the queue (high priority) + void post_task(server_task && task, bool front = false); + void post_tasks(std::vector && tasks, bool front = false); bool has_next() const; // return nullptr if should_stop() is true before receiving a result diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp index 487e70b34e..22f5b2059c 100644 --- a/tools/server/server-task.cpp +++ b/tools/server/server-task.cpp @@ -32,8 +32,8 @@ json task_params::to_json(bool only_metrics) const { } json lora = json::array(); - for (size_t i = 0; i < this->lora.size(); ++i) { - lora.push_back({{"id", i}, {"scale", this->lora[i].scale}}); + for (auto & it : this->lora) { + lora.push_back({{"id", it.first}, {"scale", it.second}}); } if (only_metrics) { @@ -145,12 +145,10 @@ json task_params::to_json(bool only_metrics) const { // task_params server_task::params_from_json_cmpl( - const llama_context * ctx, + const llama_vocab * vocab, const common_params & params_base, + const int n_ctx_slot, const json & data) { - const llama_model * model = llama_get_model(ctx); - const llama_vocab * vocab = llama_model_get_vocab(model); - task_params params; // Sampling parameter defaults are loaded from the global server context (but individual requests can still them) @@ -223,12 +221,12 @@ task_params server_task::params_from_json_cmpl( if (data.contains("lora")) { if (data.at("lora").is_array()) { - params.lora = parse_lora_request(params_base.lora_adapters, data.at("lora")); + params.lora = parse_lora_request(data.at("lora")); } else { throw std::runtime_error("Error: 'lora' must be an array of objects with 'id' and 'scale' fields"); } } else { - params.lora = params_base.lora_adapters; + params.lora = {}; } // TODO: add more sanity checks for the input parameters @@ -243,11 +241,11 @@ task_params server_task::params_from_json_cmpl( if (params.sampling.penalty_last_n == -1) { // note: should be the slot's context and not the full context, but it's ok - params.sampling.penalty_last_n = llama_n_ctx(ctx); + params.sampling.penalty_last_n = n_ctx_slot; } if (params.sampling.dry_penalty_last_n == -1) { - params.sampling.dry_penalty_last_n = llama_n_ctx(ctx); + params.sampling.dry_penalty_last_n = n_ctx_slot; } if (params.sampling.dry_base < 1.0f) { @@ -1324,6 +1322,30 @@ json server_task_result_slot_erase::to_json() { }; } +// +// server_task_result_get_lora +// + +json server_task_result_get_lora::to_json() { + json result = json::array(); + for (size_t i = 0; i < loras.size(); ++i) { + auto & lora = loras[i]; + json entry = { + {"id", i}, + {"path", lora.info.path}, + {"scale", lora.info.scale}, + {"task_name", lora.info.task_name}, + {"prompt_prefix", lora.info.prompt_prefix}, + }; + if (!lora.alora_invocation_tokens.empty()) { + entry["alora_invocation_string"] = lora.alora_invocation_string; + entry["alora_invocation_tokens"] = lora.alora_invocation_tokens; + } + result.push_back(std::move(entry)); + } + return result; +} + // // server_task_result_apply_lora // diff --git a/tools/server/server-task.h b/tools/server/server-task.h index 0759094a01..687770de5e 100644 --- a/tools/server/server-task.h +++ b/tools/server/server-task.h @@ -6,6 +6,7 @@ #include #include #include +#include // TODO: prevent including the whole server-common.h as we only use server_tokens #include "server-common.h" @@ -23,6 +24,7 @@ enum server_task_type { SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE, + SERVER_TASK_TYPE_GET_LORA, SERVER_TASK_TYPE_SET_LORA, }; @@ -60,7 +62,7 @@ struct task_params { int64_t t_max_prompt_ms = -1; // TODO: implement int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit - std::vector lora; + std::map lora; // mapping adapter ID -> scale std::vector antiprompt; std::vector response_fields; @@ -105,8 +107,10 @@ struct task_result_state { }; struct server_task { - int id = -1; // to be filled by server_queue - int index = -1; // used when there are multiple prompts (batch request) + int id = -1; // to be filled by server_queue + + // TODO @ngxson : remove this field and implement a mapping task_id -> idx in the response_reader + size_t index = 0; // used when there are multiple prompts (batch request) // used by SERVER_TASK_TYPE_CANCEL int id_target = -1; @@ -138,7 +142,7 @@ struct server_task { bool metrics_reset_bucket = false; // used by SERVER_TASK_TYPE_SET_LORA - std::vector set_lora; + std::map set_lora; // mapping adapter ID -> scale server_task() = default; @@ -149,9 +153,10 @@ struct server_task { } static task_params params_from_json_cmpl( - const llama_context * ctx, - const common_params & params_base, - const json & data); + const llama_vocab * vocab, + const common_params & params_base, + const int n_ctx_slot, + const json & data); // utility function static std::unordered_set get_list_id(const std::vector & tasks) { @@ -162,10 +167,9 @@ struct server_task { return ids; } - server_task create_child(int id_parent, int id_child, int idx) const { + server_task create_child(int id_parent, int id_child) const { server_task copy; copy.id = id_child; - copy.index = idx; copy.id_parent = id_parent; copy.params = params; copy.type = type; @@ -212,6 +216,10 @@ struct result_prompt_progress { struct server_task_result { int id = -1; int id_slot = -1; + + // TODO @ngxson : remove this field and implement a mapping task_id -> idx in the response_reader + size_t index = 0; // to be used for batched tasks + virtual bool is_error() { // only used by server_task_result_error return false; @@ -220,9 +228,6 @@ struct server_task_result { // only used by server_task_result_cmpl_* return true; } - virtual int get_index() { - return -1; - } virtual void update(task_result_state &) { // only used by server_task_result_cmpl_* } @@ -255,8 +260,6 @@ struct completion_token_output { }; struct server_task_result_cmpl_final : server_task_result { - int index = 0; - std::string content; llama_tokens tokens; @@ -289,10 +292,6 @@ struct server_task_result_cmpl_final : server_task_result { std::vector oaicompat_msg_diffs; // to be populated by update() bool is_updated = false; - virtual int get_index() override { - return index; - } - virtual bool is_stop() override { return true; // in stream mode, final responses are considered stop } @@ -318,8 +317,6 @@ struct server_task_result_cmpl_final : server_task_result { }; struct server_task_result_cmpl_partial : server_task_result { - int index = 0; - std::string content; llama_tokens tokens; @@ -340,10 +337,6 @@ struct server_task_result_cmpl_partial : server_task_result { std::vector oaicompat_msg_diffs; // to be populated by update() bool is_updated = false; - virtual int get_index() override { - return index; - } - virtual bool is_stop() override { return false; // in stream mode, partial responses are not considered stop } @@ -365,7 +358,6 @@ struct server_task_result_cmpl_partial : server_task_result { }; struct server_task_result_embd : server_task_result { - int index = 0; std::vector> embedding; int32_t n_tokens; @@ -373,10 +365,6 @@ struct server_task_result_embd : server_task_result { // response formatting task_response_type res_type = TASK_RESPONSE_TYPE_NONE; - virtual int get_index() override { - return index; - } - virtual json to_json() override; json to_json_non_oaicompat(); @@ -385,20 +373,14 @@ struct server_task_result_embd : server_task_result { }; struct server_task_result_rerank : server_task_result { - int index = 0; float score = -1e6; int32_t n_tokens; - virtual int get_index() override { - return index; - } - virtual json to_json() override; }; struct server_task_result_error : server_task_result { - int index = 0; error_type err_type = ERROR_TYPE_SERVER; std::string err_msg; @@ -460,6 +442,17 @@ struct server_task_result_slot_erase : server_task_result { virtual json to_json() override; }; +struct server_task_result_get_lora : server_task_result { + struct lora { + common_adapter_lora_info info; + std::string alora_invocation_string; + llama_tokens alora_invocation_tokens; + }; + std::vector loras; + + virtual json to_json() override; +}; + struct server_task_result_apply_lora : server_task_result { virtual json to_json() override; }; diff --git a/tools/server/server.cpp b/tools/server/server.cpp index ff650ab2ec..0fbc7b6d35 100644 --- a/tools/server/server.cpp +++ b/tools/server/server.cpp @@ -119,7 +119,7 @@ int main(int argc, char ** argv, char ** envp) { // // register API routes - server_routes routes(params, ctx_server, [&ctx_http]() { return ctx_http.is_ready.load(); }); + server_routes routes(params, ctx_server); bool is_router_server = params.model.path.empty(); std::optional models_routes{}; @@ -252,6 +252,7 @@ int main(int argc, char ** argv, char ** envp) { return 1; } + routes.update_meta(ctx_server); ctx_http.is_ready.store(true); LOG_INF("%s: model loaded\n", __func__); From e3b35ddf1ccfd3040616502cf3b405d9ba643389 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Mon, 22 Dec 2025 11:03:13 -0600 Subject: [PATCH 07/25] vulkan: Extend rope fusions to allow mrope (#18264) Extend the test-backend-ops tests as well. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 11 ++- .../vulkan-shaders/rope_funcs.glsl | 15 +++- .../vulkan-shaders/vulkan-shaders-gen.cpp | 2 + tests/test-backend-ops.cpp | 79 ++++++++++++++----- 4 files changed, 82 insertions(+), 25 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c2adca9cba..a524adbe0c 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -731,7 +731,7 @@ struct vk_device_struct { vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16; - vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16; + vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16, pipeline_rope_multi_f32_f16; vk_pipeline pipeline_rope_vision_f32, pipeline_rope_vision_f16; vk_pipeline pipeline_argsort_f32[num_argsort_pipelines]; vk_pipeline pipeline_argsort_large_f32[num_argsort_pipelines]; @@ -4077,6 +4077,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32_f16, "rope_norm_f32_f16", rope_norm_f32_f16_rte_len, rope_norm_f32_f16_rte_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32_f16, "rope_neox_f32_f16", rope_neox_f32_f16_rte_len, rope_neox_f32_f16_rte_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32_f16, "rope_multi_f32_f16", rope_multi_f32_f16_rte_len, rope_multi_f32_f16_rte_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); } else { ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -4085,6 +4086,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32_f16, "rope_norm_f32_f16", rope_norm_f32_f16_len, rope_norm_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32_f16, "rope_neox_f32_f16", rope_neox_f32_f16_len, rope_neox_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32_f16, "rope_multi_f32_f16", rope_multi_f32_f16_len, rope_multi_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); } for (uint32_t i = 0; i < num_argsort_pipelines; ++i) { @@ -8680,6 +8682,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_rope_multi_f32; } + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_multi_f32_f16; + } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { return ctx->device->pipeline_rope_multi_f16; } @@ -13076,9 +13081,9 @@ static bool ggml_vk_can_fuse_rope_set_rows(ggml_backend_vk_context * ctx, const return false; } - // Only norm/neox shaders have the fusion code + // Only norm/neox/mrope shaders have the fusion code const int mode = ((const int32_t *) rope->op_params)[2]; - if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX) { + if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_MROPE) { return false; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl index 9726b722d1..aacec98469 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl @@ -49,8 +49,8 @@ void rope_norm(const uint i0, const uint i1, rope_params p) { uint idst = i1*ne0 + i0; const uint ix = rope_a_coord(i0, i01, i02, p); - // Fusion optimization: ROPE + VIEW + SET_ROWS.. - // The rope output is viewed as a 1D tensor and offset based on a row index in data_i. + // Fusion optimization: ROPE + VIEW + SET_ROWS. + // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i. if (p.set_rows_stride != 0) { idst = i01*ne0 + i0; idst += rope_data_i[i02].x * p.set_rows_stride; @@ -91,7 +91,7 @@ void rope_neox(const uint i0, const uint i1, rope_params p) { uint idst = i1*ne0 + i0/2; const uint ix = rope_a_coord(i0/2, i01, i02, p); - // Fusion optimization: ROPE + VIEW + SET_ROWS.. + // Fusion optimization: ROPE + VIEW + SET_ROWS. // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i. if (p.set_rows_stride != 0) { idst = i01*ne0 + i0/2; @@ -132,9 +132,16 @@ void rope_multi(const uint i0, const uint i1, rope_params p) { const uint i01 = i1 % ne1; const uint i02 = i1 / ne1; - const uint idst = i1*ne0 + i0/2; + uint idst = i1*ne0 + i0/2; const uint ix = rope_a_coord(i0/2, i01, i02, p); + // Fusion optimization: ROPE + VIEW + SET_ROWS. + // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i. + if (p.set_rows_stride != 0) { + idst = i01*ne0 + i0/2; + idst += rope_data_i[i02].x * p.set_rows_stride; + } + if (i0 >= p.n_dims) { rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]); rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 92ad3bcab1..e237a8e102 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -927,6 +927,8 @@ void process_shaders() { string_to_spv("rope_multi_f32", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); string_to_spv("rope_multi_f16", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); string_to_spv("rope_multi_f16_rte", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}}); + string_to_spv("rope_multi_f32_f16", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}}); + string_to_spv("rope_multi_f32_f16_rte", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}}); string_to_spv("rope_vision_f32", "rope_vision.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); string_to_spv("rope_vision_f16", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 2cdbe66a84..6b65f6e1c7 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2329,11 +2329,13 @@ struct test_set_rows : public test_case { struct test_rope_set_rows : public test_case { const ggml_type type; const ggml_type type_idx; - const std::array ne; + const std::array ne_a; int mode; + const int n_ctx{512}; + const int n_dims{128}; std::string vars() override { - return VARS_TO_STR4(type, type_idx, ne, mode); + return VARS_TO_STR4(type, type_idx, ne_a, mode); } std::string op_desc(ggml_tensor * t) override { @@ -2345,24 +2347,51 @@ struct test_rope_set_rows : public test_case { test_rope_set_rows(ggml_type type, ggml_type type_idx, - std::array ne, + std::array ne_a, int mode) - : type(type), type_idx(type_idx), ne(ne), mode(mode) {} + : type(type), type_idx(type_idx), ne_a(ne_a), mode(mode) {} ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * src = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne[0], ne[1], ne[2], 1); - ggml_set_name(src, "src"); + ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, ne_a[0], ne_a[1], ne_a[2], 1); + ggml_set_name(a, "a"); - ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]); + const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; + const bool is_vision = mode == GGML_ROPE_TYPE_VISION; - ggml_tensor * rope = ggml_rope(ctx, src, pos, ne[0], mode); + ggml_tensor * pos; + if (is_mrope || is_vision) { + pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2] * 4); + } else { + pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]); + } + ggml_set_name(pos, "pos"); - ggml_tensor * view = ggml_view_2d(ctx, rope, ne[0] * ne[1], ne[2], rope->nb[2], 0); + float fs = 1.4245f; + float ef = 0.7465f; + float af = 1.4245f; + ggml_tensor * freq = nullptr; - ggml_tensor * dst = ggml_new_tensor_4d(ctx, type, ne[0] * ne[1], ne[2] * ne[3], 1, 1); + ggml_tensor * rope = nullptr; + if (is_mrope) { + if (is_vision) { + GGML_ASSERT(n_dims/4 > 0); + int rope_sections[4] = {n_dims/4, n_dims/4, 0, 0}; // Vision-RoPE only use first two dimension for image (x, y) coordinate + rope = ggml_rope_multi(ctx, a, pos, freq, n_dims/2, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f); + } else { + GGML_ASSERT(n_dims/3 > 0); + int rope_sections[4] = {n_dims/3, n_dims/3, n_dims/3, 0}; + rope = ggml_rope_multi(ctx, a, pos, freq, n_dims, rope_sections, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f); + } + } else { + rope = ggml_rope(ctx, a, pos, ne_a[0], mode); + } + + ggml_tensor * view = ggml_view_2d(ctx, rope, ne_a[0] * ne_a[1], ne_a[2], rope->nb[2], 0); + + ggml_tensor * dst = ggml_new_tensor_4d(ctx, type, ne_a[0] * ne_a[1], ne_a[2] * ne_a[3], 1, 1); ggml_set_name(dst, "dst"); - ggml_tensor * row_idxs = ggml_new_tensor_3d(ctx, type_idx, ne[2], 1, 1); + ggml_tensor * row_idxs = ggml_new_tensor_3d(ctx, type_idx, ne_a[2], 1, 1); ggml_set_name(row_idxs, "row_idxs"); ggml_tensor * out = ggml_set_rows(ctx, dst, view, row_idxs); @@ -2373,14 +2402,26 @@ struct test_rope_set_rows : public test_case { void initialize_tensors(ggml_context * ctx) override { for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - if (t->type == GGML_TYPE_I64 || t->type == GGML_TYPE_I32) { + if (strcmp(t->name, "row_idxs") == 0) { if (ggml_is_view_op(t->op)) { continue; } - - init_set_rows_row_ids(t, ne[2]); + init_set_rows_row_ids(t, ne_a[2]); + } else if (t->type == GGML_TYPE_I32) { + // pos + const int num_pos_ids = (mode & GGML_ROPE_TYPE_MROPE) ? ne_a[2] * 4 : ne_a[2]; + std::vector data(num_pos_ids); + for (int i = 0; i < num_pos_ids; i++) { + data[i] = rand() % n_ctx; + } + ggml_backend_tensor_set(t, data.data(), 0, num_pos_ids * sizeof(int)); } else { - init_tensor_uniform(t); + if (t->ne[0] == n_dims/2) { + // frequency factors in the range [0.9f, 1.1f] + init_tensor_uniform(t, 0.9f, 1.1f); + } else { + init_tensor_uniform(t); + } } } } @@ -6854,10 +6895,12 @@ static std::vector> make_test_cases_eval() { } } - for (int mode : { GGML_ROPE_TYPE_NORMAL, GGML_ROPE_TYPE_NEOX }) { + for (int mode : { GGML_ROPE_TYPE_NORMAL, GGML_ROPE_TYPE_NEOX, GGML_ROPE_TYPE_MROPE, GGML_ROPE_TYPE_VISION }) { for (ggml_type type : {GGML_TYPE_F16, GGML_TYPE_F32}) { - test_cases.emplace_back(new test_rope_set_rows(type, GGML_TYPE_I64, { 128, 32, 1, 100 }, mode)); - test_cases.emplace_back(new test_rope_set_rows(type, GGML_TYPE_I64, { 128, 32, 512, 1 }, mode)); + for (int ne2 : {1, 8, 512}) { + test_cases.emplace_back(new test_rope_set_rows(type, GGML_TYPE_I64, { 128, 32, ne2, 1 }, mode)); + test_cases.emplace_back(new test_rope_set_rows(type, GGML_TYPE_I64, { 128, 32, ne2, 3 }, mode)); + } } } From eb492bf43f932ddbd4ba809cb2b31d7cd8ff17e6 Mon Sep 17 00:00:00 2001 From: lhez Date: Mon, 22 Dec 2025 10:19:01 -0800 Subject: [PATCH 08/25] opencl: unpack q4_0 for adreno in get_tensor (#18278) --- ggml/src/ggml-opencl/ggml-opencl.cpp | 91 ++++++++++++++++++++++- ggml/src/ggml-opencl/kernels/cvt.cl | 21 ++++++ ggml/src/ggml-opencl/kernels/transpose.cl | 13 ++++ 3 files changed, 124 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 0d37587f60..639715537b 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -494,6 +494,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0; cl_kernel kernel_mul_mat_q4_0_f32_8x_flat; cl_kernel kernel_convert_block_q4_0_noshuffle; + cl_kernel kernel_restore_block_q4_0_noshuffle; cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat; cl_kernel kernel_mul_mv_q6_K_f32; cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat; @@ -634,6 +635,7 @@ struct ggml_backend_opencl_context { cl_kernel kernel_transpose_32; cl_kernel kernel_transpose_32_16; cl_kernel kernel_transpose_16; + cl_kernel kernel_transpose_16_buf; cl_kernel kernel_transpose_16_4x1; cl_mem A_s_d_max; // max scale buffer size for transpose @@ -806,6 +808,7 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts); CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0_noshuffle", &err), err)); + CL_CHECK((backend_ctx->kernel_restore_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0_noshuffle", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err)); CL_CHECK((backend_ctx->kernel_restore_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err)); CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err)); @@ -2004,7 +2007,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve CL_CHECK((backend_ctx->kernel_transpose_32_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32_16", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_32 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_32", &err), err)); CL_CHECK((backend_ctx->kernel_transpose_16 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16", &err), err)); - CL_CHECK((backend_ctx->kernel_transpose_16_4x1 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_4x1", &err), err)); + CL_CHECK((backend_ctx->kernel_transpose_16_buf = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_buf", &err), err)); + CL_CHECK((backend_ctx->kernel_transpose_16_4x1 = clCreateKernel(backend_ctx->program_transpose, "kernel_transpose_16_4x1", &err), err)); GGML_LOG_CONT("."); } @@ -3933,6 +3937,91 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, if (tensor->type == GGML_TYPE_Q4_0) { ggml_tensor_extra_cl_q4_0 * extra = (ggml_tensor_extra_cl_q4_0 *)tensor->extra; +#ifdef GGML_OPENCL_USE_ADRENO_KERNELS + if (use_adreno_kernels(backend_ctx, tensor)) { + cl_int err; + cl_kernel kernel; + + cl_int M = tensor->ne[1]; // ne01 + cl_int K = tensor->ne[0]; // ne00 + + GGML_ASSERT(K % 32 == 0); + GGML_ASSERT(M % 4 == 0); + + size_t size_q = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*ggml_blck_size(tensor->type)/2; + size_t size_d = (ggml_nelements(tensor)/ggml_blck_size(tensor->type))*sizeof(ggml_fp16_t); + GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size"); + + cl_mem buf_trans_q; + cl_mem buf_trans_d; + + CL_CHECK((buf_trans_q = clCreateBuffer(context, CL_MEM_READ_WRITE, + size_q, NULL, &err), err)); + CL_CHECK((buf_trans_d = clCreateBuffer(context, CL_MEM_READ_WRITE, + size_d, NULL, &err), err)); + + kernel = backend_ctx->kernel_transpose_16_buf; + + // transpose q back + cl_int stride_k_q = K/4; + size_t local_size_q[3] = {64, 1, 1}; + size_t global_size_q[3] = {(size_t)M, (size_t)stride_k_q, 1}; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_q)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_int), &M)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &stride_k_q)); + + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, + global_size_q, local_size_q, 0, NULL, NULL)); + + // transpose scales back + cl_int stride_k_d = K/32; + size_t local_size_d[3] = {64, 1, 1}; + size_t global_size_d[3] = {(size_t)M, (size_t)stride_k_d, 1}; + + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->d)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_int), &M)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_int), &stride_k_d)); + + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, + global_size_d, local_size_d, 0, NULL, NULL)); + + // unpack + cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, + ggml_nbytes(tensor), NULL, &err); + CL_CHECK(err); + + cl_uchar mask_0F = 0x0F; + cl_uchar mask_F0 = 0xF0; + + size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1}; + size_t local_work_size[] = {1, 1, 1}; + + kernel = backend_ctx->kernel_restore_block_q4_0_noshuffle; + CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &buf_trans_q)); + CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &buf_trans_d)); + CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device)); + CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_uchar), &mask_0F)); + CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_uchar), &mask_F0)); + + CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, + global_work_size, local_work_size, 0, NULL, NULL)); + + // read back to host + CL_CHECK(clEnqueueReadBuffer( + queue, data_device, CL_TRUE, offset, + size, data, 0, NULL, NULL)); + + CL_CHECK(clReleaseMemObject(data_device)); + CL_CHECK(clReleaseMemObject(buf_trans_q)); + CL_CHECK(clReleaseMemObject(buf_trans_d)); + + return; + } +#endif + cl_int err; cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE, ggml_nbytes(tensor), NULL, &err); diff --git a/ggml/src/ggml-opencl/kernels/cvt.cl b/ggml/src/ggml-opencl/kernels/cvt.cl index b26f9c5fb2..513a4d3e28 100644 --- a/ggml/src/ggml-opencl/kernels/cvt.cl +++ b/ggml/src/ggml-opencl/kernels/cvt.cl @@ -117,6 +117,27 @@ kernel void kernel_convert_block_q4_0_noshuffle( } } +kernel void kernel_restore_block_q4_0_noshuffle( + global uchar * src_q, + global half * src_d, + global struct block_q4_0 * dst, + uchar mask_0F, + uchar mask_F0 +) { + global struct block_q4_0 * b = (global struct block_q4_0 *) dst + get_global_id(0); + global uchar * q = (global uchar *) src_q + QK4_0/2*get_global_id(0); + global half * d = (global half *) src_d + get_global_id(0); + + b->d = *d; + for (int i = 0; i < QK4_0/4; ++i) { + uchar x0 = q[i + 0 ] ; + uchar x1 = q[i + QK4_0/4]; + + b->qs[2*i + 0] = convert_uchar((x0 & mask_0F) | ((x1 & mask_0F) << 4)); + b->qs[2*i + 1] = convert_uchar(((x0 & mask_F0) >> 4) | (x1 & mask_F0)); + } +} + //------------------------------------------------------------------------------ // block_mxfp4 //------------------------------------------------------------------------------ diff --git a/ggml/src/ggml-opencl/kernels/transpose.cl b/ggml/src/ggml-opencl/kernels/transpose.cl index 536dd560a9..1279b6531b 100644 --- a/ggml/src/ggml-opencl/kernels/transpose.cl +++ b/ggml/src/ggml-opencl/kernels/transpose.cl @@ -44,6 +44,19 @@ kernel void kernel_transpose_16_4x1( write_imageh(output, i * rows + j, (half4)(temp0, temp1, temp2, temp3)); } +// Transpose treating each element as 16-bit using buffer +kernel void kernel_transpose_16_buf( + global const ushort * input, + global ushort * output, + const int ldi, + const int ldo +) { + const int x = get_global_id(0); + const int y = get_global_id(1); + + output[x*ldo + y] = input[y*ldi + x]; +} + // 32-bit transpose, loading/storing a 4x4 tile of elements kernel void kernel_transpose_32( __read_only image1d_buffer_t input, From d34d5ca1e9d06d18382feb0cfb6d9d105c86272d Mon Sep 17 00:00:00 2001 From: Taimur Ahmad Date: Mon, 22 Dec 2025 23:20:23 +0500 Subject: [PATCH 09/25] llamafile: add rvv support for sgemm kernels (#18199) Co-authored-by: Rehan Qasim --- ggml/src/ggml-cpu/llamafile/sgemm.cpp | 768 ++++++++++++++++++++++++++ 1 file changed, 768 insertions(+) diff --git a/ggml/src/ggml-cpu/llamafile/sgemm.cpp b/ggml/src/ggml-cpu/llamafile/sgemm.cpp index a0cce10aa7..7dc36d4f8a 100644 --- a/ggml/src/ggml-cpu/llamafile/sgemm.cpp +++ b/ggml/src/ggml-cpu/llamafile/sgemm.cpp @@ -69,6 +69,10 @@ #define VECTOR_REGISTERS 16 #endif +#if defined(__riscv_v_intrinsic) +#define LMUL 4 +#endif + #define MM256_SET_M128I(a, b) _mm256_insertf128_si256(_mm256_castsi128_si256(b), (a), 1) namespace { @@ -175,6 +179,46 @@ inline float32x4_t madd(float32x4_t a, float32x4_t b, float32x4_t c) { } #endif +#if defined(__riscv_zvfh) +template <> +inline vfloat32m1_t madd(vfloat16mf2_t a, vfloat16mf2_t b, vfloat32m1_t c) { + return __riscv_vfwmacc_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1()); +} +inline vfloat32m2_t madd(vfloat16m1_t a, vfloat16m1_t b, vfloat32m2_t c) { + return __riscv_vfwmacc_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2()); +} +inline vfloat32m4_t madd(vfloat16m2_t a, vfloat16m2_t b, vfloat32m4_t c) { + return __riscv_vfwmacc_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4()); +} +inline vfloat32m8_t madd(vfloat16m4_t a, vfloat16m4_t b, vfloat32m8_t c) { + return __riscv_vfwmacc_vv_f32m8(c, a, b, __riscv_vsetvlmax_e32m8()); +} +inline vfloat32m1_t madd(vfloat32m1_t a, vfloat32m1_t b, vfloat32m1_t c) { + return __riscv_vfmacc_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1()); +} +inline vfloat32m2_t madd(vfloat32m2_t a, vfloat32m2_t b, vfloat32m2_t c) { + return __riscv_vfmacc_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2()); +} +inline vfloat32m4_t madd(vfloat32m4_t a, vfloat32m4_t b, vfloat32m4_t c) { + return __riscv_vfmacc_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4()); +} +inline vfloat32m8_t madd(vfloat32m8_t a, vfloat32m8_t b, vfloat32m8_t c) { + return __riscv_vfmacc_vv_f32m8(c, a, b, __riscv_vsetvlmax_e32m8()); +} +#endif + +#if defined(__riscv_zvfbfwma) +inline vfloat32m1_t madd(vbfloat16mf2_t a, vbfloat16mf2_t b, vfloat32m1_t c) { + return __riscv_vfwmaccbf16_vv_f32m1(c, a, b, __riscv_vsetvlmax_e32m1()); +} +inline vfloat32m2_t madd(vbfloat16m1_t a, vbfloat16m1_t b, vfloat32m2_t c) { + return __riscv_vfwmaccbf16_vv_f32m2(c, a, b, __riscv_vsetvlmax_e32m2()); +} +inline vfloat32m4_t madd(vbfloat16m2_t a, vbfloat16m2_t b, vfloat32m4_t c) { + return __riscv_vfwmaccbf16_vv_f32m4(c, a, b, __riscv_vsetvlmax_e32m4()); +} +#endif + //////////////////////////////////////////////////////////////////////////////////////////////////// // VECTORIZED HORIZONTAL SUM @@ -227,6 +271,25 @@ inline float hsum(__m512 x) { } #endif // __AVX512F__ +#if defined(__riscv_zvfh) +inline float hsum(vfloat32m1_t x) { + return __riscv_vfmv_f_s_f32m1_f32( + __riscv_vfredusum_vs_f32m1_f32m1(x, __riscv_vfmv_v_f_f32m1(0, 1), __riscv_vsetvlmax_e32m1())); +} +inline float hsum(vfloat32m2_t x) { + return __riscv_vfmv_f_s_f32m1_f32( + __riscv_vfredusum_vs_f32m2_f32m1(x, __riscv_vfmv_v_f_f32m1(0, 1), __riscv_vsetvlmax_e32m2())); +} +inline float hsum(vfloat32m4_t x) { + return __riscv_vfmv_f_s_f32m1_f32( + __riscv_vfredusum_vs_f32m4_f32m1(x, __riscv_vfmv_v_f_f32m1(0, 1), __riscv_vsetvlmax_e32m4())); +} +inline float hsum(vfloat32m8_t x) { + return __riscv_vfmv_f_s_f32m1_f32( + __riscv_vfredusum_vs_f32m8_f32m1(x, __riscv_vfmv_v_f_f32m1(0, 1), __riscv_vsetvlmax_e32m8())); +} +#endif + //////////////////////////////////////////////////////////////////////////////////////////////////// // VECTORIZED MEMORY LOADING @@ -315,6 +378,88 @@ template <> inline __m256bh load(const float *p) { } #endif +#if defined(__riscv_zvfh) +template <> inline vfloat16mf2_t load(const ggml_fp16_t *p) { + return __riscv_vle16_v_f16mf2(reinterpret_cast(p), __riscv_vsetvlmax_e16mf2()); +} +template <> inline vfloat16m1_t load(const ggml_fp16_t *p) { + return __riscv_vle16_v_f16m1(reinterpret_cast(p), __riscv_vsetvlmax_e16m1()); +} +template <> inline vfloat16m2_t load(const ggml_fp16_t *p) { + return __riscv_vle16_v_f16m2(reinterpret_cast(p), __riscv_vsetvlmax_e16m2()); +} +template <> inline vfloat16m4_t load(const ggml_fp16_t *p) { + return __riscv_vle16_v_f16m4(reinterpret_cast(p), __riscv_vsetvlmax_e16m4()); +} +template <> inline vfloat32m1_t load(const float *p) { + return __riscv_vle32_v_f32m1(p, __riscv_vsetvlmax_e32m1()); +} +template <> inline vfloat32m2_t load(const float *p) { + return __riscv_vle32_v_f32m2(p, __riscv_vsetvlmax_e32m2()); +} +template <> inline vfloat32m4_t load(const float *p) { + return __riscv_vle32_v_f32m4(p, __riscv_vsetvlmax_e32m4()); +} +template <> inline vfloat32m8_t load(const float *p) { + return __riscv_vle32_v_f32m8(p, __riscv_vsetvlmax_e32m8()); +} +#endif + +#if defined(__riscv_zvfbfwma) +template <> inline vbfloat16mf2_t load(const ggml_bf16_t *p) { + return __riscv_vle16_v_bf16mf2(reinterpret_cast(p), __riscv_vsetvlmax_e16mf2()); +} +template <> inline vbfloat16m1_t load(const ggml_bf16_t *p) { + return __riscv_vle16_v_bf16m1(reinterpret_cast(p), __riscv_vsetvlmax_e16m1()); +} +template <> inline vbfloat16m2_t load(const ggml_bf16_t *p) { + return __riscv_vle16_v_bf16m2(reinterpret_cast(p), __riscv_vsetvlmax_e16m2()); +} +#endif + +#if defined(__riscv_zvfh) +template T set_zero(); + +template <> inline vfloat16mf2_t set_zero() { + return __riscv_vfmv_v_f_f16mf2(0, __riscv_vsetvlmax_e16mf2()); +} +template <> inline vfloat16m1_t set_zero() { + return __riscv_vfmv_v_f_f16m1(0, __riscv_vsetvlmax_e16m1()); +} +template <> inline vfloat16m2_t set_zero() { + return __riscv_vfmv_v_f_f16m2(0, __riscv_vsetvlmax_e16m2()); +} +template <> inline vfloat16m4_t set_zero() { + return __riscv_vfmv_v_f_f16m4(0, __riscv_vsetvlmax_e16m4()); +} +template <> inline vfloat32m1_t set_zero() { + return __riscv_vfmv_v_f_f32m1(0.0f, __riscv_vsetvlmax_e32m1()); +} +template <> inline vfloat32m2_t set_zero() { + return __riscv_vfmv_v_f_f32m2(0, __riscv_vsetvlmax_e32m2()); +} +template <> inline vfloat32m4_t set_zero() { + return __riscv_vfmv_v_f_f32m4(0, __riscv_vsetvlmax_e32m4()); +} +template <> inline vfloat32m8_t set_zero() { + return __riscv_vfmv_v_f_f32m8(0, __riscv_vsetvlmax_e32m8()); +} +#endif + +#if defined(__riscv_v_intrinsic) +template size_t vlmax() { + if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e16mf2(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e16m1(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e16m2(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e16m4(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e32m1(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e32m2(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e32m4(); } + else if constexpr (std::is_same_v) { return __riscv_vsetvlmax_e32m8(); } + return 0; +} +#endif + //////////////////////////////////////////////////////////////////////////////////////////////////// // FLOATING POINT MATRIX MULTIPLICATION @@ -488,6 +633,573 @@ class tinyBLAS { const int64_t ldc; }; +#if defined(__riscv_v_intrinsic) +template +class tinyBLAS_RVV { + public: + tinyBLAS_RVV(const ggml_compute_params * params, int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc) + : params(params), A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc) { + } + + bool matmul(int64_t m, int64_t n) { + if (k % vlmax() != 0) { + return false; + } + +#if LMUL == 1 + if (m % 16 == 0 && (m/16 >= params->nth)) { + const int64_t SIZE_N = BLOCK_SIZE<6>(n); + mnpack<4, 6, 4>(m, n, SIZE_N, 12); + return true; + } + if (m % 8 == 0 ) { + const int64_t SIZE_N = BLOCK_SIZE<6>(n); + mnpack<4, 6, 2>(m, n, SIZE_N, 12); + return true; + } + if (m % 4 == 0) { + const int64_t SIZE_N = BLOCK_SIZE<6>(n); + mnpack<4, 6, 1>(m, n, SIZE_N, 12); + return true; + } +#elif LMUL == 2 + if (m % 16 == 0 && (m/16 >= params->nth)) { + const int64_t SIZE_N = BLOCK_SIZE<3>(n); + mnpack<4, 3, 4>(m, n, SIZE_N, 24); + return true; + } + if (m % 8 == 0 ) { + const int64_t SIZE_N = BLOCK_SIZE<3>(n); + mnpack<4, 3, 2>(m, n, SIZE_N, 24); + return true; + } + if (m % 4 == 0) { + const int64_t SIZE_N = BLOCK_SIZE<3>(n); + mnpack<4, 3, 1>(m, n, SIZE_N, 24); + return true; + } +#else // LMUL = 4 + if (m % 16 == 0 && (m/16 >= params->nth)) { + const int64_t SIZE_N = BLOCK_SIZE<2>(n); + mnpack<2, 2, 8>(m, n, SIZE_N, 36); + return true; + } + if (m % 8 == 0 ) { + const int64_t SIZE_N = BLOCK_SIZE<2>(n); + mnpack<2, 2, 4>(m, n, SIZE_N, 36); + return true; + } + if (m % 4 == 0) { + const int64_t SIZE_N = BLOCK_SIZE<2>(n); + mnpack<2, 2, 2>(m, n, SIZE_N, 36); + return true; + } +#endif + return false; + } + + private: + template + inline void mnpack(int64_t m, int64_t n, int64_t SIZE_N, int64_t BN) { + if (SIZE_N == RN) { + return gemm(m, n, BN); + } + if constexpr (RN > 1) { + return mnpack(m, n, SIZE_N, BN); + } else { + GGML_LOG_ERROR("mnpack<%d, %d> bloc size not supported\n", RM, (int)SIZE_N); + GGML_ASSERT(false); // we have miss something. + } + } + + inline void gemm_bloc_4x6(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv02 = set_zero(); + D Cv03 = set_zero(); + D Cv10 = set_zero(); + D Cv11 = set_zero(); + D Cv12 = set_zero(); + D Cv13 = set_zero(); + D Cv20 = set_zero(); + D Cv21 = set_zero(); + D Cv22 = set_zero(); + D Cv23 = set_zero(); + D Cv30 = set_zero(); + D Cv31 = set_zero(); + D Cv32 = set_zero(); + D Cv33 = set_zero(); + D Cv40 = set_zero(); + D Cv41 = set_zero(); + D Cv42 = set_zero(); + D Cv43 = set_zero(); + D Cv50 = set_zero(); + D Cv51 = set_zero(); + D Cv52 = set_zero(); + D Cv53 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Bv0 = load(B + ldb * (jj + 0) + l); + V Bv1 = load(B + ldb * (jj + 1) + l); + V Bv2 = load(B + ldb * (jj + 2) + l); + V Bv3 = load(B + ldb * (jj + 3) + l); + V Bv4 = load(B + ldb * (jj + 4) + l); + V Bv5 = load(B + ldb * (jj + 5) + l); + + V Av0 = load(A + lda * (ii + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv10 = madd(Av0, Bv1, Cv10); + Cv20 = madd(Av0, Bv2, Cv20); + Cv30 = madd(Av0, Bv3, Cv30); + Cv40 = madd(Av0, Bv4, Cv40); + Cv50 = madd(Av0, Bv5, Cv50); + + V Av1 = load(A + lda * (ii + 1) + l); + Cv01 = madd(Av1, Bv0, Cv01); + Cv11 = madd(Av1, Bv1, Cv11); + Cv21 = madd(Av1, Bv2, Cv21); + Cv31 = madd(Av1, Bv3, Cv31); + Cv41 = madd(Av1, Bv4, Cv41); + Cv51 = madd(Av1, Bv5, Cv51); + + V Av2 = load(A + lda * (ii + 2) + l); + Cv02 = madd(Av2, Bv0, Cv02); + Cv12 = madd(Av2, Bv1, Cv12); + Cv22 = madd(Av2, Bv2, Cv22); + Cv32 = madd(Av2, Bv3, Cv32); + Cv42 = madd(Av2, Bv4, Cv42); + Cv52 = madd(Av2, Bv5, Cv52); + + V Av3 = load(A + lda * (ii + 3) + l); + Cv03 = madd(Av3, Bv0, Cv03); + Cv13 = madd(Av3, Bv1, Cv13); + Cv23 = madd(Av3, Bv2, Cv23); + Cv33 = madd(Av3, Bv3, Cv33); + Cv43 = madd(Av3, Bv4, Cv43); + Cv53 = madd(Av3, Bv5, Cv53); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 0) + (ii + 2)] = hsum(Cv02); + C[ldc * (jj + 0) + (ii + 3)] = hsum(Cv03); + C[ldc * (jj + 1) + (ii + 0)] = hsum(Cv10); + C[ldc * (jj + 1) + (ii + 1)] = hsum(Cv11); + C[ldc * (jj + 1) + (ii + 2)] = hsum(Cv12); + C[ldc * (jj + 1) + (ii + 3)] = hsum(Cv13); + C[ldc * (jj + 2) + (ii + 0)] = hsum(Cv20); + C[ldc * (jj + 2) + (ii + 1)] = hsum(Cv21); + C[ldc * (jj + 2) + (ii + 2)] = hsum(Cv22); + C[ldc * (jj + 2) + (ii + 3)] = hsum(Cv23); + C[ldc * (jj + 3) + (ii + 0)] = hsum(Cv30); + C[ldc * (jj + 3) + (ii + 1)] = hsum(Cv31); + C[ldc * (jj + 3) + (ii + 2)] = hsum(Cv32); + C[ldc * (jj + 3) + (ii + 3)] = hsum(Cv33); + C[ldc * (jj + 4) + (ii + 0)] = hsum(Cv40); + C[ldc * (jj + 4) + (ii + 1)] = hsum(Cv41); + C[ldc * (jj + 4) + (ii + 2)] = hsum(Cv42); + C[ldc * (jj + 4) + (ii + 3)] = hsum(Cv43); + C[ldc * (jj + 5) + (ii + 0)] = hsum(Cv50); + C[ldc * (jj + 5) + (ii + 1)] = hsum(Cv51); + C[ldc * (jj + 5) + (ii + 2)] = hsum(Cv52); + C[ldc * (jj + 5) + (ii + 3)] = hsum(Cv53); + } + + inline void gemm_bloc_4x5(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv02 = set_zero(); + D Cv03 = set_zero(); + D Cv10 = set_zero(); + D Cv11 = set_zero(); + D Cv12 = set_zero(); + D Cv13 = set_zero(); + D Cv20 = set_zero(); + D Cv21 = set_zero(); + D Cv22 = set_zero(); + D Cv23 = set_zero(); + D Cv30 = set_zero(); + D Cv31 = set_zero(); + D Cv32 = set_zero(); + D Cv33 = set_zero(); + D Cv40 = set_zero(); + D Cv41 = set_zero(); + D Cv42 = set_zero(); + D Cv43 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Bv0 = load(B + ldb * (jj + 0) + l); + V Bv1 = load(B + ldb * (jj + 1) + l); + V Bv2 = load(B + ldb * (jj + 2) + l); + V Bv3 = load(B + ldb * (jj + 3) + l); + V Bv4 = load(B + ldb * (jj + 4) + l); + + V Av0 = load(A + lda * (ii + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv10 = madd(Av0, Bv1, Cv10); + Cv20 = madd(Av0, Bv2, Cv20); + Cv30 = madd(Av0, Bv3, Cv30); + Cv40 = madd(Av0, Bv4, Cv40); + + V Av1 = load(A + lda * (ii + 1) + l); + Cv01 = madd(Av1, Bv0, Cv01); + Cv11 = madd(Av1, Bv1, Cv11); + Cv21 = madd(Av1, Bv2, Cv21); + Cv31 = madd(Av1, Bv3, Cv31); + Cv41 = madd(Av1, Bv4, Cv41); + + V Av2 = load(A + lda * (ii + 2) + l); + Cv02 = madd(Av2, Bv0, Cv02); + Cv12 = madd(Av2, Bv1, Cv12); + Cv22 = madd(Av2, Bv2, Cv22); + Cv32 = madd(Av2, Bv3, Cv32); + Cv42 = madd(Av2, Bv4, Cv42); + + V Av3 = load(A + lda * (ii + 3) + l); + Cv03 = madd(Av3, Bv0, Cv03); + Cv13 = madd(Av3, Bv1, Cv13); + Cv23 = madd(Av3, Bv2, Cv23); + Cv33 = madd(Av3, Bv3, Cv33); + Cv43 = madd(Av3, Bv4, Cv43); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 0) + (ii + 2)] = hsum(Cv02); + C[ldc * (jj + 0) + (ii + 3)] = hsum(Cv03); + C[ldc * (jj + 1) + (ii + 0)] = hsum(Cv10); + C[ldc * (jj + 1) + (ii + 1)] = hsum(Cv11); + C[ldc * (jj + 1) + (ii + 2)] = hsum(Cv12); + C[ldc * (jj + 1) + (ii + 3)] = hsum(Cv13); + C[ldc * (jj + 2) + (ii + 0)] = hsum(Cv20); + C[ldc * (jj + 2) + (ii + 1)] = hsum(Cv21); + C[ldc * (jj + 2) + (ii + 2)] = hsum(Cv22); + C[ldc * (jj + 2) + (ii + 3)] = hsum(Cv23); + C[ldc * (jj + 3) + (ii + 0)] = hsum(Cv30); + C[ldc * (jj + 3) + (ii + 1)] = hsum(Cv31); + C[ldc * (jj + 3) + (ii + 2)] = hsum(Cv32); + C[ldc * (jj + 3) + (ii + 3)] = hsum(Cv33); + C[ldc * (jj + 4) + (ii + 0)] = hsum(Cv40); + C[ldc * (jj + 4) + (ii + 1)] = hsum(Cv41); + C[ldc * (jj + 4) + (ii + 2)] = hsum(Cv42); + C[ldc * (jj + 4) + (ii + 3)] = hsum(Cv43); + } + + inline void gemm_bloc_4x4(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv02 = set_zero(); + D Cv03 = set_zero(); + D Cv10 = set_zero(); + D Cv11 = set_zero(); + D Cv12 = set_zero(); + D Cv13 = set_zero(); + D Cv20 = set_zero(); + D Cv21 = set_zero(); + D Cv22 = set_zero(); + D Cv23 = set_zero(); + D Cv30 = set_zero(); + D Cv31 = set_zero(); + D Cv32 = set_zero(); + D Cv33 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Av0 = load(A + lda * (ii + 0) + l); + V Av1 = load(A + lda * (ii + 1) + l); + V Av2 = load(A + lda * (ii + 2) + l); + V Av3 = load(A + lda * (ii + 3) + l); + + V Bv0 = load(B + ldb * (jj + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv01 = madd(Av1, Bv0, Cv01); + Cv02 = madd(Av2, Bv0, Cv02); + Cv03 = madd(Av3, Bv0, Cv03); + + V Bv1 = load(B + ldb * (jj + 1) + l); + Cv10 = madd(Av0, Bv1, Cv10); + Cv11 = madd(Av1, Bv1, Cv11); + Cv12 = madd(Av2, Bv1, Cv12); + Cv13 = madd(Av3, Bv1, Cv13); + + V Bv2 = load(B + ldb * (jj + 2) + l); + Cv20 = madd(Av0, Bv2, Cv20); + Cv21 = madd(Av1, Bv2, Cv21); + Cv22 = madd(Av2, Bv2, Cv22); + Cv23 = madd(Av3, Bv2, Cv23); + + V Bv3 = load(B + ldb * (jj + 3) + l); + Cv30 = madd(Av0, Bv3, Cv30); + Cv31 = madd(Av1, Bv3, Cv31); + Cv32 = madd(Av2, Bv3, Cv32); + Cv33 = madd(Av3, Bv3, Cv33); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 0) + (ii + 2)] = hsum(Cv02); + C[ldc * (jj + 0) + (ii + 3)] = hsum(Cv03); + C[ldc * (jj + 1) + (ii + 0)] = hsum(Cv10); + C[ldc * (jj + 1) + (ii + 1)] = hsum(Cv11); + C[ldc * (jj + 1) + (ii + 2)] = hsum(Cv12); + C[ldc * (jj + 1) + (ii + 3)] = hsum(Cv13); + C[ldc * (jj + 2) + (ii + 0)] = hsum(Cv20); + C[ldc * (jj + 2) + (ii + 1)] = hsum(Cv21); + C[ldc * (jj + 2) + (ii + 2)] = hsum(Cv22); + C[ldc * (jj + 2) + (ii + 3)] = hsum(Cv23); + C[ldc * (jj + 3) + (ii + 0)] = hsum(Cv30); + C[ldc * (jj + 3) + (ii + 1)] = hsum(Cv31); + C[ldc * (jj + 3) + (ii + 2)] = hsum(Cv32); + C[ldc * (jj + 3) + (ii + 3)] = hsum(Cv33); + } + + inline void gemm_bloc_4x3(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv02 = set_zero(); + D Cv03 = set_zero(); + D Cv10 = set_zero(); + D Cv11 = set_zero(); + D Cv12 = set_zero(); + D Cv13 = set_zero(); + D Cv20 = set_zero(); + D Cv21 = set_zero(); + D Cv22 = set_zero(); + D Cv23 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Av0 = load(A + lda * (ii + 0) + l); + V Av1 = load(A + lda * (ii + 1) + l); + V Av2 = load(A + lda * (ii + 2) + l); + V Av3 = load(A + lda * (ii + 3) + l); + + V Bv0 = load(B + ldb * (jj + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv01 = madd(Av1, Bv0, Cv01); + Cv02 = madd(Av2, Bv0, Cv02); + Cv03 = madd(Av3, Bv0, Cv03); + + V Bv1 = load(B + ldb * (jj + 1) + l); + Cv10 = madd(Av0, Bv1, Cv10); + Cv11 = madd(Av1, Bv1, Cv11); + Cv12 = madd(Av2, Bv1, Cv12); + Cv13 = madd(Av3, Bv1, Cv13); + + V Bv2 = load(B + ldb * (jj + 2) + l); + Cv20 = madd(Av0, Bv2, Cv20); + Cv21 = madd(Av1, Bv2, Cv21); + Cv22 = madd(Av2, Bv2, Cv22); + Cv23 = madd(Av3, Bv2, Cv23); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 0) + (ii + 2)] = hsum(Cv02); + C[ldc * (jj + 0) + (ii + 3)] = hsum(Cv03); + C[ldc * (jj + 1) + (ii + 0)] = hsum(Cv10); + C[ldc * (jj + 1) + (ii + 1)] = hsum(Cv11); + C[ldc * (jj + 1) + (ii + 2)] = hsum(Cv12); + C[ldc * (jj + 1) + (ii + 3)] = hsum(Cv13); + C[ldc * (jj + 2) + (ii + 0)] = hsum(Cv20); + C[ldc * (jj + 2) + (ii + 1)] = hsum(Cv21); + C[ldc * (jj + 2) + (ii + 2)] = hsum(Cv22); + C[ldc * (jj + 2) + (ii + 3)] = hsum(Cv23); + } + + inline void gemm_bloc_4x2(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv02 = set_zero(); + D Cv03 = set_zero(); + D Cv10 = set_zero(); + D Cv11 = set_zero(); + D Cv12 = set_zero(); + D Cv13 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Av0 = load(A + lda * (ii + 0) + l); + V Av1 = load(A + lda * (ii + 1) + l); + V Av2 = load(A + lda * (ii + 2) + l); + V Av3 = load(A + lda * (ii + 3) + l); + + V Bv0 = load(B + ldb * (jj + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv01 = madd(Av1, Bv0, Cv01); + Cv02 = madd(Av2, Bv0, Cv02); + Cv03 = madd(Av3, Bv0, Cv03); + + V Bv1 = load(B + ldb * (jj + 1) + l); + Cv10 = madd(Av0, Bv1, Cv10); + Cv11 = madd(Av1, Bv1, Cv11); + Cv12 = madd(Av2, Bv1, Cv12); + Cv13 = madd(Av3, Bv1, Cv13); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 0) + (ii + 2)] = hsum(Cv02); + C[ldc * (jj + 0) + (ii + 3)] = hsum(Cv03); + C[ldc * (jj + 1) + (ii + 0)] = hsum(Cv10); + C[ldc * (jj + 1) + (ii + 1)] = hsum(Cv11); + C[ldc * (jj + 1) + (ii + 2)] = hsum(Cv12); + C[ldc * (jj + 1) + (ii + 3)] = hsum(Cv13); + } + + inline void gemm_bloc_4x1(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv02 = set_zero(); + D Cv03 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Av0 = load(A + lda * (ii + 0) + l); + V Av1 = load(A + lda * (ii + 1) + l); + V Av2 = load(A + lda * (ii + 2) + l); + V Av3 = load(A + lda * (ii + 3) + l); + + V Bv0 = load(B + ldb * (jj + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv01 = madd(Av1, Bv0, Cv01); + Cv02 = madd(Av2, Bv0, Cv02); + Cv03 = madd(Av3, Bv0, Cv03); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 0) + (ii + 2)] = hsum(Cv02); + C[ldc * (jj + 0) + (ii + 3)] = hsum(Cv03); + } + + inline void gemm_bloc_2x2(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + D Cv10 = set_zero(); + D Cv11 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Av0 = load(A + lda * (ii + 0) + l); + V Av1 = load(A + lda * (ii + 1) + l); + + V Bv0 = load(B + ldb * (jj + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv01 = madd(Av1, Bv0, Cv01); + + V Bv1 = load(B + ldb * (jj + 1) + l); + Cv10 = madd(Av0, Bv1, Cv10); + Cv11 = madd(Av1, Bv1, Cv11); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + C[ldc * (jj + 1) + (ii + 0)] = hsum(Cv10); + C[ldc * (jj + 1) + (ii + 1)] = hsum(Cv11); + } + + inline void gemm_bloc_2x1(int64_t ii, int64_t jj) { + size_t vl = vlmax(); + D Cv00 = set_zero(); + D Cv01 = set_zero(); + + for (int64_t l = 0; l < k; l += vl) { + V Av0 = load(A + lda * (ii + 0) + l); + V Av1 = load(A + lda * (ii + 1) + l); + + V Bv0 = load(B + ldb * (jj + 0) + l); + Cv00 = madd(Av0, Bv0, Cv00); + Cv01 = madd(Av1, Bv0, Cv01); + } + + C[ldc * (jj + 0) + (ii + 0)] = hsum(Cv00); + C[ldc * (jj + 0) + (ii + 1)] = hsum(Cv01); + } + + template + inline void gemm_bloc(int64_t ii, int64_t jj) { + if constexpr (RM == 4) { + if constexpr (RN == 6) { return gemm_bloc_4x6(ii, jj); } + if constexpr (RN == 5) { return gemm_bloc_4x5(ii, jj); } + if constexpr (RN == 4) { return gemm_bloc_4x4(ii, jj); } + if constexpr (RN == 3) { return gemm_bloc_4x3(ii, jj); } + if constexpr (RN == 2) { return gemm_bloc_4x2(ii, jj); } + if constexpr (RN == 1) { return gemm_bloc_4x1(ii, jj); } + } else if constexpr (RM == 2) { + if constexpr (RN == 2) { return gemm_bloc_2x2(ii, jj); } + if constexpr (RN == 1) { return gemm_bloc_2x1(ii, jj); } + } + } + + template + NOINLINE void gemm(int64_t m, int64_t n, int64_t BN) { + GGML_ASSERT(m % (RM * BM) == 0); + const int64_t ytiles = m / (RM * BM); + const int64_t xtiles = (n + RN -1) / RN; + const int64_t jj_RN = (xtiles - (xtiles * RN - n)); + + // "round" bloc_size to "nearest" BN + const int64_t NB_BN = xtiles < BN ? 1 : (xtiles + BN / 2) / BN; + const int64_t SIZE_BN = xtiles % NB_BN == 0 ? xtiles / NB_BN : xtiles / NB_BN + 1; + const int64_t jj_BN = (NB_BN - (NB_BN * SIZE_BN - xtiles)); + const int64_t nb_job = ytiles * NB_BN; + + if (params->ith == 0) { + GGML_ASSERT( jj_BN * SIZE_BN + (NB_BN - jj_BN) * (SIZE_BN - 1) == xtiles); + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + ggml_threadpool_chunk_set(params->threadpool, params->nth); + } + + ggml_barrier(params->threadpool); + + int64_t job = params->ith; + while (job < nb_job) { + const int64_t ii = (job % ytiles) * RM * BM; + const int64_t jb = job / ytiles; + const int64_t jr0 = BLOC_POS(jb , jj_BN, SIZE_BN); + const int64_t jrN = BLOC_POS(jb+1, jj_BN, SIZE_BN); + + const int64_t jj0 = BLOC_POS(jr0, jj_RN, RN); + const int64_t jj2 = BLOC_POS(jrN, jj_RN, RN); + const int64_t jj1 = jj2 < jj_RN * RN ? jj2 : jj_RN * RN; + + for (int64_t bi = 0; bi < BM * RM; bi += RM) { + int64_t jj = jj0; + for (; jj < jj1; jj += RN) { + gemm_bloc(ii + bi, jj); + } + if constexpr (RN > 1) { + for (; jj < jj2; jj += RN - 1) { + gemm_bloc(ii + bi, jj); + } + } + GGML_ASSERT(jj == jj2); + } + + job = ggml_threadpool_chunk_add(params->threadpool, 1); + } + + ggml_barrier(params->threadpool); + return; + } + + const ggml_compute_params * params; + const TA *const A; + const TB *const B; + TC *const C; + const int64_t k; + const int64_t lda; + const int64_t ldb; + const int64_t ldc; +}; +#endif + ////////////////////////////////////////////////////////////////////////////////////////// // QUANT ZERO MATRIX MULTIPLICATION @@ -2657,6 +3369,24 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 params->ith, params->nth}; tb.matmul(m, n); return true; +#elif defined(__riscv_zvfh) + #if LMUL == 1 + tinyBLAS_RVV tb{ params, + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc}; + #elif LMUL == 2 + tinyBLAS_RVV tb{ params, + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc}; + #else // LMUL = 4 + tinyBLAS_RVV tb{ params, + k, (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc}; + #endif + return tb.matmul(m, n); #else return false; #endif @@ -2699,6 +3429,24 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 tb.matmul(m, n); return true; } +#elif defined(__riscv_zvfbfwma) + #if LMUL == 1 + tinyBLAS_RVV tb{ params, + k, (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc}; + #elif LMUL == 2 + tinyBLAS_RVV tb{ params, + k, (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc}; + #else // LMUL = 4 + tinyBLAS_RVV tb{ params, + k, (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc}; + #endif + return tb.matmul(m, n); #endif return false; } @@ -2748,6 +3496,26 @@ bool llamafile_sgemm(const struct ggml_compute_params * params, int64_t m, int64 (float *)C, ldc}; return tb.matmul(m, n); } +#elif defined(__riscv_zvfh) + if (Btype == GGML_TYPE_F16) { + #if LMUL == 1 + tinyBLAS_RVV tb{ params, + k, (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc}; + #elif LMUL == 2 + tinyBLAS_RVV tb{ params, + k, (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc}; + #else // LMUL = 4 + tinyBLAS_RVV tb{ params, + k, (const ggml_fp16_t *)A, lda, + (const ggml_fp16_t *)B, ldb, + (float *)C, ldc}; + #endif + return tb.matmul(m, n); + } #endif return false; } From 179fd82a722cbca2cf5fc7cb072324782e762318 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 22 Dec 2025 19:30:19 +0100 Subject: [PATCH 10/25] gen-docs: automatically update markdown file (#18294) * gen-docs: automatically update markdown file * also strip whitespace * do not add extra newline * update TOC --- examples/gen-docs/gen-docs.cpp | 111 ++++++++++++++----- tools/cli/README.md | 188 ++++++++++++++++++++++++++++++++- tools/completion/README.md | 186 ++++++++++++++++++++++++++++++-- tools/server/README.md | 26 +++-- 4 files changed, 465 insertions(+), 46 deletions(-) diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp index dc76c4cf53..0aa33e8245 100644 --- a/examples/gen-docs/gen-docs.cpp +++ b/examples/gen-docs/gen-docs.cpp @@ -2,57 +2,74 @@ #include "common.h" #include +#include #include // Export usage message (-h) to markdown format +// Automatically update the markdown docs -static void write_table_header(std::ofstream & file) { - file << "| Argument | Explanation |\n"; - file << "| -------- | ----------- |\n"; +#define HELP_START_MARKER "" +#define HELP_END_MARKER "" +#define NOTE_MESSAGE "" + +struct md_file { + llama_example ex; + std::string fname; + std::string specific_section_header; +}; + +std::vector md_files = { + {LLAMA_EXAMPLE_CLI, "tools/cli/README.md", "CLI-specific params"}, + {LLAMA_EXAMPLE_COMPLETION, "tools/completion/README.md", "Completion-specific params"}, + {LLAMA_EXAMPLE_SERVER, "tools/server/README.md", "Server-specific params"}, +}; + +static void write_table_header(std::ostringstream & ss) { + ss << "| Argument | Explanation |\n"; + ss << "| -------- | ----------- |\n"; } -static void write_table_entry(std::ofstream & file, const common_arg & opt) { - file << "| `"; +static void write_table_entry(std::ostringstream & ss, const common_arg & opt) { + ss << "| `"; // args auto all_args = opt.get_args(); for (const auto & arg : all_args) { if (arg == all_args.front()) { - file << arg; - if (all_args.size() > 1) file << ", "; + ss << arg; + if (all_args.size() > 1) ss << ", "; } else { - file << arg << (arg != all_args.back() ? ", " : ""); + ss << arg << (arg != all_args.back() ? ", " : ""); } } // value hint if (opt.value_hint) { std::string md_value_hint(opt.value_hint); string_replace_all(md_value_hint, "|", "\\|"); - file << " " << md_value_hint; + ss << " " << md_value_hint; } if (opt.value_hint_2) { std::string md_value_hint_2(opt.value_hint_2); string_replace_all(md_value_hint_2, "|", "\\|"); - file << " " << md_value_hint_2; + ss << " " << md_value_hint_2; } // help text std::string md_help(opt.help); + md_help = string_strip(md_help); string_replace_all(md_help, "\n", "
"); string_replace_all(md_help, "|", "\\|"); - file << "` | " << md_help << " |\n"; + ss << "` | " << md_help << " |\n"; } -static void write_table(std::ofstream & file, std::vector & opts) { - write_table_header(file); +static void write_table(std::ostringstream & ss, std::vector & opts) { + write_table_header(ss); for (const auto & opt : opts) { - write_table_entry(file, *opt); + write_table_entry(ss, *opt); } } -static void export_md(std::string fname, llama_example ex, std::string name) { - std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc); - +static void write_help(std::ostringstream & ss, const md_file & md) { common_params params; - auto ctx_arg = common_params_parser_init(params, ex); + auto ctx_arg = common_params_parser_init(params, md.ex); std::vector common_options; std::vector sparam_options; @@ -68,18 +85,58 @@ static void export_md(std::string fname, llama_example ex, std::string name) { } } - file << "**Common params**\n\n"; - write_table(file, common_options); - file << "\n\n**Sampling params**\n\n"; - write_table(file, sparam_options); - file << "\n\n**" << name << "-specific params**\n\n"; - write_table(file, specific_options); + ss << HELP_START_MARKER << "\n\n"; + + ss << NOTE_MESSAGE << "\n\n"; + + ss << "### Common params\n\n"; + write_table(ss, common_options); + ss << "\n\n### Sampling params\n\n"; + write_table(ss, sparam_options); + ss << "\n\n### " << md.specific_section_header << "\n\n"; + write_table(ss, specific_options); + + ss << "\n" << HELP_END_MARKER; } int main(int, char **) { - // TODO: add CLI - export_md("autogen-completion.md", LLAMA_EXAMPLE_COMPLETION, "Tool"); - export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER, "Server"); + for (const auto & md : md_files) { + std::ifstream infile(md.fname); + if (!infile.is_open()) { + fprintf(stderr, "failed to open file '%s' for reading\n", md.fname.c_str()); + return 1; + } + + std::ostringstream ss; + ss << infile.rdbuf(); + infile.close(); + + std::string content = ss.str(); + + size_t help_start = content.find(HELP_START_MARKER); + size_t help_end = content.find(HELP_END_MARKER); + + if (help_start == std::string::npos || help_end == std::string::npos || help_end <= help_start) { + fprintf(stderr, "failed to find help markers in file '%s'\n", md.fname.c_str()); + return 1; + } + + std::ostringstream new_help_ss; + write_help(new_help_ss, md); + std::string new_help = new_help_ss.str(); + + content = content.substr(0, help_start) + new_help + content.substr(help_end + strlen(HELP_END_MARKER)); + + std::ofstream outfile(md.fname); + if (!outfile.is_open()) { + fprintf(stderr, "failed to open file '%s' for writing\n", md.fname.c_str()); + return 1; + } + outfile << content; + outfile.close(); + + printf("Updated help in '%s'\n", md.fname.c_str()); + } return 0; } diff --git a/tools/cli/README.md b/tools/cli/README.md index 1333ed77b7..7b8b8692e9 100644 --- a/tools/cli/README.md +++ b/tools/cli/README.md @@ -1 +1,187 @@ -TODO +# llama.cpp/tools/cli + +## Usage + + + + + +### Common params + +| Argument | Explanation | +| -------- | ----------- | +| `-h, --help, --usage` | print usage and exit | +| `--version` | show version and build info | +| `-cl, --cache-list` | show list of models in cache | +| `--completion-bash` | print source-able bash completion script for llama.cpp | +| `--verbose-prompt` | print a verbose prompt before generation (default: false) | +| `-t, --threads N` | number of CPU threads to use during generation (default: -1)
(env: LLAMA_ARG_THREADS) | +| `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) | +| `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") | +| `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask | +| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0) | +| `--prio N` | set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0) | +| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50) | +| `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) | +| `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch | +| `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) | +| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) | +| `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) | +| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | +| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity)
(env: LLAMA_ARG_N_PREDICT) | +| `-b, --batch-size N` | logical maximum batch size (default: 2048)
(env: LLAMA_ARG_BATCH) | +| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | +| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) | +| `--swa-full` | use full-size SWA cache (default: false)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
(env: LLAMA_ARG_SWA_FULL) | +| `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')
(env: LLAMA_ARG_FLASH_ATTN) | +| `-p, --prompt PROMPT` | prompt to start generation with; for system message, use -sys | +| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)
(env: LLAMA_ARG_PERF) | +| `-f, --file FNAME` | a file containing the prompt (default: none) | +| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) | +| `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | +| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | +| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N
(env: LLAMA_ARG_ROPE_SCALE) | +| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | +| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | +| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | +| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | +| `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | +| `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | +| `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | +| `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | +| `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | +| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | +| `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | +| `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | +| `--list-devices` | print list of available devices and exit | +| `-ot, --override-tensor =,...` | override tensor buffer type | +| `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU
(env: LLAMA_ARG_CPU_MOE) | +| `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU
(env: LLAMA_ARG_N_CPU_MOE) | +| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM (default: -1)
(env: LLAMA_ARG_N_GPU_LAYERS) | +| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(env: LLAMA_ARG_SPLIT_MODE) | +| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1
(env: LLAMA_ARG_TENSOR_SPLIT) | +| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)
(env: LLAMA_ARG_MAIN_GPU) | +| `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')
(env: LLAMA_ARG_FIT) | +| `-fitt, --fit-target MiB` | target margin per device for --fit option, default: 1024
(env: LLAMA_ARG_FIT_TARGET) | +| `-fitc, --fit-ctx N` | minimum ctx size that can be set by --fit option, default: 4096
(env: LLAMA_ARG_FIT_CTX) | +| `--check-tensors` | check model tensor data for invalid values (default: false) | +| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false | +| `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) | +| `--lora FNAME` | path to LoRA adapter (use comma-separated values to load multiple adapters) | +| `--lora-scaled FNAME:SCALE,...` | path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)
note: use comma-separated values | +| `--control-vector FNAME` | add a control vector
note: use comma-separated values to add multiple control vectors | +| `--control-vector-scaled FNAME:SCALE,...` | add a control vector with user defined scaling SCALE
note: use comma-separated values (format: FNAME:SCALE,...) | +| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive | +| `-m, --model FNAME` | model path to load
(env: LLAMA_ARG_MODEL) | +| `-mu, --model-url MODEL_URL` | model download url (default: unused)
(env: LLAMA_ARG_MODEL_URL) | +| `-dr, --docker-repo [/][:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.
example: gemma3
(default: unused)
(env: LLAMA_ARG_DOCKER_REPO) | +| `-hf, -hfr, --hf-repo /[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.
mmproj is also downloaded automatically if available. to disable, add --no-mmproj
example: unsloth/phi-4-GGUF:q4_k_m
(default: unused)
(env: LLAMA_ARG_HF_REPO) | +| `-hfd, -hfrd, --hf-repo-draft /[:quant]` | Same as --hf-repo, but for the draft model (default: unused)
(env: LLAMA_ARG_HFD_REPO) | +| `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)
(env: LLAMA_ARG_HF_FILE) | +| `-hfv, -hfrv, --hf-repo-v /[:quant]` | Hugging Face model repository for the vocoder model (default: unused)
(env: LLAMA_ARG_HF_REPO_V) | +| `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)
(env: LLAMA_ARG_HF_FILE_V) | +| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | +| `--log-disable` | Log disable | +| `--log-file FNAME` | Log to file
(env: LLAMA_LOG_FILE) | +| `--log-colors [on\|off\|auto]` | Set colored logging ('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal
(env: LLAMA_LOG_COLORS) | +| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) | +| `--offline` | Offline mode: forces use of cache, prevents network access
(env: LLAMA_OFFLINE) | +| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:
- 0: generic output
- 1: error
- 2: warning
- 3: info
- 4: debug
(default: 3)

(env: LLAMA_LOG_VERBOSITY) | +| `--log-prefix` | Enable prefix in log messages
(env: LLAMA_LOG_PREFIX) | +| `--log-timestamps` | Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS) | +| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) | +| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) | + + +### Sampling params + +| Argument | Explanation | +| -------- | ----------- | +| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature) | +| `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | +| `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | +| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | +| `--temp N` | temperature (default: 0.8) | +| `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | +| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | +| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | +| `--top-nsigma N` | top-n-sigma sampling (default: -1.0, -1.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.0, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.1, 1.0 = disabled) | +| `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) | +| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | +| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | +| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | +| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--dry-base N` | set DRY sampling base value (default: 1.75) | +| `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | +| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | +| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers | +| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | +| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | +| `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | +| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | +| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | +| `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | +| `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | +| `--grammar-file FNAME` | file to read grammar from | +| `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | +| `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | + + +### CLI-specific params + +| Argument | Explanation | +| -------- | ----------- | +| `--display-prompt, --no-display-prompt` | whether to print prompt at generation (default: true) | +| `-co, --color [on\|off\|auto]` | Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal | +| `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS) | +| `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM) | +| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | +| `-sys, --system-prompt PROMPT` | system prompt to use with model (if applicable, depending on chat template) | +| `--show-timings, --no-show-timings` | whether to show timing information after each response (default: true)
(env: LLAMA_ARG_SHOW_TIMINGS) | +| `-sysf, --system-prompt-file FNAME` | a file containing the system prompt (default: none) | +| `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode | +| `-sp, --special` | special tokens output enabled (default: false) | +| `-cnv, --conversation, -no-cnv, --no-conversation` | whether to run in conversation mode:
- does not print special tokens and suffix/prefix
- interactive mode is also enabled
(default: auto enabled if chat template is available) | +| `-st, --single-turn` | run conversation for a single turn only, then exit when done
will not be interactive if first turn is predefined with --prompt
(default: false) | +| `-mli, --multiline-input` | allows you to write or paste multiple lines without ending each in '\' | +| `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) | +| `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md
note: if -hf is used, this argument can be omitted
(env: LLAMA_ARG_MMPROJ) | +| `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md
(env: LLAMA_ARG_MMPROJ_URL) | +| `--mmproj-auto, --no-mmproj, --no-mmproj-auto` | whether to use multimodal projector file (if available), useful when using -hf (default: enabled)
(env: LLAMA_ARG_MMPROJ_AUTO) | +| `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)
(env: LLAMA_ARG_MMPROJ_OFFLOAD) | +| `--image, --audio FILE` | path to an image or audio file. use with multimodal models, use comma-separated values for multiple files | +| `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MIN_TOKENS) | +| `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MAX_TOKENS) | +| `-otd, --override-tensor-draft =,...` | override tensor buffer type for draft model | +| `-cmoed, --cpu-moe-draft` | keep all Mixture of Experts (MoE) weights in the CPU for the draft model
(env: LLAMA_ARG_CPU_MOE_DRAFT) | +| `-ncmoed, --n-cpu-moe-draft N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model
(env: LLAMA_ARG_N_CPU_MOE_DRAFT) | +| `--chat-template-kwargs STRING` | sets additional params for the json template parser
(env: LLAMA_CHAT_TEMPLATE_KWARGS) | +| `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(env: LLAMA_ARG_JINJA) | +| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK) | +| `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | +| `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles | +| `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX) | +| `--draft-min, --draft-n-min N` | minimum number of draft tokens to use for speculative decoding (default: 0)
(env: LLAMA_ARG_DRAFT_MIN) | +| `--draft-p-min P` | minimum speculative decoding probability (greedy) (default: 0.8)
(env: LLAMA_ARG_DRAFT_P_MIN) | +| `-cd, --ctx-size-draft N` | size of the prompt context for the draft model (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE_DRAFT) | +| `-devd, --device-draft ` | comma-separated list of devices to use for offloading the draft model (none = don't offload)
use --list-devices to see a list of available devices | +| `-ngld, --gpu-layers-draft, --n-gpu-layers-draft N` | number of layers to store in VRAM for the draft model
(env: LLAMA_ARG_N_GPU_LAYERS_DRAFT) | +| `-md, --model-draft FNAME` | draft model for speculative decoding (default: unused)
(env: LLAMA_ARG_MODEL_DRAFT) | +| `--spec-replace TARGET DRAFT` | translate the string in TARGET into DRAFT if the draft model and main model are not compatible | +| `--gpt-oss-20b-default` | use gpt-oss-20b (note: can download weights from the internet) | +| `--gpt-oss-120b-default` | use gpt-oss-120b (note: can download weights from the internet) | +| `--vision-gemma-4b-default` | use Gemma 3 4B QAT (note: can download weights from the internet) | +| `--vision-gemma-12b-default` | use Gemma 3 12B QAT (note: can download weights from the internet) | + + diff --git a/tools/completion/README.md b/tools/completion/README.md index 57ef394213..391488579e 100644 --- a/tools/completion/README.md +++ b/tools/completion/README.md @@ -5,13 +5,14 @@ This example program allows you to use various LLaMA language models easily and ## Table of Contents 1. [Quick Start](#quick-start) -2. [Common Options](#common-options) -3. [Input Prompts](#input-prompts) -4. [Interaction](#interaction) -5. [Context Management](#context-management) -6. [Generation Flags](#generation-flags) -7. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options) -8. [Additional Options](#additional-options) +2. [Usage](#usage) +3. [Common Options](#common-options) +4. [Input Prompts](#input-prompts) +5. [Interaction](#interaction) +6. [Context Management](#context-management) +7. [Generation Flags](#generation-flags) +8. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options) +9. [Additional Options](#additional-options) ## Quick Start @@ -82,6 +83,177 @@ Once downloaded, place your model in the models folder in llama.cpp. llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` +## Usage + + + + + +### Common params + +| Argument | Explanation | +| -------- | ----------- | +| `-h, --help, --usage` | print usage and exit | +| `--version` | show version and build info | +| `-cl, --cache-list` | show list of models in cache | +| `--completion-bash` | print source-able bash completion script for llama.cpp | +| `--verbose-prompt` | print a verbose prompt before generation (default: false) | +| `-t, --threads N` | number of CPU threads to use during generation (default: -1)
(env: LLAMA_ARG_THREADS) | +| `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) | +| `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") | +| `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask | +| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0) | +| `--prio N` | set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0) | +| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50) | +| `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) | +| `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch | +| `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) | +| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) | +| `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) | +| `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | +| `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled)
(env: LLAMA_ARG_N_PREDICT) | +| `-b, --batch-size N` | logical maximum batch size (default: 2048)
(env: LLAMA_ARG_BATCH) | +| `-ub, --ubatch-size N` | physical maximum batch size (default: 512)
(env: LLAMA_ARG_UBATCH) | +| `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) | +| `--swa-full` | use full-size SWA cache (default: false)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
(env: LLAMA_ARG_SWA_FULL) | +| `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')
(env: LLAMA_ARG_FLASH_ATTN) | +| `-p, --prompt PROMPT` | prompt to start generation with; for system message, use -sys | +| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)
(env: LLAMA_ARG_PERF) | +| `-f, --file FNAME` | a file containing the prompt (default: none) | +| `-bf, --binary-file FNAME` | binary file containing the prompt (default: none) | +| `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | +| `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | +| `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N
(env: LLAMA_ARG_ROPE_SCALE) | +| `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | +| `--rope-freq-scale N` | RoPE frequency scaling factor, expands context by a factor of 1/N
(env: LLAMA_ARG_ROPE_FREQ_SCALE) | +| `--yarn-orig-ctx N` | YaRN: original context size of model (default: 0 = model training context size)
(env: LLAMA_ARG_YARN_ORIG_CTX) | +| `--yarn-ext-factor N` | YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation)
(env: LLAMA_ARG_YARN_EXT_FACTOR) | +| `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | +| `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | +| `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | +| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | +| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | +| `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | +| `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | +| `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | +| `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | +| `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | +| `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | +| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | +| `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | +| `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | +| `--list-devices` | print list of available devices and exit | +| `-ot, --override-tensor =,...` | override tensor buffer type | +| `-cmoe, --cpu-moe` | keep all Mixture of Experts (MoE) weights in the CPU
(env: LLAMA_ARG_CPU_MOE) | +| `-ncmoe, --n-cpu-moe N` | keep the Mixture of Experts (MoE) weights of the first N layers in the CPU
(env: LLAMA_ARG_N_CPU_MOE) | +| `-ngl, --gpu-layers, --n-gpu-layers N` | max. number of layers to store in VRAM (default: -1)
(env: LLAMA_ARG_N_GPU_LAYERS) | +| `-sm, --split-mode {none,layer,row}` | how to split the model across multiple GPUs, one of:
- none: use one GPU only
- layer (default): split layers and KV across GPUs
- row: split rows across GPUs
(env: LLAMA_ARG_SPLIT_MODE) | +| `-ts, --tensor-split N0,N1,N2,...` | fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1
(env: LLAMA_ARG_TENSOR_SPLIT) | +| `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)
(env: LLAMA_ARG_MAIN_GPU) | +| `-fit, --fit [on\|off]` | whether to adjust unset arguments to fit in device memory ('on' or 'off', default: 'on')
(env: LLAMA_ARG_FIT) | +| `-fitt, --fit-target MiB` | target margin per device for --fit option, default: 1024
(env: LLAMA_ARG_FIT_TARGET) | +| `-fitc, --fit-ctx N` | minimum ctx size that can be set by --fit option, default: 4096
(env: LLAMA_ARG_FIT_CTX) | +| `--check-tensors` | check model tensor data for invalid values (default: false) | +| `--override-kv KEY=TYPE:VALUE,...` | advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false | +| `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) | +| `--lora FNAME` | path to LoRA adapter (use comma-separated values to load multiple adapters) | +| `--lora-scaled FNAME:SCALE,...` | path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)
note: use comma-separated values | +| `--control-vector FNAME` | add a control vector
note: use comma-separated values to add multiple control vectors | +| `--control-vector-scaled FNAME:SCALE,...` | add a control vector with user defined scaling SCALE
note: use comma-separated values (format: FNAME:SCALE,...) | +| `--control-vector-layer-range START END` | layer range to apply the control vector(s) to, start and end inclusive | +| `-m, --model FNAME` | model path to load
(env: LLAMA_ARG_MODEL) | +| `-mu, --model-url MODEL_URL` | model download url (default: unused)
(env: LLAMA_ARG_MODEL_URL) | +| `-dr, --docker-repo [/][:quant]` | Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.
example: gemma3
(default: unused)
(env: LLAMA_ARG_DOCKER_REPO) | +| `-hf, -hfr, --hf-repo /[:quant]` | Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.
mmproj is also downloaded automatically if available. to disable, add --no-mmproj
example: unsloth/phi-4-GGUF:q4_k_m
(default: unused)
(env: LLAMA_ARG_HF_REPO) | +| `-hfd, -hfrd, --hf-repo-draft /[:quant]` | Same as --hf-repo, but for the draft model (default: unused)
(env: LLAMA_ARG_HFD_REPO) | +| `-hff, --hf-file FILE` | Hugging Face model file. If specified, it will override the quant in --hf-repo (default: unused)
(env: LLAMA_ARG_HF_FILE) | +| `-hfv, -hfrv, --hf-repo-v /[:quant]` | Hugging Face model repository for the vocoder model (default: unused)
(env: LLAMA_ARG_HF_REPO_V) | +| `-hffv, --hf-file-v FILE` | Hugging Face model file for the vocoder model (default: unused)
(env: LLAMA_ARG_HF_FILE_V) | +| `-hft, --hf-token TOKEN` | Hugging Face access token (default: value from HF_TOKEN environment variable)
(env: HF_TOKEN) | +| `--log-disable` | Log disable | +| `--log-file FNAME` | Log to file
(env: LLAMA_LOG_FILE) | +| `--log-colors [on\|off\|auto]` | Set colored logging ('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal
(env: LLAMA_LOG_COLORS) | +| `-v, --verbose, --log-verbose` | Set verbosity level to infinity (i.e. log all messages, useful for debugging) | +| `--offline` | Offline mode: forces use of cache, prevents network access
(env: LLAMA_OFFLINE) | +| `-lv, --verbosity, --log-verbosity N` | Set the verbosity threshold. Messages with a higher verbosity will be ignored. Values:
- 0: generic output
- 1: error
- 2: warning
- 3: info
- 4: debug
(default: 3)

(env: LLAMA_LOG_VERBOSITY) | +| `--log-prefix` | Enable prefix in log messages
(env: LLAMA_LOG_PREFIX) | +| `--log-timestamps` | Enable timestamps in log messages
(env: LLAMA_LOG_TIMESTAMPS) | +| `-ctkd, --cache-type-k-draft TYPE` | KV cache data type for K for the draft model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K_DRAFT) | +| `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) | + + +### Sampling params + +| Argument | Explanation | +| -------- | ----------- | +| `--samplers SAMPLERS` | samplers that will be used for generation in the order, separated by ';'
(default: penalties;dry;top_n_sigma;top_k;typ_p;top_p;min_p;xtc;temperature) | +| `-s, --seed SEED` | RNG seed (default: -1, use random seed for -1) | +| `--sampler-seq, --sampling-seq SEQUENCE` | simplified sequence for samplers that will be used (default: edskypmxt) | +| `--ignore-eos` | ignore end of stream token and continue generating (implies --logit-bias EOS-inf) | +| `--temp N` | temperature (default: 0.8) | +| `--top-k N` | top-k sampling (default: 40, 0 = disabled)
(env: LLAMA_ARG_TOP_K) | +| `--top-p N` | top-p sampling (default: 0.9, 1.0 = disabled) | +| `--min-p N` | min-p sampling (default: 0.1, 0.0 = disabled) | +| `--top-nsigma N` | top-n-sigma sampling (default: -1.0, -1.0 = disabled) | +| `--xtc-probability N` | xtc probability (default: 0.0, 0.0 = disabled) | +| `--xtc-threshold N` | xtc threshold (default: 0.1, 1.0 = disabled) | +| `--typical N` | locally typical sampling, parameter p (default: 1.0, 1.0 = disabled) | +| `--repeat-last-n N` | last n tokens to consider for penalize (default: 64, 0 = disabled, -1 = ctx_size) | +| `--repeat-penalty N` | penalize repeat sequence of tokens (default: 1.0, 1.0 = disabled) | +| `--presence-penalty N` | repeat alpha presence penalty (default: 0.0, 0.0 = disabled) | +| `--frequency-penalty N` | repeat alpha frequency penalty (default: 0.0, 0.0 = disabled) | +| `--dry-multiplier N` | set DRY sampling multiplier (default: 0.0, 0.0 = disabled) | +| `--dry-base N` | set DRY sampling base value (default: 1.75) | +| `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | +| `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | +| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers | +| `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | +| `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | +| `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | +| `--mirostat-lr N` | Mirostat learning rate, parameter eta (default: 0.1) | +| `--mirostat-ent N` | Mirostat target entropy, parameter tau (default: 5.0) | +| `-l, --logit-bias TOKEN_ID(+/-)BIAS` | modifies the likelihood of token appearing in the completion,
i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',
or `--logit-bias 15043-1` to decrease likelihood of token ' Hello' | +| `--grammar GRAMMAR` | BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '') | +| `--grammar-file FNAME` | file to read grammar from | +| `-j, --json-schema SCHEMA` | JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | +| `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | + + +### Completion-specific params + +| Argument | Explanation | +| -------- | ----------- | +| `--display-prompt, --no-display-prompt` | whether to print prompt at generation (default: true) | +| `-co, --color [on\|off\|auto]` | Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')
'auto' enables colors when output is to a terminal | +| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | +| `-sys, --system-prompt PROMPT` | system prompt to use with model (if applicable, depending on chat template) | +| `-sysf, --system-prompt-file FNAME` | a file containing the system prompt (default: none) | +| `-ptc, --print-token-count N` | print token count every N tokens (default: -1) | +| `--prompt-cache FNAME` | file to cache prompt state for faster startup (default: none) | +| `--prompt-cache-all` | if specified, saves user input and generations to cache as well | +| `--prompt-cache-ro` | if specified, uses the prompt cache but does not update it | +| `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode | +| `-sp, --special` | special tokens output enabled (default: false) | +| `-cnv, --conversation, -no-cnv, --no-conversation` | whether to run in conversation mode:
- does not print special tokens and suffix/prefix
- interactive mode is also enabled
(default: auto enabled if chat template is available) | +| `-st, --single-turn` | run conversation for a single turn only, then exit when done
will not be interactive if first turn is predefined with --prompt
(default: false) | +| `-i, --interactive` | run in interactive mode (default: false) | +| `-if, --interactive-first` | run in interactive mode and wait for input right away (default: false) | +| `-mli, --multiline-input` | allows you to write or paste multiple lines without ending each in '\' | +| `--in-prefix-bos` | prefix BOS to user inputs, preceding the `--in-prefix` string | +| `--in-prefix STRING` | string to prefix user inputs with (default: empty) | +| `--in-suffix STRING` | string to suffix after user inputs with (default: empty) | +| `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) | +| `-gan, --grp-attn-n N` | group-attention factor (default: 1)
(env: LLAMA_ARG_GRP_ATTN_N) | +| `-gaw, --grp-attn-w N` | group-attention width (default: 512)
(env: LLAMA_ARG_GRP_ATTN_W) | +| `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: disabled)
(env: LLAMA_ARG_JINJA) | +| `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK) | +| `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | +| `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | +| `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | +| `--simple-io` | use basic IO for better compatibility in subprocesses and limited consoles | + + + ## Common Options In this section, we cover the most commonly used options for running the `llama-completion` program with the LLaMA models: diff --git a/tools/server/README.md b/tools/server/README.md index 29ce254652..1ae5eae4c6 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -23,9 +23,11 @@ For the ful list of features, please refer to [server's changelog](https://githu ## Usage - + -**Common params** + + +### Common params | Argument | Explanation | | -------- | ----------- | @@ -38,13 +40,13 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-tb, --threads-batch N` | number of threads to use during batch and prompt processing (default: same as --threads) | | `-C, --cpu-mask M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range (default: "") | | `-Cr, --cpu-range lo-hi` | range of CPUs for affinity. Complements --cpu-mask | -| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0)
| -| `--prio N` | set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0)
| -| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50)
| +| `--cpu-strict <0\|1>` | use strict CPU placement (default: 0) | +| `--prio N` | set process/thread priority : low(-1), normal(0), medium(1), high(2), realtime(3) (default: 0) | +| `--poll <0...100>` | use polling level to wait for work (0 - no polling, default: 50) | | `-Cb, --cpu-mask-batch M` | CPU affinity mask: arbitrarily long hex. Complements cpu-range-batch (default: same as --cpu-mask) | | `-Crb, --cpu-range-batch lo-hi` | ranges of CPUs for affinity. Complements --cpu-mask-batch | | `--cpu-strict-batch <0\|1>` | use strict CPU placement (default: same as --cpu-strict) | -| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0)
| +| `--prio-batch N` | set process/thread priority : 0-normal, 1-medium, 2-high, 3-realtime (default: 0) | | `--poll-batch <0\|1>` | use polling to wait for work (default: same as --poll) | | `-c, --ctx-size N` | size of the prompt context (default: 0, 0 = loaded from model)
(env: LLAMA_ARG_CTX_SIZE) | | `-n, --predict, --n-predict N` | number of tokens to predict (default: -1, -1 = infinity)
(env: LLAMA_ARG_N_PREDICT) | @@ -114,7 +116,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-ctvd, --cache-type-v-draft TYPE` | KV cache data type for V for the draft model
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V_DRAFT) | -**Sampling params** +### Sampling params | Argument | Explanation | | -------- | ----------- | @@ -138,7 +140,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--dry-base N` | set DRY sampling base value (default: 1.75) | | `--dry-allowed-length N` | set allowed length for DRY sampling (default: 2) | | `--dry-penalty-last-n N` | set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size) | -| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers
| +| `--dry-sequence-breaker STRING` | add sequence breaker for DRY sampling, clearing out default breakers ('\n', ':', '"', '*') in the process; use "none" to not use any sequence breakers | | `--dynatemp-range N` | dynamic temperature range (default: 0.0, 0.0 = disabled) | | `--dynatemp-exp N` | dynamic temperature exponent (default: 1.0) | | `--mirostat N` | use Mirostat sampling.
Top K, Nucleus and Locally Typical samplers are ignored if used.
(default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | @@ -151,7 +153,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object
For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead | -**Server-specific params** +### Server-specific params | Argument | Explanation | | -------- | ----------- | @@ -159,7 +161,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-cram, --cache-ram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM) | | `-kvu, --kv-unified` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)
(env: LLAMA_ARG_KV_UNIFIED) | | `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | -| `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode
| +| `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode | | `-sp, --special` | special tokens output enabled (default: false) | | `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) | | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | @@ -208,8 +210,9 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | | `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled

(env: LLAMA_ARG_PREFILL_ASSISTANT) | -| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled)
| +| `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled) | | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | +| `--sleep-idle-seconds SECONDS` | number of seconds of idleness after which the server will sleep (default: -1; -1 = disabled) | | `-td, --threads-draft N` | number of threads to use during generation (default: same as --threads) | | `-tbd, --threads-batch-draft N` | number of threads to use during batch and prompt processing (default: same as --threads-draft) | | `--draft, --draft-n, --draft-max N` | number of tokens to draft for speculative decoding (default: 16)
(env: LLAMA_ARG_DRAFT_MAX) | @@ -234,6 +237,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--vision-gemma-4b-default` | use Gemma 3 4B QAT (note: can download weights from the internet) | | `--vision-gemma-12b-default` | use Gemma 3 12B QAT (note: can download weights from the internet) | + Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. From bf6bc3c1551491b5eb019f0fb56c743f25d3eb79 Mon Sep 17 00:00:00 2001 From: Shouyu <65317431+joeldushouyu@users.noreply.github.com> Date: Mon, 22 Dec 2025 13:56:52 -0500 Subject: [PATCH 11/25] ggml-hexagon: gelu optimization (#18151) * feat: working gelu with src0 put on vtcm * feat: gelu ping-pong for both in and out * fix: fixu compile error * break: distinguish dma ddr->vtcm and vtcm->ddr operation * fix: fix dma queue size * break: update dma api to either pop src or dst ptr * fix: fix activation vtcm allocation issue for src1 when swapperd * refactor: ping-pong gelu logic to avoid unnecessary if else * dma: improved queue interface and prefetch handling * gelu: fix N+2 block prefetch --------- Co-authored-by: Max Krasnyansky --- ggml/src/ggml-hexagon/ggml-hexagon.cpp | 2 +- ggml/src/ggml-hexagon/htp/act-ops.c | 148 +++++++++++++++---------- ggml/src/ggml-hexagon/htp/htp-dma.c | 16 +-- ggml/src/ggml-hexagon/htp/htp-dma.h | 61 +++++++--- ggml/src/ggml-hexagon/htp/hvx-utils.h | 12 +- ggml/src/ggml-hexagon/htp/main.c | 3 +- ggml/src/ggml-hexagon/htp/matmul-ops.c | 40 +++---- 7 files changed, 175 insertions(+), 107 deletions(-) diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 6a00abacc3..853a5bda1e 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -2668,7 +2668,7 @@ static void ggml_hexagon_unary(const struct ggml_tensor * op, uint32_t flags) { req.op = HTP_OP_UNARY_SILU; supported = true; } - else if (ggml_get_unary_op(dst) == GGML_UNARY_OP_GELU){ + else if (ggml_get_unary_op(dst) == GGML_UNARY_OP_GELU) { req.op = HTP_OP_UNARY_GELU; supported = true; } diff --git a/ggml/src/ggml-hexagon/htp/act-ops.c b/ggml/src/ggml-hexagon/htp/act-ops.c index 586b5c1f92..7e488456ee 100644 --- a/ggml/src/ggml-hexagon/htp/act-ops.c +++ b/ggml/src/ggml-hexagon/htp/act-ops.c @@ -263,7 +263,8 @@ static void unary_gelu_fp32_per_thread(const struct htp_tensor * src0, struct htp_spad * dst_spad, uint32_t nth, uint32_t ith, - uint32_t src0_nrows_per_thread) { + uint32_t src0_nrows_per_thread, + dma_queue * dma_queue) { htp_act_preamble2; uint64_t t1, t2; @@ -271,6 +272,8 @@ static void unary_gelu_fp32_per_thread(const struct htp_tensor * src0, const size_t src0_row_size = nb01; const size_t dst_row_size = nb1; + const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN); + const size_t dst_row_size_aligned = htp_round_up(dst_row_size, VLEN); const uint32_t src0_nrows = ne01 * ne02 * ne03; @@ -282,60 +285,81 @@ static void unary_gelu_fp32_per_thread(const struct htp_tensor * src0, return; } - int is_aligned = 1; - int opt_path = 0; - if (!htp_is_aligned((void *) src0->data, VLEN) || !htp_is_aligned((void *) dst->data, VLEN)) { - is_aligned = 0; - FARF(HIGH, "silu-f32: unaligned addresses in elementwise op, possibly slower execution\n"); - } - if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) { - opt_path = 1; + const uint8_t * data_src0 = (const uint8_t *) src0->data; + uint8_t * data_dst = (uint8_t *) dst->data; + + uint8_t * src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread); + uint8_t * dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread); + + // While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0 + size_t src0_spad_half_size = src0_spad->size_per_thread / 2; + size_t dst_spad_half_size = dst_spad->size_per_thread / 2; + + // In gelu = x*sigmoid(x*1.702) + const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block + + if (BLOCK == 0) { + FARF(ERROR, "gelu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", + src0_spad->size_per_thread, src0_row_size_aligned); + return; } - const uint8_t * restrict data_src0 = (const uint8_t *) src0->data; - uint8_t * restrict data_dst = (uint8_t *) dst->data; + // See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379 + for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) { + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_row_size); - uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_row_size); + // Dummy DMA transation for sequencing (interleaving dst,src,dst,...) + dma_queue_push_vtcm_to_ddr(dma_queue, + dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)), + dst_row_size, dst_row_size_aligned, 0); + + dma_queue_push_ddr_to_vtcm(dma_queue, + dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)), + src0_row_size_aligned, src0_row_size, block_size); + } - const int BLOCK = 8; for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) { - const uint32_t block_end = MIN(ir + BLOCK, src0_end_row); + const uint32_t block_size = MIN(BLOCK, src0_end_row - ir); - // Prefetch next block - if (block_end < src0_end_row) { - const float * restrict prefetch_ptr = (float *) (data_src0 + (block_end * src0_row_size)); - htp_l2fetch(prefetch_ptr, 1, block_end * src0_row_size, src0_row_size); - } + float* dst_spad = (float *) dma_queue_pop(dma_queue).src; + float* src0_spad = (float *) dma_queue_pop(dma_queue).dst; - // Process rows in current block - for (uint32_t ib = ir; ib < block_end; ib++) { - const float * restrict src0 = (float *) (data_src0 + (ib * src0_row_size)); - float * restrict dst = (float *) (data_dst + (ib * dst_row_size)); + for (uint32_t ib = 0; ib < block_size; ib++) { + const float* src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float)); + float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float)); // gelu = x * sigmoid(1.702 * x) // current implementation - if (1 == opt_path) { - hvx_mul_scalar_f32((const uint8_t *) src0, (float) 1.702, (uint8_t *) src0_spad_data, ne0); - hvx_fast_sigmoid_f32((const uint8_t *) src0_spad_data, (uint8_t *) src0_spad_data, ne0); - hvx_mul_f32_opt((const uint8_t *) src0, src0_spad_data, (uint8_t *) dst, ne0); - } else { - hvx_mul_scalar_f32( (const uint8_t *) src0, (float)1.702, (uint8_t *) src0_spad_data, ne0); - hvx_sigmoid_f32((const uint8_t *) src0_spad_data, (uint8_t *) src0_spad_data, ne0); - hvx_mul_f32((const uint8_t *) src0, src0_spad_data, (uint8_t *) dst, ne0); - } + hvx_mul_scalar_f32((const uint8_t *) src0_spad_ptr, (float) 1.702, (uint8_t *) dst_spad_ptr, ne0); + hvx_fast_sigmoid_f32((const uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, ne0); + hvx_mul_f32_opt((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, ne0); + } + + dma_queue_push_vtcm_to_ddr(dma_queue, + dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), + dst_row_size, dst_row_size_aligned, block_size); + + // prefetch N+2 loop iteration if any + const uint32_t pref_block = (ir + BLOCK * 2); + if (pref_block < src0_end_row) { + const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block); + dma_queue_push_ddr_to_vtcm(dma_queue, + dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)), + src0_row_size_aligned, src0_row_size, pref_block_size); } } + dma_queue_flush(dma_queue); + t2 = HAP_perf_get_qtimer_count(); - FARF(HIGH, "gelu-f32 %d/%d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, opt_path, ne00, ne01, ne02, + FARF(HIGH, "gelu-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1)); } static void unary_gelu_fp32(unsigned int n, unsigned int i, void * data) { struct htp_ops_context * octx = (struct htp_ops_context *) data; unary_gelu_fp32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i, - octx->src0_nrows_per_thread); + octx->src0_nrows_per_thread, octx->ctx->dma[i]); } @@ -468,21 +492,45 @@ static int execute_op_activations_fp32(struct htp_ops_context * octx) { const uint32_t n_threads = octx->n_threads; const uint32_t src0_nrows = src0->ne[1] * src0->ne[2] * src0->ne[3]; - const size_t src0_row_size = src0->nb[1]; - const size_t src1_row_size = src1->ne[0] ? src1->nb[1] : src0->nb[1]; - const size_t dst_row_size = dst->nb[1]; + size_t src0_row_size = src0->nb[1]; + size_t src1_row_size = src1->nb[1]; // zero bytes if src1 is not used + size_t dst_row_size = dst->nb[1]; + const bool src1_valid = src1->ne[0]; + if (!src1_valid) { + src1_row_size = src0_row_size; + } + + const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN); + const size_t src1_row_size_aligned = htp_round_up(src1_row_size, VLEN); + const size_t dst_row_size_aligned = htp_round_up(dst_row_size, VLEN); // VTCM scratchpads for all tensors // N rows per thread, padded to HVX vector size - octx->dst_spad.size = htp_round_up(dst_row_size, 128) * octx->n_threads; - octx->src0_spad.size = htp_round_up(src0_row_size, 128) * octx->n_threads; - octx->src1_spad.size = htp_round_up(src1_row_size, 128) * octx->n_threads; - size_t spad_size = octx->src0_spad.size + octx->src1_spad.size + octx->dst_spad.size; + size_t spad_size_per_row = (src0_row_size_aligned + src1_row_size_aligned) + dst_row_size_aligned; + size_t vtcm_row_per_thread = (octx->ctx->vtcm_size)/ (n_threads* spad_size_per_row); + + // Make sure the reserved vtcm size is sufficient + if(vtcm_row_per_thread ==0){ + FARF(ERROR, "act-%s : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n", op_type, octx->ctx->vtcm_size, + spad_size_per_row * n_threads); + return HTP_STATUS_VTCM_TOO_SMALL; + } + + octx->src0_spad.size_per_thread = src0_row_size_aligned * vtcm_row_per_thread; + octx->src1_spad.size_per_thread = src1_row_size_aligned * vtcm_row_per_thread; + octx->dst_spad.size_per_thread = dst_row_size_aligned * vtcm_row_per_thread; + + octx->dst_spad.size = n_threads* octx->dst_spad.size_per_thread; + octx->src0_spad.size = n_threads* octx->src0_spad.size_per_thread; + octx->src1_spad.size = n_threads* octx->src1_spad.size_per_thread; + + octx->src0_spad.data = octx->ctx->vtcm_base; + octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; + octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; if (src1->ne[0]) { - FARF(HIGH, - "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", + FARF(HIGH, "%s: %ux%ux%ux%u x %ux%ux%ux%u -> %ux%ux%ux%u : src0-spad-size %u src1-spad-size %u dst-spad-size %u\n", op_type, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); @@ -492,20 +540,8 @@ static int execute_op_activations_fp32(struct htp_ops_context * octx) { octx->src0_spad.size, octx->src1_spad.size, octx->dst_spad.size); } - // Make sure the reserved vtcm size is sufficient - if (octx->ctx->vtcm_size < spad_size) { - FARF(ERROR, "act-%s : current VTCM reservation %zu is too small, needed %zu\n", op_type, octx->ctx->vtcm_size, - spad_size); - return HTP_STATUS_VTCM_TOO_SMALL; - } - - octx->src0_spad.data = octx->ctx->vtcm_base; - octx->src1_spad.data = octx->src0_spad.data + octx->src0_spad.size; - octx->dst_spad.data = octx->src1_spad.data + octx->src1_spad.size; - if (!(octx->flags & HTP_OPFLAGS_SKIP_COMPUTE)) { uint32_t n_jobs = MIN(n_threads, src0_nrows); - octx->src0_nrows_per_thread = (src0_nrows + n_jobs - 1) / n_jobs; worker_pool_run_func(octx->ctx->worker_pool, act_op_func, octx, n_jobs); } diff --git a/ggml/src/ggml-hexagon/htp/htp-dma.c b/ggml/src/ggml-hexagon/htp/htp-dma.c index 10c54b45ee..880c4542a0 100644 --- a/ggml/src/ggml-hexagon/htp/htp-dma.c +++ b/ggml/src/ggml-hexagon/htp/htp-dma.c @@ -34,12 +34,12 @@ dma_queue * dma_queue_create(size_t capacity) { q->desc = (hexagon_udma_descriptor_type1_t *) memalign(64, capacity * sizeof(hexagon_udma_descriptor_type1_t)); memset(q->desc, 0, capacity * sizeof(hexagon_udma_descriptor_type1_t)); - q->dst = (void **) memalign(4, capacity * sizeof(void *)); - memset(q->dst, 0, capacity * sizeof(void *)); + q->dptr = (dma_ptr *) memalign(4, capacity * sizeof(dma_ptr)); + memset(q->dptr, 0, capacity * sizeof(dma_ptr)); q->tail = &q->desc[capacity - 1]; - if (!q->desc && !q->dst) { + if (!q->desc && !q->dptr) { FARF(ERROR, "%s: failed to allocate DMA queue items\n", __FUNCTION__); return NULL; } @@ -54,16 +54,10 @@ void dma_queue_delete(dma_queue * q) { return; } free(q->desc); - free(q->dst); + free(q->dptr); free(q); } void dma_queue_flush(dma_queue * q) { - while (1) { - uint32_t s = dmwait() & 0x3; - if (s == HEXAGON_UDMA_DM0_STATUS_IDLE) { - break; - } - } - q->tail = NULL; + while (dma_queue_pop(q).dst != NULL) ; } diff --git a/ggml/src/ggml-hexagon/htp/htp-dma.h b/ggml/src/ggml-hexagon/htp/htp-dma.h index 7d3fc4078c..32fd06e7d4 100644 --- a/ggml/src/ggml-hexagon/htp/htp-dma.h +++ b/ggml/src/ggml-hexagon/htp/htp-dma.h @@ -11,10 +11,15 @@ extern "C" { #endif +typedef struct { + void *dst; + const void *src; +} dma_ptr; + typedef struct { hexagon_udma_descriptor_type1_t * desc; // descriptor pointers hexagon_udma_descriptor_type1_t * tail; // tail pointer - void ** dst; // dst pointers + dma_ptr * dptr; // dst/src pointers uint32_t push_idx; uint32_t pop_idx; uint32_t capacity; @@ -49,13 +54,20 @@ static inline unsigned int dmwait(void) { return ret; } -static inline bool dma_queue_push(dma_queue * q, - void * dst, - const void * src, - size_t dst_row_size, - size_t src_row_size, - size_t nrows) { +static inline dma_ptr dma_make_ptr(void *dst, const void *src) +{ + dma_ptr p = { dst, src }; + return p; +} + +static inline bool dma_queue_push(dma_queue * q, + dma_ptr dptr, + size_t dst_row_size, + size_t src_row_size, + size_t width, // width in bytes. number of bytes to transfer per row + size_t nrows) { if (((q->push_idx + 1) & q->idx_mask) == q->pop_idx) { + FARF(ERROR, "dma-push: queue full\n"); return false; } @@ -75,18 +87,18 @@ static inline bool dma_queue_push(dma_queue * q, #endif desc->order = 0; desc->dstate = HEXAGON_UDMA_DESC_DSTATE_INCOMPLETE; - desc->src = (void *) src; - desc->dst = (void *) dst; + desc->src = (void *) dptr.src; + desc->dst = (void *) dptr.dst; desc->allocation = 0; desc->padding = 0; - desc->roiwidth = src_row_size; + desc->roiwidth = width; desc->roiheight = nrows; desc->srcstride = src_row_size; desc->dststride = dst_row_size; desc->srcwidthoffset = 0; desc->dstwidthoffset = 0; - q->dst[q->push_idx] = dst; + q->dptr[q->push_idx] = dptr; dmlink(q->tail, desc); q->tail = desc; @@ -96,9 +108,28 @@ static inline bool dma_queue_push(dma_queue * q, return true; } -static inline uint8_t * dma_queue_pop(dma_queue * q) { +static inline bool dma_queue_push_ddr_to_vtcm(dma_queue * q, + dma_ptr dptr, + size_t dst_row_size, + size_t src_row_size, + size_t nrows) { + return dma_queue_push(q, dptr, dst_row_size, src_row_size, src_row_size, nrows); +} + + +static inline bool dma_queue_push_vtcm_to_ddr(dma_queue * q, + dma_ptr dptr, + size_t dst_row_size, + size_t src_row_size, + size_t nrows) { + return dma_queue_push(q, dptr, dst_row_size, src_row_size, dst_row_size, nrows); +} + +static inline dma_ptr dma_queue_pop(dma_queue * q) { + dma_ptr dptr = { NULL }; + if (q->push_idx == q->pop_idx) { - return NULL; + return dptr; } hexagon_udma_descriptor_type1_t * desc = &q->desc[q->pop_idx]; @@ -112,11 +143,11 @@ static inline uint8_t * dma_queue_pop(dma_queue * q) { // FARF(ERROR, "dma-pop: waiting for DMA : %u\n", q->pop_idx); } - uint8_t * dst = (uint8_t *) q->dst[q->pop_idx]; + dptr = q->dptr[q->pop_idx]; // FARF(ERROR, "dma-pop: i %u dst %p\n", q->pop_idx, dst); q->pop_idx = (q->pop_idx + 1) & q->idx_mask; - return dst; + return dptr; } #ifdef __cplusplus diff --git a/ggml/src/ggml-hexagon/htp/hvx-utils.h b/ggml/src/ggml-hexagon/htp/hvx-utils.h index 566048297d..d2d5d23636 100644 --- a/ggml/src/ggml-hexagon/htp/hvx-utils.h +++ b/ggml/src/ggml-hexagon/htp/hvx-utils.h @@ -980,8 +980,6 @@ static inline void hvx_fast_sigmoid_f32(const uint8_t * restrict src, uint8_t * int step_of_1 = num_elems >> 5; int remaining = num_elems - step_of_1 * VLEN_FP32; - assert(remaining == 0); - const HVX_Vector * restrict v_src = (HVX_Vector *) src; HVX_Vector * restrict v_dst = (HVX_Vector *) dst; @@ -996,8 +994,16 @@ static inline void hvx_fast_sigmoid_f32(const uint8_t * restrict src, uint8_t * for (int i = 0; i < step_of_1; i++) { v_dst[i] = hvx_vec_fast_sigmoid_fp32_guard(v_src[i], one, max_exp, min_exp); } -} + if (remaining > 0) { + const float * srcf = ((const float *) src) + step_of_1* VLEN_FP32; + float * dstf = (float *) dst + step_of_1*VLEN_FP32; + + HVX_Vector in = *(HVX_UVector *) srcf; + HVX_Vector out = hvx_vec_fast_sigmoid_fp32_guard(in, one, max_exp, min_exp); + hvx_vec_store_u((void *) dstf, remaining * SIZEOF_FP32, out); + } +} static inline void hvx_sigmoid_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems){ int step_of_1 = num_elems >> 5; // divby 32, because 32 float = 128 bytes per HVX vector diff --git a/ggml/src/ggml-hexagon/htp/main.c b/ggml/src/ggml-hexagon/htp/main.c index 656c369d0a..fb5508a560 100644 --- a/ggml/src/ggml-hexagon/htp/main.c +++ b/ggml/src/ggml-hexagon/htp/main.c @@ -299,7 +299,8 @@ AEEResult htp_iface_start(remote_handle64 handle, uint32 sess_id, uint64 dsp_que ctx->n_threads = n_hvx; for (int i = 0; i < ctx->n_threads; i++) { - ctx->dma[i] = dma_queue_create(HTP_SPAD_SRC0_NROWS * 2); + // see discussion https://github.com/ggml-org/llama.cpp/pull/18151#discussion_r2632388541 + ctx->dma[i] = dma_queue_create(64); } // init worker pool diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c index 0c9188244d..f14523d485 100644 --- a/ggml/src/ggml-hexagon/htp/matmul-ops.c +++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c @@ -1127,13 +1127,13 @@ static void matmul(struct htp_matmul_type * mt, if (is0 >= HTP_SPAD_SRC0_NROWS) { break; } - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } // Process src0 rows for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) { - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; #pragma unroll(2) for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) { @@ -1146,7 +1146,7 @@ static void matmul(struct htp_matmul_type * mt, const int pr0 = (ir0 + HTP_SPAD_SRC0_NROWS); const int is0 = (pr0 - src0_start_row) % HTP_SPAD_SRC0_NROWS; if (pr0 < src0_end_row_x2) { - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } } @@ -1155,9 +1155,9 @@ static void matmul(struct htp_matmul_type * mt, if (src0_end_row != src0_end_row_x2) { uint32_t ir0 = src0_end_row_x2; const int is0 = (ir0 - src0_start_row); - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 1); - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; #pragma unroll(2) for (uint32_t ir1 = 0; ir1 < src1_nrows; ++ir1) { @@ -1229,20 +1229,20 @@ static void matvec(struct htp_matmul_type * mt, if (is0 >= HTP_SPAD_SRC0_NROWS) { break; } - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } // Process src0 rows for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) { - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; mt->vec_dot_rx2(ne00, &tmp[ir0 - src0_start_row], ss0, src0_row_size_padded, src1_col); // Prefetch next (n + spad_nrows) row const uint32_t pr0 = (ir0 + HTP_SPAD_SRC0_NROWS); const uint32_t is0 = (pr0 - src0_start_row) % HTP_SPAD_SRC0_NROWS; if (pr0 < src0_end_row_x2) { - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } } @@ -1251,9 +1251,9 @@ static void matvec(struct htp_matmul_type * mt, if (src0_end_row != src0_end_row_x2) { const uint32_t ir0 = src0_end_row_x2; const uint32_t is0 = (ir0 - src0_start_row); - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 1); - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; mt->vec_dot(ne00, &tmp[ir0 - src0_start_row], ss0, src1_col); } @@ -1343,13 +1343,13 @@ static void matmul_id(struct htp_matmul_type * mt, if (is0 >= HTP_SPAD_SRC0_NROWS) { break; } - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } // Process src0 rows for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) { - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; for (uint32_t cid = 0; cid < cne1; ++cid) { struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, cid); @@ -1368,7 +1368,7 @@ static void matmul_id(struct htp_matmul_type * mt, const int pr0 = (ir0 + HTP_SPAD_SRC0_NROWS); const int is0 = (pr0 - src0_start_row) % HTP_SPAD_SRC0_NROWS; if (pr0 < src0_end_row_x2) { - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } } @@ -1377,9 +1377,9 @@ static void matmul_id(struct htp_matmul_type * mt, if (src0_end_row != src0_end_row_x2) { uint32_t ir0 = src0_end_row_x2; const uint32_t is0 = (ir0 - src0_start_row); - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 1); - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; for (uint32_t cid = 0; cid < cne1; ++cid) { struct mmid_row_mapping row_mapping = MMID_MATRIX_ROW(cur_a, cid); @@ -1467,20 +1467,20 @@ static void matvec_id(struct htp_matmul_type * mt, if (is0 >= HTP_SPAD_SRC0_NROWS) { break; } - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } // Process src0 rows for (uint32_t ir0 = src0_start_row; ir0 < src0_end_row_x2; ir0 += 2) { - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; mt->vec_dot_rx2(ne00, &dst_row[ir0], ss0, src0_row_size_padded, src1_col); // Prefetch next (n + spad_nrows) row const int pr0 = (ir0 + HTP_SPAD_SRC0_NROWS); const int is0 = (pr0 - src0_start_row) % HTP_SPAD_SRC0_NROWS; if (pr0 < src0_end_row_x2) { - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + pr0 * src0_row_size), src0_row_size_padded, src0_row_size, 2); } } @@ -1489,9 +1489,9 @@ static void matvec_id(struct htp_matmul_type * mt, if (src0_end_row != src0_end_row_x2) { uint32_t ir0 = src0_end_row_x2; const uint32_t is0 = (ir0 - src0_start_row); - dma_queue_push(dma_queue, spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size, + dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(spad_src0 + is0 * src0_row_size_padded, src0_row + ir0 * src0_row_size), src0_row_size_padded, src0_row_size, 1); - const uint8_t * ss0 = dma_queue_pop(dma_queue); + const uint8_t * ss0 = dma_queue_pop(dma_queue).dst; mt->vec_dot(ne00, &dst_row[ir0], ss0, src1_col); } } From 8f48807380305a5985df78f67e29862664c9afec Mon Sep 17 00:00:00 2001 From: compilade Date: Mon, 22 Dec 2025 14:25:16 -0500 Subject: [PATCH 12/25] gguf-py : do not align the data start offset (#18291) The safetensors format doesn't require alignment. --- gguf-py/gguf/utility.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/gguf-py/gguf/utility.py b/gguf-py/gguf/utility.py index 4918ae971a..154351d8ed 100644 --- a/gguf-py/gguf/utility.py +++ b/gguf-py/gguf/utility.py @@ -110,7 +110,6 @@ class SafetensorRemote: """ BASE_DOMAIN = "https://huggingface.co" - ALIGNMENT = 8 # bytes @classmethod def get_list_tensors_hf_model(cls, model_id: str) -> dict[str, RemoteTensor]: @@ -204,9 +203,6 @@ class SafetensorRemote: # Calculate the data start offset data_start_offset = 8 + metadata_length - alignment = SafetensorRemote.ALIGNMENT - if data_start_offset % alignment != 0: - data_start_offset += alignment - (data_start_offset % alignment) # Check if we have enough data to read the metadata if len(raw_data) < 8 + metadata_length: @@ -298,7 +294,6 @@ class SafetensorsLocal: Custom parsing gives a bit more control over the memory usage. The official safetensors library doesn't expose file ranges. """ - ALIGNMENT = 8 # bytes tensors: dict[str, LocalTensor] @@ -316,9 +311,6 @@ class SafetensorsLocal: raise ValueError(f"Failed to parse safetensors metadata as JSON: {e}") data_start_offset = f.tell() - alignment = self.ALIGNMENT - if data_start_offset % alignment != 0: - data_start_offset += alignment - (data_start_offset % alignment) tensors: dict[str, LocalTensor] = {} for name, meta in metadata.items(): From dfc959b886d2613a4d3f06d8f3523628c654e8a3 Mon Sep 17 00:00:00 2001 From: Ryan Mangeno <160974989+ryan-mangeno@users.noreply.github.com> Date: Mon, 22 Dec 2025 18:28:19 -0500 Subject: [PATCH 13/25] model : Granite Embedding support (#15641) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ModernBERT but without `head.norm` so will currently fail to convert and run any other ModernBERT models, PRs with `head.norm` support welcome! * constants and tensor mappings for modern bert support, model not supported yet but working on getting conversion to work for encoder only * conversion now working, hf -> gguf * working on support, now working on building graph * some cleanup * cleanup * continuing * correct tensor shape for qkv * fixed tensor mappings and working on buildin graph * tensor debugging now works -> (llama-eval-callback), instead of simulated gate split with views, GEGLU is now used which does exactly this * cleanup * cleanup * cleanup * more cleanup * ubatch issues, the assert for checking equal seqs in llama-graph.cpp when building attention keeps failing, setting ubatch size to 1 when running llama-embedding with --ubatch-size 1 makes it work, but needs to be looked into more * added cls token per previous modern bert attempt, still working on checking out the rest * fixed pre tokenizer and still working through previous pr * working through previous attemp, implimented more accurate conversion per previous attempt, added local sliding window attention that alternates every third layer * fixed pre tokenizer * working on swa with local and global alternating attention * some cleanup and now fails on build attn * starting to work, and some cleanup, currently failing on last layer construction in graph build * alternating rope implemented and modern bert graph build succeeds * fixed asser for equal ubatch seq * cleanup * added mask check in vocab * fixed alternating rope, the hparams.rope_freq_base_train and hparams.rope_freq_base_train_swa were the same and i set them to correct values * reuse variable * removed repeat * standard swa method can be used instead of a new enum being LLAMA_SWA_TYPE_LOCAL * correct swa layer indexing, is supposed to be 0, 3, 6 ... instead of 1, 4, 7 ... * more modular hparam setting * replaced attn out norm with ffn_norm and cosine similarity between hf embds and llama.cpp embds went way up, from 0.05 to 0.24, replaced the cacheless kv with swa todo per the previous conversion * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update convert_hf_to_gguf_update.py Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-vocab.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret * Update src/llama-graph.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-arch.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * removed redundant hparam set * enums for model sizes * conversion for modern-bert model supported rather than just granite-small * Update src/llama-model.cpp Co-authored-by: Gabe Goodhart * Update src/llama-model.cpp Co-authored-by: Gabe Goodhart * fixed ordering of enum for freq_base_swa * fixed where I added residual, now gives much much better embeddings~ * readded cacheless logic * removing whitespace * conversion now working for swa pattern - dense every n layers * modern bert put into seperate src file * removing whitespace * fixed whitespace and newline errors in editorconfig job * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret * better naming convention, n_swa_pattern -> swa_period * reusing sliding_window_pattern key rather than making new dense_every_n_layers key, and adding writing and reading support * fixing pyright type-check fail * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/gguf_writer.py Co-authored-by: Sigbjørn Skjæret * Update src/llama-hparams.h Co-authored-by: Sigbjørn Skjæret * Update src/llama-model-saver.cpp Co-authored-by: Sigbjørn Skjæret * Update src/models/modern-bert.cpp Co-authored-by: Sigbjørn Skjæret * Update src/models/modern-bert.cpp Co-authored-by: Sigbjørn Skjæret * Update src/models/modern-bert.cpp Co-authored-by: Sigbjørn Skjæret * Update gguf-py/gguf/gguf_writer.py Co-authored-by: Sigbjørn Skjæret * Update src/models/modern-bert.cpp Co-authored-by: Sigbjørn Skjæret * Update src/models/modern-bert.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model-loader.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model-loader.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model-loader.cpp Co-authored-by: Sigbjørn Skjæret * added descriptions in llama-model * fixed tensor mappings for conversion * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret * mapping name for size * nits * unused --------- Co-authored-by: Sigbjørn Skjæret Co-authored-by: Gabe Goodhart --- convert_hf_to_gguf.py | 33 +++++++++ convert_hf_to_gguf_update.py | 1 + gguf-py/gguf/constants.py | 16 +++++ gguf-py/gguf/gguf_writer.py | 11 ++- gguf-py/gguf/tensor_mapping.py | 11 +++ src/CMakeLists.txt | 1 + src/llama-arch.cpp | 17 +++++ src/llama-arch.h | 3 + src/llama-model-loader.cpp | 23 ++++++ src/llama-model-loader.h | 2 + src/llama-model.cpp | 68 ++++++++++++++++++ src/llama-model.h | 3 + src/llama-vocab.cpp | 10 ++- src/models/models.h | 5 ++ src/models/modern-bert.cpp | 126 +++++++++++++++++++++++++++++++++ 15 files changed, 327 insertions(+), 3 deletions(-) create mode 100644 src/models/modern-bert.cpp diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 22f703e6ad..16c5acf346 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1212,6 +1212,9 @@ class TextModel(ModelBase): if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756": # ref: https://huggingface.co/JetBrains/Mellum-4b-base res = "mellum" + if chkhsh == "a0b64b4385f123663873756336c085744376d015ff328bb1d901598f63c44152": + # ref: https://huggingface.co/answerdotai/ModernBERT-base + res = "modern-bert" if chkhsh == "49fc0303c9e0d2c2c565c510f64b2d9b271276acdcdadff733249eda9f7d59df": # ref: https://huggingface.co/arcee-ai/Trinity-Tokenizer res = "afmoe" @@ -9999,6 +10002,36 @@ class SmallThinkerModel(TextModel): raise ValueError(f"Unprocessed experts: {experts}") +@ModelBase.register("ModernBertModel", "ModernBertForMaskedLM", "ModernBertForSequenceClassification") +class ModernBertModel(BertModel): + model_arch = gguf.MODEL_ARCH.MODERN_BERT + + def set_vocab(self): + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + self.gguf_writer.add_add_sep_token(True) + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_sliding_window(self.hparams["local_attention"]) + if (sliding_window_pattern := self.hparams.get("global_attn_every_n_layers")) is not None: + self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern) + self.gguf_writer.add_rope_freq_base_swa(self.rope_parameters.get("sliding_attention", {"rope_theta": self.hparams.get("local_rope_theta")})["rope_theta"]) + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) + self.gguf_writer.add_vocab_size(self.hparams["vocab_size"]) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # these layers act as MLM head, so we don't need them + if name.startswith("decoder."): + return [] + + if name.startswith("model."): + name = name[6:] + + return super().modify_tensors(data_torch, name, bid) + + @ModelBase.register("ApertusForCausalLM") class ApertusModel(LlamaModel): model_arch = gguf.MODEL_ARCH.APERTUS diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py index 5e8456a7ea..4378378309 100755 --- a/convert_hf_to_gguf_update.py +++ b/convert_hf_to_gguf_update.py @@ -139,6 +139,7 @@ models = [ {"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"}, {"name": "exaone4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", }, {"name": "mellum", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", }, + {"name": "modern-bert", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", }, {"name": "afmoe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/arcee-ai/Trinity-Tokenizer", }, {"name": "bailingmoe2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", }, {"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", }, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index cab8f2901a..41d3bd4faf 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -181,6 +181,7 @@ class Keys: DIMENSION_COUNT = "{arch}.rope.dimension_count" DIMENSION_SECTIONS = "{arch}.rope.dimension_sections" FREQ_BASE = "{arch}.rope.freq_base" + FREQ_BASE_SWA = "{arch}.rope.freq_base_swa" SCALING_TYPE = "{arch}.rope.scaling.type" SCALING_FACTOR = "{arch}.rope.scaling.factor" SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" @@ -354,6 +355,7 @@ class MODEL_ARCH(IntEnum): STARCODER = auto() REFACT = auto() BERT = auto() + MODERN_BERT = auto() NOMIC_BERT = auto() NOMIC_BERT_MOE = auto() NEO_BERT = auto() @@ -747,6 +749,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.STARCODER: "starcoder", MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", + MODEL_ARCH.MODERN_BERT: "modern-bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", MODEL_ARCH.NOMIC_BERT_MOE: "nomic-bert-moe", MODEL_ARCH.NEO_BERT: "neo-bert", @@ -1367,6 +1370,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.CLS, MODEL_TENSOR.CLS_OUT, ], + MODEL_ARCH.MODERN_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.CLS, + MODEL_TENSOR.CLS_OUT, + ], MODEL_ARCH.NOMIC_BERT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 9e6ff3ac77..6a4a504f8d 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -774,8 +774,12 @@ class GGUFWriter: def add_shared_kv_layers(self, value: int) -> None: self.add_uint32(Keys.Attention.SHARED_KV_LAYERS.format(arch=self.arch), value) - def add_sliding_window_pattern(self, value: Sequence[bool]) -> None: - self.add_array(Keys.Attention.SLIDING_WINDOW_PATTERN.format(arch=self.arch), value) + def add_sliding_window_pattern(self, value: int | Sequence[bool]) -> None: + key = Keys.Attention.SLIDING_WINDOW_PATTERN.format(arch=self.arch) + if isinstance(value, int): + self.add_uint32(key, value) + else: + self.add_array(key, value) def add_dense_features_dims(self, dense:str, in_f:int, out_f:int) -> None: self.add_uint32(Keys.LLM.DENSE_FEAT_IN_SIZE.format(arch=self.arch, dense=dense), in_f) @@ -886,6 +890,9 @@ class GGUFWriter: def add_value_residual_mix_lora_rank(self, length: int) -> None: self.add_uint32(Keys.Attention.VALUE_RESIDUAL_MIX_LORA_RANK.format(arch=self.arch), length) + def add_rope_freq_base_swa(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE_SWA.format(arch=self.arch), value) + def add_gate_lora_rank(self, length: int) -> None: self.add_uint32(Keys.Attention.GATE_LORA_RANK.format(arch=self.arch), length) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 301aafa910..276720fcde 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -17,6 +17,7 @@ class TensorNameMap: "embed_tokens", # embeddinggemma "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert nomic-bert + "embeddings.tok_embeddings", # modern-bert "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 @@ -46,6 +47,7 @@ class TensorNameMap: MODEL_TENSOR.TOKEN_EMBD_NORM: ( "word_embeddings_layernorm", # bloom "embeddings.LayerNorm", # bert + "embeddings.norm", # modern-bert "emb_ln", # nomic-bert "transformer.norm", # openelm "rwkv.blocks.0.pre_ln", # rwkv @@ -75,6 +77,7 @@ class TensorNameMap: "head.out", # wavtokenizer "lm_head", # llama4 "model.transformer.ff_out", # llada + "head.decoder", # modern-bert ), MODEL_TENSOR.DENSE_2_OUT: ( "dense_2_out", # embeddinggemma @@ -104,6 +107,7 @@ class TensorNameMap: "backbone.final_layer_norm", # wavtokenizer "model.norm", # llama4 "model.transformer.ln_f", # llada + "final_norm", # modern-bert "model.norm", # cogvlm ), @@ -151,6 +155,7 @@ class TensorNameMap: "model.layers.{bid}.input_layernorm", # llama4 "layers.{bid}.input_layernorm", # embeddinggemma "transformer_encoder.{bid}.attention_norm", # neobert + "layers.{bid}.attn_norm", # modern-bert "model.layers.{bid}.operator_norm", # lfm2 "model.transformer.blocks.{bid}.attn_norm", # llada "layers.{bid}.input_layernorm", # qwen3-embedding @@ -187,6 +192,7 @@ class TensorNameMap: "encoder.layers.{bid}.self_attention.query_key_value", # chatglm "transformer.layers.{bid}.attn.qkv_proj", # openelm "transformer_encoder.{bid}.qkv", # neobert + "layers.{bid}.attn.Wqkv", # modern-bert "model.layers.{bid}.self_attn.language_expert_query_key_value", # cogvlm ), @@ -261,6 +267,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.linear_attn", # deci "layers.{bid}.attention.wo", # llama-pth "encoder.layer.{bid}.attention.output.dense", # bert + "layers.{bid}.attn.Wo", # modern-bert "transformer.layer.{bid}.attention.out_lin", # distillbert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon @@ -344,6 +351,7 @@ class TensorNameMap: "layers.{bid}.post_attention_layernorm", # qwen3-embedding "model.layers.{bid}.feedforward_layernorm", # apertus "model.layers.{bid}.pre_mlp_layernorm", # kormo + "layers.{bid}.mlp_norm" # modern-bert ), # Pre feed-forward norm @@ -407,6 +415,7 @@ class TensorNameMap: "layers.{bid}.mlp.up_proj", # embeddinggemma "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert + "layers.{bid}.mlp.Wi", # modern-bert "transformer.layer.{bid}.ffn.lin1", # distillbert "transformer.h.{bid}.mlp.fc_in", # gpt-j "transformer.h.{bid}.mlp.linear_3", # refact @@ -521,6 +530,7 @@ class TensorNameMap: "layers.{bid}.mlp.down_proj", # embeddinggemma "layers.{bid}.feed_forward.w2", # llama-pth "encoder.layer.{bid}.output.dense", # bert + "layers.{bid}.mlp.Wo", # modern-bert "transformer.layer.{bid}.ffn.lin2", # distillbert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon @@ -1122,6 +1132,7 @@ class TensorNameMap: "classifier.dense", # roberta "pre_classifier", # distillbert "dense", # neobert + "head.dense", # modern-bert ), MODEL_TENSOR.CLS_OUT: ( diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt index 4192af7c0c..4ca8974916 100644 --- a/src/CMakeLists.txt +++ b/src/CMakeLists.txt @@ -90,6 +90,7 @@ add_library(llama models/mamba.cpp models/minicpm3.cpp models/minimax-m2.cpp + models/modern-bert.cpp models/mpt.cpp models/nemotron-h.cpp models/nemotron.cpp diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index d0eaf317f7..80f44ae1bf 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -20,6 +20,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_STARCODER, "starcoder" }, { LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_BERT, "bert" }, + { LLM_ARCH_MODERN_BERT, "modern-bert" }, { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, { LLM_ARCH_NOMIC_BERT_MOE, "nomic-bert-moe" }, { LLM_ARCH_NEO_BERT, "neo-bert" }, @@ -204,6 +205,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_GATE_LORA_RANK, "%s.attention.gate_lora_rank" }, { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" }, { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" }, + { LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, "%s.attention.sliding_window_pattern" }, { LLM_KV_ATTENTION_SCALE, "%s.attention.scale" }, { LLM_KV_ATTENTION_OUTPUT_SCALE, "%s.attention.output_scale" }, { LLM_KV_ATTENTION_TEMPERATURE_LENGTH, "%s.attention.temperature_length" }, @@ -214,6 +216,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, + { LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" }, { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, @@ -778,6 +781,20 @@ static std::set llm_get_tensor_names(llm_arch arch) { LLM_TENSOR_CLS, LLM_TENSOR_CLS_OUT, }; + case LLM_ARCH_MODERN_BERT: + return { + LLM_TENSOR_TOKEN_EMBD, + LLM_TENSOR_TOKEN_EMBD_NORM, + LLM_TENSOR_OUTPUT_NORM, + LLM_TENSOR_ATTN_NORM, + LLM_TENSOR_ATTN_OUT, + LLM_TENSOR_ATTN_QKV, + LLM_TENSOR_FFN_DOWN, + LLM_TENSOR_FFN_UP, + LLM_TENSOR_FFN_NORM, + LLM_TENSOR_CLS, + LLM_TENSOR_CLS_OUT, + }; case LLM_ARCH_JINA_BERT_V2: return { LLM_TENSOR_TOKEN_EMBD, diff --git a/src/llama-arch.h b/src/llama-arch.h index 6cbf9b1f89..a53bc39d18 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -24,6 +24,7 @@ enum llm_arch { LLM_ARCH_STARCODER, LLM_ARCH_REFACT, LLM_ARCH_BERT, + LLM_ARCH_MODERN_BERT, LLM_ARCH_NOMIC_BERT, LLM_ARCH_NOMIC_BERT_MOE, LLM_ARCH_NEO_BERT, @@ -208,6 +209,7 @@ enum llm_kv { LLM_KV_ATTENTION_GATE_LORA_RANK, LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, LLM_KV_ATTENTION_SLIDING_WINDOW, + LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, LLM_KV_ATTENTION_SCALE, LLM_KV_ATTENTION_OUTPUT_SCALE, LLM_KV_ATTENTION_TEMPERATURE_LENGTH, @@ -218,6 +220,7 @@ enum llm_kv { LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_DIMENSION_SECTIONS, LLM_KV_ROPE_FREQ_BASE, + LLM_KV_ROPE_FREQ_BASE_SWA, LLM_KV_ROPE_SCALE_LINEAR, LLM_KV_ROPE_SCALING_TYPE, LLM_KV_ROPE_SCALING_FACTOR, diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index 33a76dba40..5003b4fbf5 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -462,6 +462,29 @@ namespace GGUFMeta { return get_key_or_arr(llm_kv(kid), result, n, required); } + bool llama_model_loader::get_key_or_arr(enum llm_kv kid, uint32_t & result, bool required) { + const std::string key = llm_kv(kid); + + const int id = gguf_find_key(meta.get(), key.c_str()); + + if (id < 0) { + if (required) { + throw std::runtime_error(format("key not found in model: %s", key.c_str())); + } + return false; + } + + // throw and error if type is an array + if (gguf_get_kv_type(meta.get(), id) == GGUF_TYPE_ARRAY) { + if (required) { + throw std::runtime_error(format("expected scalar, found array for key: %s", key.c_str())); + } + return false; + } + + return get_key(key, result, required); + } + // TODO: this is not very clever - figure out something better template bool llama_model_loader::get_key_or_arr>(enum llm_kv kid, std::array & result, uint32_t n, bool required); template bool llama_model_loader::get_key_or_arr>(enum llm_kv kid, std::array & result, uint32_t n, bool required); diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index 0380c92fde..d13299ad3f 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -131,6 +131,8 @@ struct llama_model_loader { template bool get_key_or_arr(enum llm_kv kid, T & result, uint32_t n, bool required = true); + bool get_key_or_arr(enum llm_kv kid, uint32_t & result, bool required = true); + std::string get_arch_name() const; enum llm_arch get_arch() const; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index d2270e8f2d..87fefd576c 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -31,12 +31,14 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_17M: return "17M"; case LLM_TYPE_22M: return "22M"; case LLM_TYPE_33M: return "33M"; + case LLM_TYPE_47M: return "47M"; case LLM_TYPE_60M: return "60M"; case LLM_TYPE_70M: return "70M"; case LLM_TYPE_80M: return "80M"; case LLM_TYPE_109M: return "109M"; case LLM_TYPE_137M: return "137M"; case LLM_TYPE_140M: return "140M"; + case LLM_TYPE_149M: return "149M"; case LLM_TYPE_160M: return "160M"; case LLM_TYPE_190M: return "190M"; case LLM_TYPE_220M: return "220M"; @@ -46,6 +48,7 @@ const char * llm_type_name(llm_type type) { case LLM_TYPE_335M: return "335M"; case LLM_TYPE_350M: return "350M"; case LLM_TYPE_360M: return "360M"; + case LLM_TYPE_395M: return "395M"; case LLM_TYPE_410M: return "410M"; case LLM_TYPE_450M: return "450M"; case LLM_TYPE_475M: return "475M"; @@ -875,6 +878,34 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: type = LLM_TYPE_UNKNOWN; } } break; + case LLM_ARCH_MODERN_BERT: + { + const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); + if (found_swa && hparams.n_swa > 0) { + uint32_t swa_period = 3; + hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC; + + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa); + ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, swa_period, false); + hparams.set_swa_pattern(swa_period); + } else { + hparams.swa_type = LLAMA_SWA_TYPE_NONE; + } + + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false); + + switch (hparams.n_layer) { + case 12: + type = LLM_TYPE_47M; break; // granite-embedding-small + case 22: + type = LLM_TYPE_149M; break; // modern-bert-base + case 28: + type = LLM_TYPE_395M; break; // modern-bert-large + default: type = LLM_TYPE_UNKNOWN; + } + } break; case LLM_ARCH_JINA_BERT_V2: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -3155,6 +3186,37 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0); } } break; + case LLM_ARCH_MODERN_BERT: + { + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + + for(int i = 0; i < n_layer; ++i) { + auto& layer = layers[i]; + + if ( i != 0 ) { + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + } else{ + // layer 0 uses identity + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED); + } + + + layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); + + layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, 2 * n_ff}, 0); + layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + } + + cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED); + cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, hparams.n_cls_out}, TENSOR_NOT_REQUIRED); + cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {hparams.n_cls_out}, TENSOR_NOT_REQUIRED); + + } break; case LLM_ARCH_NEO_BERT: { tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -7089,6 +7151,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params, case LLM_ARCH_NOMIC_BERT_MOE: case LLM_ARCH_NEO_BERT: case LLM_ARCH_WAVTOKENIZER_DEC: + case LLM_ARCH_MODERN_BERT: case LLM_ARCH_GEMMA_EMBEDDING: case LLM_ARCH_DREAM: case LLM_ARCH_LLADA: @@ -7248,6 +7311,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const { { llm = std::make_unique(*this, params); } break; + case LLM_ARCH_MODERN_BERT: + { + llm = std::make_unique>(*this, params); + } break; case LLM_ARCH_NEO_BERT: { llm = std::make_unique(*this, params); @@ -7816,6 +7883,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_DBRX: case LLM_ARCH_BERT: case LLM_ARCH_JINA_BERT_V3: + case LLM_ARCH_MODERN_BERT: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_NOMIC_BERT_MOE: case LLM_ARCH_STABLELM: diff --git a/src/llama-model.h b/src/llama-model.h index c6eb953188..7f560d462f 100644 --- a/src/llama-model.h +++ b/src/llama-model.h @@ -24,12 +24,14 @@ enum llm_type { LLM_TYPE_17M, LLM_TYPE_22M, LLM_TYPE_33M, + LLM_TYPE_47M, LLM_TYPE_60M, LLM_TYPE_70M, LLM_TYPE_80M, LLM_TYPE_109M, LLM_TYPE_137M, LLM_TYPE_140M, + LLM_TYPE_149M, LLM_TYPE_160M, LLM_TYPE_190M, LLM_TYPE_220M, @@ -39,6 +41,7 @@ enum llm_type { LLM_TYPE_335M, LLM_TYPE_350M, LLM_TYPE_360M, + LLM_TYPE_395M, LLM_TYPE_410M, LLM_TYPE_450M, LLM_TYPE_475M, diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp index 7b01a2edfe..cd4092ca07 100644 --- a/src/llama-vocab.cpp +++ b/src/llama-vocab.cpp @@ -1878,7 +1878,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { tokenizer_pre == "jina-v2-es" || tokenizer_pre == "jina-v2-de" || tokenizer_pre == "a.x-4.0" || - tokenizer_pre == "mellum") { + tokenizer_pre == "mellum" || + tokenizer_pre == "modern-bert" ) { pre_type = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "jina-v1-en" || @@ -2528,6 +2529,13 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) { for (const auto * token : {"", "", "<|endoftext|>"}) { _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false); } + } else if (_contains_any(model_name, {"modern-bert"})) { + if (token_to_id.count("[MASK]") == 0 ) { + LLAMA_LOG_WARN("%s: Mask token missing in vocab!\n", __func__); + } + else { + _set_token_attr("[MASK]", LLAMA_TOKEN_ATTR_LSTRIP, true); + } } } } diff --git a/src/models/models.h b/src/models/models.h index ffb36acc61..53a5810659 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -327,6 +327,11 @@ struct llm_build_mistral3 : public llm_graph_context { llm_build_mistral3(const llama_model & model, const llm_graph_params & params); }; +template +struct llm_build_modern_bert : public llm_graph_context { + llm_build_modern_bert(const llama_model & model, const llm_graph_params & params); +}; + struct llm_build_mpt : public llm_graph_context { llm_build_mpt(const llama_model & model, const llm_graph_params & params); }; diff --git a/src/models/modern-bert.cpp b/src/models/modern-bert.cpp new file mode 100644 index 0000000000..c7809bdedf --- /dev/null +++ b/src/models/modern-bert.cpp @@ -0,0 +1,126 @@ +#include "models.h" + +template +llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + ggml_tensor * cur; + ggml_tensor * inpL; + ggml_tensor * inp_pos = build_inp_pos(); + + // construct input embeddings (token, type, position) + inpL = build_inp_embd(model.tok_embd); + cb(inpL, "inp_embd", -1); + + // embed layer norm + inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1); + cb(inpL, "inp_norm", -1); + + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + auto * inp_attn = build_attn_inp_no_cache(); + + for (int il = 0; il < n_layer; ++il) { + float freq_base_l = 0.0f; + + if constexpr (iswa) { + freq_base_l = model.get_rope_freq_base(cparams, il); + } else { + freq_base_l = freq_base; + } + + cur = inpL; + + // attention layer norm + if (model.layers[il].attn_norm) { + cur = build_norm(inpL, + model.layers[il].attn_norm, NULL, + LLM_NORM, il); + cb(cur, "attn_norm", il); + } + + // self attention + cur = build_lora_mm(model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + const size_t type_size = ggml_type_size(cur->type); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd)); + ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd)); + ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa)); + + // RoPE + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, + n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, nullptr, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il); + cb(cur, "kqv_out", il); + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + // re-add the layer input + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + // attention layer norm + cur = build_norm(ffn_inp, + model.layers[il].ffn_norm, NULL, + LLM_NORM, il); + cb(cur, "ffn_norm", il); + + cur = build_ffn(cur, + model.layers[il].ffn_up, NULL, NULL, + NULL, NULL, NULL, + model.layers[il].ffn_down, NULL, NULL, + NULL, + LLM_FFN_GEGLU, LLM_FFN_SEQ, il); + + // attentions bypass the intermediate layer + cur = ggml_add(ctx0, cur, ffn_inp); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, + model.output_norm, NULL, + LLM_NORM, -1); + cb(cur, "final_norm_out", -1); + + if (hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { + // extracting cls token + cur = ggml_view_1d(ctx0, cur, hparams.n_embd, 0); + cb(cur, "cls_pooled_embd", -1); + } + + cb(cur, "res_embd", -1); + res->t_embd = cur; + ggml_build_forward_expand(gf, cur); +} + +// Explicit template instantiations +template struct llm_build_modern_bert; +template struct llm_build_modern_bert; From 96e33a814e100025648d12eb6a827154b48277d9 Mon Sep 17 00:00:00 2001 From: Alessandro98-git <61804547+Alessandro98-git@users.noreply.github.com> Date: Tue, 23 Dec 2025 03:04:57 +0100 Subject: [PATCH 14/25] model : fix div-by-zero for Nemotron V2 (#18309) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * llama-model : fix Nemotron V2 crash by moving MoE parameters calculation * remove whitespace --------- Co-authored-by: Sigbjørn Skjæret --- src/llama-model.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 87fefd576c..0d5bcc64fe 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -5243,9 +5243,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) { const int64_t n_group = hparams.ssm_n_group; const int64_t d_in_proj = 2*d_inner + 2*n_group*d_state + n_ssm_head; - const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used; - const int64_t n_ff_shexp = hparams.n_ff_shexp; - // embeddings tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); @@ -5297,6 +5294,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) { layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED); } else { if (n_expert != 0) { + const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used; + const int64_t n_ff_shexp = hparams.n_ff_shexp; + 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 }, 0); From a6a552e4ec43c2b6ed29b05d8da6921c59f05ed7 Mon Sep 17 00:00:00 2001 From: Neo Zhang Date: Tue, 23 Dec 2025 12:59:12 +0800 Subject: [PATCH 15/25] [SYCL] replace llama-cli by llama-completion to rm the impact to test script (#18290) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * replace llama-cli by llama-completion to rm the impact to test script * Update examples/sycl/run-llama2.sh Co-authored-by: Sigbjørn Skjæret * Update examples/sycl/run-llama2.sh Co-authored-by: Sigbjørn Skjæret * Update examples/sycl/run-llama3.sh Co-authored-by: Sigbjørn Skjæret * Update examples/sycl/run-llama3.sh Co-authored-by: Sigbjørn Skjæret * Update examples/sycl/win-run-llama2.bat Co-authored-by: Sigbjørn Skjæret * Update examples/sycl/win-run-llama3.bat Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Neo Zhang Jianyu Co-authored-by: Sigbjørn Skjæret --- examples/sycl/run-llama2.sh | 4 ++-- examples/sycl/run-llama3.sh | 4 ++-- examples/sycl/win-run-llama2.bat | 2 +- examples/sycl/win-run-llama3.bat | 2 +- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index a018e45197..cf23619ee0 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -22,9 +22,9 @@ if [ $# -gt 0 ]; then GGML_SYCL_DEVICE=$1 echo "use $GGML_SYCL_DEVICE as main GPU" #use signle GPU only - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none else #use multiple GPUs with same max compute units - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} fi diff --git a/examples/sycl/run-llama3.sh b/examples/sycl/run-llama3.sh index 4770255703..feee5165e9 100755 --- a/examples/sycl/run-llama3.sh +++ b/examples/sycl/run-llama3.sh @@ -24,8 +24,8 @@ export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 if [ $# -gt 0 ]; then GGML_SYCL_DEVICE=$1 echo "Using $GGML_SYCL_DEVICE as the main GPU" - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none else #use multiple GPUs with same max compute units - ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-completion -m ${MODEL_FILE} -no-cnv -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} fi diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index b654f88f62..32ff673ae2 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -8,4 +8,4 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" :: support malloc device memory more than 4GB. set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 -.\build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 99 -s 0 +.\build\bin\llama-completion.exe -m models\llama-2-7b.Q4_0.gguf -no-cnv -p %INPUT2% -n 400 -e -ngl 99 -s 0 diff --git a/examples/sycl/win-run-llama3.bat b/examples/sycl/win-run-llama3.bat index 608b834f60..ea4ae69d6c 100644 --- a/examples/sycl/win-run-llama3.bat +++ b/examples/sycl/win-run-llama3.bat @@ -8,4 +8,4 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" :: support malloc device memory more than 4GB. set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1 -.\build\bin\llama-cli.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p %INPUT2% -n 400 -s 0 -e -ngl 99 +.\build\bin\llama-completion.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -no-cnv -p %INPUT2% -n 400 -s 0 -e -ngl 99 From 847c35f7d5cbc686dfd5ae70f5702bafb84a8be9 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 23 Dec 2025 07:27:37 +0100 Subject: [PATCH 16/25] model-conversion : add trust_remote_code for embedding scripts (#18288) This commit adds the trust_remote_code=True parameter when loading models and configurations in the embedding model conversion scripts. It also adds a cast to float for models that might use a data type that is not supported by python, for example bfloat16. The motivation for this is that some models may require custom code to be executed during loading, and setting trust_remote_code to True avoids getting prompted for confirmation. Future work will consolidate the embedding conversion scripts with the causal conversion scripts to avoid code duplication. But in the mean time it would be nice to have this fix in place. --- .../scripts/embedding/run-original-model.py | 8 ++++---- examples/model-conversion/scripts/utils/semantic_check.py | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/model-conversion/scripts/embedding/run-original-model.py b/examples/model-conversion/scripts/embedding/run-original-model.py index 640e200a97..39f054d0e0 100755 --- a/examples/model-conversion/scripts/embedding/run-original-model.py +++ b/examples/model-conversion/scripts/embedding/run-original-model.py @@ -45,7 +45,7 @@ if use_sentence_transformers: else: tokenizer = AutoTokenizer.from_pretrained(model_path) - config = AutoConfig.from_pretrained(model_path) + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) # This can be used to override the sliding window size for manual testing. This # can be useful to verify the sliding window attention mask in the original model @@ -64,12 +64,12 @@ else: try: model_class = getattr(importlib.import_module(unreleased_module_path), class_name) - model = model_class.from_pretrained(model_path, config=config) + model = model_class.from_pretrained(model_path, config=config, trust_remote_code=True) except (ImportError, AttributeError) as e: print(f"Failed to import or load model: {e}") exit(1) else: - model = AutoModel.from_pretrained(model_path, config=config) + model = AutoModel.from_pretrained(model_path, config=config, trust_remote_code=True) print(f"Model class: {type(model)}") print(f"Model file: {type(model).__module__}") @@ -123,7 +123,7 @@ with torch.no_grad(): outputs = model(**encoded) hidden_states = outputs.last_hidden_state # Shape: [batch_size, seq_len, hidden_size] - all_embeddings = hidden_states[0].cpu().numpy() # Shape: [seq_len, hidden_size] + all_embeddings = hidden_states[0].float().cpu().numpy() # Shape: [seq_len, hidden_size] print(f"Hidden states shape: {hidden_states.shape}") print(f"All embeddings shape: {all_embeddings.shape}") diff --git a/examples/model-conversion/scripts/utils/semantic_check.py b/examples/model-conversion/scripts/utils/semantic_check.py index 2ac8b6b7b4..e64c000497 100644 --- a/examples/model-conversion/scripts/utils/semantic_check.py +++ b/examples/model-conversion/scripts/utils/semantic_check.py @@ -166,7 +166,7 @@ def main(): # Load the python model to get configuration information and also to load the tokenizer. print("Loading model and tokenizer using AutoTokenizer:", args.model_path) tokenizer = AutoTokenizer.from_pretrained(args.model_path) - config = AutoConfig.from_pretrained(args.model_path) + config = AutoConfig.from_pretrained(args.model_path, trust_remote_code=True) if unreleased_model_name: model_name_lower = unreleased_model_name.lower() @@ -186,9 +186,9 @@ def main(): exit(1) else: if args.causal: - model = AutoModelForCausalLM.from_pretrained(args.model_path) + model = AutoModelForCausalLM.from_pretrained(args.model_path, trust_remote_code=True) else: - model = AutoModel.from_pretrained(args.model_path) + model = AutoModel.from_pretrained(args.model_path, trust_remote_code=True) encoded = tokenizer(prompt, return_tensors="pt") tokens = tokenizer.convert_ids_to_tokens(encoded['input_ids'][0]) From ed759777178e7a30d064b81e35d1fc320bf3970b Mon Sep 17 00:00:00 2001 From: nullname Date: Tue, 23 Dec 2025 15:13:24 +0800 Subject: [PATCH 17/25] ggml-hexagon: create generalized functions for cpu side op (#17500) * refactor: replace ggml_hexagon_mul_mat with template-based binary operation for improved flexibility * refactor: replace ggml_hexagon_mul_mat_id with template-based binary operation for improved flexibility * refactor: initialize buffer types and streamline dspqueue_buffers_init calls for clarity * add comment * refactor: remove redundant buffer checks in hexagon supported operations * wip * add missing include to fix weak symbol warning * add ggml_hexagon_op_generic * refactor: simplify tensor operation initialization and buffer management in hexagon implementation * refactor: streamline hexagon operation initialization and buffer management * refactor: update function signatures and streamline request handling in hexagon operations * wip * ggml-hexagon: clean up code formatting and improve unary operation handling * wip * rename * fix: add support for permuted F16 tensors and enhance quantization checks in matrix operations * refactor: replace ggml_hexagon_mul_mat with template-based binary operation for improved flexibility refactor: replace ggml_hexagon_mul_mat_id with template-based binary operation for improved flexibility refactor: initialize buffer types and streamline dspqueue_buffers_init calls for clarity refactor: remove redundant buffer checks in hexagon supported operations add missing include to fix weak symbol warning add ggml_hexagon_op_generic refactor: simplify tensor operation initialization and buffer management in hexagon implementation refactor: streamline hexagon operation initialization and buffer management refactor: update function signatures and streamline request handling in hexagon operations ggml-hexagon: clean up code formatting and improve unary operation handling fix: add support for permuted F16 tensors and enhance quantization checks in matrix operations # Conflicts: # ggml/src/ggml-hexagon/ggml-hexagon.cpp * hexagon: fix merge conflicts * hexagon: minor cleanup for buffer support checks * hexagon: factor out op_desc and the overal op logging * hexagon: further simplify and cleanup op dispatch logic * snapdragon: update adb scripts to use llama-cli and llama-completion * fix pipeline failure --------- Co-authored-by: Max Krasnyansky --- docs/backend/hexagon/README.md | 10 +- docs/backend/hexagon/developer.md | 2 +- ggml/src/ggml-hexagon/ggml-hexagon.cpp | 994 ++++++----------------- ggml/src/ggml-hexagon/htp-utils.h | 1 + ggml/src/ggml-hexagon/op-desc.h | 153 ++++ scripts/snapdragon/adb/run-cli.sh | 18 +- scripts/snapdragon/adb/run-completion.sh | 53 ++ 7 files changed, 452 insertions(+), 779 deletions(-) create mode 100644 ggml/src/ggml-hexagon/op-desc.h create mode 100755 scripts/snapdragon/adb/run-completion.sh diff --git a/docs/backend/hexagon/README.md b/docs/backend/hexagon/README.md index 85f136ef9e..00ec3a7e71 100644 --- a/docs/backend/hexagon/README.md +++ b/docs/backend/hexagon/README.md @@ -106,7 +106,7 @@ Here are some examples of running various llama.cpp tools via ADB. Simple question for Llama-3.2-1B ``` -~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-cli.sh -no-cnv -p "what is the most popular cookie in the world?" +~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-completion.sh -p "what is the most popular cookie in the world?" ... ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1 ggml-hex: Hexagon Arch version v79 @@ -136,7 +136,7 @@ llama_memory_breakdown_print: | - HTP0-REPACK | 504 = Summary request for OLMoE-1B-7B. This is a large model that requires two HTP sessions/devices ``` -~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-cli.sh -f surfing.txt -no-cnv +~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-completion.sh -f surfing.txt ... ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1 ggml-hex: Hexagon Arch version v81 @@ -234,6 +234,6 @@ build: 6a8cf8914 (6733) Examples: - `GGML_HEXAGON_OPMASK=0x1 llama-cli ...` - Ops are enqueued but NPU-side processing is stubbed out - `GGML_HEXAGON_OPMASK=0x3 llama-cli ...` - NPU performs dynamic quantization and skips the rest - `GGML_HEXAGON_OPMASK=0x7 llama-cli ...` - Full queuing and processing of Ops (default) + `GGML_HEXAGON_OPMASK=0x1 llama-completion ...` - Ops are enqueued but NPU-side processing is stubbed out + `GGML_HEXAGON_OPMASK=0x3 llama-completion ...` - NPU performs dynamic quantization and skips the rest + `GGML_HEXAGON_OPMASK=0x7 llama-completion ...` - Full queuing and processing of Ops (default) diff --git a/docs/backend/hexagon/developer.md b/docs/backend/hexagon/developer.md index 200a7aabc0..fc4d160e93 100644 --- a/docs/backend/hexagon/developer.md +++ b/docs/backend/hexagon/developer.md @@ -49,7 +49,7 @@ Each Hexagon device behaves like a GPU from the offload and model splitting pers Here is an example of running GPT-OSS-20B model on a newer Snapdragon device with 16GB of DDR. ``` -M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-cli.sh -no-cnv -f surfing.txt -n 32 +M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-completion.sh -f surfing.txt -n 32 ... LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp index 853a5bda1e..13b96d61f8 100644 --- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp +++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp @@ -7,9 +7,10 @@ #include #include +#include #include -#include #include +#include #ifdef _WIN32 # include @@ -36,6 +37,7 @@ #include "ggml-hexagon.h" #include "ggml-impl.h" #include "ggml-quants.h" +#include "op-desc.h" #include "htp-msg.h" #include "htp_iface.h" @@ -55,9 +57,6 @@ static int opt_opsync = 0; // synchronous ops #define HEX_VERBOSE(...) \ if (opt_verbose) GGML_LOG_DEBUG(__VA_ARGS__) -#define HEX_PROFILE(...) \ - if (opt_profile) GGML_LOG_INFO(__VA_ARGS__) - static inline uint64_t hex_is_aligned(void * addr, uint32_t align) { return ((size_t) addr & (align - 1)) == 0; } @@ -85,128 +84,30 @@ static const char * status_to_str(uint32_t status) { // ** debug helpers -static inline int hex_format_tensor_dims(char * str, const struct ggml_tensor * t) { - if (t->ne[2] == 1 && t->ne[3] == 1) { - return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]); - } else { - return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]); - } +static void ggml_hexagon_dump_op_exec(const std::string &sess_name, const ggml_tensor * op, const uint32_t req_flags) { + if (!opt_verbose) return; + + op_desc desc(op); + GGML_LOG_DEBUG("ggml-hex: %s execute-op %s: %s : %s : %s : %s : %s : flags 0x%x\n", sess_name.c_str(), + ggml_op_name(op->op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, req_flags); } -static inline void hex_format_op_dims(char * str, const struct ggml_tensor * t) { - char * p = str; +static void ggml_hexagon_dump_op_supp(const std::string &sess_name, const struct ggml_tensor * op, bool supp) { + if (!opt_verbose) return; - // append src0 and src1 (if any) - if (t->src[0]) { - p += hex_format_tensor_dims(p, t->src[0]); - - for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { - p += sprintf(p, " x "); - p += hex_format_tensor_dims(p, t->src[i]); - } - - p += sprintf(p, " -> "); - } - - // format self dims separately for better visual alignment - char self[64]; - hex_format_tensor_dims(self, t); - - p += sprintf(p, "%s", self); + op_desc desc(op); + GGML_LOG_DEBUG("ggml-hex: %s supports-op %s : %s : %s : %s : %s : %s : %s\n", sess_name.c_str(), + ggml_op_name(op->op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, supp ? "yes" : "no"); } -static inline int hex_format_tensor_strides(char * str, const struct ggml_tensor * t) { - const char * c = ggml_is_contiguous(t) ? "" : "!"; +static void ggml_hexagon_dump_op_prof(const std::string &sess_name, const ggml_tensor * op, + uint32_t op_usec, uint32_t op_cycles, uint32_t op_pkts, uint64_t call_usec) { + if (!opt_profile) return; - if (t->ne[2] == 1 && t->ne[3] == 1) { - return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c); - } else { - return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], - (size_t) t->nb[3], c); - } -} - -static inline void hex_format_op_strides(char * str, const struct ggml_tensor * t) { - char * p = str; - - // append src0 and src1 (if any) - if (t->src[0]) { - p += hex_format_tensor_strides(p, t->src[0]); - - for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { - p += sprintf(p, " x "); - p += hex_format_tensor_strides(p, t->src[i]); - } - - p += sprintf(p, " -> "); - } - - // format self dims separately for better visual alignment - char self[64]; - hex_format_tensor_strides(self, t); - - p += sprintf(p, "%s", self); -} - -static inline void hex_format_op_types(char * str, const struct ggml_tensor * t) { - char * p = str; - - // append src0 and src1 (if any) - if (t->src[0]) { - p += sprintf(p, "%s", ggml_type_name(t->src[0]->type)); - - for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { - p += sprintf(p, " x "); - p += sprintf(p, "%s", ggml_type_name(t->src[i]->type)); - } - - p += sprintf(p, " -> "); - } - - p += sprintf(p, "%s", ggml_type_name(t->type)); -} - -static inline const char * hex_tensor_buff_name(const struct ggml_tensor * t) { - if (t->buffer) { - return ggml_backend_buffer_name(t->buffer); - } - return "NONE"; -} - -static inline void hex_format_op_buffs(char * str, const struct ggml_tensor * t) { - char * p = str; - - // append src0 and src1 (if any) - if (t->src[0]) { - p += sprintf(p, "%s", hex_tensor_buff_name(t->src[0])); - - for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { - p += sprintf(p, " x "); - p += sprintf(p, "%s", hex_tensor_buff_name(t->src[i])); - } - - p += sprintf(p, " -> "); - } - - p += sprintf(p, "%s", hex_tensor_buff_name(t)); -} - -static inline void hex_format_op_names(char * str, const struct ggml_tensor * t) { - char * p = str; - - // append src0 and src1 (if any) - if (t->src[0]) { - p += sprintf(p, "%s", t->src[0]->name); - - for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { - p += sprintf(p, " x "); - p += sprintf(p, "%s", t->src[i]->name); - } - - p += sprintf(p, " -> "); - } - - p += sprintf(p, "%s", t->name); + op_desc desc(op); + GGML_LOG_DEBUG("ggml-hex: %s profile-op %s: %s : %s : %s : %s : %s : op-usec %u op-cycles %u op-pkts %u (%f) call-usec %llu\n", sess_name.c_str(), + ggml_op_name(op->op), desc.names, desc.dims, desc.types, desc.strides, desc.buffs, + op_usec, op_cycles, op_pkts, (float) op_cycles / op_pkts, (unsigned long long) call_usec); } // ** backend sessions @@ -221,8 +122,8 @@ struct ggml_hexagon_session { void enqueue(struct htp_general_req &req, struct dspqueue_buffer *bufs, uint32_t n_bufs, bool sync = false); void flush(); - ggml_backend_buffer_type buffer_type; - ggml_backend_buffer_type repack_buffer_type; + ggml_backend_buffer_type buffer_type = {}; + ggml_backend_buffer_type repack_buffer_type = {}; std::string name; remote_handle64 handle; @@ -241,23 +142,6 @@ struct ggml_hexagon_session { uint32_t prof_pkts; }; -static inline void hex_print_op_info(const ggml_tensor * op, ggml_hexagon_session * sess, const uint32_t req_flags) { - char dims[64 * GGML_MAX_SRC]; - char strides[64 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s %s: %s : %s : %s : %s : %s: flags 0x%x\n", sess->name.c_str(), ggml_op_name(op->op), - names, dims, types, strides, buffs, req_flags); -} - void ggml_hexagon_session::enqueue(struct htp_general_req &req, struct dspqueue_buffer *bufs, uint32_t n_bufs, bool sync) { // Bump pending flag (cleared in the session::flush once we get the responce) this->op_pending++; // atomic inc @@ -1598,7 +1482,7 @@ static ggml_backend_buffer_t ggml_backend_hexagon_buffer_type_alloc_buffer( try { ggml_backend_hexagon_buffer_context * ctx = new ggml_backend_hexagon_buffer_context(sess, size, false /*repack*/); return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, ctx, size); - } catch (std::exception const &exc) { + } catch (const std::exception & exc) { GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer context: %s\n", sess->name.c_str(), exc.what()); return nullptr; } @@ -1610,7 +1494,7 @@ static ggml_backend_buffer_t ggml_backend_hexagon_repack_buffer_type_alloc_buffe try { ggml_backend_hexagon_buffer_context * ctx = new ggml_backend_hexagon_buffer_context(sess, size, true /*repack*/); return ggml_backend_buffer_init(buffer_type, ggml_backend_hexagon_buffer_interface, ctx, size); - } catch (std::exception const &exc) { + } catch (const std::exception & exc) { GGML_LOG_ERROR("ggml-hex: %s failed to allocate buffer context: %s\n", sess->name.c_str(), exc.what()); return nullptr; } @@ -1697,8 +1581,8 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { } // Save the IDs - this->session_id = n.session_id; - this->domain_id = n.effective_domain_id; + this->session_id = n.session_id; + this->domain_id = n.effective_domain_id; this->valid_session = true; } @@ -1751,7 +1635,7 @@ void ggml_hexagon_session::allocate(int dev_id) noexcept(false) { this->valid_handle = true; GGML_LOG_INFO("ggml-hex: new session: %s : session-id %d domain-id %d uri %s handle 0x%lx\n", this->name.c_str(), - this->session_id, this->domain_id, session_uri, (unsigned long) this->handle); + this->session_id, this->domain_id, session_uri, (unsigned long) this->handle); // Enable FastRPC QoS mode { @@ -1838,11 +1722,8 @@ void ggml_hexagon_session::release() noexcept(true) { } ggml_hexagon_session::ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) noexcept(false) { - buffer_type.context = nullptr; - repack_buffer_type.context = nullptr; - - buffer_type.device = dev; - repack_buffer_type.device = dev; + buffer_type.device = dev; + repack_buffer_type.device = dev; try { allocate(dev_id); @@ -1852,7 +1733,7 @@ ggml_hexagon_session::ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) n repack_buffer_type.iface = ggml_backend_hexagon_repack_buffer_type_interface; repack_buffer_type.context = new ggml_backend_hexagon_buffer_type_context(this->name + "-REPACK", this); - } catch (std::exception const &exc) { + } catch (const std::exception & exc) { release(); throw; } @@ -1861,8 +1742,8 @@ ggml_hexagon_session::ggml_hexagon_session(int dev_id, ggml_backend_dev_t dev) n ggml_hexagon_session::~ggml_hexagon_session() noexcept(true) { release(); - delete static_cast(buffer_type.context); - delete static_cast(repack_buffer_type.context); + delete static_cast(buffer_type.context); + delete static_cast(repack_buffer_type.context); } // ** backend interface @@ -1930,15 +1811,6 @@ static bool hex_supported_dims(const struct ggml_tensor * x, const struct ggml_t return true; } -template -static inline bool hex_supported_buffer(const struct ggml_hexagon_session * sess, _TTensor... tensors) { - return ([&]() -> bool { - return !tensors || !tensors->buffer || - (ggml_backend_buffer_is_hexagon(tensors->buffer) && - ggml_backend_hexagon_buffer_get_sess(tensors->buffer) == sess); - }() && ...); -} - static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * sess, const struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; @@ -1976,17 +1848,16 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s break; case GGML_TYPE_F16: + if (src0->nb[1] < src0->nb[0]) { + GGML_LOG_DEBUG("ggml_hexagon_supported_mul_mat: permuted F16 src0 not supported\n"); + return false; + } break; default: return false; } - // src0 & src1 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, src1, dst)) { - return false; - } - return true; } @@ -2029,12 +1900,6 @@ static bool ggml_hexagon_supported_mul_mat_id(const struct ggml_hexagon_session return false; } - // src0 (weights) must be repacked and mapped to the same session - // src1 & sr2 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, src1, src2, dst)) { - return false; - } - return true; } @@ -2064,18 +1929,12 @@ static bool ggml_hexagon_supported_binary(const struct ggml_hexagon_session * se return false; } - // src0, src1 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, src1, dst)) { - return false; - } - return true; } static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * sess, const struct ggml_tensor * op) { const struct ggml_tensor * src0 = op->src[0]; const struct ggml_tensor * src1 = op->src[1]; - const struct ggml_tensor * src2 = op->src[2]; const struct ggml_tensor * dst = op; if (!hex_supported_src0_type(src0->type)) { @@ -2096,11 +1955,6 @@ static bool ggml_hexagon_supported_add_id(const struct ggml_hexagon_session * se return false; } - // src0, src1 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, src1, src2, dst)) { - return false; - } - return true; } @@ -2123,11 +1977,6 @@ static bool ggml_hexagon_supported_unary(const struct ggml_hexagon_session * ses return false; } - // src0 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, dst)) { - return false; - } - return true; } @@ -2160,17 +2009,6 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session } } - // src0, src1 & dst must be mapped to the same session - if(src1){ - if (!hex_supported_buffer(sess, src0, src1, dst)) { - return false; - } - }else{ - if (!hex_supported_buffer(sess, src0, dst)) { - return false; - } - } - return true; } @@ -2219,11 +2057,6 @@ static bool ggml_hexagon_supported_softmax(const struct ggml_hexagon_session * s } } - // src0, src1 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, src1, dst)) { - return false; - } - return true; } @@ -2274,16 +2107,28 @@ static bool ggml_hexagon_supported_rope(const struct ggml_hexagon_session * sess } } - // src0, src1, src2 & dst must be mapped to the same session - if (!hex_supported_buffer(sess, src0, src1, src2, dst)) { - return false; - } - return true; } +enum dspqbuf_type { + DSPQBUF_TYPE_DSP_WRITE_CPU_READ = 0, + DSPQBUF_TYPE_CPU_WRITE_DSP_READ, + DSPQBUF_TYPE_CONSTANT, +}; + +static void dspqbuf_dump(dspqueue_buffer * d, const struct ggml_tensor * t, dspqbuf_type type) { + if (opt_verbose < 2) return; + + auto buf = static_cast(t->buffer->context); + auto sess = buf->sess; + + GGML_LOG_DEBUG("ggml-hex: %s dspqbuf : %s base-addr %p base-size %zu data %p offset %u size %u\n", sess->name.c_str(), + t->name, (void *) buf->base, buf->size, (void *) d->ptr, (unsigned int) d->offset, + (unsigned int) d->size); +} + // Init hexagon tensor from GGML tensor and Hexagon buffer -static void init_htp_tensor(htp_tensor * h, const ggml_tensor * t) { +static void htp_req_tensor_init(htp_tensor * h, const ggml_tensor * t) { h->data = 0; // updated by the receiver h->type = t->type; h->ne[0] = t->ne[0]; @@ -2296,53 +2141,52 @@ static void init_htp_tensor(htp_tensor * h, const ggml_tensor * t) { h->nb[3] = t->nb[3]; } -static size_t dspqueue_buffers_init(dspqueue_buffer * buf, const ggml_tensor * t, bool flush_host, bool flush_htp) { +static size_t htp_req_buff_init(htp_tensor *h, dspqueue_buffer * d, const ggml_tensor * t, dspqbuf_type type) { if (!t) { return 0; } - memset(buf, 0, sizeof(*buf)); - auto tensor_buf = static_cast(t->buffer->context); - buf->fd = tensor_buf->fd; - buf->ptr = t->data; - buf->offset = (uint8_t *) t->data - tensor_buf->base; - buf->size = ggml_nbytes(t); - buf->flags = (flush_host ? DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER : 0); // Flush CPU - buf->flags |= (flush_htp ? DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT : 0); // Invalidate DSP + auto buf = static_cast(t->buffer->context); + + memset(d, 0, sizeof(*d)); + d->fd = buf->fd; + d->ptr = t->data; + d->offset = (uint8_t *) t->data - buf->base; + d->size = ggml_nbytes(t); + + switch (type) { + case DSPQBUF_TYPE_DSP_WRITE_CPU_READ: + // Flush CPU + d->flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER; + break; + case DSPQBUF_TYPE_CPU_WRITE_DSP_READ: + // Flush CPU, Invalidate DSP + d->flags = DSPQUEUE_BUFFER_FLAG_FLUSH_SENDER | DSPQUEUE_BUFFER_FLAG_INVALIDATE_RECIPIENT; + break; + default: + // Constant buffer, no cache maintenance + d->flags = 0; + break; + } + + htp_req_tensor_init(h, t); + + dspqbuf_dump(d, t, type); + return 1; } -static ggml_hexagon_session * get_session_from_tensor(const ggml_tensor * t) { - return static_cast(t->buffer->context)->sess; -} +typedef size_t (*htp_req_init_func_t)(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * op); -static void hex_dump_dspbuf(const struct ggml_tensor * t, const dspqueue_buffer * d) { - auto buf = static_cast(t->buffer->context); - auto sess = buf->sess; +template +static inline void ggml_hexagon_dispatch_op(ggml_hexagon_session *sess, const struct ggml_tensor * op, uint32_t flags) { + uint64_t t = ggml_time_us(); - HEX_VERBOSE("ggml-hex: %s dspqbuf : %s base-addr %p base-size %zu data %p offset %u size %u\n", sess->name.c_str(), - t->name, (void *) buf->base, buf->size, (void *) d->ptr, (unsigned int) d->offset, - (unsigned int) d->size); -} - -static void ggml_hexagon_mul_mat(const struct ggml_tensor * op, uint32_t flags) { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - const struct ggml_tensor * dst = op; - - uint64_t t1, t2; - t1 = ggml_time_us(); - - // Construct HTP message + // Construct HTP request htp_general_req req; - req.op = HTP_OP_MUL_MAT; + memset(&req, 0, sizeof(req)); + req.flags = flags; - - init_htp_tensor(&req.src0, src0); - init_htp_tensor(&req.src1, src1); - init_htp_tensor(&req.dst, dst); - - // Use opmask to override flags if (!(opt_opmask & HTP_OPMASK_QUANTIZE)) { req.flags |= HTP_OPFLAGS_SKIP_QUANTIZE; } @@ -2350,342 +2194,111 @@ static void ggml_hexagon_mul_mat(const struct ggml_tensor * op, uint32_t flags) req.flags |= HTP_OPFLAGS_SKIP_COMPUTE; } - dspqueue_buffer bufs[3]; - - // First buffer Weights. - // The content is static, there is no need to do any cache management - dspqueue_buffers_init(bufs, src0, false, false); - - // Second buffer Input Activations. This is a buffer that the CPU - // writes and the DSP reads, so we'll need to flush CPU caches and - // invalidate DSP ones. On platforms with I/O coherency support the - // framework will automatically skip cache operations where possible. - dspqueue_buffers_init(&bufs[1], src1, true, true); - - // Third buffer Output Activations. We'll handle DSP - // cache maintenance in the response message but need to flush - // CPU caches to ensure any previously written dirty lines are - // written out before writes from the DSP start. - dspqueue_buffers_init(&bufs[2], dst, true, false); - - auto * sess = get_session_from_tensor(src0); - - if (opt_verbose) { - hex_print_op_info(op, sess, req.flags); - if (opt_verbose > 1) { - hex_dump_dspbuf(src0, &bufs[0]); - hex_dump_dspbuf(src1, &bufs[1]); - hex_dump_dspbuf(dst, &bufs[2]); - } - } + ggml_hexagon_dump_op_exec(sess->name, op, req.flags); if ((opt_opmask & HTP_OPMASK_QUEUE)) { - sess->enqueue(req, bufs, 3, opt_opsync); + dspqueue_buffer bufs[HTP_MAX_PACKET_BUFFERS]; + size_t n_bufs = _init_req_func(&req, bufs, op); + sess->enqueue(req, bufs, n_bufs, opt_opsync); } - t2 = ggml_time_us(); + t = ggml_time_us() - t; - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u x %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles %u op-pkts %u (%f) " - "call-usec %llu\n", - sess->name.c_str(), ggml_op_name(op->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], - (uint32_t) src1->ne[2], (uint32_t) src1->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); + ggml_hexagon_dump_op_prof(sess->name, op, sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, t); } -static void ggml_hexagon_mul_mat_id(const struct ggml_tensor * op, uint32_t flags) { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - const struct ggml_tensor * src2 = op->src[2]; - const struct ggml_tensor * dst = op; - - uint64_t t1, t2; - t1 = ggml_time_us(); - - // Construct HTP message - htp_general_req req; - req.op = HTP_OP_MUL_MAT_ID; - req.flags = flags; - - init_htp_tensor(&req.src0, src0); - init_htp_tensor(&req.src1, src1); - init_htp_tensor(&req.src2, src2); - init_htp_tensor(&req.dst, dst); - - // Use opmask to override flags - if (!(opt_opmask & HTP_OPMASK_QUANTIZE)) { - req.flags |= HTP_OPFLAGS_SKIP_QUANTIZE; - } - if (!(opt_opmask & HTP_OPMASK_COMPUTE)) { - req.flags |= HTP_OPFLAGS_SKIP_COMPUTE; - } - - dspqueue_buffer bufs[4]; - // First buffer Weights. - // The content is static, there is no need to do any cache management - dspqueue_buffers_init(bufs, src0, false, false); - - // Second buffer Input Activations. This is a buffer that the CPU - // writes and the DSP reads, so we'll need to flush CPU caches and - // invalidate DSP ones. On platforms with I/O coherency support the - // framework will automatically skip cache operations where possible. - dspqueue_buffers_init(&bufs[1], src1, true, true); - - // Third buffer expert IDs. This is a buffer that the CPU - // writes and the DSP reads, so we'll need to flush CPU caches and - // invalidate DSP ones. On platforms with I/O coherency support the - // framework will automatically skip cache operations where possible. - dspqueue_buffers_init(&bufs[2], src2, true, true); - - // Forth buffer Output Activations. We'll handle DSP - // cache maintenance in the response message but need to flush - // CPU caches to ensure any previously written dirty lines are - // written out before writes from the DSP start. - dspqueue_buffers_init(&bufs[3], dst, true, false); - - auto * sess = get_session_from_tensor(src0); - - if (opt_verbose) { - hex_print_op_info(op, sess, req.flags); - if (opt_verbose > 1) { - hex_dump_dspbuf(src0, &bufs[0]); - hex_dump_dspbuf(src1, &bufs[1]); - hex_dump_dspbuf(src2, &bufs[2]); - hex_dump_dspbuf(dst, &bufs[3]); - } - } - - if ((opt_opmask & HTP_OPMASK_QUEUE)) { - sess->enqueue(req, bufs, 4, opt_opsync); - } - - t2 = ggml_time_us(); - - HEX_PROFILE( - "ggml-hex: %s matmul-id %s %u:%u:%u:%u x %s %u:%u:%u:%u (%s %u:%u:%u:%u) -> %s %u:%u:%u:%u : op-usec %u " - "op-cycles %u op-pkts %u (%f) call-usec %llu\n", - sess->name.c_str(), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], (uint32_t) src0->ne[2], - (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], (uint32_t) src1->ne[2], - (uint32_t) src1->ne[3], src2->name, (uint32_t) src2->ne[0], (uint32_t) src2->ne[1], (uint32_t) src2->ne[2], - (uint32_t) src2->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], - (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); -} - -static void ggml_hexagon_binary(const struct ggml_tensor * op, uint32_t flags) { - const struct ggml_tensor * node = op; - const struct ggml_tensor * src0 = node->src[0]; - const struct ggml_tensor * src1 = node->src[1]; - const struct ggml_tensor * dst = node; - - uint64_t t1 = 0; - uint64_t t2 = 0; - - t1 = ggml_time_us(); - - // Construct HTP message - htp_general_req req; - req.flags = flags; - - // Use opmask to override flags - if (!(opt_opmask & HTP_OPMASK_QUANTIZE)) { - req.flags |= HTP_OPFLAGS_SKIP_QUANTIZE; - } - if (!(opt_opmask & HTP_OPMASK_COMPUTE)) { - req.flags |= HTP_OPFLAGS_SKIP_COMPUTE; - } - - switch (node->op) { +template +static inline size_t init_binary_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) { + switch (t->op) { + case GGML_OP_MUL_MAT: + req->op = HTP_OP_MUL_MAT; + break; case GGML_OP_MUL: - req.op = HTP_OP_MUL; + req->op = HTP_OP_MUL; break; case GGML_OP_ADD: - req.op = HTP_OP_ADD; + req->op = HTP_OP_ADD; break; case GGML_OP_SUB: - req.op = HTP_OP_SUB; + req->op = HTP_OP_SUB; break; default: - GGML_ABORT("ggml-hex: binary : unsupported op:%d\n", node->op); + GGML_ABORT("ggml-hex: binary : unsupported op: %d\n", t->op); + break; } - init_htp_tensor(&req.src0, src0); - init_htp_tensor(&req.src1, src1); - init_htp_tensor(&req.dst, dst); + // src0: Weights (mulmat) or First Operand (binary op). + // If constant (e.g. weights), no cache management is needed. + // src1: Input Activations (mulmat) or Second Operand (binary op). - dspqueue_buffer bufs[3]; - // First buffer = First Operand of Binary op - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - dspqueue_buffers_init(bufs, src0, true, true); + size_t n_bufs = 0; + n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], _is_src0_constant ? DSPQBUF_TYPE_CONSTANT : DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src1, &bufs[n_bufs], t->src[1], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->dst, &bufs[n_bufs], t, DSPQBUF_TYPE_DSP_WRITE_CPU_READ); - // Second buffer = Second Operand of Binary op - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - dspqueue_buffers_init(&bufs[1], src1, true, true); - - // Third buffer = Output Activations. We'll handle DSP - // cache maintenance in the response message but need to flush - // CPU caches to ensure any previously written dirty lines are - // written out before writes from the DSP start. - dspqueue_buffers_init(&bufs[2], dst, true, false); - - auto * sess = get_session_from_tensor(src0); - - if (opt_verbose) { - hex_print_op_info(op, sess, req.flags); - if (opt_verbose > 1) { - hex_dump_dspbuf(src0, &bufs[0]); - hex_dump_dspbuf(src1, &bufs[1]); - hex_dump_dspbuf(dst, &bufs[2]); - } - } - - if ((opt_opmask & HTP_OPMASK_QUEUE)) { - sess->enqueue(req, bufs, 3, opt_opsync); - } - - t2 = ggml_time_us(); - - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u x %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles %u op-pkts %u (%f) " - "call-usec %llu\n", - sess->name.c_str(), ggml_op_name(node->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], - (uint32_t) src1->ne[2], (uint32_t) src1->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); + return n_bufs; } -static void ggml_hexagon_add_id(const struct ggml_tensor * op, uint32_t flags) { - const struct ggml_tensor * node = op; - const struct ggml_tensor * src0 = node->src[0]; - const struct ggml_tensor * src1 = node->src[1]; - const struct ggml_tensor * src2 = node->src[2]; - const struct ggml_tensor * dst = node; - - uint64_t t1 = 0; - uint64_t t2 = 0; - - t1 = ggml_time_us(); - - // Construct HTP message - htp_general_req req; - req.flags = flags; - - // Use opmask to override flags - if (!(opt_opmask & HTP_OPMASK_QUANTIZE)) { - req.flags |= HTP_OPFLAGS_SKIP_QUANTIZE; - } - if (!(opt_opmask & HTP_OPMASK_COMPUTE)) { - req.flags |= HTP_OPFLAGS_SKIP_COMPUTE; - } - - switch (node->op) { +template +static inline size_t init_binary_id_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) { + switch (t->op) { + case GGML_OP_MUL_MAT_ID: + req->op = HTP_OP_MUL_MAT_ID; + break; case GGML_OP_ADD_ID: - req.op = HTP_OP_ADD_ID; + req->op = HTP_OP_ADD_ID; break; default: - GGML_ABORT("ggml-hex: unsupported op:%d\n", node->op); + GGML_ABORT("ggml-hex: unsupported op: %d\n", t->op); } - init_htp_tensor(&req.src0, src0); - init_htp_tensor(&req.src1, src1); - init_htp_tensor(&req.src2, src2); - init_htp_tensor(&req.dst, dst); + // src0: Weights (mulmat) or Input Activations (other op). + // If constant, no cache management is needed. + // src1: Input Activations (mulmat) or Second Operand (binary op). + // src2: Expert IDs (mulmat) or Activated Experts (other op). - dspqueue_buffer bufs[4]; - // First buffer = input activations - dspqueue_buffers_init(bufs, src0, true, true); - // Second buffer = experts bias - dspqueue_buffers_init(&bufs[1], src1, true, true); - // Third buffer = activated experts - dspqueue_buffers_init(&bufs[2], src2, true, true); - // Forth buffer = output activations - dspqueue_buffers_init(&bufs[3], dst, true, true); + size_t n_bufs = 0; + n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], _is_src0_constant ? DSPQBUF_TYPE_CONSTANT : DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src1, &bufs[n_bufs], t->src[1], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src2, &bufs[n_bufs], t->src[2], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->dst, &bufs[n_bufs], t, DSPQBUF_TYPE_DSP_WRITE_CPU_READ); - auto * sess = get_session_from_tensor(src0); - - if (opt_verbose) { - hex_print_op_info(op, sess, req.flags); - if (opt_verbose > 1) { - hex_dump_dspbuf(src0, &bufs[0]); - hex_dump_dspbuf(src1, &bufs[1]); - hex_dump_dspbuf(src2, &bufs[2]); - hex_dump_dspbuf(dst, &bufs[3]); - } - } - - if ((opt_opmask & HTP_OPMASK_QUEUE)) { - sess->enqueue(req, bufs, 4, opt_opsync); - } - - t2 = ggml_time_us(); - - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u x %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles %u op-pkts %u (%f) " - "call-usec %llu\n", - sess->name.c_str(), ggml_op_name(node->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], - (uint32_t) src1->ne[2], (uint32_t) src1->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); + return n_bufs; } -static void ggml_hexagon_unary(const struct ggml_tensor * op, uint32_t flags) { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - const struct ggml_tensor * dst = op; - - uint64_t t1 = 0; - uint64_t t2 = 0; - - t1 = ggml_time_us(); - - // Construct HTP message - htp_general_req req; - - memset(&req, 0, sizeof(htp_general_req)); - memcpy(&req.op_params, &op->op_params, sizeof(op->op_params)); - req.flags = flags; +static inline size_t init_unary_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) { + memcpy(&req->op_params, &t->op_params, sizeof(t->op_params)); bool supported = false; - switch (op->op) { + switch (t->op) { case GGML_OP_RMS_NORM: - req.op = HTP_OP_RMS_NORM; + req->op = HTP_OP_RMS_NORM; supported = true; break; case GGML_OP_UNARY: - if (ggml_get_unary_op(dst) == GGML_UNARY_OP_SILU) { - req.op = HTP_OP_UNARY_SILU; + if (ggml_get_unary_op(t) == GGML_UNARY_OP_SILU) { + req->op = HTP_OP_UNARY_SILU; supported = true; - } - else if (ggml_get_unary_op(dst) == GGML_UNARY_OP_GELU) { - req.op = HTP_OP_UNARY_GELU; + } else if (ggml_get_unary_op(t) == GGML_UNARY_OP_GELU) { + req->op = HTP_OP_UNARY_GELU; supported = true; } break; case GGML_OP_GLU: - if (ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU) { - req.op = HTP_OP_GLU_SWIGLU; + if (ggml_get_glu_op(t) == GGML_GLU_OP_SWIGLU) { + req->op = HTP_OP_GLU_SWIGLU; supported = true; - } else if (ggml_get_glu_op(dst) == GGML_GLU_OP_SWIGLU_OAI) { - req.op = HTP_OP_GLU_SWIGLU_OAI; + } else if (ggml_get_glu_op(t) == GGML_GLU_OP_SWIGLU_OAI) { + req->op = HTP_OP_GLU_SWIGLU_OAI; supported = true; } break; case GGML_OP_SOFT_MAX: - req.op = HTP_OP_SOFTMAX; + req->op = HTP_OP_SOFTMAX; supported = true; break; @@ -2694,194 +2307,28 @@ static void ggml_hexagon_unary(const struct ggml_tensor * op, uint32_t flags) { } if (!supported) { - GGML_ABORT("ggml-hex: unary : unsupported op:%d\n", op->op); + GGML_ABORT("ggml-hex: unary : unsupported op: %d\n", t->op); } - init_htp_tensor(&req.dst, dst); - init_htp_tensor(&req.src0, src0); - if (src1) { - init_htp_tensor(&req.src1, src1); - } + size_t n_bufs = 0; + n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src1, &bufs[n_bufs], t->src[1], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->dst, &bufs[n_bufs], t, DSPQBUF_TYPE_DSP_WRITE_CPU_READ); - // Use opmask to override flags - if (!(opt_opmask & HTP_OPMASK_QUANTIZE)) { - req.flags |= HTP_OPFLAGS_SKIP_QUANTIZE; - } - if (!(opt_opmask & HTP_OPMASK_COMPUTE)) { - req.flags |= HTP_OPFLAGS_SKIP_COMPUTE; - } - - dspqueue_buffer bufs[3]; - - // First buffer = Only Operand of Unary op - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - size_t n_bufs = dspqueue_buffers_init(bufs, src0, true, true); - - // Second buffer(nullable) = Second Operand of Binary op - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - n_bufs += dspqueue_buffers_init(&bufs[n_bufs], src1, true, true); - - // Second or third buffer = Output Activations. We'll handle DSP - // Second buffer = Output Activations. We'll handle DSP - // cache maintenance in the response message but need to flush - // CPU caches to ensure any previously written dirty lines are - // written out before writes from the DSP start. - n_bufs += dspqueue_buffers_init(&bufs[n_bufs], dst, true, false); - - // Primary DSP session from the src0 tensor - auto * sess = get_session_from_tensor(src0); - - if (opt_verbose) { - hex_print_op_info(op, sess, req.flags); - if (opt_verbose > 1) { - hex_dump_dspbuf(src0, &bufs[0]); - if (src1) { - hex_dump_dspbuf(src1, &bufs[1]); - hex_dump_dspbuf(dst, &bufs[2]); - } else { - hex_dump_dspbuf(dst, &bufs[1]); - } - } - } - - if ((opt_opmask & HTP_OPMASK_QUEUE)) { - sess->enqueue(req, bufs, n_bufs, opt_opsync); - } - - t2 = ggml_time_us(); - - if (src1) { - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u x %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles %u op-pkts %u " - "(%f) call-usec %llu\n", - sess->name.c_str(), ggml_op_name(op->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], - (uint32_t) src1->ne[2], (uint32_t) src1->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); - } else { - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles %u op-pkts %u (%f) call-usec " - "%llu\n", - sess->name.c_str(), ggml_op_name(op->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); - } + return n_bufs; } -static void ggml_hexagon_rope(const struct ggml_tensor * op, uint32_t flags) { - const struct ggml_tensor * src0 = op->src[0]; - const struct ggml_tensor * src1 = op->src[1]; - const struct ggml_tensor * src2 = op->src[2]; - const struct ggml_tensor * dst = op; +static inline size_t init_rope_req(htp_general_req * req, dspqueue_buffer * bufs, const ggml_tensor * t) { + memcpy(&req->op_params, &t->op_params, sizeof(t->op_params)); + req->op = HTP_OP_ROPE; - uint64_t t1 = 0; - uint64_t t2 = 0; + size_t n_bufs = 0; + n_bufs += htp_req_buff_init(&req->src0, &bufs[n_bufs], t->src[0], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src1, &bufs[n_bufs], t->src[1], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->src2, &bufs[n_bufs], t->src[2], DSPQBUF_TYPE_CPU_WRITE_DSP_READ); + n_bufs += htp_req_buff_init(&req->dst, &bufs[n_bufs], t, DSPQBUF_TYPE_DSP_WRITE_CPU_READ); - t1 = ggml_time_us(); - - // Construct HTP message - htp_general_req req; - - memset(&req, 0, sizeof(htp_general_req)); - memcpy(&req.op_params, &op->op_params, sizeof(op->op_params)); - req.flags = flags; - req.op = HTP_OP_ROPE; - - init_htp_tensor(&req.dst, dst); - init_htp_tensor(&req.src0, src0); - init_htp_tensor(&req.src1, src1); - if (src2) { - init_htp_tensor(&req.src2, src2); - } - - // Use opmask to override flags - if (!(opt_opmask & HTP_OPMASK_QUANTIZE)) { - req.flags |= HTP_OPFLAGS_SKIP_QUANTIZE; - } - if (!(opt_opmask & HTP_OPMASK_COMPUTE)) { - req.flags |= HTP_OPFLAGS_SKIP_COMPUTE; - } - - dspqueue_buffer bufs[4]; - - // First buffer - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - size_t n_bufs = dspqueue_buffers_init(bufs, src0, true, true); - - // Second buffer - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - n_bufs += dspqueue_buffers_init(&bufs[n_bufs], src1, true, true); - - // Third buffer(nullable) - // This is a buffer that the CPU writes and the DSP reads, so we'll - // need to flush CPU caches and invalidate DSP ones. On platforms - // with I/O coherency support the framework will automatically skip - // cache operations where possible. - n_bufs += dspqueue_buffers_init(&bufs[n_bufs], src2, true, true); - - // Final buffer = Output Activations. We'll handle DSP - // Second buffer = Output Activations. We'll handle DSP - // cache maintenance in the response message but need to flush - // CPU caches to ensure any previously written dirty lines are - // written out before writes from the DSP start. - n_bufs += dspqueue_buffers_init(&bufs[n_bufs], dst, true, false); - - // Primary DSP session from the src0 tensor - auto * sess = get_session_from_tensor(src0); - - if (opt_verbose) { - hex_print_op_info(op, sess, req.flags); - if (opt_verbose > 1) { - hex_dump_dspbuf(src0, &bufs[0]); - if (src1) { - hex_dump_dspbuf(src1, &bufs[1]); - hex_dump_dspbuf(dst, &bufs[2]); - } else { - hex_dump_dspbuf(dst, &bufs[1]); - } - } - } - - if ((opt_opmask & HTP_OPMASK_QUEUE)) { - sess->enqueue(req, bufs, n_bufs, opt_opsync); - } - - t2 = ggml_time_us(); - - if (src2) { - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u x %s %u:%u:%u:%u x %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles " - "%u op-pkts %u (%f) call-usec %llu\n", - sess->name.c_str(), ggml_op_name(op->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], - (uint32_t) src1->ne[2], (uint32_t) src1->ne[3], src2->name, (uint32_t) src2->ne[0], (uint32_t) src2->ne[1], - (uint32_t) src2->ne[2], (uint32_t) src2->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); - } else { - HEX_PROFILE( - "ggml-hex: %s %s %s %u:%u:%u:%u x %s %u:%u:%u:%u -> %s %u:%u:%u:%u : op-usec %u op-cycles %u op-pkts %u " - "(%f) call-usec %llu\n", - sess->name.c_str(), ggml_op_name(op->op), src0->name, (uint32_t) src0->ne[0], (uint32_t) src0->ne[1], - (uint32_t) src0->ne[2], (uint32_t) src0->ne[3], src1->name, (uint32_t) src1->ne[0], (uint32_t) src1->ne[1], - (uint32_t) src1->ne[2], (uint32_t) src1->ne[3], dst->name, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], - (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], sess->prof_usecs, sess->prof_cycles, sess->prof_pkts, - (float) sess->prof_cycles / sess->prof_pkts, (unsigned long long) t2 - t1); - } + return n_bufs; } static const char * ggml_backend_hexagon_name(ggml_backend_t backend) { @@ -2896,7 +2343,7 @@ static void ggml_backend_hexagon_free(ggml_backend_t backend) { } static inline bool op_reuse_src1(const ggml_tensor * op1, const ggml_tensor * op0) { - return (op0 && op0->src[1] == op1->src[1]); + return (op0 && op0->src[1] == op1->src[1] && ggml_is_quantized(op0->src[0]->type) && ggml_is_quantized(op1->src[1]->type)); } static inline bool is_compute_op(ggml_tensor *node) @@ -2946,43 +2393,50 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg switch (node->op) { case GGML_OP_MUL_MAT: - ggml_hexagon_mul_mat(node, flags); + if (ggml_is_quantized(node->src[0]->type)) { + ggml_hexagon_dispatch_op>(sess, node, flags); + } else { + ggml_hexagon_dispatch_op>(sess, node, flags); + } prev_quant_op = node; break; case GGML_OP_MUL_MAT_ID: - ggml_hexagon_mul_mat_id(node, flags); + if (ggml_is_quantized(node->src[0]->type)) { + ggml_hexagon_dispatch_op>(sess, node, flags); + } else { + ggml_hexagon_dispatch_op>(sess, node, flags); + } prev_quant_op = node; break; case GGML_OP_MUL: case GGML_OP_ADD: case GGML_OP_SUB: - ggml_hexagon_binary(node, flags); + ggml_hexagon_dispatch_op>(sess, node, flags); break; case GGML_OP_ADD_ID: - ggml_hexagon_add_id(node, flags); + ggml_hexagon_dispatch_op>(sess, node, flags); break; case GGML_OP_RMS_NORM: - ggml_hexagon_unary(node, flags); + ggml_hexagon_dispatch_op(sess, node, flags); break; case GGML_OP_UNARY: - if (ggml_get_unary_op(node) == GGML_UNARY_OP_SILU) { - ggml_hexagon_unary(node, flags); - } else if (ggml_get_unary_op(node) == GGML_UNARY_OP_GELU) { - ggml_hexagon_unary(node, flags); + if ((ggml_get_unary_op(node) == GGML_UNARY_OP_SILU) || + (ggml_get_unary_op(node) == GGML_UNARY_OP_GELU)) { + ggml_hexagon_dispatch_op(sess, node, flags); } break; case GGML_OP_GLU: if ((ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU) || - (ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU_OAI)) { - ggml_hexagon_unary(node, flags); + (ggml_get_glu_op(node) == GGML_GLU_OP_SWIGLU_OAI)) { + ggml_hexagon_dispatch_op(sess, node, flags); } break; case GGML_OP_SOFT_MAX: - ggml_hexagon_unary(node, flags); + ggml_hexagon_dispatch_op(sess, node, flags); break; case GGML_OP_ROPE: - ggml_hexagon_rope(node, flags); + ggml_hexagon_dispatch_op(sess, node, flags); break; default: @@ -3111,8 +2565,8 @@ static void ggml_backend_hexagon_graph_optimize(ggml_backend_t backend, ggml_cgr // and perform the reorder over the fused nodes. after the reorder is done, we unfuse for (int i = 0; i < n; i++) { node_info node = { - /*.node =*/ gf->nodes[i], - /*.fused =*/ {}, + /*.node =*/gf->nodes[i], + /*.fused =*/{}, }; // fuse only ops that start with these operations @@ -3263,9 +2717,38 @@ static ggml_backend_buffer_type_t ggml_backend_hexagon_device_get_repack_buffer_ return &sess->repack_buffer_type; } +static bool ggml_hexagon_supported_buffer(ggml_hexagon_session *sess, const struct ggml_tensor * t) { + if (t && t->buffer) { + if (ggml_backend_buffer_is_hexagon(t->buffer) == false) return false; // not our buffer + if (ggml_backend_hexagon_buffer_get_sess(t->buffer) != sess) return false; // wrong session + } + return true; +} + +static bool ggml_hexagon_supported_buffers(ggml_hexagon_session *sess, const struct ggml_tensor * t) { + // all srcs & dsts must be mapped to the same session + if (!ggml_hexagon_supported_buffer(sess, t)) { + return false; + } + + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (!ggml_hexagon_supported_buffer(sess, t->src[i])) { + return false; + } + } + + return true; +} + static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { auto sess = static_cast(dev->context); + // all srcs & dsts must be mapped to the same session + if (!ggml_hexagon_supported_buffers(sess, op)) { + ggml_hexagon_dump_op_supp(sess->name, op, false); + return false; + } + bool supp = false; switch (op->op) { case GGML_OP_NONE: @@ -3303,20 +2786,21 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons break; case GGML_OP_UNARY: - if (ggml_get_unary_op(op) == GGML_UNARY_OP_SILU) { - supp = ggml_hexagon_supported_activations(sess, op); + { + const auto unary_op = ggml_get_unary_op(op); + if (unary_op == GGML_UNARY_OP_SILU || unary_op == GGML_UNARY_OP_GELU) { + supp = ggml_hexagon_supported_activations(sess, op); + } + break; } - else if (ggml_get_unary_op(op) == GGML_UNARY_OP_GELU){ - supp = ggml_hexagon_supported_activations(sess, op); - } - break; - case GGML_OP_GLU: - if ((ggml_get_glu_op(op) == GGML_GLU_OP_SWIGLU) || (ggml_get_glu_op(op) == GGML_GLU_OP_SWIGLU_OAI) ) { - supp = ggml_hexagon_supported_activations(sess, op); + { + const auto glu_op = ggml_get_glu_op(op); + if ((glu_op == GGML_GLU_OP_SWIGLU) || (glu_op == GGML_GLU_OP_SWIGLU_OAI)) { + supp = ggml_hexagon_supported_activations(sess, op); + } + break; } - break; - case GGML_OP_ROPE: supp = ggml_hexagon_supported_rope(sess, op); break; @@ -3325,26 +2809,8 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons break; } - if (opt_verbose) { - char dims[64 * GGML_MAX_SRC]; - char strides[64 * GGML_MAX_SRC]; - char types[16 * GGML_MAX_SRC]; - char buffs[64 * GGML_MAX_SRC]; - char names[64 * GGML_MAX_SRC]; - - hex_format_op_dims(dims, op); - hex_format_op_strides(strides, op); - hex_format_op_types(types, op); - hex_format_op_buffs(buffs, op); - hex_format_op_names(names, op); - - HEX_VERBOSE("ggml-hex: %s device-supports-op %s : %s : %s : %s : %s : %s : (%d)\n", sess->name.c_str(), - ggml_op_name(op->op), names, dims, types, strides, buffs, (int) supp); - } - + ggml_hexagon_dump_op_supp(sess->name, op, supp); return supp; - - GGML_UNUSED(dev); } static bool ggml_backend_hexagon_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { @@ -3413,7 +2879,7 @@ ggml_hexagon_registry::ggml_hexagon_registry(ggml_backend_reg_t reg) { } } - if(opt_arch < 75) { + if (opt_arch < 75) { opt_ndev = 1; GGML_LOG_WARN("ggml-hex: forcing ndev to 1 for SoCs archs lower than v75.\n"); } @@ -3422,11 +2888,11 @@ ggml_hexagon_registry::ggml_hexagon_registry(ggml_backend_reg_t reg) { // Create devices / sessions for (size_t i = 0; i < opt_ndev; i++) { - devices[i].iface = ggml_backend_hexagon_device_i; - devices[i].reg = reg; + devices[i].iface = ggml_backend_hexagon_device_i; + devices[i].reg = reg; try { devices[i].context = new ggml_hexagon_session(i, &devices[i]); - } catch (std::exception const &exc) { + } catch (const std::exception & exc) { GGML_LOG_ERROR("ggml-hex: failed to create device/session %zu\n", i); devices[i].context = nullptr; } diff --git a/ggml/src/ggml-hexagon/htp-utils.h b/ggml/src/ggml-hexagon/htp-utils.h index 1a48f5dcbd..7bbae3a0b7 100644 --- a/ggml/src/ggml-hexagon/htp-utils.h +++ b/ggml/src/ggml-hexagon/htp-utils.h @@ -8,6 +8,7 @@ extern "C" { #include #include #include +#include #include /* Offset to differentiate HLOS and Hexagon error codes. diff --git a/ggml/src/ggml-hexagon/op-desc.h b/ggml/src/ggml-hexagon/op-desc.h new file mode 100644 index 0000000000..a1e8ddd8b9 --- /dev/null +++ b/ggml/src/ggml-hexagon/op-desc.h @@ -0,0 +1,153 @@ +#ifndef OP_DESC_H +#define OP_DESC_H + +#define GGML_COMMON_IMPL_CPP +#include "ggml-backend-impl.h" +#include "ggml-common.h" + +#include +#include + +struct op_desc { + char strides[64 * GGML_MAX_SRC]; + char dims[64 * GGML_MAX_SRC]; + char types[16 * GGML_MAX_SRC]; + char buffs[64 * GGML_MAX_SRC]; + char names[64 * GGML_MAX_SRC]; + + int format_tensor_dims(char * str, const struct ggml_tensor * t) { + if (t->ne[2] == 1 && t->ne[3] == 1) { + return sprintf(str, "%d:%d", (int) t->ne[0], (int) t->ne[1]); + } else { + return sprintf(str, "%d:%d:%d:%d", (int) t->ne[0], (int) t->ne[1], (int) t->ne[2], (int) t->ne[3]); + } + } + + void format_op_dims(char * str, const struct ggml_tensor * t) { + char * p = str; + + // append src0 and src1 (if any) + if (t->src[0]) { + p += format_tensor_dims(p, t->src[0]); + + for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { + p += sprintf(p, " x "); + p += format_tensor_dims(p, t->src[i]); + } + + p += sprintf(p, " -> "); + } + + // format self dims separately for better visual alignment + char self[64]; + format_tensor_dims(self, t); + + p += sprintf(p, "%s", self); + } + + int format_tensor_strides(char * str, const struct ggml_tensor * t) { + const char * c = ggml_is_contiguous(t) ? "" : "!"; + + if (t->ne[2] == 1 && t->ne[3] == 1) { + return sprintf(str, "%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], c); + } else { + return sprintf(str, "%zu:%zu:%zu:%zu%s", (size_t) t->nb[0], (size_t) t->nb[1], (size_t) t->nb[2], (size_t) t->nb[3], c); + } + } + + void format_op_strides(char * str, const struct ggml_tensor * t) { + char * p = str; + + // append src0 and src1 (if any) + if (t->src[0]) { + p += format_tensor_strides(p, t->src[0]); + + for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { + p += sprintf(p, " x "); + p += format_tensor_strides(p, t->src[i]); + } + + p += sprintf(p, " -> "); + } + + // format self dims separately for better visual alignment + char self[64]; + format_tensor_strides(self, t); + + p += sprintf(p, "%s", self); + } + + void format_op_types(char * str, const struct ggml_tensor * t) { + char * p = str; + + // append src0 and src1 (if any) + if (t->src[0]) { + p += sprintf(p, "%s", ggml_type_name(t->src[0]->type)); + + for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { + p += sprintf(p, " x "); + p += sprintf(p, "%s", ggml_type_name(t->src[i]->type)); + } + + p += sprintf(p, " -> "); + } + + p += sprintf(p, "%s", ggml_type_name(t->type)); + } + + const char * tensor_buff_name(const struct ggml_tensor * t) { + if (t->buffer) { + return ggml_backend_buffer_name(t->buffer); + } + return "NONE"; + } + + void format_op_buffs(char * str, const struct ggml_tensor * t) { + char * p = str; + + // append src0 and src1 (if any) + if (t->src[0]) { + p += sprintf(p, "%s", tensor_buff_name(t->src[0])); + + for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { + p += sprintf(p, " x "); + p += sprintf(p, "%s", tensor_buff_name(t->src[i])); + } + + p += sprintf(p, " -> "); + } + + p += sprintf(p, "%s", tensor_buff_name(t)); + } + + void format_op_names(char * str, const struct ggml_tensor * t) { + char * p = str; + + // append src0 and src1 (if any) + if (t->src[0]) { + p += sprintf(p, "%s", t->src[0]->name); + + for (int i = 1; i < GGML_MAX_SRC && t->src[i]; i++) { + p += sprintf(p, " x "); + p += sprintf(p, "%s", t->src[i]->name); + } + + p += sprintf(p, " -> "); + } + + p += sprintf(p, "%s", t->name); + } + + void format(const ggml_tensor * op) { + format_op_dims(dims, op); + format_op_strides(strides, op); + format_op_types(types, op); + format_op_buffs(buffs, op); + format_op_names(names, op); + } + + op_desc() {} + op_desc(const ggml_tensor * op) { format(op); } +}; + +#endif // OP_DESC_H diff --git a/scripts/snapdragon/adb/run-cli.sh b/scripts/snapdragon/adb/run-cli.sh index cc5e47c2d6..8a3053c859 100755 --- a/scripts/snapdragon/adb/run-cli.sh +++ b/scripts/snapdragon/adb/run-cli.sh @@ -18,17 +18,17 @@ model="Llama-3.2-3B-Instruct-Q4_0.gguf" device="HTP0" [ "$D" != "" ] && device="$D" -verbose= -[ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V" - experimental= [ "$E" != "" ] && experimental="GGML_HEXAGON_EXPERIMENTAL=$E" +verbose= +[ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V" cli_opts="$cli_opts -v" + sched= [ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v" profile= -[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" +[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" cli_opts="$cli_opts -v" opmask= [ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK" @@ -45,9 +45,9 @@ adb $adbserial shell " \ cd $basedir; ulimit -c unlimited; \ LD_LIBRARY_PATH=$basedir/$branch/lib \ ADSP_LIBRARY_PATH=$basedir/$branch/lib \ - $verbose $experimental $sched $opmask $profile $nhvx $ndev \ - ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \ - --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ - --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \ - -ngl 99 --device $device $cli_opts $@ \ + $verbose $experimental $sched $opmask $profile $nhvx $ndev \ + ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model \ + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ + --ctx-size 8192 --batch-size 128 -fa on \ + -ngl 99 --device $device $cli_opts $@ \ " diff --git a/scripts/snapdragon/adb/run-completion.sh b/scripts/snapdragon/adb/run-completion.sh new file mode 100755 index 0000000000..bb7ba5e671 --- /dev/null +++ b/scripts/snapdragon/adb/run-completion.sh @@ -0,0 +1,53 @@ +#!/bin/sh +# + +# Basedir on device +basedir=/data/local/tmp/llama.cpp + +cli_opts= + +branch=. +[ "$B" != "" ] && branch=$B + +adbserial= +[ "$S" != "" ] && adbserial="-s $S" + +model="Llama-3.2-3B-Instruct-Q4_0.gguf" +[ "$M" != "" ] && model="$M" + +device="HTP0" +[ "$D" != "" ] && device="$D" + +experimental= +[ "$E" != "" ] && experimental="GGML_HEXAGON_EXPERIMENTAL=$E" + +verbose= +[ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V" cli_opts="$cli_opts -v" + +sched= +[ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v" + +profile= +[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1" cli_opts="$cli_opts -v" + +opmask= +[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK" + +nhvx= +[ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX" + +ndev= +[ "$NDEV" != "" ] && ndev="GGML_HEXAGON_NDEV=$NDEV" + +set -x + +adb $adbserial shell " \ + cd $basedir; ulimit -c unlimited; \ + LD_LIBRARY_PATH=$basedir/$branch/lib \ + ADSP_LIBRARY_PATH=$basedir/$branch/lib \ + $verbose $experimental $sched $opmask $profile $nhvx $ndev \ + ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model \ + --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1 \ + --ctx-size 8192 --batch-size 128 -fa on \ + -ngl 99 -no-cnv --device $device $cli_opts $@ \ +" From 12ee1763a6f6130ce820a366d220bbadff54b818 Mon Sep 17 00:00:00 2001 From: Chris Rohlf Date: Tue, 23 Dec 2025 04:56:49 -0500 Subject: [PATCH 18/25] rpc : add check for rpc buffer type (#18242) --- ggml/src/ggml-rpc/ggml-rpc.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index 13cf1f5f9d..e7890a5ee9 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -571,6 +571,10 @@ static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { return ctx->base_ptr; } +static bool ggml_backend_buffer_is_rpc(ggml_backend_buffer_t buffer) { + return buffer->iface.free_buffer == ggml_backend_rpc_buffer_free_buffer; +} + static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { rpc_tensor result; if (!tensor) { @@ -580,7 +584,7 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { result.id = reinterpret_cast(tensor); result.type = tensor->type; - if (tensor->buffer) { + if (tensor->buffer && ggml_backend_buffer_is_rpc(tensor->buffer)) { ggml_backend_buffer_t buffer = tensor->buffer; ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; result.buffer = ctx != nullptr ? ctx->remote_ptr : 0; @@ -664,10 +668,6 @@ static void ggml_backend_rpc_buffer_get_tensor(ggml_backend_buffer_t buffer, con RPC_STATUS_ASSERT(status); } -static bool ggml_backend_buffer_is_rpc(ggml_backend_buffer_t buffer) { - return buffer->iface.free_buffer == ggml_backend_rpc_buffer_free_buffer; -} - static bool ggml_backend_rpc_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_rpc(src->buffer)) { // check if src and dst are on the same server From 8e3ead6e4d1a4c455c8dac1b4690c49366769856 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Tue, 23 Dec 2025 14:07:25 +0100 Subject: [PATCH 19/25] model-conversion : add device option to run-org-model.py (#18318) * model-conversion : add device option to run-org-model.py This commit refactors the `run-org-model.py` script to include a `--device` argument, to allow users to specify the device on which to run the model (e.g., cpu, cuda, mps, auto). It also extracts a few common functions to prepare for future changes where some code duplication will be removed which there currently exists in embedding scripts. The Makefile is also been updated to pass the device argument, for example: ```console (venv) $ make causal-verify-logits DEVICE=cpu ``` * fix error handling and remove parser reference This commit fixes the error handling which previously referenced an undefined 'parser' variable. --- examples/model-conversion/Makefile | 4 +- .../scripts/causal/run-org-model.py | 272 ++++++++++-------- 2 files changed, 154 insertions(+), 122 deletions(-) diff --git a/examples/model-conversion/Makefile b/examples/model-conversion/Makefile index 25b0514b29..f8dc525a77 100644 --- a/examples/model-conversion/Makefile +++ b/examples/model-conversion/Makefile @@ -25,6 +25,8 @@ define quantize_model @echo "Export the quantized model path to $(2) variable in your environment" endef +DEVICE ?= auto + ### ### Casual Model targets/recipes ### @@ -53,7 +55,7 @@ causal-convert-mm-model: causal-run-original-model: $(call validate_model_path,causal-run-original-model) - @MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py + @MODEL_PATH="$(MODEL_PATH)" ./scripts/causal/run-org-model.py --device "$(DEVICE)" causal-run-converted-model: @CONVERTED_MODEL="$(CONVERTED_MODEL)" ./scripts/causal/run-converted-model.sh diff --git a/examples/model-conversion/scripts/causal/run-org-model.py b/examples/model-conversion/scripts/causal/run-org-model.py index 14bb12fe68..b12173a1fb 100755 --- a/examples/model-conversion/scripts/causal/run-org-model.py +++ b/examples/model-conversion/scripts/causal/run-org-model.py @@ -4,149 +4,179 @@ import argparse import os import sys import importlib +import torch +import numpy as np + from pathlib import Path +from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig # Add parent directory to path for imports sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..')) - -from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig -import torch -import numpy as np from utils.common import debug_hook -parser = argparse.ArgumentParser(description="Process model with specified path") -parser.add_argument("--model-path", "-m", help="Path to the model") -parser.add_argument("--prompt-file", "-f", help="Optional prompt file", required=False) -parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose debug output") -args = parser.parse_args() +def parse_arguments(): + parser = argparse.ArgumentParser(description="Process model with specified path") + parser.add_argument("--model-path", "-m", help="Path to the model") + parser.add_argument("--prompt-file", "-f", help="Optional prompt file", required=False) + parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose debug output") + parser.add_argument("--device", "-d", help="Device to use (cpu, cuda, mps, auto)", default="auto") + return parser.parse_args() -model_path = os.environ.get("MODEL_PATH", args.model_path) -if model_path is None: - parser.error( - "Model path must be specified either via --model-path argument or MODEL_PATH environment variable" - ) +def load_model_and_tokenizer(model_path, device="auto"): + print("Loading model and tokenizer using AutoTokenizer:", model_path) + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + multimodal = False + full_config = config -### If you want to dump RoPE activations, uncomment the following lines: -### === START ROPE DEBUG === -# from utils.common import setup_rope_debug -# setup_rope_debug("transformers.models.apertus.modeling_apertus") -### == END ROPE DEBUG === - - -print("Loading model and tokenizer using AutoTokenizer:", model_path) -tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) -config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) -multimodal = False -full_config = config - -print("Model type: ", config.model_type) -if "vocab_size" not in config and "text_config" in config: - config = config.text_config - multimodal = True -print("Vocab size: ", config.vocab_size) -print("Hidden size: ", config.hidden_size) -print("Number of layers: ", config.num_hidden_layers) -print("BOS token id: ", config.bos_token_id) -print("EOS token id: ", config.eos_token_id) - -unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME") -if unreleased_model_name: - model_name_lower = unreleased_model_name.lower() - unreleased_module_path = ( - f"transformers.models.{model_name_lower}.modular_{model_name_lower}" - ) - class_name = f"{unreleased_model_name}ForCausalLM" - print(f"Importing unreleased model module: {unreleased_module_path}") - - try: - model_class = getattr( - importlib.import_module(unreleased_module_path), class_name - ) - model = model_class.from_pretrained( - model_path - ) # Note: from_pretrained, not fromPretrained - except (ImportError, AttributeError) as e: - print(f"Failed to import or load model: {e}") - exit(1) -else: - if multimodal: - model = AutoModelForImageTextToText.from_pretrained( - model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=full_config - ) + # Determine device_map based on device argument + if device == "cpu": + device_map = {"": "cpu"} + print("Forcing CPU usage") + elif device == "auto": + device_map = "auto" else: - model = AutoModelForCausalLM.from_pretrained( - model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config + device_map = {"": device} + + print("Model type: ", config.model_type) + if "vocab_size" not in config and "text_config" in config: + config = config.text_config + multimodal = True + + print("Vocab size: ", config.vocab_size) + print("Hidden size: ", config.hidden_size) + print("Number of layers: ", config.num_hidden_layers) + print("BOS token id: ", config.bos_token_id) + print("EOS token id: ", config.eos_token_id) + + unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME") + if unreleased_model_name: + model_name_lower = unreleased_model_name.lower() + unreleased_module_path = ( + f"transformers.models.{model_name_lower}.modular_{model_name_lower}" ) + class_name = f"{unreleased_model_name}ForCausalLM" + print(f"Importing unreleased model module: {unreleased_module_path}") -if args.verbose: - for name, module in model.named_modules(): - if len(list(module.children())) == 0: # only leaf modules - module.register_forward_hook(debug_hook(name)) + try: + model_class = getattr(importlib.import_module(unreleased_module_path), class_name) + model = model_class.from_pretrained( + model_path, + device_map=device_map, + offload_folder="offload", + trust_remote_code=True, + config=config + ) + except (ImportError, AttributeError) as e: + print(f"Failed to import or load model: {e}") + exit(1) + else: + if multimodal: + model = AutoModelForImageTextToText.from_pretrained( + model_path, + device_map=device_map, + offload_folder="offload", + trust_remote_code=True, + config=full_config + ) + else: + model = AutoModelForCausalLM.from_pretrained( + model_path, + device_map=device_map, + offload_folder="offload", + trust_remote_code=True, + config=config + ) -model_name = os.path.basename(model_path) -# Printing the Model class to allow for easier debugging. This can be useful -# when working with models that have not been publicly released yet and this -# migth require that the concrete class is imported and used directly instead -# of using AutoModelForCausalLM. -print(f"Model class: {model.__class__.__name__}") + print(f"Model class: {model.__class__.__name__}") -device = next(model.parameters()).device -if args.prompt_file: - with open(args.prompt_file, encoding='utf-8') as f: - prompt = f.read() -elif os.getenv("MODEL_TESTING_PROMPT"): - prompt = os.getenv("MODEL_TESTING_PROMPT") -else: - prompt = "Hello, my name is" -input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) + return model, tokenizer, config -print(f"Input tokens: {input_ids}") -print(f"Input text: {repr(prompt)}") -print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}") +def enable_torch_debugging(model): + for name, module in model.named_modules(): + if len(list(module.children())) == 0: # only leaf modules + module.register_forward_hook(debug_hook(name)) -batch_size = 512 +def get_prompt(args): + if args.prompt_file: + with open(args.prompt_file, encoding='utf-8') as f: + return f.read() + elif os.getenv("MODEL_TESTING_PROMPT"): + return os.getenv("MODEL_TESTING_PROMPT") + else: + return "Hello, my name is" -with torch.no_grad(): - past = None - outputs = None - for i in range(0, input_ids.size(1), batch_size): - print(f"Processing chunk with tokens {i} to {i + batch_size}") - chunk = input_ids[:, i:i + batch_size] - outputs = model(chunk.to(model.device), past_key_values=past, use_cache=True) - past = outputs.past_key_values +def main(): + args = parse_arguments() + model_path = os.environ.get("MODEL_PATH", args.model_path) + if model_path is None: + print("Error: Model path must be specified either via --model-path argument or MODEL_PATH environment variable") + sys.exit(1) - logits = outputs.logits # type: ignore - # Extract logits for the last token (next token prediction) - last_logits = logits[0, -1, :].float().cpu().numpy() + model, tokenizer, config = load_model_and_tokenizer(model_path, args.device) - print(f"Logits shape: {logits.shape}") - print(f"Last token logits shape: {last_logits.shape}") - print(f"Vocab size: {len(last_logits)}") + if args.verbose: + enable_torch_debugging(model) - data_dir = Path("data") - data_dir.mkdir(exist_ok=True) - bin_filename = data_dir / f"pytorch-{model_name}.bin" - txt_filename = data_dir / f"pytorch-{model_name}.txt" + model_name = os.path.basename(model_path) - # Save to file for comparison - last_logits.astype(np.float32).tofile(bin_filename) + # Iterate over the model parameters (the tensors) and get the first one + # and use it to get the device the model is on. + device = next(model.parameters()).device + prompt = get_prompt(args) + input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) - # Also save as text file for easy inspection - with open(txt_filename, "w") as f: - for i, logit in enumerate(last_logits): - f.write(f"{i}: {logit:.6f}\n") + print(f"Input tokens: {input_ids}") + print(f"Input text: {repr(prompt)}") + print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}") - # Print some sample logits for quick verification - print(f"First 10 logits: {last_logits[:10]}") - print(f"Last 10 logits: {last_logits[-10:]}") + batch_size = 512 - # Show top 5 predicted tokens - top_indices = np.argsort(last_logits)[-5:][::-1] - print("Top 5 predictions:") - for idx in top_indices: - token = tokenizer.decode([idx]) - print(f" Token {idx} ({repr(token)}): {last_logits[idx]:.6f}") + with torch.no_grad(): + past = None + outputs = None + for i in range(0, input_ids.size(1), batch_size): + print(f"Processing chunk with tokens {i} to {i + batch_size}") + chunk = input_ids[:, i:i + batch_size] + outputs = model(chunk.to(model.device), past_key_values=past, use_cache=True) + past = outputs.past_key_values - print(f"Saved bin logits to: {bin_filename}") - print(f"Saved txt logist to: {txt_filename}") + logits = outputs.logits # type: ignore + + # Extract logits for the last token (next token prediction) + last_logits = logits[0, -1, :].float().cpu().numpy() + + print(f"Logits shape: {logits.shape}") + print(f"Last token logits shape: {last_logits.shape}") + print(f"Vocab size: {len(last_logits)}") + + data_dir = Path("data") + data_dir.mkdir(exist_ok=True) + bin_filename = data_dir / f"pytorch-{model_name}.bin" + txt_filename = data_dir / f"pytorch-{model_name}.txt" + + # Save to file for comparison + last_logits.astype(np.float32).tofile(bin_filename) + + # Also save as text file for easy inspection + with open(txt_filename, "w") as f: + for i, logit in enumerate(last_logits): + f.write(f"{i}: {logit:.6f}\n") + + # Print some sample logits for quick verification + print(f"First 10 logits: {last_logits[:10]}") + print(f"Last 10 logits: {last_logits[-10:]}") + + # Show top 5 predicted tokens + top_indices = np.argsort(last_logits)[-5:][::-1] + print("Top 5 predictions:") + for idx in top_indices: + token = tokenizer.decode([idx]) + print(f" Token {idx} ({repr(token)}): {last_logits[idx]:.6f}") + + print(f"Saved bin logits to: {bin_filename}") + print(f"Saved txt logist to: {txt_filename}") + +if __name__ == "__main__": + main() From 849d021104aeedfae7dcceb6ee1ca10ae3e7e9b7 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 23 Dec 2025 14:39:36 +0100 Subject: [PATCH 20/25] server: fix crash with model not having BOS/EOS (#18321) --- tools/server/server-context.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index a132b87c84..e2e7bcb9e6 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -2784,6 +2784,12 @@ server_response_reader server_context::get_response_reader() { server_context_meta server_context::get_meta() const { auto tool_use_src = common_chat_templates_source(impl->chat_templates.get(), "tool_use"); + + auto bos_id = llama_vocab_bos(impl->vocab); + auto eos_id = llama_vocab_eos(impl->vocab); + auto bos_token_str = bos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx, bos_id, true) : ""; + auto eos_token_str = eos_id != LLAMA_TOKEN_NULL ? common_token_to_piece(impl->ctx, eos_id, true) : ""; + return server_context_meta { /* build_info */ build_info, /* model_name */ impl->model_name, @@ -2798,8 +2804,8 @@ server_context_meta server_context::get_meta() const { /* chat_template */ common_chat_templates_source(impl->chat_templates.get()), /* chat_template_tool_use */ tool_use_src ? tool_use_src : "", - /* bos_token_str */ common_token_to_piece(impl->ctx, llama_vocab_bos(impl->vocab), true), - /* eos_token_str */ common_token_to_piece(impl->ctx, llama_vocab_eos(impl->vocab), true), + /* bos_token_str */ bos_token_str, + /* eos_token_str */ eos_token_str, /* fim_pre_token */ llama_vocab_fim_pre(impl->vocab), /* fim_sub_token */ llama_vocab_fim_suf(impl->vocab), /* fim_mid_token */ llama_vocab_fim_mid(impl->vocab), From 5b6c9bc0f3c8f55598b9999b65aff7ce4119bc15 Mon Sep 17 00:00:00 2001 From: Pascal Date: Tue, 23 Dec 2025 15:48:03 +0100 Subject: [PATCH 21/25] webui: apply webui_settings on first load (#18223) * webui: apply webui_settings on first load The webui_settings from /props were not applied on initial load when default_generation_settings.params was null Now syncs whenever serverProps is available, regardless of params, works for both single-model and router modes * chore: update webui build output --- tools/server/public/index.html.gz | Bin 1451262 -> 1451233 bytes .../webui/src/lib/stores/settings.svelte.ts | 9 ++++----- tools/server/webui/src/routes/+layout.svelte | 2 +- 3 files changed, 5 insertions(+), 6 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 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zaMTD0^W4xIC*N2AmWo=1YiW$Ov$9d8L6xKaf>%2Jg^%?3-C`=m7LIhE zalfO>Wb(p+n<0*G$UzUE6nLiKWLmTHO}aR&eoU9_9~L74FP9J?7NmcV0DNj2cXx4~ z$+4;1zxw8z+cfRU%%AJEW>w9C&* z<10Lj%4xECyDMjh%m)&$+;%o-6D}(f1&{&!sxNBeP=nl;-AlM?il_e-_PI;I3Il)&);u9{L|IR2%~w9 zYeWgYfioA?93*GjCIA2A0glMy^Z8T!c=BL4{)|7KJUE&Tub$p{@_;TrPw)H-3qe-v HU>rdJEbK^( diff --git a/tools/server/webui/src/lib/stores/settings.svelte.ts b/tools/server/webui/src/lib/stores/settings.svelte.ts index e163833bfb..cda940ba7e 100644 --- a/tools/server/webui/src/lib/stores/settings.svelte.ts +++ b/tools/server/webui/src/lib/stores/settings.svelte.ts @@ -294,15 +294,14 @@ class SettingsStore { * This sets up the default values from /props endpoint */ syncWithServerDefaults(): void { - const serverParams = serverStore.defaultParams; - if (!serverParams) { - console.warn('No server parameters available for initialization'); + const propsDefaults = this.getServerDefaults(); + + if (Object.keys(propsDefaults).length === 0) { + console.warn('No server defaults available for initialization'); return; } - const propsDefaults = this.getServerDefaults(); - for (const [key, propsValue] of Object.entries(propsDefaults)) { const currentValue = getConfigValue(this.config, key); diff --git a/tools/server/webui/src/routes/+layout.svelte b/tools/server/webui/src/routes/+layout.svelte index a14dfb633c..095827b9ca 100644 --- a/tools/server/webui/src/routes/+layout.svelte +++ b/tools/server/webui/src/routes/+layout.svelte @@ -119,7 +119,7 @@ $effect(() => { const serverProps = serverStore.props; - if (serverProps?.default_generation_settings?.params) { + if (serverProps) { settingsStore.syncWithServerDefaults(); } }); From 5ee4e43f2629fc88c3c3ff428a47ffb842fa8d84 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Tue, 23 Dec 2025 21:49:05 +0100 Subject: [PATCH 22/25] server: return_progress to also report 0% processing state (#18305) --- tools/server/server-context.cpp | 6 ++++++ tools/server/tests/unit/test_chat_completion.py | 13 +++++++++++-- 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index e2e7bcb9e6..94825dc862 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -2313,6 +2313,12 @@ private: slot.n_prompt_tokens_processed = 0; slot.prompt.tokens.keep_first(n_past); + + // send initial 0% progress update if needed + // this is to signal the client that the request has started processing + if (slot.task->params.stream && slot.task->params.return_progress) { + send_partial_response(slot, {}, true); + } } if (!slot.can_split()) { diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py index 64f3158b98..5f5de415cf 100644 --- a/tools/server/tests/unit/test_chat_completion.py +++ b/tools/server/tests/unit/test_chat_completion.py @@ -434,8 +434,8 @@ def test_context_size_exceeded_stream(): @pytest.mark.parametrize( "n_batch,batch_count,reuse_cache", [ - (64, 3, False), - (64, 1, True), + (64, 4, False), + (64, 2, True), ] ) def test_return_progress(n_batch, batch_count, reuse_cache): @@ -462,10 +462,18 @@ def test_return_progress(n_batch, batch_count, reuse_cache): res = make_cmpl_request() last_progress = None total_batch_count = 0 + for data in res: cur_progress = data.get("prompt_progress", None) if cur_progress is None: continue + if total_batch_count == 0: + # first progress report must have n_cache == n_processed + assert cur_progress["total"] > 0 + assert cur_progress["cache"] == cur_progress["processed"] + if reuse_cache: + # when reusing cache, we expect some cached tokens + assert cur_progress["cache"] > 0 if last_progress is not None: assert cur_progress["total"] == last_progress["total"] assert cur_progress["cache"] == last_progress["cache"] @@ -473,6 +481,7 @@ def test_return_progress(n_batch, batch_count, reuse_cache): total_batch_count += 1 last_progress = cur_progress + # last progress should indicate completion (all tokens processed) assert last_progress is not None assert last_progress["total"] > 0 assert last_progress["processed"] == last_progress["total"] From 10355dc7d0d854c474d49610de16fde9c6679cf7 Mon Sep 17 00:00:00 2001 From: ddh0 Date: Wed, 24 Dec 2025 00:19:12 -0600 Subject: [PATCH 23/25] common: add `LLAMA_ARG_OVERRIDE_TENSOR` env var for `-ot` arg (#18267) --- common/arg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index 1302065498..fded0bd260 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -2087,7 +2087,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex "override tensor buffer type", [](common_params & params, const std::string & value) { parse_tensor_buffer_overrides(value, params.tensor_buft_overrides); } - )); + ).set_env("LLAMA_ARG_OVERRIDE_TENSOR")); add_opt(common_arg( {"-otd", "--override-tensor-draft"}, "=,...", "override tensor buffer type for draft model", [](common_params & params, const std::string & value) { From cf2ffc02bc8a6515bd258391704d1daa2f1b5a69 Mon Sep 17 00:00:00 2001 From: TianHao324 Date: Wed, 24 Dec 2025 14:55:33 +0800 Subject: [PATCH 24/25] CANN: Uses yarn_ramp cache in ROPE (#17725) --- ggml/src/ggml-cann/aclnn_ops.cpp | 14 ++++++++------ ggml/src/ggml-cann/common.h | 4 ++++ 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index 835b53f659..dff72a277a 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -2338,19 +2338,19 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx, // Step1.2: prepare rope_yarn_ramp, if this part updated, should update theta_scale_tensor. // TODO: acl_yarn_ramp_tensor use rope cache. bool yarn_ramp_tensor_updated = false; - ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool()); acl_tensor_ptr acl_yarn_ramp_tensor; if (ext_factor != 0 && (theta_scale_updated || ctx.rope_cache.theta_scale_length != theta_scale_length || ctx.rope_cache.freq_scale != freq_scale)) { yarn_ramp_tensor_updated = true; - + if (ctx.rope_cache.yarn_ramp_cache != nullptr) { + ACL_CHECK(aclrtFree(ctx.rope_cache.yarn_ramp_cache)); + } + ACL_CHECK(aclrtMalloc(&ctx.rope_cache.yarn_ramp_cache, theta_scale_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST)); // -rope_yarn_ramp // const float y = (i0 / 2 - low) / MAX(0.001f, high - low); // return MIN(1, MAX(0, y)) - 1; - yarn_ramp_allocator.alloc(theta_scale_length * sizeof(float)); - void * yarn_ramp_buffer = yarn_ramp_allocator.get(); acl_yarn_ramp_tensor = - ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float), theta_scale_ne, theta_scale_nb, 1); + ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float), theta_scale_ne, theta_scale_nb, 1); float zero_value = 0, one_value = 1; float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]); acl_scalar_ptr low = ggml_cann_create_scalar(&corr_dims[0], aclDataType::ACL_FLOAT); @@ -2380,8 +2380,10 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx, acl_scalar_ptr freq_scale_1_sc = ggml_cann_create_scalar(&freq_scale_1, aclDataType::ACL_FLOAT); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor.get(), freq_scale_1_sc.get()); GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor.get(), freq_scale_sc.get(), one.get()); + } else { + acl_yarn_ramp_tensor = + ggml_cann_create_tensor(ctx.rope_cache.yarn_ramp_cache, ACL_FLOAT, sizeof(float), theta_scale_ne, theta_scale_nb, 1); } - // Step 1.3: update theta_scale_tensor according to ext_factor or freq_scale. if (ext_factor != 0) { if (theta_scale_updated || yarn_ramp_tensor_updated) { diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h index 45c7294e68..3a461ef1a7 100644 --- a/ggml/src/ggml-cann/common.h +++ b/ggml/src/ggml-cann/common.h @@ -318,6 +318,9 @@ struct ggml_cann_rope_cache { if (position_select_index_host) { free(position_select_index_host); } + if (yarn_ramp_cache) { + ACL_CHECK(aclrtFree(yarn_ramp_cache)); + } } bool equal(int64_t theta_scale_length, @@ -370,6 +373,7 @@ struct ggml_cann_rope_cache { float * theta_scale_exp_host = nullptr; int * position_select_index_host = nullptr; void * position_select_index = nullptr; + void * yarn_ramp_cache = nullptr; // sin/cos cache, used only to accelerate first layer on each device void * sin_cache = nullptr; void * cos_cache = nullptr; From 7f459c98e716054b61323440b49896b397375397 Mon Sep 17 00:00:00 2001 From: Ruben Ortlam Date: Wed, 24 Dec 2025 08:59:14 +0100 Subject: [PATCH 25/25] vulkan: use fewer FA rows for small cache runs (#18280) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 56 ++++++++++++++-------------- 1 file changed, 29 insertions(+), 27 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index a524adbe0c..4f50d378cd 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -379,18 +379,18 @@ enum FaCodePath { }; struct vk_fa_pipeline_state { - vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, FaCodePath path, bool aligned, bool f32acc) - : HSK(HSK), HSV(HSV), small_rows(small_rows), path(path), aligned(aligned), f32acc(f32acc) {} + vk_fa_pipeline_state(uint32_t HSK, uint32_t HSV, bool small_rows, bool small_cache, FaCodePath path, bool aligned, bool f32acc) + : HSK(HSK), HSV(HSV), small_rows(small_rows), small_cache(small_cache), path(path), aligned(aligned), f32acc(f32acc) {} uint32_t HSK, HSV; - bool small_rows; + bool small_rows, small_cache; FaCodePath path; bool aligned; bool f32acc; bool operator<(const vk_fa_pipeline_state &b) const { - return std::tie(HSK, HSV, small_rows, path, aligned, f32acc) < - std::tie(b.HSK, b.HSV, b.small_rows, b.path, b.aligned, b.f32acc); + return std::tie(HSK, HSV, small_rows, small_cache, path, aligned, f32acc) < + std::tie(b.HSK, b.HSV, b.small_rows, b.small_cache, b.path, b.aligned, b.f32acc); } }; @@ -2582,10 +2582,10 @@ static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events static constexpr uint32_t flash_attention_num_small_rows = 32; static constexpr uint32_t scalar_flash_attention_num_small_rows = 1; -static uint32_t get_fa_scalar_num_large_rows(uint32_t hsk, uint32_t hsv) { +static uint32_t get_fa_scalar_num_large_rows(uint32_t hsk, uint32_t hsv, bool small_cache) { if (hsv >= 192) { return 2; - } else if ((hsv | hsk) & 8) { + } else if ((hsv | hsk) & 8 || small_cache) { return 4; } else { return 8; @@ -2607,9 +2607,8 @@ static uint32_t get_fa_num_small_rows(FaCodePath path) { } } -static std::array fa_rows_cols(FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows) { +static std::array fa_rows_cols(FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) { GGML_UNUSED(clamp); - GGML_UNUSED(hsv); if (path == FA_SCALAR) { if (small_rows) { @@ -2618,9 +2617,9 @@ static std::array fa_rows_cols(FaCodePath path, uint32_t hsk, uint3 if ((hsv | hsk) & 8) { // HSV/HSK not being a multiple of 16 makes D_split smaller, which makes cols_per_iter // larger, and Bc needs to be >= cols_per_thread. 64 is large enough, 32 is not. - return {get_fa_scalar_num_large_rows(hsk, hsv), 64}; + return {get_fa_scalar_num_large_rows(hsk, hsv, small_cache), 64}; } else { - return {get_fa_scalar_num_large_rows(hsk, hsv), 32}; + return {get_fa_scalar_num_large_rows(hsk, hsv, small_cache), 32}; } } } @@ -2649,8 +2648,8 @@ static std::array fa_rows_cols(FaCodePath path, uint32_t hsk, uint3 return {64, 64}; } -static uint32_t fa_align(FaCodePath path, uint32_t hsk, uint32_t hsv, ggml_type type, bool small_rows) { - return fa_rows_cols(path, hsk, hsv, 0, type, small_rows)[1]; +static uint32_t fa_align(FaCodePath path, uint32_t hsk, uint32_t hsv, ggml_type type, bool small_rows, bool small_cache) { + return fa_rows_cols(path, hsk, hsv, 0, type, small_rows, small_cache)[1]; } static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector& warptile, bool mul_mat_id, ggml_type src0_type) { @@ -2992,11 +2991,11 @@ static void ggml_vk_load_shaders(vk_device& device) { align, disable_robustness, require_full_subgroups, required_subgroup_size); }; - auto const &fa_wg_denoms = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows) -> std::array { - return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows)[0], 1, 1}; + auto const &fa_wg_denoms = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) -> std::array { + return {fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache)[0], 1, 1}; }; - auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows) -> std::vector { + auto const &fa_spec_constants = [&](FaCodePath path, uint32_t hsk, uint32_t hsv, uint32_t clamp, ggml_type type, bool small_rows, bool small_cache) -> std::vector { // For large number of rows, 128 invocations seems to work best. // For small number of rows (e.g. N==1), 256 works better. But matrix granularity for 256 is 32, so we // can't use 256 for D==80. @@ -3006,7 +3005,7 @@ static void ggml_vk_load_shaders(vk_device& device) { uint32_t wg_size = (path == FA_SCALAR || path == FA_COOPMAT1) ? scalar_flash_attention_workgroup_size : ((small_rows && (D % 32) == 0) ? 256 : 128); - auto rows_cols = fa_rows_cols(path, hsk, hsv, clamp, type, small_rows); + auto rows_cols = fa_rows_cols(path, hsk, hsv, clamp, type, small_rows, small_cache); // D_split can't be larger than a subgroup because we use subgroupShuffle to reduce it. // D_split can't be larger than the LSB of D divided by 4 due to vectorization in the shader. @@ -3021,21 +3020,22 @@ static void ggml_vk_load_shaders(vk_device& device) { uint32_t HSK = fa.first.HSK; \ uint32_t HSV = fa.first.HSV; \ bool small_rows = fa.first.small_rows; \ + bool small_cache = fa.first.small_cache; \ FaCodePath path = fa.first.path; \ bool aligned = fa.first.aligned; \ bool f32acc = fa.first.f32acc; \ if (path == FAPATH) { \ if (aligned) { \ if (f32acc) { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_align(FAPATH,HSK,HSV,TYPE,small_rows), true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ } else { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows), fa_align(FAPATH,HSK,HSV,TYPE,small_rows), true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_aligned_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,0,TYPE,small_rows,small_cache), fa_align(FAPATH,HSK,HSV,TYPE,small_rows,small_cache), true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ } \ } else { \ if (f32acc) { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows), 1, true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f32acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ } else { \ - ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows), 1, true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ + ggml_vk_create_pipeline(device, fa.second, "flash_attn_f32_f16_f16acc" #NAMELC, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _len, flash_attn_f32_f16_ ## NAMELC ## _f16acc ## SUFFIX ## _data, "main", 6, sizeof(vk_flash_attn_push_constants), fa_wg_denoms(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), fa_spec_constants(FAPATH, HSK,HSV,1,TYPE,small_rows,small_cache), 1, true, true, (FAPATH==FA_COOPMAT1 ? 32 : 0)); \ } \ } \ } \ @@ -8008,11 +8008,11 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx } } -static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const uint32_t hsk, uint32_t hsv) { +static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const uint32_t hsk, uint32_t hsv, bool small_cache) { // Needs to be kept up to date on shader changes GGML_UNUSED(hsv); const uint32_t wg_size = scalar_flash_attention_workgroup_size; - const uint32_t Br = get_fa_scalar_num_large_rows(hsk, hsv); + const uint32_t Br = get_fa_scalar_num_large_rows(hsk, hsv, small_cache); const uint32_t Bc = scalar_flash_attention_Bc; const uint32_t tmpsh = wg_size * sizeof(float); @@ -8136,6 +8136,8 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx uint32_t workgroups_y = (uint32_t)neq2; uint32_t workgroups_z = (uint32_t)neq3; + const bool small_cache = nek1 < 1024; + // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga. // For coopmat2 FA, we always use the small size (which is still pretty large for gqa). uint32_t max_gqa; @@ -8143,7 +8145,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx case FA_SCALAR: case FA_COOPMAT1: // We may switch from coopmat1 to scalar, so use the scalar limit for both - max_gqa = get_fa_scalar_num_large_rows(HSK, HSV); + max_gqa = get_fa_scalar_num_large_rows(HSK, HSV, small_cache); break; case FA_COOPMAT2: max_gqa = get_fa_num_small_rows(FA_COOPMAT2); @@ -8177,7 +8179,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx // with large hsk/hsv, scalar path may need to use small_rows to fit in shared memory if (path == FA_SCALAR && - !ggml_vk_flash_attn_scalar_shmem_support(ctx->device, HSK, HSV)) { + !ggml_vk_flash_attn_scalar_shmem_support(ctx->device, HSK, HSV, small_cache)) { small_rows = true; } @@ -8193,7 +8195,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx v_stride /= 4; } - uint32_t alignment = fa_align(path, HSK, HSV, k->type, small_rows); + uint32_t alignment = fa_align(path, HSK, HSV, k->type, small_rows, small_cache); bool aligned = (KV % alignment) == 0 && // the "aligned" shader variant will forcibly align strides, for performance (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0; @@ -8205,7 +8207,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx bool f32acc = path == FA_SCALAR || dst->op_params[3] == GGML_PREC_F32; - vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, path, aligned, f32acc); + vk_fa_pipeline_state fa_pipeline_state(HSK, HSV, small_rows, small_cache, path, aligned, f32acc); vk_pipeline pipeline = nullptr;

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