diff --git a/arguments.py b/arguments.py index 48b3f40..ef13b1b 100644 --- a/arguments.py +++ b/arguments.py @@ -1,72 +1,11 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - from dataclasses import dataclass, field -from typing import Optional, Self -from enum import Enum - - -class DatasetType(Enum): - TEXT = 1 - S2S = 2 - HUB = 3 - CHAT = 4 - - @staticmethod - def to_string(dtype: Self) -> str: - if dtype == DatasetType.TEXT: - return "text" - elif dtype == DatasetType.S2S: - return "s2s" - elif dtype == DatasetType.HUB: - return "hub" - elif dtype == DatasetType.CHAT: - return "chat" - return "invalid" - - @staticmethod - def from_string(string: str): - if string == str(DatasetType.TEXT): - return DatasetType.TEXT - elif string == str(DatasetType.S2S): - return DatasetType.S2S - elif string == str(DatasetType.HUB): - return DatasetType.HUB - elif string == str(DatasetType.CHAT): - return DatasetType.CHAT - return None - - def __str__(self): - return DatasetType.to_string(self) +from typing import Optional @dataclass class DataArguments: dataset: str = field( - metadata={"help": "The dataset to train on"} - ) - dataset_type: str = field( - default="text", metadata={"help": f"The type of dataset, set to one of {[e for e in DatasetType]}"} - ) - dataset_chat_template: str | None = field( - default=None, metadata={"help": "overrides the chat template to be the one set here"} + metadata={"help": "A json file (s2s) or text file with the dataset to train on"} ) eval_dataset_size: int = field( default=512, metadata={"help": "Size of validation dataset."} @@ -87,6 +26,10 @@ class DataArguments: default=False, metadata={"help": "If this is set the dataset is assumed to be a name of a hf-hub dataset"} ) + block_size: int = field( + default=512, + metadata={"help": "size of the blocks the text is split into for training"}, + ) @dataclass @@ -122,9 +65,8 @@ class TrainingArguments(): ) resume: bool = field(default=False, metadata={"help": 'Resume from previous checkpoint'}) ddp_find_unused_parameters: bool = field(default=True, metadata={"help": 'set if trainer should try to find unused parameters'}) - output_dir: str = field(default='./output', metadata={"help": 'The output dir for checkpoints'}) + output_dir: str = field(default='./output', metadata={"help": 'The output dir for logs and checkpoints'}) per_device_train_batch_size: int = field(default=1, metadata={"help": 'The training batch size per GPU. Increase for better speed.'}) - per_device_eval_batch_size: int = field(default=1, metadata={"help": 'The eval batch size per GPU. Increase for better speed.'}) gradient_accumulation_steps: int = field(default=16, metadata={"help": 'How many gradients to accumulate before to perform an optimizer step'}) epochs: int = field(default=3, metadata={"help": 'How many epochs to train for'}) weight_decay: float = field(default=0.0, metadata={"help": 'The L2 weight decay rate of AdamW'}) @@ -140,7 +82,6 @@ class TrainingArguments(): metadata={"help": 'Learning rate schedule. Constant a bit better than cosine, and has advantage for analysis'}) warmup_steps: float = field(default=0, metadata={"help": 'number of steps to do a warmup for'}) logging_steps: int = field(default=10, metadata={"help": 'The frequency of update steps after which to log the loss'}) - logging_dir: str = field(default='./log', metadata={"help": 'The output dir for logs'}) group_by_length: bool = field(default=False, metadata={"help": 'Group sequences into batches with same length. Saves memory and speeds up training considerably.'}) save_steps: int = field(default=250, metadata={"help": 'How often to save a model'}) @@ -151,5 +92,5 @@ class TrainingArguments(): max_instant_params: int = field(default=0, metadata={"help": "Maximum amount of paramters to optimize per step in millions"}) churn_percent: int = field(default=100, metadata={"help": "The percentage of active parameters to replace when changeing active parameters"}) eval_steps: int = field(default=-1, metadata={"help": "Number of optimization steps after wich to compute the evaluation loss"}) - eval_prompt: str | None = field(default=None, metadata={"help": "A prompt to used during eval to check if the model is learning"}) + eval_prompt: str = field(default=None, metadata={"help": "A prompt to used during eval to check if the model is learning"}) reshufle_steps: int = field(default=50, metadata={"help": "Number of steps to take before changing the active parameters"}) diff --git a/datamodules.py b/datamodules.py index 05b9ff2..0e36a6d 100644 --- a/datamodules.py +++ b/datamodules.py @@ -1,49 +1,27 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - import copy import torch import typing import datasets import itertools import transformers -import os from dataclasses import dataclass from torch.nn.utils.rnn import pad_sequence -from tqdm import tqdm -from arguments import DataArguments, DatasetType +from arguments import DataArguments IGNORE_INDEX = -100 -def group_texts(examples, source_max_len: int): +def group_texts(examples, block_size: int): # Concatenate all texts. concatenated_examples = {k: list(itertools.chain(*examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can # customize this part to your needs. - if total_length >= source_max_len: - total_length = (total_length // source_max_len) * source_max_len + if total_length >= block_size: + total_length = (total_length // block_size) * block_size # Split by chunks of max_len. - result = {k: [t[i: i + source_max_len] for i in range(0, total_length, source_max_len)] for k, t in concatenated_examples.items()} + result = {k: [t[i: i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items()} result["labels"] = result["input_ids"].copy() return result @@ -157,7 +135,7 @@ def create_data_module_s2s(tokenizer: transformers.PreTrainedTokenizer, data_arg eval_dataset = dataset['eval'] else: print('Splitting train dataset in train and validation according to `eval_dataset_size`') - dataset = dataset['train'].train_test_split( + dataset = dataset.train_test_split( test_size=data_args.eval_dataset_size, shuffle=True, seed=42 ) eval_dataset = dataset['test'] @@ -197,7 +175,7 @@ def create_data_module_hub(tokenizer: transformers.PreTrainedTokenizer, data_arg eval_dataset = dataset['eval'] else: print('Splitting train dataset in train and validation according to `eval_dataset_size`') - dataset = dataset['train'].train_test_split( + dataset = dataset.train_test_split( test_size=data_args.eval_dataset_size, shuffle=True, seed=42 ) eval_dataset = dataset['test'] @@ -220,15 +198,14 @@ def create_data_module_hub(tokenizer: transformers.PreTrainedTokenizer, data_arg ) -def create_data_module_txt(tokenizer: transformers.PreTrainedTokenizer, - data_args: DataArguments, do_train: bool, do_eval: bool, do_predict: bool) -> typing.Dict: +def create_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args: DataArguments, do_train: bool, do_eval: bool, do_predict: bool) -> typing.Dict: try: dataset = datasets.load_dataset('text', data_files={'train': [data_args.dataset]}) except FileNotFoundError as ex: raise ValueError(f"Error loading dataset from {data_args.dataset}, {ex}") - if data_args.source_max_len > tokenizer.model_max_length: - raise ValueError(f"Max source length of {data_args.source_max_len} is larger than the maximum size supported by the model: {tokenizer.model_max_length}") + if data_args.block_size > tokenizer.model_max_length: + raise ValueError(f"Block size of {data_args.block_size} is larger than the maximum size supported by the model: {tokenizer.model_max_length}") def add_newline_fn(example): example['text'] = example['text'] + '\n' @@ -241,7 +218,10 @@ def create_data_module_txt(tokenizer: transformers.PreTrainedTokenizer, eval_dataset = dataset['eval'] else: print('Splitting train dataset in train and validation according to `eval_dataset_size`') - dataset = dataset['train'].train_test_split(test_size=data_args.eval_dataset_size, shuffle=False) + breakpoint() + dataset = dataset['train'].train_test_split( + test_size=data_args.eval_dataset_size, shuffle=True, seed=42 + ) eval_dataset = dataset['test'] if 'train' in dataset: @@ -253,14 +233,14 @@ def create_data_module_txt(tokenizer: transformers.PreTrainedTokenizer, lambda example: tokenizer(example['text']), batched=True, remove_columns='text', - num_proc=os.cpu_count(), + num_proc=32, load_from_cache_file=True) train_dataset_tokenized = train_dataset_tokenized.map( - lambda example: group_texts(example, data_args.source_max_len), + lambda example: group_texts(example, data_args.block_size), batched=True, - num_proc=max(1, min(os.cpu_count(), int(len(train_dataset_tokenized['input_ids']) / (data_args.source_max_len * 10)))), + num_proc=32, load_from_cache_file=True, - desc=f"Grouping texts in chunks of {data_args.source_max_len}") + desc=f"Grouping texts in chunks of {data_args.block_size}") eval_dataset_tokenized = None if eval_dataset is not None: @@ -268,18 +248,13 @@ def create_data_module_txt(tokenizer: transformers.PreTrainedTokenizer, lambda example: tokenizer(example['text']), batched=True, remove_columns='text', - num_proc=os.cpu_count()) + num_proc=32) eval_dataset_tokenized = eval_dataset_tokenized.map( - lambda example: group_texts(example, data_args.source_max_len), + lambda example: group_texts(example, data_args.block_size), batched=True, - num_proc=max(1, min(os.cpu_count(), int(len(eval_dataset_tokenized['input_ids']) / (data_args.source_max_len * 10)))), + num_proc=32, load_from_cache_file=True, - desc=f"Grouping texts in chunks of {data_args.source_max_len}") - - for ids in train_dataset_tokenized['input_ids']: - assert len(ids) == data_args.source_max_len - for ids in eval_dataset_tokenized['input_ids']: - assert len(ids) == data_args.source_max_len + desc=f"Grouping texts in chunks of {data_args.block_size}") return dict( train_dataset=train_dataset_tokenized if do_train else None, @@ -287,84 +262,3 @@ def create_data_module_txt(tokenizer: transformers.PreTrainedTokenizer, predict_dataset=eval_dataset_tokenized if do_predict else None, data_collator=transformers.default_data_collator ) - - -def create_data_module_chat(tokenizer, data_args, do_train, do_eval, do_predict): - try: - dataset = datasets.Dataset.from_json(path_or_paths=data_args.dataset) - except FileNotFoundError as ex: - raise ValueError(f"Error loading dataset from {data_args.dataset}, {ex}") - - if data_args.dataset_chat_template is not None: - tokenizer.chat_template = data_args.dataset_chat_template - - target_len = data_args.source_max_len * 0.5 - grouped_chats = list() - last_len = 0 - for row in tqdm(dataset, desc="Grouping chat messages"): - content_length = len(tokenizer(row['content'])['input_ids']) - if last_len + content_length <= target_len and len(grouped_chats) > 0: - grouped_chats[-1]['chat'].append(row) - last_len += content_length - else: - last_len = 0 - grouped_chats.append({'chat': [row]}) - dataset = datasets.Dataset.from_list(grouped_chats) - dataset = dataset.map(lambda x: {"text": tokenizer.apply_chat_template(x["chat"], tokenize=False, add_generation_prompt=False)}) - dataset.remove_columns('chat') - - eval_dataset = None - if do_eval or do_predict: - print('Splitting train dataset in train and validation according to `eval_dataset_size`') - dataset_split = dataset.train_test_split(test_size=data_args.eval_dataset_size, shuffle=True) - train_dataset = dataset_split["train"] - eval_dataset = dataset_split["test"] - - data_collator = DataCollatorForCausalLMText( - tokenizer=tokenizer, - max_len=data_args.source_max_len, - ) - return dict( - train_dataset=train_dataset if do_train else None, - eval_dataset=eval_dataset, - predict_dataset=eval_dataset, - data_collator=data_collator - ) - - -def get_data_loaders(tokenizer, data_args: DataArguments, batch_size: int, eval_batch_size: int, - do_train: bool, do_eval: bool, do_predict: bool = False): - data_type = DatasetType.from_string(data_args.dataset_type) - if data_type == DatasetType.S2S: - print("Loading dataset in s2s mode") - data_module = create_data_module_s2s(tokenizer, data_args, do_train, do_eval, do_predict) - elif data_type == DatasetType.HUB: - print("Loading dataset from hub, expecting alpaca style") - data_module = create_data_module_hub(tokenizer, data_args, do_train, do_eval, do_predict) - elif data_type == DatasetType.TEXT: - print("Loading dataset in txt mode") - data_module = create_data_module_txt(tokenizer, data_args, do_train, do_eval, do_predict) - elif data_type == DatasetType.CHAT: - print("Loading dataset in chat mode") - data_module = create_data_module_chat(tokenizer, data_args, do_train, do_eval, do_predict) - else: - raise RuntimeError("Unkown dataset type") - - train_dataloader = None - eval_dataloader = None - - if do_train: - train_dataloader = torch.utils.data.DataLoader( - data_module['train_dataset'], - shuffle=True, - collate_fn=data_module['data_collator'], - batch_size=batch_size - ) - if do_eval: - eval_dataloader = torch.utils.data.DataLoader( - data_module['eval_dataset'], - shuffle=True, - collate_fn=data_module['data_collator'], - batch_size=eval_batch_size - ) - return train_dataloader, eval_dataloader diff --git a/dyntrainmodel.py b/dyntrainmodel.py index 68ce7c0..e6a1638 100644 --- a/dyntrainmodel.py +++ b/dyntrainmodel.py @@ -1,23 +1,3 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - from transformers import AutoModelForCausalLM import torch from utils import replace_module @@ -88,9 +68,7 @@ class LinearGroup: class DyntrainModel: def __init__(self, model_name_or_path: str, cache_dir: str | None, quantize: bool, - target_active_params: int, train_static_params: bool, - reshuffle_fraction: float, gradient_checkpointing: bool, - trust_remote_code: bool = False): + target_active_params: int, reshuffle_fraction: float, gradient_checkpointing: bool, trust_remote_code: bool = False): self.model = AutoModelForCausalLM.from_pretrained( model_name_or_path, cache_dir=cache_dir, @@ -104,7 +82,6 @@ class DyntrainModel: raise RuntimeError("reshuffle_percent must be between 0.1 and 1.0") self.devices = list[torch.device]() self.inital_reshufle = True - self.train_static_params = train_static_params if gradient_checkpointing: self.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) @@ -190,14 +167,8 @@ class DyntrainModel: def staticParameterCount(self) -> int: return sum(p.numel() for p in self.staticParameters()) - def activeDynamicParameterCount(self) -> int: - return sum(p.numel() for p in self.dynamicParameters() if p.requires_grad) - def activeParameterCount(self) -> int: - if self.train_static_params: - total_params = self.dynamicParameters() + self.staticParameters() - else: - total_params = self.dynamicParameters() + total_params = self.dynamicParameters() + self.staticParameters() return sum(p.numel() for p in total_params if p.requires_grad) def getDistanceAndErrorSample(self) -> (torch.Tensor, torch.Tensor): @@ -216,7 +187,7 @@ class DyntrainModel: params = self.activeParameterCount() if params >= self.target_active_params: - raise RuntimeError("Insuficant active parameters to suffle active") + RuntimeError("Insuficant active parameters to suffle active") while params < self.target_active_params and len(self.frozen_linear_groups) > 0: i = randint(0, len(self.frozen_linear_groups) - 1) group = self.frozen_linear_groups.pop(i) @@ -228,7 +199,7 @@ class DyntrainModel: active_params = self.activeParameterCount() - assert self.target_active_params * 1.4 > active_params and self.target_active_params * 0.6 < active_params + assert self.target_active_params * 1.3 > active_params and self.target_active_params * 0.7 < active_params def activeParamtersByDevice(self) -> list[int]: out = [0] * len(self.devices) @@ -242,7 +213,7 @@ class DyntrainModel: for i, count in enumerate(active_counts): memory = torch.cuda.get_device_properties(self.devices[i]).total_memory if i == 0: - memory = int(memory * 0.5) + memory = int(memory * 0.8) bits_per_param.append(count / memory) max_index, max_bits_per_param = max(enumerate(active_counts), key=lambda x: x[1]) @@ -252,7 +223,7 @@ class DyntrainModel: if group.getDevice() is self.devices[max_index]: memory = torch.cuda.get_device_properties(self.devices[max_index]).total_memory if max_index == 0: - memory = int(memory * 0.5) + memory = int(memory * 0.8) swing = group.paramCount() / memory if max_bits_per_param - swing > min_bits_per_param + swing: group.inplaceTo(device=self.devices[min_index]) diff --git a/gpl-3.0.txt b/gpl-3.0.txt deleted file mode 100644 index f288702..0000000 --- a/gpl-3.0.txt +++ /dev/null @@ -1,674 +0,0 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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But first, please read -. diff --git a/modules.py b/modules.py index 061f6a6..9ff9dee 100644 --- a/modules.py +++ b/modules.py @@ -1,23 +1,3 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - import torch import bitsandbytes as bnb import torch.multiprocessing as multiprocessing @@ -128,7 +108,7 @@ class DynamicConvertingLinear(Linear): class DynamicQantizedLinear(Linear): def __init__(self, in_features: int, out_features: int, bias: bool, active_device: torch.device, cold_device: torch.device, - output_dtype=None, compute_dtype=None, output_device=None, cold_dtype=torch.float32): + output_dtype=None, compute_dtype=None, output_device=None): super().__init__(in_features, out_features, bias, cold_device, torch.float32) self.active_device = active_device self.cold_device = cold_device @@ -140,8 +120,8 @@ class DynamicQantizedLinear(Linear): self.bias_quantized = None self.bias_state = None self.block_size = 128 - #self.weight_start = self.weight.clone().detach() - self.cold_dtype = cold_dtype + self.quant_type = 'nf4' + self.weight_start = self.weight.clone().detach() @classmethod def fromLinear(cls, in_module: torch.nn.Linear, active_device: torch.device = torch.device("cuda:0"), cold_device: torch.device = torch.device("cpu"), @@ -151,19 +131,19 @@ class DynamicQantizedLinear(Linear): compute_dtype=compute_dtype, output_device=output_device) new_module.weight = torch.nn.Parameter(in_module.weight.to(torch.float32).to(cold_device)) new_module.bias = torch.nn.Parameter(in_module.bias.to(torch.float32).to(cold_device)) if new_module.bias is not None else None - #new_module.weight_start = new_module.weight.clone().detach() + new_module.weight_start = new_module.weight.clone().detach() return new_module def compress(self) -> None: - weight = self.weight.contiguous().to(torch.float16).to(self.active_device) + weight = self.weight.contiguous().to(torch.float16).cuda(self.active_device) self.weight_quantized, self.weight_state = bnb.functional.quantize_blockwise(weight, blocksize=self.block_size) if self.bias is not None: - bias = self.bias.contiguous().to(torch.float16).to(self.active_device) + bias = self.bias.contiguous().to(torch.float16).cuda(self.active_device) self.bias_quantized, self.bias_state = bnb.functional.quantize_blockwise(bias, blocksize=self.block_size) frozen = self.isFrozen() - self.weight = torch.nn.Parameter(self.weight.to(self.cold_dtype).to(self.cold_device)) - self.bias = torch.nn.Parameter(self.bias.to(self.cold_dtype).to(self.cold_device)) if self.bias is not None else None + self.weight = torch.nn.Parameter(self.weight.to(self.cold_device)) + self.bias = torch.nn.Parameter(self.bias.to(self.cold_device)) if self.bias is not None else None self.setFrozen(frozen, False) def decompress(self) -> None: @@ -171,16 +151,16 @@ class DynamicQantizedLinear(Linear): self.weight_state = None self.bias_quantized = None self.bias_state = None - #self.weight_start = self.weight.clone().detach().to(self.cold_device) - self.weight = torch.nn.Parameter(self.weight.to(self.active_device).to(torch.float32)) + self.weight_start = self.weight.clone().detach().to(self.cold_device) + self.weight = torch.nn.Parameter(self.weight.to(self.active_device)) if self.bias_quantized: - self.bias = torch.nn.Parameter(self.bias.to(self.active_device).to(torch.float32)) + self.bias = torch.nn.Parameter(self.bias.to(self.active_device)) def getDistanceAndError(self) -> tuple[torch.Tensor, torch.Tensor]: original_weight = self.weight.contiguous().to(self.active_device).to(torch.float16) quantized_original_weight, quantized_original_state = bnb.functional.quantize_blockwise(original_weight, blocksize=self.block_size) dequantized_original_weight = bnb.functional.dequantize_blockwise(quantized_original_weight, quantized_original_state).to(original_weight.dtype) - distance = torch.zeros((2)) #(self.weight_start - self.weight.to(self.cold_device)).to(torch.float32) + distance = (self.weight_start - self.weight.to(self.cold_device)).to(torch.float32) error = (dequantized_original_weight - original_weight).to(torch.float32) return (distance, error) diff --git a/tokenizer.py b/tokenizer.py index cf8a04a..c16f3df 100644 --- a/tokenizer.py +++ b/tokenizer.py @@ -1,23 +1,3 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - import transformers from arguments import ModelArguments @@ -50,13 +30,13 @@ def smart_tokenizer_and_embedding_resize( def get_tokenizer(model, cache_dir, model_args: ModelArguments): - tokenizer_path = model_args.tokenizer if model_args.tokenizer is not None else model_args.model_name_or_path - print(f'Tokenizer: {tokenizer_path}') + print(f'Tokenizer: {model_args.tokenizer if model_args.tokenizer is not None else model_args.model_name_or_path}') tokenizer = transformers.AutoTokenizer.from_pretrained( - tokenizer_path, + model_args.tokenizer if model_args.tokenizer is not None else model_args.model_name_or_path, cache_dir=cache_dir, padding_side="right", use_fast=False, + eos_token="[EOS]", tokenizer_type='llama' if 'llama' in model_args.model_name_or_path else None, trust_remote_code=model_args.trust_remote_code ) diff --git a/train_dynamic.py b/train_dynamic.py index 0a64e19..96ff497 100644 --- a/train_dynamic.py +++ b/train_dynamic.py @@ -1,24 +1,6 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - import transformers +from transformers import get_scheduler + import torch from torch.utils import tensorboard import os @@ -26,10 +8,9 @@ import shutil import math from tqdm.auto import tqdm import gc -import sys from arguments import DataArguments, ModelArguments, TrainingArguments -from datamodules import get_data_loaders +from datamodules import create_data_module_s2s, create_data_module, create_data_module_hub from tokenizer import get_tokenizer from dyntrainmodel import DyntrainModel @@ -38,16 +19,7 @@ from dyntrainmodel import DyntrainModel def save_model(model, global_step: int, output_dir: str, max_checkpoints: int = 0): output_chkpt_dir = f"step_{global_step}" if global_step >= 0 else "" output_dir = os.path.join(output_dir, output_chkpt_dir) - - print(f"saveing model to {output_chkpt_dir}") - - temperature = model.generation_config.temperature - top_p = model.generation_config.top_p - model.generation_config.temperature = None - model.generation_config.top_p = None model.save_pretrained(output_dir) - model.generation_config.temperature = temperature - model.generation_config.top_p = top_p if max_checkpoints > 0: files = [f for f in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, f)) and f.startswith("step_")] @@ -85,85 +57,37 @@ def get_optimizer(dyamic_parameters: list[torch.nn.Parameter], static_parameters return optimizer -def move_optimizer_param(param, device: torch.device, device_map: dict): - if isinstance(param, torch.Tensor): - move_device = device if device is not None else device_map[id(param)] - assert device is not None or move_device != torch.device("cpu") - old_device = param.device - param.data = param.data.to(move_device) - if param._grad is not None: - param._grad.data = param._grad.data.to(move_device) - if device is not None and id(param) not in device_map: - device_map[id(param)] = old_device - assert old_device != torch.device("cpu") - elif isinstance(param, dict): - for subparam in param.values(): - move_optimizer_param(subparam, device, device_map) - - -def suspend_optimizer(optimizer) -> dict: - device_map = dict() - for param in optimizer.state.values(): - move_optimizer_param(param, torch.device("cpu"), device_map) - return device_map - - -def resume_optimizer(optimizer, device_map: dict): - for param in optimizer.state.values(): - move_optimizer_param(param, None, device_map) - - def evaluate(model: DyntrainModel, tokenizer, dataloader: torch.utils.data.DataLoader, globalstep: int, - log_writer: tensorboard.SummaryWriter, eval_prompt: str | None = None): - with torch.no_grad(): - loss = torch.zeros((1), device="cuda:0") - model.model.eval() + log_writer: tensorboard.SummaryWriter, eval_prompt: str = None): + print("*** Eval ***") + loss = torch.zeros((1), device="cuda:0") + model.model.eval() + for batch in dataloader: + for key in batch: + batch[key] = batch[key].to("cuda:0") + outputs = model.model(**batch) + loss += outputs.loss + loss = loss / len(dataloader) + log_writer.add_scalar("Loss/Eval", loss, globalstep) + print(f"Eval Loss {loss.item()}") + return loss.item() - for batch in tqdm(dataloader, desc="Doing eval"): - for key in batch: - batch[key] = batch[key].to("cuda:0") - outputs = model.model(**batch) - loss += outputs.loss - loss = loss / len(dataloader) - log_writer.add_scalar("Loss/Eval", loss, globalstep) - print(f"Eval Loss {loss.item()}") - - if eval_prompt is not None: - input_ids = tokenizer(eval_prompt, return_tensors="pt").input_ids.to(model.devices[0]) - attention_mask = torch.ones(input_ids.shape, device=model.devices[0], requires_grad=False) - outputs = model.model.generate(input_ids, attention_mask=attention_mask, do_sample=True, temperature=1, - max_new_tokens=100, min_new_tokens=100) - response_decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] - print(f"Eval generation: {response_decoded}") - log_writer.add_text("Text/Eval", response_decoded, globalstep) - model.model.train() - - -def max_vram_allocated(): - max_vram_alloc = 0 - for i in range(0, torch.cuda.device_count()): - max_vram_alloc = max(torch.cuda.memory_allocated(i), max_vram_alloc) - return max_vram_alloc - - -def min_vram_allocated(): - max_vram_alloc = sys.maxsize - for i in range(0, torch.cuda.device_count()): - max_vram_alloc = min(torch.cuda.memory_allocated(i), max_vram_alloc) - return max_vram_alloc + if eval_prompt is not None: + input_ids = tokenizer(eval_prompt, return_tensors="pt").input_ids.to(model.devices[0]) + attention_mask = torch.ones(input_ids.shape, device=model.devices[0], requires_grad=False) + outputs = model.generate(input_ids, attention_mask=attention_mask, do_sample=True, temperature=1, max_new_tokens=100) + response_decoded = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] + print(f"Eval generation: response_decoded") + log_writer.add_text("Text/Eval", response_decoded, globalstep) def train(model_args: ModelArguments, data_args: DataArguments, training_args: TrainingArguments): - log_writer = tensorboard.SummaryWriter(log_dir=training_args.logging_dir) + log_writer = tensorboard.SummaryWriter() - model = DyntrainModel(model_args.model_name_or_path, training_args.cache_dir, - quantize=model_args.quantize, - target_active_params=int(training_args.max_instant_params * 1e6), - train_static_params=training_args.train_non_linear_layers, - reshuffle_fraction=training_args.churn_percent / 100.0, - gradient_checkpointing=True, - trust_remote_code=True) + model = DyntrainModel(model_args.model_name_or_path, training_args.cache_dir, target_active_params=int(training_args.max_instant_params * 1e6), + reshuffle_fraction=training_args.churn_percent / 100.0, gradient_checkpointing=True, trust_remote_code=True, + quantize=model_args.quantize) devices = list(torch.device(i) for i in range(0, torch.cuda.device_count())) model.toDevices(devices) model.reshuffleActive() @@ -172,15 +96,32 @@ def train(model_args: ModelArguments, data_args: DataArguments, training_args: T paramter_count = sum(p.numel() for p in model.model.parameters()) active_paramter_count = sum(p.numel() for p in model.model.parameters() if p.requires_grad) static_parameter_count = model.staticParameterCount() if training_args.train_non_linear_layers else 0 - print(f"Training model with {paramter_count / 1e6}m parameters and {active_paramter_count / 1e6}m " + print(f"Training model with {paramter_count / 1e6}m parameters and {active_paramter_count / 1e6}m" f"instantanous active paramters of which {static_parameter_count} are static") tokenizer = get_tokenizer(model.model, training_args.cache_dir, model_args) - train_dataloader, eval_dataloader = get_data_loaders(tokenizer, data_args, - training_args.per_device_train_batch_size, - training_args.per_device_eval_batch_size, - training_args.do_train, training_args.do_eval) + if data_args.dataset.endswith("json"): + print("Loading dataset in s2s mode") + data_module = create_data_module_s2s(tokenizer, data_args, training_args.do_train, training_args.do_eval, False) + elif data_args.data_from_hub: + data_module = create_data_module_hub(tokenizer, data_args, training_args.do_train, training_args.do_eval, False) + else: + print("Loading dataset in txt mode") + data_module = create_data_module(tokenizer, data_args, training_args.do_train, training_args.do_eval, False) + dataset = {k: v for k, v in data_module.items() if k != 'predict_dataset'} + train_dataloader = torch.utils.data.DataLoader( + dataset['train_dataset'], + shuffle=True, + collate_fn=dataset['data_collator'], + batch_size=training_args.per_device_train_batch_size + ) if dataset['train_dataset'] is not None else None + eval_dataloader = torch.utils.data.DataLoader( + dataset['eval_dataset'], + shuffle=True, + collate_fn=dataset['data_collator'], + batch_size=training_args.per_device_train_batch_size + ) if dataset['eval_dataset'] is not None else None dynamic_param_ratio = (model.staticParameterCount() + model.dynamicParameterCount()) / model.dynamicParameterCount() steps_per_epoch = math.ceil(len(train_dataloader) / training_args.gradient_accumulation_steps) if train_dataloader is not None else 1 @@ -194,7 +135,7 @@ def train(model_args: ModelArguments, data_args: DataArguments, training_args: T training_args.adam_epsilon, training_args.adam8bit) - lr_scheduler = transformers.get_scheduler( + lr_scheduler = get_scheduler( name=training_args.lr_scheduler_type, optimizer=optimizer, num_warmup_steps=training_args.warmup_steps, @@ -206,11 +147,13 @@ def train(model_args: ModelArguments, data_args: DataArguments, training_args: T global_step = 0 model.model.train() for epoch in range(0, training_args.epochs): + model.model.train() print("*** Train ***") - print(f'Vram used for model before training starts: {torch.cuda.memory_allocated()/(1024.0**3):.2f}') + print(f'Vram used for model before training starts: {torch.cuda.memory_allocated()/(1024.0*1024.0)}') for step, batch in enumerate(train_dataloader): for key in batch: batch[key] = batch[key].to("cuda:0") + outputs = model.model(**batch) loss = outputs.loss / training_args.gradient_accumulation_steps loss.backward() @@ -221,54 +164,48 @@ def train(model_args: ModelArguments, data_args: DataArguments, training_args: T optimizer.step() lr_scheduler.step() - progress_bar.set_postfix_str(f"Loss: {loss.item():.2f} Max: {max_vram_allocated()/(1024.0**3):.2f}GB" - f" Min: {min_vram_allocated()/(1024.0**3):.2f}GB") - model.model.zero_grad() - if global_step > 0: - if global_step % training_args.reshufle_steps == 0 and training_args.max_instant_params != 0: - print("Reshuffleing") - lr_scheduler.optimizer = None - del optimizer - # distance, error = model.getDistanceAndErrorSample() - # log_writer.add_histogram("Distances/Train", distance, max_bins=50) - # log_writer.add_histogram("Errors/Train", error, max_bins=50) + if global_step % 5 == 0: + print(f"Train Loss {loss.item()}") - model.reshuffleActive() - model.balanceActive() - log_writer.add_scalar("Parameters/train", model.activeParameterCount(), global_step) - optimizer = get_optimizer(model.dynamicParameters(), - model.staticParameters() if training_args.train_non_linear_layers else None, - training_args.learning_rate, - training_args.learning_rate / dynamic_param_ratio, - training_args.weight_decay, - training_args.adam_epsilon, - training_args.adam8bit) - lr_scheduler.optimizer = optimizer + if global_step % training_args.reshufle_steps == 0 and training_args.max_instant_params != 0: + print("Reshuffleing") + lr_scheduler.optimizer = None + del optimizer + # distance, error = model.getDistanceAndErrorSample() + # log_writer.add_histogram("Distances/Train", distance, max_bins=50) + # log_writer.add_histogram("Errors/Train", error, max_bins=50) - if global_step % training_args.save_steps == 0: - save_model(model.model, global_step, training_args.output_dir, training_args.max_checkpoints) - if training_args.eval_steps > 0 and global_step % training_args.eval_steps == 0: - device_map = suspend_optimizer(optimizer) - evaluate(model, tokenizer, eval_dataloader, global_step, log_writer, training_args.eval_prompt) - resume_optimizer(optimizer, device_map) + model.reshuffleActive() + model.balanceActive() + log_writer.add_scalar("Parameters/train", model.activeParameterCount(), global_step) + optimizer = get_optimizer(model.dynamicParameters(), + model.staticParameters() if training_args.train_non_linear_layers else None, + training_args.learning_rate, + training_args.learning_rate / dynamic_param_ratio, + training_args.weight_decay, + training_args.adam_epsilon, + training_args.adam8bit) + lr_scheduler.optimizer = optimizer global_step += 1 progress_bar.update() + if global_step > 0: + if global_step % training_args.save_steps == 0: + save_model(model.model, global_step, training_args.output_dir, training_args.max_checkpoints) + if training_args.eval_steps > 0 and global_step % training_args.save_steps == 0: + evaluate(model, eval_dataloader, global_step, log_writer, training_args.eval_prompt) if training_args.flush_allocator: gc.collect() torch.cuda.empty_cache() if training_args.do_eval and training_args.eval_steps == -1: - device_map = suspend_optimizer(optimizer) - evaluate(model, tokenizer, eval_dataloader, global_step, log_writer, training_args.eval_prompt) - resume_optimizer(optimizer, device_map) - - del optimizer + evaluate(model, eval_dataloader, global_step, log_writer, training_args.eval_prompt) + # Evaluation if training_args.do_eval: - evaluate(model, tokenizer, eval_dataloader, global_step, log_writer, training_args.eval_prompt) + evaluate(model, eval_dataloader, global_step, log_writer, training_args.eval_prompt) save_model(model.model, global_step, training_args.output_dir) diff --git a/tune.sh b/tune.sh deleted file mode 100755 index 2a59aae..0000000 --- a/tune.sh +++ /dev/null @@ -1,54 +0,0 @@ -#!/bin/sh -# -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . -# - -BASE_DIR=$(dirname "$0") -VENV_DIR=$(venvget) - -export MAX_JOBS=48 - -export ROCR_VISIBLE_DEVICES="1,2" -source $VENV_DIR/bin/activate - -python $SCRIPTS/train_dyamic/train_dynamic.py \ - --model_name_or_path "huggyllama/llama-7b" \ - --dataset "tatsu-lab/alpaca" \ - --dataset_type "hub" \ - --eval_dataset_size 200 \ - --source_max_len 1024 \ - --do_train \ - --do_eval \ - --eval_steps 100 \ - --reshufle_steps 50 \ - --per_device_train_batch_size 2 \ - --per_device_eval_batch_size 1 \ - --gradient_checkpointing True \ - --gradient_accumulation_steps 4 \ - --epochs 3 \ - --logging_dir $BASE_DIR/log \ - --logging_steps 5 \ - --learning_rate 1e-6 \ - --save_steps 500 \ - --output_dir $BASE_DIR/llama-7b-quant \ - --adam8bit \ - --churn_percent 100\ - --max_instant_params 3000 \ - --quantize diff --git a/utils.py b/utils.py index 8f99d16..c58bc06 100644 --- a/utils.py +++ b/utils.py @@ -1,23 +1,3 @@ - -# QRotaryTraining - A novel method for fully training all parameters of large -# language models (llms) while using less device memory than traditional methods. -# Copyright (C) 2024 Carl Philipp Klemm -# -# This file is part of QRotaryTraining. -# -# QRotaryTraining is free software: you can redistribute it and/or modify -# it under the terms of the GNU General Public License as published by -# the Free Software Foundation, either version 3 of the License, or -# (at your option) any later version. -# -# QRotaryTraining is distributed in the hope that it will be useful, -# but WITHOUT ANY WARRANTY; without even the implied warranty of -# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -# GNU General Public License for more details. -# -# You should have received a copy of the GNU General Public License -# along with QRotaryTraining. If not, see . - from peft.utils import _get_submodules import torch