From: johnson442 Date: Fri, 14 Jul 2023 17:58:51 +0000 (+0100) Subject: starcoder : mmap (and gpu) example (#338) X-Git-Tag: upstream/0.0.1642~1313 X-Git-Url: https://git.djapps.eu/?a=commitdiff_plain;h=d2b178ee747cfb0a0a1892da362d068080659170;p=pkg%2Fggml%2Fsources%2Fggml starcoder : mmap (and gpu) example (#338) * Add basic mmap & GPU offload starcoder example * starcode-mmap : adapt to new ggml API --------- Co-authored-by: Georgi Gerganov --- diff --git a/examples/common.cpp b/examples/common.cpp index 7d215ae1..57f5039f 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -29,6 +29,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.seed = std::stoi(argv[++i]); } else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); + } else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") { + params.n_gpu_layers = std::stoi(argv[++i]); } else if (arg == "-p" || arg == "--prompt") { params.prompt = argv[++i]; } else if (arg == "-n" || arg == "--n_predict") { @@ -89,6 +91,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stderr, " -h, --help show this help message and exit\n"); fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n"); fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); + fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers); fprintf(stderr, " -p PROMPT, --prompt PROMPT\n"); fprintf(stderr, " prompt to start generation with (default: random)\n"); fprintf(stderr, " -f FNAME, --file FNAME\n"); diff --git a/examples/common.h b/examples/common.h index f9740a3c..f51bee61 100644 --- a/examples/common.h +++ b/examples/common.h @@ -33,6 +33,8 @@ struct gpt_params { bool interactive = false; int32_t interactive_port = -1; + + int32_t n_gpu_layers = 0; }; bool gpt_params_parse(int argc, char ** argv, gpt_params & params); diff --git a/examples/starcoder/CMakeLists.txt b/examples/starcoder/CMakeLists.txt index 4c25b4d9..557f4e5d 100644 --- a/examples/starcoder/CMakeLists.txt +++ b/examples/starcoder/CMakeLists.txt @@ -5,9 +5,27 @@ set(TEST_TARGET starcoder) add_executable(${TEST_TARGET} main.cpp) target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml) +# +# starcoder-mmap + +set(TEST_TARGET starcoder-mmap) +add_executable(${TEST_TARGET} starcoder-mmap.cpp) +target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml) + # # starcoder-quantize set(TEST_TARGET starcoder-quantize) add_executable(${TEST_TARGET} quantize.cpp) target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml) + +# +# For GPU offloading + +if (GGML_CUBLAS) + add_compile_definitions(GGML_USE_CUBLAS) +endif() +if (GGML_CLBLAST) + add_compile_definitions(GGML_USE_CLBLAST) +endif() + diff --git a/examples/starcoder/starcoder-mmap.cpp b/examples/starcoder/starcoder-mmap.cpp new file mode 100644 index 00000000..094c441d --- /dev/null +++ b/examples/starcoder/starcoder-mmap.cpp @@ -0,0 +1,1124 @@ +#include "ggml/ggml.h" + +#include "common.h" +#include "common-ggml.h" + +#include +#include +#include +#include +#include +#include +#include +#include + +// mmap +#include +#include +#include +#include + +#ifdef GGML_USE_CUBLAS +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_CLBLAST +#include "ggml-opencl.h" +#endif + +// default hparams (GPT-2 117M) +// https://huggingface.co/bigcode/gpt_bigcode-santacoder/blob/main/config.json +struct starcoder_hparams { + int32_t n_vocab = 49280; + int32_t n_ctx = 2048; + int32_t n_embd = 2048; + int32_t n_head = 16; + int32_t n_layer = 24; + int32_t ftype = 1; +}; + +struct starcoder_layer { + // normalization + struct ggml_tensor * ln_1_g; + struct ggml_tensor * ln_1_b; + + struct ggml_tensor * ln_2_g; + struct ggml_tensor * ln_2_b; + + // attention + struct ggml_tensor * c_attn_attn_w; + struct ggml_tensor * c_attn_attn_b; + + struct ggml_tensor * c_attn_proj_w; + struct ggml_tensor * c_attn_proj_b; + + // mlp + struct ggml_tensor * c_mlp_fc_w; + struct ggml_tensor * c_mlp_fc_b; + + struct ggml_tensor * c_mlp_proj_w; + struct ggml_tensor * c_mlp_proj_b; +}; + +struct llama_buffer { + uint8_t * addr = NULL; + size_t size = 0; + + llama_buffer() = default; + + void resize(size_t len) { +#ifdef GGML_USE_METAL + free(addr); + int result = posix_memalign((void **) &addr, getpagesize(), len); + if (result == 0) { + memset(addr, 0, len); + } + else { + addr = NULL; + } +#else + delete[] addr; + addr = new uint8_t[len]; +#endif + size = len; + } + + ~llama_buffer() { +#ifdef GGML_USE_METAL + free(addr); +#else + delete[] addr; +#endif + addr = NULL; + } + + // disable copy and move + llama_buffer(const llama_buffer&) = delete; + llama_buffer(llama_buffer&&) = delete; + llama_buffer& operator=(const llama_buffer&) = delete; + llama_buffer& operator=(llama_buffer&&) = delete; +}; + + +struct kv_cache { + struct ggml_tensor * k; + struct ggml_tensor * v; + + struct ggml_context * ctx = NULL; + + //std::vector buf; + llama_buffer buf; + + int n; +}; + +struct starcoder_model { + starcoder_hparams hparams; + + // normalization + struct ggml_tensor * ln_f_g; + struct ggml_tensor * ln_f_b; + + struct ggml_tensor * wte; // position embedding + struct ggml_tensor * wpe; // token embedding + struct ggml_tensor * lm_head; // language model head + + std::vector layers; + + // key + value memory + //struct ggml_tensor * memory_k; + //struct ggml_tensor * memory_v; + struct kv_cache cache; + + // model memory mapped file + void * mm_addr = NULL; + uint64_t mm_length = 0; + + // + struct ggml_context * ctx; + std::map tensors; +}; + +// From PR #613 (https://github.com/ggerganov/llama.cpp/pull/613) +static void *mmap_file(const char *fname, uint64_t *mm_length) { +#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES) + HANDLE hFile = CreateFileA(fname, + GENERIC_READ, + FILE_SHARE_READ | FILE_SHARE_WRITE | FILE_SHARE_DELETE, + NULL, + OPEN_EXISTING, + FILE_ATTRIBUTE_NORMAL | FILE_ATTRIBUTE_NOT_CONTENT_INDEXED, + NULL); + if (hFile == INVALID_HANDLE_VALUE) return 0; + LARGE_INTEGER fileSize; + fileSize.QuadPart = -1; + GetFileSizeEx(hFile, &fileSize); + int64_t length = fileSize.QuadPart; + HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL); + CloseHandle(hFile); + if (!hMapping) return 0; + void *addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0); + CloseHandle(hMapping); + if (!addr) return 0; +#else + int fd = open(fname, O_RDONLY); + if (fd == -1) return 0; + int64_t length = lseek(fd, 0, SEEK_END); + void *addr = mmap(NULL, length, PROT_READ, MAP_SHARED, fd, 0); + close(fd); + if (addr == MAP_FAILED) return 0; +#endif + *mm_length = length; + return addr; +} + +static void munmap_file(void * addr, size_t length) { +#if defined(_WIN32) && !defined(_POSIX_MAPPED_FILES) + UnmapViewOfFile(addr); +#else + munmap(addr, length); +#endif +} + +// load the model's weights from a file +bool starcoder_model_load(const std::string & fname, starcoder_model & model, gpt_vocab & vocab, int32_t n_gpu_layers) { + printf("%s: loading model from '%s'\n", __func__, fname.c_str()); + + auto fin = std::ifstream(fname, std::ios::binary); + if (!fin) { + fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str()); + return false; + } + + std::vector f_buf(1024*1024); + fin.rdbuf()->pubsetbuf(f_buf.data(), f_buf.size()); + + fin.seekg(0, fin.end); + const size_t file_size = fin.tellg(); + fin.seekg(0); + + // verify magic + { + uint32_t magic; + fin.read((char *) &magic, sizeof(magic)); + //if (magic != 0x67676a74) { + if (magic != 0x67676d6c) { + fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str()); + return false; + } + } + + // load hparams + { + auto & hparams = model.hparams; + + fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); + fin.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx)); + fin.read((char *) &hparams.n_embd, sizeof(hparams.n_embd)); + fin.read((char *) &hparams.n_head, sizeof(hparams.n_head)); + fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer)); + fin.read((char *) &hparams.ftype, sizeof(hparams.ftype)); + + const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR; + + printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab); + printf("%s: n_ctx = %d\n", __func__, hparams.n_ctx); + printf("%s: n_embd = %d\n", __func__, hparams.n_embd); + printf("%s: n_head = %d\n", __func__, hparams.n_head); + printf("%s: n_layer = %d\n", __func__, hparams.n_layer); + printf("%s: ftype = %d\n", __func__, hparams.ftype); + printf("%s: qntvr = %d\n", __func__, qntvr); + + hparams.ftype %= GGML_QNT_VERSION_FACTOR; + } + + // load vocab + { + int32_t n_vocab = 0; + fin.read((char *) &n_vocab, sizeof(n_vocab)); + + if (n_vocab != model.hparams.n_vocab) { + fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n", + __func__, fname.c_str(), n_vocab, model.hparams.n_vocab); + return false; + } + + std::string word; + std::vector buf(128); + + for (int i = 0; i < n_vocab; i++) { + uint32_t len; + fin.read((char *) &len, sizeof(len)); + + buf.resize(len); + fin.read((char *) buf.data(), len); + word.assign(buf.data(), len); + + vocab.token_to_id[word] = i; + vocab.id_to_token[i] = word; + + // if (i < 10) fprintf(stderr, "%.s: vocab[%d] = '%s'\n", __func__, i, word.c_str()); + } + + // Add StarChat special tokens. + for (const std::string & token : { + "<|system|>", + "<|user|>", + "<|assistant|>", + "<|end|>", + }) { + if (vocab.token_to_id.find(token) != vocab.token_to_id.end()) { + vocab.add_special_token(token); + } + } + } + + char *mm_addr = NULL; + model.mm_addr = mmap_file(fname.c_str(), &model.mm_length); + if (model.mm_addr == NULL) { + fprintf(stderr, "%s: failed to mmap '%s'\n", __func__, fname.c_str()); + return false; + } + mm_addr = (char *)model.mm_addr; + fprintf(stderr, "%s: ggml map size = %6.2f MB\n", __func__, model.mm_length/(1024.0*1024.0)); + + // for the big tensors, we have the option to store the data in 16-bit floats or quantized + // in order to save memory and also to speed up the computation + ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype)); + if (wtype == GGML_TYPE_COUNT) { + fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", + __func__, fname.c_str(), model.hparams.ftype); + return false; + } + + auto & ctx = model.ctx; + + size_t ctx_size = 0; + + { + const auto & hparams = model.hparams; + + + + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + const int n_ctx = hparams.n_ctx; + const int n_vocab = hparams.n_vocab; + + const int head_dim = n_embd / hparams.n_head; + const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head + const int kv_dim = kv_heads * head_dim; + + + /* + ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_g + ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // ln_f_b + + ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // wte + ctx_size += n_ctx*n_embd*ggml_type_sizef(GGML_TYPE_F32); // wpe + ctx_size += n_vocab*n_embd*ggml_type_sizef(wtype); // lm_head + + ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_g + ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_1_b + + ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_g + ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // ln_2_b + + ctx_size += n_layer*((n_embd + 2*kv_dim)*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w // TODO: + ctx_size += n_layer*( (n_embd + 2*kv_dim)*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_attn_b + + ctx_size += n_layer*(n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_proj_w + ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_b + + ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_fc_w + ctx_size += n_layer*( 4*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_fc_b + + ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_sizef(wtype)); // c_mlp_proj_w + ctx_size += n_layer*( n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_mlp_proj_b + + ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k + ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v + */ + + ctx_size += (6 + 12*n_layer)*512; // object overhead + + //printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); + printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0)); + } + + // create the ggml context + { + struct ggml_init_params params = { + /*.mem_size =*/ ctx_size, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + model.ctx = ggml_init(params); + if (!model.ctx) { + fprintf(stderr, "%s: ggml_init() failed\n", __func__); + return false; + } + } + + // prepare memory for the weights + { + const auto & hparams = model.hparams; + + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + const int n_ctx = hparams.n_ctx; + const int n_vocab = hparams.n_vocab; + + const int head_dim = n_embd / hparams.n_head; + const int kv_heads = hparams.n_head; // 1 if MQA else hparams.n_head + const int kv_dim = kv_heads * head_dim; + + model.layers.resize(n_layer); + + model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); + model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx); + model.lm_head = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); + + // map by name + model.tensors["model/ln_f/g"] = model.ln_f_g; + model.tensors["model/ln_f/b"] = model.ln_f_b; + + model.tensors["model/wte"] = model.wte; + model.tensors["model/wpe"] = model.wpe; + model.tensors["model/lm_head"] = model.lm_head; + + for (int i = 0; i < n_layer; ++i) { + auto & layer = model.layers[i]; + + layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + layer.ln_2_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + layer.ln_2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + layer.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd + 2*kv_dim); + layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd + 2*kv_dim); + + layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd); + layer.c_attn_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); //TODO: 4*n_embd = config.n_inner + layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd); + + layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4*n_embd, n_embd); + layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + // map by name + model.tensors["model/h" + std::to_string(i) + "/ln_1/g"] = layer.ln_1_g; + model.tensors["model/h" + std::to_string(i) + "/ln_1/b"] = layer.ln_1_b; + + model.tensors["model/h" + std::to_string(i) + "/ln_2/g"] = layer.ln_2_g; + model.tensors["model/h" + std::to_string(i) + "/ln_2/b"] = layer.ln_2_b; + + model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w; + model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b; + + model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w; + model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b; + + model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"] = layer.c_mlp_fc_w; + model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"] = layer.c_mlp_fc_b; + + model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"] = layer.c_mlp_proj_w; + model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"] = layer.c_mlp_proj_b; + } + } + + // key + value memory + { + const auto & hparams = model.hparams; + + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + const int n_ctx = hparams.n_ctx; + + const int n_mem = n_layer*n_ctx; + const int n_elements = n_embd*n_mem; + + model.cache.buf.resize(2u*n_elements*ggml_type_size(GGML_TYPE_F16) + 2u*1024*1024); + + struct ggml_init_params c_params; + c_params.mem_size = model.cache.buf.size; + c_params.mem_buffer = model.cache.buf.addr; + c_params.no_alloc = false; + + model.cache.ctx = ggml_init(c_params); + + if (!model.cache.ctx) { + fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); + return false; + } + + model.cache.k = ggml_new_tensor_1d(model.cache.ctx, GGML_TYPE_F16, n_elements); + model.cache.v = ggml_new_tensor_1d(model.cache.ctx, GGML_TYPE_F16, n_elements); + + const size_t memory_size = ggml_nbytes(model.cache.k) + ggml_nbytes(model.cache.v); + + printf("%s: kv_cache memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem); + } + + // load weights + { + size_t total_size = 0; + + bool has_lm_head = false; + + while (true) { + int32_t n_dims; + int32_t length; + int32_t ttype; + + fin.read(reinterpret_cast(&n_dims), sizeof(n_dims)); + fin.read(reinterpret_cast(&length), sizeof(length)); + fin.read(reinterpret_cast(&ttype), sizeof(ttype)); + + if (fin.eof()) { + break; + } + + int32_t nelements = 1; + int32_t ne[2] = { 1, 1 }; + for (int i = 0; i < n_dims; ++i) { + fin.read(reinterpret_cast(&ne[i]), sizeof(ne[i])); + nelements *= ne[i]; + } + + std::string name(length, 0); + fin.read(&name[0], length); + + if (model.tensors.find(name.data()) == model.tensors.end()) { + fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data()); + return false; + } + + auto tensor = model.tensors[name.data()]; + + if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) { + fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n", + __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]); + return false; + } + if (ggml_nelements(tensor) != nelements) { + fprintf(stderr, "%s: tensor '%s' has wrong size in model file. got %d, expected %d\n", + __func__, name.data(), (int) ggml_nelements(tensor), nelements); + return false; + } + + // for debugging + if (0) { + printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor)); + } + + const size_t bpe = ggml_type_size(ggml_type(ttype)); + + if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) { + fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n", + __func__, name.data(), ggml_nbytes(tensor), nelements*bpe); + return false; + } + + // mmap + size_t offset = fin.tellg(); + size_t tensor_data_size = ggml_nbytes(tensor); + //offset = (offset + 31) & -32; + tensor->data = mm_addr + offset; + fin.seekg(offset + tensor_data_size); + total_size += tensor_data_size; + + // GPT-2 models share the WTE tensor as the LM head + if (name == "model/wte" && has_lm_head == false) { + // Dont know if this is required, test models have an lm_head + model.lm_head->data = tensor->data; + } + + if (name == "model/lm_head") { + has_lm_head = true; + } + } + + printf("%s: model size = %8.2f MB\n", __func__, total_size/1024.0/1024.0); + } + + fin.close(); + +#ifdef GGML_USE_CUBLAS + { + const auto & hparams = model.hparams; + const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); + + fprintf(stderr, "%s: [cublas] offloading %d layers to GPU\n", __func__, n_gpu); + + size_t vram_total = 0; + + for (int i = 0; i < n_gpu; ++i) { + const auto & layer = model.layers[i]; + + layer.c_attn_attn_w->backend = GGML_BACKEND_GPU; + ggml_cuda_transform_tensor((uint8_t *)layer.c_attn_attn_w->data, layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w); + + layer.c_attn_proj_w->backend = GGML_BACKEND_GPU; + ggml_cuda_transform_tensor((uint8_t *)layer.c_attn_proj_w->data, layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w); + + layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU; + ggml_cuda_transform_tensor((uint8_t *)layer.c_mlp_fc_w->data, layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w); + + layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU; + ggml_cuda_transform_tensor((uint8_t *)layer.c_mlp_proj_w->data, layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w); + } + + ggml_cuda_set_scratch_size(0); // disable scratch + + //if (n_gpu_layers > (int) hparams.n_layer) { + // fprintf(stderr, "%s: [cublas] offloading output layer to GPU\n", __func__); + // ggml_cuda_transform_tensor(model.output); vram_total += ggml_nbytes(model.output); + //} + + fprintf(stderr, "%s: [cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); + } +#elif defined(GGML_USE_CLBLAST) + //From koboldcpp + { + const auto & hparams = model.hparams; + size_t vram_total = 0; + const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); + fprintf(stderr, "%s: [opencl] offloading %d layers to GPU\n", __func__, n_gpu); + for (int i = 0; i < n_gpu; ++i) { + const auto & layer = model.layers[i]; + layer.c_attn_attn_w->backend = GGML_BACKEND_GPU; + layer.c_attn_proj_w->backend = GGML_BACKEND_GPU; + layer.c_mlp_fc_w->backend = GGML_BACKEND_GPU; + layer.c_mlp_proj_w->backend = GGML_BACKEND_GPU; + ggml_cl_transform_tensor(layer.c_attn_attn_w->data,layer.c_attn_attn_w); vram_total += ggml_nbytes(layer.c_attn_attn_w); + ggml_cl_transform_tensor(layer.c_attn_proj_w->data,layer.c_attn_proj_w); vram_total += ggml_nbytes(layer.c_attn_proj_w); + ggml_cl_transform_tensor(layer.c_mlp_fc_w->data,layer.c_mlp_fc_w); vram_total += ggml_nbytes(layer.c_mlp_fc_w); + ggml_cl_transform_tensor(layer.c_mlp_proj_w->data,layer.c_mlp_proj_w); vram_total += ggml_nbytes(layer.c_mlp_proj_w); + } + fprintf(stderr, "%s: [opencl] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024); + } + #endif + + return true; +} + +// evaluate the transformer +// +// - model: the model +// - n_threads: number of threads to use +// - n_past: the context size so far +// - embd_inp: the embeddings of the tokens in the context +// - embd_w: the predicted logits for the next token +// +bool starcoder_eval( + const starcoder_model & model, + const int n_threads, + const int n_past, + const std::vector & embd_inp, + std::vector & embd_w, + size_t & mem_per_token) { + + const int N = embd_inp.size(); + + const auto & hparams = model.hparams; + + auto & cache = model.cache; + + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + const int n_ctx = hparams.n_ctx; + const int n_head = hparams.n_head; + const int n_vocab = hparams.n_vocab; + + // Scratch is too small for large n_batch (256) + //static size_t buf_size = 256u*1024*1024; + static size_t buf_size = 256u*1024*1024*2; + static void * buf = malloc(buf_size); + + // use 2 scratch buffers + // TODO: very hacky solution - reimplement in a more elegant way + static size_t scr0_size = 256u*1024*1024*2; + static void * scr0 = malloc(scr0_size); + + static size_t scr1_size = 256u*1024*1024*2; + static void * scr1 = malloc(scr1_size); + + if (mem_per_token > 0 && mem_per_token*N > buf_size) { + const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead + printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new); + + // reallocate + buf_size = buf_size_new; + buf = realloc(buf, buf_size); + if (buf == nullptr) { + fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size); + return false; + } + } + + struct ggml_init_params params = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ buf, + /*.no_alloc =*/ false, + }; + + struct ggml_context * ctx0 = ggml_init(params); + struct ggml_cgraph gf = {}; + + struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + + + memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd)); + + struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + for (int i = 0; i < N; ++i) { + ((int32_t *) position->data)[i] = n_past + i; + } + + // wte + wpe + struct ggml_tensor * inpL = + ggml_add(ctx0, + ggml_get_rows(ctx0, model.wte, embd), + ggml_get_rows(ctx0, model.wpe, position)); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * cur; + + ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); + + // norm + { + // [ 768, N] + cur = ggml_norm(ctx0, inpL); + + // cur = ln_1_g*cur + ln_1_b + // [ 768, N] + cur = ggml_add(ctx0, + ggml_mul(ctx0, + ggml_repeat(ctx0, model.layers[il].ln_1_g, cur), + cur), + ggml_repeat(ctx0, model.layers[il].ln_1_b, cur)); + } + + // attn + // [2304, 768] - model.layers[il].c_attn_attn_w + // [2304, 1] - model.layers[il].c_attn_attn_b + // [ 768, N] - cur (in) + // [2304, N] - cur (out) + // + // cur = attn_w*cur + attn_b + // [2304, N] + { + cur = ggml_mul_mat(ctx0, + model.layers[il].c_attn_attn_w, + cur); + + cur = ggml_add(ctx0, + ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur), + cur); + } + + // self-attention + { + struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd); + struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd); + struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd); + + // store key and value to memory + if (N >= 1) { + struct ggml_tensor * k = ggml_view_1d(ctx0, cache.k, N*n_embd, (ggml_element_size(cache.k)*n_embd)*(il*n_ctx + n_past)); + struct ggml_tensor * v = ggml_view_1d(ctx0, cache.v, N*n_embd, (ggml_element_size(cache.v)*n_embd)*(il*n_ctx + n_past)); + + ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k)); + ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v)); + } + + // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3) + // [64, N, 12] + struct ggml_tensor * Q = + ggml_permute(ctx0, + ggml_cpy(ctx0, + Qcur, + ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)), + 0, 2, 1, 3); + + // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3) + // [64, n_past + N, 12] + struct ggml_tensor * K = + ggml_permute(ctx0, + ggml_reshape_3d(ctx0, + ggml_view_1d(ctx0, cache.k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(cache.k)*n_embd), + n_embd/n_head, n_head, n_past + N), + 0, 2, 1, 3); //TODO: need to be tiled + + // GG: flash attention + //struct ggml_tensor * V = + // ggml_cpy(ctx0, + // ggml_permute(ctx0, + // ggml_reshape_3d(ctx0, + // ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd), + // n_embd/n_head, n_head, n_past + N), + // 1, 2, 0, 3), + // ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head)); + + //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true); + + // K * Q + // [n_past + N, N, 12] + struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); //TODO: check if it broadcasts + + // KQ_scaled = KQ / sqrt(n_embd/n_head) + // [n_past + N, N, 12] + struct ggml_tensor * KQ_scaled = + ggml_scale_inplace(ctx0, + KQ, + ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)) + ); + + // KQ_masked = mask_past(KQ_scaled) + // [n_past + N, N, 12] + struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); + + // KQ = soft_max(KQ_masked) + // [n_past + N, N, 12] + struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); + + // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() + // [n_past + N, 64, 12] + struct ggml_tensor * V_trans = + ggml_cpy(ctx0, + ggml_permute(ctx0, + ggml_reshape_3d(ctx0, + ggml_view_1d(ctx0, cache.v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(cache.v)*n_embd), + n_embd/n_head, n_head, n_past + N), + 1, 2, 0, 3), + ggml_new_tensor_3d(ctx0, cache.v->type, n_past + N, n_embd/n_head, n_head)); + + // KQV = transpose(V) * KQ_soft_max + // [64, N, 12] + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max); + + // KQV_merged = KQV.permute(0, 2, 1, 3) + // [64, 12, N] + struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + + // cur = KQV_merged.contiguous().view(n_embd, N) + // [768, N] + cur = ggml_cpy(ctx0, + KQV_merged, + ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); + } + + // projection + // [ 768, 768] - model.layers[il].c_attn_proj_w + // [ 768, 1] - model.layers[il].c_attn_proj_b + // [ 768, N] - cur (in) + // [ 768, N] - cur (out) + // + // cur = proj_w*cur + proj_b + // [768, N] + { + cur = ggml_mul_mat(ctx0, + model.layers[il].c_attn_proj_w, + cur); + + cur = ggml_add(ctx0, + ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur), + cur); + } + + // add the input + cur = ggml_add(ctx0, cur, inpL); + + struct ggml_tensor * inpFF = cur; + + ggml_set_scratch(ctx0, { 0, scr1_size, scr1, }); + + // feed-forward network + { + // norm + { + cur = ggml_norm(ctx0, inpFF); + + // cur = ln_2_g*cur + ln_2_b + // [ 768, N] + cur = ggml_add(ctx0, + ggml_mul(ctx0, + ggml_repeat(ctx0, model.layers[il].ln_2_g, cur), + cur), + ggml_repeat(ctx0, model.layers[il].ln_2_b, cur)); + } + + // fully connected + // [3072, 768] - model.layers[il].c_mlp_fc_w + // [3072, 1] - model.layers[il].c_mlp_fc_b + // [ 768, N] - cur (in) + // [3072, N] - cur (out) + // + // cur = fc_w*cur + fc_b + // [3072, N] + cur = ggml_mul_mat(ctx0, + model.layers[il].c_mlp_fc_w, + cur); + + cur = ggml_add(ctx0, + ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur), + cur); + + // GELU activation + // [3072, N] + cur = ggml_gelu(ctx0, cur); + + // projection + // [ 768, 3072] - model.layers[il].c_mlp_proj_w + // [ 768, 1] - model.layers[il].c_mlp_proj_b + // [3072, N] - cur (in) + // [ 768, N] - cur (out) + // + // cur = proj_w*cur + proj_b + // [768, N] + cur = ggml_mul_mat(ctx0, + model.layers[il].c_mlp_proj_w, + cur); + + cur = ggml_add(ctx0, + ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur), + cur); + } + + // input for next layer + inpL = ggml_add(ctx0, cur, inpFF); + } + + ggml_set_scratch(ctx0, { 0, scr0_size, scr0, }); + + // norm + { + // [ 768, N] + inpL = ggml_norm(ctx0, inpL); + + // inpL = ln_f_g*inpL + ln_f_b + // [ 768, N] + inpL = ggml_add(ctx0, + ggml_mul(ctx0, + ggml_repeat(ctx0, model.ln_f_g, inpL), + inpL), + ggml_repeat(ctx0, model.ln_f_b, inpL)); + } + + ggml_set_scratch(ctx0, { 0, 0, nullptr, }); + + // inpL = WTE * inpL + // [ 768, 50257] - model.lm_head + // [ 768, N] - inpL + inpL = ggml_mul_mat(ctx0, model.lm_head, inpL); + + // logits -> probs + //inpL = ggml_soft_max_inplace(ctx0, inpL); + + // run the computation + ggml_build_forward_expand(&gf, inpL); + ggml_graph_compute_with_ctx(ctx0, &gf, n_threads); + + //if (n_past%100 == 0) { + // ggml_graph_print (&gf); + // ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot"); + //} + + //embd_w.resize(n_vocab*N); + //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N); + + // return result just for the last token + embd_w.resize(n_vocab); + memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab); + + if (mem_per_token == 0) { + mem_per_token = ggml_used_mem(ctx0)/N; + } + //printf("used_mem = %zu MB\n", ggml_used_mem(ctx0)/(1024*1024)); + + ggml_free(ctx0); + + return true; +} + + +int main(int argc, char ** argv) { + ggml_time_init(); + + const int64_t t_main_start_us = ggml_time_us(); + + gpt_params params; + params.model = "models/gpt-2-117M/ggml-model.bin"; + + if (gpt_params_parse(argc, argv, params) == false) { + return 1; + } + + if (params.seed < 0) { + params.seed = time(NULL); + } + + printf("%s: seed = %d\n", __func__, params.seed); + + std::mt19937 rng(params.seed); + if (params.prompt.empty()) { + params.prompt = gpt_random_prompt(rng); + } + + int64_t t_load_us = 0; + + gpt_vocab vocab; + starcoder_model model; + + // load the model + { + const int64_t t_start_us = ggml_time_us(); + + if (!starcoder_model_load(params.model, model, vocab, params.n_gpu_layers)) { + fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str()); + return 1; + } + + t_load_us = ggml_time_us() - t_start_us; + + test_gpt_tokenizer(vocab, params.token_test); + } + + int n_past = 0; + + int64_t t_sample_us = 0; + int64_t t_predict_us = 0; + + std::vector logits; + + // tokenize the prompt + std::vector embd_inp = ::gpt_tokenize(vocab, params.prompt); + + params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size()); + + printf("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); + printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); + for (int i = 0; i < embd_inp.size(); i++) { + printf("%s: token[%d] = %6d, %s\n", __func__, i, embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str()); + } + printf("\n\n"); + + // Handle StarChat "<|end|>" token. + gpt_vocab::id starchat_end_token = -1; + { + const auto it = vocab.token_to_id.find("<|end|>"); + if (it != vocab.token_to_id.end()) { + starchat_end_token = it->second; + } + } + + // submit the input prompt token-by-token + // this reduces the memory usage during inference, at the cost of a bit of speed at the beginning + std::vector embd; + + // determine the required inference memory per token: + size_t mem_per_token = 0; + printf("Calling starcoder_eval\n"); + starcoder_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); + + for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) { + // predict + if (embd.size() > 0) { + const int64_t t_start_us = ggml_time_us(); + + if (!starcoder_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { + printf("Failed to predict\n"); + return 1; + } + + // Should input processing count towards t_predict? + if (i > embd_inp.size()) { + t_predict_us += ggml_time_us() - t_start_us; + } + } + + n_past += embd.size(); + embd.clear(); + + if (i >= embd_inp.size()) { + // sample next token + const int top_k = params.top_k; + const float top_p = params.top_p; + const float temp = params.temp; + + const int n_vocab = model.hparams.n_vocab; + + gpt_vocab::id id = 0; + + { + const int64_t t_start_sample_us = ggml_time_us(); + + id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng); + + t_sample_us += ggml_time_us() - t_start_sample_us; + } + + // add it to the context + embd.push_back(id); + } else { + // if here, it means we are still processing the input prompt + for (int k = i; k < embd_inp.size(); k++) { + embd.push_back(embd_inp[k]); + if (embd.size() >= params.n_batch) { + break; + } + } + i += embd.size() - 1; + } + + // display text + for (auto id : embd) { + printf("%s", vocab.id_to_token[id].c_str()); + } + fflush(stdout); + + // check if model is santacoder + if (model.hparams.n_layer <= 30 && embd.back() == 49152) { + break; + } + // check if model is starcoder + else if (embd.back() == 0) { //TODO: this is only for starcoder + break; + } + // Handle StarChat "<|end|>" token. + else if (embd.back() == starchat_end_token) { + //break; + } + } + + // report timing + { + const int64_t t_main_end_us = ggml_time_us(); + + printf("\n\n"); + printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token); + printf("%s: load time = %8.2f ms\n", __func__, t_load_us/1000.0f); + printf("%s: sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f); + //Shouldnt the input prompt be subracted? + printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/(n_past - embd_inp.size())); + //printf("%s: predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past); + + printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); + } + + ggml_free(model.ctx); + + if (model.mm_addr) { + munmap_file(model.mm_addr, model.mm_length); + } + + return 0; +}