diff --git a/Makefile b/Makefile index 456beee62..f02ce99d0 100644 --- a/Makefile +++ b/Makefile @@ -248,7 +248,7 @@ gpttype_adapter.o: gpttype_adapter.cpp $(CXX) $(CXXFLAGS) -c $< -o $@ clean: - rm -vf *.o main quantize_llama quantize_gpt2 quantize_gptj quantize-stats perplexity embedding benchmark-q4_0-matmult main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe koboldcpp.dll koboldcpp_openblas.dll koboldcpp_noavx2.dll koboldcpp_openblas_noavx2.dll koboldcpp_clblast.dll koboldcpp_cublas.dll koboldcpp.so koboldcpp_openblas.so koboldcpp_noavx2.so koboldcpp_openblas_noavx2.so koboldcpp_clblast.so koboldcpp_cublas.so gptj.exe gpt2.exe + rm -vf *.o main quantize_llama quantize_gpt2 quantize_gptj quantize_neox quantize-stats perplexity embedding benchmark-q4_0-matmult main.exe quantize_llama.exe quantize_gptj.exe quantize_gpt2.exe quantize_neox.exe koboldcpp.dll koboldcpp_openblas.dll koboldcpp_noavx2.dll koboldcpp_openblas_noavx2.dll koboldcpp_clblast.dll koboldcpp_cublas.dll koboldcpp.so koboldcpp_openblas.so koboldcpp_noavx2.so koboldcpp_openblas_noavx2.so koboldcpp_clblast.so koboldcpp_cublas.so gptj.exe gpt2.exe main: examples/main/main.cpp ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) @@ -283,6 +283,9 @@ quantize_gptj: ggml.o llama.o otherarch/tools/gptj_quantize.cpp quantize_gpt2: ggml.o llama.o otherarch/tools/gpt2_quantize.cpp $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) +quantize_neox: ggml.o llama.o otherarch/tools/neox_quantize.cpp + $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + quantize-stats: examples/quantize-stats/quantize-stats.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) diff --git a/gpttype_adapter.cpp b/gpttype_adapter.cpp index 26c4a3f13..fa41f8ccb 100644 --- a/gpttype_adapter.cpp +++ b/gpttype_adapter.cpp @@ -21,6 +21,7 @@ #include "otherarch/gpt2_v1.cpp" #include "otherarch/gpt2_v2.cpp" #include "otherarch/rwkv.cpp" +#include "otherarch/neox.cpp" //return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt) static FileFormat file_format = FileFormat::BADFORMAT; @@ -30,6 +31,7 @@ static gptj_model_v1 gptj_ctx_v1; static gptj_model gptj_ctx_v2; static gpt2_v1_model gpt2_ctx_v1; static gpt2_model gpt2_ctx_v2; +static stablelm_model neox_ctx; static rwkv_context * rwkv_ctx_v1; static llama_context_params llama_ctx_params; static llama_context * llama_ctx_v1; @@ -66,7 +68,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in blasbatchsize = inputs.blasbatchsize; params.memory_f16 = inputs.f16_kv; params.n_ctx = inputs.max_context_length; - gptj_ctx_v1.hparams.n_ctx = gptj_ctx_v2.hparams.n_ctx = gpt2_ctx_v1.hparams.n_ctx = gpt2_ctx_v2.hparams.n_ctx = params.n_ctx; + neox_ctx.hparams.n_ctx = gptj_ctx_v1.hparams.n_ctx = gptj_ctx_v2.hparams.n_ctx = gpt2_ctx_v1.hparams.n_ctx = gpt2_ctx_v2.hparams.n_ctx = params.n_ctx; printf("System Info: %s\n", llama_print_system_info()); diff --git a/koboldcpp.dll b/koboldcpp.dll deleted file mode 100644 index 787f9af9c..000000000 Binary files a/koboldcpp.dll and /dev/null differ diff --git a/koboldcpp_clblast.dll b/koboldcpp_clblast.dll deleted file mode 100644 index f580ac008..000000000 Binary files a/koboldcpp_clblast.dll and /dev/null differ diff --git a/koboldcpp_noavx2.dll b/koboldcpp_noavx2.dll deleted file mode 100644 index fc3488569..000000000 Binary files a/koboldcpp_noavx2.dll and /dev/null differ diff --git a/koboldcpp_openblas.dll b/koboldcpp_openblas.dll deleted file mode 100644 index 3193704d9..000000000 Binary files a/koboldcpp_openblas.dll and /dev/null differ diff --git a/koboldcpp_openblas_noavx2.dll b/koboldcpp_openblas_noavx2.dll deleted file mode 100644 index f56254ee2..000000000 Binary files a/koboldcpp_openblas_noavx2.dll and /dev/null differ diff --git a/otherarch/neox.cpp b/otherarch/neox.cpp new file mode 100644 index 000000000..ff4a7ae7e --- /dev/null +++ b/otherarch/neox.cpp @@ -0,0 +1,744 @@ +#include "ggml.h" +#include "otherarch.h" + +#include "utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +// load the model's weights from a file +bool stablelm_model_load(const std::string & fname, stablelm_model & model, gpt_vocab & vocab) { + printf("%s: loading model from '%s' - please wait ...\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; + } + + // verify magic + { + uint32_t magic; + fin.read((char *) &magic, sizeof(magic)); + 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.n_rot, sizeof(hparams.n_rot)); + fin.read((char *) &hparams.ftype, sizeof(hparams.ftype)); + + 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: n_rot = %d\n", __func__, hparams.n_rot); + printf("%s: ftype = %d\n", __func__, hparams.ftype); + } + + // load vocab + { + const int32_t n_vocab = model.hparams.n_vocab; + + std::string word; + for (int i = 0; i < n_vocab; i++) { + uint32_t len; + fin.read((char *) &len, sizeof(len)); + + word.resize(len); + fin.read((char *) word.data(), len); + + vocab.token_to_id[word] = i; + vocab.id_to_token[i] = word; + } + } + + // 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_TYPE_COUNT; + switch (model.hparams.ftype) { + case 0: wtype = GGML_TYPE_F32; break; + case 1: wtype = GGML_TYPE_F16; break; + case 2: wtype = GGML_TYPE_Q4_0; break; + case 3: wtype = GGML_TYPE_Q4_1; break; + case 5: wtype = GGML_TYPE_Q4_2; break; + case 6: wtype = GGML_TYPE_Q4_3; break; + default: + { + fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", + __func__, fname.c_str(), model.hparams.ftype); + return false; + } + } + + const ggml_type wtype2 = GGML_TYPE_F32; + + 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; + + 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_embd*n_vocab*ggml_type_sizef(wtype); // wte + + ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // lmh_g + //ctx_size += n_vocab*ggml_type_sizef(GGML_TYPE_F32); // lmh_b + + 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*(3*n_embd*n_embd*ggml_type_sizef(wtype)); // c_attn_attn_w + ctx_size += n_layer*( 3*n_embd*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*n_embd*ggml_type_sizef(GGML_TYPE_F32)); // c_attn_proj_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*(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 + 16*n_layer)*256; // object overhead + + printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0)); + } + + // create the ggml context + { + struct ggml_init_params params = { + .mem_size = ctx_size, + .mem_buffer = NULL, + .no_alloc = false, + }; + + 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; + + model.layers.resize(n_layer); + + model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); + + 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.lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab); + //model.lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab); + + // map by name + model.tensors["gpt_neox.embed_in.weight"] = model.wte; + + model.tensors["gpt_neox.final_layer_norm.weight"] = model.ln_f_g; + model.tensors["gpt_neox.final_layer_norm.bias"] = model.ln_f_b; + + model.tensors["embed_out.weight"] = model.lmh_g; + //model.tensors["lm_head.bias"] = model.lmh_b; + + 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.c_attn_attn_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 3*n_embd); + layer.c_attn_attn_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd); + + 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.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_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4*n_embd); + 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["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.weight"] = layer.ln_1_g; + model.tensors["gpt_neox.layers." + std::to_string(i) + ".input_layernorm.bias"] = layer.ln_1_b; + + model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.weight"] = layer.c_attn_attn_w; + model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.query_key_value.bias"] = layer.c_attn_attn_b; + + model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.weight"] = layer.c_attn_proj_w; + model.tensors["gpt_neox.layers." + std::to_string(i) + ".attention.dense.bias"] = layer.c_attn_proj_b; + + model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.weight"] = layer.ln_2_g; + model.tensors["gpt_neox.layers." + std::to_string(i) + ".post_attention_layernorm.bias"] = layer.ln_2_b; + + model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.weight"] = layer.c_mlp_fc_w; + model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_h_to_4h.bias"] = layer.c_mlp_fc_b; + + model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.weight"] = layer.c_mlp_proj_w; + model.tensors["gpt_neox.layers." + std::to_string(i) + ".mlp.dense_4h_to_h.bias"] = 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.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements); + model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements); + + const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v); + + printf("%s: memory_size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem); + } + + // load weights + { + int n_tensors = 0; + size_t total_size = 0; + + printf("%s: ", __func__); + + while (true) { + int32_t n_dims; + int32_t length; + int32_t ftype; + + fin.read(reinterpret_cast(&n_dims), sizeof(n_dims)); + fin.read(reinterpret_cast(&length), sizeof(length)); + fin.read(reinterpret_cast(&ftype), sizeof(ftype)); + + 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 (ggml_nelements(tensor) != nelements) { + fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data()); + return false; + } + + if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) { + fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%5d, %5d], expected [%5d, %5d]\n", + __func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], ne[0], ne[1]); + return false; + } + + if (0) { + static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", "q4_2", }; + printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1], ftype_str[ftype], ggml_nbytes(tensor)/1024.0/1024.0, ggml_nbytes(tensor)); + } + + size_t bpe = 0; + + switch (ftype) { + case 0: bpe = ggml_type_size(GGML_TYPE_F32); break; + case 1: bpe = ggml_type_size(GGML_TYPE_F16); break; + case 2: bpe = ggml_type_size(GGML_TYPE_Q4_0); assert(ne[0] % 64 == 0); break; + case 3: bpe = ggml_type_size(GGML_TYPE_Q4_1); assert(ne[0] % 64 == 0); break; + case 5: bpe = ggml_type_size(GGML_TYPE_Q4_2); assert(ne[0] % 64 == 0); break; + case 6: bpe = ggml_type_size(GGML_TYPE_Q4_3); assert(ne[0] % 64 == 0); break; + default: + { + fprintf(stderr, "%s: unknown ftype %d in model file\n", __func__, ftype); + return false; + } + }; + + 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; + } + + fin.read(reinterpret_cast(tensor->data), ggml_nbytes(tensor)); + + //printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0); + total_size += ggml_nbytes(tensor); + if (++n_tensors % 8 == 0) { + printf("."); + fflush(stdout); + } + } + + printf(" done\n"); + + printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size/1024.0/1024.0, n_tensors); + } + + fin.close(); + + 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 stablelm_eval( + const stablelm_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; + + 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; + const int n_rot = hparams.n_rot; + + static size_t buf_size = 256u*1024*1024; + static void * buf = malloc(buf_size); + + if (mem_per_token > 0 && mem_per_token*N*1.2 > buf_size) { + const size_t buf_size_new = 1.4*(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 = { .n_threads = n_threads }; + + struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd)); + + // wte + struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte, embd); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * cur; + + // self-attention + { + { + cur = ggml_norm(ctx0, inpL); + + 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)); + } + + // compute QKV + { + 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); + } + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 0*sizeof(float)*n_embd/n_head)); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 1*sizeof(float)*n_embd/n_head)); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd/n_head, n_head, N, cur->nb[1]/n_head, cur->nb[1], 2*sizeof(float)*n_embd/n_head)); + + // using mode = 2 for GPT-NeoX mode + Qcur = ggml_rope(ctx0, Qcur, n_past, n_rot, 2); + Kcur = ggml_rope(ctx0, Kcur, n_past, n_rot, 2); + + // store key and value to memory + { + Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd, N)); + + struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past)); + struct ggml_tensor * v = ggml_view_2d(ctx0, model.memory_v, N, n_embd, + ( n_ctx)*ggml_element_size(model.memory_v), + (il*n_ctx)*ggml_element_size(model.memory_v)*n_embd + n_past*ggml_element_size(model.memory_v)); + + 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) + struct ggml_tensor * Q = + ggml_permute(ctx0, + Qcur, + 0, 2, 1, 3); + + // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3) + struct ggml_tensor * K = + ggml_permute(ctx0, + ggml_reshape_3d(ctx0, + ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd), + n_embd/n_head, n_head, n_past + N), + 0, 2, 1, 3); + + // K * Q + struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); + + // KQ_scaled = KQ / sqrt(n_embd/n_head) + struct ggml_tensor * KQ_scaled = + ggml_scale(ctx0, + KQ, + ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head)) + ); + + // KQ_masked = mask_past(KQ_scaled) + struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); + + // KQ = soft_max(KQ_masked) + struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); + + // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous() + struct ggml_tensor * V = + ggml_view_3d(ctx0, model.memory_v, + n_past + N, n_embd/n_head, n_head, + n_ctx*ggml_element_size(model.memory_v), + n_ctx*ggml_element_size(model.memory_v)*n_embd/n_head, + il*n_ctx*ggml_element_size(model.memory_v)*n_embd); + + // KQV = transpose(V) * KQ_soft_max + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); + + // KQV_merged = KQV.permute(0, 2, 1, 3) + struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + + // cur = KQV_merged.contiguous().view(n_embd, N) + cur = ggml_cpy(ctx0, + KQV_merged, + ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); + + // projection + { + 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); + } + } + + struct ggml_tensor * inpFF = cur; + + // feed-forward network + // this is independent of the self-attention result, so it could be done in parallel to the self-attention + { + // post attention layer norm + // note here we pass inpL instead of cur + { + cur = ggml_norm(ctx0, inpL); + + 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)); + } + + 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 + cur = ggml_gelu(ctx0, cur); + + // projection + // cur = proj_w*cur + proj_b + 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); + } + + // layer input + FF + cur = ggml_add(ctx0, cur, inpFF); + + // input for next layer + inpL = ggml_add(ctx0, cur, inpL); + } + + // norm + { + inpL = ggml_norm(ctx0, inpL); + + // inpL = ln_f_g*inpL + ln_f_b + inpL = ggml_add(ctx0, + ggml_mul(ctx0, + ggml_repeat(ctx0, model.ln_f_g, inpL), + inpL), + ggml_repeat(ctx0, model.ln_f_b, inpL)); + } + + // lm_head + { + inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL); + + //inpL = ggml_add(ctx0, + // ggml_repeat(ctx0, model.lmh_b, inpL), + // inpL); + } + + // logits -> probs + //inpL = ggml_soft_max(ctx0, inpL); + + // run the computation + ggml_build_forward_expand(&gf, inpL); + ggml_graph_compute (ctx0, &gf); + + //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 for just 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\n", ggml_used_mem(ctx0)); + + 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/stablelm-base-alpha-3b/ggml-model-f16.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()) { +// if( !isatty(STDIN_FILENO) ){ +// std::string line; +// while( std::getline(std::cin, line) ){ +// params.prompt = params.prompt + "\n" + line; +// } +// } else { +// params.prompt = gpt_random_prompt(rng); +// } +// } + +// int64_t t_load_us = 0; + +// gpt_vocab vocab; +// stablelm_model model; + +// // load the model +// { +// const int64_t t_start_us = ggml_time_us(); + +// if (!stablelm_model_load(params.model, model, vocab)) { +// 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; +// } + +// 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: 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"); + +// std::vector embd; + +// // determine the required inference memory per token: +// size_t mem_per_token = 0; +// stablelm_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 (!stablelm_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) { +// printf("Failed to predict\n"); +// return 1; +// } + +// 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); + +// // end of text token +// if (embd.back() == 0) { +// 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); +// 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); + +// return 0; +// } \ No newline at end of file diff --git a/otherarch/otherarch.h b/otherarch/otherarch.h index c011e9506..b8a1951e5 100644 --- a/otherarch/otherarch.h +++ b/otherarch/otherarch.h @@ -213,6 +213,64 @@ struct gpt2_model { std::map tensors; }; +// default hparams (StableLM 3B) +struct stablelm_hparams { + int32_t n_vocab = 50257; + int32_t n_ctx = 4096; + int32_t n_embd = 4096; + int32_t n_head = 32; + int32_t n_layer = 16; + int32_t n_rot = 32; // rotary_pct * (n_embd / n_head) + int32_t ftype = 1; +}; + +struct stablelm_layer { + // pre normalization + struct ggml_tensor * ln_1_g; + struct ggml_tensor * ln_1_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; + + // post normalization + struct ggml_tensor * ln_2_g; + struct ggml_tensor * ln_2_b; + + // ff + 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 stablelm_model { + stablelm_hparams hparams; + + // normalization + struct ggml_tensor * ln_f_g; + struct ggml_tensor * ln_f_b; + + struct ggml_tensor * wte; // position embedding + + struct ggml_tensor * lmh_g; // language model head + //struct ggml_tensor * lmh_b; // language model bias + + std::vector layers; + + // key + value memory + struct ggml_tensor * memory_k; + struct ggml_tensor * memory_v; + + // + struct ggml_context * ctx; + std::map tensors; +}; + struct rwkv_layer { struct ggml_rwkv_tensor * ln1_weight; struct ggml_rwkv_tensor * ln1_bias; diff --git a/otherarch/tools/convert_hf_neox.py b/otherarch/tools/convert_hf_neox.py new file mode 100644 index 000000000..4e1f8f01b --- /dev/null +++ b/otherarch/tools/convert_hf_neox.py @@ -0,0 +1,116 @@ +import sys +import struct +import json +import torch +import numpy as np + +from transformers import AutoModelForCausalLM, AutoTokenizer + +if len(sys.argv) < 3: + print("Usage: convert-h5-to-ggml.py dir-model [use-f32]\n") + print(" ftype == 0 -> float32") + print(" ftype == 1 -> float16") + sys.exit(1) + +# output in the same directory as the model +dir_model = sys.argv[1] +fname_out = sys.argv[1] + "/ggml-model.bin" + +with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: + encoder = json.load(f) + +with open(dir_model + "/config.json", "r", encoding="utf-8") as f: + hparams = json.load(f) + +# possible data types +# ftype == 0 -> float32 +# ftype == 1 -> float16 +# +# map from ftype to string +ftype_str = ["f32", "f16"] + +ftype = 1 +if len(sys.argv) > 2: + ftype = int(sys.argv[2]) + if ftype < 0 or ftype > 1: + print("Invalid ftype: " + str(ftype)) + sys.exit(1) + fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + + +tokenizer = AutoTokenizer.from_pretrained(dir_model) +model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True) +#print (model) + +#print(tokenizer.encode('I believe the meaning of life is')) + +list_vars = model.state_dict() +for name in list_vars.keys(): + print(name, list_vars[name].shape, list_vars[name].dtype) + +fout = open(fname_out, "wb") + +print(hparams) + +fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex +fout.write(struct.pack("i", hparams["vocab_size"])) +fout.write(struct.pack("i", hparams["max_position_embeddings"])) +fout.write(struct.pack("i", hparams["hidden_size"])) +fout.write(struct.pack("i", hparams["num_attention_heads"])) +fout.write(struct.pack("i", hparams["num_hidden_layers"])) +fout.write(struct.pack("i", int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])))) +fout.write(struct.pack("i", ftype)) + +# TODO: temporary hack to not deal with implementing the tokenizer +dot_token = tokenizer.encode('.')[0] +for i in range(hparams["vocab_size"]): + text = tokenizer.decode([dot_token, i]).encode('utf-8') + # remove the first byte (it's always '.') + text = text[1:] + fout.write(struct.pack("i", len(text))) + fout.write(text) + +for name in list_vars.keys(): + data = list_vars[name].squeeze().numpy() + print("Processing variable: " + name + " with shape: ", data.shape) + + # we don't need these + if name.endswith(".attention.masked_bias") or \ + name.endswith(".attention.bias") or \ + name.endswith(".attention.rotary_emb.inv_freq"): + print(" Skipping variable: " + name) + continue + + n_dims = len(data.shape); + + # ftype == 0 -> float32, ftype == 1 -> float16 + ftype_cur = 0; + if ftype != 0: + if name[-7:] == ".weight" and n_dims == 2: + print(" Converting to float16") + data = data.astype(np.float16) + ftype_cur = 1 + else: + print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + else: + if data.dtype != np.float32: + print(" Converting to float32") + data = data.astype(np.float32) + ftype_cur = 0 + + # header + str = name.encode('utf-8') + fout.write(struct.pack("iii", n_dims, len(str), ftype_cur)) + for i in range(n_dims): + fout.write(struct.pack("i", data.shape[n_dims - 1 - i])) + fout.write(str); + + # data + data.tofile(fout) + +fout.close() + +print("Done. Output file: " + fname_out) +print("") \ No newline at end of file diff --git a/otherarch/tools/neox_quantize.cpp b/otherarch/tools/neox_quantize.cpp new file mode 100644 index 000000000..60a27df53 --- /dev/null +++ b/otherarch/tools/neox_quantize.cpp @@ -0,0 +1,338 @@ +#include "ggml.h" + +#include "otherarch/utils.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// default hparams (StableLM 3B) +struct stablelm_hparams { + int32_t n_vocab = 50257; + int32_t n_ctx = 4096; + int32_t n_embd = 4096; + int32_t n_head = 32; + int32_t n_layer = 16; + int32_t n_rot = 32; // 0.25 * (n_embd / n_head) + int32_t ftype = 1; +}; + +// quantize a model +bool stablelm_model_quantize(const std::string & fname_inp, const std::string & fname_out, int itype) { + ggml_type type = GGML_TYPE_Q4_1; + + switch (itype) { + case 2: type = GGML_TYPE_Q4_0; break; + case 3: type = GGML_TYPE_Q4_1; break; + case 5: type = GGML_TYPE_Q4_2; break; + case 6: type = GGML_TYPE_Q4_3; break; + default: fprintf(stderr, "%s: invalid quantization type %d\n", __func__, itype); return 1; + }; + + if (!ggml_is_quantized(type)) { + fprintf(stderr, "%s: invalid quantization type %d\n", __func__, type); + return false; + } + + gpt_vocab vocab; + + printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str()); + + auto finp = std::ifstream(fname_inp, std::ios::binary); + if (!finp) { + fprintf(stderr, "%s: failed to open '%s' for reading\n", __func__, fname_inp.c_str()); + return false; + } + + auto fout = std::ofstream(fname_out, std::ios::binary); + if (!fout) { + fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname_out.c_str()); + return false; + } + + // verify magic + { + uint32_t magic; + finp.read((char *) &magic, sizeof(magic)); + if (magic != 0x67676d6c) { + fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str()); + return false; + } + + fout.write((char *) &magic, sizeof(magic)); + } + + stablelm_hparams hparams; + + // load hparams + { + finp.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); + finp.read((char *) &hparams.n_ctx, sizeof(hparams.n_ctx)); + finp.read((char *) &hparams.n_embd, sizeof(hparams.n_embd)); + finp.read((char *) &hparams.n_head, sizeof(hparams.n_head)); + finp.read((char *) &hparams.n_layer, sizeof(hparams.n_layer)); + finp.read((char *) &hparams.n_rot, sizeof(hparams.n_rot)); + finp.read((char *) &hparams.ftype, sizeof(hparams.ftype)); + + 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); + + fout.write((char *) &hparams.n_vocab, sizeof(hparams.n_vocab)); + fout.write((char *) &hparams.n_ctx, sizeof(hparams.n_ctx)); + fout.write((char *) &hparams.n_embd, sizeof(hparams.n_embd)); + fout.write((char *) &hparams.n_head, sizeof(hparams.n_head)); + fout.write((char *) &hparams.n_layer, sizeof(hparams.n_layer)); + fout.write((char *) &hparams.n_rot, sizeof(hparams.n_rot)); + fout.write((char *) &itype, sizeof(hparams.ftype)); + } + + // load vocab + { + const int32_t n_vocab = hparams.n_vocab; + + std::string word; + for (int i = 0; i < n_vocab; i++) { + uint32_t len; + finp.read ((char *) &len, sizeof(len)); + fout.write((char *) &len, sizeof(len)); + + word.resize(len); + finp.read ((char *) word.data(), len); + fout.write((char *) word.data(), len); + + vocab.token_to_id[word] = i; + vocab.id_to_token[i] = word; + } + } + + // load weights + { + size_t total_size_org = 0; + size_t total_size_new = 0; + + std::vector work; + + std::vector data_u8; + std::vector data_f16; + std::vector data_f32; + + std::vector hist_all(1 << 4, 0); + + while (true) { + int32_t n_dims; + int32_t length; + int32_t ftype; + + finp.read(reinterpret_cast(&n_dims), sizeof(n_dims)); + finp.read(reinterpret_cast(&length), sizeof(length)); + finp.read(reinterpret_cast(&ftype), sizeof(ftype)); + + if (finp.eof()) { + break; + } + + int32_t nelements = 1; + int32_t ne[2] = { 1, 1 }; + for (int i = 0; i < n_dims; ++i) { + finp.read (reinterpret_cast(&ne[i]), sizeof(ne[i])); + nelements *= ne[i]; + } + + std::string name(length, 0); + finp.read (&name[0], length); + + { + static const char * ftype_str[] = { "f32", "f16", "q4_0", "q4_1", "q4_2" }; + printf("%64s - [%5d, %5d], type = %6s ", name.data(), ne[0], ne[1], ftype_str[ftype]); + } + + // regexes of tensor names to be quantized + const std::vector k_names = { + ".*weight", + }; + + bool quantize = false; + for (const auto & s : k_names) { + if (std::regex_match(name, std::regex(s))) { + quantize = true; + break; + } + } + + // quantize only 2D tensors + quantize &= (n_dims == 2); + + if (quantize) { + if (ftype != 0 && ftype != 1) { + fprintf(stderr, "%s: unsupported ftype %d for integer quantization\n", __func__, ftype); + return false; + } + + if (ftype == 1) { + data_f16.resize(nelements); + finp.read(reinterpret_cast(data_f16.data()), nelements * sizeof(ggml_fp16_t)); + data_f32.resize(nelements); + for (int i = 0; i < nelements; ++i) { + data_f32[i] = ggml_fp16_to_fp32(data_f16[i]); + } + } else { + data_f32.resize(nelements); + finp.read(reinterpret_cast(data_f32.data()), nelements * sizeof(float)); + } + + ftype = itype; + } else { + const int bpe = (ftype == 0) ? sizeof(float) : sizeof(uint16_t); + + data_u8.resize(nelements*bpe); + finp.read(reinterpret_cast(data_u8.data()), nelements * bpe); + } + + fout.write(reinterpret_cast(&n_dims), sizeof(n_dims)); + fout.write(reinterpret_cast(&length), sizeof(length)); + fout.write(reinterpret_cast(&ftype), sizeof(ftype)); + for (int i = 0; i < n_dims; ++i) { + fout.write(reinterpret_cast(&ne[i]), sizeof(ne[i])); + } + fout.write(&name[0], length); + + if (quantize) { + printf("quantizing .. "); + work.resize(nelements); // for quantization + + size_t cur_size = 0; + std::vector hist_cur(1 << 4, 0); + + switch (type) { + case GGML_TYPE_Q4_0: + { + cur_size = ggml_quantize_q4_0(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q4_1: + { + cur_size = ggml_quantize_q4_1(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q4_2: + { + cur_size = ggml_quantize_q4_2(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data()); + } break; + case GGML_TYPE_Q4_3: + { + cur_size = ggml_quantize_q4_3(data_f32.data(), work.data(), nelements, ne[0], hist_cur.data()); + } break; + default: + { + fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, type); + return false; + } + } + + fout.write(reinterpret_cast(work.data()), cur_size); + total_size_new += cur_size; + + printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0); + for (int i = 0; i < hist_cur.size(); ++i) { + hist_all[i] += hist_cur[i]; + } + + for (int i = 0; i < hist_cur.size(); ++i) { + printf("%5.3f ", hist_cur[i] / (float)nelements); + } + printf("\n"); + } else { + printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0); + fout.write(reinterpret_cast(data_u8.data()), data_u8.size()); + total_size_new += data_u8.size(); + } + + total_size_org += nelements * sizeof(float); + } + + printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); + printf("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0); + + { + int64_t sum_all = 0; + for (int i = 0; i < hist_all.size(); ++i) { + sum_all += hist_all[i]; + } + + printf("%s: hist: ", __func__); + for (int i = 0; i < hist_all.size(); ++i) { + printf("%5.3f ", hist_all[i] / (float)sum_all); + } + printf("\n"); + } + } + + finp.close(); + fout.close(); + + return true; +} + +// usage: +// ./stablelm2-quantize models/stablelm2-117M/ggml-model.bin models/stablelm2-117M/ggml-model-quant.bin type +// +int main(int argc, char ** argv) { + + ggml_time_init(); + + if (argc != 4) { + fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]); + fprintf(stderr, " type = 2 - q4_0\n"); + fprintf(stderr, " type = 3 - q4_1\n"); + fprintf(stderr, " type = 5 - q4_2\n"); + fprintf(stderr, " type = 6 - q4_3\n"); + return 1; + } + + // needed to initialize f16 tables + { + struct ggml_init_params params = { 0, NULL, false }; + struct ggml_context * ctx = ggml_init(params); + ggml_free(ctx); + } + + const std::string fname_inp = argv[1]; + const std::string fname_out = argv[2]; + + const int itype = atoi(argv[3]); + + const int64_t t_main_start_us = ggml_time_us(); + + int64_t t_quantize_us = 0; + + // load the model + { + const int64_t t_start_us = ggml_time_us(); + + if (!stablelm_model_quantize(fname_inp, fname_out, itype)) { + fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str()); + return 1; + } + + t_quantize_us = ggml_time_us() - t_start_us; + } + + // report timing + { + const int64_t t_main_end_us = ggml_time_us(); + + printf("\n"); + printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0f); + printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f); + } + + return 0; +} \ No newline at end of file