From 38d152160898b0173ffe4dc7df5daadcbd2eceb0 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 1 Mar 2024 07:36:47 +0000 Subject: [PATCH 01/11] [SYCL] Use batched mul_mat pathway (#5591) * Use batched mul_mat pathway * rm extra line * Explicitly state scaled data type --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 107 +++++++++++++++++++++----------------------------- 1 file changed, 44 insertions(+), 63 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a054ec8b92bac..6f391b0c650ad 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -12726,6 +12726,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(dst->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src1->backend != GGML_BACKEND_TYPE_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32 || (src1->ne[2] == 1 && src1->ne[3] == 1)); GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); @@ -13269,31 +13270,23 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, int64_t i03 = i13 / r3; int64_t i02 = i12 / r2; - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12 + i13*nb13; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } -static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, - const ggml_tensor *src1, - ggml_tensor *dst) try { +static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT); GGML_ASSERT(src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - - GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); - - GGML_TENSOR_LOCALS(int64_t, nb0, src0, nb); - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); - - GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); + GGML_TENSOR_BINARY_OP_LOCALS - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + const int64_t ne_dst = ggml_nelements(dst); SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; @@ -13312,11 +13305,16 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; // convert src1 to fp16 - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - GGML_ASSERT(to_fp16_sycl != nullptr); - - sycl_pool_alloc src1_as_f16(ne1); - to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream); + sycl_pool_alloc src1_f16_alloc; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + const int64_t ne_src1 = ggml_nelements(src1); + src1_f16_alloc.alloc(ne_src1); + GGML_ASSERT(to_fp16_sycl != nullptr); + to_fp16_sycl(src1_ddf, src1_f16_alloc.get(), ne_src1, main_stream); + } + sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf + : src1_f16_alloc.get(); sycl_pool_alloc dst_f16; char * dst_t; @@ -13337,20 +13335,12 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, const void * alpha = &alpha_f16; const void * beta = &beta_f16; - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - dst_t = (char *) dst_f16.alloc(ne); - - nbd2 /= sizeof(float) / sizeof(sycl::half); - nbd3 /= sizeof(float) / sizeof(sycl::half); - } else { - dst_t = (char *) dst_ddf; - - cu_compute_type = dpct::library_data_t::real_float; - cu_data_type = dpct::library_data_t::real_float; + // TODO: Renable (dst->op_params[0] =! GGML_PREC_DEFAULT) pathway + // once oneMKL open source supports half, half, float, float: datatypes + dst_t = (char *) dst_f16.alloc(ne_dst); - alpha = &alpha_f32; - beta = &beta_f32; - } + nbd2 /= sizeof(float) / sizeof(sycl::half); + nbd3 /= sizeof(float) / sizeof(sycl::half); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -13386,10 +13376,10 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, - nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half), - (const char *)src1_as_f16.get(), dpct::library_data_t::real_half, - nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta, - (char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float), + nb01 / nb00, nb02 / nb00, + (const char *)src1_f16, dpct::library_data_t::real_half, + nb11 / nb10, nb12 / nb10, beta, + (char *)dst_t, cu_data_type, ne01, nb2 / nb0, ne12 * ne13, cu_compute_type))); } else { // use syclGemmBatchedEx @@ -13409,44 +13399,35 @@ static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, {sycl::aspect::fp16}); main_stream->submit([&](sycl::handler &cgh) { - const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get(); - const void **ptrs_src_get_ct3 = ptrs_src.get(); - void **ptrs_dst_get_ct4 = ptrs_dst.get(); - + const void **ptrs_src_get = ptrs_src.get(); + void **ptrs_dst_get = ptrs_dst.get(); + size_t nb12_scaled = src1->type == GGML_TYPE_F16 ? nb12 : nb12 / 2; + size_t nb13_scaled = src1->type == GGML_TYPE_F16 ? nb13 : nb13 / 2; cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) { k_compute_batched_ptrs( - src0_as_f16, src1_as_f16_get_ct1, - dst_t, ptrs_src_get_ct3, - ptrs_dst_get_ct4, ne12, ne13, ne23, - nb02, nb03, nb12, nb13, nbd2, nbd3, r2, - r3, item_ct1); + src0_as_f16, src1_f16, + dst_t, ptrs_src_get, + ptrs_dst_get, ne12, ne13, ne23, + nb02, nb03, nb12_scaled, nb13_scaled, + nbd2, nbd3, r2, r3, item_ct1); }); }); } - /* - DPCT1010:95: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this - code. - */ - SYCL_CHECK(0); - SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), - dpct::library_data_t::real_half, nb01 / sizeof(sycl::half), + dpct::library_data_t::real_half, nb01 / nb00, (const void **)(ptrs_src.get() + 1 * ne23), - dpct::library_data_t::real_half, nb11 / sizeof(float), beta, + dpct::library_data_t::real_half, nb11 / nb10, beta, (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, cu_compute_type))); } #endif - if (dst->op_params[0] == GGML_PREC_DEFAULT) { - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream); - } + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16.get(), dst_ddf, ne_dst, main_stream); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -13491,10 +13472,10 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 // KQV single-batch // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); ggml_sycl_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n"); - ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst); + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); + ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); } else if (src0->type == GGML_TYPE_F32) { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); From f105471ef6aa4727afac8240da398590d7277f45 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Mar 2024 09:59:43 +0200 Subject: [PATCH 02/11] server : fix newlines in help (#5785) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index bf20e0cf1c9c7..45c4aec4de502 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2080,8 +2080,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); printf(" --chat-template JINJA_TEMPLATE\n"); printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); printf(" Note: only commonly used templates are accepted, since we don't have jinja parser\n"); From 6ea0f010ff6967034528d9e0b8330b9b0f0b7c13 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Fri, 1 Mar 2024 08:54:53 +0000 Subject: [PATCH 03/11] ci : add Ubuntu 22 Vulkan CI run (#5789) --- .github/workflows/build.yml | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 66ad85938ca16..9144f926628a5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -145,6 +145,28 @@ jobs: cd build ctest -L main --verbose + ubuntu-22-cmake-vulkan: + runs-on: ubuntu-22.04 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential libvulkan-dev + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake -DLLAMA_VULKAN=ON .. + cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 From 5cb02b4a012bb16c6c699c0c62c05ffa653eee0f Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 1 Mar 2024 10:08:08 +0100 Subject: [PATCH 04/11] server: allow to override threads server pool with --threads-http (#5794) --- examples/server/README.md | 1 + examples/server/server.cpp | 16 ++++++++++++++++ 2 files changed, 17 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index 0e9bd7fd404ba..ad35306c60c4e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -18,6 +18,7 @@ The project is under active development, and we are [looking for feedback and co - `--threads N`, `-t N`: Set the number of threads to use during generation. - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. +- `--threads-http N`: number of threads in the http server pool to process requests (default: `std::thread::hardware_concurrency()`) - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 45c4aec4de502..eea98796663d6 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -43,6 +43,7 @@ struct server_params { int32_t write_timeout = 600; bool slots_endpoint = true; bool metrics_endpoint = false; + int n_threads_http = -1; }; bool server_verbose = false; @@ -2012,6 +2013,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); + printf(" --threads-http N number of threads in the http server pool to process requests (default: hardware concurrency)\n"); printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); printf(" --rope-scaling {none,linear,yarn}\n"); printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); @@ -2298,6 +2300,15 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads_batch = std::stoi(argv[i]); } + else if (arg == "--threads-http") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + sparams.n_threads_http = std::stoi(argv[i]); + } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) @@ -3449,6 +3460,11 @@ int main(int argc, char **argv) }*/ //); + if (sparams.n_threads_http > 0) { + log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); + svr.new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + } + LOG_INFO("HTTP server listening", log_data); // run the HTTP server in a thread - see comment below std::thread t([&]() From 9600d59e010c18f5872580a21734ea1bf1968d04 Mon Sep 17 00:00:00 2001 From: Douglas Hanley Date: Fri, 1 Mar 2024 03:15:36 -0600 Subject: [PATCH 05/11] unicode : switch to multimap based nfd_map (#5799) * switch to multimap based nfd_map due to compile time issues * simplify multimap keys * dont construct new locale every time --- llama.cpp | 11 +- unicode.h | 566 +++++++++++++++++++++++++++++------------------------- 2 files changed, 312 insertions(+), 265 deletions(-) diff --git a/llama.cpp b/llama.cpp index 62699ce52e197..a35f07aa47cda 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8947,10 +8947,10 @@ struct llm_tokenizer_wpm { std::vector codepoints = codepoints_from_utf8(text); std::vector nfd_codepoints; for (uint32_t code : codepoints) { - auto it = nfd_map.find(code); - if (it != nfd_map.end()) { - for (uint32_t c : it->second) { - nfd_codepoints.push_back(c); + auto it = nfd_map.equal_range(code); + if (it.first != it.second) { + for (auto jt = it.first; jt != it.second; jt++) { + nfd_codepoints.push_back(jt->second); } } else { nfd_codepoints.push_back(code); @@ -9001,12 +9001,13 @@ struct llm_tokenizer_wpm { } uint32_t to_lower(uint32_t code) { + static const std::locale locale("en_US.UTF-8"); #if defined(_WIN32) if (code > 0xFFFF) { return code; } #endif - return std::tolower(wchar_t(code), std::locale("en_US.UTF-8")); + return std::tolower(wchar_t(code), locale); } bool is_ascii_punct(uint32_t code) { diff --git a/unicode.h b/unicode.h index 620e2b5806c24..f6be4549bff10 100644 --- a/unicode.h +++ b/unicode.h @@ -1,6 +1,7 @@ #pragma once #include +#include #include #include #include @@ -223,266 +224,311 @@ static const std::vector> control_ranges = { {0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF}, }; -static const std::unordered_map> nfd_map = { -{0xC0, {0x41, 0x300}}, {0xC1, {0x41, 0x301}}, {0xC2, {0x41, 0x302}}, {0xC3, {0x41, 0x303}}, {0xC4, {0x41, 0x308}}, {0xC5, {0x41, 0x30A}}, {0xC7, {0x43, 0x327}}, {0xC8, {0x45, 0x300}}, -{0xC9, {0x45, 0x301}}, {0xCA, {0x45, 0x302}}, {0xCB, {0x45, 0x308}}, {0xCC, {0x49, 0x300}}, {0xCD, {0x49, 0x301}}, {0xCE, {0x49, 0x302}}, {0xCF, {0x49, 0x308}}, {0xD1, {0x4E, 0x303}}, -{0xD2, {0x4F, 0x300}}, {0xD3, {0x4F, 0x301}}, {0xD4, {0x4F, 0x302}}, {0xD5, {0x4F, 0x303}}, {0xD6, {0x4F, 0x308}}, {0xD9, {0x55, 0x300}}, {0xDA, {0x55, 0x301}}, {0xDB, {0x55, 0x302}}, -{0xDC, {0x55, 0x308}}, {0xDD, {0x59, 0x301}}, {0xE0, {0x61, 0x300}}, {0xE1, {0x61, 0x301}}, {0xE2, {0x61, 0x302}}, {0xE3, {0x61, 0x303}}, {0xE4, {0x61, 0x308}}, {0xE5, {0x61, 0x30A}}, -{0xE7, {0x63, 0x327}}, {0xE8, {0x65, 0x300}}, {0xE9, {0x65, 0x301}}, {0xEA, {0x65, 0x302}}, {0xEB, {0x65, 0x308}}, {0xEC, {0x69, 0x300}}, {0xED, {0x69, 0x301}}, {0xEE, {0x69, 0x302}}, -{0xEF, {0x69, 0x308}}, {0xF1, {0x6E, 0x303}}, {0xF2, {0x6F, 0x300}}, {0xF3, {0x6F, 0x301}}, {0xF4, {0x6F, 0x302}}, {0xF5, {0x6F, 0x303}}, {0xF6, {0x6F, 0x308}}, {0xF9, {0x75, 0x300}}, -{0xFA, {0x75, 0x301}}, {0xFB, {0x75, 0x302}}, {0xFC, {0x75, 0x308}}, {0xFD, {0x79, 0x301}}, {0xFF, {0x79, 0x308}}, {0x100, {0x41, 0x304}}, {0x101, {0x61, 0x304}}, {0x102, {0x41, 0x306}}, -{0x103, {0x61, 0x306}}, {0x104, {0x41, 0x328}}, {0x105, {0x61, 0x328}}, {0x106, {0x43, 0x301}}, {0x107, {0x63, 0x301}}, {0x108, {0x43, 0x302}}, {0x109, {0x63, 0x302}}, {0x10A, {0x43, 0x307}}, -{0x10B, {0x63, 0x307}}, {0x10C, {0x43, 0x30C}}, {0x10D, {0x63, 0x30C}}, {0x10E, {0x44, 0x30C}}, {0x10F, {0x64, 0x30C}}, {0x112, {0x45, 0x304}}, {0x113, {0x65, 0x304}}, {0x114, {0x45, 0x306}}, -{0x115, {0x65, 0x306}}, {0x116, {0x45, 0x307}}, {0x117, {0x65, 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{0x65E3}}, {0x2F8CC, {0x66F8}}, {0x2F8CD, {0x6649}}, {0x2F8CE, {0x3B19}}, -{0x2F8CF, {0x6691}}, {0x2F8D0, {0x3B08}}, {0x2F8D1, {0x3AE4}}, {0x2F8D2, {0x5192}}, {0x2F8D3, {0x5195}}, {0x2F8D4, {0x6700}}, {0x2F8D5, {0x669C}}, {0x2F8D6, {0x80AD}}, {0x2F8D7, {0x43D9}}, -{0x2F8D8, {0x6717}}, {0x2F8D9, {0x671B}}, {0x2F8DA, {0x6721}}, {0x2F8DB, {0x675E}}, {0x2F8DC, {0x6753}}, {0x2F8DD, {0x233C3}}, {0x2F8DE, {0x3B49}}, {0x2F8DF, {0x67FA}}, {0x2F8E0, {0x6785}}, -{0x2F8E1, {0x6852}}, {0x2F8E2, {0x6885}}, {0x2F8E3, {0x2346D}}, {0x2F8E4, {0x688E}}, {0x2F8E5, {0x681F}}, {0x2F8E6, {0x6914}}, {0x2F8E7, {0x3B9D}}, {0x2F8E8, {0x6942}}, {0x2F8E9, {0x69A3}}, -{0x2F8EA, {0x69EA}}, {0x2F8EB, {0x6AA8}}, {0x2F8EC, {0x236A3}}, {0x2F8ED, {0x6ADB}}, {0x2F8EE, {0x3C18}}, {0x2F8EF, {0x6B21}}, {0x2F8F0, {0x238A7}}, {0x2F8F1, {0x6B54}}, {0x2F8F2, {0x3C4E}}, -{0x2F8F3, {0x6B72}}, {0x2F8F4, {0x6B9F}}, {0x2F8F5, {0x6BBA}}, {0x2F8F6, {0x6BBB}}, {0x2F8F7, {0x23A8D}}, {0x2F8F8, {0x21D0B}}, {0x2F8F9, {0x23AFA}}, {0x2F8FA, {0x6C4E}}, {0x2F8FB, {0x23CBC}}, -{0x2F8FC, {0x6CBF}}, {0x2F8FD, {0x6CCD}}, {0x2F8FE, {0x6C67}}, {0x2F8FF, {0x6D16}}, {0x2F900, {0x6D3E}}, {0x2F901, {0x6D77}}, {0x2F902, {0x6D41}}, {0x2F903, {0x6D69}}, {0x2F904, {0x6D78}}, -{0x2F905, {0x6D85}}, {0x2F906, {0x23D1E}}, {0x2F907, {0x6D34}}, {0x2F908, {0x6E2F}}, {0x2F909, {0x6E6E}}, {0x2F90A, {0x3D33}}, {0x2F90B, {0x6ECB}}, {0x2F90C, {0x6EC7}}, {0x2F90D, {0x23ED1}}, -{0x2F90E, {0x6DF9}}, {0x2F90F, {0x6F6E}}, {0x2F910, {0x23F5E}}, {0x2F911, {0x23F8E}}, {0x2F912, {0x6FC6}}, {0x2F913, {0x7039}}, {0x2F914, {0x701E}}, {0x2F915, {0x701B}}, {0x2F916, {0x3D96}}, -{0x2F917, {0x704A}}, {0x2F918, {0x707D}}, {0x2F919, {0x7077}}, {0x2F91A, {0x70AD}}, {0x2F91B, {0x20525}}, {0x2F91C, {0x7145}}, {0x2F91D, {0x24263}}, {0x2F91E, {0x719C}}, {0x2F91F, {0x243AB}}, -{0x2F920, {0x7228}}, {0x2F921, {0x7235}}, {0x2F922, {0x7250}}, {0x2F923, {0x24608}}, {0x2F924, {0x7280}}, {0x2F925, {0x7295}}, {0x2F926, {0x24735}}, {0x2F927, {0x24814}}, {0x2F928, {0x737A}}, -{0x2F929, {0x738B}}, {0x2F92A, {0x3EAC}}, {0x2F92B, {0x73A5}}, {0x2F92C, {0x3EB8}}, {0x2F92D, {0x3EB8}}, {0x2F92E, {0x7447}}, {0x2F92F, {0x745C}}, {0x2F930, {0x7471}}, {0x2F931, {0x7485}}, -{0x2F932, {0x74CA}}, {0x2F933, {0x3F1B}}, {0x2F934, {0x7524}}, {0x2F935, {0x24C36}}, {0x2F936, {0x753E}}, {0x2F937, {0x24C92}}, {0x2F938, {0x7570}}, {0x2F939, {0x2219F}}, {0x2F93A, {0x7610}}, -{0x2F93B, {0x24FA1}}, {0x2F93C, {0x24FB8}}, {0x2F93D, {0x25044}}, {0x2F93E, {0x3FFC}}, {0x2F93F, {0x4008}}, {0x2F940, {0x76F4}}, {0x2F941, {0x250F3}}, {0x2F942, {0x250F2}}, {0x2F943, {0x25119}}, -{0x2F944, {0x25133}}, {0x2F945, {0x771E}}, {0x2F946, {0x771F}}, {0x2F947, {0x771F}}, {0x2F948, {0x774A}}, {0x2F949, {0x4039}}, {0x2F94A, {0x778B}}, {0x2F94B, {0x4046}}, {0x2F94C, {0x4096}}, -{0x2F94D, {0x2541D}}, {0x2F94E, {0x784E}}, {0x2F94F, {0x788C}}, {0x2F950, {0x78CC}}, {0x2F951, {0x40E3}}, {0x2F952, {0x25626}}, {0x2F953, {0x7956}}, {0x2F954, {0x2569A}}, {0x2F955, {0x256C5}}, -{0x2F956, {0x798F}}, {0x2F957, {0x79EB}}, {0x2F958, {0x412F}}, {0x2F959, {0x7A40}}, {0x2F95A, {0x7A4A}}, {0x2F95B, {0x7A4F}}, {0x2F95C, {0x2597C}}, {0x2F95D, {0x25AA7}}, {0x2F95E, {0x25AA7}}, -{0x2F95F, {0x7AEE}}, {0x2F960, {0x4202}}, {0x2F961, {0x25BAB}}, {0x2F962, {0x7BC6}}, {0x2F963, {0x7BC9}}, {0x2F964, {0x4227}}, {0x2F965, {0x25C80}}, {0x2F966, {0x7CD2}}, {0x2F967, {0x42A0}}, -{0x2F968, {0x7CE8}}, {0x2F969, {0x7CE3}}, {0x2F96A, {0x7D00}}, {0x2F96B, {0x25F86}}, {0x2F96C, {0x7D63}}, {0x2F96D, {0x4301}}, {0x2F96E, {0x7DC7}}, {0x2F96F, {0x7E02}}, {0x2F970, {0x7E45}}, -{0x2F971, {0x4334}}, {0x2F972, {0x26228}}, {0x2F973, {0x26247}}, {0x2F974, {0x4359}}, {0x2F975, {0x262D9}}, {0x2F976, {0x7F7A}}, {0x2F977, {0x2633E}}, {0x2F978, {0x7F95}}, {0x2F979, {0x7FFA}}, -{0x2F97A, {0x8005}}, {0x2F97B, {0x264DA}}, {0x2F97C, {0x26523}}, {0x2F97D, {0x8060}}, {0x2F97E, {0x265A8}}, {0x2F97F, {0x8070}}, {0x2F980, {0x2335F}}, {0x2F981, {0x43D5}}, {0x2F982, {0x80B2}}, -{0x2F983, {0x8103}}, {0x2F984, {0x440B}}, {0x2F985, {0x813E}}, {0x2F986, {0x5AB5}}, {0x2F987, {0x267A7}}, {0x2F988, {0x267B5}}, {0x2F989, {0x23393}}, {0x2F98A, {0x2339C}}, {0x2F98B, {0x8201}}, -{0x2F98C, {0x8204}}, {0x2F98D, {0x8F9E}}, {0x2F98E, {0x446B}}, {0x2F98F, {0x8291}}, {0x2F990, {0x828B}}, {0x2F991, {0x829D}}, {0x2F992, {0x52B3}}, {0x2F993, {0x82B1}}, {0x2F994, {0x82B3}}, -{0x2F995, {0x82BD}}, {0x2F996, {0x82E6}}, {0x2F997, {0x26B3C}}, {0x2F998, {0x82E5}}, {0x2F999, {0x831D}}, {0x2F99A, {0x8363}}, {0x2F99B, {0x83AD}}, {0x2F99C, {0x8323}}, {0x2F99D, {0x83BD}}, -{0x2F99E, {0x83E7}}, {0x2F99F, {0x8457}}, {0x2F9A0, {0x8353}}, {0x2F9A1, {0x83CA}}, {0x2F9A2, {0x83CC}}, {0x2F9A3, {0x83DC}}, {0x2F9A4, {0x26C36}}, {0x2F9A5, {0x26D6B}}, {0x2F9A6, {0x26CD5}}, -{0x2F9A7, {0x452B}}, {0x2F9A8, {0x84F1}}, {0x2F9A9, {0x84F3}}, {0x2F9AA, {0x8516}}, {0x2F9AB, {0x273CA}}, {0x2F9AC, {0x8564}}, {0x2F9AD, {0x26F2C}}, {0x2F9AE, {0x455D}}, {0x2F9AF, {0x4561}}, -{0x2F9B0, {0x26FB1}}, {0x2F9B1, {0x270D2}}, {0x2F9B2, {0x456B}}, {0x2F9B3, {0x8650}}, {0x2F9B4, {0x865C}}, {0x2F9B5, {0x8667}}, {0x2F9B6, {0x8669}}, {0x2F9B7, {0x86A9}}, {0x2F9B8, {0x8688}}, -{0x2F9B9, {0x870E}}, {0x2F9BA, {0x86E2}}, {0x2F9BB, {0x8779}}, {0x2F9BC, {0x8728}}, {0x2F9BD, {0x876B}}, {0x2F9BE, {0x8786}}, {0x2F9BF, {0x45D7}}, {0x2F9C0, {0x87E1}}, {0x2F9C1, {0x8801}}, -{0x2F9C2, {0x45F9}}, {0x2F9C3, {0x8860}}, {0x2F9C4, {0x8863}}, {0x2F9C5, {0x27667}}, {0x2F9C6, {0x88D7}}, {0x2F9C7, {0x88DE}}, {0x2F9C8, {0x4635}}, {0x2F9C9, {0x88FA}}, {0x2F9CA, {0x34BB}}, -{0x2F9CB, {0x278AE}}, {0x2F9CC, {0x27966}}, {0x2F9CD, {0x46BE}}, {0x2F9CE, {0x46C7}}, {0x2F9CF, {0x8AA0}}, {0x2F9D0, {0x8AED}}, {0x2F9D1, {0x8B8A}}, {0x2F9D2, {0x8C55}}, {0x2F9D3, {0x27CA8}}, -{0x2F9D4, {0x8CAB}}, {0x2F9D5, {0x8CC1}}, {0x2F9D6, {0x8D1B}}, {0x2F9D7, {0x8D77}}, {0x2F9D8, {0x27F2F}}, {0x2F9D9, {0x20804}}, {0x2F9DA, {0x8DCB}}, {0x2F9DB, {0x8DBC}}, {0x2F9DC, {0x8DF0}}, -{0x2F9DD, {0x208DE}}, {0x2F9DE, {0x8ED4}}, {0x2F9DF, {0x8F38}}, {0x2F9E0, {0x285D2}}, {0x2F9E1, {0x285ED}}, {0x2F9E2, {0x9094}}, {0x2F9E3, {0x90F1}}, {0x2F9E4, {0x9111}}, {0x2F9E5, {0x2872E}}, -{0x2F9E6, {0x911B}}, {0x2F9E7, {0x9238}}, {0x2F9E8, {0x92D7}}, {0x2F9E9, {0x92D8}}, {0x2F9EA, {0x927C}}, {0x2F9EB, {0x93F9}}, {0x2F9EC, {0x9415}}, {0x2F9ED, {0x28BFA}}, {0x2F9EE, {0x958B}}, -{0x2F9EF, {0x4995}}, {0x2F9F0, {0x95B7}}, {0x2F9F1, {0x28D77}}, {0x2F9F2, {0x49E6}}, {0x2F9F3, {0x96C3}}, {0x2F9F4, {0x5DB2}}, {0x2F9F5, {0x9723}}, {0x2F9F6, {0x29145}}, {0x2F9F7, {0x2921A}}, -{0x2F9F8, {0x4A6E}}, {0x2F9F9, {0x4A76}}, {0x2F9FA, {0x97E0}}, {0x2F9FB, {0x2940A}}, {0x2F9FC, {0x4AB2}}, {0x2F9FD, {0x29496}}, {0x2F9FE, {0x980B}}, {0x2F9FF, {0x980B}}, {0x2FA00, {0x9829}}, -{0x2FA01, {0x295B6}}, {0x2FA02, {0x98E2}}, {0x2FA03, {0x4B33}}, {0x2FA04, {0x9929}}, {0x2FA05, {0x99A7}}, {0x2FA06, {0x99C2}}, {0x2FA07, {0x99FE}}, {0x2FA08, {0x4BCE}}, {0x2FA09, {0x29B30}}, -{0x2FA0A, {0x9B12}}, {0x2FA0B, {0x9C40}}, {0x2FA0C, {0x9CFD}}, {0x2FA0D, {0x4CCE}}, {0x2FA0E, {0x4CED}}, {0x2FA0F, {0x9D67}}, {0x2FA10, {0x2A0CE}}, {0x2FA11, {0x4CF8}}, {0x2FA12, {0x2A105}}, -{0x2FA13, {0x2A20E}}, {0x2FA14, {0x2A291}}, {0x2FA15, {0x9EBB}}, {0x2FA16, {0x4D56}}, {0x2FA17, {0x9EF9}}, {0x2FA18, {0x9EFE}}, {0x2FA19, {0x9F05}}, {0x2FA1A, {0x9F0F}}, {0x2FA1B, {0x9F16}}, -{0x2FA1D, {0x2A600}}, +static const std::multimap nfd_map = { +{0xC0, 0x41}, {0xC0, 0x300}, {0xC1, 0x41}, {0xC1, 0x301}, {0xC2, 0x41}, {0xC2, 0x302}, {0xC3, 0x41}, {0xC3, 0x303}, {0xC4, 0x41}, {0xC4, 0x308}, {0xC5, 0x41}, {0xC5, 0x30A}, {0xC7, 0x43}, +{0xC7, 0x327}, {0xC8, 0x45}, {0xC8, 0x300}, {0xC9, 0x45}, {0xC9, 0x301}, {0xCA, 0x45}, {0xCA, 0x302}, {0xCB, 0x45}, {0xCB, 0x308}, {0xCC, 0x49}, {0xCC, 0x300}, {0xCD, 0x49}, {0xCD, 0x301}, +{0xCE, 0x49}, {0xCE, 0x302}, {0xCF, 0x49}, {0xCF, 0x308}, {0xD1, 0x4E}, {0xD1, 0x303}, {0xD2, 0x4F}, {0xD2, 0x300}, {0xD3, 0x4F}, {0xD3, 0x301}, {0xD4, 0x4F}, {0xD4, 0x302}, {0xD5, 0x4F}, +{0xD5, 0x303}, {0xD6, 0x4F}, {0xD6, 0x308}, {0xD9, 0x55}, {0xD9, 0x300}, {0xDA, 0x55}, {0xDA, 0x301}, {0xDB, 0x55}, {0xDB, 0x302}, {0xDC, 0x55}, {0xDC, 0x308}, {0xDD, 0x59}, {0xDD, 0x301}, +{0xE0, 0x61}, {0xE0, 0x300}, {0xE1, 0x61}, {0xE1, 0x301}, {0xE2, 0x61}, {0xE2, 0x302}, {0xE3, 0x61}, {0xE3, 0x303}, {0xE4, 0x61}, {0xE4, 0x308}, {0xE5, 0x61}, {0xE5, 0x30A}, {0xE7, 0x63}, +{0xE7, 0x327}, {0xE8, 0x65}, {0xE8, 0x300}, {0xE9, 0x65}, {0xE9, 0x301}, {0xEA, 0x65}, {0xEA, 0x302}, {0xEB, 0x65}, {0xEB, 0x308}, {0xEC, 0x69}, {0xEC, 0x300}, {0xED, 0x69}, {0xED, 0x301}, +{0xEE, 0x69}, {0xEE, 0x302}, {0xEF, 0x69}, {0xEF, 0x308}, {0xF1, 0x6E}, {0xF1, 0x303}, {0xF2, 0x6F}, {0xF2, 0x300}, {0xF3, 0x6F}, {0xF3, 0x301}, {0xF4, 0x6F}, {0xF4, 0x302}, {0xF5, 0x6F}, +{0xF5, 0x303}, {0xF6, 0x6F}, {0xF6, 0x308}, {0xF9, 0x75}, {0xF9, 0x300}, {0xFA, 0x75}, {0xFA, 0x301}, {0xFB, 0x75}, {0xFB, 0x302}, {0xFC, 0x75}, {0xFC, 0x308}, {0xFD, 0x79}, {0xFD, 0x301}, +{0xFF, 0x79}, {0xFF, 0x308}, {0x100, 0x41}, {0x100, 0x304}, {0x101, 0x61}, {0x101, 0x304}, {0x102, 0x41}, {0x102, 0x306}, {0x103, 0x61}, {0x103, 0x306}, {0x104, 0x41}, {0x104, 0x328}, {0x105, 0x61}, 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0x3781}, {0x2F877, 0x5C60}, {0x2F878, 0x5C6E}, {0x2F879, 0x5CC0}, {0x2F87A, 0x5C8D}, {0x2F87B, 0x21DE4}, {0x2F87C, 0x5D43}, {0x2F87D, 0x21DE6}, +{0x2F87E, 0x5D6E}, {0x2F87F, 0x5D6B}, {0x2F880, 0x5D7C}, {0x2F881, 0x5DE1}, {0x2F882, 0x5DE2}, {0x2F883, 0x382F}, {0x2F884, 0x5DFD}, {0x2F885, 0x5E28}, {0x2F886, 0x5E3D}, {0x2F887, 0x5E69}, +{0x2F888, 0x3862}, {0x2F889, 0x22183}, {0x2F88A, 0x387C}, {0x2F88B, 0x5EB0}, {0x2F88C, 0x5EB3}, {0x2F88D, 0x5EB6}, {0x2F88E, 0x5ECA}, {0x2F88F, 0x2A392}, {0x2F890, 0x5EFE}, {0x2F891, 0x22331}, +{0x2F892, 0x22331}, {0x2F893, 0x8201}, {0x2F894, 0x5F22}, {0x2F895, 0x5F22}, {0x2F896, 0x38C7}, {0x2F897, 0x232B8}, {0x2F898, 0x261DA}, {0x2F899, 0x5F62}, {0x2F89A, 0x5F6B}, {0x2F89B, 0x38E3}, +{0x2F89C, 0x5F9A}, {0x2F89D, 0x5FCD}, {0x2F89E, 0x5FD7}, {0x2F89F, 0x5FF9}, {0x2F8A0, 0x6081}, {0x2F8A1, 0x393A}, {0x2F8A2, 0x391C}, {0x2F8A3, 0x6094}, {0x2F8A4, 0x226D4}, {0x2F8A5, 0x60C7}, +{0x2F8A6, 0x6148}, {0x2F8A7, 0x614C}, {0x2F8A8, 0x614E}, {0x2F8A9, 0x614C}, {0x2F8AA, 0x617A}, {0x2F8AB, 0x618E}, {0x2F8AC, 0x61B2}, {0x2F8AD, 0x61A4}, {0x2F8AE, 0x61AF}, {0x2F8AF, 0x61DE}, +{0x2F8B0, 0x61F2}, {0x2F8B1, 0x61F6}, {0x2F8B2, 0x6210}, {0x2F8B3, 0x621B}, {0x2F8B4, 0x625D}, {0x2F8B5, 0x62B1}, {0x2F8B6, 0x62D4}, {0x2F8B7, 0x6350}, {0x2F8B8, 0x22B0C}, {0x2F8B9, 0x633D}, +{0x2F8BA, 0x62FC}, {0x2F8BB, 0x6368}, {0x2F8BC, 0x6383}, {0x2F8BD, 0x63E4}, {0x2F8BE, 0x22BF1}, {0x2F8BF, 0x6422}, {0x2F8C0, 0x63C5}, {0x2F8C1, 0x63A9}, {0x2F8C2, 0x3A2E}, {0x2F8C3, 0x6469}, +{0x2F8C4, 0x647E}, {0x2F8C5, 0x649D}, {0x2F8C6, 0x6477}, {0x2F8C7, 0x3A6C}, {0x2F8C8, 0x654F}, {0x2F8C9, 0x656C}, {0x2F8CA, 0x2300A}, {0x2F8CB, 0x65E3}, {0x2F8CC, 0x66F8}, {0x2F8CD, 0x6649}, +{0x2F8CE, 0x3B19}, {0x2F8CF, 0x6691}, {0x2F8D0, 0x3B08}, {0x2F8D1, 0x3AE4}, {0x2F8D2, 0x5192}, {0x2F8D3, 0x5195}, {0x2F8D4, 0x6700}, {0x2F8D5, 0x669C}, {0x2F8D6, 0x80AD}, {0x2F8D7, 0x43D9}, +{0x2F8D8, 0x6717}, {0x2F8D9, 0x671B}, {0x2F8DA, 0x6721}, {0x2F8DB, 0x675E}, {0x2F8DC, 0x6753}, {0x2F8DD, 0x233C3}, {0x2F8DE, 0x3B49}, {0x2F8DF, 0x67FA}, {0x2F8E0, 0x6785}, {0x2F8E1, 0x6852}, +{0x2F8E2, 0x6885}, {0x2F8E3, 0x2346D}, {0x2F8E4, 0x688E}, {0x2F8E5, 0x681F}, {0x2F8E6, 0x6914}, {0x2F8E7, 0x3B9D}, {0x2F8E8, 0x6942}, {0x2F8E9, 0x69A3}, {0x2F8EA, 0x69EA}, {0x2F8EB, 0x6AA8}, +{0x2F8EC, 0x236A3}, {0x2F8ED, 0x6ADB}, {0x2F8EE, 0x3C18}, {0x2F8EF, 0x6B21}, {0x2F8F0, 0x238A7}, {0x2F8F1, 0x6B54}, {0x2F8F2, 0x3C4E}, {0x2F8F3, 0x6B72}, {0x2F8F4, 0x6B9F}, {0x2F8F5, 0x6BBA}, +{0x2F8F6, 0x6BBB}, {0x2F8F7, 0x23A8D}, {0x2F8F8, 0x21D0B}, {0x2F8F9, 0x23AFA}, {0x2F8FA, 0x6C4E}, {0x2F8FB, 0x23CBC}, {0x2F8FC, 0x6CBF}, {0x2F8FD, 0x6CCD}, {0x2F8FE, 0x6C67}, {0x2F8FF, 0x6D16}, +{0x2F900, 0x6D3E}, {0x2F901, 0x6D77}, {0x2F902, 0x6D41}, {0x2F903, 0x6D69}, {0x2F904, 0x6D78}, {0x2F905, 0x6D85}, {0x2F906, 0x23D1E}, {0x2F907, 0x6D34}, {0x2F908, 0x6E2F}, {0x2F909, 0x6E6E}, +{0x2F90A, 0x3D33}, {0x2F90B, 0x6ECB}, {0x2F90C, 0x6EC7}, {0x2F90D, 0x23ED1}, {0x2F90E, 0x6DF9}, {0x2F90F, 0x6F6E}, {0x2F910, 0x23F5E}, {0x2F911, 0x23F8E}, {0x2F912, 0x6FC6}, {0x2F913, 0x7039}, +{0x2F914, 0x701E}, {0x2F915, 0x701B}, {0x2F916, 0x3D96}, {0x2F917, 0x704A}, {0x2F918, 0x707D}, {0x2F919, 0x7077}, {0x2F91A, 0x70AD}, {0x2F91B, 0x20525}, {0x2F91C, 0x7145}, {0x2F91D, 0x24263}, +{0x2F91E, 0x719C}, {0x2F91F, 0x243AB}, {0x2F920, 0x7228}, {0x2F921, 0x7235}, {0x2F922, 0x7250}, {0x2F923, 0x24608}, {0x2F924, 0x7280}, {0x2F925, 0x7295}, {0x2F926, 0x24735}, {0x2F927, 0x24814}, +{0x2F928, 0x737A}, {0x2F929, 0x738B}, {0x2F92A, 0x3EAC}, {0x2F92B, 0x73A5}, {0x2F92C, 0x3EB8}, {0x2F92D, 0x3EB8}, {0x2F92E, 0x7447}, {0x2F92F, 0x745C}, {0x2F930, 0x7471}, {0x2F931, 0x7485}, +{0x2F932, 0x74CA}, {0x2F933, 0x3F1B}, {0x2F934, 0x7524}, {0x2F935, 0x24C36}, {0x2F936, 0x753E}, {0x2F937, 0x24C92}, {0x2F938, 0x7570}, {0x2F939, 0x2219F}, {0x2F93A, 0x7610}, {0x2F93B, 0x24FA1}, +{0x2F93C, 0x24FB8}, {0x2F93D, 0x25044}, {0x2F93E, 0x3FFC}, {0x2F93F, 0x4008}, {0x2F940, 0x76F4}, {0x2F941, 0x250F3}, {0x2F942, 0x250F2}, {0x2F943, 0x25119}, {0x2F944, 0x25133}, {0x2F945, 0x771E}, +{0x2F946, 0x771F}, {0x2F947, 0x771F}, {0x2F948, 0x774A}, {0x2F949, 0x4039}, {0x2F94A, 0x778B}, {0x2F94B, 0x4046}, {0x2F94C, 0x4096}, {0x2F94D, 0x2541D}, {0x2F94E, 0x784E}, {0x2F94F, 0x788C}, +{0x2F950, 0x78CC}, {0x2F951, 0x40E3}, {0x2F952, 0x25626}, {0x2F953, 0x7956}, {0x2F954, 0x2569A}, {0x2F955, 0x256C5}, {0x2F956, 0x798F}, {0x2F957, 0x79EB}, {0x2F958, 0x412F}, {0x2F959, 0x7A40}, +{0x2F95A, 0x7A4A}, {0x2F95B, 0x7A4F}, {0x2F95C, 0x2597C}, {0x2F95D, 0x25AA7}, {0x2F95E, 0x25AA7}, {0x2F95F, 0x7AEE}, {0x2F960, 0x4202}, {0x2F961, 0x25BAB}, {0x2F962, 0x7BC6}, {0x2F963, 0x7BC9}, +{0x2F964, 0x4227}, {0x2F965, 0x25C80}, {0x2F966, 0x7CD2}, {0x2F967, 0x42A0}, {0x2F968, 0x7CE8}, {0x2F969, 0x7CE3}, {0x2F96A, 0x7D00}, {0x2F96B, 0x25F86}, {0x2F96C, 0x7D63}, {0x2F96D, 0x4301}, +{0x2F96E, 0x7DC7}, {0x2F96F, 0x7E02}, {0x2F970, 0x7E45}, {0x2F971, 0x4334}, {0x2F972, 0x26228}, {0x2F973, 0x26247}, {0x2F974, 0x4359}, {0x2F975, 0x262D9}, {0x2F976, 0x7F7A}, {0x2F977, 0x2633E}, +{0x2F978, 0x7F95}, {0x2F979, 0x7FFA}, {0x2F97A, 0x8005}, {0x2F97B, 0x264DA}, {0x2F97C, 0x26523}, {0x2F97D, 0x8060}, {0x2F97E, 0x265A8}, {0x2F97F, 0x8070}, {0x2F980, 0x2335F}, {0x2F981, 0x43D5}, +{0x2F982, 0x80B2}, {0x2F983, 0x8103}, {0x2F984, 0x440B}, {0x2F985, 0x813E}, {0x2F986, 0x5AB5}, {0x2F987, 0x267A7}, {0x2F988, 0x267B5}, {0x2F989, 0x23393}, {0x2F98A, 0x2339C}, {0x2F98B, 0x8201}, +{0x2F98C, 0x8204}, {0x2F98D, 0x8F9E}, {0x2F98E, 0x446B}, {0x2F98F, 0x8291}, {0x2F990, 0x828B}, {0x2F991, 0x829D}, {0x2F992, 0x52B3}, {0x2F993, 0x82B1}, {0x2F994, 0x82B3}, {0x2F995, 0x82BD}, +{0x2F996, 0x82E6}, {0x2F997, 0x26B3C}, {0x2F998, 0x82E5}, {0x2F999, 0x831D}, {0x2F99A, 0x8363}, {0x2F99B, 0x83AD}, {0x2F99C, 0x8323}, {0x2F99D, 0x83BD}, {0x2F99E, 0x83E7}, {0x2F99F, 0x8457}, +{0x2F9A0, 0x8353}, {0x2F9A1, 0x83CA}, {0x2F9A2, 0x83CC}, {0x2F9A3, 0x83DC}, {0x2F9A4, 0x26C36}, {0x2F9A5, 0x26D6B}, {0x2F9A6, 0x26CD5}, {0x2F9A7, 0x452B}, {0x2F9A8, 0x84F1}, {0x2F9A9, 0x84F3}, +{0x2F9AA, 0x8516}, {0x2F9AB, 0x273CA}, {0x2F9AC, 0x8564}, {0x2F9AD, 0x26F2C}, {0x2F9AE, 0x455D}, {0x2F9AF, 0x4561}, {0x2F9B0, 0x26FB1}, {0x2F9B1, 0x270D2}, {0x2F9B2, 0x456B}, {0x2F9B3, 0x8650}, +{0x2F9B4, 0x865C}, {0x2F9B5, 0x8667}, {0x2F9B6, 0x8669}, {0x2F9B7, 0x86A9}, {0x2F9B8, 0x8688}, {0x2F9B9, 0x870E}, {0x2F9BA, 0x86E2}, {0x2F9BB, 0x8779}, {0x2F9BC, 0x8728}, {0x2F9BD, 0x876B}, +{0x2F9BE, 0x8786}, {0x2F9BF, 0x45D7}, {0x2F9C0, 0x87E1}, {0x2F9C1, 0x8801}, {0x2F9C2, 0x45F9}, {0x2F9C3, 0x8860}, {0x2F9C4, 0x8863}, {0x2F9C5, 0x27667}, {0x2F9C6, 0x88D7}, {0x2F9C7, 0x88DE}, +{0x2F9C8, 0x4635}, {0x2F9C9, 0x88FA}, {0x2F9CA, 0x34BB}, {0x2F9CB, 0x278AE}, {0x2F9CC, 0x27966}, {0x2F9CD, 0x46BE}, {0x2F9CE, 0x46C7}, {0x2F9CF, 0x8AA0}, {0x2F9D0, 0x8AED}, {0x2F9D1, 0x8B8A}, +{0x2F9D2, 0x8C55}, {0x2F9D3, 0x27CA8}, {0x2F9D4, 0x8CAB}, {0x2F9D5, 0x8CC1}, {0x2F9D6, 0x8D1B}, {0x2F9D7, 0x8D77}, {0x2F9D8, 0x27F2F}, {0x2F9D9, 0x20804}, {0x2F9DA, 0x8DCB}, {0x2F9DB, 0x8DBC}, +{0x2F9DC, 0x8DF0}, {0x2F9DD, 0x208DE}, {0x2F9DE, 0x8ED4}, {0x2F9DF, 0x8F38}, {0x2F9E0, 0x285D2}, {0x2F9E1, 0x285ED}, {0x2F9E2, 0x9094}, {0x2F9E3, 0x90F1}, {0x2F9E4, 0x9111}, {0x2F9E5, 0x2872E}, +{0x2F9E6, 0x911B}, {0x2F9E7, 0x9238}, {0x2F9E8, 0x92D7}, {0x2F9E9, 0x92D8}, {0x2F9EA, 0x927C}, {0x2F9EB, 0x93F9}, {0x2F9EC, 0x9415}, {0x2F9ED, 0x28BFA}, {0x2F9EE, 0x958B}, {0x2F9EF, 0x4995}, +{0x2F9F0, 0x95B7}, {0x2F9F1, 0x28D77}, {0x2F9F2, 0x49E6}, {0x2F9F3, 0x96C3}, {0x2F9F4, 0x5DB2}, {0x2F9F5, 0x9723}, {0x2F9F6, 0x29145}, {0x2F9F7, 0x2921A}, {0x2F9F8, 0x4A6E}, {0x2F9F9, 0x4A76}, +{0x2F9FA, 0x97E0}, {0x2F9FB, 0x2940A}, {0x2F9FC, 0x4AB2}, {0x2F9FD, 0x29496}, {0x2F9FE, 0x980B}, {0x2F9FF, 0x980B}, {0x2FA00, 0x9829}, {0x2FA01, 0x295B6}, {0x2FA02, 0x98E2}, {0x2FA03, 0x4B33}, +{0x2FA04, 0x9929}, {0x2FA05, 0x99A7}, {0x2FA06, 0x99C2}, {0x2FA07, 0x99FE}, {0x2FA08, 0x4BCE}, {0x2FA09, 0x29B30}, {0x2FA0A, 0x9B12}, {0x2FA0B, 0x9C40}, {0x2FA0C, 0x9CFD}, {0x2FA0D, 0x4CCE}, +{0x2FA0E, 0x4CED}, {0x2FA0F, 0x9D67}, {0x2FA10, 0x2A0CE}, {0x2FA11, 0x4CF8}, {0x2FA12, 0x2A105}, {0x2FA13, 0x2A20E}, {0x2FA14, 0x2A291}, {0x2FA15, 0x9EBB}, {0x2FA16, 0x4D56}, {0x2FA17, 0x9EF9}, +{0x2FA18, 0x9EFE}, {0x2FA19, 0x9F05}, {0x2FA1A, 0x9F0F}, {0x2FA1B, 0x9F16}, {0x2FA1D, 0x2A600}, }; static std::string codepoint_to_utf8(uint32_t cp) { From 3ab8b3a92ede46df88bc5a2dfca3777de4a2b2b6 Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Fri, 1 Mar 2024 12:39:06 +0100 Subject: [PATCH 06/11] llama : cleanup unused mmq flags (#5772) * cleanup unused --no-mul-mat-q,-nommq, -mmq, --mul-mat-q, mul_mat_q * remove: mul_mat_q in compare llama bench and usage * update llama-bench --------- Co-authored-by: slaren --- common/common.cpp | 2 -- common/common.h | 1 - examples/batched-bench/batched-bench.cpp | 18 +++++--------- examples/llama-bench/README.md | 1 - examples/llama-bench/llama-bench.cpp | 30 +++--------------------- examples/server/server.cpp | 8 ------- llama.cpp | 3 --- llama.h | 1 - scripts/compare-llama-bench.py | 2 +- 9 files changed, 10 insertions(+), 56 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 18289755c9ceb..bf1ed8a66fea6 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1281,7 +1281,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.n_batch = params.n_batch; cparams.n_threads = params.n_threads; cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; - cparams.mul_mat_q = params.mul_mat_q; cparams.seed = params.seed; cparams.logits_all = params.logits_all; cparams.embedding = params.embedding; @@ -1725,7 +1724,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false"); fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false"); fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); diff --git a/common/common.h b/common/common.h index 25003df2600d1..ab62bdb822d71 100644 --- a/common/common.h +++ b/common/common.h @@ -115,7 +115,6 @@ struct gpt_params { bool kl_divergence = false; // compute KL-divergence - bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index b4b8a38e1bcf5..19aff18aefde7 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -32,16 +32,15 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] \n" , argv[0]); + printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] \n" , argv[0]); printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); - printf(" example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); + printf(" example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); return 1 ; } int n_kv_max = 2048; int is_pp_shared = 0; int n_gpu_layers = 0; - int mmq = 0; std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; std::vector n_tg = { 128, 256, }; @@ -65,19 +64,15 @@ int main(int argc, char ** argv) { } if (argc >= 6) { - mmq = std::atoi(argv[5]); + n_pp = parse_list(argv[5]); } if (argc >= 7) { - n_pp = parse_list(argv[6]); + n_tg = parse_list(argv[6]); } if (argc >= 8) { - n_tg = parse_list(argv[7]); - } - - if (argc >= 9) { - n_pl = parse_list(argv[8]); + n_pl = parse_list(argv[7]); } // init LLM @@ -106,7 +101,6 @@ int main(int argc, char ** argv) { ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_max; ctx_params.n_batch = 512; - ctx_params.mul_mat_q = mmq; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -159,7 +153,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 374e40a7dc108..10f37b4418897 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -35,7 +35,6 @@ options: -mg, --main-gpu (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - -mmq, --mul-mat-q <0|1> (default: 1) -ts, --tensor_split (default: 0) -r, --repetitions (default: 5) -o, --output (default: md) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 8fec3d43ddfdd..c2155b2ac4696 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -176,7 +176,6 @@ struct cmd_params { std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; - std::vector mul_mat_q; std::vector> tensor_split; std::vector use_mmap; int reps; @@ -196,7 +195,6 @@ static const cmd_params cmd_params_defaults = { /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, - /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, /* reps */ 5, @@ -221,7 +219,6 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); - printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); @@ -383,13 +380,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); - } else if (arg == "-mmq" || arg == "--mul-mat-q") { - if (++i >= argc) { - invalid_param = true; - break; - } - auto p = split(argv[i], split_delim); - params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; @@ -466,7 +456,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } - if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } @@ -486,7 +475,6 @@ struct cmd_params_instance { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; @@ -518,7 +506,6 @@ struct cmd_params_instance { cparams.n_batch = n_batch; cparams.type_k = type_k; cparams.type_v = type_v; - cparams.mul_mat_q = mul_mat_q; cparams.offload_kqv = !no_kv_offload; return cparams; @@ -538,7 +525,6 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) - for (const auto & mmq : params.mul_mat_q) for (const auto & nkvo : params.no_kv_offload) for (const auto & nt : params.n_threads) { for (const auto & n_prompt : params.n_prompt) { @@ -557,7 +543,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -580,7 +565,6 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, - /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, }; @@ -616,7 +600,6 @@ struct test { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; - bool mul_mat_q; std::vector tensor_split; bool use_mmap; int n_prompt; @@ -639,7 +622,6 @@ struct test { split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; - mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; @@ -713,7 +695,7 @@ struct test { "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", "use_mmap", + "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -733,7 +715,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q" || field == "use_mmap") { + field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -767,7 +749,7 @@ struct test { std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), + tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -931,9 +913,6 @@ struct markdown_printer : public printer { if (field == "n_threads") { return "threads"; } - if (field == "mul_mat_q") { - return "mmq"; - } if (field == "no_kv_offload") { return "nkvo"; } @@ -974,9 +953,6 @@ struct markdown_printer : public printer { if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { fields.emplace_back("split_mode"); } - if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.emplace_back("mul_mat_q"); - } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { fields.emplace_back("no_kv_offload"); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index eea98796663d6..2b2f4a0f4a48b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2390,14 +2390,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUBLAS - } - else if (arg == "--no-mul-mat-q" || arg == "-nommq") - { -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) - params.mul_mat_q = false; -#else - LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); #endif // GGML_USE_CUBLAS } else if (arg == "--main-gpu" || arg == "-mg") diff --git a/llama.cpp b/llama.cpp index a35f07aa47cda..073fd3b703037 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1645,7 +1645,6 @@ struct llama_cparams { float yarn_beta_slow; float defrag_thold; - bool mul_mat_q; bool offload_kqv; bool do_pooling; @@ -11633,7 +11632,6 @@ struct llama_context_params llama_context_default_params() { /*.cb_eval_user_data =*/ nullptr, /*.type_k =*/ GGML_TYPE_F16, /*.type_v =*/ GGML_TYPE_F16, - /*.mul_mat_q =*/ true, /*.logits_all =*/ false, /*.embedding =*/ false, /*.offload_kqv =*/ true, @@ -11785,7 +11783,6 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_beta_fast = params.yarn_beta_fast; cparams.yarn_beta_slow = params.yarn_beta_slow; cparams.defrag_thold = params.defrag_thold; - cparams.mul_mat_q = params.mul_mat_q; cparams.offload_kqv = params.offload_kqv; cparams.do_pooling = params.do_pooling; diff --git a/llama.h b/llama.h index 4d0ebe37d3f9b..ed51f478a7b21 100644 --- a/llama.h +++ b/llama.h @@ -255,7 +255,6 @@ extern "C" { enum ggml_type type_v; // data type for V cache // Keep the booleans together to avoid misalignment during copy-by-value. - bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embedding; // embedding mode only bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 70737f976c5b5..39c3e52e52789 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -31,7 +31,7 @@ "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters", "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO", - "mul_mat_q": "MMQ", "tensor_split": "Tensor split" + "tensor_split": "Tensor split" } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. From f49a5356865ced0eca1df9f9d84631dfef71b9dc Mon Sep 17 00:00:00 2001 From: Miwa / Ensan <63481257+ensan-hcl@users.noreply.github.com> Date: Fri, 1 Mar 2024 22:48:56 +0900 Subject: [PATCH 07/11] common : fix flag `--logits-all` to `--all-logits` (#5805) --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index bf1ed8a66fea6..938c428cf0fa2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1015,7 +1015,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); printf(" --no-penalize-nl do not penalize newline token\n"); printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n"); + printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); From e7433867288d2f142cffe596f3751bda5d7ee2c7 Mon Sep 17 00:00:00 2001 From: kunal-vaishnavi <115581922+kunal-vaishnavi@users.noreply.github.com> Date: Fri, 1 Mar 2024 06:08:08 -0800 Subject: [PATCH 08/11] gemma : fix bfloat16 -> float16 conversion issue (#5810) --- convert-hf-to-gguf.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index ae30b2a76971a..d3e8ec1f60c70 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1811,16 +1811,15 @@ def write_tensors(self): tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) for name, data_torch in self.get_tensors(): - # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 - if name.endswith("norm.weight"): - data_torch = data_torch + 1 - old_dtype = data_torch.dtype # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 data = data_torch.squeeze().numpy() # map tensor names From c2224f003bf9cf558b1a3c57033563e11a4de9a5 Mon Sep 17 00:00:00 2001 From: ddpasa <112642920+ddpasa@users.noreply.github.com> Date: Fri, 1 Mar 2024 18:00:00 +0100 Subject: [PATCH 09/11] ggml-vulkan: fix VULKAN_CHECK_RESULTS flag, which was previously broken (#5813) --- ggml-vulkan.cpp | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 896c290b243ab..ae9cb3c1c1c6e 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -5428,7 +5428,8 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -5540,7 +5541,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5550,10 +5552,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu->size) { - src0_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src0_size >= buffer_gpu->size) { + src0_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5583,7 +5586,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5593,10 +5597,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu->size) { - src1_size = extra->buffer_gpu->size - offset; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (offset + src1_size >= buffer_gpu->size) { + src1_size = buffer_gpu->size - offset; } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5643,11 +5648,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_RMS_NORM) { tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { - if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); - } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); - } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { @@ -5753,11 +5754,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu->size) { - tensor_size = extra->buffer_gpu->size - (extra->offset); + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + if (extra->offset + tensor_size >= buffer_gpu->size) { + tensor_size = buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; From 38d16b142624bdd7c41d9955752b7f7b59c5e048 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Mar 2024 20:00:58 +0200 Subject: [PATCH 10/11] server : remove api_like_OAI.py proxy script (#5808) --- README.md | 1 + examples/server/README.md | 17 +-- examples/server/api_like_OAI.py | 228 -------------------------------- 3 files changed, 3 insertions(+), 243 deletions(-) delete mode 100755 examples/server/api_like_OAI.py diff --git a/README.md b/README.md index 5401e197f67d7..67717c1e38b34 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics +- The `api_like_OAI.py` script has been removed - use `server` instead ([#5766](https://github.com/ggerganov/llama.cpp/issues/5766#issuecomment-1969037761)) - Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631 - Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590 diff --git a/examples/server/README.md b/examples/server/README.md index ad35306c60c4e..397ee825278bd 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -326,7 +326,7 @@ Notice that each `probs` is an array of length `n_probs`. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - `total_slots` - the total number of slots for process requests (defined by `--parallel` option) -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. +- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. *Options:* @@ -528,20 +528,7 @@ bash chat.sh ### API like OAI -API example using Python Flask: [api_like_OAI.py](api_like_OAI.py) -This example must be used with server.cpp - -```sh -python api_like_OAI.py -``` - -After running the API server, you can use it in Python by setting the API base URL. - -```python -openai.api_base = "http://:port" -``` - -Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API +The HTTP server supports OAI-like API ### Extending or building alternative Web Front End diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py deleted file mode 100755 index 607fe49d3ff15..0000000000000 --- a/examples/server/api_like_OAI.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -import argparse -from flask import Flask, jsonify, request, Response -import urllib.parse -import requests -import time -import json - - -app = Flask(__name__) -slot_id = -1 - -parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") -parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.') -parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ") -parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ") -parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ") -parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '')", default="") -parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080') -parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="") -parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1') -parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081) - -args = parser.parse_args() - -def is_present(json, key): - try: - buf = json[key] - except KeyError: - return False - if json[key] == None: - return False - return True - -#convert chat to prompt -def convert_chat(messages): - - system_n = args.system_name - user_n = args.user_name - ai_n = args.ai_name - stop = args.stop - - prompt = "" + args.chat_prompt + stop - - for line in messages: - if (line["role"] == "system"): - prompt += f"{system_n}{line['content']}{stop}" - if (line["role"] == "user"): - prompt += f"{user_n}{line['content']}{stop}" - if (line["role"] == "assistant"): - prompt += f"{ai_n}{line['content']}{stop}" - prompt += ai_n.rstrip() - - return prompt - -def make_postData(body, chat=False, stream=False): - postData = {} - if (chat): - postData["prompt"] = convert_chat(body["messages"]) - else: - postData["prompt"] = body["prompt"] - if(is_present(body, "temperature")): postData["temperature"] = body["temperature"] - if(is_present(body, "top_k")): postData["top_k"] = body["top_k"] - if(is_present(body, "top_p")): postData["top_p"] = body["top_p"] - if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"] - if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"] - if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"] - if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"] - if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"] - if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"] - if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"] - if(is_present(body, "seed")): postData["seed"] = body["seed"] - if(is_present(body, "grammar")): postData["grammar"] = body["grammar"] - if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()] - if (args.stop != ""): - postData["stop"] = [args.stop] - else: - postData["stop"] = [] - if(is_present(body, "stop")): postData["stop"] += body["stop"] - postData["n_keep"] = -1 - postData["stream"] = stream - postData["cache_prompt"] = True - postData["slot_id"] = slot_id - return postData - -def make_resData(data, chat=False, promptToken=[]): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion" if (chat) else "text_completion", - "created": int(time.time()), - "truncated": data["truncated"], - "model": "LLaMA_CPP", - "usage": { - "prompt_tokens": data["tokens_evaluated"], - "completion_tokens": data["tokens_predicted"], - "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"] - } - } - if (len(promptToken) != 0): - resData["promptToken"] = promptToken - if (chat): - #only one choice is supported - resData["choices"] = [{ - "index": 0, - "message": { - "role": "assistant", - "content": data["content"], - }, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - else: - #only one choice is supported - resData["choices"] = [{ - "text": data["content"], - "index": 0, - "logprobs": None, - "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - }] - return resData - -def make_resData_stream(data, chat=False, time_now = 0, start=False): - resData = { - "id": "chatcmpl" if (chat) else "cmpl", - "object": "chat.completion.chunk" if (chat) else "text_completion.chunk", - "created": time_now, - "model": "LLaMA_CPP", - "choices": [ - { - "finish_reason": None, - "index": 0 - } - ] - } - slot_id = data.get("slot_id") - if (chat): - if (start): - resData["choices"][0]["delta"] = { - "role": "assistant" - } - else: - resData["choices"][0]["delta"] = { - "content": data["content"] - } - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - else: - resData["choices"][0]["text"] = data["content"] - if (data["stop"]): - resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" - - return resData - - -@app.route('/chat/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS']) -def chat_completions(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=True, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=True, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - resData = make_resData_stream({}, chat=True, time_now=time_now, start=True) - yield 'data: {}\n\n'.format(json.dumps(resData)) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - - -@app.route('/completions', methods=['POST', 'OPTIONS']) -@app.route('/v1/completions', methods=['POST', 'OPTIONS']) -def completion(): - if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): - return Response(status=403) - if request.method == 'OPTIONS': - return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - body = request.get_json() - stream = False - tokenize = False - if(is_present(body, "stream")): stream = body["stream"] - if(is_present(body, "tokenize")): tokenize = body["tokenize"] - postData = make_postData(body, chat=False, stream=stream) - - promptToken = [] - if (tokenize): - tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() - promptToken = tokenData["tokens"] - - if (not stream): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) - print(data.json()) - resData = make_resData(data.json(), chat=False, promptToken=promptToken) - return jsonify(resData) - else: - def generate(): - data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) - time_now = int(time.time()) - for line in data.iter_lines(): - if line: - decoded_line = line.decode('utf-8') - resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now) - yield 'data: {}\n\n'.format(json.dumps(resData)) - return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"}) - -if __name__ == '__main__': - app.run(args.host, port=args.port) From c29af7e2252d288f2ea58a7d437c1cb7c0abf160 Mon Sep 17 00:00:00 2001 From: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Date: Sat, 2 Mar 2024 01:00:46 +0530 Subject: [PATCH 11/11] llama : add StarCoder2 support (#5795) * Add support for starcoder2 * handle rope type * skip rope freq and rotary embeddings from being serialized * resolve comments * Update llama.cpp * remove redundant changes * handle `rope-theta` * llama : change starcoder2 rope type * address comment --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 8 +- gguf-py/gguf/constants.py | 21 ++++ gguf-py/gguf/tensor_mapping.py | 2 + llama.cpp | 199 +++++++++++++++++++++++++++++++++ 4 files changed, 229 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index d3e8ec1f60c70..28b92ac385367 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -96,9 +96,11 @@ def set_gguf_parameters(self): if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) + if (rope_theta := self.hparams.get("rope_theta")) is not None: + self.gguf_writer.add_rope_freq_base(rope_theta) if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) - if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon"], optional=True)) is not None: + if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: self.gguf_writer.add_layer_norm_eps(f_norm_eps) if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) @@ -220,6 +222,8 @@ def from_model_architecture(model_architecture): return NomicBertModel if model_architecture == "GemmaForCausalLM": return GemmaModel + if model_architecture == "Starcoder2ForCausalLM": + return Model return Model def _is_model_safetensors(self) -> bool: @@ -281,6 +285,8 @@ def _get_model_architecture(self) -> gguf.MODEL_ARCH: return gguf.MODEL_ARCH.NOMIC_BERT if arch == "GemmaForCausalLM": return gguf.MODEL_ARCH.GEMMA + if arch == "Starcoder2ForCausalLM": + return gguf.MODEL_ARCH.STARCODER2 raise NotImplementedError(f'Architecture "{arch}" not supported!') diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 8f9139d1b7eca..5db760cb14900 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -112,6 +112,7 @@ class MODEL_ARCH(IntEnum): INTERNLM2 = auto() MINICPM = auto() GEMMA = auto() + STARCODER2 = auto() class MODEL_TENSOR(IntEnum): @@ -169,6 +170,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.INTERNLM2: "internlm2", MODEL_ARCH.MINICPM: "minicpm", MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.STARCODER2: "starcoder2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -526,6 +528,21 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_UP, MODEL_TENSOR.FFN_NORM, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -554,6 +571,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8610037767fb6..db2ec9704a441 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -210,6 +210,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert + "model.layers.{bid}.mlp.c_fc", # starcoder2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -256,6 +257,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert + "model.layers.{bid}.mlp.c_proj", # starcoder2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 073fd3b703037..b1db5b1797dc5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -211,6 +211,7 @@ enum llm_arch { LLM_ARCH_INTERNLM2, LLM_ARCH_MINICPM, LLM_ARCH_GEMMA, + LLM_ARCH_STARCODER2, LLM_ARCH_UNKNOWN, }; @@ -238,6 +239,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_INTERNLM2, "internlm2" }, { LLM_ARCH_MINICPM, "minicpm" }, { LLM_ARCH_GEMMA, "gemma" }, + { LLM_ARCH_STARCODER2, "starcoder2" }, }; enum llm_kv { @@ -779,6 +781,24 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_STARCODER2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -3320,6 +3340,16 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_STARCODER2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 30: model.type = e_model::MODEL_3B; break; + case 32: model.type = e_model::MODEL_7B; break; + case 40: model.type = e_model::MODEL_15B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4490,6 +4520,56 @@ static bool llm_load_tensors( layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); } } break; + case LLM_ARCH_STARCODER2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + + } + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + // optional bias tensors + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + + // optional bias tensors + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -7559,6 +7639,120 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_starcoder2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -7705,6 +7899,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_gemma(); } break; + case LLM_ARCH_STARCODER2: + { + result = llm.build_starcoder2(); + } break; default: GGML_ASSERT(false); } @@ -12084,6 +12282,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_QWEN2: case LLM_ARCH_PHI2: case LLM_ARCH_GEMMA: + case LLM_ARCH_STARCODER2: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here