From: Michael Verrilli Date: Sat, 6 May 2023 05:51:45 +0000 (-0400) Subject: examples : add dolly-v2 sample inference (#132) X-Git-Tag: upstream/0.0.1642~1493 X-Git-Url: https://git.djapps.eu/?a=commitdiff_plain;h=ff6e03cbcd9bf6e9fa41d49f2495c042efae4dc6;p=pkg%2Fggml%2Fsources%2Fggml examples : add dolly-v2 sample inference (#132) * Vocab support for special tokens * Initial dolly-v2 commit * update README --- diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index f7519cf6..fc776c71 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -10,3 +10,4 @@ add_subdirectory(gpt-j) add_subdirectory(whisper) add_subdirectory(mnist) add_subdirectory(stablelm) +add_subdirectory(dolly-v2) diff --git a/examples/common.cpp b/examples/common.cpp index fc45999b..08130119 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -192,14 +192,34 @@ std::map json_parse(const std::string & fname) { return result; } +void gpt_vocab::add_special_token(const std::string &token) { + special_tokens.push_back(token); +} + + std::vector gpt_tokenize(const gpt_vocab & vocab, const std::string & text) { std::vector words; + // first split the text into words { std::string str = text; std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"; + // Generate the subpattern from the special_tokens vector if it's not empty + if (!vocab.special_tokens.empty()) { + std::string special_tokens_subpattern; + for (const auto &token : vocab.special_tokens) { + if (!special_tokens_subpattern.empty()) { + special_tokens_subpattern += "|"; + } + special_tokens_subpattern += token; + } + + // Modify the regex pattern with the generated special tokens subpattern + pat = special_tokens_subpattern + "|" + pat; + } + std::regex re(pat); std::smatch m; diff --git a/examples/common.h b/examples/common.h index b08e5760..bd66f09a 100644 --- a/examples/common.h +++ b/examples/common.h @@ -53,6 +53,9 @@ struct gpt_vocab { std::map token_to_id; std::map id_to_token; + std::vector special_tokens; + + void add_special_token(const std::string &token); }; // poor-man's JSON parsing diff --git a/examples/dolly-v2/CMakeLists.txt b/examples/dolly-v2/CMakeLists.txt new file mode 100644 index 00000000..b2d55563 --- /dev/null +++ b/examples/dolly-v2/CMakeLists.txt @@ -0,0 +1,13 @@ +# +# dollyv2 + +set(TEST_TARGET dollyv2) +add_executable(${TEST_TARGET} main.cpp) +target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml) + +# +# dollyv2-quantize + +set(TEST_TARGET dollyv2-quantize) +add_executable(${TEST_TARGET} quantize.cpp) +target_link_libraries(${TEST_TARGET} PRIVATE ggml common common-ggml) diff --git a/examples/dolly-v2/README.md b/examples/dolly-v2/README.md new file mode 100644 index 00000000..14069733 --- /dev/null +++ b/examples/dolly-v2/README.md @@ -0,0 +1,217 @@ +# Dolly-V2 + +Transformer architecture: GPT-NeoX + +Modeled from examples/stablelm + +Ref: https://github.com/databrickslabs/dolly + +Ref: https://github.com/stability-AI/stableLM/#stablelm-alpha + +## Usage + +```bash +# get the repo and build it +git clone https://github.com/ggerganov/ggml +cd ggml +mkdir build && cd build +cmake .. +make -j + +# get the Dolly-V2 3B model +git clone https://huggingface.co/databricks/dolly-v2-3b + +# convert model to FP16 +python3 ../examples/dolly-v2/convert-h5-to-ggml.py ./dolly-v2-3b/ 1 + +# run inference using FP16 precision +./bin/dollyv2 -m ./dolly-v2-3b/ggml-model-f16.bin -p "State the meaning of life." -t 6 -n 64 + +main: seed = 1683218142 +dollyv2_model_load: loading model from './dolly-v2-3b/ggml-model-f16.bin' - please wait ... +dollyv2_model_load: n_vocab = 50280 +dollyv2_model_load: n_ctx = 2048 +dollyv2_model_load: n_embd = 2560 +dollyv2_model_load: n_head = 32 +dollyv2_model_load: n_layer = 32 +dollyv2_model_load: n_rot = 20 +dollyv2_model_load: ftype = 1 +dollyv2_model_load: ggml ctx size = 7374.91 MB +dollyv2_model_load: memory_size = 640.00 MB, n_mem = 65536 +dollyv2_model_load: ................................................ done +dollyv2_model_load: model size = 5295.10 MB / num tensors = 388 +main: number of tokens in prompt = 32 +main: token[0] = 30003, Below +main: token[1] = 310, is +main: token[2] = 271, an +main: token[3] = 9775, instruction +main: token[4] = 326, that +main: token[5] = 8631, describes +main: token[6] = 247, a +main: token[7] = 4836, task +main: token[8] = 964, . +main: token[9] = 19566, Write +main: token[10] = 247, a +main: token[11] = 2380, response +main: token[12] = 326, that +main: token[13] = 20420, appropriately +main: token[14] = 29141, completes +main: token[15] = 253, the +main: token[16] = 2748, request +main: token[17] = 964, . +main: token[18] = 187, + +main: token[19] = 187, + +main: token[20] = 50278, ### Instruction: +main: token[21] = 187, + +main: token[22] = 5443, State +main: token[23] = 253, the +main: token[24] = 4495, meaning +main: token[25] = 273, of +main: token[26] = 1495, life +main: token[27] = 964, . +main: token[28] = 187, + +main: token[29] = 187, + +main: token[30] = 50279, ### Response: +main: token[31] = 187, + + +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +State the meaning of life. + +### Response: +The meaning of life is to love and be loved. + +### End + +main: mem per token = 16136720 bytes +main: load time = 2202.58 ms +main: sample time = 2.57 ms +main: predict time = 1497.14 ms / 33.27 ms per token +main: total time = 6187.27 ms +``` + +## 5-bit integer quantization mode + +```bash +# quantize the model to 5-bits using Q5_0 quantization +./bin/dollyv2-quantize ./dolly-v2-3b/ggml-model-f16.bin ./dolly-v2-3b/ggml-model-q5_0.bin 8 + +# run the quantized model +./bin/dollyv2 -m ./dolly-v2-3b/ggml-model-q5_0.bin -p "State the meaning of life." -t 6 -n 64 + +main: seed = 1683218518 +dollyv2_model_load: loading model from './dolly-v2-3b/ggml-model-q5_0.bin' - please wait ... +dollyv2_model_load: n_vocab = 50280 +dollyv2_model_load: n_ctx = 2048 +dollyv2_model_load: n_embd = 2560 +dollyv2_model_load: n_head = 32 +dollyv2_model_load: n_layer = 32 +dollyv2_model_load: n_rot = 20 +dollyv2_model_load: ftype = 8 +dollyv2_model_load: ggml ctx size = 3902.68 MB +dollyv2_model_load: memory_size = 640.00 MB, n_mem = 65536 +dollyv2_model_load: ................................................ done +dollyv2_model_load: model size = 1822.87 MB / num tensors = 388 +main: number of tokens in prompt = 32 +main: token[0] = 30003, Below +main: token[1] = 310, is +main: token[2] = 271, an +main: token[3] = 9775, instruction +main: token[4] = 326, that +main: token[5] = 8631, describes +main: token[6] = 247, a +main: token[7] = 4836, task +main: token[8] = 964, . +main: token[9] = 19566, Write +main: token[10] = 247, a +main: token[11] = 2380, response +main: token[12] = 326, that +main: token[13] = 20420, appropriately +main: token[14] = 29141, completes +main: token[15] = 253, the +main: token[16] = 2748, request +main: token[17] = 964, . +main: token[18] = 187, + +main: token[19] = 187, + +main: token[20] = 50278, ### Instruction: +main: token[21] = 187, + +main: token[22] = 5443, State +main: token[23] = 253, the +main: token[24] = 4495, meaning +main: token[25] = 273, of +main: token[26] = 1495, life +main: token[27] = 964, . +main: token[28] = 187, + +main: token[29] = 187, + +main: token[30] = 50279, ### Response: +main: token[31] = 187, + + +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +State the meaning of life. + +### Response: +The meaning of life is the discovery of the true self. + +### End + +main: mem per token = 16127760 bytes +main: load time = 1011.09 ms +main: sample time = 2.79 ms +main: predict time = 1271.62 ms / 27.64 ms per token +main: total time = 2802.51 ms +``` + +## Notes + +- No guarantees for correctness +- The tokenizer is currently hacked - probably works only for English +- Non-parallel residual is not supported +- Contributions and improvements are welcome + +## Note about possible bug +**There might be some issue with this implementation - not 100% sure. +The embeddings magnitude increases after each layer which is unexpected. +To observe this, uncomment the following line:** +https://github.com/ggerganov/ggml/blob/abea4b7609c14b837015ab625e3ac36c4708dd03/src/ggml.c#L9208 +``` +... +p[ 0] = 65.5842 +p[ 1] = 61.6951 +p[ 2] = 59.3500 +p[ 3] = 61.2421 +p[ 4] = 65.9653 +p[ 5] = 59.4936 +p[ 6] = 58.4164 +p[ 0] = -209.6351 +p[ 1] = -214.0987 +p[ 2] = -217.0928 +p[ 3] = -215.0267 +p[ 4] = -208.2430 +p[ 5] = -215.3692 +p[ 6] = -214.1981 +p[ 0] = -301.0286 +p[ 1] = -308.6521 +p[ 2] = -310.7513 +p[ 3] = -307.0832 +p[ 4] = -299.9238 +p[ 5] = -306.0667 +p[ 6] = -302.1777 +... +``` +**Instead, I think the magnitude should remain around `1`. +See https://github.com/ggerganov/llama.cpp/issues/1063#issuecomment-1527730562 for more analysis** diff --git a/examples/dolly-v2/convert-h5-to-ggml.py b/examples/dolly-v2/convert-h5-to-ggml.py new file mode 100644 index 00000000..ecbe2fad --- /dev/null +++ b/examples/dolly-v2/convert-h5-to-ggml.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("") diff --git a/examples/dolly-v2/main.cpp b/examples/dolly-v2/main.cpp new file mode 100644 index 00000000..5825e838 --- /dev/null +++ b/examples/dolly-v2/main.cpp @@ -0,0 +1,796 @@ +#include "ggml/ggml.h" + +#include "common.h" +#include "common-ggml.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +// default hparams (Dolly-V2 3B) +struct dollyv2_hparams { + int32_t n_vocab = 50254; // tokenizer.vocab_size + int32_t n_ctx = 2048; // model.config.max_position_embeddings + int32_t n_embd = 2560; // model.config.hidden_size + int32_t n_head = 32; // model.config.num_attention_heads + int32_t n_layer = 32; // model.config.num_hidden_layers + int32_t n_rot = 20; // rotary_pct[25%] * (n_embd / n_head) + int32_t ftype = GGML_FTYPE_MOSTLY_F16; +}; + +const std::string INSTRUCTION_KEY = "### Instruction:"; +const std::string RESPONSE_KEY = "### Response:"; +const std::string END_KEY = "### End"; +const std::string INTRO_BLURB = "Below is an instruction that describes a task. Write a response that appropriately completes the request."; + +// dollyv2 prompt format +std::string promptForGenerationFormat(const std::string& instruction) { + return INTRO_BLURB + "\n\n" + INSTRUCTION_KEY + "\n" + instruction + "\n\n" + RESPONSE_KEY + "\n"; +} + +struct dollyv2_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 dollyv2_model { + dollyv2_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; +}; + +// load the model's weights from a file +bool dollyv2_model_load(const std::string & fname, dollyv2_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; + } + + vocab.add_special_token("### End"); + vocab.add_special_token("### Instruction:"); + vocab.add_special_token("### Response:"); + } + + // 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; + + 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 + + // unmapped: attention.rotary_emb, mlp.act + + 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 int64_t n_mem = n_layer*n_ctx; + const int64_t 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 = %lld\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 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 (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; + } + + // 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; + } + + fin.read(reinterpret_cast(tensor->data), ggml_nbytes(tensor)); + + 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 dollyv2_eval( + const dollyv2_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 > 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 = { .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) { + const int64_t t_main_start_us = ggml_time_us(); + + gpt_params params; + params.model = "models/dolly-v2-3b/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); + } + } + + std::string prompt = promptForGenerationFormat(params.prompt); + + int64_t t_load_us = 0; + + gpt_vocab vocab; + dollyv2_model model; + + // load the model + { + const int64_t t_start_us = ggml_time_us(); + + if (!dollyv2_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, 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; + dollyv2_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token); + + int32_t end_token = vocab.token_to_id["### End"]; + + 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 (!dollyv2_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 || (end_token > 0 && embd.back() == 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); + 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; +} diff --git a/examples/dolly-v2/quantize.cpp b/examples/dolly-v2/quantize.cpp new file mode 100644 index 00000000..6df1a061 --- /dev/null +++ b/examples/dolly-v2/quantize.cpp @@ -0,0 +1,168 @@ +#include "ggml/ggml.h" + +#include "common.h" +#include "common-ggml.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, ggml_ftype ftype) { + 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 *) &ftype, 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; + } + } + + // regexes of tensor names to be quantized + const std::vector to_quant = { + ".*weight", + }; + + if (!ggml_common_quantize_0(finp, fout, ftype, to_quant, {})) { + fprintf(stderr, "%s: failed to quantize model '%s'\n", __func__, fname_inp.c_str()); + return false; + } + + 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) { + if (argc != 4) { + fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]); + ggml_print_ftypes(stderr); + 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 ggml_ftype ftype = ggml_parse_ftype(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, ggml_ftype(ftype))) { + 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; +}