diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index a75bc976f..491d67676 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -5,9 +5,10 @@ FROM ubuntu:$UBUNTU_VERSION as build RUN apt-get update && \ apt-get install -y build-essential python3 python3-pip +COPY requirements.txt requirements.txt + RUN pip install --upgrade pip setuptools wheel \ - && pip install numpy requests sentencepiece tqdm \ - && pip install torch --index-url https://download.pytorch.org/whl/cpu + && pip install -r requirements.txt WORKDIR /app diff --git a/README.md b/README.md index c88e0de28..78215c9ce 100644 --- a/README.md +++ b/README.md @@ -192,10 +192,10 @@ ls ./models 65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model # install Python dependencies -python3 -m pip install torch numpy sentencepiece +python3 -m pip install -r requirements.txt # convert the 7B model to ggml FP16 format -python3 convert-pth-to-ggml.py models/7B/ 1 +python3 convert.py models/7B/ # quantize the model to 4-bits (using method 2 = q4_0) ./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin 2 diff --git a/configs/alpaca-native-enhanced.txt b/configs/alpaca-native-enhanced.txt new file mode 100644 index 000000000..109d31592 --- /dev/null +++ b/configs/alpaca-native-enhanced.txt @@ -0,0 +1,21 @@ +--ctx_size 2048 +--batch_size 16 +--repeat_penalty 1.15 +--temp 0.4 +--top_k 30 +--top_p 0.18 + +--interactive-first +--keep -1 + +--ins-prefix-bos +--ins-prefix "\n\nUser: " +--ins-suffix "\n\nAssistant: " +--reverse-prompt "User: " + +-p "You are an AI language model designed to assist the User by answering their questions, offering advice, and engaging in casual conversation in a friendly, helpful, and informative manner. You respond clearly, coherently, and you consider the conversation history. + +User: Hey, how's it going? + +Assistant: Hey there! I'm doing great, thank you. What can I help you with today? Let's have a fun chat!" + diff --git a/configs/alpaca.txt b/configs/alpaca.txt new file mode 100644 index 000000000..99a3ab47e --- /dev/null +++ b/configs/alpaca.txt @@ -0,0 +1,9 @@ +--clean-interface +--interactive-first +--keep -1 +--ins-prefix-bos +--ins-prefix "\n\n### Instruction:\n\n" +--ins-suffix "\n\n### Response:\n\n" +--reverse-prompt "### Instruction:\n\n" + +-p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n" diff --git a/configs/chat-with-bob.txt b/configs/chat-with-bob.txt new file mode 100644 index 000000000..0caa749a3 --- /dev/null +++ b/configs/chat-with-bob.txt @@ -0,0 +1,15 @@ +--interactive-first +--keep -1 +--ins-prefix-bos +--ins-prefix "\nUser: " +--ins-suffix "\nBob: " +--reverse-prompt "User: " +--rm-trailing-space-workaround + +-p "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision. + +User: Hello, Bob. +Bob: Hello. How may I help you today? +User: Please tell me the largest city in Europe. +Bob: Sure. The largest city in Europe is Moscow, the capital of Russia." + diff --git a/configs/llama.txt b/configs/llama.txt new file mode 100644 index 000000000..9d23e75ac --- /dev/null +++ b/configs/llama.txt @@ -0,0 +1,3 @@ +--interactive-first +--keep -1 +--temp 0.1 diff --git a/configs/vicuna-simple.txt b/configs/vicuna-simple.txt new file mode 100644 index 000000000..efa60d96a --- /dev/null +++ b/configs/vicuna-simple.txt @@ -0,0 +1,7 @@ +--interactive-first +--keep -1 +--ins-prefix-bos +--ins-prefix "\n### Human: " +--ins-suffix "\n### Assistant: " +--reverse-prompt "### Human: " +--rm-trailing-space-workaround diff --git a/configs/vicuna-stop.txt b/configs/vicuna-stop.txt new file mode 100644 index 000000000..911d067ef --- /dev/null +++ b/configs/vicuna-stop.txt @@ -0,0 +1,8 @@ +--interactive-first +--keep -1 +--ins-prefix-bos +--ins-prefix "\n### Human: " +--ins-suffix "\n### Assistant: " +--reverse-prompt "### Human: " +--stop-prompt "### Assistant: " +--rm-trailing-space-workaround diff --git a/configs/vicuna.txt b/configs/vicuna.txt new file mode 100644 index 000000000..6d811410a --- /dev/null +++ b/configs/vicuna.txt @@ -0,0 +1,9 @@ +--interactive-first +--keep -1 +--ins-prefix-bos +--ins-prefix "\n### Human: " +--ins-suffix "\n### Assistant: " +--reverse-prompt "### Human: " +--rm-trailing-space-workaround + +-p "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions." diff --git a/convert-ggml-to-pth.py b/convert-ggml-to-pth.py deleted file mode 100644 index 25a44237a..000000000 --- a/convert-ggml-to-pth.py +++ /dev/null @@ -1,299 +0,0 @@ -# Author: github.com/ductai199x -import argparse -import os -import struct - -import numpy as np -import torch -from numba import njit -from tqdm.auto import tqdm - - -def read_header(fin): - values = struct.unpack("i" * 9, fin.read(4 * 9)) - _, _, vocab_size, dim, multiple_of, n_heads, n_layers, rot, ftype = values - return { - "vocab_size": vocab_size, - "dim": dim, - "multiple_of": multiple_of, - "n_heads": n_heads, - "n_layers": n_layers, - }, ftype - - -def read_tokens(fin, vocab_size): - tokens = [] - for _ in range(vocab_size): - text_len = struct.unpack("i", fin.read(4))[0] - text_bytes = fin.read(text_len) - try: - text = text_bytes.decode() - except UnicodeDecodeError: - text = text_bytes.decode(errors="replace") - score = struct.unpack("f", fin.read(4))[0] - tokens.append((text, score)) - return tokens - - -@njit -def dequantize_weights_numba(fin_data, n_rows, n_cols): - qk = 32 - nb = n_cols // qk - bs = 4 + (qk // 2) - - weights = np.zeros((n_rows, n_cols), dtype=np.float32) - data_pos = 0 - - for row in range(n_rows): - for block in range(nb): - d = np.frombuffer(fin_data[data_pos : data_pos + 4], dtype=np.float32)[0] - data_pos += 4 - packed_values = fin_data[data_pos : data_pos + (qk // 2)] - data_pos += qk // 2 - - for i in range(qk // 2): - packed_value = packed_values[i] - v0 = np.float32((packed_value & 0b00001111) - 8) * d - v1 = np.float32((packed_value >> 4) - 8) * d - - weights[row, block * qk + 2 * i] = v0 - weights[row, block * qk + 2 * i + 1] = v1 - - return weights - - -def dequantize_weights(fin, n_rows, n_cols): - qk = 32 - nb = n_cols // qk - data_size = n_rows * n_cols // 2 + n_rows * nb * 4 - fin_data = fin.read(data_size) - return dequantize_weights_numba(fin_data, n_rows, n_cols) - - -def read_variables(fin): - model = {} - pbar = tqdm(total=os.path.getsize(fin.name), unit="B", unit_scale=True, desc="Reading variables") - while True: - start_pos = fin.tell() - try: - n_dims, name_length, ftype_cur = struct.unpack("iii", fin.read(4 * 3)) - except struct.error: - break - - shape = tuple(struct.unpack("i" * n_dims, fin.read(4 * n_dims))) - shape = shape[::-1] - name = fin.read(name_length).decode() - - # ensure tensor data is aligned - tensor_data_offset = fin.tell() - tensor_data_offset = (tensor_data_offset + 31) & -32 - fin.seek(tensor_data_offset) - - if ftype_cur == 2: - # 4-bit quantized weights - dtype = np.uint8 - data = dequantize_weights(fin, shape[0], shape[1]) - data = data.reshape(shape) - elif ftype_cur == 0: - dtype = np.float32 - data_size = np.prod(shape) - data = np.fromfile(fin, dtype=dtype, count=data_size).reshape(shape) - elif ftype_cur == 1: - dtype = np.float16 - data_size = np.prod(shape) - data = np.fromfile(fin, dtype=dtype, count=data_size).reshape(shape) - - model[name] = torch.tensor(data, dtype=torch.float32 if dtype == np.float32 else torch.float16) - - pbar.update(fin.tell() - start_pos) - - return model - - -def convert_to_hf_format(model, hparams): - # This works for llama 7B, need to test with other models - n_layers = hparams["n_layers"] - n_heads = hparams["n_heads"] - dim = hparams["dim"] - dims_per_head = dim // n_heads - base = 10000.0 - inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)) - - # permute for sliced rotary - def permute(w): - return w.view(n_heads, dim // n_heads // 2, 2, dim).transpose(1, 2).reshape(dim, dim) - - state_dict = {} - for layer_i in range(n_layers): - state_dict.update( - { - f"model.layers.{layer_i}.self_attn.q_proj.weight": permute( - model[f"layers.{layer_i}.attention.wq.weight"] - ), - f"model.layers.{layer_i}.self_attn.k_proj.weight": permute( - model[f"layers.{layer_i}.attention.wk.weight"] - ), - f"model.layers.{layer_i}.self_attn.v_proj.weight": model[ - f"layers.{layer_i}.attention.wv.weight" - ], - f"model.layers.{layer_i}.self_attn.o_proj.weight": model[ - f"layers.{layer_i}.attention.wo.weight" - ], - f"model.layers.{layer_i}.mlp.gate_proj.weight": model[ - f"layers.{layer_i}.feed_forward.w1.weight" - ], - f"model.layers.{layer_i}.mlp.down_proj.weight": model[ - f"layers.{layer_i}.feed_forward.w2.weight" - ], - f"model.layers.{layer_i}.mlp.up_proj.weight": model[ - f"layers.{layer_i}.feed_forward.w3.weight" - ], - f"model.layers.{layer_i}.input_layernorm.weight": model[ - f"layers.{layer_i}.attention_norm.weight" - ], - f"model.layers.{layer_i}.post_attention_layernorm.weight": model[ - f"layers.{layer_i}.ffn_norm.weight" - ], - } - ) - state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq - state_dict.update( - { - "model.embed_tokens.weight": model["tok_embeddings.weight"], - "model.norm.weight": model["norm.weight"], - "lm_head.weight": model["output.weight"], - } - ) - - return state_dict - - -def chat(model, hparams, llama_dir): - from transformers import (GenerationConfig, LlamaForCausalLM, - LlamaTokenizer, StoppingCriteria, - StoppingCriteriaList) - from transformers.models.llama.configuration_llama import LlamaConfig - - class StoppingCriteriaSub(StoppingCriteria): - def __init__(self): - super().__init__() - - def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, stops=[]): - print(tokenizer.decode(input_ids[0]), end="", flush=True) - if input_ids[0][-1] == 13: - return True - - return False - - config = LlamaConfig( - vocab_size=hparams["vocab_size"], - dim=hparams["dim"], - num_hidden_layers=hparams["n_layers"], - num_attention_heads=hparams["n_heads"], - ) - - llama = LlamaForCausalLM(config=config) - llama.load_state_dict(state_dict=model, strict=True) - tokenizer = LlamaTokenizer.from_pretrained(llama_dir) - - device = torch.device("cpu") - llama = llama.to(device) - - ctx = """You are AI. -This is a dialog, where User interacts with AI. AI is helpful, kind, obedient, honest, respectful, direct, concise, should try to protect User's privacy, and knows its own limits. Also, AI must answer User and AI cannot stop the conversation by itself. -User: Hello, AI. -AI: Hello! How can I assist you today? -""" - print(ctx.rstrip("\n")) - while True: - print("-" * 60) - prompt = input("User: ") - if ctx != "": - ctx = f"{ctx}User: {prompt}\n" - else: - ctx = f"{prompt}\nAI:" - - ctx = (ctx[-1920:]) if len(ctx) >= 2048 else ctx - - print("-" * 60) - if len(ctx.strip()) > 0: - input_ids = tokenizer(ctx, return_tensors="pt")["input_ids"].to(device) - generation_config = GenerationConfig( - temperature=0.8, - top_p=0.95, - top_k=50, - repetition_penalty=1.1764, - ) - with torch.no_grad(): - generation_output = llama.generate( - input_ids=input_ids, - generation_config=generation_config, - return_dict_in_generate=True, - output_scores=True, - max_length=2048, - do_sample=True, - stopping_criteria=StoppingCriteriaList([StoppingCriteriaSub()]), - ) - s = generation_output.sequences[0] - decoded = tokenizer.decode(s) - ctx = f"{decoded}\n" - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--input_dir", "-i", type=str, required=True, help="The input directory containing the ggml files." - ) - parser.add_argument( - "--prefix", - "-p", - type=str, - required=True, - help="The prefix of the ggml files (ggml-model-f16 or ggml-model-q4_0).", - ) - parser.add_argument( - "--hf", - action="store_true", - help="Whether to save the model in the Hugging Face format. (default: False)", - ) - parser.add_argument( - "--chat", "-c", action="store_true", help="Whether to open a chat with the model. (default: False)" - ) - args = parser.parse_args() - - llama_dir = os.path.abspath(f"{args.input_dir}/../") - - ggml_files = sorted( - [f"{args.input_dir}/{f}" for f in os.listdir(args.input_dir) if f.startswith(args.prefix)] - ) - - fin = open(ggml_files[0], "rb") - hparams, ftype = read_header(fin) - tokens = read_tokens(fin, hparams["vocab_size"]) - model = read_variables(fin) - - for f in tqdm(ggml_files[1:]): - fin = open(f, "rb") - read_header(fin) - read_tokens(fin, hparams["vocab_size"]) - model.update(read_variables(fin)) - - if args.hf: - model = convert_to_hf_format(model, hparams) - - pth_ckpt = { - "state_dict": model, - "hparams": hparams, - "tokens": tokens, - } - - torch.save(pth_ckpt, f"{args.input_dir}/{args.prefix}-to-torch.pth") - - if args.chat: - if not args.hf: - model = convert_to_hf_format(model, hparams) - chat(model, hparams, llama_dir) - - -if __name__ == "__main__": - main() diff --git a/convert-gpt4all-to-ggml.py b/convert-gpt4all-to-ggml.py deleted file mode 100644 index b1a5e0560..000000000 --- a/convert-gpt4all-to-ggml.py +++ /dev/null @@ -1,107 +0,0 @@ -#!/usr/bin/env python3 - -# -# TODO: deduplicate GPT4All with convert-unversioned-ggml-to-ggml.py -# - -# Original by https://github.com/eiz -# https://github.com/ggerganov/llama.cpp/issues/324#issuecomment-1476227818 -import argparse -import glob -import os -import struct -import sys -from sentencepiece import SentencePieceProcessor - -HPARAMS = keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"] - -def parse_args(): - parser = argparse.ArgumentParser(description='Upgrade a GPT4All model to the current format') - parser.add_argument('gpt4all_model', help='path to gpt4all-lora-quantized.bin') - parser.add_argument('tokenizer_model', help='path to LLaMA tokenizer.model file') - return parser.parse_args() - -def read_header(f_in): - struct_fmt = "i" * (3 + len(HPARAMS)) - struct_size = struct.calcsize(struct_fmt) - buf = f_in.read(struct_size) - return struct.unpack(struct_fmt, buf) - -def write_header(f_out, header): - (magic, vocab_size, dim, multiple_of, n_heads, n_layers, rot, ftype) = header - - if magic != 0x67676d6c: - raise Exception('Invalid file magic. Must be an old style ggml file.') - - values = [ - 0x67676d66, # magic: ggml in hex - 1, # file version - vocab_size, - dim, - multiple_of, - n_heads, - n_layers, - rot, - ftype - ] - f_out.write(struct.pack("i" * len(values), *values)) - -def write_tokens(fout, tokenizer): - for i in range(tokenizer.vocab_size()): - if tokenizer.is_unknown(i): - text = " \u2047 ".encode() - elif tokenizer.is_control(i): - text = b"" - elif tokenizer.is_byte(i): - piece = tokenizer.id_to_piece(i) - if len(piece) != 6: - print(f"Invalid token: {piece}") - sys.exit(1) - byte_value = int(piece[3:-1], 16) - text = struct.pack("B", byte_value) - else: - text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode() - fout.write(struct.pack("i", len(text))) - fout.write(text) - fout.write(struct.pack("f", tokenizer.get_score(i))) - - # TODO: GPT4All - add extra token - text = "".encode() - fout.write(struct.pack("i", len(text))) - fout.write(text) - fout.write(struct.pack("f", 0.0)) - -def read_tokens(f_in, tokenizer): - for i in range(tokenizer.vocab_size()): - len_b = f_in.read(4) - (length,) = struct.unpack("i", len_b) - f_in.read(length) - -def copy_all_data(f_out, f_in): - while True: - buf = f_in.read(1024 * 1024) - if not buf: - break - f_out.write(buf) - -def convert_one_file(path_in, tokenizer): - path_tmp = f"{path_in}.tmp" - path_orig= f"{path_in}.orig" - print(f"converting {path_in}") - with open(path_in, "rb") as f_in, open(path_tmp, "wb") as f_out: - write_header(f_out, read_header(f_in)) - read_tokens(f_in, tokenizer) - write_tokens(f_out, tokenizer) - copy_all_data(f_out, f_in) - os.rename(path_in, path_orig) - os.rename(path_tmp, path_in) - -def main(): - args = parse_args() - - tokenizer = SentencePieceProcessor(args.tokenizer_model) - - convert_one_file(args.gpt4all_model, tokenizer) - -if __name__ == "__main__": - main() diff --git a/convert-gptq-to-ggml.py b/convert-gptq-to-ggml.py deleted file mode 100644 index 42e99c2ff..000000000 --- a/convert-gptq-to-ggml.py +++ /dev/null @@ -1,172 +0,0 @@ -# Convert a GPTQ quantized LLaMA model to a ggml compatible file -# Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa -# -import os -import re -import sys -import json -import struct -import numpy as np -import torch -from sentencepiece import SentencePieceProcessor - -if len(sys.argv) != 4: - print("Usage: convert-gptq-to-ggml.py llamaXXb-4bit.pt tokenizer.model out.bin\n") - sys.exit(1) - -fname_model = sys.argv[1] -fname_tokenizer = sys.argv[2] -dir_out = sys.argv[3] - -model = torch.load(fname_model, map_location="cpu") - -n_vocab, n_embd = model['model.embed_tokens.weight'].shape -n_layer = 1 + max(int(m.group(1)) for name in model - if (m := re.match(r'model\.layers\.([0-9]+)', name))) - -# hardcoded: -n_mult = 256 -n_head = {32: 32, 40: 40, 60: 52, 80: 64}[n_layer] - -tokenizer = SentencePieceProcessor(fname_tokenizer) - -assert tokenizer.vocab_size() == n_vocab - -fname_out = sys.argv[3] - -fout = open(fname_out, "wb") - -fout.write(struct.pack("i", 0x67676d66)) # magic: ggmf in hex -fout.write(struct.pack("i", 1)) # file version -fout.write(struct.pack("i", n_vocab)) -fout.write(struct.pack("i", n_embd)) -fout.write(struct.pack("i", n_mult)) -fout.write(struct.pack("i", n_head)) -fout.write(struct.pack("i", n_layer)) -fout.write(struct.pack("i", n_embd // n_head)) # rot (obsolete) -fout.write(struct.pack("i", 4)) - - -# This loop unchanged from convert-pth-to-ggml.py: -for i in range(tokenizer.vocab_size()): - if tokenizer.is_unknown(i): - text = " \u2047 ".encode() - elif tokenizer.is_control(i): - text = b"" - elif tokenizer.is_byte(i): - piece = tokenizer.id_to_piece(i) - if len(piece) != 6: - print(f"Invalid token: {piece}") - sys.exit(1) - byte_value = int(piece[3:-1], 16) - text = struct.pack("B", byte_value) - else: - text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode() - fout.write(struct.pack("i", len(text))) - fout.write(text) - fout.write(struct.pack("f", tokenizer.get_score(i))) - -def write_header(shape, dst_name, ftype_cur): - sname = dst_name.encode() - fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur)) - fout.write(struct.pack("i" * len(shape), *shape[::-1])) - fout.write(sname) - - # ensure tensor data is aligned - tensor_data_offset = fout.tell() - tensor_data_offset = (tensor_data_offset + 31) & -32 - fout.seek(tensor_data_offset) - -def convert_non_q4(src_name, dst_name): - v = model[src_name] - shape = v.shape - print(f"Processing non-Q4 variable: {src_name} with shape: {shape} and type: {v.dtype}") - if len(shape) == 1: - print(" Converting to float32") - v = v.to(torch.float32) - - ftype_cur = {torch.float16: 1, torch.float32: 0}[v.dtype] - - # header - write_header(shape, dst_name, ftype_cur) - - # data - v.numpy().tofile(fout) - -def convert_q4(src_name, dst_name, permute=False): - zeros = model[f"{src_name}.zeros"].numpy() - scales = model[f"{src_name}.scales"].numpy() - bias = model[f"{src_name}.bias"].numpy() - qweight = model[f"{src_name}.qweight"].numpy().T # transpose - - # Q4_1 does not support bias; good thing the bias is always all zeros. - assert not np.any(bias) - - # Each int32 item is actually 8 int4 items packed together, and it's transposed. - shape = (qweight.shape[0], qweight.shape[1] * 8) - - print(f"Processing Q4 variable: {src_name} with shape: {shape}") - - # The output format has the int4 weights in groups of 32 rather than 8. - # It looks like this: - # For each row: - # For each group of 32 columns: - # - addend (float32, 4 bytes) - # - scale (float32, 4 bytes) - # - weights (int4 * 32, 16 bytes) - # Note that in the input, the scales and addends are shared between all - # the columns in a row, so we end up wasting quite a bit of memory with - # repeated scales and addends. - - addends = -zeros # flip sign - - # Since the output format is mixed between integers and floats, we have - # to hackily view the floats as int32s just so numpy will let us - # concatenate them. - addends_view = addends.view(dtype=np.int32) - scales_view = scales.view(dtype=np.int32) - - # Split into groups of 4 columns (i.e. 32 columns of quantized data): - grouped = qweight.reshape([qweight.shape[0], qweight.shape[1] // 4, 4]) - - # Repeat addends and scales: - addends_rep = np.atleast_3d(addends_view).repeat(grouped.shape[1], axis=1) - scales_rep = np.atleast_3d(scales_view).repeat(grouped.shape[1], axis=1) - - blob = np.concatenate([scales_rep, addends_rep, grouped], axis=2, casting='no') - - if permute: - # Permute some rows to undo the permutation done by convert_llama_weights_to_hf.py. - # This can be done after the above conversion because it doesn't affect column order/layout. - blob = (blob.reshape(n_head, 2, shape[0] // n_head // 2, *blob.shape[1:]) - .swapaxes(1, 2) - .reshape(blob.shape)) - - # header - write_header(shape, dst_name, 3) # ftype = Q4_1 - - # data - blob.tofile(fout) - -convert_non_q4("model.embed_tokens.weight", "tok_embeddings.weight") -convert_non_q4("model.norm.weight", "norm.weight") -convert_non_q4("lm_head.weight", "output.weight") - -for i in range(n_layer): - convert_q4(f"model.layers.{i}.self_attn.q_proj", f"layers.{i}.attention.wq.weight", permute=True) - convert_q4(f"model.layers.{i}.self_attn.k_proj", f"layers.{i}.attention.wk.weight", permute=True) - convert_q4(f"model.layers.{i}.self_attn.v_proj", f"layers.{i}.attention.wv.weight") - convert_q4(f"model.layers.{i}.self_attn.o_proj", f"layers.{i}.attention.wo.weight") - - convert_q4(f"model.layers.{i}.mlp.gate_proj", f"layers.{i}.feed_forward.w1.weight") - convert_q4(f"model.layers.{i}.mlp.down_proj", f"layers.{i}.feed_forward.w2.weight") - convert_q4(f"model.layers.{i}.mlp.up_proj", f"layers.{i}.feed_forward.w3.weight") - - convert_non_q4(f"model.layers.{i}.input_layernorm.weight", f"layers.{i}.attention_norm.weight") - convert_non_q4(f"model.layers.{i}.post_attention_layernorm.weight", f"layers.{i}.ffn_norm.weight") - - -fout.close() - -print(f"Done. Output file: {fname_out}") -print() diff --git a/convert-pth-to-ggml.py b/convert-pth-to-ggml.py index dcef2f6a3..f87ac270c 100644 --- a/convert-pth-to-ggml.py +++ b/convert-pth-to-ggml.py @@ -1,274 +1,11 @@ -# Convert a LLaMA model checkpoint to a ggjt compatible file -# -# Load the model using Torch -# Iterate over all variables and write them to a binary file. -# -# For each variable, write the following: -# - Number of dimensions (int) -# - Name length (int) -# - Dimensions (int[n_dims]) -# - Name (char[name_length]) -# - Data (float[n_dims]) -# -# At the start of the ggml file we write the model parameters -# and vocabulary. -# +# Compatibility stub import argparse -import os -import sys -import json -import struct -import numpy as np -import torch -from sentencepiece import SentencePieceProcessor +import convert -QK = 32 - -GGML_TYPE_Q4_0 = 0 -GGML_TYPE_Q4_1 = 1 -GGML_TYPE_I8 = 2 -GGML_TYPE_I16 = 3 -GGML_TYPE_I32 = 4 -GGML_TYPE_F16 = 5 -GGML_TYPE_F32 = 6 - -WTYPES = { - 0: GGML_TYPE_F32, - 1: GGML_TYPE_F16, - 2: GGML_TYPE_Q4_0, - 3: GGML_TYPE_Q4_1, -} - -GGML_BLCK_SIZE = { - GGML_TYPE_Q4_0: QK, - GGML_TYPE_Q4_1: QK, - GGML_TYPE_I8: 1, - GGML_TYPE_I16: 1, - GGML_TYPE_I32: 1, - GGML_TYPE_F16: 1, - GGML_TYPE_F32: 1, -} - -GGML_TYPE_SIZE = { - GGML_TYPE_Q4_0: 4 + QK//2, - GGML_TYPE_Q4_1: 4*2 + QK//2, - GGML_TYPE_I8: 1, - GGML_TYPE_I16: 2, - GGML_TYPE_I32: 4, - GGML_TYPE_F16: 2, - GGML_TYPE_F32: 4, -} - -def ggml_nelements(shape): - r = 1 - for i in shape: - r *= i - return r - -def ggml_nbytes(shape, ftype): - x = ggml_nelements(shape) - t = WTYPES[ftype] - x *= GGML_TYPE_SIZE[t] - x //= GGML_BLCK_SIZE[t] - return x - -def parse_args(): - parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file') - parser.add_argument('dir_model', help='directory containing the model checkpoint') - parser.add_argument('ftype', help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1) - parser.add_argument('vocab_only', help='only write vocab to file', type=int, default=0, nargs='?') - return parser.parse_args() - -def get_n_parts(dim): - mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8} - n_parts = mappings.get(dim) - if n_parts is None: - print(f"Invalid dim: {dim}") - sys.exit(1) - - print(f"n_parts = {n_parts}\n") - return n_parts - -def load_hparams_and_tokenizer(dir_model): - # `dir_model` is something like `models/7B` or `models/7B/`. - # "tokenizer.model" is expected under model's parent dir. - # When `dir_model` is a symlink, f"{dir_model}/../tokenizer.model" would not be found. - # Let's use the model's parent dir directly. - model_parent_dir = os.path.dirname(os.path.normpath(dir_model)) - fname_hparams = f"{dir_model}/params.json" - fname_tokenizer = f"{model_parent_dir}/tokenizer.model" - with open(fname_hparams, "r") as f: - hparams = json.load(f) - print(hparams) - tokenizer = SentencePieceProcessor(fname_tokenizer) - hparams.update({"vocab_size": tokenizer.vocab_size()}) - return hparams, tokenizer - -def write_header(fout, hparams, ftype): - keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"] - values = [ - 0x67676a74, # magic: ggjt in hex - 1, # file version - *[hparams[key] for key in keys], - hparams["dim"] // hparams["n_heads"], # rot (obsolete) - ftype - ] - fout.write(struct.pack("i" * len(values), *values)) - -def write_tokens(fout, tokenizer): - for i in range(tokenizer.vocab_size()): - if tokenizer.is_unknown(i): - text = " \u2047 ".encode() - elif tokenizer.is_control(i): - text = b"" - elif tokenizer.is_byte(i): - piece = tokenizer.id_to_piece(i) - if len(piece) != 6: - print(f"Invalid token: {piece}") - sys.exit(1) - byte_value = int(piece[3:-1], 16) - text = struct.pack("B", byte_value) - else: - text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode() - fout.write(struct.pack("i", len(text))) - fout.write(text) - fout.write(struct.pack("f", tokenizer.get_score(i))) - -def process_and_write_variables(fout, model, ftype, part_id, n_parts): - for name, datao in model.items(): - if name.endswith("freqs"): - continue - - # remove dimensions with a single element - data = datao.numpy().squeeze() - partshape = data.shape - n_dims = len(data.shape) - assert n_dims in (1, 2) - - print(f"Processing variable: {name} with shape: {partshape} and type: {datao.dtype}") - - # coerce single-dimensional tensors from float16 to float32 - ftype_cur = 1 - if ftype == 0 or n_dims == 1: - print(" Converting to float32") - data = data.astype(np.float32) - ftype_cur = 0 - blck_size = GGML_BLCK_SIZE[WTYPES[ftype_cur]] - type_size = GGML_TYPE_SIZE[WTYPES[ftype_cur]] - - # determine dimension along which multipart tensor is sharded - # - # split_dim 0 regex: - # - output.* - # - layers.*.attention.wq.weight - # - layers.*.attention.wk.weight - # - layers.*.attention.wv.weight - # - layers.*.feed_forward.w1.weight - # - layers.*.feed_forward.w3.weight - # - # split_dim 1 regex: - # - tok_embeddings.* - # - layers.*.attention.wo.weight - # - layers.*.feed_forward.w2.weight - # - if n_dims > 1: - split_dim = 1 - if "tok_embeddings" in name: - split_dim = 1 - elif "layers" in name: - if "attention.wo.weight" in name: - split_dim = 1 - elif "feed_forward.w2.weight" in name: - split_dim = 1 - else: - split_dim = 0 - elif "output" in name: - split_dim = 0 - - # output tensor header - fullshape = list(partshape) - if n_dims > 1: - fullshape[split_dim] *= n_parts - sname = name.encode() - fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur)) - for dim in reversed(fullshape): - fout.write(struct.pack("i", dim)) - fout.write(sname) - - # ensure tensor data is aligned - tensor_data_offset = fout.tell() - while tensor_data_offset % QK != 0: - fout.write(struct.pack("B", 0)) - tensor_data_offset += 1 - - # output unified mappable tensor data - if n_dims == 1 or n_parts == 1: - # copy tensor which we thankfully received in one piece - if part_id == 0: - data.tofile(fout) - elif split_dim == 0: - # reassemble multifile tensor containing some of the rows - rows_per_chunk = partshape[0] - current_row = part_id * rows_per_chunk - bytes_per_row = fullshape[1] // blck_size * type_size - offset = current_row * bytes_per_row - fout.seek(tensor_data_offset + offset) - data.tofile(fout) - elif split_dim == 1: - # reassemble multifile tensor containing some of the cols - cols_per_chunk = partshape[1] - current_col = part_id * cols_per_chunk - bytes_per_row = fullshape[1] // blck_size * type_size - offset_current_col = current_col // blck_size * type_size - for row in range(partshape[0]): - offset_row = row * bytes_per_row - offset = offset_row + offset_current_col - fout.seek(tensor_data_offset + offset) - data[row].tofile(fout) - - # advance file position to next tensor - fout.seek(tensor_data_offset + ggml_nbytes(fullshape, ftype_cur)) - -def main(): - args = parse_args() - dir_model = args.dir_model - ftype = args.ftype - ftype_str = ["f32", "f16"] - hparams, tokenizer = load_hparams_and_tokenizer(dir_model) - - print(args) - - # if only writing vocab to file - if args.vocab_only: - fname_model = f"{dir_model}/consolidated.00.pth" - fname_out = f"{dir_model}/ggml-vocab.bin" - print(f"Extracting only the vocab from '{fname_model}'\n") - with open(fname_out, "wb") as fout: - write_header(fout, hparams, ftype) - write_tokens(fout, tokenizer) - print(f"Done. Output file: {fname_out}\n") - return - - n_parts = get_n_parts(hparams["dim"]) - fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin" - - # we output a single file for ggml - with open(fname_out, "wb") as fout: - write_header(fout, hparams, ftype) - write_tokens(fout, tokenizer) - offset_of_tensors = fout.tell() - # the tensors we load could be split across multiple files - for part_id in range(n_parts): - fout.seek(offset_of_tensors) - print(f"Processing part {part_id+1} of {n_parts}\n") - fname_model = f"{dir_model}/consolidated.0{part_id}.pth" - model = torch.load(fname_model, map_location="cpu") - process_and_write_variables(fout, model, ftype, part_id, n_parts) - del model - - print(f"Done. Output file: {fname_out}\n") - -if __name__ == "__main__": - main() +parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file') +parser.add_argument('dir_model', help='directory containing the model checkpoint') +parser.add_argument('ftype', help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1) +args = parser.parse_args() +convert.main(['--outtype', 'f16' if args.ftype == 1 else 'f32', '--', args.dir_model]) diff --git a/convert-unversioned-ggml-to-ggml.py b/convert-unversioned-ggml-to-ggml.py deleted file mode 100644 index 5151d9081..000000000 --- a/convert-unversioned-ggml-to-ggml.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python3 -# Original by https://github.com/eiz -# https://github.com/ggerganov/llama.cpp/issues/324#issuecomment-1476227818 -import argparse -import glob -import os -import struct -import sys -from sentencepiece import SentencePieceProcessor - -HPARAMS = keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"] - -def parse_args(): - parser = argparse.ArgumentParser(description='Upgrade old ggml model files to the current format') - parser.add_argument('dir_model', help='directory containing ggml .bin files') - parser.add_argument('tokenizer_model', help='path to LLaMA tokenizer.model file') - return parser.parse_args() - -def read_header(f_in): - struct_fmt = "i" * (3 + len(HPARAMS)) - struct_size = struct.calcsize(struct_fmt) - buf = f_in.read(struct_size) - return struct.unpack(struct_fmt, buf) - -def write_header(f_out, header): - (magic, vocab_size, dim, multiple_of, n_heads, n_layers, rot, ftype) = header - - if magic != 0x67676d6c: - raise Exception('Invalid file magic. Must be an old style ggml file.') - - values = [ - 0x67676d66, # magic: ggml in hex - 1, # file version - vocab_size, - dim, - multiple_of, - n_heads, - n_layers, - rot, - ftype - ] - f_out.write(struct.pack("i" * len(values), *values)) - -def write_tokens(fout, tokenizer): - for i in range(tokenizer.vocab_size()): - if tokenizer.is_unknown(i): - text = " \u2047 ".encode() - elif tokenizer.is_control(i): - text = b"" - elif tokenizer.is_byte(i): - piece = tokenizer.id_to_piece(i) - if len(piece) != 6: - print(f"Invalid token: {piece}") - sys.exit(1) - byte_value = int(piece[3:-1], 16) - text = struct.pack("B", byte_value) - else: - text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode() - fout.write(struct.pack("i", len(text))) - fout.write(text) - fout.write(struct.pack("f", tokenizer.get_score(i))) - -def read_tokens(f_in, tokenizer): - for i in range(tokenizer.vocab_size()): - len_b = f_in.read(4) - (length,) = struct.unpack("i", len_b) - f_in.read(length) - -def copy_all_data(f_out, f_in): - while True: - buf = f_in.read(1024 * 1024) - if not buf: - break - f_out.write(buf) - -def convert_one_file(path_in, tokenizer): - path_tmp = f"{path_in}.tmp" - path_orig= f"{path_in}.orig" - print(f"converting {path_in}") - with open(path_in, "rb") as f_in, open(path_tmp, "wb") as f_out: - write_header(f_out, read_header(f_in)) - read_tokens(f_in, tokenizer) - write_tokens(f_out, tokenizer) - copy_all_data(f_out, f_in) - os.rename(path_in, path_orig) - os.rename(path_tmp, path_in) - -def main(): - args = parse_args() - files = [] - files.extend(glob.glob(f"{args.dir_model}/*.bin")) - files.extend(glob.glob(f"{args.dir_model}/*.bin.*")) - - tokenizer = SentencePieceProcessor(args.tokenizer_model) - - for file in files: - convert_one_file(file, tokenizer) - -if __name__ == "__main__": - main() diff --git a/convert.py b/convert.py new file mode 100644 index 000000000..056dc618d --- /dev/null +++ b/convert.py @@ -0,0 +1,1145 @@ +import argparse +import concurrent.futures +import copy +import enum +import faulthandler +import functools +import io +import itertools +import json +import math +import mmap +import pickle +import re +import signal +import struct +import sys +import zipfile +from abc import ABCMeta, abstractmethod +from dataclasses import dataclass +from pathlib import Path +from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, + Literal, Optional, Sequence, Tuple, TypeVar, Union) + +import numpy as np +from sentencepiece import SentencePieceProcessor # type: ignore + +if TYPE_CHECKING: + from typing_extensions import TypeAlias + +if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): + faulthandler.register(signal.SIGUSR1) + +NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' + + +@dataclass(frozen=True) +class UnquantizedDataType: + name: str + + +DT_F16 = UnquantizedDataType('F16') +DT_F32 = UnquantizedDataType('F32') +DT_I32 = UnquantizedDataType('I32') +DT_BF16 = UnquantizedDataType('BF16') + + +@dataclass(frozen=True) +class QuantizedDataType: + groupsize: int + have_addends: bool + have_g_idx: bool + + +DT_Q4_0 = QuantizedDataType(groupsize=32, have_addends=False, have_g_idx=False) +DT_Q4_1 = QuantizedDataType(groupsize=32, have_addends=True, have_g_idx=False) + +DataType = Union[UnquantizedDataType, QuantizedDataType] + +DATA_TYPE_TO_FTYPE: Dict[DataType, int] = { + DT_F32: 0, + DT_F16: 1, + DT_Q4_0: 2, + DT_Q4_1: 3, +} + +FTYPE_TO_DATA_TYPE: Dict[int, DataType] = \ + {ftype: dtype for (dtype, ftype) in DATA_TYPE_TO_FTYPE.items()} + +DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = { + DT_F16: np.dtype(np.float16), + DT_F32: np.dtype(np.float32), + DT_I32: np.dtype(np.int32), +} + +NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \ + {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()} + + +class GGMLFileType(enum.Enum): + AllF32 = 0 + MostlyF16 = 1 # except 1d tensors + MostlyQ4_0 = 2 # except 1d tensors + MostlyQ4_1 = 3 # except 1d tensors + PerLayerIsQ4_1 = 4 # but tok_embeddings.weight and output.weight are F16 + + def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType: + if len(tensor.shape) == 1: + # 1D tensors are always F32. + return DT_F32 + elif self == GGMLFileType.AllF32: + return DT_F32 + elif self == GGMLFileType.MostlyF16: + return DT_F16 + elif self == GGMLFileType.MostlyQ4_0: + return DT_Q4_0 + elif self == GGMLFileType.MostlyQ4_1: + return DT_Q4_1 + elif self == GGMLFileType.PerLayerIsQ4_1: + if name in ('output.weight', 'tok_embeddings.weight'): + return DT_F16 + else: + return DT_Q4_1 + else: + raise ValueError(self) + + +def make_tensors_list() -> List[str]: + ret = [ + 'tok_embeddings.weight', + 'norm.weight', + 'output.weight', + ] + for i in range(80): # maximum number of layer + ret += [ + f'layers.{i}.attention.wq.weight', + f'layers.{i}.attention.wk.weight', + f'layers.{i}.attention.wv.weight', + f'layers.{i}.attention.wo.weight', + f'layers.{i}.attention_norm.weight', + f'layers.{i}.feed_forward.w1.weight', + f'layers.{i}.feed_forward.w2.weight', + f'layers.{i}.feed_forward.w3.weight', + f'layers.{i}.atttention_norm.weight', + f'layers.{i}.ffn_norm.weight', + ] + return ret + + +TENSORS_LIST = make_tensors_list() +TENSORS_SET = set(TENSORS_LIST) + + +@dataclass +class Params: + n_vocab: int + n_embd: int + n_mult: int + n_head: int + n_layer: int + file_type: GGMLFileType + + @staticmethod + def guessed(model: 'LazyModel', file_type: GGMLFileType) -> 'Params': + n_vocab, n_embd = model["tok_embeddings.weight"].shape + + return Params( + n_vocab=n_vocab, + n_embd=n_embd, + n_mult=256, + n_head=n_embd // 128, + n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model), + file_type=file_type, + ) + + +class SentencePieceVocab: + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: + self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) + added_tokens: Dict[str, int] + if fname_added_tokens is not None: + added_tokens = json.load(open(fname_added_tokens)) + else: + added_tokens = {} + vocab_size: int = self.sentencepiece_tokenizer.vocab_size() + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base: int = vocab_size + self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + self.fname_added_tokens = fname_added_tokens + + def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + text: bytes + if tokenizer.is_unknown(i): + text = " \u2047 ".encode("utf-8") + elif tokenizer.is_control(i): + text = b"" + elif tokenizer.is_byte(i): + piece = tokenizer.id_to_piece(i) + if len(piece) != 6: + raise Exception(f"Invalid token: {piece}") + byte_value = int(piece[3:-1], 16) + text = struct.pack("B", byte_value) + else: + text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8") + score: float = tokenizer.get_score(i) + yield text, score + + def added_tokens(self) -> Iterable[Tuple[bytes, float]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score + + def all_tokens(self) -> Iterable[Tuple[bytes, float]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class GGMLVocab: + def __init__(self, tokens: List[Tuple[bytes, float]]): + self.tokens = tokens + self.vocab_size = len(tokens) + + def all_tokens(self) -> Iterable[Tuple[bytes, float]]: + return self.tokens + + def __repr__(self) -> str: + return f"" + + +Vocab = Union[SentencePieceVocab, GGMLVocab] + + +def permute(weights: NDArray, n_head: int) -> NDArray: + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + +def dequantize_q4(qvalues_pack32: NDArray, scales: NDArray, addends: Optional[NDArray], g_idx: Optional[NDArray]) -> NDArray: + # First reinterpret each row from a list of int32s containing 8 values each + # to a list of uint8s containing 2 values each. + qvalues_pack8 = qvalues_pack32.view(np.uint8) + + # Then split out the two values per int8 (which requires an actual + # conversion because numpy doesn't natively support int4s). + qvalues = np.zeros([qvalues_pack8.shape[0], qvalues_pack8.shape[1] * 2], dtype=np.uint8) + qvalues[:, 0::2] = qvalues_pack8 & 0xf + qvalues[:, 1::2] = qvalues_pack8 >> 4 + + assert addends is None or addends.shape == scales.shape + assert qvalues.shape[0] == scales.shape[0] + assert qvalues.shape[1] % scales.shape[1] == 0 + if g_idx is None: + repeat_count = qvalues.shape[1] // scales.shape[1] + scales = scales[:, :, np.newaxis] + if addends is not None: + addends = addends[:, :, np.newaxis] + # Reshape so that the below computation broadcasts over scales and addends: + qvalues.shape = (qvalues.shape[0], scales.shape[1], int(repeat_count)) + else: + # In this case the scale and addend is selected for each column by g_idx: + assert addends is not None + scales = scales[:, g_idx] + addends = addends[:, g_idx] + if addends is None: + # Q4_0 + qvalues = qvalues.view(np.int8) + qvalues -= 8 + # And do the actual 'value = scale * qvalue + addend' computation. + values = scales * qvalues + if addends is not None: + values += addends + if g_idx is None: + values.shape = (values.shape[0], values.shape[1] * values.shape[2]) + return values + + +class Tensor(metaclass=ABCMeta): + data_type: DataType + + @abstractmethod + def astype(self, data_type: DataType) -> 'Tensor': ... + @abstractmethod + def permute(self, n_head: int) -> 'Tensor': ... + @abstractmethod + def to_ggml(self) -> 'GGMLCompatibleTensor': ... + + +class UnquantizedTensor(Tensor): + def __init__(self, ndarray: NDArray) -> None: + assert isinstance(ndarray, np.ndarray) + self.ndarray = ndarray + self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] + + def astype(self, data_type: DataType) -> Tensor: + dtype = DATA_TYPE_TO_NUMPY[data_type] + return UnquantizedTensor(self.ndarray.astype(dtype)) + + def to_ggml(self) -> 'UnquantizedTensor': + return self + + def permute(self, n_head: int) -> 'UnquantizedTensor': + return UnquantizedTensor(permute(self.ndarray, n_head)) + + +def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: + tensor = lazy_tensor.load() + assert isinstance(tensor, UnquantizedTensor) + + # double-check: + actual_shape = list(tensor.ndarray.shape) + assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape) + if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype: + if convert: + tensor.ndarray = tensor.ndarray.astype(expected_dtype) + else: + raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}') + + return tensor.ndarray + + +class GGMLQuantizedTensor(Tensor): + data_type: QuantizedDataType + + def __init__(self, ndarray: NDArray, shape: List[int], data_type: DataType) -> None: + rows, columns = shape + assert data_type in (DT_Q4_1, DT_Q4_0) # for now + assert isinstance(data_type, QuantizedDataType) # redundant, but mypy complains without this + assert columns % data_type.groupsize == 0 + words_in_block = 6 if data_type == DT_Q4_1 else 5 + self.ndarray = ndarray.view(dtype=np.uint32).reshape((rows, columns // data_type.groupsize, words_in_block)) + self.shape = shape[:] + self.data_type = data_type + + def astype(self, data_type: DataType) -> Tensor: + if data_type == self.data_type: + return self + scales = self.ndarray[:, :, 0].view(np.float32) + if self.data_type.have_addends: + addends = self.ndarray[:, :, 1].view(np.float32) + else: + addends = None + qweights = self.ndarray[:, :, -4:].reshape([self.shape[0], self.shape[1] // 8]) + + dq = dequantize_q4(qweights, scales, addends, g_idx=None) + return UnquantizedTensor(dq).astype(data_type) + + def to_ggml(self) -> 'GGMLQuantizedTensor': + return self + + def permute(self, n_head: int) -> 'GGMLQuantizedTensor': + return GGMLQuantizedTensor(permute(self.ndarray, n_head), self.shape, self.data_type) + + +GGMLCompatibleTensor = Union[UnquantizedTensor, GGMLQuantizedTensor] + + +class DeferredPermutedTensor(Tensor): + def __init__(self, base: Tensor, n_head: int) -> None: + self.base = base + self.n_head = n_head + self.data_type = self.base.data_type + + def astype(self, data_type: DataType) -> Tensor: + return self.base.astype(data_type).permute(self.n_head) + + def to_ggml(self) -> GGMLCompatibleTensor: + return self.base.to_ggml().permute(self.n_head) + + def permute(self, n_head: int) -> Tensor: + raise Exception("shouldn't permute twice") + + +class GPTQForLLaMaQuantizedTensor(Tensor): + def __init__(self, model: 'LazyModel', namebase: str) -> None: + qweight = load_unquantized(model[f"{namebase}.qweight"], np.int32) + scales = load_unquantized(model[f"{namebase}.scales"], np.float32, convert=True) + + bias = model.get(f"{namebase}.bias") + if bias is not None: + # Q4_1 does not support bias; good thing the bias is always all zeros. + assert not np.any(load_unquantized(bias)) + + if f"{namebase}.zeros" in model: + zeros = load_unquantized(model[f"{namebase}.zeros"], np.float32) + else: + qzeros = load_unquantized(model[f"{namebase}.qzeros"], np.int32) + assert qzeros.dtype == np.int32 + zeros = dequantize_q4(qzeros, scales, scales, g_idx=None) + assert zeros.dtype == np.float32 + + assert zeros.shape == scales.shape + + # Output is transposed compared to the input, and addends have their sign flipped. + # Scales and zeros similarly must be transposed but only for newer + # versions of GPTQ-for-LLaMa; the older versions can be identified by + # having shape (n_embd, 1). + qweight = qweight.T + if scales.shape[1] != 1: + scales = scales.T + zeros = zeros.T + + # Output also has signs flipped for the addends. + self.qweight = qweight + self.scales = scales + self.addends = -zeros + + self.g_idx: Optional[NDArray] + if f"{namebase}.g_idx" in model: + self.g_idx = load_unquantized(model[f"{namebase}.g_idx"], np.int32) + assert self.g_idx.shape == (qweight.shape[1] * 8,) + else: + self.g_idx = None + + self.shape = [self.qweight.shape[0], self.qweight.shape[1] * 8] + self.data_type = QuantizedDataType(groupsize=self.groupsize(), have_addends=True, + have_g_idx=(self.g_idx is not None)) + + def inspect(self, row: int, col: int) -> None: + '''For debugging.''' + qweight = (self.qweight[row, col // 8] >> (4 * (col & 7))) & 0xf + if self.g_idx is not None: + group = self.g_idx[col] + else: + group = int(col // self.groupsize()) + scale = self.scales[row, group] + addend = self.addends[row, group] + with np.printoptions(precision=None, suppress=True): + print(f'scale:{scale} addend:{addend} qweight:{qweight}') + print('possible values:', np.arange(16) * scale + addend) + print('actual value:', qweight * scale + addend) + + def astype(self, data_type: DataType) -> Tensor: + if isinstance(data_type, QuantizedDataType): + assert self.g_idx is None and data_type.have_addends is True and data_type.have_g_idx is False + return self.regroup(data_type.groupsize) + + dequantized = dequantize_q4(np.ascontiguousarray(self.qweight), self.scales, self.addends, self.g_idx) + return UnquantizedTensor(dequantized).astype(data_type) + + def groupsize(self) -> int: + assert self.addends.shape == self.scales.shape + assert self.shape[1] % self.scales.shape[1] == 0 + return self.shape[1] // self.scales.shape[1] + + def regroup(self, new_groupsize: int = 32) -> 'GPTQForLLaMaQuantizedTensor': + # Old versions of GPTQ-for-LLaMa shared scales and addends between all the + # columns in a row. Newer versions share them between every set of N + # columns in a row, where N is the `groupsize` parameter, usually 128. The + # output format shares them between every set of 32 columns. To handle + # this, duplicate scales and addends for every smaller group. + # (In the above, 'row' and 'column' are in the sense of the output.) + assert self.g_idx is None + old_groupsize = self.groupsize() + assert old_groupsize >= new_groupsize and old_groupsize % new_groupsize == 0, old_groupsize + ret = copy.copy(self) + ret.addends = self.addends.repeat(old_groupsize // new_groupsize, axis=1) + ret.scales = self.scales.repeat(old_groupsize // new_groupsize, axis=1) + ret.data_type = QuantizedDataType(groupsize=new_groupsize, have_addends=True, have_g_idx=False) + return ret + + def permute(self, n_head: int) -> Tensor: + return DeferredPermutedTensor(self, n_head) + + def to_ggml(self) -> GGMLQuantizedTensor: + # The output format looks like this: + # For each row: + # For each group of 32 columns: + # - addend (float32, 4 bytes) + # - scale (float32, 4 bytes) + # - weights (int4 * 32, 16 bytes) + + if self.groupsize() != 32: + raise Exception("should have been regrouped before converting to ggml") + + # Since the output format is mixed between integers and floats, we have + # to hackily view the floats as int32s just so numpy will let us + # concatenate them. + addends_view = self.addends.view(dtype=np.int32)[:, :, np.newaxis] + scales_view = self.scales.view(dtype=np.int32)[:, :, np.newaxis] + + # Split into groups of 4 columns (i.e. 32 columns of quantized data): + grouped = self.qweight.reshape([self.qweight.shape[0], self.qweight.shape[1] // 4, 4]) + + # And concatenate: + grouped = np.concatenate([scales_view, addends_view, grouped], axis=2, casting='no') + + return GGMLQuantizedTensor(grouped, self.shape, DT_Q4_1) + + +@dataclass +class LazyTensor: + _load: Callable[[], Tensor] + shape: List[int] + data_type: DataType + description: str + + def load(self) -> Tensor: + ret = self._load() + assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description) + return ret + + def astype(self, data_type: DataType) -> 'LazyTensor': + self.validate_conversion_to(data_type) + + def load() -> Tensor: + return self.load().astype(data_type) + return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}') + + def validate_conversion_to(self, data_type: DataType) -> None: + if data_type == self.data_type: + return + if isinstance(data_type, QuantizedDataType): + if not isinstance(self.data_type, QuantizedDataType): + raise Exception(f"Can't turn an unquantized tensor into a quantized type ({data_type})") + if self.data_type.have_g_idx: + sys.stderr.write("Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), which is not yet natively supported by GGML. For now you can still convert this model by passing `--outtype f16` to dequantize, but that will result in a much larger output file for no quality benefit.\n") + sys.exit(1) + assert not data_type.have_g_idx and self.data_type.have_addends and data_type.have_addends + + +LazyModel = Dict[str, LazyTensor] + + +@dataclass +class ModelPlus: + model: LazyModel + paths: List[Path] # Where this was read from. + format: Literal['ggml', 'torch', 'safetensors'] + vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab. + + +def merge_sharded(models: List[LazyModel]) -> LazyModel: + # Original LLaMA models have each file contain one part of each tensor. + # Use a dict instead of a set to preserve order. + names = {name: None for model in models for name in model} + + def convert(name: str) -> LazyTensor: + lazy_tensors: List[LazyTensor] = [model[name] for model in models] + if len(lazy_tensors) == 1: + # only one file; don't go through this procedure since there might + # be quantized tensors + return lazy_tensors[0] + if len(lazy_tensors[0].shape) == 1: + # the tensor is just duplicated in every file + return lazy_tensors[0] + if name.startswith('tok_embeddings.') or \ + name.endswith('.attention.wo.weight') or \ + name.endswith('.feed_forward.w2.weight'): + # split by columns + axis = 1 + else: + # split by rows + axis = 0 + concatenated_shape = list(lazy_tensors[0].shape) + concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors) + + def load() -> UnquantizedTensor: + ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors] + concatenated: NDArray = np.concatenate(ndarrays, axis=axis) + return UnquantizedTensor(concatenated) + description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]' + return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description) + return {name: convert(name) for name in names} + + +def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus: + formats = set(mp.format for mp in models_plus) + assert len(formats) == 1, "different formats?" + format = formats.pop() + paths = [path for mp in models_plus for path in mp.paths] + # Use the first non-None vocab, if any. + try: + vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None) + except StopIteration: + vocab = None + + if any("model.embed_tokens.weight" in mp.model for mp in models_plus): + # Transformers models put different tensors in different files, but + # don't split indivdual tensors between files. + model: LazyModel = {} + for mp in models_plus: + model.update(mp.model) + else: + model = merge_sharded([mp.model for mp in models_plus]) + + return ModelPlus(model, paths, format, vocab) + + +def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().permute(n_head) + return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + + +def convert_transformers_to_orig(model: LazyModel) -> LazyModel: + out: LazyModel = {} + out["tok_embeddings.weight"] = model["model.embed_tokens.weight"] + out["norm.weight"] = model["model.norm.weight"] + out["output.weight"] = model["lm_head.weight"] + + n_head = model["model.layers.0.self_attn.q_proj.weight"].shape[1] // 128 + for i in itertools.count(): + if f"model.layers.{i}.self_attn.q_proj.weight" not in model: + break + out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], n_head) + out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] + + out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"] + out[f"layers.{i}.feed_forward.w2.weight"] = model[f"model.layers.{i}.mlp.down_proj.weight"] + out[f"layers.{i}.feed_forward.w3.weight"] = model[f"model.layers.{i}.mlp.up_proj.weight"] + + out[f"layers.{i}.attention_norm.weight"] = model[f"model.layers.{i}.input_layernorm.weight"] + out[f"layers.{i}.ffn_norm.weight"] = model[f"model.layers.{i}.post_attention_layernorm.weight"] + return out + + +def handle_quantization(model: LazyModel) -> LazyModel: + '''Convert a model with entries for 'foo.qweight', 'foo.scales', etc. + (which resolve to UnquantizedTensors with the raw data) to one with entries + for 'foo.weight' (which resolve to QuantizedTensors). + ''' + def convert(name: str) -> Tuple[str, LazyTensor]: + if name.endswith(".qweight"): + namebase = name.rsplit('.', 1)[0] + orig_name = namebase + ".weight" + + lazy_tensor = model[name] + assert len(lazy_tensor.shape) == 2 + real_shape = [lazy_tensor.shape[1], lazy_tensor.shape[0] * 8] + + # Calculate type. This replicates the logic in + # GPTQForLLaMaQuantizedTensor (which is executed when the modelis + # actually loaded). + lazy_scales = model[f"{namebase}.scales"] + scales_width = 1 if lazy_scales.shape[1] == 1 else lazy_scales.shape[0] + assert real_shape[1] % scales_width == 0 + groupsize = real_shape[1] // scales_width + have_g_idx = f"{namebase}.g_idx" in model + data_type = QuantizedDataType(groupsize=groupsize, have_addends=True, have_g_idx=have_g_idx) + + def load() -> Tensor: + return GPTQForLLaMaQuantizedTensor(model, namebase) + + return (orig_name, LazyTensor(load, real_shape, data_type, '[quantized]')) + else: + return (name, model[name]) + return dict(convert(name) for name in model) + +# Functionality that simulates `torch.load` but where individual tensors are +# only loaded into memory on demand, not all at once. +# PyTorch can't do this natively as of time of writing: +# - https://github.com/pytorch/pytorch/issues/64327 +# This allows us to de-shard without multiplying RAM usage, and also +# conveniently drops the PyTorch dependency (though we still need numpy). + + +@dataclass +class LazyStorageKind: + data_type: DataType + + +@dataclass +class LazyStorage: + load: Callable[[int, int], NDArray] + kind: LazyStorageKind + description: str + + +class LazyUnpickler(pickle.Unpickler): + def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile): + super().__init__(fp) + self.data_base_path = data_base_path + self.zip_file = zip_file + + def persistent_load(self, pid: Any) -> Any: + assert pid[0] == 'storage' + assert isinstance(pid[1], LazyStorageKind) + data_type = pid[1].data_type + filename_stem = pid[2] + filename = self.data_base_path + '/' + filename_stem + info = self.zip_file.getinfo(filename) + + def load(offset: int, elm_count: int) -> NDArray: + dtype = DATA_TYPE_TO_NUMPY.get(data_type) + if dtype is None: + raise Exception("tensor stored in unsupported format") + fp = self.zip_file.open(info) + fp.seek(offset * dtype.itemsize) + size = elm_count * dtype.itemsize + data = fp.read(size) + assert len(data) == size + return np.frombuffer(data, dtype) + description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' + return LazyStorage(load=load, kind=pid[1], description=description) + + def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, # pyright: ignore[reportSelfClsParameterName] + requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: + assert isinstance(storage, LazyStorage) + + def load() -> UnquantizedTensor: + elm_count = stride[0] * size[0] + return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size)) + description = f'pickled storage_offset={storage_offset} in {storage.description}' + return LazyTensor(load, list(size), storage.kind.data_type, description) + + CLASSES: Dict[Any, Any] = { + ('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2, + ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), + ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), + ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), + ('torch', 'IntStorage'): LazyStorageKind(DT_I32), + } + + def find_class(self, module: str, name: str) -> Any: + if not module.startswith('torch'): + return super().find_class(module, name) + return self.CLASSES[(module, name)] + + +def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: + zf = zipfile.ZipFile(outer_fp) + pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')] + assert len(pickle_paths) == 1, pickle_paths + pickle_fp = zf.open(pickle_paths[0], 'r') + unpickler = LazyUnpickler(pickle_fp, + data_base_path=pickle_paths[0][:-4], + zip_file=zf) + model = unpickler.load() + as_dict = dict(model.items()) + return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) + + +SAFETENSORS_DATA_TYPES: Dict[str, DataType] = { + 'F16': DT_F16, + 'F32': DT_F32, + 'I32': DT_I32, +} + + +def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: + header_size, = struct.unpack(' LazyTensor: + data_type = SAFETENSORS_DATA_TYPES[info['dtype']] + numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] + shape: List[int] = info['shape'] + begin, end = info['data_offsets'] + assert 0 <= begin <= end <= len(byte_buf) + assert end - begin == math.prod(shape) * numpy_dtype.itemsize + buf = byte_buf[begin:end] + + def load() -> UnquantizedTensor: + return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) + description = f'safetensors begin={begin} end={end} type={data_type} path={path}' + return LazyTensor(load, shape, data_type, description) + model = {name: convert(info) for (name, info) in header.items()} + return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None) + + +def must_read(fp: IO[bytes], length: int) -> bytes: + ret = fp.read(length) + if len(ret) < length: + raise Exception("unexpectedly reached end of file") + return ret + + +def lazy_load_ggml_file(fp: IO[bytes], path: Path) -> ModelPlus: + magic = must_read(fp, 4)[::-1] + if magic in (b'ggmf', b'ggjt'): + version, = struct.unpack("i", must_read(fp, 4)) + assert version == 1 + else: + assert magic == b'ggml' + version = None + n_vocab, n_embd, n_mult, n_head, n_layer, rot, file_type = struct.unpack('<7i', must_read(fp, 28)) + + tokens: List[Tuple[bytes, float]] = [] + for i in range(n_vocab): + if i == 32000: + # HACK: GPT4All messed with the format without changing the magic + # number. Specifically, they changed the vocab section to contain + # `n_vocab - 1` tokens instead of `n_vocab` (i.e. omitting the + # extra pad token). Try to detect if we're reading a file like + # this. + orig_pos = fp.tell() + fp.seek(20, io.SEEK_CUR) + is_gpt4all = fp.read(21) == b'tok_embeddings.weight' + fp.seek(orig_pos) + if is_gpt4all: + break + + length, = struct.unpack("i", must_read(fp, 4)) + text = must_read(fp, length) + if magic != b'ggml': + score, = struct.unpack("f", must_read(fp, 4)) + tokens.append((text, score)) + vocab = GGMLVocab(tokens) if magic != b'ggml' else None + + model: LazyModel = {} + # Use mmap for the actual data to avoid race conditions with the file offset. + mapped = memoryview(mmap.mmap(fp.fileno(), 0, access=mmap.ACCESS_READ)) + + def read_tensor() -> None: # this is a function so that variables captured in `load` don't change + shape_len, name_len, ftype = struct.unpack("iii", must_read(fp, 12)) + assert 0 <= shape_len <= 3 + shape: List[int] = list(struct.unpack(f"{shape_len}i", must_read(fp, 4 * shape_len))) + shape = shape[::-1] + name = must_read(fp, name_len).decode('utf-8') + data_type = FTYPE_TO_DATA_TYPE[ftype] + + if magic == b'ggjt': + fp.seek((fp.tell() + 31) & -32) + + if data_type == DT_Q4_1: + # See GPTQForLLaMaQuantizedTensor.ggml_ndarray() + size = 24 * (shape[1] // 32) * shape[0] + elif data_type == DT_Q4_0: + size = 20 * (shape[1] // 32) * shape[0] + else: + numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] + elm_count = math.prod(shape) + size = elm_count * numpy_dtype.itemsize + offset = fp.tell() + buf = mapped[offset:offset+size] + fp.seek(size, io.SEEK_CUR) + + def load() -> Tensor: + if isinstance(data_type, QuantizedDataType): + ndarray = np.frombuffer(buf, dtype=np.uint32) + return GGMLQuantizedTensor(ndarray, shape, data_type) + else: + return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) + description = f'ggml offset={offset} type={data_type} path={path}' + model[name] = LazyTensor(load, shape, data_type, description) + + while fp.read(1) != b'': + fp.seek(-1, io.SEEK_CUR) + read_tensor() + + return ModelPlus(model=model, paths=[path], format='ggml', vocab=vocab) + + +@functools.lru_cache(maxsize=None) +def lazy_load_file(path: Path) -> ModelPlus: + fp = open(path, 'rb') + first8 = fp.read(8) + fp.seek(0) + if first8[:2] == b'PK': + # A zip file, i.e. PyTorch format + return lazy_load_torch_file(fp, path) + elif first8[2:4] == b'gg': + # GGML format + return lazy_load_ggml_file(fp, path) + elif struct.unpack(' Iterable[Out]: + '''Parallel map, but with backpressure. If the caller doesn't call `next` + fast enough, this will stop calling `func` at some point rather than + letting results pile up in memory. Specifically, there is a max of one + output value buffered per thread.''' + with concurrent.futures.ThreadPoolExecutor() as executor: + futures: List[concurrent.futures.Future[Out]] = [] + items_rev = list(iterable)[::-1] + for i in range(min(concurrency, len(items_rev))): + futures.append(executor.submit(func, items_rev.pop())) + while futures: + result = futures.pop(0).result() + if items_rev: + futures.append(executor.submit(func, items_rev.pop())) + yield result + + +def check_vocab_size(params: Params, vocab: Vocab) -> None: + if params.n_vocab != vocab.vocab_size: + # GGMLVocab comes from the same file as the model so shouldn't mismatch: + assert isinstance(vocab, SentencePieceVocab) + if params.n_vocab == vocab.vocab_size_base: + print("Ignoring added_tokens.json since model matches vocab size without it.") + vocab.added_tokens_list = [] + vocab.vocab_size = vocab.vocab_size_base + return + msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}" + if vocab.fname_added_tokens is not None: + msg += f" combined with {vocab.fname_added_tokens}" + msg += f" has {vocab.vocab_size})." + if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None: + msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." + raise Exception(msg) + + +class OutputFile: + def __init__(self, fname_out: Path) -> None: + self.fout = open(fname_out, "wb") + + def write_file_header(self, params: Params) -> None: + self.fout.write(b"ggjt"[::-1]) # magic + values = [ + 1, # file version + params.n_vocab, + params.n_embd, + params.n_mult, + params.n_head, + params.n_layer, + params.n_embd // params.n_head, # rot (obsolete) + params.file_type.value, + ] + self.fout.write(struct.pack("i" * len(values), *values)) + + def write_tensor_header(self, name: str, shape: Sequence[int], data_type: DataType) -> None: + sname = name.encode('utf-8') + self.fout.write(struct.pack("iii", len(shape), len(sname), DATA_TYPE_TO_FTYPE[data_type])) + self.fout.write(struct.pack("i" * len(shape), *shape[::-1])) + self.fout.write(sname) + self.fout.seek((self.fout.tell() + 31) & -32) + + def write_vocab(self, vocab: Vocab) -> None: + for text, score in vocab.all_tokens(): + self.fout.write(struct.pack("i", len(text))) + self.fout.write(text) + self.fout.write(struct.pack("f", score)) + + @staticmethod + def write_vocab_only(fname_out: Path, vocab: Vocab) -> None: + of = OutputFile(fname_out) + params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, + n_head=1, n_layer=0, file_type=GGMLFileType.AllF32) + of = OutputFile(fname_out) + of.write_file_header(params) + of.write_vocab(vocab) + of.fout.close() + + @staticmethod + def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None: + check_vocab_size(params, vocab) + of = OutputFile(fname_out) + of.write_file_header(params) + print("Writing vocab...") + of.write_vocab(vocab) + + def do_item(item: Tuple[str, LazyTensor]) -> NDArray: + name, lazy_tensor = item + return lazy_tensor.load().to_ggml().ndarray + + ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8) + for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): + size = ' x '.join(map(str, lazy_tensor.shape)) + print(f"[{i+1}/{len(model)}] Writing tensor {name}, size {size}...") + of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type) + ndarray.tofile(of.fout) + of.fout.close() + + +def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType: + wq_type = model["layers.0.attention.wq.weight"].data_type + if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): + return GGMLFileType.AllF32 + if output_type_str == "f16" or (output_type_str is None and wq_type == DT_F16): + return GGMLFileType.MostlyF16 + if output_type_str == "q4_1" or (output_type_str is None and isinstance(wq_type, QuantizedDataType) and + wq_type.have_addends): + if isinstance(model["output.weight"].data_type, QuantizedDataType): + return GGMLFileType.MostlyQ4_1 + else: + return GGMLFileType.PerLayerIsQ4_1 + if output_type_str == "q4_0" or (output_type_str is None and isinstance(wq_type, QuantizedDataType)): + return GGMLFileType.MostlyQ4_0 + name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} + raise Exception(f"Unexpected combination of types: {name_to_type}") + + +def do_necessary_conversions(model: LazyModel) -> LazyModel: + model = handle_quantization(model) + + if "lm_head.weight" in model: + model = convert_transformers_to_orig(model) + model = filter_and_sort_tensors(model) + + return model + + +def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: + return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) + for (name, tensor) in model.items()} + + +def nth_multifile_path(path: Path, n: int) -> Optional[Path]: + '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return + the nth path in the model. + ''' + # Support the following patterns: + patterns: List[Tuple[str, str]] = [ + # - x.00.pth, x.01.pth, etc. + (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), + # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. + (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'), + # x.bin, x.bin.1, etc. + (r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}') + ] + for regex, replacement in patterns: + if re.search(regex, path.name): + new_path = path.with_name(re.sub(regex, replacement, path.name)) + if new_path.exists(): + return new_path + return None + + +def find_multifile_paths(path: Path) -> List[Path]: + '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return + the whole list of paths in the model. + ''' + ret: List[Path] = [] + for i in itertools.count(): + nth_path = nth_multifile_path(path, i) + if nth_path is None: + break + ret.append(nth_path) + if not ret: + # No matches. This should only happen if the file was named, e.g., + # foo.0, and there was no file named foo. Oh well, try to process it + # as a single file. + return [path] + return ret + + +def load_some_model(path: Path) -> ModelPlus: + '''Load a model of any supported format.''' + # Be extra-friendly and accept either a file or a directory: + if path.is_dir(): + globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt"] + files = [file for glob in globs for file in path.glob(glob)] + if not files: + # Try GGML too, but with lower priority, since if both a non-GGML + # model and a GGML model exist in the same directory, we assume the + # latter was converted from the former. + files = list(path.glob("ggml-model*.bin*")) + if not files: + raise Exception(f"Can't find model in directory {path}") + if len(files) > 1: + raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}") + path = files[0] + + paths = find_multifile_paths(path) + models_plus: List[ModelPlus] = [] + for path in paths: + print(f"Loading model file {path}") + models_plus.append(lazy_load_file(path)) + + model_plus = merge_multifile_models(models_plus) + return model_plus + + +def filter_and_sort_tensors(model: LazyModel) -> LazyModel: + return {name: model[name] for name in TENSORS_LIST if name in model} + + +def load_vocab(path: Path) -> SentencePieceVocab: + # Be extra-friendly and accept either a file or a directory. Also, if it's + # a directory, it might be the model directory, and tokenizer.model might + # be in the parent of that. + if path.is_dir(): + path2 = path / "tokenizer.model" + # Use `.parent` instead of /.. to handle the symlink case better. + path3 = path.parent / "tokenizer.model" + if path2.exists(): + path = path2 + elif path3.exists(): + path = path3 + else: + raise FileNotFoundError(f"Could not find tokenizer.model in {path} or its parent; if it's in another directory, pass the directory as --vocab-dir") + added_tokens_path = path.parent / "added_tokens.json" + print(f"Loading vocab file {path}") + return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) + + +def default_outfile(model_paths: List[Path], params: Params) -> Path: + namestr = { + GGMLFileType.AllF32: "f32", + GGMLFileType.MostlyF16: "f16", + GGMLFileType.MostlyQ4_1: "q4_1", + GGMLFileType.PerLayerIsQ4_1: "q4_1", + }[params.file_type] + ret = model_paths[0].parent / f"ggml-model-{namestr}.bin" + if ret in model_paths: + sys.stderr.write(f"Error: Default output path ({ret}) would overwrite the input. Please explicitly specify a path using --outfile.\n") + sys.exit(1) + return ret + + +def do_dump_model(model_plus: ModelPlus) -> None: + print(f"model_plus.paths = {model_plus.paths!r}") + print(f"model_plus.format = {model_plus.format!r}") + print(f"model_plus.vocab = {model_plus.vocab!r}") + for name, lazy_tensor in model_plus.model.items(): + print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") + + +def main(args_in: Optional[List[str]] = None) -> None: + parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") + parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") + parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outtype", choices=["f32", "f16", "q4_1"], help="output format (default: based on input)") + parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") + args = parser.parse_args(args_in) + + vocab: Vocab + if args.dump_single: + model_plus = lazy_load_file(args.model) + do_dump_model(model_plus) + elif args.vocab_only: + vocab = load_vocab(args.vocab_dir or args.model) + assert args.outfile, "need --outfile if using --vocab-only" + outfile = args.outfile + OutputFile.write_vocab_only(outfile, vocab) + print(f"Wrote {outfile}") + else: + model_plus = load_some_model(args.model) + if args.dump: + do_dump_model(model_plus) + return + if model_plus.vocab is not None and args.vocab_dir is None: + vocab = model_plus.vocab + else: + vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent + vocab = load_vocab(vocab_dir) + model = model_plus.model + model = do_necessary_conversions(model) + output_type = pick_output_type(model, args.outtype) + model = convert_to_output_type(model, output_type) + params = Params.guessed(model, output_type) + outfile = args.outfile or default_outfile(model_plus.paths, params) + OutputFile.write_all(outfile, params, model, vocab) + print(f"Wrote {outfile}") + + +if __name__ == '__main__': + main() diff --git a/examples/common.cpp b/examples/common.cpp index 0772dbfe1..eaa5aceea 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -2,10 +2,13 @@ #include #include +#include #include +#include #include #include #include +#include #if defined (_WIN32) #include @@ -23,6 +26,43 @@ extern "C" __declspec(dllimport) int __stdcall WideCharToMultiByte(unsigned int #define CP_UTF8 65001 #endif +void split_args(const std::string & args_string, std::vector & output_args) +{ + std::string current_arg = ""; + bool in_quotes = false; + char quote_type; + + for (char c : args_string) { + if (c == '"' || c == '\'') { + if (!in_quotes) { + in_quotes = true; + quote_type = c; + } else if (quote_type == c) { + in_quotes = false; + } else { + current_arg += c; + } + } else if (in_quotes) { + current_arg += c; + } else if (std::isspace(c)) { + if (current_arg != "") { + output_args.push_back(current_arg); + current_arg = ""; + } + } else { + current_arg += c; + } + } + + if (current_arg != "") { + output_args.push_back(current_arg); + } +} + +std::string unescape(const std::string & str) { + return std::regex_replace(str, std::regex("\\\\n"), "\n"); +} + bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { // determine sensible default number of threads. // std::thread::hardware_concurrency may not be equal to the number of cores, or may return 0. @@ -40,28 +80,11 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { std::string arg; gpt_params default_params; + // get additional arguments from config files + std::vector args; for (int i = 1; i < argc; i++) { arg = argv[i]; - - if (arg == "-s" || arg == "--seed") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.seed = std::stoi(argv[i]); - } else if (arg == "-t" || arg == "--threads") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads = std::stoi(argv[i]); - } else if (arg == "-p" || arg == "--prompt") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.prompt = argv[i]; - } else if (arg == "-f" || arg == "--file") { + if (arg == "--config") { if (++i >= argc) { invalid_param = true; break; @@ -72,85 +95,153 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } + std::string args_string; + std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(args_string)); + if (args_string.back() == '\n') { + args_string.pop_back(); + } + split_args(args_string, args); + for (int j = 0; j < args.size(); j++) { + args[j] = unescape(args[j]); + } + } else { + args.emplace_back(argv[i]); + } + } + + // parse args + int args_c = static_cast(args.size()); + for (int i = 0; i < args_c && !invalid_param; i++) { + arg = args[i]; + + if (arg == "-s" || arg == "--seed") { + if (++i >= args_c) { + invalid_param = true; + break; + } + params.seed = std::stoi(args[i]); + } else if (arg == "-t" || arg == "--threads") { + if (++i >= args_c) { + invalid_param = true; + break; + } + params.n_threads = std::stoi(args[i]); + } else if (arg == "-p" || arg == "--prompt") { + if (++i >= args_c) { + invalid_param = true; + break; + } + params.prompt = args[i]; + } else if (arg == "-f" || arg == "--file") { + if (++i >= args_c) { + invalid_param = true; + break; + } + std::ifstream file(args[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", args[i].c_str()); + invalid_param = true; + break; + } std::copy(std::istreambuf_iterator(file), std::istreambuf_iterator(), back_inserter(params.prompt)); if (params.prompt.back() == '\n') { params.prompt.pop_back(); } } else if (arg == "-n" || arg == "--n_predict") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.n_predict = std::stoi(argv[i]); + params.n_predict = std::stoi(args[i]); } else if (arg == "--top_k") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.top_k = std::stoi(argv[i]); + params.top_k = std::stoi(args[i]); } else if (arg == "-c" || arg == "--ctx_size") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.n_ctx = std::stoi(argv[i]); + params.n_ctx = std::stoi(args[i]); } else if (arg == "--memory_f32") { params.memory_f16 = false; } else if (arg == "--top_p") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.top_p = std::stof(argv[i]); + params.top_p = std::stof(args[i]); } else if (arg == "--temp") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.temp = std::stof(argv[i]); + params.temp = std::stof(args[i]); } else if (arg == "--repeat_last_n") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.repeat_last_n = std::stoi(argv[i]); + params.repeat_last_n = std::stoi(args[i]); } else if (arg == "--repeat_penalty") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.repeat_penalty = std::stof(argv[i]); + params.repeat_penalty = std::stof(args[i]); } else if (arg == "-b" || arg == "--batch_size") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.n_batch = std::stoi(argv[i]); + params.n_batch = std::stoi(args[i]); params.n_batch = std::min(512, params.n_batch); } else if (arg == "--keep") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.n_keep = std::stoi(argv[i]); + params.n_keep = std::stoi(args[i]); } else if (arg == "-m" || arg == "--model") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.model = argv[i]; + params.model = args[i]; } else if (arg == "-i" || arg == "--interactive") { params.interactive = true; } else if (arg == "--embedding") { params.embedding = true; + } else if (arg == "--clean-interface") { + params.clean_interface = true; } else if (arg == "--interactive-start") { params.interactive = true; } else if (arg == "--interactive-first") { params.interactive_start = true; } else if (arg == "-ins" || arg == "--instruct") { - params.instruct = true; + fprintf(stderr, "\n\nWarning: instruct mode is deprecated! Use: \n" + "--clean-interface " + "--interactive-first " + "--keep -1 " + "--ins-prefix-bos " + "--ins-prefix \"\\n\\n### Instruction:\\n\\n\" " + "--ins-suffix \"\\n\\n### Response:\\n\\n\" " + "-r \"### Instruction:\\n\\n\" " + "\n\n"); + // params.instruct = true; + params.clean_interface = true; + params.interactive_start = true; + params.n_keep = -1; + params.instruct_prefix_bos = true; + params.instruct_prefix = "\n\n### Instruction:\n\n"; + params.instruct_suffix = "\n\n### Response:\n\n"; + params.antiprompt.push_back("### Instruction:\n\n"); } else if (arg == "--color") { params.use_color = true; + } else if (arg == "--disable-multiline") { + params.multiline_mode = false; } else if (arg == "--mlock") { params.use_mlock = true; } else if (arg == "--no-mmap") { @@ -160,65 +251,94 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } else if (arg == "--verbose-prompt") { params.verbose_prompt = true; } else if (arg == "-r" || arg == "--reverse-prompt") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.antiprompt.push_back(argv[i]); + params.antiprompt.push_back(args[i]); + } else if (arg == "--stop-prompt") { + if (++i >= args_c) { + invalid_param = true; + break; + } + params.stopprompt.push_back(args[i]); + } else if (arg == "--rm-trailing-space-workaround") { + params.rm_trailing_space_workaround = true; } else if (arg == "--perplexity") { params.perplexity = true; } else if (arg == "--ignore-eos") { params.ignore_eos = true; } else if (arg == "--n_parts") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.n_parts = std::stoi(argv[i]); + params.n_parts = std::stoi(args[i]); } else if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, default_params); + gpt_print_usage(argv[0], default_params); exit(0); } else if (arg == "--random-prompt") { params.random_prompt = true; } else if (arg == "--in-prefix") { - if (++i >= argc) { + if (++i >= args_c) { invalid_param = true; break; } - params.input_prefix = argv[i]; + params.input_prefix = args[i]; + } else if (arg == "--ins-prefix-bos") { + params.instruct_prefix_bos = true; + } else if (arg == "--ins-prefix") { + if (++i >= args_c) { + invalid_param = true; + break; + } + params.instruct_prefix = args[i]; + } else if (arg == "--ins-suffix-bos") { + params.instruct_suffix_bos = true; + } else if (arg == "--ins-suffix") { + if (++i >= args_c) { + invalid_param = true; + break; + } + params.instruct_suffix = args[i]; } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - gpt_print_usage(argc, argv, default_params); + gpt_print_usage(argv[0], default_params); exit(1); } } if (invalid_param) { fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - gpt_print_usage(argc, argv, default_params); + gpt_print_usage(argv[0], default_params); exit(1); } return true; } -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { - fprintf(stderr, "usage: %s [options]\n", argv[0]); +void gpt_print_usage(char * argv_0, const gpt_params & params) { + fprintf(stderr, "usage: %s [options]\n", argv_0); fprintf(stderr, "\n"); fprintf(stderr, "options:\n"); fprintf(stderr, " -h, --help show this help message and exit\n"); fprintf(stderr, " -i, --interactive run in interactive mode\n"); fprintf(stderr, " --interactive-first run in interactive mode and wait for input right away\n"); - fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n"); + fprintf(stderr, " --clean-interface hides input prefix & suffix and displays '>' instead\n"); fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n"); fprintf(stderr, " run in interactive mode and poll user input upon seeing PROMPT (can be\n"); fprintf(stderr, " specified more than once for multiple prompts).\n"); fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n"); + fprintf(stderr, " --disable-multiline disable multiline mode (use Ctrl+D on Linux/Mac and Ctrl+Z then Return on Windows to toggle multiline)\n"); fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for <= 0)\n"); fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); fprintf(stderr, " -p PROMPT, --prompt PROMPT\n"); fprintf(stderr, " prompt to start generation with (default: empty)\n"); fprintf(stderr, " --random-prompt start with a randomized prompt.\n"); fprintf(stderr, " --in-prefix STRING string to prefix user inputs with (default: empty)\n"); + fprintf(stderr, " --ins-prefix STRING (instruct) prefix user inputs with tokenized string (default: empty)\n"); + fprintf(stderr, " --ins-prefix-bos (instruct) prepend bos token to instruct prefix.\n"); + fprintf(stderr, " --ins-suffix STRING (instruct) suffix user inputs with tokenized string (default: empty)\n"); + fprintf(stderr, " --ins-suffix-bos (instruct) prepend bos token to instruct suffix.\n"); fprintf(stderr, " -f FNAME, --file FNAME\n"); fprintf(stderr, " prompt file to start generation.\n"); fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d, -1 = infinity)\n", params.n_predict); @@ -328,3 +448,61 @@ void win32_utf8_encode(const std::wstring & wstr, std::string & str) { str = strTo; } #endif + +bool get_input_text(std::string & input_text, bool eof_toggled_multiline_mode) { + bool another_line = true; + bool is_eof_multiline_toggled = false; + do { + std::string line; +#if defined(_WIN32) + auto & stdcin = std::wcin; + std::wstring wline; + if (!std::getline(stdcin, wline)) { + // input stream is bad or EOF received + if (stdcin.bad()) { + fprintf(stderr, "%s: error: input stream bad\n", __func__); + return 1; + } + } + win32_utf8_encode(wline, line); +#else + auto & stdcin = std::cin; + if (!std::getline(stdcin, line)) { + // input stream is bad or EOF received + if (stdcin.bad()) { + fprintf(stderr, "%s: error: input stream bad\n", __func__); + return 1; + } + } +#endif + if (stdcin.eof()) { + stdcin.clear(); + stdcin.seekg(0, std::ios::beg); + if (!eof_toggled_multiline_mode) { + another_line = false; + } else { + is_eof_multiline_toggled = !is_eof_multiline_toggled; + if (is_eof_multiline_toggled) { + input_text += line; + continue; + } + } + } + if (!eof_toggled_multiline_mode) { + if (line.empty() || line.back() != '\\') { + another_line = false; + } else { + line.pop_back(); // Remove the continue character + } + } else { + if (!is_eof_multiline_toggled) { + another_line = false; + } + } + input_text += line; + if (another_line) { + input_text += '\n'; // Append the line to the result + } + } while (another_line); + return true; +} diff --git a/examples/common.h b/examples/common.h index 1ea6f7445..df8e4c6cc 100644 --- a/examples/common.h +++ b/examples/common.h @@ -14,14 +14,14 @@ // struct gpt_params { - int32_t seed = -1; // RNG seed - int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); - int32_t n_predict = 128; // new tokens to predict - int32_t repeat_last_n = 64; // last n tokens to penalize - int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) - int32_t n_ctx = 512; // context size - int32_t n_batch = 8; // batch size for prompt processing - int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t seed = -1; // RNG seed + int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); // max 4 threads (default) + int32_t n_predict = 128; // new tokens to predict + int32_t repeat_last_n = 64; // last n tokens to penalize + int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions) + int32_t n_ctx = 512; // context size + int32_t n_batch = 8; // batch size for prompt processing + int32_t n_keep = 0; // number of tokens to keep from initial prompt (-1 for all) // sampling parameters int32_t top_k = 40; @@ -33,8 +33,15 @@ struct gpt_params { std::string prompt = ""; std::string input_prefix = ""; // string to prefix user inputs with + std::string instruct_prefix = ""; // prefix user inputs with tokenized string + bool instruct_prefix_bos = false; // prepend bos token to instruct prefix + std::string instruct_suffix = ""; // suffix user inputs with tokenized string + bool instruct_suffix_bos = false; // prepend bos token to instruct suffix std::vector antiprompt; // string upon seeing which more user input is prompted + std::vector stopprompt; // string upon seeing which more user input is prompted (without adding instruct prefixes and suffixes) + + bool rm_trailing_space_workaround = false; // workaround for removing trailing space from reverse/stop prompts bool memory_f16 = true; // use f16 instead of f32 for memory kv bool random_prompt = false; // do not randomize prompt if none provided @@ -51,11 +58,14 @@ struct gpt_params { bool use_mlock = false; // use mlock to keep model in memory bool mem_test = false; // compute maximum memory usage bool verbose_prompt = false; // print prompt tokens before generation + + bool clean_interface = false; // hides input prefix & suffix and displays '>' + bool multiline_mode = true; // enables multi-line mode, to send input press CTRL+D on Linux/Max, Ctrl+Z then Return on Windows }; bool gpt_params_parse(int argc, char ** argv, gpt_params & params); -void gpt_print_usage(int argc, char ** argv, const gpt_params & params); +void gpt_print_usage(char * argv_0, const gpt_params & params); std::string gpt_random_prompt(std::mt19937 & rng); @@ -95,3 +105,5 @@ void set_console_color(console_state & con_st, console_color_t color); void win32_console_init(bool enable_color); void win32_utf8_encode(const std::wstring & wstr, std::string & str); #endif + +bool get_input_text(std::string & input_text, bool escape_newline_mode); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index c22c7f436..e6ce77134 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -34,7 +34,8 @@ llama_context * ctx; #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) void sigint_handler(int signo) { set_console_color(con_st, CONSOLE_COLOR_DEFAULT); - printf("\n"); // this also force flush stdout. + fflush(stdout); + fflush(stderr); if (signo == SIGINT) { if (!is_interacting) { is_interacting=true; @@ -144,6 +145,8 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } + bool instruct_mode = !params.instruct_prefix.empty() || !params.instruct_suffix.empty(); + // params.prompt = R"(// this function checks if the number n is prime //bool is_prime(int n) {)"; @@ -206,22 +209,20 @@ int main(int argc, char ** argv) { } // number of tokens to keep when resetting context - if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size() || params.instruct) { + if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size()) { params.n_keep = (int)embd_inp.size(); } // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true); - const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false); - - // in instruct mode, we inject a prefix and a suffix to each input by the user - if (params.instruct) { - params.interactive_start = true; - params.antiprompt.push_back("###"); + const auto inp_pfx = ::llama_tokenize(ctx, params.instruct_prefix, params.instruct_prefix_bos); + std::string instruct_suffix = params.instruct_suffix; + if (params.rm_trailing_space_workaround) { + if (instruct_suffix.back() == ' ') { instruct_suffix.pop_back(); } } + const auto inp_sfx = ::llama_tokenize(ctx, instruct_suffix, params.instruct_suffix_bos); // enable interactive mode if reverse prompt or interactive start is specified - if (params.antiprompt.size() != 0 || params.interactive_start) { + if (params.antiprompt.size() != 0 || params.stopprompt.size() != 0 || params.interactive_start) { params.interactive = true; } @@ -263,10 +264,21 @@ int main(int argc, char ** argv) { fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str()); } } + if (params.stopprompt.size()) { + for (auto stopprompt : params.stopprompt) { + fprintf(stderr, "Stop prompt: '%s'\n", stopprompt.c_str()); + } + } if (!params.input_prefix.empty()) { fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); } + if (!params.instruct_prefix.empty()) { + fprintf(stderr, "Instruct prefix %s: '%s'\n", params.instruct_prefix_bos ? "(with bos token)" : "", params.instruct_prefix.c_str()); + } + if (!params.instruct_suffix.empty()) { + fprintf(stderr, "Instruct suffix %s: '%s'\n", params.instruct_suffix_bos ? "(with bos token)" : "", params.instruct_suffix.c_str()); + } } fprintf(stderr, "sampling: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty); @@ -282,12 +294,29 @@ int main(int argc, char ** argv) { #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) " - Press Ctrl+C to interject at any time.\n" #endif - " - Press Return to return control to LLaMa.\n" - " - If you want to submit another line, end your input in '\\'.\n\n"); + ); + if (params.multiline_mode) { + fprintf(stderr, " - Press Return to return control to LLaMa.\n" +#if defined (_WIN32) + " - [MULTILINE MODE] Press Ctrl+Z then Return (EOF) to toggle.\n\n"); +#else + " - [MULTILINE MODE] Press Ctrl+D (EOF) to toggle.\n\n"); +#endif + } + else { + fprintf(stderr, " - Press Return to return control to LLaMa.\n" + " - If you want to submit another line, end your input in '\\'.\n\n"); + } is_interacting = params.interactive_start; } - bool is_antiprompt = false; + struct Antiprompt { + bool any = false; + bool trailing_space = false; + size_t len; + bool is_stop_prompt = false; + } antiprompt; + bool input_noecho = false; int n_past = 0; @@ -357,7 +386,7 @@ int main(int argc, char ** argv) { } // replace end of text token with newline token when in interactive mode - if (id == llama_token_eos() && params.interactive && !params.instruct) { + if (id == llama_token_eos() && params.interactive && !instruct_mode) { id = llama_token_newline.front(); if (params.antiprompt.size() != 0) { // tokenize and inject first reverse prompt @@ -405,27 +434,72 @@ int main(int argc, char ** argv) { // check if we should prompt the user for more if (params.interactive && (int) embd_inp.size() <= n_consumed) { - // check for reverse prompt - if (params.antiprompt.size()) { + // check for reverse prompt or stop prompt + if (params.antiprompt.size() || params.stopprompt.size()) { std::string last_output; for (auto id : last_n_tokens) { last_output += llama_token_to_str(ctx, id); } - is_antiprompt = false; + antiprompt.any = false; + antiprompt.is_stop_prompt = false; // Check if each of the reverse prompts appears at the end of the output. - for (std::string & antiprompt : params.antiprompt) { - if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) { + for (std::string & prompt : params.antiprompt) { + if (params.rm_trailing_space_workaround) { + antiprompt.trailing_space = prompt.back() == ' '; + antiprompt.len = prompt.length() - (antiprompt.trailing_space ? 1 : 0); + } + if (last_output.find(prompt.c_str(), last_output.length() - antiprompt.len, antiprompt.len) != std::string::npos) { is_interacting = true; - is_antiprompt = true; + antiprompt.any = true; set_console_color(con_st, CONSOLE_COLOR_USER_INPUT); fflush(stdout); break; } } + if (!antiprompt.any) { + for (std::string & prompt : params.stopprompt) { + if (params.rm_trailing_space_workaround) { + antiprompt.trailing_space = prompt.back() == ' '; + antiprompt.len = prompt.length() - (antiprompt.trailing_space ? 1 : 0); + } + if (last_output.find(prompt.c_str(), last_output.length() - antiprompt.len, antiprompt.len) != std::string::npos) { + is_interacting = true; + antiprompt.any = true; + antiprompt.is_stop_prompt = true; + set_console_color(con_st, CONSOLE_COLOR_USER_INPUT); + fflush(stdout); + break; + } + } + } } - if (n_past > 0 && is_interacting) { + if (n_past > 0 && is_interacting) + { + std::string buffer; + if (!params.clean_interface && !params.instruct_prefix.empty() && !antiprompt.any) { + // avoid printing again user's new line (TODO: try to revert enter press and print newline) + int i = params.instruct_prefix.front() == '\n' ? 1 : 0; + for (; i < inp_pfx.size(); i++) { + printf("%s", llama_token_to_str(ctx, inp_pfx[i])); + } + fflush(stdout); + } + if (params.rm_trailing_space_workaround) { + // add only if not stopprompt (as stopprompt could be used to pause + // assistant and then continue without input - adding back trailing + // space may mess it up.) + if (!antiprompt.is_stop_prompt && antiprompt.any && antiprompt.trailing_space) { + // add back removed trailing space to buffer(workaround) + buffer += ' '; + if (!params.clean_interface) { + printf("%s", buffer.c_str()); + } + fflush(stdout); + } + } + // potentially set color to indicate we are taking user input set_console_color(con_st, CONSOLE_COLOR_USER_INPUT); @@ -434,43 +508,39 @@ int main(int argc, char ** argv) { signal(SIGINT, sigint_handler); #endif - if (params.instruct) { + if (params.clean_interface) { printf("\n> "); } - std::string buffer; if (!params.input_prefix.empty()) { buffer += params.input_prefix; printf("%s", buffer.c_str()); } - std::string line; - bool another_line = true; - do { -#if defined(_WIN32) - std::wstring wline; - if (!std::getline(std::wcin, wline)) { - // input stream is bad or EOF received - return 0; - } - win32_utf8_encode(wline, line); -#else - if (!std::getline(std::cin, line)) { - // input stream is bad or EOF received - return 0; - } -#endif - if (line.empty() || line.back() != '\\') { - another_line = false; - } else { - line.pop_back(); // Remove the continue character - } - buffer += line + '\n'; // Append the line to the result - } while (another_line); + if (!get_input_text(buffer, params.multiline_mode)) { + // input stream is bad + return 1; + } + if (!antiprompt.is_stop_prompt) { + buffer += "\n"; + } // done taking input, reset color set_console_color(con_st, CONSOLE_COLOR_DEFAULT); + if (!params.clean_interface && !params.instruct_suffix.empty() && !antiprompt.is_stop_prompt) { + // avoid printing again user's new line (TODO: try to revert enter press and print newline) + int i = params.instruct_suffix.front() == '\n' ? 1 : 0; + for (; i < inp_sfx.size(); i++) { + printf("%s", llama_token_to_str(ctx, inp_sfx[i])); + } + // if (remove trailing space workaround) { + // We won't add back removed trailing space here, because assistant continues here, + // and it may mess up it's output (remove trailing space workaround). + // } + fflush(stdout); + } + // Add tokens to embd only if the input buffer is non-empty // Entering a empty line lets the user pass control back if (buffer.length() > 1) { @@ -478,8 +548,8 @@ int main(int argc, char ** argv) { if (command(buffer, params, n_ctx) == 0) { // this is not a command, run normally. is_command = false; - // instruct mode: insert instruction prefix - if (params.instruct && !is_antiprompt) { + // insert input prefix + if (!params.instruct_prefix.empty() && !antiprompt.any) { n_consumed = embd_inp.size(); embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); } @@ -487,11 +557,9 @@ int main(int argc, char ** argv) { auto line_inp = ::llama_tokenize(ctx, buffer, false); embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); - // instruct mode: insert response suffix - if (params.instruct) { + // insert response suffix + if (!params.instruct_suffix.empty() && !antiprompt.is_stop_prompt) { embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - } - n_remain -= line_inp.size(); } else { // this was a command, so we need to stop anything more from printing. @@ -509,7 +577,7 @@ int main(int argc, char ** argv) { // end of text token if (!embd.empty() && embd.back() == llama_token_eos()) { - if (params.instruct) { + if (instruct_mode) { is_interacting = true; } else { fprintf(stderr, " [end of text]\n"); diff --git a/flake.nix b/flake.nix index 91d2edd79..5363052b1 100644 --- a/flake.nix +++ b/flake.nix @@ -10,7 +10,6 @@ inherit system; }; llama-python = pkgs.python310.withPackages (ps: with ps; [ - torch numpy sentencepiece ]); diff --git a/ggml.c b/ggml.c index 42e3ee314..ce48b78ad 100644 --- a/ggml.c +++ b/ggml.c @@ -2344,14 +2344,14 @@ static void ggml_vec_dot_q4_1(const int n, float * restrict s, const void * rest #if defined(__ARM_FEATURE_DOTPROD) // dot product into int32x4_t - int32x4_t p_0 = vdotq_s32(vdupq_n_s32(0), v0_0l, v1_0l); - int32x4_t p_1 = vdotq_s32(vdupq_n_s32(0), v0_1l, v1_1l); + uint32x4_t p_0 = vdotq_u32(vdupq_n_u32(0), v0_0l, v1_0l); + uint32x4_t p_1 = vdotq_u32(vdupq_n_u32(0), v0_1l, v1_1l); - p_0 = vdotq_s32(p_0, v0_0h, v1_0h); - p_1 = vdotq_s32(p_1, v0_1h, v1_1h); + p_0 = vdotq_u32(p_0, v0_0h, v1_0h); + p_1 = vdotq_u32(p_1, v0_1h, v1_1h); - sum11 += x0->d*y0->d*vaddvq_s32(p_0); - sum11 += x1->d*y1->d*vaddvq_s32(p_1); + sum11 += x0->d*y0->d*vaddvq_u32(p_0); + sum11 += x1->d*y1->d*vaddvq_u32(p_1); #else const uint16x8_t pl0l = vmull_u8(vget_low_u8 (v0_0l), vget_low_u8 (v1_0l)); const uint16x8_t pl0h = vmull_u8(vget_high_u8(v0_0l), vget_high_u8(v1_0l)); @@ -2712,9 +2712,12 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = { "FLASH_ATTN", "FLASH_FF", + + "MAP_UNARY", + "MAP_BINARY", }; -static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36"); +static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2757,9 +2760,12 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "flash_attn(x)", "flash_ff(x)", + + "f(x)", + "f(x,y)", }; -static_assert(GGML_OP_COUNT == 36, "GGML_OP_COUNT != 36"); +static_assert(GGML_OP_COUNT == 38, "GGML_OP_COUNT != 38"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); @@ -3054,9 +3060,11 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { return NULL; } + const size_t mem_size = (params.mem_size + GGML_MEM_ALIGN - 1) & ~(GGML_MEM_ALIGN - 1); + *ctx = (struct ggml_context) { - /*.mem_size =*/ params.mem_size, - /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(params.mem_size), + /*.mem_size =*/ mem_size, + /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : GGML_ALIGNED_MALLOC(mem_size), /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, /*.no_alloc =*/ params.no_alloc, /*.n_objects =*/ 0, @@ -3066,7 +3074,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { /*.scratch_save =*/ { 0, 0, NULL, }, }; - GGML_ASSERT(ctx->mem_buffer != NULL); // check for allocation failure + GGML_ASSERT(ctx->mem_buffer != NULL); ggml_assert_aligned(ctx->mem_buffer); @@ -4905,6 +4913,90 @@ struct ggml_tensor * ggml_flash_ff( return result; } +// ggml_map_unary + +struct ggml_tensor * ggml_map_unary_impl_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_unary_op_f32_t fun, + bool inplace) { + bool is_node = false; + + if (!inplace && a->grad) { + is_node = true; + } + + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); + *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_MAP_UNARY; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->opt[0] = addr_tensor; + + return result; +} + +struct ggml_tensor * ggml_map_unary_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_unary_op_f32_t fun) { + return ggml_map_unary_impl_f32(ctx, a, fun, false); +} + +struct ggml_tensor * ggml_map_unary_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_unary_op_f32_t fun) { + return ggml_map_unary_impl_f32(ctx, a, fun, true); +} + +// ggml_map_binary + +struct ggml_tensor * ggml_map_binary_impl_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_binary_op_f32_t fun, + bool inplace) { + GGML_ASSERT(ggml_are_same_shape(a, b)); + + bool is_node = false; + + if (!inplace && (a->grad || b->grad)) { + is_node = true; + } + + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); + *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_MAP_BINARY; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = b; + result->opt[0] = addr_tensor; + + return result; +} + +struct ggml_tensor * ggml_map_binary_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_binary_op_f32_t fun) { + return ggml_map_binary_impl_f32(ctx, a, b, fun, false); +} + +struct ggml_tensor * ggml_map_binary_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_binary_op_f32_t fun) { + return ggml_map_binary_impl_f32(ctx, a, b, fun, true); +} + //////////////////////////////////////////////////////////////////////////////// void ggml_set_param( @@ -7507,6 +7599,8 @@ static void ggml_compute_forward_rope_f32( // row index used to determine which thread to use int ir = 0; + const float theta_scale = powf(10000.0, -2.0f/n_dims); + for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) { const int p = (mode == 0 ? n_past + i2 : i2); @@ -7514,11 +7608,13 @@ static void ggml_compute_forward_rope_f32( if (ir++ < ir0) continue; if (ir > ir1) break; - for (int i0 = 0; i0 < n_dims; i0 += 2) { - const float theta = powf(10000.0, ((float)-i0)/n_dims); + float theta = (float)p; - const float cos_theta = cosf(p*theta); - const float sin_theta = sinf(p*theta); + for (int i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cosf(theta); + const float sin_theta = sinf(theta); + + theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -7580,6 +7676,8 @@ static void ggml_compute_forward_rope_f16( // row index used to determine which thread to use int ir = 0; + const float theta_scale = powf(10000.0, -2.0f/n_dims); + for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = (mode == 0 ? 0 : n_past); i2 < ne2; i2++) { const int p = (mode == 0 ? n_past + i2 : i2); @@ -7587,11 +7685,13 @@ static void ggml_compute_forward_rope_f16( if (ir++ < ir0) continue; if (ir > ir1) break; - for (int i0 = 0; i0 < n_dims; i0 += 2) { - const float theta = powf(10000.0, ((float)-i0)/n_dims); + float theta = (float)p; - const float cos_theta = cosf(p*theta); - const float sin_theta = sinf(p*theta); + for (int i0 = 0; i0 < n_dims; i0 += 2) { + const float cos_theta = cosf(theta); + const float sin_theta = sinf(theta); + + theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -8865,6 +8965,111 @@ static void ggml_compute_forward_flash_ff( } } +// ggml_compute_forward_map_unary + +static void ggml_compute_forward_map_unary_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert( dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + + +static void ggml_compute_forward_map_unary( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst, + const ggml_unary_op_f32_t fun) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_unary_f32(params, src0, dst, fun); + } break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_I8: + case GGML_TYPE_I16: + case GGML_TYPE_I32: + case GGML_TYPE_F16: + case GGML_TYPE_COUNT: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_map_binary + +static void ggml_compute_forward_map_binary_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert( dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + assert(src1->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + fun(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1])), + (float *) ((char *) src1->data + i*(src1->nb[1]))); + } +} + + +static void ggml_compute_forward_map_binary( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst, + const ggml_binary_op_f32_t fun) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_binary_f32(params, src0, src1, dst, fun); + } break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_I8: + case GGML_TYPE_I16: + case GGML_TYPE_I32: + case GGML_TYPE_F16: + case GGML_TYPE_COUNT: + { + GGML_ASSERT(false); + } break; + } +} + ///////////////////////////////// static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { @@ -9014,6 +9219,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_flash_ff(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], tensor->opt[2], tensor); } break; + case GGML_OP_MAP_UNARY: + { + const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->opt[0]->data); + ggml_compute_forward_map_unary(params, tensor->src0, tensor, fun); + } + break; + case GGML_OP_MAP_BINARY: + { + const ggml_binary_op_f32_t fun = *((ggml_binary_op_f32_t *)tensor->opt[0]->data); + ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun); + } + break; case GGML_OP_NONE: { // nop @@ -9273,6 +9490,11 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // not supported } break; + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + { + GGML_ASSERT(false); // not supported + } break; case GGML_OP_NONE: { // nop @@ -9765,6 +9987,11 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) work_size = MAX(work_size, cur); } break; + case GGML_OP_MAP_UNARY: + case GGML_OP_MAP_BINARY: + { + node->n_tasks = 1; + } break; case GGML_OP_NONE: { node->n_tasks = 1; diff --git a/ggml.h b/ggml.h index c06c09e06..bdff0b4de 100644 --- a/ggml.h +++ b/ggml.h @@ -253,6 +253,9 @@ enum ggml_op { GGML_OP_FLASH_ATTN, GGML_OP_FLASH_FF, + GGML_OP_MAP_UNARY, + GGML_OP_MAP_BINARY, + GGML_OP_COUNT, }; @@ -652,6 +655,21 @@ struct ggml_tensor * ggml_flash_ff( struct ggml_tensor * c0, struct ggml_tensor * c1); +// Mapping operations +typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *); +typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *); + +struct ggml_tensor * ggml_map_unary_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_unary_op_f32_t fun); + +struct ggml_tensor * ggml_map_binary_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_binary_op_f32_t fun); + // // automatic differentiation // diff --git a/migrate-ggml-2023-03-30-pr613.py b/migrate-ggml-2023-03-30-pr613.py deleted file mode 100644 index b6ef2476e..000000000 --- a/migrate-ggml-2023-03-30-pr613.py +++ /dev/null @@ -1,311 +0,0 @@ -# Migrate ggml file(s) with ggmf magic to ggml file with ggjt magic -# -# We caused a breaking change to the file format on 2023-03-30 in: -# https://github.com/ggerganov/llama.cpp/pull/613 -# -# (1) If you still have the Meta LLaMA .pth files, then close this -# file now; you can just run `convert-pth-to-ggml.py` again to -# migrate to the new format. The tool is easier to use too. It -# isn't necessary anymore to manage split output files because -# the new format always combines things into a single file. -# -# (2) If you deleted the Meta LLaMA .pth files due to save on disk -# space, then this tool is intended to help you. Please check -# out the instructions below. -# -# USAGE -# -# python migrate-ggml-2023-03-30-pr613.py INPUT OUTPUT -# -# PREREQUISITES -# -# pip install numpy -# cd llama.cpp -# make -j4 -# -# EXAMPLE (7B MODEL) -# -# # you can replace all the 'f16' with 'q4_0' if you're using quantized weights -# python migrate-ggml-2023-03-30-pr613.py models/7B/ggml-model-f16.bin models/7B/ggml-model-f16-ggjt.bin -# -# # check that it works -# ./main -m models/7B/ggml-model-f16-ggjt.bin -p 'Question: Do you love me?' -# -# # you can delete the old files -# rm -f models/7B/ggml-model-f16.bin -# mv models/7B/ggml-model-f16-ggjt.bin models/7B/ggml-model-f16.bin -# -# EXAMPLE (13B MODEL) -# -# # you can replace all the 'f16' with 'q4_0' if you're using quantized weights -# python migrate-ggml-2023-03-30-pr613.py models/13B/ggml-model-f16.bin models/13B/ggml-model-f16-ggjt.bin -# -# # check that it works -# ./main -m models/13B/ggml-model-f16-ggjt.bin -p 'Question: Do you love me?' -# -# # you can delete the old files -# rm -f models/13B/ggml-model-f16.bin* -# mv models/13B/ggml-model-f16-ggjt.bin models/13B/ggml-model-f16.bin -# - -import argparse -import os -import sys -import json -import struct -import numpy as np - -QK = 32 - -GGML_TYPE_Q4_0 = 0 -GGML_TYPE_Q4_1 = 1 -GGML_TYPE_I8 = 2 -GGML_TYPE_I16 = 3 -GGML_TYPE_I32 = 4 -GGML_TYPE_F16 = 5 -GGML_TYPE_F32 = 6 - -WTYPE_NAMES = { - 0: "F32", - 1: "F16", - 2: "Q4_0", - 3: "Q4_1", -} - -WTYPES = { - 0: GGML_TYPE_F32, - 1: GGML_TYPE_F16, - 2: GGML_TYPE_Q4_0, - 3: GGML_TYPE_Q4_1, -} - -GGML_BLCK_SIZE = { - GGML_TYPE_Q4_0: QK, - GGML_TYPE_Q4_1: QK, - GGML_TYPE_I8: 1, - GGML_TYPE_I16: 1, - GGML_TYPE_I32: 1, - GGML_TYPE_F16: 1, - GGML_TYPE_F32: 1, -} - -GGML_TYPE_SIZE = { - GGML_TYPE_Q4_0: 4 + QK//2, - GGML_TYPE_Q4_1: 4*2 + QK//2, - GGML_TYPE_I8: 1, - GGML_TYPE_I16: 2, - GGML_TYPE_I32: 4, - GGML_TYPE_F16: 2, - GGML_TYPE_F32: 4, -} - -HPARAMS = [ - 'magic', # int32 - 'version', # int32 - 'n_vocab', # int32 - 'n_embd', # int32 - 'n_mult', # int32 - 'n_head', # int32 - 'n_layer', # int32 - 'n_rot', # int32 - 'f16', # int32 -] - -def read_hparams(fin): - struct_fmt = "i" * len(HPARAMS) - struct_size = struct.calcsize(struct_fmt) - buf = fin.read(struct_size) - ints = struct.unpack(struct_fmt, buf) - hparams = dict(zip(HPARAMS, ints)) - return hparams - -def write_hparams(fout, hparams): - struct_fmt = "i" * len(HPARAMS) - struct_size = struct.calcsize(struct_fmt) - ints = [hparams[h] for h in HPARAMS] - fout.write(struct.pack(struct_fmt, *ints)) - -def read_tokens(fin, hparams): - tokens = [] - for i in range(hparams['n_vocab']): - len_b = fin.read(4) - (length,) = struct.unpack("i", len_b) - word = fin.read(length) - score_b = fin.read(4) - (score,) = struct.unpack("f", score_b) - tokens.append((word, score)) - return tokens - -def write_tokens(fout, tokens): - for word, score in tokens: - fout.write(struct.pack("i", len(word))) - fout.write(word) - fout.write(struct.pack("f", score)) - -def ggml_nelements(shape): - r = 1 - for i in shape: - r *= i - return r - -def ggml_nbytes(shape, ftype): - x = ggml_nelements(shape) - t = WTYPES[ftype] - x *= GGML_TYPE_SIZE[t] - x //= GGML_BLCK_SIZE[t] - return x - -def copy_tensors(fin, fout, part_id, n_parts): - while True: - - b = fin.read(4) - if not b: break - (n_dims,) = struct.unpack("i", b) - b = fin.read(4) - (length,) = struct.unpack("i", b) - b = fin.read(4) - (ftype,) = struct.unpack("i", b) - - assert n_dims in (1, 2) - - partshape = list(range(n_dims)) - for i in range(n_dims): - b = fin.read(4) - partshape[i] = struct.unpack("i", b)[0] - partshape = list(reversed(partshape)) - - name = fin.read(length) - data = fin.read(ggml_nbytes(partshape, ftype)) - - blck_size = GGML_BLCK_SIZE[WTYPES[ftype]] - type_size = GGML_TYPE_SIZE[WTYPES[ftype]] - - print(f"Processing tensor {name} with shape: {partshape} and type: {WTYPE_NAMES[ftype]}") - - # determine dimension along which multipart tensor is sharded - # - # split_dim 0 regex: - # - output.* - # - layers.*.attention.wq.weight - # - layers.*.attention.wk.weight - # - layers.*.attention.wv.weight - # - layers.*.feed_forward.w1.weight - # - layers.*.feed_forward.w3.weight - # - # split_dim 1 regex: - # - tok_embeddings.* - # - layers.*.attention.wo.weight - # - layers.*.feed_forward.w2.weight - # - if n_dims > 1: - split_dim = 1 - if b"tok_embeddings" in name: - split_dim = 1 - elif b"layers" in name: - if b"attention.wo.weight" in name: - split_dim = 1 - elif b"feed_forward.w2.weight" in name: - split_dim = 1 - else: - split_dim = 0 - elif b"output" in name: - split_dim = 0 - - # output tensor header - fullshape = list(partshape) - if n_dims > 1: - fullshape[split_dim] *= n_parts - fout.write(struct.pack("iii", n_dims, len(name), ftype)) - for dim in reversed(fullshape): - fout.write(struct.pack("i", dim)) - fout.write(name) - - # ensure tensor data is aligned - tensor_data_offset = fout.tell() - while tensor_data_offset % QK != 0: - fout.write(struct.pack("B", 0)) - tensor_data_offset += 1 - - # output unified mappable tensor data - if n_dims == 1 or n_parts == 1: - # copy tensor which we thankfully received in one piece - if part_id == 0: - fout.write(data) - elif split_dim == 0: - # reassemble multifile tensor containing some of the rows - rows_per_chunk = partshape[0] - current_row = part_id * rows_per_chunk - bytes_per_row = fullshape[1] // blck_size * type_size - offset = current_row * bytes_per_row - fout.seek(tensor_data_offset + offset) - fout.write(data) - elif split_dim == 1: - # reassemble multifile tensor containing some of the cols - cols_per_chunk = partshape[1] - current_col = part_id * cols_per_chunk - bpr = partshape[1] // blck_size * type_size - bytes_per_row = fullshape[1] // blck_size * type_size - offset_current_col = current_col // blck_size * type_size - for row in range(partshape[0]): - offset_row = row * bytes_per_row - offset = offset_row + offset_current_col - fout.seek(tensor_data_offset + offset) - fout.write(data[row * bpr:row * bpr + bpr]) - - # advance file position to next tensor - fout.seek(tensor_data_offset + ggml_nbytes(fullshape, ftype)) - -def parse_args(): - parser = argparse.ArgumentParser(description='Migrate from GGML to new GGJT file format') - parser.add_argument('fin_path', help='your old ggml file (leave out the .1 .2 etc.)') - parser.add_argument('fout_path', help='your new ggjt file name') - return parser.parse_args() - -def main(): - args = parse_args() - assert args.fin_path - assert args.fout_path - assert args.fin_path != args.fout_path - - with open(args.fin_path, "rb") as fin: - hparams = read_hparams(fin) - tokens = read_tokens(fin, hparams) - - if hparams['magic'] == 0x67676a74: # ggjt - print(f"{args.fin_path}: input ggml has already been converted to 'ggjt' magic\n") - sys.exit(1) - - if hparams['magic'] != 0x67676d66: # ggmf - print(f"{args.fin_path}: input ggml file doesn't have expected 'ggmf' magic: {hparams['magic']:#x}\n") - sys.exit(1) - - hparams['magic'] = 0x67676a74 # ggjt - - # count number of multipart files by convention - n_parts = 1 - while True: - if os.path.exists(f"{args.fin_path}.{n_parts}"): - n_parts += 1 - else: - break - - # we output a single file for ggml - with open(args.fout_path, "wb") as fout: - write_hparams(fout, hparams) - write_tokens(fout, tokens) - offset_of_tensors = fout.tell() - # the tensors we load could be split across multiple files - for part_id in range(n_parts): - fout.seek(offset_of_tensors) - print(f"Processing part {part_id+1} of {n_parts}\n") - fin_path = args.fin_path - if part_id > 0: - fin_path += f".{part_id}" - with open(fin_path, "rb") as fin: - read_tokens(fin, read_hparams(fin)) - copy_tensors(fin, fout, part_id, n_parts) - - print(f"Done. Output file: {args.fout_path}\n") - -if __name__ == "__main__": - main() diff --git a/prompts/alpaca.txt b/prompts/alpaca.txt deleted file mode 100644 index 2224bdeb0..000000000 --- a/prompts/alpaca.txt +++ /dev/null @@ -1 +0,0 @@ -Below is an instruction that describes a task. Write a response that appropriately completes the request. diff --git a/prompts/chat-with-bob.txt b/prompts/chat-with-bob.txt deleted file mode 100644 index ad494d831..000000000 --- a/prompts/chat-with-bob.txt +++ /dev/null @@ -1,7 +0,0 @@ -Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision. - -User: Hello, Bob. -Bob: Hello. How may I help you today? -User: Please tell me the largest city in Europe. -Bob: Sure. The largest city in Europe is Moscow, the capital of Russia. -User: \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..f3944951a --- /dev/null +++ b/requirements.txt @@ -0,0 +1,2 @@ +numpy==1.24 +sentencepiece==0.1.97