diff --git a/aiserver.py b/aiserver.py index 6256a49d..7536a1a0 100644 --- a/aiserver.py +++ b/aiserver.py @@ -452,6 +452,7 @@ def emit(*args, **kwargs): return _emit(*args, **kwargs) except AttributeError: return socketio.emit(*args, **kwargs) +utils.emit = emit # marshmallow/apispec setup from apispec import APISpec @@ -879,7 +880,7 @@ def device_config(config): print(f"{colors.RED}Please enter an integer between -1 and {n_layers}.{colors.END}") logger.init_ok("Final device configuration:", status="Info") - device_list(n_layers) + device_list(n_layers, primary=breakmodel.primary_device) # If all layers are on the same device, use the old GPU generation mode while(len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] == 0): @@ -1360,6 +1361,8 @@ def general_startup(override_args=None): args = parser.parse_args(shlex.split(os.environ["KOBOLDAI_ARGS"])) else: args = parser.parse_args() + + utils.args = args set_logger_verbosity(args.verbosity) quiesce_logger(args.quiesce) @@ -1796,7 +1799,9 @@ def patch_transformers(): if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) - PreTrainedModel.from_pretrained = new_from_pretrained + if(not hasattr(PreTrainedModel, "_kai_patched")): + PreTrainedModel.from_pretrained = new_from_pretrained + PreTrainedModel._kai_patched = True if(hasattr(modeling_utils, "get_checkpoint_shard_files")): old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): @@ -2662,7 +2667,9 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal if not args.no_aria2: utils.aria2_hook(pretrained_model_name_or_path, **kwargs) return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) - PreTrainedModel.from_pretrained = new_from_pretrained + if(not hasattr(PreTrainedModel, "_kai_patched")): + PreTrainedModel.from_pretrained = new_from_pretrained + PreTrainedModel._kai_patched = True if(hasattr(modeling_utils, "get_checkpoint_shard_files")): old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): diff --git a/prompt_tuner.py b/prompt_tuner.py new file mode 100644 index 00000000..f37a8718 --- /dev/null +++ b/prompt_tuner.py @@ -0,0 +1,1082 @@ +import abc +import os +import sys +import math +import numpy as np +import termcolor +import contextlib +import traceback +import random +import zipfile +import json +import uuid +import datetime +import base64 +import pickle +import hashlib +import itertools +import functools +import bisect +import eventlet +import packaging +import gc +import time +from tqdm.auto import tqdm +import torch +import torch.nn.functional as F +from torch.nn import Embedding, CrossEntropyLoss +import transformers +from transformers import __version__ as transformers_version +from transformers import AutoTokenizer, GPT2Tokenizer, AutoConfig, AutoModelForCausalLM, GPTNeoForCausalLM, PreTrainedModel, modeling_utils +import accelerate +import accelerate.utils +from mkultra.tuning import GPTPromptTuningMixin, GPTNeoPromptTuningLM +from mkultra.soft_prompt import SoftPrompt +from typing import Dict, List, Optional, TextIO, Union + +import logging +logging.getLogger("urllib3").setLevel(logging.ERROR) + +import breakmodel +import torch_lazy_loader +import utils + +use_breakmodel = True + + +class colors: + PURPLE = '\033[95m' + BLUE = '\033[94m' + CYAN = '\033[96m' + GREEN = '\033[92m' + YELLOW = '\033[93m' + RED = '\033[91m' + END = '\033[0m' + UNDERLINE = '\033[4m' + +class Send_to_socketio(object): + def write(self, bar): + print(bar, end="") + time.sleep(0.01) + try: + if utils.emit is not None: + utils.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True) + except: + pass + +def patch_transformers_download(): + global transformers + import copy, requests, tqdm, time + class Send_to_socketio(object): + def write(self, bar): + bar = bar.replace("\r", "").replace("\n", "") + if bar != "": + try: + print(bar, end="\r") + if utils.emit is not None: + utils.emit('from_server', {'cmd': 'model_load_status', 'data': bar.replace(" ", " ")}, broadcast=True) + eventlet.sleep(seconds=0) + except: + pass + def http_get( + url: str, + temp_file: transformers.utils.hub.BinaryIO, + proxies=None, + resume_size=0, + headers: transformers.utils.hub.Optional[transformers.utils.hub.Dict[str, str]] = None, + file_name: transformers.utils.hub.Optional[str] = None, + ): + """ + Download remote file. Do not gobble up errors. + """ + headers = copy.deepcopy(headers) + if resume_size > 0: + headers["Range"] = f"bytes={resume_size}-" + r = requests.get(url, stream=True, proxies=proxies, headers=headers) + transformers.utils.hub._raise_for_status(r) + content_length = r.headers.get("Content-Length") + total = resume_size + int(content_length) if content_length is not None else None + # `tqdm` behavior is determined by `utils.logging.is_progress_bar_enabled()` + # and can be set using `utils.logging.enable/disable_progress_bar()` + if url[-11:] != 'config.json': + progress = tqdm.tqdm( + unit="B", + unit_scale=True, + unit_divisor=1024, + total=total, + initial=resume_size, + desc=f"Downloading {file_name}" if file_name is not None else "Downloading", + file=Send_to_socketio(), + ) + for chunk in r.iter_content(chunk_size=1024): + if chunk: # filter out keep-alive new chunks + if url[-11:] != 'config.json': + progress.update(len(chunk)) + temp_file.write(chunk) + if url[-11:] != 'config.json': + progress.close() + + transformers.utils.hub.http_get = http_get + + +def patch_transformers(): + global transformers + + patch_transformers_download() + + old_from_pretrained = PreTrainedModel.from_pretrained.__func__ + @classmethod + def new_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): + utils.num_shards = None + utils.current_shard = 0 + utils.from_pretrained_model_name = pretrained_model_name_or_path + utils.from_pretrained_index_filename = None + utils.from_pretrained_kwargs = kwargs + utils.bar = None + if utils.args is None or not utils.args.no_aria2: + utils.aria2_hook(pretrained_model_name_or_path, **kwargs) + return old_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) + if(not hasattr(PreTrainedModel, "_kai_patched")): + PreTrainedModel.from_pretrained = new_from_pretrained + PreTrainedModel._kai_patched = True + if(hasattr(modeling_utils, "get_checkpoint_shard_files")): + old_get_checkpoint_shard_files = modeling_utils.get_checkpoint_shard_files + def new_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs): + utils.num_shards = utils.get_num_shards(index_filename) + utils.from_pretrained_index_filename = index_filename + return old_get_checkpoint_shard_files(pretrained_model_name_or_path, index_filename, *args, **kwargs) + modeling_utils.get_checkpoint_shard_files = new_get_checkpoint_shard_files + + # Some versions of transformers 4.17.0.dev0 are affected by + # https://github.com/huggingface/transformers/issues/15736 + # This is a workaround for those versions of transformers. + if(transformers_version == "4.17.0.dev0"): + try: + from transformers.models.xglm.modeling_xglm import XGLMSinusoidalPositionalEmbedding + except ImportError: + pass + else: + @torch.no_grad() + def new_forward(self, input_ids: torch.Tensor = None, inputs_embeds: torch.Tensor = None, past_key_values_length: int = 0): + bsz, seq_len = inputs_embeds.size()[:-1] + input_shape = inputs_embeds.size()[:-1] + sequence_length = input_shape[1] + position_ids = torch.arange( + past_key_values_length + self.padding_idx + 1, past_key_values_length + sequence_length + self.padding_idx + 1, dtype=torch.long, device=inputs_embeds.device + ).unsqueeze(0).expand(input_shape).contiguous() + max_pos = self.padding_idx + 1 + seq_len + past_key_values_length + if max_pos > self.weights.size(0): + self.make_weights(max_pos + self.offset, self.embedding_dim, self.padding_idx) + return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() + XGLMSinusoidalPositionalEmbedding.forward = new_forward + + + # Fix a bug in OPTForCausalLM where self.lm_head is the wrong size + if(packaging.version.parse("4.19.0.dev0") <= packaging.version.parse(transformers_version) < packaging.version.parse("4.20.0")): + try: + from transformers import OPTForCausalLM, OPTModel + except ImportError: + pass + else: + # This is the same as the original __init__ but with + # config.hidden_size + # replaced with + # config.word_embed_proj_dim + def new_init(self, config): + super(OPTForCausalLM, self).__init__(config) + self.model = OPTModel(config) + self.lm_head = torch.nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False) + self.post_init() + OPTForCausalLM.__init__ = new_init + + +def device_list(n_layers, primary=None, selected=None): + device_count = torch.cuda.device_count() + if(device_count < 2): + primary = None + gpu_blocks = breakmodel.gpu_blocks + (device_count - len(breakmodel.gpu_blocks))*[0] + print(f"{colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{colors.END}") + for i in range(device_count): + name = torch.cuda.get_device_name(i) + if(len(name) > 47): + name = "..." + name[-44:] + row_color = colors.END + sep_color = colors.YELLOW + print(f"{row_color}{colors.YELLOW + '->' + row_color if i == selected else ' '} {'(primary)' if i == primary else ' '*9} {i:3} {sep_color}|{row_color} {gpu_blocks[i]:3} {sep_color}|{row_color} {name}{colors.END}") + row_color = colors.END + sep_color = colors.YELLOW + print(f"{row_color}{colors.YELLOW + '->' + row_color if -1 == selected else ' '} {' '*9} N/A {sep_color}|{row_color} {breakmodel.disk_blocks:3} {sep_color}|{row_color} (Disk cache){colors.END}") + print(f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){colors.END}") + + +def move_model_to_devices(model, usegpu, gpu_device): + global generator + + if(not use_breakmodel): + if(usegpu): + model = model.half().to(gpu_device) + else: + model = model.to('cpu').float() + generator = model.generate + return + + for key, value in model.state_dict().items(): + target_dtype = torch.float32 if breakmodel.primary_device == "cpu" else torch.float16 + if(value.dtype is not target_dtype): + accelerate.utils.set_module_tensor_to_device(model, key, target_dtype) + disk_blocks = breakmodel.disk_blocks + gpu_blocks = breakmodel.gpu_blocks + ram_blocks = len(utils.layers_module_names) - sum(gpu_blocks) + cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks)) + device_map = {} + for name in utils.layers_module_names: + layer = int(name.rsplit(".", 1)[1]) + device = ("disk" if layer < disk_blocks else "cpu") if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks) + device_map[name] = device + for name in utils.get_missing_module_names(model, list(device_map.keys())): + device_map[name] = breakmodel.primary_device + breakmodel.dispatch_model_ex(model, device_map, main_device=breakmodel.primary_device, offload_buffers=True, offload_dir="accelerate-disk-cache") + gc.collect() + generator = model.generate + return + + +_PromptTuningPreTrainedModel = Union["UniversalPromptTuningMixin", GPTPromptTuningMixin, transformers.PreTrainedModel] + +class _WTEDummy: + def __init__(self, model: transformers.PreTrainedModel): + self.model = model + + @property + def wte(self: "_WTEDummy"): + return self.model.get_input_embeddings() + + @wte.setter + def wte(self: "_WTEDummy", v): + self.model.set_input_embeddings(v) + +class _WTEMixin: + @property + def wte(self: Union["_WTEMixin", transformers.PreTrainedModel]): + return self.get_input_embeddings() + + @wte.setter + def wte(self: Union["_WTEMixin", transformers.PreTrainedModel], v): + self.set_input_embeddings(v) + + +class UniversalPromptTuningMixin: + @classmethod + def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs): + model: _PromptTuningPreTrainedModel = super().from_pretrained(pretrained_model_name_or_path, **kwargs) + + if not hasattr(model, "transformer"): + model.transformer = _WTEDummy(model) + elif not hasattr(model.transformer, "wte"): + assert isinstance(model.transformer, type) + model.transformer.__class__ = type("_UniversalPromptTuning" + model.transformer.__class__.__name__, (_WTEMixin, model.transformer.__class__), {}) + + model.__class__ = type("_UniversalPromptTuning" + model.__class__.__name__, (UniversalPromptTuningMixin, model.__class__), {}) + + for param in model.parameters(): + param.requires_grad = False + model.initialize_soft_prompt() + + return model + + def forward( + self: _PromptTuningPreTrainedModel, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + return_dict: Optional[bool] = None, + **kwargs, + ): + assert input_ids is not None + assert input_ids.ndim == 2 + + input_ids = F.pad(input_ids, (self.learned_embedding.size(0), 0, 0, 0), value=self.transformer.wte.weight.size(0) // 2) + + if labels is not None: + labels = self._extend_labels(labels) + + if attention_mask is not None: + attention_mask = self._extend_attention_mask(attention_mask) + + old_embedding_call = Embedding.__call__ + model = self + + def new_embedding_call(self, input_ids, *args, **kwargs): + inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs) + if model.transformer.wte is self: + assert inputs_embeds.ndim == 3 + inputs_embeds[:, :model.learned_embedding.size(0), :] = model.learned_embedding[None] + return inputs_embeds + + Embedding.__call__ = new_embedding_call + + try: + return super().forward( + input_ids=input_ids, + attention_mask=attention_mask, + labels=labels, + use_cache=use_cache, + return_dict=return_dict, + ) + finally: + Embedding.__call__ = old_embedding_call + +for k in dir(GPTPromptTuningMixin): + v = getattr(GPTPromptTuningMixin, k) + _v = getattr(UniversalPromptTuningMixin, k, None) + if _v is None or (_v is getattr(object, k, None) and callable(_v) and not isinstance(_v, type)): + setattr(UniversalPromptTuningMixin, k, v) + + +class AutoPromptTuningLM(UniversalPromptTuningMixin, transformers.AutoModelForCausalLM): + def __init__(self, config): + super().__init__(config) + + +default_quiet = False + + +def get_tokenizer(model_id, revision=None) -> transformers.PreTrainedTokenizerBase: + if(os.path.isdir(model_id)): + try: + tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache", use_fast=False) + except Exception as e: + try: + tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache") + except Exception as e: + try: + tokenizer = GPT2Tokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache") + except Exception as e: + tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=revision, cache_dir="cache") + elif(os.path.isdir("models/{}".format(model_id.replace('/', '_')))): + try: + tokenizer = AutoTokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache", use_fast=False) + except Exception as e: + try: + tokenizer = AutoTokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache") + except Exception as e: + try: + tokenizer = GPT2Tokenizer.from_pretrained("models/{}".format(model_id.replace('/', '_')), revision=revision, cache_dir="cache") + except Exception as e: + tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=revision, cache_dir="cache") + else: + try: + tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache", use_fast=False) + except Exception as e: + try: + tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache") + except Exception as e: + try: + tokenizer = GPT2Tokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache") + except Exception as e: + tokenizer = GPT2Tokenizer.from_pretrained("gpt2", revision=revision, cache_dir="cache") + + @contextlib.contextmanager + def _kai_no_prefix(): + add_bos_token = getattr(tokenizer, "add_bos_token", False) + add_prefix_space = getattr(tokenizer, "add_prefix_space", False) + tokenizer.add_bos_token = False + tokenizer.add_prefix_space = False + try: + yield + finally: + tokenizer.add_bos_token = add_bos_token + tokenizer.add_prefix_space = add_prefix_space + + tokenizer._kai_no_prefix = _kai_no_prefix + return tokenizer + + +class ConfigurationError(Exception): + def __init__(self, msg: str = "Unknown error", code: int = 1, quiet: Optional[bool] = None): + if quiet is None: + quiet = default_quiet + super().__init__(msg) + self.code = code + self.quiet = quiet + + +class TrainerBase(abc.ABC): + @abc.abstractmethod + def startup(self, step: int) -> None: + ... + + @abc.abstractmethod + def get_batch(self, step: int, size: int) -> np.ndarray: + ... + + @abc.abstractmethod + def get_num_sequences(self) -> int: + ... + + @abc.abstractmethod + def get_initial_soft_embeddings(self, model: transformers.PreTrainedModel) -> SoftPrompt: + ... + + @abc.abstractmethod + def tokenize_dataset_callback(self, tokenizer: transformers.PreTrainedTokenizerBase, text: str) -> List[int]: + ... + + class TrainerData: + def __init__(self): + self.__lazy_load_spec: Optional[dict] = None + self.model_spec: Optional[dict] = None + self.tokenizer_id: Optional[str] = None + self.newlinemode: Optional[str] = None + self.ckpt_path: Optional[str] = None + self.save_file: Optional[str] = None + self.params: Optional[dict] = None + self.stparams: Optional[dict] = None + self.gradient_accumulation_steps = -1 + self.soft_in_dim = -1 + self.prompt_method = "tokens" + self.prompt_seed = 42 + + @property + def lazy_load_spec(self): + print("WARNING: `TrainerData.lazy_load_spec` is currently unused", file=sys.stderr) + return self.__lazy_load_spec + + @lazy_load_spec.setter + def lazy_load_spec(self, value: Optional[dict]): + print("WARNING: `TrainerData.lazy_load_spec` is currently unused", file=sys.stderr) + self.__lazy_load_spec = value + + @property + def kaiming_size(self): # backwards compatibility + return self.soft_in_dim + + @kaiming_size.setter + def kaiming_size(self, value: int): # backwards compatibility + self.prompt_method = "kaiming" + self.soft_in_dim = value + + data: TrainerData + + def __init__(self, universe: Optional[int] = None, quiet=False): + self.quiet = quiet + self.universe = universe + self.data = self.TrainerData() + self._spmodule: Optional[str] = None + if universe is not None: + print("WARNING: The `universe` argument of `TrainerBase.__init__` is currently unused", file=sys.stderr) + + def raise_configuration_error(self, msg, **kwargs): + if "quiet" not in kwargs: + kwargs["quiet"] = self.quiet + raise ConfigurationError(msg, **kwargs) + + def _get_model_config(self) -> transformers.configuration_utils.PretrainedConfig: + REVISION = None + if(os.path.isdir(self.data.ckpt_path)): + model_config = AutoConfig.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache") + elif(os.path.isdir("models/{}".format(self.data.ckpt_path.replace('/', '_')))): + model_config = AutoConfig.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache") + else: + model_config = AutoConfig.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache") + return model_config + + def get_hf_checkpoint_metadata(self) -> bool: + params = {} + model_config = self._get_model_config() + params["tokenizer_id"] = self.data.ckpt_path + tokenizer = get_tokenizer(self.data.ckpt_path) + params["newlinemode"] = params.get( + "newlinemode", "s" if model_config.model_type == "xglm" else "n" + ) + params["max_batch_size"] = 2048 + with tokenizer._kai_no_prefix(): + params["eos_token"] = ( + [50259, 50259] if model_config.model_type == "xglm" and model_config.eos_token_id == 50259 else [model_config.eos_token_id] + ) + params["seq"] = 2048 + self.data.params = params + return True + + def get_tokenizer(self) -> transformers.PreTrainedTokenizerBase: + return get_tokenizer(self.data.ckpt_path) + + def save_data(self): + pass + + def export_to_kobold(self, output_file: str, name: str, author: str, supported: str, description: str): + try: + z = torch.load(self.data.save_file) + assert z["step"] > 0 + assert z["tensor"].ndim == 2 and "opt_state" in z + assert z["tensor"].shape[0] < self.data.params["max_batch_size"] + self.data.soft_in_dim = z["tensor"].shape[0] + except AssertionError: + self.raise_configuration_error("MKUSP file is corrupted.", code=14) + + tensor = z["tensor"] + + meta = { + "name": name, + "author": author, + "supported": supported, + "description": description, + } + if len(meta["author"].strip()) == 0: + meta.pop("author") + meta["supported"] = list(map(lambda m: m.strip(), supported.split(","))) + + with zipfile.ZipFile(output_file, "w", compression=zipfile.ZIP_LZMA) as z: + with z.open("tensor.npy", "w") as f: + np.save(f, tensor.detach().cpu().numpy(), allow_pickle=False) + with zipfile.ZipFile(output_file, "a", compression=zipfile.ZIP_STORED) as z: + with z.open("meta.json", "w") as f: + f.write(json.dumps(meta, indent=2).encode("utf-8")) + + def export_to_mkultra(self, output_file: str, soft_prompt_name: str, soft_prompt_description: str): + try: + z = torch.load(self.data.save_file) + assert z["step"] > 0 + assert z["tensor"].ndim == 2 and "opt_state" in z + assert z["tensor"].shape[0] < self.data.params["max_batch_size"] + self.data.soft_in_dim = z["tensor"].shape[0] + _step = z["step"] + except AssertionError: + self.raise_configuration_error("MKUSP file is corrupted.", code=14) + + tensor = z["tensor"] + + with open(output_file, "w") as f: + json.dump( + { + "metadata": { + "step": _step, + "loss": float(z["loss"]), + "uuid": str(uuid.uuid4()), + "name": soft_prompt_name, + "description": soft_prompt_description, + "epoch": datetime.datetime.now().timestamp(), + }, + "tensor": base64.b64encode( + pickle.dumps( + tensor.detach().cpu(), + protocol=4, + ), + ).decode("ascii"), + }, + f, + ) + + def tokenize_dataset( + self, + dataset_path: Union[str, TextIO], + output_file: Union[str, TextIO], + batch_size=2048, + epochs=1, + use_ftfy=True, + shuffle_seed: Optional[Union[int, float, str, bytes, bytearray]] = 1729, + ): + dataset_path = dataset_path.replace("\\", "/") + output_file = output_file.replace("\\", "/") + if not isinstance(batch_size, int) or batch_size < 1: + self.raise_configuration_error( + "batch_size must be an integer greater than zero.", code=9 + ) + if ( + not isinstance(epochs, int) and not isinstance(epochs, float) + ) or epochs <= 0: + self.raise_configuration_error( + "epochs must be an int or float greater than zero.", code=10 + ) + if isinstance(output_file, str) and output_file.endswith("/"): + self.raise_configuration_error( + "output_file should be the path to a file, not a directory.", code=11 + ) + if isinstance(dataset_path, str) and not os.path.exists(dataset_path): + self.raise_configuration_error( + "dataset_path is not set to a valid file or directory.", code=12 + ) + + if use_ftfy: + import ftfy + + tokenizer = self.get_tokenizer() + + batch_size = min( + batch_size, + self.data.params["max_batch_size"] - self.data.soft_in_dim, + ) + assert batch_size >= 0 + print( + termcolor.colored( + "\nIf you see a warning somewhere below about token indices, ignore it. That warning is normal.\n", + "magenta", + ) + ) + print("Batch size:", batch_size) + print(termcolor.colored("Tokenizing your dataset...\n", "magenta")) + + if not isinstance(dataset_path, str): + files = [dataset_path] + elif os.path.isfile(dataset_path): + files = [dataset_path] + else: + files = sorted( + os.path.join(dataset_path, filename) + for filename in os.listdir(dataset_path) + ) + if shuffle_seed is not None: + random.Random(shuffle_seed).shuffle(files) + tokens = [] + eos = tokenizer.decode(self.data.params["eos_token"]) + for path in files: + if isinstance(path, str): + f = open(path) + else: + f = path + try: + text = f.read() + if use_ftfy: + text = ftfy.fix_text(text) + text = text.replace("<|endoftext|>", eos) + tokens.extend(self.tokenize_dataset_callback(tokenizer, text)) + finally: + if isinstance(path, str): + f.close() + + print("Dataset size (in tokens):", len(tokens)) + if len(tokens) < batch_size + 1: + self.raise_configuration_error( + "Your dataset is too small! The number of tokens has to be greater than the batch size. Try increasing the epochs.", + code=13, + ) + tail = len(tokens) % (batch_size + 1) + if tail: + print( + f"We're removing the last {tail} tokens from your dataset to make the length a multiple of {batch_size+1}." + ) + tokens = tokens[:-tail] + + tokens = np.array(tokens, dtype=np.uint16).reshape((-1, batch_size + 1)) + sequences_per_epoch = tokens.shape[0] + _epochs = math.ceil(epochs) + if _epochs > 1: + rng = np.random.Generator(np.random.PCG64(1729)) + tokens = np.concatenate( + ( + tokens, + *(rng.permutation(tokens, axis=0) for i in range(_epochs - 1)), + ), + axis=0, + ) + tokens = tokens[: math.ceil(epochs * sequences_per_epoch)] + print(f"Total sequences in your dataset: {tokens.shape[0]}") + + if isinstance(output_file, str): + f = open(output_file, "w") + else: + f = output_file + try: + np.save(output_file, tokens) + finally: + if isinstance(output_file, str): + f.close() + + def train( + self, + breakmodel_primary_device: Optional[Union[str, int, torch.device]] = None, + breakmodel_gpulayers: Optional[List[int]] = None, + breakmodel_disklayers = 0, + ): + if breakmodel_gpulayers is None: + breakmodel_gpulayers = [] + if breakmodel_primary_device is None: + breakmodel_primary_device = 0 if sum(x if x >= 0 else 1 for x in breakmodel_gpulayers) else "cpu" + + if self.data.params is not None and "max_batch_size" not in self.data.params: + self.data.params["max_batch_size"] = 2048 + + if not os.path.exists(self.data.save_file): + print("We are starting a brand new soft-tuning session.\n") + self.startup(step=-1) + if self.data.soft_in_dim <= 0: + self.raise_configuration_error( + "You have not set a soft prompt size.", code=6 + ) + step = 0 + else: + # If we're resuming a soft-tuning session, the soft prompt tensor is + # already in the save file and we just have to decode it. + try: + z = torch.load(self.data.save_file) + assert z["step"] > 0 + assert z["tensor"].ndim == 2 and "opt_state" in z + assert z["tensor"].shape[0] < self.data.params["max_batch_size"] + self.data.soft_in_dim = z["tensor"].shape[0] + step = z["step"] + opt_state = z["opt_state"] + except AssertionError: + self.raise_configuration_error("MKUSP file is corrupted.", code=14) + print(f"We're resuming a previous soft-tuning session at step {step+1}.\n") + self.startup(step=step + 1) + soft_embeddings = z["tensor"] + + REVISION = None + + patch_transformers() + + model: _PromptTuningPreTrainedModel + + model_config = self._get_model_config() + n_layers = utils.num_layers(model_config) + breakmodel_gpulayers = [x if x >= 0 else n_layers for x in breakmodel_gpulayers] + + convert_to_float16 = True + hascuda = torch.cuda.is_available() + usegpu = hascuda and not breakmodel_disklayers and len(breakmodel_gpulayers) == 1 and breakmodel_gpulayers[0] == n_layers + gpu_device = breakmodel_primary_device + use_breakmodel = bool(hascuda or breakmodel_disklayers or sum(breakmodel_gpulayers)) + + assert len(breakmodel_gpulayers) <= torch.cuda.device_count() + assert sum(breakmodel_gpulayers) + breakmodel_disklayers <= n_layers + + breakmodel.gpu_blocks = breakmodel_gpulayers + breakmodel.disk_blocks = breakmodel_disklayers + disk_blocks = breakmodel.disk_blocks + gpu_blocks = breakmodel.gpu_blocks + ram_blocks = ram_blocks = n_layers - sum(gpu_blocks) + cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks)) + + device_list(ram_blocks, primary=breakmodel.primary_device) + + def lazy_load_callback(model_dict: Dict[str, Union[torch_lazy_loader.LazyTensor, torch.Tensor]], f, **_): + if lazy_load_callback.nested: + return + lazy_load_callback.nested = True + + device_map: Dict[str, Union[str, int]] = {} + + @functools.lru_cache(maxsize=None) + def get_original_key(key): + return max((original_key for original_key in utils.module_names if original_key.endswith(key)), key=len) + + for key, value in model_dict.items(): + original_key = get_original_key(key) + if isinstance(value, torch_lazy_loader.LazyTensor) and not any(original_key.startswith(n) for n in utils.layers_module_names): + device_map[key] = gpu_device if hascuda and usegpu else "cpu" if not hascuda or not use_breakmodel else breakmodel.primary_device + else: + layer = int(max((n for n in utils.layers_module_names if original_key.startswith(n)), key=len).rsplit(".", 1)[1]) + device = gpu_device if hascuda and usegpu else "disk" if layer < disk_blocks and layer < ram_blocks else "cpu" if not hascuda or not use_breakmodel else "shared" if layer < ram_blocks else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks) + device_map[key] = device + + if utils.num_shards is None or utils.current_shard == 0: + utils.offload_index = {} + if os.path.isdir("accelerate-disk-cache"): + # Delete all of the files in the disk cache folder without deleting the folder itself to allow people to create symbolic links for this folder + # (the folder doesn't contain any subfolders so os.remove will do just fine) + for filename in os.listdir("accelerate-disk-cache"): + try: + os.remove(os.path.join("accelerate-disk-cache", filename)) + except OSError: + pass + os.makedirs("accelerate-disk-cache", exist_ok=True) + if utils.num_shards is not None: + num_tensors = len(utils.get_sharded_checkpoint_num_tensors(utils.from_pretrained_model_name, utils.from_pretrained_index_filename, **utils.from_pretrained_kwargs)) + else: + num_tensors = len(device_map) + print(flush=True) + utils.bar = tqdm(total=num_tensors, desc="Loading model tensors", file=Send_to_socketio()) + + with zipfile.ZipFile(f, "r") as z: + try: + last_storage_key = None + f = None + current_offset = 0 + able_to_pin_layers = True + if utils.num_shards is not None: + utils.current_shard += 1 + for key in sorted(device_map.keys(), key=lambda k: (model_dict[k].key, model_dict[k].seek_offset)): + storage_key = model_dict[key].key + if storage_key != last_storage_key or model_dict[key].seek_offset < current_offset: + last_storage_key = storage_key + if isinstance(f, zipfile.ZipExtFile): + f.close() + f = z.open(f"archive/data/{storage_key}") + current_offset = 0 + if current_offset != model_dict[key].seek_offset: + f.read(model_dict[key].seek_offset - current_offset) + current_offset = model_dict[key].seek_offset + device = device_map[key] + size = functools.reduce(lambda x, y: x * y, model_dict[key].shape, 1) + dtype = model_dict[key].dtype + nbytes = size if dtype is torch.bool else size * ((torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits >> 3) + #print(f"Transferring <{key}> to {f'({device.upper()})' if isinstance(device, str) else '[device ' + str(device) + ']'} ... ", end="", flush=True) + model_dict[key] = model_dict[key].materialize(f, map_location="cpu") + # if model_dict[key].dtype is torch.float32: + # fp32_model = True + if convert_to_float16 and breakmodel.primary_device != "cpu" and hascuda and (use_breakmodel or usegpu) and model_dict[key].dtype is torch.float32: + model_dict[key] = model_dict[key].to(torch.float16) + if breakmodel.primary_device == "cpu" or (not usegpu and not use_breakmodel and model_dict[key].dtype is torch.float16): + model_dict[key] = model_dict[key].to(torch.float32) + if device == "shared": + model_dict[key] = model_dict[key].to("cpu").detach_() + if able_to_pin_layers: + try: + model_dict[key] = model_dict[key].pin_memory() + except: + able_to_pin_layers = False + elif device == "disk": + accelerate.utils.offload_weight(model_dict[key], get_original_key(key), "accelerate-disk-cache", index=utils.offload_index) + model_dict[key] = model_dict[key].to("meta") + else: + model_dict[key] = model_dict[key].to(device) + #print("OK", flush=True) + current_offset += nbytes + utils.bar.update(1) + finally: + if utils.num_shards is None or utils.current_shard >= utils.num_shards: + if utils.offload_index: + for name, tensor in utils.named_buffers: + if name not in utils.offload_index: + accelerate.utils.offload_weight(tensor, name, "accelerate-disk-cache", index=utils.offload_index) + accelerate.utils.save_offload_index(utils.offload_index, "accelerate-disk-cache") + utils.bar.close() + utils.bar = None + lazy_load_callback.nested = False + if isinstance(f, zipfile.ZipExtFile): + f.close() + + lazy_load_callback.nested = False + + # Since we're using lazy loader, we need to figure out what the model's hidden layers are called + with torch_lazy_loader.use_lazy_torch_load(dematerialized_modules=True, use_accelerate_init_empty_weights=True): + try: + metamodel = AutoModelForCausalLM.from_config(model_config) + except Exception as e: + metamodel = GPTNeoForCausalLM.from_config(model_config) + utils.layers_module_names = utils.get_layers_module_names(metamodel) + utils.module_names = list(metamodel.state_dict().keys()) + utils.named_buffers = list(metamodel.named_buffers(recurse=True)) + + with torch_lazy_loader.use_lazy_torch_load(callback=lazy_load_callback, dematerialized_modules=True): + if(os.path.isdir(self.data.ckpt_path)): + try: + model = AutoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache") + except Exception as e: + if("out of memory" in traceback.format_exc().lower()): + raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") + model = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache") + elif(os.path.isdir("models/{}".format(self.data.ckpt_path.replace('/', '_')))): + try: + model = AutoPromptTuningLM.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache") + except Exception as e: + if("out of memory" in traceback.format_exc().lower()): + raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") + model = GPTNeoPromptTuningLM.from_pretrained("models/{}".format(self.data.ckpt_path.replace('/', '_')), revision=REVISION, cache_dir="cache") + else: + try: + model = AutoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache") + except Exception as e: + if("out of memory" in traceback.format_exc().lower()): + raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.") + model = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache") + + if(hascuda): + if(usegpu): + model = model.half().to(gpu_device) + elif(use_breakmodel): # Use both RAM and VRAM (breakmodel) + move_model_to_devices(model, usegpu, gpu_device) + elif(__import__("breakmodel").disk_blocks > 0): + move_model_to_devices(model, usegpu, gpu_device) + else: + model = model.to('cpu').float() + elif(__import__("breakmodel").disk_blocks > 0): + move_model_to_devices(model, usegpu, gpu_device) + else: + model.to('cpu').float() + + if step == 0: + soft_embeddings = self.get_initial_soft_embeddings(model) + else: + soft_embeddings = SoftPrompt.from_inputs_embeds(soft_embeddings) + model.set_soft_prompt(soft_embeddings) + + steps = self.get_num_sequences() // self.data.gradient_accumulation_steps + warmup_steps = max(1, round(steps * self.data.stparams["warmup"])) + + beta1: Optional[float] = self.data.stparams.get("beta1", 0.0) + if beta1 == 0.0: + beta1 = None + optimizer = transformers.Adafactor( + params=(model.get_soft_params(),), + scale_parameter=False, + relative_step=False, + warmup_init=False, + lr=self.data.stparams["lr"], + beta1=beta1, + decay_rate=self.data.stparams.get("decay_rate", -0.8), + weight_decay=self.data.stparams.get("weight_decay", 0.1), + ) + if step != 0: + optimizer.load_state_dict(opt_state) + scheduler = transformers.get_cosine_with_hard_restarts_schedule_with_warmup( + optimizer=optimizer, + num_warmup_steps=warmup_steps, + num_training_steps=steps - warmup_steps, + num_cycles=(steps - warmup_steps) // self.data.stparams.get("training_steps_per_cycle", 56), + ) + + torch.cuda.empty_cache() + optimizer.state['step'] = step + cross_entropy_loss = CrossEntropyLoss() + + def save_mkusp( + loss, + grad_norm, + ): + with open(self.data.save_file, "wb") as f: + torch.save( + { + "tensor": soft_embeddings.get_inputs_embeds(), + "opt_state": optimizer.state_dict(), + "step": step, + "loss": loss, + "grad_norm": grad_norm, + }, + f, + ) + self.save_data() + + bar1 = tqdm(initial=step + 1, total=steps, desc="CURRENT TRAINING STEP") + + while step < steps: + step += 1 + model.train() + + total_loss = total_grad = total_grad_norm = 0 + + # Get the next sequences from the dataset + block = torch.tensor(np.int32(self.get_batch(step, self.data.gradient_accumulation_steps))).to(model.transformer.wte.weight.device) + + for sequence in tqdm(block, desc="GRADIENT ACCUMULATION", leave=False): + # input_ids is the context to the model (without the soft prompt) and labels is what we expect the model to generate (the -100s represent soft prompt tokens for which loss is not calculated) + input_ids = sequence[:-1].unsqueeze(0).detach() + labels = torch.cat((torch.full((model.get_soft_params().size(0) - 1,), -100, device=sequence.device), sequence), dim=-1).unsqueeze(0).detach() + + # Give the context to the model and compare the model's output logits with the labels to compute the loss + logits = model(input_ids=input_ids, labels=input_ids).logits + loss: torch.Tensor = cross_entropy_loss(logits.view(-1, model.transformer.wte.weight.size(0)), labels.view(-1)) + total_loss += loss.detach() + + # Compute the gradient of the loss function and add it to model.get_soft_params().grad (model.get_soft_params().grad += gradient) + loss.backward() + + total_grad_norm += torch.linalg.norm(model.get_soft_params().grad.detach() - total_grad) + total_grad = model.get_soft_params().grad.detach() + + del input_ids + del labels + del logits + torch.cuda.empty_cache() + + mean_loss = (total_loss / self.data.gradient_accumulation_steps).item() + mean_grad_norm = (total_grad_norm / self.data.gradient_accumulation_steps).item() + + # Apply the optimization algorithm using the accumulated gradients, which changes the contents of the soft prompt matrix very slightly to reduce the loss + optimizer.step() + lr = optimizer.param_groups[0]["lr"] + scheduler.step() + optimizer.zero_grad() + + # Save checkpoint every few steps + if step == 1 or step % self.data.stparams["save_every"] == 0: + save_mkusp(mean_loss, mean_grad_norm) + + bar1.set_postfix({"loss": mean_loss, "grad_norm": mean_grad_norm, "learning_rate": lr}) + bar1.update() + + +class BasicTrainer(TrainerBase): + class TrainerData(TrainerBase.TrainerData): + def __init__(self): + super().__init__() + self.dataset_file: Optional[str] = None + self.initial_softprompt: Optional[List[int]] = None + + data: "BasicTrainer.TrainerData" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.dataset: Optional[np.ndarray] = None + + def startup(self, step: int) -> None: + if self.get_num_sequences() < self.data.gradient_accumulation_steps: + self.raise_configuration_error( + "Your dataset is too small! gradient_accumulation_steps must be less than or equal to the number of sequences.", + code=101, + ) + if ( + self.data.prompt_method == "tokens" + and step < 0 + and self.data.initial_softprompt is None + ): + self.raise_configuration_error( + "You have not set an initial soft prompt string.", code=103 + ) + if self.data.prompt_method == "tokens" and step < 0: + self.data.soft_in_dim = len(self.data.initial_softprompt) + + def get_batch(self, step: int, size: int) -> np.ndarray: + return self.dataset[(step - 1) * size : step * size] + + def get_num_sequences(self) -> int: + if self.dataset is None: + if self.data.dataset_file is None or not os.path.exists( + self.data.dataset_file + ): + self.raise_configuration_error( + f"Dataset file not found at {repr(self.data.dataset_file)}", + code=102, + ) + self.dataset = np.load(self.data.dataset_file, mmap_mode="r") + assert self.dataset.ndim >= 2 + assert self.dataset.shape[0] >= 2 + return self.dataset.shape[0] + + def get_initial_soft_embeddings(self, model: transformers.PreTrainedModel) -> SoftPrompt: + if self.data.prompt_method == "vocab_sample": + rng = np.random.Generator( + np.random.PCG64( + [ + self.data.prompt_seed, + int.from_bytes(hashlib.sha256(model.config.model_type.encode("utf8")).digest()[:4], "little"), + ] + ) + ) + tokenizer = self.get_tokenizer() + with tokenizer._kai_no_prefix(): + special_tokens = set( + itertools.chain.from_iterable( + tokenizer.encode(str(v)) + for v in tokenizer.special_tokens_map_extended.values() + ) + ) + sample_space = [ + k for k in range(model.get_input_embeddings().weight.shape[-2]) if k not in special_tokens + ] + sample = rng.choice(sample_space, self.data.soft_in_dim, False) + return SoftPrompt.from_inputs_embeds(model.get_input_embeddings()(torch.tensor(sample, dtype=torch.int32, device=model.get_input_embeddings().weight.device))) + elif self.data.prompt_method == "tokens": + return SoftPrompt.from_inputs_embeds(model.get_input_embeddings()(torch.tensor(self.data.initial_softprompt, dtype=torch.int32, device=model.get_input_embeddings().weight.device))) + self.raise_configuration_error( + f"Unknown prompt method {repr(self.data.prompt_method)}", code=104 + ) + + def tokenize_dataset_callback( + self, tokenizer: transformers.PreTrainedTokenizerBase, text: str + ) -> List[int]: + if self.data.newlinemode == "s": + text = text.replace("\n", "") + with tokenizer._kai_no_prefix(): + return tokenizer.encode(text) + self.data.params["eos_token"] diff --git a/utils.py b/utils.py index a5bea541..baa74add 100644 --- a/utils.py +++ b/utils.py @@ -27,6 +27,7 @@ except ImportError: HAS_ACCELERATE = False vars = None +args = None num_shards: Optional[int] = None current_shard = 0 from_pretrained_model_name = "" @@ -40,6 +41,8 @@ named_buffers: Optional[List[tuple]] = None default_sampler_order = [6, 0, 1, 2, 3, 4, 5] +emit = None + #==================================================================# # Decorator to prevent a function's actions from being run until # at least x seconds have passed without the function being called @@ -198,6 +201,7 @@ def _download_with_aria2(aria2_config: str, total_length: int, directory: str = pass import transformers + aria2_port = 6799 if vars is None else vars.aria2_port lengths = {} s = requests.Session() s.mount("http://", requests.adapters.HTTPAdapter(max_retries=requests.adapters.Retry(total=120, backoff_factor=1))) @@ -208,9 +212,9 @@ def _download_with_aria2(aria2_config: str, total_length: int, directory: str = with tempfile.NamedTemporaryFile("w+b", delete=False) as f: f.write(aria2_config) f.flush() - p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(vars.aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", directory, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {use_auth_token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) + p = subprocess.Popen(["aria2c", "-x", "10", "-s", "10", "-j", "10", "--enable-rpc=true", f"--rpc-secret={secret}", "--rpc-listen-port", str(aria2_port), "--disable-ipv6", "--file-allocation=trunc", "--allow-overwrite", "--auto-file-renaming=false", "-d", directory, "-i", f.name, "-U", transformers.file_utils.http_user_agent(user_agent)] + (["-c"] if not force_download else []) + ([f"--header='Authorization: Bearer {use_auth_token}'"] if use_auth_token else []), stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL) while p.poll() is None: - r = s.post(f"http://localhost:{vars.aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"] + r = s.post(f"http://localhost:{aria2_port}/jsonrpc", json={"jsonrpc": "2.0", "id": "kai", "method": "aria2.tellActive", "params": [f"token:{secret}"]}).json()["result"] if not r: s.close() if bar is not None: @@ -602,4 +606,4 @@ def get_missing_module_names(model: PreTrainedModel, names: List[str]) -> List[s else: recurse(c[1], head=name + ".") recurse(model) - return missing_names + return missing_names \ No newline at end of file