├── llama_recipes ├── inference │ ├── __pycache__ │ │ ├── __init__.cpython-39.pyc │ │ └── model_utils.cpython-39.pyc │ ├── __init__.py │ ├── model_utils.py │ ├── chat_utils.py │ ├── checkpoint_converter_fsdp_hf.py │ └── safety_utils.py ├── data │ ├── __init__.py │ ├── concatenator.py │ └── sampler.py ├── datasets │ ├── grammar_dataset │ │ ├── __init__.py │ │ ├── grammar_dataset.py │ │ └── grammar_dataset_process.ipynb │ ├── __init__.py │ ├── samsum_dataset.py │ └── alpaca_dataset.py ├── configs │ ├── __init__.py │ ├── peft.py │ ├── fsdp.py │ ├── datasets.py │ └── training.py ├── utils │ ├── __init__.py │ ├── fsdp_utils.py │ ├── memory_utils.py │ ├── dataset_utils.py │ ├── config_utils.py │ └── train_utils.py ├── policies │ ├── __init__.py │ ├── activation_checkpointing_functions.py │ ├── mixed_precision.py │ ├── wrapping.py │ └── anyprecision_optimizer.py └── model_checkpointing │ ├── __init__.py │ └── checkpoint_handler.py ├── data ├── demo_infer.json └── demo_train.json ├── run_infer.sh ├── run_infer_logit.sh ├── run_test.sh ├── README.md ├── requirements.txt ├── README_en.md ├── finetuning.py └── inference.py /llama_recipes/inference/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WalkerMitty/Fast-Llama2/HEAD/llama_recipes/inference/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /llama_recipes/inference/__pycache__/model_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/WalkerMitty/Fast-Llama2/HEAD/llama_recipes/inference/__pycache__/model_utils.cpython-39.pyc -------------------------------------------------------------------------------- /llama_recipes/data/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. -------------------------------------------------------------------------------- /data/demo_infer.json: -------------------------------------------------------------------------------- 1 | [{"conversations": [{"from": "human", "value": "QUESTION1"}, {"from": "gpt", "value": ""}]},{"conversations": [{"from": "human", "value": "QUESTION2"}, {"from": "gpt", "value": ""}]}] -------------------------------------------------------------------------------- /llama_recipes/inference/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. -------------------------------------------------------------------------------- /llama_recipes/datasets/grammar_dataset/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | -------------------------------------------------------------------------------- /llama_recipes/configs/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from llama_recipes.configs.peft import lora_config, llama_adapter_config, prefix_config 5 | from llama_recipes.configs.fsdp import fsdp_config 6 | from llama_recipes.configs.training import train_config 7 | -------------------------------------------------------------------------------- /run_infer.sh: -------------------------------------------------------------------------------- 1 | CUDA_VISIBLE_DEVICES=3 python inference.py \ 2 | --model_name /data/hfmodel/PLMs/llama27b_hf \ 3 | --peft_model loras/decisioner-100-epoch40 \ 4 | --max_new_tokens 8 \ 5 | --do_sample false \ 6 | --num_beams 1 \ 7 | --start 0 \ 8 | --end -1 \ 9 | --eval_file ./data/demo_infer.json \ 10 | --bsz 16 \ 11 | --max_length 256 \ 12 | --generate_file './record/conflict_2-baseline-decision.file' 13 | 14 | -------------------------------------------------------------------------------- /llama_recipes/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from llama_recipes.utils.memory_utils import MemoryTrace 5 | from llama_recipes.utils.dataset_utils import * 6 | from llama_recipes.utils.fsdp_utils import fsdp_auto_wrap_policy 7 | from llama_recipes.utils.train_utils import * -------------------------------------------------------------------------------- /run_infer_logit.sh: -------------------------------------------------------------------------------- 1 | CUDA_VISIBLE_DEVICES=4 python inference.py \ 2 | --model_name /data/pretrained_models/llama27b_hf \ 3 | --peft_model loras/checker-sample \ 4 | --max_new_tokens 4 \ 5 | --num_beams 1 \ 6 | --start 0 \ 7 | --end -1 \ 8 | --eval_file /data/train_file/conflict_checker_4-h.json \ 9 | --bsz 16 \ 10 | --output_logits \ 11 | --max_length 256 \ 12 | --token_k 3 \ 13 | --generate_file './record/conflict_checker_answer_4-h.json' 14 | 15 | -------------------------------------------------------------------------------- /llama_recipes/policies/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from llama_recipes.policies.mixed_precision import * 5 | from llama_recipes.policies.wrapping import * 6 | from llama_recipes.policies.activation_checkpointing_functions import apply_fsdp_checkpointing 7 | from llama_recipes.policies.anyprecision_optimizer import AnyPrecisionAdamW 8 | -------------------------------------------------------------------------------- /llama_recipes/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from llama_recipes.datasets.grammar_dataset.grammar_dataset import get_dataset as get_grammar_dataset 5 | from llama_recipes.datasets.alpaca_dataset import InstructionDataset as get_alpaca_dataset 6 | from llama_recipes.datasets.samsum_dataset import get_preprocessed_samsum as get_samsum_dataset -------------------------------------------------------------------------------- /llama_recipes/model_checkpointing/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from llama_recipes.model_checkpointing.checkpoint_handler import ( 5 | load_model_checkpoint, 6 | save_model_checkpoint, 7 | load_optimizer_checkpoint, 8 | save_optimizer_checkpoint, 9 | save_model_and_optimizer_sharded, 10 | load_model_sharded, 11 | load_sharded_model_single_gpu 12 | ) 13 | -------------------------------------------------------------------------------- /run_test.sh: -------------------------------------------------------------------------------- 1 | CUDA_VISIBLE_DEVICES=4,5,6,7 nohup torchrun --nnodes 1 --nproc_per_node 4 --master_port 29504 finetuning.py \ 2 | --enable_fsdp \ 3 | --model_name /data/hfmodel/PLMs/llama27b_hf \ 4 | --peft_method lora \ 5 | --use_peft true \ 6 | --dataset grammar_dataset \ 7 | --save_model \ 8 | --dist_checkpoint_root_folder model_checkpoints \ 9 | --dist_checkpoint_folder fine-tuned \ 10 | --fsdp_config.pure_bf16 \ 11 | --lr 5e-6 \ 12 | --output_dir loras/decisioner-100-epoch60-prompt \ 13 | --train_split ./data/demo_train.json \ 14 | --batch_size_training 128 \ 15 | --lora_path '' \ 16 | --step_size 1 \ 17 | --num_epochs 10 > logs/decisioner-100-epoch60-prompt.log 2>&1 & 18 | -------------------------------------------------------------------------------- /llama_recipes/configs/peft.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from dataclasses import dataclass, field 5 | from typing import List 6 | 7 | @dataclass 8 | class lora_config: 9 | r: int=16 10 | lora_alpha: int=32 11 | target_modules: List[str] = field(default_factory=lambda: ['q_proj','v_proj']) 12 | bias= "none" 13 | task_type: str= "CAUSAL_LM" 14 | lora_dropout: float=0.05 15 | inference_mode: bool = False 16 | 17 | @dataclass 18 | class llama_adapter_config: 19 | adapter_len: int= 10 20 | adapter_layers: int= 30 21 | task_type: str= "CAUSAL_LM" 22 | 23 | @dataclass 24 | class prefix_config: 25 | num_virtual_tokens: int=30 26 | task_type: str= "CAUSAL_LM" 27 | -------------------------------------------------------------------------------- /llama_recipes/configs/fsdp.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from dataclasses import dataclass 5 | 6 | from torch.distributed.fsdp import ShardingStrategy 7 | from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType 8 | 9 | @dataclass 10 | class fsdp_config: 11 | mixed_precision: bool=True 12 | use_fp16: bool=False 13 | sharding_strategy: ShardingStrategy = ShardingStrategy.FULL_SHARD 14 | checkpoint_type: StateDictType = StateDictType.SHARDED_STATE_DICT # alternatively can use SHARDED_STATE_DICT save one file per rank, and can resize the world-size. 15 | fsdp_activation_checkpointing: bool=True 16 | fsdp_cpu_offload: bool=False 17 | pure_bf16: bool = False 18 | optimizer: str= "AdamW" 19 | 20 | -------------------------------------------------------------------------------- /llama_recipes/configs/datasets.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from dataclasses import dataclass 5 | 6 | 7 | @dataclass 8 | class samsum_dataset: 9 | dataset: str = "samsum_dataset" 10 | train_split: str = "train" 11 | test_split: str = "validation" 12 | 13 | 14 | @dataclass 15 | class grammar_dataset: 16 | dataset: str = "grammar_dataset" 17 | train_split: str = "train.json" 18 | test_split: str = "test.json" 19 | 20 | 21 | @dataclass 22 | class alpaca_dataset: 23 | dataset: str = "alpaca_dataset" 24 | train_split: str = "train" 25 | test_split: str = "val" 26 | data_path: str = "src/llama_recipes/datasets/alpaca_data.json" 27 | 28 | 29 | @dataclass 30 | class custom_dataset: 31 | dataset: str = "custom_dataset" 32 | file: str = "examples/custom_dataset.py" 33 | train_split: str = "train" 34 | test_split: str = "validation" -------------------------------------------------------------------------------- /llama_recipes/inference/model_utils.py: -------------------------------------------------------------------------------- 1 | # This software may be used and distributed according to the terms of the GNU General Public License version 3. 2 | 3 | from peft import PeftModel 4 | from transformers import LlamaForCausalLM, LlamaConfig 5 | 6 | # Function to load the main model for text generation 7 | def load_model(model_name, quantization): 8 | model = LlamaForCausalLM.from_pretrained( 9 | model_name, 10 | return_dict=True, 11 | load_in_8bit=quantization, 12 | device_map="auto", 13 | low_cpu_mem_usage=True, 14 | ) 15 | return model 16 | 17 | 18 | # Function to load the PeftModel for performance optimization 19 | def load_peft_model(model, peft_model): 20 | peft_model = PeftModel.from_pretrained(model, peft_model) 21 | return peft_model 22 | 23 | # Loading the model from config to load FSDP checkpoints into that 24 | def load_llama_from_config(config_path): 25 | model_config = LlamaConfig.from_pretrained(config_path) 26 | model = LlamaForCausalLM(config=model_config) 27 | return model 28 | 29 | -------------------------------------------------------------------------------- /llama_recipes/policies/activation_checkpointing_functions.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from functools import partial 5 | 6 | from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import ( 7 | checkpoint_wrapper, 8 | CheckpointImpl, 9 | apply_activation_checkpointing, 10 | ) 11 | from transformers.models.llama.modeling_llama import LlamaDecoderLayer 12 | 13 | non_reentrant_wrapper = partial( 14 | checkpoint_wrapper, 15 | checkpoint_impl=CheckpointImpl.NO_REENTRANT, 16 | ) 17 | 18 | check_fn = lambda submodule: isinstance(submodule, LlamaDecoderLayer) 19 | 20 | 21 | def apply_fsdp_checkpointing(model): 22 | """apply activation checkpointing to model 23 | returns None as model is updated directly 24 | """ 25 | print(f"--> applying fsdp activation checkpointing...") 26 | 27 | apply_activation_checkpointing( 28 | model, checkpoint_wrapper_fn=non_reentrant_wrapper, check_fn=check_fn 29 | ) 30 | -------------------------------------------------------------------------------- /llama_recipes/policies/mixed_precision.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import torch 5 | 6 | from torch.distributed.fsdp import ( 7 | MixedPrecision, 8 | ) 9 | 10 | # requires grad scaler in main loop 11 | fpSixteen = MixedPrecision( 12 | param_dtype=torch.float16, 13 | # Gradient communication precision. 14 | reduce_dtype=torch.float16, 15 | # Buffer precision. 16 | buffer_dtype=torch.float16, 17 | ) 18 | 19 | bfSixteen = MixedPrecision( 20 | param_dtype=torch.bfloat16, 21 | # Gradient communication precision. 22 | reduce_dtype=torch.bfloat16, 23 | # Buffer precision. 24 | buffer_dtype=torch.bfloat16, 25 | cast_forward_inputs=True, 26 | ) 27 | 28 | bfSixteen_mixed = MixedPrecision( 29 | param_dtype=torch.float32, 30 | reduce_dtype=torch.bfloat16, 31 | buffer_dtype=torch.bfloat16, 32 | ) 33 | 34 | fp32_policy = MixedPrecision( 35 | param_dtype=torch.float32, 36 | reduce_dtype=torch.float32, 37 | buffer_dtype=torch.float32, 38 | ) 39 | -------------------------------------------------------------------------------- /llama_recipes/policies/wrapping.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import functools 5 | 6 | from transformers.models.llama.modeling_llama import LlamaDecoderLayer 7 | from torch.distributed.fsdp.wrap import ( 8 | transformer_auto_wrap_policy, 9 | size_based_auto_wrap_policy, 10 | ) 11 | 12 | 13 | def get_size_policy(min_params=1e8): 14 | num_wrap_policy = functools.partial( 15 | size_based_auto_wrap_policy, min_num_params=min_params 16 | ) 17 | return num_wrap_policy 18 | 19 | 20 | def get_llama_wrapper(): 21 | """we register our main layer class and use the fsdp transformer wrapping policy 22 | ensures embedding layers are in the root fsdp unit for shared access and that fsdp units map to transformer layers 23 | """ 24 | # ==== use new transformer wrapper 25 | 26 | llama_auto_wrap_policy = functools.partial( 27 | transformer_auto_wrap_policy, 28 | transformer_layer_cls={ 29 | LlamaDecoderLayer, 30 | }, 31 | ) 32 | 33 | return llama_auto_wrap_policy 34 | -------------------------------------------------------------------------------- /llama_recipes/data/concatenator.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from tqdm import tqdm 5 | from itertools import chain 6 | 7 | from torch.utils.data import Dataset 8 | 9 | 10 | class ConcatDataset(Dataset): 11 | def __init__(self, dataset, chunk_size=4096): 12 | self.dataset = dataset 13 | self.chunk_size = chunk_size 14 | 15 | self.samples = [] 16 | 17 | buffer = { 18 | "input_ids": [], 19 | "attention_mask": [], 20 | "labels": [], 21 | } 22 | 23 | for sample in tqdm(self.dataset, desc="Preprocessing dataset", dynamic_ncols=True): 24 | buffer = {k: v + sample[k] for k,v in buffer.items()} 25 | 26 | while len(next(iter(buffer.values()))) > self.chunk_size: 27 | self.samples.append({k: v[:self.chunk_size] for k,v in buffer.items()}) 28 | buffer = {k: v[self.chunk_size:] for k,v in buffer.items()} 29 | 30 | def __getitem__(self, idx): 31 | return self.samples[idx] 32 | 33 | def __len__(self): 34 | return len(self.samples) 35 | -------------------------------------------------------------------------------- /llama_recipes/utils/fsdp_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | def fsdp_auto_wrap_policy(model, transformer_layer_name): 5 | import functools 6 | 7 | from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy 8 | 9 | from peft.tuners import PrefixEncoder, PromptEmbedding, PromptEncoder 10 | 11 | def lambda_policy_fn(module): 12 | if ( 13 | len(list(module.named_children())) == 0 14 | and getattr(module, "weight", None) is not None 15 | and module.weight.requires_grad 16 | ): 17 | return True 18 | return False 19 | 20 | lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn) 21 | transformer_wrap_policy = functools.partial( 22 | transformer_auto_wrap_policy, 23 | transformer_layer_cls=( 24 | PrefixEncoder, 25 | PromptEncoder, 26 | PromptEmbedding, 27 | transformer_layer_name, 28 | # FullyShardedDataParallelPlugin.get_module_class_from_name( 29 | # model, transformer_layer_name 30 | # ), 31 | ), 32 | ) 33 | 34 | auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy]) 35 | return auto_wrap_policy -------------------------------------------------------------------------------- /llama_recipes/datasets/samsum_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | # For dataset details visit: https://huggingface.co/datasets/samsum 5 | 6 | import copy 7 | import datasets 8 | 9 | 10 | def get_preprocessed_samsum(dataset_config, tokenizer, split): 11 | dataset = datasets.load_dataset("samsum", split=split) 12 | 13 | prompt = ( 14 | f"Summarize this dialog:\n{{dialog}}\n---\nSummary:\n" 15 | ) 16 | 17 | def apply_prompt_template(sample): 18 | return { 19 | "prompt": prompt.format(dialog=sample["dialogue"]), 20 | "summary": sample["summary"], 21 | } 22 | 23 | dataset = dataset.map(apply_prompt_template, remove_columns=list(dataset.features)) 24 | 25 | def tokenize_add_label(sample): 26 | prompt = tokenizer.encode(tokenizer.bos_token + sample["prompt"], add_special_tokens=False) 27 | summary = tokenizer.encode(sample["summary"] + tokenizer.eos_token, add_special_tokens=False) 28 | 29 | sample = { 30 | "input_ids": prompt + summary, 31 | "attention_mask" : [1] * (len(prompt) + len(summary)), 32 | "labels": [-100] * len(prompt) + summary, 33 | } 34 | 35 | return sample 36 | 37 | dataset = dataset.map(tokenize_add_label, remove_columns=list(dataset.features)) 38 | 39 | return dataset 40 | -------------------------------------------------------------------------------- /llama_recipes/configs/training.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from dataclasses import dataclass 5 | 6 | 7 | @dataclass 8 | class train_config: 9 | model_name: str="PATH/to/LLAMA/7B" 10 | lora_path: str="" 11 | enable_fsdp: bool=False 12 | low_cpu_fsdp: bool=False 13 | run_validation: bool=False 14 | batch_size_training: int=4 15 | batching_strategy: str="padding" #alternative: padding 16 | context_length: int=128 17 | gradient_accumulation_steps: int=1 18 | gradient_clipping: bool = False 19 | gradient_clipping_threshold: float = 1.0 20 | num_epochs: int=1 21 | num_workers_dataloader: int=1 22 | lr: float=1e-4 23 | weight_decay: float=0.0 24 | gamma: float= 0.85 25 | step_size:int=1 26 | seed: int=42 27 | use_fp16: bool=False 28 | mixed_precision: bool=True 29 | val_batch_size: int=1 30 | dataset = "samsum_dataset" 31 | peft_method: str = "lora" # None , llama_adapter, prefix 32 | use_peft: bool=False 33 | output_dir: str = "PATH/to/save/PEFT/model" 34 | freeze_layers: bool = False 35 | num_freeze_layers: int = 1 36 | quantization: bool = False 37 | one_gpu: bool = False 38 | save_model: bool = True 39 | dist_checkpoint_root_folder: str="PATH/to/save/FSDP/model" # will be used if using FSDP 40 | dist_checkpoint_folder: str="fine-tuned" # will be used if using FSDP 41 | save_optimizer: bool=False # will be used if using FSDP 42 | use_fast_kernels: bool = False # Enable using SDPA from PyTroch Accelerated Transformers, make use Flash Attention and Xformer memory-efficient kernels 43 | -------------------------------------------------------------------------------- /llama_recipes/data/sampler.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import random 5 | from itertools import islice 6 | 7 | import numpy as np 8 | import torch 9 | 10 | 11 | class LengthBasedBatchSampler(torch.utils.data.BatchSampler): 12 | def __init__(self, data_source, batch_size: int, drop_last: bool, shuffle: bool=True) -> None: 13 | if isinstance(next(iter(data_source)), dict): 14 | first_key = next(iter(next(iter(data_source)).keys())) 15 | self.lengths = [len(d[first_key]) for d in data_source] 16 | else: 17 | self.lengths = [len(d) for d in data_source] 18 | self.batch_size = batch_size 19 | self.drop_last = drop_last 20 | self.shuffle = shuffle 21 | 22 | def __iter__(self): 23 | ids = np.argsort(self.lengths) 24 | if self.drop_last: 25 | ids = ids[:len(ids) // self.batch_size * self.batch_size] 26 | 27 | batches = [ids[i:i+self.batch_size] for i in range(0, len(ids), self.batch_size)] 28 | 29 | if self.shuffle: 30 | random.shuffle(batches) 31 | 32 | for b in batches: 33 | yield b 34 | 35 | def __len__(self): 36 | if self.drop_last: 37 | return len(self.lengths) // self.batch_size 38 | else: 39 | return len(self.lengths) // self.batch_size + (len(self.lengths) % self.batch_size > 0) 40 | 41 | 42 | class DistributedLengthBasedBatchSampler(torch.utils.data.BatchSampler): 43 | def __init__(self, data_source, batch_size: int, num_replicas: int, rank: int, shuffle: bool = True, seed: int = 0) -> None: 44 | random.seed(seed) 45 | self.batch_sampler = LengthBasedBatchSampler( 46 | data_source, batch_size=batch_size, drop_last=True, shuffle=shuffle 47 | ) 48 | self.num_replicas = num_replicas 49 | self.rank = rank 50 | 51 | def __iter__(self): 52 | max_length = len(self.batch_sampler) // self.num_replicas * self.num_replicas 53 | return islice(self.batch_sampler, self.rank, max_length, self.num_replicas) 54 | 55 | def __len__(self): 56 | return len(self.batch_sampler) // self.num_replicas 57 | -------------------------------------------------------------------------------- /llama_recipes/inference/chat_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import json 5 | from typing import List, Literal, TypedDict 6 | 7 | 8 | Role = Literal["user", "assistant"] 9 | 10 | 11 | class Message(TypedDict): 12 | role: Role 13 | content: str 14 | 15 | 16 | Dialog = List[Message] 17 | 18 | B_INST, E_INST = "[INST]", "[/INST]" 19 | B_SYS, E_SYS = "<>\n", "\n<>\n\n" 20 | def format_tokens(dialogs, tokenizer): 21 | prompt_tokens = [] 22 | for dialog in dialogs: 23 | if dialog[0]["role"] == "system": 24 | dialog = [ 25 | { 26 | "role": dialog[1]["role"], 27 | "content": B_SYS 28 | + dialog[0]["content"] 29 | + E_SYS 30 | + dialog[1]["content"], 31 | } 32 | ] + dialog[2:] 33 | assert all([msg["role"] == "user" for msg in dialog[::2]]) and all( 34 | [msg["role"] == "assistant" for msg in dialog[1::2]] 35 | ), ( 36 | "model only supports 'system','user' and 'assistant' roles, " 37 | "starting with user and alternating (u/a/u/a/u...)" 38 | ) 39 | """ 40 | Please verify that your tokenizer support adding "[INST]", "[/INST]" to your inputs. 41 | Here, we are adding it manually. 42 | """ 43 | dialog_tokens: List[int] = sum( 44 | [ 45 | tokenizer.encode( 46 | f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ", 47 | ) + [tokenizer.eos_token_id] 48 | for prompt, answer in zip(dialog[::2], dialog[1::2]) 49 | ], 50 | [], 51 | ) 52 | assert ( 53 | dialog[-1]["role"] == "user" 54 | ), f"Last message must be from user, got {dialog[-1]['role']}" 55 | dialog_tokens += tokenizer.encode( 56 | f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}", 57 | ) 58 | prompt_tokens.append(dialog_tokens) 59 | return prompt_tokens 60 | 61 | 62 | def read_dialogs_from_file(file_path): 63 | with open(file_path, 'r') as file: 64 | dialogs = json.load(file) 65 | return dialogs 66 | -------------------------------------------------------------------------------- /llama_recipes/datasets/alpaca_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | # For dataset details visit: https://crfm.stanford.edu/2023/03/13/alpaca.html 5 | 6 | import copy 7 | import json 8 | 9 | import torch 10 | from torch.utils.data import Dataset 11 | 12 | 13 | PROMPT_DICT = { 14 | "prompt_input": ( 15 | "Below is an instruction that describes a task, paired with an input that provides further context. " 16 | "Write a response that appropriately completes the request.\n\n" 17 | "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" 18 | ), 19 | "prompt_no_input": ( 20 | "Below is an instruction that describes a task. " 21 | "Write a response that appropriately completes the request.\n\n" 22 | "### Instruction:\n{instruction}\n\n### Response:" 23 | ), 24 | } 25 | 26 | class InstructionDataset(Dataset): 27 | def __init__(self, dataset_config, tokenizer, partition="train"): 28 | self.ann = json.load(open(dataset_config.data_path)) 29 | if partition == "train": 30 | self.ann = self.ann 31 | else: 32 | self.ann = self.ann[:200] 33 | 34 | self.tokenizer = tokenizer 35 | 36 | def __len__(self): 37 | return len(self.ann) 38 | 39 | def __getitem__(self, index): 40 | IGNORE_INDEX = -100 # The default setting in CrossEntropyLoss 41 | 42 | 43 | ann = self.ann[index] 44 | if ann.get("input", "") == "": 45 | prompt = PROMPT_DICT["prompt_no_input"].format_map(ann) 46 | else: 47 | prompt = PROMPT_DICT["prompt_input"].format_map(ann) 48 | example = prompt + ann["output"] 49 | prompt = torch.tensor( 50 | self.tokenizer.encode(prompt), dtype=torch.int64 51 | ) 52 | example = self.tokenizer.encode(example) 53 | example.append(self.tokenizer.eos_token_id) 54 | example = torch.tensor( 55 | example, dtype=torch.int64 56 | ) 57 | labels = copy.deepcopy(example) 58 | labels[: len(prompt)] = -1 59 | example_mask = example.ge(0) 60 | label_mask = labels.ge(0) 61 | example[~example_mask] = 0 62 | labels[~label_mask] = IGNORE_INDEX 63 | 64 | return { 65 | "input_ids": example.tolist(), 66 | "labels": labels.tolist(), 67 | "attention_mask":example_mask.tolist(), 68 | } 69 | -------------------------------------------------------------------------------- /llama_recipes/utils/memory_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import gc 5 | import psutil 6 | import threading 7 | 8 | import torch 9 | 10 | def byte2gb(x): 11 | return int(x / 2**30) 12 | # This context manager is used to track the peak memory usage of the process 13 | class MemoryTrace: 14 | def __enter__(self): 15 | gc.collect() 16 | torch.cuda.empty_cache() 17 | torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero 18 | self.begin = byte2gb(torch.cuda.memory_allocated()) 19 | self.process = psutil.Process() 20 | self.cpu_begin = byte2gb(self.cpu_mem_used()) 21 | self.peak_monitoring = True 22 | peak_monitor_thread = threading.Thread(target=self.peak_monitor_func) 23 | peak_monitor_thread.daemon = True 24 | peak_monitor_thread.start() 25 | return self 26 | 27 | def cpu_mem_used(self): 28 | """get resident set size memory for the current process""" 29 | return self.process.memory_info().rss 30 | 31 | def peak_monitor_func(self): 32 | self.cpu_peak = -1 33 | 34 | while True: 35 | self.cpu_peak = max(self.cpu_mem_used(), self.cpu_peak) 36 | 37 | # can't sleep or will not catch the peak right (this comment is here on purpose) 38 | # time.sleep(0.001) # 1msec 39 | 40 | if not self.peak_monitoring: 41 | break 42 | 43 | def __exit__(self, *exc): 44 | self.peak_monitoring = False 45 | 46 | gc.collect() 47 | torch.cuda.empty_cache() 48 | self.end = byte2gb(torch.cuda.memory_allocated()) 49 | self.peak = byte2gb(torch.cuda.max_memory_allocated()) 50 | cuda_info = torch.cuda.memory_stats() 51 | self.peak_active_gb = byte2gb(cuda_info["active_bytes.all.peak"]) 52 | self.cuda_malloc_retires = cuda_info.get("num_alloc_retries", 0) 53 | self.peak_active_gb = byte2gb(cuda_info["active_bytes.all.peak"]) 54 | self.m_cuda_ooms = cuda_info.get("num_ooms", 0) 55 | self.used = byte2gb(self.end - self.begin) 56 | self.peaked = byte2gb(self.peak - self.begin) 57 | self.max_reserved = byte2gb(torch.cuda.max_memory_reserved()) 58 | 59 | self.cpu_end = self.cpu_mem_used() 60 | self.cpu_used = byte2gb(self.cpu_end - self.cpu_begin) 61 | self.cpu_peaked = byte2gb(self.cpu_peak - self.cpu_begin) 62 | # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}") -------------------------------------------------------------------------------- /llama_recipes/utils/dataset_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import importlib 5 | from functools import partial 6 | from pathlib import Path 7 | 8 | import torch 9 | 10 | from llama_recipes.datasets import ( 11 | get_grammar_dataset, 12 | get_alpaca_dataset, 13 | get_samsum_dataset, 14 | ) 15 | 16 | 17 | def load_module_from_py_file(py_file: str) -> object: 18 | """ 19 | This method loads a module from a py file which is not in the Python path 20 | """ 21 | module_name = Path(py_file).name 22 | loader = importlib.machinery.SourceFileLoader(module_name, py_file) 23 | spec = importlib.util.spec_from_loader(module_name, loader) 24 | module = importlib.util.module_from_spec(spec) 25 | 26 | loader.exec_module(module) 27 | 28 | return module 29 | 30 | 31 | def get_custom_dataset(dataset_config, tokenizer, split: str): 32 | if ":" in dataset_config.file: 33 | module_path, func_name = dataset_config.file.split(":") 34 | else: 35 | module_path, func_name = dataset_config.file, "get_custom_dataset" 36 | 37 | if not module_path.endswith(".py"): 38 | raise ValueError(f"Dataset file {module_path} is not a .py file.") 39 | 40 | module_path = Path(module_path) 41 | if not module_path.is_file(): 42 | raise FileNotFoundError(f"Dataset py file {module_path.as_posix()} does not exist or is not a file.") 43 | 44 | module = load_module_from_py_file(module_path.as_posix()) 45 | try: 46 | return getattr(module, func_name)(dataset_config, tokenizer, split) 47 | except AttributeError as e: 48 | print(f"It seems like the given method name ({func_name}) is not present in the dataset .py file ({module_path.as_posix()}).") 49 | raise e 50 | 51 | 52 | DATASET_PREPROC = { 53 | "alpaca_dataset": partial(get_alpaca_dataset), 54 | "grammar_dataset": get_grammar_dataset, 55 | "samsum_dataset": get_samsum_dataset, 56 | "custom_dataset": get_custom_dataset, 57 | } 58 | 59 | 60 | def get_preprocessed_dataset( 61 | tokenizer, dataset_config, split: str = "train" 62 | ) -> torch.utils.data.Dataset: 63 | if not dataset_config.dataset in DATASET_PREPROC: 64 | raise NotImplementedError(f"{dataset_config.dataset} is not (yet) implemented") 65 | 66 | def get_split(): 67 | return ( 68 | dataset_config.train_split 69 | if split == "train" 70 | else dataset_config.test_split 71 | ) 72 | 73 | return DATASET_PREPROC[dataset_config.dataset]( 74 | dataset_config, 75 | tokenizer, 76 | get_split(), 77 | ) 78 | -------------------------------------------------------------------------------- /llama_recipes/inference/checkpoint_converter_fsdp_hf.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | # from accelerate import init_empty_weights, load_checkpoint_and_dispatch 5 | 6 | import fire 7 | import os 8 | import sys 9 | import yaml 10 | 11 | from transformers import LlamaTokenizer 12 | 13 | from llama_recipes.inference.model_utils import load_llama_from_config 14 | 15 | # Get the current file's directory 16 | current_directory = os.path.dirname(os.path.abspath(__file__)) 17 | 18 | # Get the parent directory 19 | parent_directory = os.path.dirname(current_directory) 20 | 21 | # Append the parent directory to sys.path 22 | sys.path.append(parent_directory) 23 | from model_checkpointing import load_sharded_model_single_gpu 24 | 25 | def main( 26 | fsdp_checkpoint_path="", # Path to FSDP Sharded model checkpoints 27 | consolidated_model_path="", # Path to save the HF converted model checkpoints 28 | HF_model_path_or_name="" # Path/ name of the HF model that include config.json and tokenizer_config.json (e.g. meta-llama/Llama-2-7b-chat-hf) 29 | ): 30 | 31 | try: 32 | file_name = 'train_params.yaml' 33 | # Combine the directory and file name to create the full path 34 | train_params_path = os.path.join(fsdp_checkpoint_path, file_name) 35 | # Open the file 36 | with open(train_params_path, 'r') as file: 37 | # Load the YAML data 38 | data = yaml.safe_load(file) 39 | 40 | # Access the 'model_name' field 41 | HF_model_path_or_name = data.get('model_name') 42 | 43 | print(f"Model name: {HF_model_path_or_name}") 44 | except FileNotFoundError: 45 | print(f"The file {train_params_path} does not exist.") 46 | HF_model_path_or_name = input("Please enter the model name: ") 47 | print(f"Model name: {HF_model_path_or_name}") 48 | except Exception as e: 49 | print(f"An error occurred: {e}") 50 | 51 | 52 | #load the HF model definition from config 53 | model_def = load_llama_from_config(HF_model_path_or_name) 54 | print("model is loaded from config") 55 | #load the FSDP sharded checkpoints into the model 56 | model = load_sharded_model_single_gpu(model_def, fsdp_checkpoint_path) 57 | print("model is loaded from FSDP checkpoints") 58 | #loading the tokenizer form the model_path 59 | tokenizer = LlamaTokenizer.from_pretrained(HF_model_path_or_name) 60 | tokenizer.save_pretrained(consolidated_model_path) 61 | #save the FSDP sharded checkpoints in HF format 62 | model.save_pretrained(consolidated_model_path) 63 | print(f"HuggingFace model checkpoints has been saved in {consolidated_model_path}") 64 | if __name__ == "__main__": 65 | fire.Fire(main) 66 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [English](./README_en.md) 2 | 3 | **Note:** 这些代码用于Llama2指令微调，适配自官方仓库。删掉了不必要功能，方便上手，添加了一些实用功能。 4 | 5 | ### 添加的部分： 6 | - 加载训练好的lora继续训练 7 | - 推理输出文本的同时输出权重 8 | - 修改了scheduler逻辑，只有当loss增加时才减小lr 9 | 10 | ## step1: 数据构建与环境准备 11 | 环境为python3.9，其余环境见requirements.txt 12 | 13 | 指令微调数据集是一系列的question, answer 对，只需要将question和answer填入下面模板（见/data/demo*.json) 14 | ```json 15 | [{"conversations": [{"from": "human", "value": "QUESTION1"}, {"from": "gpt", "value": "ANSWER1"}]}] 16 | ``` 17 | 18 | ## step2: 微调 19 | 20 | ```shell 21 | bash run_test.sh 22 | ``` 23 | 24 | ## step3: 推理 25 | 26 | ```shell 27 | bash run_infer.sh #不输出logit 28 | bash run_infer_logit.sh #输出logit 29 | ``` 30 | 31 | ## details 32 | 下面是run_test.sh的细节 33 | - 默认是Lora微调，如果是全参数微调，则删掉use_peft和peft_method 34 | - dataset参数不用改，grammar_dataset只是一个模板 35 | - lr比较重要，我在不同的数据集上采用的是1e-3,1e-4,1e-5,1e-6 36 | - output_dir loras权重存储位置 37 | - train_split 训练集的路径 38 | - batch_size_training 根据自己的显存改，注意数据量必须 >= batch_size_training* num_gpus 39 | - lora_path 如果为空字符串，则自动初始化权重。否则将加载这个lora路径继续训练 40 | - step size 控制改变lr的频率的，如果step size为1，则每个epoch结束后判断是否需要减小lr 41 | ```shell 42 | CUDA_VISIBLE_DEVICES=4,5,6,7 nohup torchrun --nnodes 1 --nproc_per_node 4 --master_port 29504 finetuning.py \ 43 | --enable_fsdp \ 44 | --model_name /data/hfmodel/PLMs/llama27b_hf \ 45 | --peft_method lora \ 46 | --use_peft true \ 47 | --dataset grammar_dataset \ 48 | --save_model \ 49 | --dist_checkpoint_root_folder model_checkpoints \ 50 | --dist_checkpoint_folder fine-tuned \ 51 | --fsdp_config.pure_bf16 \ 52 | --lr 5e-5 \ 53 | --output_dir loras/decisioner-100-epoch60-prompt \ 54 | --train_split ./data/demo_train.json \ 55 | --batch_size_training 128 \ 56 | --lora_path '' \ 57 | --step_size 1 \ 58 | --num_epochs 10 > logs/decisioner-100-epoch60-prompt.log 2>&1 & 59 | 60 | ``` 61 | 62 | 下面是 run_infer.sh的细节 63 | 64 | - 仅支持单卡推理，多卡请结合start, end参数手动并行。start和end分别是需要推理数据的起始index和结束index。默认参数表示推理全量数据 65 | - eval_file 需要推理的数据集 66 | - generate_file 生成的LLM answer数据集（每一行对应一个answer） 67 | ```shell 68 | CUDA_VISIBLE_DEVICES=3 python inference.py \ 69 | --model_name /data/hfmodel/PLMs/llama27b_hf \ 70 | --peft_model loras/decisioner-100-epoch40 \ 71 | --max_new_tokens 8 \ 72 | --do_sample false \ 73 | --num_beams 1 \ 74 | --start 0 \ 75 | --end -1 \ 76 | --eval_file ./data/demo_infer.json \ 77 | --bsz 16 \ 78 | --max_length 256 \ 79 | --generate_file './record/conflict_2-baseline-decision.file' 80 | ``` 81 | 82 | 下面是 run_infer_logit.sh的细节 83 | 84 | - token_k 每个token输出前k个最大的logits (这里是没有softmax的) 85 | - generate_file 只能是json格式，存储有answer和logits信息 86 | 87 | ```shell 88 | CUDA_VISIBLE_DEVICES=4 python inference.py \ 89 | --model_name /data/pretrained_models/llama27b_hf \ 90 | --peft_model loras/checker-sample \ 91 | --max_new_tokens 4 \ 92 | --num_beams 1 \ 93 | --start 0 \ 94 | --end -1 \ 95 | --eval_file ./data/demo_infer.json \ 96 | --bsz 16 \ 97 | --output_logits \ 98 | --max_length 256 \ 99 | --token_k 3 \ 100 | --generate_file './record/conflict_checker_answer_4-h.json' 101 | 102 | 103 | ``` 104 | ## Reference 105 | 106 | https://github.com/meta-llama/llama-recipes 107 | -------------------------------------------------------------------------------- /llama_recipes/datasets/grammar_dataset/grammar_dataset.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | # For dataset details visit: https://huggingface.co/datasets/jfleg 5 | # For download and preparation see: recipes/ft_datasets/grammar_dataset/grammar_dataset_process.ipynb 6 | 7 | 8 | from datasets import load_dataset 9 | from pathlib import Path 10 | 11 | from torch.utils.data import Dataset 12 | 13 | 14 | class grammar(Dataset): 15 | def __init__( 16 | self, 17 | tokenizer, 18 | csv_name=None, 19 | ): 20 | 21 | try: 22 | # self.dataset = load_dataset( 23 | # "csv", 24 | # data_files={"train": [csv_name]}, # "eval": "grammar_validation.csv"}, 25 | # delimiter=",", 26 | # ) 27 | self.dataset = load_dataset('json', data_files=csv_name) 28 | except Exception as e: 29 | print("Loading of grammar dataset failed! Please see recipes/ft_datasets/grammar_dataset/grammar_dataset_process.ipynb for details on how to download the dataset.") 30 | raise e 31 | 32 | # self.dataset = load_dataset("wikihow", "all", data_dir="data/", split=type_path) 33 | # if num_samples: 34 | # self.dataset = self.dataset.select(list(range(0, num_samples))) 35 | self.tokenizer = tokenizer 36 | self.print_text = False # print_text 37 | 38 | def __len__(self): 39 | return self.dataset["train"].shape[0] 40 | 41 | def convert_to_features(self, example_batch): 42 | 43 | # Create prompt and tokenize contexts and questions 44 | 45 | if self.print_text: 46 | print("Input Text: ", self.clean_text(example_batch["text"])) 47 | 48 | # input_ = example_batch["input"] 49 | # target_ = example_batch["target"] 50 | input_ = example_batch['conversations'][0]['value'] 51 | target_ = example_batch['conversations'][1]['value'] 52 | 53 | # prompt = f"Correct this to standard English: {input_}\n---\nCorrected: " 54 | prompt = input_ 55 | prompt_ids = self.tokenizer.encode(self.tokenizer.bos_token + prompt, add_special_tokens=False) 56 | label_ids = self.tokenizer.encode(target_ + self.tokenizer.eos_token, add_special_tokens=False) 57 | # print('len',len(prompt_ids)+len(label_ids)) 58 | 59 | sample = { 60 | "input_ids": prompt_ids + label_ids, 61 | "attention_mask": [1] * len(prompt_ids + label_ids), 62 | "labels": [-100] * len(prompt_ids) + label_ids 63 | } 64 | 65 | return sample 66 | 67 | def __getitem__(self, index): 68 | return self.convert_to_features(self.dataset["train"][int(index)]) 69 | #['train'][0]['conversations'][0]['value'] 70 | 71 | 72 | def get_dataset( 73 | dataset_config, tokenizer, csv_name=None 74 | ): 75 | """cover function for handling loading the working dataset""" 76 | """dataset loading""" 77 | # if csv_name is None: 78 | # currPath = Path.cwd() / "datasets_grammar" / "grammar_train.csv" 79 | # print(f"Loading dataset {currPath}") 80 | # csv_name = str(currPath) 81 | dataset = grammar( 82 | tokenizer=tokenizer, 83 | csv_name=csv_name, 84 | ) 85 | 86 | return dataset 87 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | absl-py==2.1.0 2 | accelerate==0.25.0 3 | aiohttp==3.9.1 4 | aiosignal==1.3.1 5 | annotated-types==0.6.0 6 | appdirs==1.4.4 7 | async-timeout==4.0.3 8 | attrs==23.1.0 9 | beautifulsoup4==4.12.3 10 | bitsandbytes==0.41.3 11 | black==23.11.0 12 | Brotli==1.1.0 13 | bypy==1.8.4 14 | certifi==2023.11.17 15 | charset-normalizer==3.3.2 16 | chex==0.1.86 17 | click==8.1.7 18 | coloredlogs==15.0.1 19 | datasets==2.15.0 20 | deepspeed==0.14.0 21 | dill==0.3.7 22 | emoji==2.10.1 23 | etils==1.5.2 24 | filelock==3.13.1 25 | fire==0.5.0 26 | flax==0.8.2 27 | frozenlist==1.4.0 28 | fsspec==2023.10.0 29 | gdown==5.1.0 30 | hjson==3.1.0 31 | huggingface-hub==0.19.4 32 | humanfriendly==10.0 33 | idna==3.6 34 | importlib_metadata==7.1.0 35 | importlib_resources==6.4.0 36 | inflate64==1.0.0 37 | jax==0.4.25 38 | jaxlib==0.4.25 39 | Jinja2==3.1.2 40 | joblib==1.3.2 41 | jsonlines==4.0.0 42 | loralib==0.1.2 43 | markdown-it-py==3.0.0 44 | MarkupSafe==2.1.3 45 | mdurl==0.1.2 46 | ml-dtypes==0.3.2 47 | mpmath==1.3.0 48 | msgpack==1.0.8 49 | multidict==6.0.4 50 | multiprocess==0.70.15 51 | multivolumefile==0.2.3 52 | mypy-extensions==1.0.0 53 | nest-asyncio==1.6.0 54 | networkx==3.2.1 55 | ninja==1.11.1.1 56 | nltk==3.8.1 57 | numpy==1.26.2 58 | nvidia-cublas-cu11==11.11.3.6 59 | nvidia-cublas-cu12==12.1.3.1 60 | nvidia-cuda-cupti-cu11==11.8.87 61 | nvidia-cuda-cupti-cu12==12.1.105 62 | nvidia-cuda-nvrtc-cu11==11.8.89 63 | nvidia-cuda-nvrtc-cu12==12.1.105 64 | nvidia-cuda-runtime-cu11==11.8.89 65 | nvidia-cuda-runtime-cu12==12.1.105 66 | nvidia-cudnn-cu11==8.7.0.84 67 | nvidia-cudnn-cu12==8.9.2.26 68 | nvidia-cufft-cu11==10.9.0.58 69 | nvidia-cufft-cu12==11.0.2.54 70 | nvidia-curand-cu11==10.3.0.86 71 | nvidia-curand-cu12==10.3.2.106 72 | nvidia-cusolver-cu11==11.4.1.48 73 | nvidia-cusolver-cu12==11.4.5.107 74 | nvidia-cusparse-cu11==11.7.5.86 75 | nvidia-cusparse-cu12==12.1.0.106 76 | nvidia-nccl-cu11==2.19.3 77 | nvidia-nccl-cu12==2.18.1 78 | nvidia-nvjitlink-cu12==12.3.101 79 | nvidia-nvtx-cu11==11.8.86 80 | nvidia-nvtx-cu12==12.1.105 81 | opt-einsum==3.3.0 82 | optax==0.2.2 83 | optimum==1.15.0 84 | orbax-checkpoint==0.5.7 85 | packaging==23.2 86 | pandas==2.1.3 87 | pathspec==0.11.2 88 | peft==0.7.0 89 | Pillow==9.3.0 90 | pip==23.3.1 91 | platformdirs==4.1.0 92 | protobuf==4.25.1 93 | psutil==5.9.6 94 | py-cpuinfo==9.0.0 95 | py7zr==0.20.8 96 | pyarrow==14.0.1 97 | pyarrow-hotfix==0.6 98 | pybcj==1.0.2 99 | pycryptodomex==3.19.0 100 | pydantic==2.6.4 101 | pydantic_core==2.16.3 102 | Pygments==2.17.2 103 | pynvml==11.5.0 104 | pyppmd==1.1.0 105 | PySocks==1.7.1 106 | python-dateutil==2.8.2 107 | pytorch-triton==2.1.0+bcad9dabe1 108 | pytz==2023.3.post1 109 | PyYAML==6.0.1 110 | pyzstd==0.15.9 111 | regex==2023.10.3 112 | requests==2.31.0 113 | requests-toolbelt==1.0.0 114 | rich==13.7.1 115 | safetensors==0.4.1 116 | scipy==1.11.4 117 | sentencepiece==0.1.99 118 | setuptools==68.0.0 119 | six==1.16.0 120 | soupsieve==2.5 121 | stanza==1.8.1 122 | supar==1.1.4 123 | sympy==1.12 124 | tensorstore==0.1.56 125 | termcolor==2.4.0 126 | texttable==1.7.0 127 | tokenizers==0.15.0 128 | toml==0.10.2 129 | tomli==2.0.1 130 | toolz==0.12.1 131 | torch==2.2.0.dev20231208+cu118 132 | torchaudio==2.2.0.dev20231208+cu118 133 | torchvision==0.17.0.dev20231208+cu118 134 | tqdm==4.66.1 135 | transformers==4.35.2 136 | trimesh==4.2.4 137 | triton==2.1.0 138 | typing_extensions==4.8.0 139 | tzdata==2023.3 140 | urllib3==2.1.0 141 | wheel==0.41.2 142 | xxhash==3.4.1 143 | yarl==1.9.4 144 | zipp==3.18.1 145 | -------------------------------------------------------------------------------- /README_en.md: -------------------------------------------------------------------------------- 1 | **Note:** These codes are for instruction tuning with Llama2, adapted from the official repository. Unnecessary features have been removed for ease of use, and some practical features have been added. 2 | 3 | ### Added Features: 4 | - Load pre-trained Lora for continued training 5 | - Output logits during inference 6 | - Modified scheduler logic to decrease learning rate only when loss increases 7 | 8 | ## step1: Data Preparation && Environment 9 | Python==3.9 others in requirements.txt 10 | 11 | SFT dataset consists of a series of question-answer pairs. Simply fill in the questions and answers in the template below (see /data/demo*.json). 12 | ```json 13 | [{"conversations": [{"from": "human", "value": "QUESTION1"}, {"from": "gpt", "value": "ANSWER1"}]}] 14 | ``` 15 | 16 | ## step2: Fine tuning 17 | 18 | ```shell 19 | bash run_test.sh 20 | ``` 21 | 22 | ## step3: Inference 23 | 24 | ```shell 25 | bash run_infer.sh # without logits 26 | bash run_infer_logit.sh # with logits 27 | ``` 28 | 29 | ## details 30 | 31 | Here are the details of run_test.sh: 32 | 33 | - By default, it's Lora fine-tuning. Remove ``use_peft`` and ``peft_method`` for full-parameters tuning. 34 | - No need to change the ``dataset`` parameter, grammar_dataset is just a template. 35 | - ``lr`` is quite important. I used 1e-3, 1e-4, 1e-5, 1e-6 on different datasets. 36 | - ``output_dir`` is where Lora weights are stored. 37 | - If ``lora_path`` is an empty string, weights will be automatically initialized. Otherwise, it will load weights from this Lora path for continued training. 38 | - ``step size`` controls the frequency of lr changes. If ``step size`` is 1, lr will be evaluated for decrease after each epoch. 39 | - dataset size should larger than ``batch_size_training`` * num_gpus 40 | ```shell 41 | CUDA_VISIBLE_DEVICES=4,5,6,7 nohup torchrun --nnodes 1 --nproc_per_node 4 --master_port 29504 finetuning.py \ 42 | --enable_fsdp \ 43 | --model_name /data/hfmodel/PLMs/llama27b_hf \ 44 | --peft_method lora \ 45 | --use_peft true \ 46 | --dataset grammar_dataset \ 47 | --save_model \ 48 | --dist_checkpoint_root_folder model_checkpoints \ 49 | --dist_checkpoint_folder fine-tuned \ 50 | --fsdp_config.pure_bf16 \ 51 | --lr 5e-6 \ 52 | --output_dir loras/decisioner-100-epoch60-prompt \ 53 | --train_split ./data/demo_train.json \ 54 | --batch_size_training 128 \ 55 | --lora_path '' \ 56 | --step_size 1 \ 57 | --num_epochs 10 > logs/decisioner-100-epoch60-prompt.log 2>&1 & 58 | 59 | ``` 60 | 61 | Here are the details of run_infer.sh: 62 | 63 | - Supports single-GPU inference only. For multi-GPU, manually parallelize with ``start`` and ``end`` parameters, which indicate the starting and ending indices of data for inference. Default parameters infer all data. 64 | - ``eval_file`` is the dataset to be inferred. 65 | - ``generate_file`` stores the generated LLM answer dataset (each line corresponds to an answer). 66 | 67 | ```shell 68 | CUDA_VISIBLE_DEVICES=3 python inference.py \ 69 | --model_name /data/hfmodel/PLMs/llama27b_hf \ 70 | --peft_model loras/decisioner-100-epoch40 \ 71 | --max_new_tokens 8 \ 72 | --do_sample false \ 73 | --num_beams 1 \ 74 | --start 0 \ 75 | --end -1 \ 76 | --eval_file ./data/demo_infer.json \ 77 | --bsz 16 \ 78 | --max_length 256 \ 79 | --generate_file './record/conflict_2-baseline-decision.file' 80 | ``` 81 | 82 | Here are the details of run_infer_logit.sh: 83 | 84 | - ``token_k`` outputs the top k logits for each token (before softmax). 85 | - ``generate_file`` must be in JSON format, storing both answer and logits information. 86 | 87 | ```shell 88 | CUDA_VISIBLE_DEVICES=4 python inference.py \ 89 | --model_name /data/pretrained_models/llama27b_hf \ 90 | --peft_model loras/checker-sample \ 91 | --max_new_tokens 4 \ 92 | --num_beams 1 \ 93 | --start 0 \ 94 | --end -1 \ 95 | --eval_file ./data/demo_infer.json \ 96 | --bsz 16 \ 97 | --output_logits \ 98 | --max_length 256 \ 99 | --token_k 3 \ 100 | --generate_file './record/conflict_checker_answer_4-h.json' 101 | 102 | 103 | ``` 104 | ## Reference 105 | 106 | https://github.com/meta-llama/llama-recipes 107 | -------------------------------------------------------------------------------- /llama_recipes/utils/config_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import inspect 5 | from dataclasses import asdict 6 | 7 | import torch.distributed as dist 8 | from torch.utils.data import DistributedSampler 9 | from peft import ( 10 | LoraConfig, 11 | AdaptionPromptConfig, 12 | PrefixTuningConfig, 13 | ) 14 | from transformers import default_data_collator 15 | from transformers.data import DataCollatorForSeq2Seq 16 | 17 | from llama_recipes.configs import datasets, lora_config, llama_adapter_config, prefix_config, train_config 18 | from llama_recipes.data.sampler import LengthBasedBatchSampler, DistributedLengthBasedBatchSampler 19 | from llama_recipes.utils.dataset_utils import DATASET_PREPROC 20 | 21 | 22 | def update_config(config, **kwargs): 23 | if isinstance(config, (tuple, list)): 24 | for c in config: 25 | update_config(c, **kwargs) 26 | else: 27 | for k, v in kwargs.items(): 28 | if hasattr(config, k): 29 | setattr(config, k, v) 30 | elif "." in k: 31 | # allow --some_config.some_param=True 32 | config_name, param_name = k.split(".") 33 | if type(config).__name__ == config_name: 34 | if hasattr(config, param_name): 35 | setattr(config, param_name, v) 36 | else: 37 | # In case of specialized config we can warm user 38 | print(f"Warning: {config_name} does not accept parameter: {k}") 39 | elif isinstance(config, train_config): 40 | print(f"Warning: unknown parameter {k}") 41 | 42 | 43 | def generate_peft_config(train_config, kwargs): 44 | configs = (lora_config, llama_adapter_config, prefix_config) 45 | peft_configs = (LoraConfig, AdaptionPromptConfig, PrefixTuningConfig) 46 | names = tuple(c.__name__.rstrip("_config") for c in configs) 47 | 48 | assert train_config.peft_method in names, f"Peft config not found: {train_config.peft_method}" 49 | 50 | config = configs[names.index(train_config.peft_method)]() 51 | 52 | update_config(config, **kwargs) 53 | params = asdict(config) 54 | peft_config = peft_configs[names.index(train_config.peft_method)](**params) 55 | 56 | return peft_config 57 | 58 | 59 | def generate_dataset_config(train_config, kwargs): 60 | names = tuple(DATASET_PREPROC.keys()) 61 | 62 | assert train_config.dataset in names, f"Unknown dataset: {train_config.dataset}" 63 | 64 | dataset_config = {k:v for k, v in inspect.getmembers(datasets)}[train_config.dataset]() 65 | 66 | update_config(dataset_config, **kwargs) 67 | 68 | return dataset_config 69 | 70 | 71 | def get_dataloader_kwargs(train_config, dataset, tokenizer, mode): 72 | kwargs = {} 73 | batch_size = train_config.batch_size_training if mode=="train" else train_config.val_batch_size 74 | if train_config.batching_strategy == "padding": 75 | if train_config.enable_fsdp: 76 | kwargs["batch_sampler"] = DistributedLengthBasedBatchSampler( 77 | dataset, 78 | batch_size=batch_size, 79 | rank=dist.get_rank(), 80 | num_replicas=dist.get_world_size(), 81 | shuffle=mode=="train", 82 | ) 83 | else: 84 | kwargs["batch_sampler"] = LengthBasedBatchSampler(dataset, batch_size, drop_last=True, shuffle=mode=="train") 85 | kwargs["collate_fn"] = DataCollatorForSeq2Seq(tokenizer) 86 | elif train_config.batching_strategy == "packing": 87 | if train_config.enable_fsdp: 88 | kwargs["sampler"] = DistributedSampler( 89 | dataset, 90 | rank=dist.get_rank(), 91 | num_replicas=dist.get_world_size(), 92 | shuffle=mode=="train", 93 | ) 94 | kwargs["batch_size"] = batch_size 95 | kwargs["drop_last"] = True 96 | kwargs["collate_fn"] = default_data_collator 97 | else: 98 | raise ValueError(f"Unknown batching strategy: {train_config.batching_strategy}") 99 | 100 | return kwargs 101 | -------------------------------------------------------------------------------- /llama_recipes/policies/anyprecision_optimizer.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | # AnyPrecisionAdamW: a flexible precision AdamW optimizer 5 | # with optional Kahan summation for high precision weight updates. 6 | # Allows direct control over momentum, variance and auxiliary compensation 7 | # buffer dtypes. 8 | # Optional Kahan summation is used to offset precision reduction for 9 | # the weight updates. This allows full training in BFloat16 (equal or 10 | # better than FP32 results in many cases) due to high precision weight upates. 11 | 12 | import torch 13 | from torch.optim.optimizer import Optimizer 14 | 15 | 16 | class AnyPrecisionAdamW(Optimizer): 17 | def __init__( 18 | self, 19 | params, 20 | lr=1e-3, 21 | betas=(0.9, 0.999), 22 | eps=1e-8, 23 | weight_decay=0.0, 24 | use_kahan_summation=False, 25 | momentum_dtype=torch.bfloat16, 26 | variance_dtype=torch.bfloat16, 27 | compensation_buffer_dtype=torch.bfloat16, 28 | ): 29 | """ 30 | Args: 31 | params (iterable): iterable of parameters to optimize or dicts defining 32 | parameter groups 33 | lr (float, optional): learning rate (default: 1e-3) 34 | betas (Tuple[float, float], optional): coefficients used for computing 35 | running averages of gradient and its square (default: (0.9, 0.999)) 36 | eps (float, optional): term added to the denominator to improve 37 | numerical stability (default: 1e-8) 38 | weight_decay (float, optional): weight decay coefficient (default: 1e-2) 39 | 40 | # Any Precision specific 41 | use_kahan_summation = creates auxiliary buffer to ensure high precision 42 | model param updates (default: False) 43 | momentum_dtype = dtype for momentum (default: BFloat32) 44 | variance_dtype = dtype for uncentered variance (default: BFloat16) 45 | compensation_buffer_dtype = dtype for Kahan summation 46 | buffer (default: BFloat16) 47 | 48 | # Usage 49 | This optimizer implements optimizer states, and Kahan summation 50 | for high precision updates, all in user controlled dtypes. 51 | Defaults are variance in BF16, Momentum in FP32. 52 | This can be run in FSDP mixed precision, amp, or full precision, 53 | depending on what training pipeline you wish to work with. 54 | 55 | Setting to use_kahan_summation = False, and changing momentum and 56 | variance dtypes to FP32, reverts this to a standard AdamW optimizer. 57 | 58 | """ 59 | defaults = dict( 60 | lr=lr, 61 | betas=betas, 62 | eps=eps, 63 | weight_decay=weight_decay, 64 | use_kahan_summation=use_kahan_summation, 65 | momentum_dtype=momentum_dtype, 66 | variance_dtype=variance_dtype, 67 | compensation_buffer_dtype=compensation_buffer_dtype, 68 | ) 69 | 70 | super().__init__(params, defaults) 71 | 72 | @torch.no_grad() 73 | def step(self, closure=None): 74 | """Performs a single optimization step. 75 | Args: 76 | closure (callable, optional): A closure that reevaluates the model 77 | and returns the loss. 78 | """ 79 | 80 | if closure is not None: 81 | with torch.enable_grad(): 82 | # to fix linter, we do not keep the returned loss for use atm. 83 | closure() 84 | 85 | for group in self.param_groups: 86 | 87 | beta1, beta2 = group["betas"] 88 | lr = group["lr"] 89 | weight_decay = group["weight_decay"] 90 | eps = group["eps"] 91 | use_kahan_summation = group["use_kahan_summation"] 92 | 93 | momentum_dtype = group["momentum_dtype"] 94 | variance_dtype = group["variance_dtype"] 95 | compensation_buffer_dtype = group["compensation_buffer_dtype"] 96 | 97 | for p in group["params"]: 98 | if p.grad is None: 99 | continue 100 | 101 | if p.grad.is_sparse: 102 | raise RuntimeError( 103 | "AnyPrecisionAdamW does not support sparse gradients" 104 | ) 105 | 106 | state = self.state[p] 107 | 108 | # State initialization 109 | if len(state) == 0: 110 | 111 | state["step"] = torch.tensor(0.0) 112 | 113 | # momentum - EMA of gradient values 114 | state["exp_avg"] = torch.zeros_like( 115 | p, 116 | dtype=momentum_dtype, 117 | ) 118 | 119 | # variance uncentered - EMA of squared gradient values 120 | state["exp_avg_sq"] = torch.zeros_like( 121 | p, 122 | dtype=variance_dtype, 123 | ) 124 | 125 | # optional Kahan summation - accumulated error tracker 126 | if use_kahan_summation: 127 | state["compensation"] = torch.zeros_like( 128 | p, 129 | dtype=compensation_buffer_dtype, 130 | ) 131 | 132 | # main processing ------------------------- 133 | 134 | # update the steps for each param group update 135 | state["step"] += 1 136 | step = state["step"] 137 | 138 | exp_avg = state["exp_avg"] 139 | exp_avg_sq = state["exp_avg_sq"] 140 | 141 | grad = p.grad 142 | 143 | # weight decay, AdamW style 144 | if weight_decay: 145 | p.data.mul_(1 - lr * weight_decay) 146 | 147 | # update momentum 148 | exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) 149 | 150 | # update uncentered variance 151 | exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) 152 | 153 | # adjust using bias1 154 | bias_correction1 = 1 - beta1**step 155 | 156 | step_size = lr / bias_correction1 157 | 158 | # adjust using bias2 159 | denom_correction = (1 - beta2**step) ** 0.5 # avoids math import 160 | 161 | centered_variance = (exp_avg_sq.sqrt() / denom_correction).add_( 162 | eps, alpha=1 163 | ) 164 | 165 | # lr update to compensation 166 | if use_kahan_summation: 167 | compensation = state["compensation"] 168 | 169 | compensation.addcdiv_(exp_avg, centered_variance, value=-step_size) 170 | 171 | # update weights with compensation (Kahan summation) 172 | # save error back to compensation for next iteration 173 | temp_buffer = p.detach().clone() 174 | p.data.add_(compensation) 175 | compensation.add_(temp_buffer.sub_(p.data)) 176 | 177 | else: 178 | # usual AdamW updates 179 | p.data.addcdiv_(exp_avg, centered_variance, value=-step_size) -------------------------------------------------------------------------------- /llama_recipes/model_checkpointing/checkpoint_handler.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | from pathlib import Path 5 | from datetime import datetime 6 | import torch 7 | import time 8 | 9 | from torch.distributed.fsdp import ( 10 | FullyShardedDataParallel as FSDP, 11 | StateDictType, 12 | FullStateDictConfig, # general model non-sharded, non-flattened params 13 | LocalStateDictConfig, # flattened params, usable only by FSDP 14 | # ShardedStateDictConfig, # un-flattened param but shards, usable by other parallel schemes. 15 | ) 16 | 17 | from torch.distributed._shard.checkpoint import ( 18 | FileSystemReader, 19 | FileSystemWriter, 20 | save_state_dict, 21 | load_state_dict, 22 | ) 23 | from torch.distributed.checkpoint.default_planner import ( 24 | DefaultSavePlanner, 25 | DefaultLoadPlanner, 26 | ) 27 | 28 | 29 | from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType 30 | import torch.distributed._shard.checkpoint as dist_cp 31 | import torch.distributed as dist 32 | 33 | 34 | def get_date_of_run(): 35 | """create date and time for file save uniqueness 36 | example: 2022-05-07-08:31:12_PM' 37 | """ 38 | date_of_run = datetime.now().strftime("%Y-%m-%d-%I:%M:%S_%p") 39 | print(f"--> current date and time of run = {date_of_run}") 40 | return date_of_run 41 | 42 | 43 | # create singleton saving policies to avoid making over and over 44 | fullstate_save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 45 | 46 | 47 | def load_model_sharded(model, rank, cfg): 48 | # torch.manual_seed(103) 49 | folder_name = ( 50 | cfg.dist_checkpoint_root_folder 51 | + "/" 52 | + cfg.dist_checkpoint_folder 53 | + "-" 54 | + cfg.model_name 55 | ) 56 | 57 | load_dir = Path.cwd() / folder_name 58 | 59 | if not load_dir.exists(): 60 | if rank == 0: 61 | print(f"No sharded_state_dict checkpoint directory found...skipping") 62 | return 63 | if rank == 0: 64 | print(f"loading model from model path: {load_dir} ") 65 | reader = FileSystemReader(load_dir) 66 | 67 | with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 68 | checkpoint = {"model": model.state_dict()} 69 | if rank == 0: 70 | ck = checkpoint.keys() 71 | print(f" checkpoint key len = {len(ck)} and \n keys = {ck}") 72 | 73 | dist_cp.load_state_dict( 74 | state_dict=checkpoint, 75 | storage_reader=reader, 76 | ) 77 | if rank == 0: 78 | print(f"checkpoint after load_state_dict()") 79 | ck = checkpoint.keys() 80 | print(f" checkpoint key len = {len(ck)} and \n keys = {ck}") 81 | model.load_state_dict(checkpoint["model"]) 82 | if rank == 0: 83 | print(f"Sharded state checkpoint loaded from {load_dir}") 84 | 85 | 86 | def save_model_and_optimizer_sharded(model, rank, cfg,optim=None): 87 | """save model and optimizer via sharded_state_dict to save_dir""" 88 | 89 | folder_name = ( 90 | cfg.dist_checkpoint_root_folder 91 | + "/" 92 | + cfg.dist_checkpoint_folder 93 | + "-" 94 | + cfg.model_name 95 | ) 96 | 97 | save_dir = Path.cwd() / folder_name 98 | if rank == 0: 99 | print(f"Saving model to {save_dir}") 100 | 101 | distributed_writer = dist_cp.FileSystemWriter( 102 | save_dir, 103 | ) 104 | t0 = time.perf_counter() 105 | 106 | with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 107 | 108 | state_dict = {"model": model.state_dict()} 109 | if optim is not None: 110 | state_dict["optim"] = FSDP.optim_state_dict(model, optim) 111 | 112 | dist_cp.save_state_dict( 113 | state_dict=state_dict, 114 | storage_writer=distributed_writer, 115 | planner=DefaultSavePlanner(), 116 | 117 | ) 118 | dist.barrier() 119 | t1 = time.perf_counter() 120 | if rank == 0: 121 | print(f"Sharded state checkpoint saved to {save_dir}") 122 | print( 123 | f"Checkpoint Time = {t1-t0:.4f}\n" 124 | ) 125 | def save_model_checkpoint( 126 | model, 127 | optimizer, 128 | rank, 129 | cfg, 130 | epoch=1, 131 | ): 132 | """saving model via rank0 cpu streaming and full_state_dict""" 133 | 134 | with FSDP.state_dict_type( 135 | model, StateDictType.FULL_STATE_DICT, fullstate_save_policy 136 | ): 137 | cpu_state = model.state_dict() 138 | 139 | print(f"saving process: rank {rank} done w model state_dict\n") 140 | 141 | 142 | if rank == 0: 143 | print(f"--> saving model ...") 144 | # create save path 145 | folder_name = ( 146 | cfg.dist_checkpoint_root_folder 147 | + "/" 148 | + cfg.dist_checkpoint_folder 149 | + "-" 150 | + cfg.model_name 151 | ) 152 | save_dir = Path.cwd() / folder_name 153 | save_dir.mkdir(parents=True, exist_ok=True) 154 | save_name = cfg.model_name + "-" + str(epoch) + ".pt" 155 | save_full_path = str(save_dir) + "/" + save_name 156 | 157 | # save model 158 | torch.save(cpu_state, save_full_path) 159 | 160 | 161 | print(f"model checkpoint saved for epoch {epoch} at {save_full_path}\n") 162 | 163 | 164 | 165 | def load_model_checkpoint(model, rank, cfg): 166 | """load local checkpoint to rank0 cpu 167 | must be called * before * passing to FSDP""" 168 | 169 | if rank != 0: 170 | return 171 | 172 | # where is the checkpoint at... 173 | full_state_dict_model_path = ( 174 | Path.cwd() / cfg.checkpoint_folder / cfg.checkpoint_model_filename 175 | ) 176 | # is it present... 177 | if not full_state_dict_model_path.is_file(): 178 | print( 179 | f"model checkpoint {full_state_dict_model_path} not present. Returning..." 180 | ) 181 | return 182 | 183 | 184 | model_checkpoint = torch.load(full_state_dict_model_path) 185 | # integrate into loaded model 186 | model.load_state_dict(model_checkpoint) 187 | 188 | 189 | print(f"model checkpoint loaded to rank0 cpu") 190 | 191 | 192 | def save_optimizer_checkpoint(model, optimizer, rank, cfg, epoch=1): 193 | """save optimizer state via full state dict""" 194 | 195 | 196 | print(f"--> optim state call on rank {rank}\n") 197 | 198 | # pull all sharded optimizer states to rank0 cpu... 199 | 200 | optim_state = FSDP.full_optim_state_dict(model, optimizer) 201 | 202 | 203 | print(f"optim state dict ready on {rank} and len of {len(optim_state)}\n") 204 | 205 | if rank == 0: 206 | folder_name = ( 207 | cfg.dist_checkpoint_root_folder 208 | + "/" 209 | + cfg.dist_checkpoint_folder 210 | + "-" 211 | + cfg.model_name 212 | ) 213 | save_dir = Path.cwd() / folder_name 214 | save_dir.mkdir(parents=True, exist_ok=True) 215 | 216 | opt_save_name = ( 217 | "optimizer" + "-" + cfg.model_name + "-" + str(epoch) + ".pt" 218 | ) 219 | opt_save_full_path = save_dir / opt_save_name 220 | 221 | print(f"--> saving optimizer state...") 222 | 223 | torch.save(optim_state, opt_save_full_path) 224 | 225 | print(f"--> saved {opt_save_full_path} to disk") 226 | 227 | 228 | def load_optimizer_checkpoint(model, optimizer_checkpoint_path, rank): 229 | """load an fsdp optimizer full_state checkpoint using scatter method 230 | this ensures only rank 0 loads the optimizer state dict and scatters to other ranks 231 | """ 232 | 233 | 234 | if not optimizer_checkpoint_path.is_file(): 235 | print( 236 | f"warning - optimizer checkpoint not present {optimizer_checkpoint_path}. Returning. " 237 | ) 238 | return 239 | 240 | full_osd = None 241 | 242 | if rank == 0: 243 | full_osd = torch.load(optimizer_checkpoint_path) 244 | 245 | # called from all ranks, though only rank0 has a valid param for full_osd 246 | sharded_osd = FSDP.scatter_full_optim_state_dict(full_osd, model) 247 | 248 | print(f"optimizer shard loaded on rank {rank}") 249 | 250 | def load_sharded_model_single_gpu(model,model_path): 251 | 252 | reader = FileSystemReader(model_path) 253 | 254 | state_dict = { 255 | "model": model.state_dict() 256 | } 257 | 258 | dist_cp.load_state_dict( 259 | state_dict=state_dict, 260 | storage_reader= FileSystemReader(model_path), 261 | no_dist=True, 262 | ) 263 | 264 | model.load_state_dict(state_dict["model"]) 265 | 266 | print(f"Sharded state checkpoint loaded from {model_path}") 267 | return model -------------------------------------------------------------------------------- /finetuning.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import os 5 | from pkg_resources import packaging 6 | 7 | import fire 8 | import random 9 | import torch 10 | import torch.optim as optim 11 | from peft import get_peft_model, prepare_model_for_int8_training,PeftModel 12 | from torch.distributed.fsdp import ( 13 | FullyShardedDataParallel as FSDP, 14 | ) 15 | from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload 16 | from torch.optim.lr_scheduler import StepLR 17 | from transformers import ( 18 | LlamaForCausalLM, 19 | LlamaTokenizer, 20 | LlamaConfig, 21 | ) 22 | from transformers.models.llama.modeling_llama import LlamaDecoderLayer 23 | 24 | from llama_recipes.configs import fsdp_config as FSDP_CONFIG 25 | from llama_recipes.configs import train_config as TRAIN_CONFIG 26 | from llama_recipes.data.concatenator import ConcatDataset 27 | from llama_recipes.policies import AnyPrecisionAdamW, apply_fsdp_checkpointing 28 | 29 | from llama_recipes.utils import fsdp_auto_wrap_policy 30 | from llama_recipes.utils.config_utils import ( 31 | update_config, 32 | generate_peft_config, 33 | generate_dataset_config, 34 | get_dataloader_kwargs, 35 | ) 36 | from llama_recipes.utils.dataset_utils import get_preprocessed_dataset 37 | 38 | from llama_recipes.utils.train_utils import ( 39 | train, 40 | freeze_transformer_layers, 41 | setup, 42 | setup_environ_flags, 43 | clear_gpu_cache, 44 | print_model_size, 45 | get_policies 46 | ) 47 | 48 | 49 | def main(**kwargs): 50 | # Update the configuration for the training and sharding process 51 | train_config, fsdp_config = TRAIN_CONFIG(), FSDP_CONFIG() 52 | update_config((train_config, fsdp_config), **kwargs) 53 | 54 | # Set the seeds for reproducibility 55 | torch.cuda.manual_seed(train_config.seed) 56 | torch.manual_seed(train_config.seed) 57 | random.seed(train_config.seed) 58 | 59 | if train_config.enable_fsdp: 60 | setup() 61 | # torchrun specific 62 | local_rank = int(os.environ["LOCAL_RANK"]) 63 | rank = int(os.environ["RANK"]) 64 | world_size = int(os.environ["WORLD_SIZE"]) 65 | 66 | if torch.distributed.is_initialized(): 67 | torch.cuda.set_device(local_rank) 68 | clear_gpu_cache(local_rank) 69 | setup_environ_flags(rank) 70 | 71 | # Load the pre-trained model and setup its configuration 72 | use_cache = False if train_config.enable_fsdp else None 73 | if train_config.enable_fsdp and train_config.low_cpu_fsdp: 74 | """ 75 | for FSDP, we can save cpu memory by loading pretrained model on rank0 only. 76 | this avoids cpu oom when loading large models like llama 70B, in which case 77 | model alone would consume 2+TB cpu mem (70 * 4 * 8). This will add some comms 78 | overhead and currently requires latest nightly. 79 | """ 80 | v = packaging.version.parse(torch.__version__) 81 | verify_latest_nightly = v.is_devrelease and v.dev >= 20230701 82 | if not verify_latest_nightly: 83 | raise Exception("latest pytorch nightly build is required to run with low_cpu_fsdp config, " 84 | "please install latest nightly.") 85 | if rank == 0: 86 | model = LlamaForCausalLM.from_pretrained( 87 | train_config.model_name, 88 | load_in_8bit=True if train_config.quantization else None, 89 | device_map="auto" if train_config.quantization else None, 90 | use_cache=use_cache, 91 | ) 92 | else: 93 | llama_config = LlamaConfig.from_pretrained(train_config.model_name) 94 | llama_config.use_cache = use_cache 95 | with torch.device("meta"): 96 | model = LlamaForCausalLM(llama_config) 97 | 98 | else: 99 | model = LlamaForCausalLM.from_pretrained( 100 | train_config.model_name, 101 | load_in_8bit=True if train_config.quantization else None, 102 | device_map="auto" if train_config.quantization else None, 103 | use_cache=use_cache, 104 | ) 105 | if train_config.enable_fsdp and train_config.use_fast_kernels: 106 | """ 107 | For FSDP and FSDP+PEFT, setting 'use_fast_kernels' will enable 108 | using of Flash Attention or Xformer memory-efficient kernels 109 | based on the hardware being used. This would speed up fine-tuning. 110 | """ 111 | try: 112 | from optimum.bettertransformer import BetterTransformer 113 | model = BetterTransformer.transform(model) 114 | except ImportError: 115 | print("Module 'optimum' not found. Please install 'optimum' it before proceeding.") 116 | 117 | # Load the tokenizer and add special tokens 118 | tokenizer = LlamaTokenizer.from_pretrained(train_config.model_name) 119 | tokenizer.pad_token_id = tokenizer.eos_token_id 120 | 121 | print_model_size(model, train_config, rank if train_config.enable_fsdp else 0) 122 | 123 | # Prepare the model for int8 training if quantization is enabled 124 | if train_config.quantization: 125 | model = prepare_model_for_int8_training(model) 126 | 127 | # Convert the model to bfloat16 if fsdp and pure_bf16 is enabled 128 | if train_config.enable_fsdp and fsdp_config.pure_bf16: 129 | model.to(torch.bfloat16) 130 | 131 | if train_config.use_peft: 132 | peft_config = generate_peft_config(train_config, kwargs) 133 | if train_config.lora_path == '': 134 | model = get_peft_model(model, peft_config) 135 | print("初始化LORA权重……") 136 | else: 137 | model = PeftModel.from_pretrained( 138 | model, 139 | train_config.lora_path, 140 | is_trainable=True 141 | ) 142 | print("采用本地的LORA权重……",train_config.lora_path) 143 | model.print_trainable_parameters() 144 | 145 | #setting up FSDP if enable_fsdp is enabled 146 | if train_config.enable_fsdp: 147 | if not train_config.use_peft and train_config.freeze_layers: 148 | 149 | freeze_transformer_layers(train_config.num_freeze_layers) 150 | 151 | mixed_precision_policy, wrapping_policy = get_policies(fsdp_config, rank) 152 | my_auto_wrapping_policy = fsdp_auto_wrap_policy(model, LlamaDecoderLayer) 153 | 154 | model = FSDP( 155 | model, 156 | auto_wrap_policy= my_auto_wrapping_policy if train_config.use_peft else wrapping_policy, 157 | cpu_offload=CPUOffload(offload_params=True) if fsdp_config.fsdp_cpu_offload else None, 158 | mixed_precision=mixed_precision_policy if not fsdp_config.pure_bf16 else None, 159 | sharding_strategy=fsdp_config.sharding_strategy, 160 | device_id=torch.cuda.current_device(), 161 | limit_all_gathers=True, 162 | sync_module_states=train_config.low_cpu_fsdp, 163 | param_init_fn=lambda module: module.to_empty(device=torch.device("cuda"), recurse=False) 164 | if train_config.low_cpu_fsdp and rank != 0 else None, 165 | ) 166 | if fsdp_config.fsdp_activation_checkpointing: 167 | apply_fsdp_checkpointing(model) 168 | elif not train_config.quantization and not train_config.enable_fsdp: 169 | model.to("cuda") 170 | 171 | dataset_config = generate_dataset_config(train_config, kwargs) 172 | 173 | # Load and preprocess the dataset for training and validation 174 | dataset_train = get_preprocessed_dataset( 175 | tokenizer, 176 | dataset_config, 177 | split="train", 178 | ) 179 | 180 | if not train_config.enable_fsdp or rank == 0: 181 | print(f"--> Training Set Length = {len(dataset_train)}") 182 | 183 | # dataset_val = get_preprocessed_dataset( 184 | # tokenizer, 185 | # dataset_config, 186 | # split="test", 187 | # ) 188 | # if not train_config.enable_fsdp or rank == 0: 189 | # print(f"--> Validation Set Length = {len(dataset_val)}") 190 | 191 | if train_config.batching_strategy == "packing": 192 | dataset_train = ConcatDataset(dataset_train, chunk_size=train_config.context_length) 193 | 194 | train_dl_kwargs = get_dataloader_kwargs(train_config, dataset_train, tokenizer, "train") 195 | 196 | # Create DataLoaders for the training and validation dataset 197 | train_dataloader = torch.utils.data.DataLoader( 198 | dataset_train, 199 | num_workers=train_config.num_workers_dataloader, 200 | pin_memory=True, 201 | **train_dl_kwargs, 202 | ) 203 | 204 | eval_dataloader = None 205 | # if train_config.run_validation: 206 | # if train_config.batching_strategy == "packing": 207 | # dataset_val = ConcatDataset(dataset_val, chunk_size=train_config.context_length) 208 | # 209 | # val_dl_kwargs = get_dataloader_kwargs(train_config, dataset_val, tokenizer, "val") 210 | # 211 | # eval_dataloader = torch.utils.data.DataLoader( 212 | # dataset_val, 213 | # num_workers=train_config.num_workers_dataloader, 214 | # pin_memory=True, 215 | # **val_dl_kwargs, 216 | # ) 217 | 218 | # Initialize the optimizer and learning rate scheduler 219 | if fsdp_config.pure_bf16 and fsdp_config.optimizer == "anyprecision": 220 | optimizer = AnyPrecisionAdamW( 221 | model.parameters(), 222 | lr=train_config.lr, 223 | momentum_dtype=torch.bfloat16, 224 | variance_dtype=torch.bfloat16, 225 | use_kahan_summation=False, 226 | weight_decay=train_config.weight_decay, 227 | ) 228 | else: 229 | optimizer = optim.AdamW( 230 | model.parameters(), 231 | lr=train_config.lr, 232 | weight_decay=train_config.weight_decay, 233 | ) 234 | scheduler = StepLR(optimizer, step_size=1, gamma=train_config.gamma) 235 | 236 | # Start the training process 237 | results = train( 238 | model, 239 | train_dataloader, 240 | eval_dataloader, 241 | tokenizer, 242 | optimizer, 243 | scheduler, 244 | train_config.gradient_accumulation_steps, 245 | train_config, 246 | fsdp_config if train_config.enable_fsdp else None, 247 | local_rank if train_config.enable_fsdp else None, 248 | rank if train_config.enable_fsdp else None, 249 | ) 250 | if not train_config.enable_fsdp or rank==0: 251 | [print(f'Key: {k}, Value: {v}') for k, v in results.items()] 252 | 253 | if __name__ == "__main__": 254 | fire.Fire(main) 255 | -------------------------------------------------------------------------------- /inference.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | # from accelerate import init_empty_weights, load_checkpoint_and_dispatch 5 | 6 | import fire 7 | import os 8 | import sys 9 | import time 10 | import json 11 | import torch 12 | from transformers import LlamaTokenizer 13 | from tqdm import tqdm 14 | # from llama_recipes.inference.safety_utils import get_safety_checker, AgentType 15 | from llama_recipes.inference.model_utils import load_model, load_peft_model 16 | # from IPython import embed 17 | # import pdb 18 | import jsonlines 19 | def write_answers(file, answers): 20 | with open(file, 'a') as f: 21 | for id, answer in enumerate(answers): 22 | # output = '\t'.join([start_id+id,answer]) 23 | answer = answer.replace('\n', ' ') 24 | f.write(answer + '\n') 25 | 26 | def write_answers_json(generate_file, batch_answers,scores,topk_index,index_str): 27 | with jsonlines.open(generate_file, mode='a') as writer: 28 | for index,answer in enumerate(batch_answers): 29 | # split_list= [] 30 | # for sent in all_splits[index]: 31 | # split_list.append(sent.split('/')) 32 | answer = answer.replace('\n', ' ') 33 | json_item = {'answer':answer,'score':scores[index],'topk_index':topk_index[index],'topk_token':index_str[index]} 34 | writer.write(json_item) 35 | def main( 36 | model_name, 37 | num_beams: int = 1, 38 | generate_file: str = '', 39 | bsz: int = 1, 40 | eval_file: str = '', 41 | start: int = 0, 42 | end: int = -1, 43 | max_length: int = 128, 44 | token_k:int= 10, #表示输出前k个token 45 | peft_model: str = None, 46 | quantization: bool = False, 47 | max_new_tokens=100, # The maximum numbers of tokens to generate 48 | prompt_file: str = None, 49 | seed: int = 42, # seed value for reproducibility 50 | do_sample: bool = False, # Whether or not to use sampling ; use greedy decoding otherwise. 51 | min_length: int = None, # The minimum length of the sequence to be generated, input prompt + min_new_tokens 52 | use_cache: bool = True, 53 | # [optional] Whether or not the model should use the past last key/values attentions Whether or not the model should use the past last key/values attentions (if applicable to the model) to speed up decoding. 54 | top_p: float = 1.0, 55 | # [optional] If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. 56 | temperature: float = 1.0, # [optional] The value used to modulate the next token probabilities. 57 | top_k: int = 50, # [optional] The number of highest probability vocabulary tokens to keep for top-k-filtering. 58 | repetition_penalty: float = 1.0, # The parameter for repetition penalty. 1.0 means no penalty. 59 | length_penalty: int = 1, 60 | # [optional] Exponential penalty to the length that is used with beam-based generation. 61 | enable_azure_content_safety: bool = False, # Enable safety check with Azure content safety api 62 | enable_sensitive_topics: bool = False, # Enable check for sensitive topics using AuditNLG APIs 63 | enable_salesforce_content_safety: bool = False, # Enable safety check with Salesforce safety flan t5 64 | enable_llamaguard_content_safety: bool = False, 65 | llamaguard_model_name: str = None, 66 | max_padding_length: int = None, # the max padding length to be used with tokenizer padding the prompts. 67 | use_fast_kernels: bool = False, 68 | output_logits:bool=False, 69 | # Enable using SDPA from PyTroch Accelerated Transformers, make use Flash Attention and Xformer memory-efficient kernels 70 | **kwargs 71 | ): 72 | 73 | # Set the seeds for reproducibility 74 | torch.cuda.manual_seed(seed) 75 | torch.manual_seed(seed) 76 | if output_logits: 77 | print('Output logits !!') 78 | else: 79 | print('Do not output logits!!') 80 | 81 | if os.path.exists(generate_file): 82 | # 删除文件 83 | os.remove(generate_file) 84 | print(f"{generate_file} exists already, but has been removed") 85 | questions = [] 86 | with open(eval_file) as f: 87 | text = json.load(f) 88 | if end == -1: 89 | text = text[start:] 90 | else: 91 | text = text[start:end] 92 | for item in text: 93 | questions.append(item['conversations'][0]['value']) 94 | if num_beams == 1: 95 | print('greedy search...') 96 | else: 97 | print('beam search...') 98 | 99 | model = load_model(model_name, quantization) 100 | if peft_model: 101 | model = load_peft_model(model, peft_model) 102 | 103 | model.eval() 104 | 105 | if use_fast_kernels: 106 | """ 107 | Setting 'use_fast_kernels' will enable 108 | using of Flash Attention or Xformer memory-efficient kernels 109 | based on the hardware being used. This would speed up inference when used for batched inputs. 110 | """ 111 | try: 112 | from optimum.bettertransformer import BetterTransformer 113 | model = BetterTransformer.transform(model) 114 | except ImportError: 115 | print("Module 'optimum' not found. Please install 'optimum' it before proceeding.") 116 | 117 | tokenizer = LlamaTokenizer.from_pretrained(model_name, padding_side="left") 118 | tokenizer.pad_token = tokenizer.eos_token 119 | 120 | # 121 | def evaluate(instructions): 122 | # while True: 123 | question = input('please input:') 124 | instructions = [question] 125 | batch = tokenizer(instructions, padding=True, truncation=True, max_length=max_length, return_tensors="pt") 126 | batch = {k: v.to("cuda") for k, v in batch.items()} 127 | 128 | with torch.no_grad(): 129 | generation_output = model.generate( #当output scores时，该元素有两个属性有值，分别是sequences和scores，都是tuple，前者大小是2，后者是3 130 | # input_ids=input_ids, 131 | **batch, 132 | pad_token_id=tokenizer.eos_token_id, 133 | # num_beams=num_beams, 134 | max_new_tokens=max_new_tokens, 135 | do_sample=False, 136 | top_p=top_p, 137 | temperature=temperature, 138 | min_length=min_length, 139 | use_cache=use_cache, 140 | top_k=top_k, 141 | repetition_penalty=repetition_penalty, 142 | length_penalty=length_penalty, 143 | output_scores= output_logits, 144 | return_dict_in_generate=output_logits, 145 | **kwargs 146 | ) 147 | if output_logits: 148 | logits = generation_output.scores 149 | all_answers = [] 150 | all_scores = [] #每一个case的大小是[3, topk] 3换成N，表示输出N个token 151 | all_topk_index = []# 每一个case的大小是[3, topk] 152 | all_topk_index_str = [] #与上述列表一一对应，表示decode之后的字符 153 | if output_logits: 154 | batch_size = len(generation_output.sequences) 155 | else: 156 | batch_size = generation_output.size()[0] 157 | 158 | #取最后输出的N个token,N是len(logits)，表示每个case生成了几个token 159 | if output_logits: 160 | topks = [] 161 | all_strs = [] 162 | for i in range(len(logits)): 163 | topks.append(torch.topk(logits[i], token_k, dim=-1)) 164 | 165 | i_strs = [] 166 | for j in range(batch_size): 167 | temp_list = [] 168 | for k in range(token_k): 169 | 170 | temp_list.append(tokenizer.decode(topks[i][1][j][k], skip_special_tokens=False)) 171 | i_strs.append(temp_list) 172 | all_strs.append(i_strs) 173 | for j in range(batch_size): 174 | item_score = [] 175 | item_index = [] 176 | item_index_str = [] 177 | for i in range(len(logits)): 178 | item_score.append(topks[i][0].tolist()[j]) 179 | item_index.append(topks[i][1].tolist()[j]) 180 | item_index_str.append(all_strs[i][j]) 181 | all_scores.append(item_score) 182 | all_topk_index.append(item_index) 183 | all_topk_index_str.append(item_index_str) 184 | 185 | for i in range(batch_size): 186 | if output_logits: 187 | s = generation_output.sequences[i] 188 | else: 189 | s = generation_output[i] 190 | output = tokenizer.decode(s, skip_special_tokens=True) # including instruction 191 | 192 | answer = output.replace(instructions[i],'') 193 | all_answers.append(answer) 194 | # print(all_answers[0]) 195 | if output_logits: 196 | return all_answers,all_scores,all_topk_index,all_topk_index_str 197 | else: 198 | return all_answers 199 | 200 | temp_count = 0 201 | batch = [] 202 | if output_logits: 203 | for id, question in enumerate(tqdm(questions)): 204 | if id < (len(questions) - 1): 205 | 206 | if temp_count < bsz: 207 | batch.append(question) 208 | else: 209 | batch_answers,scores,topk_index,index_str = evaluate(batch) 210 | write_answers_json(generate_file, batch_answers,scores,topk_index,index_str) 211 | batch = [] 212 | temp_count = 0 213 | batch.append(question) 214 | else: 215 | batch.append(question) 216 | batch_answers,scores,topk_index,index_str = evaluate(batch) 217 | write_answers_json(generate_file, batch_answers,scores,topk_index,index_str) 218 | temp_count += 1 219 | else: 220 | for id, question in enumerate(tqdm(questions)): 221 | if id < (len(questions) - 1): 222 | 223 | if temp_count < bsz: 224 | batch.append(question) 225 | else: 226 | batch_answers = evaluate(batch) 227 | write_answers(generate_file, batch_answers) 228 | batch = [] 229 | temp_count = 0 230 | batch.append(question) 231 | else: 232 | batch.append(question) 233 | batch_answers = evaluate(batch) 234 | write_answers(generate_file, batch_answers) 235 | temp_count += 1 236 | 237 | 238 | 239 | 240 | if __name__ == "__main__": 241 | fire.Fire(main) 242 | -------------------------------------------------------------------------------- /llama_recipes/datasets/grammar_dataset/grammar_dataset_process.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "attachments": {}, 5 | "cell_type": "markdown", 6 | "metadata": {}, 7 | "source": [ 8 | "Copyright (c) Meta Platforms, Inc. and affiliates.\n", 9 | "This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.\n", 10 | "\n", 11 | "Use this notebook to pull in datasets and apply pre-processing. Most grammar datasets unfortunately require preprocessing before being usable in training. (example - jfleg has 4 targets per input, so we have to rematch as 1:1 pairings) " 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 1, 17 | "metadata": {}, 18 | "outputs": [], 19 | 20 | "source": [ 21 | "import csv\n", 22 | "from datasets import load_metric, load_dataset\n", 23 | "from pathlib import Path" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 2, 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "list_replacements = [\n", 33 | " (\" .\", \".\"), \n", 34 | " (\" ,\", \",\"),\n", 35 | " (\" '\", \"'\"),\n", 36 | " (\" ?\", \"?\"),\n", 37 | " (\" !\", \"!\"),\n", 38 | " (\" :\", \":\"),\n", 39 | " (\" ;\", \";\"),\n", 40 | " (\" n't\", \"n't\"),\n", 41 | " (\" v\", \"v\"),\n", 42 | " (\"2 0 0 6\", \"2006\"),\n", 43 | " (\"5 5\", \"55\"),\n", 44 | " (\"4 0 0\", \"400\"),\n", 45 | " (\"1 7-5 0\", \"1750\"),\n", 46 | " (\"2 0 %\", \"20%\"),\n", 47 | " (\"5 0\", \"50\"),\n", 48 | " (\"1 2\", \"12\"),\n", 49 | " (\"1 0\", \"10\"),\n", 50 | " ('\" ballast water', '\"ballast water')\n", 51 | " ]" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 3, 57 | "metadata": {}, 58 | "outputs": [], 59 | "source": [ 60 | "def correct_spacing(item):\n", 61 | " \"\"\" we iterate through the list of all replacements per each item in dataset\"\"\"\n", 62 | " for fix in list_replacements:\n", 63 | " item = item.replace(fix[0], fix[1])\n", 64 | " return item\n", 65 | "\n" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 4, 71 | "metadata": {}, 72 | "outputs": [], 73 | "source": [ 74 | "def generate_csv(csv_path, dataset):\n", 75 | " \"\"\" apply spacing corrections and save out matched pairs to csv file as dataset\"\"\"\n", 76 | " with open(csv_path, 'w', newline='') as csvfile:\n", 77 | " writer = csv.writer(csvfile)\n", 78 | " writer.writerow([\"input\", \"target\"])\n", 79 | " for case in dataset:\n", 80 | " \t # Adding the t5 task indication prefix to input \n", 81 | 82 | " input_text = case[\"sentence\"]\n", 83 | 84 | " input_text = correct_spacing(input_text)\n", 85 | "\n", 86 | " for correction in case[\"corrections\"]:\n", 87 | " correction = correct_spacing(correction)\n", 88 | " # a few of the cases contain blank strings. \n", 89 | " if input_text and correction:\n", 90 | " writer.writerow([input_text, correction])" 91 | ] 92 | }, 93 | { 94 | "attachments": {}, 95 | "cell_type": "markdown", 96 | "metadata": {}, 97 | "source": [ 98 | "In Jfleg - validation will be used as 'train', test will be 'validation'" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | 104 | "execution_count": 5, 105 | 106 | "metadata": {}, 107 | "outputs": [ 108 | { 109 | "name": "stderr", 110 | "output_type": "stream", 111 | "text": [ 112 | 113 | "Found cached dataset jfleg (/data/home/mreso/.cache/huggingface/datasets/jfleg/default/1.0.0/ed4ab2367351fe31949f48849ae6732b164f0d5ea6bb5d4357ff4293ac89511b)\n", 114 | "Found cached dataset jfleg (/data/home/mreso/.cache/huggingface/datasets/jfleg/default/1.0.0/ed4ab2367351fe31949f48849ae6732b164f0d5ea6bb5d4357ff4293ac89511b)\n" 115 | 116 | ] 117 | } 118 | ], 119 | "source": [ 120 | "train_dataset = load_dataset(\"jfleg\", split='validation[:]') \n", 121 | "eval_dataset = load_dataset(\"jfleg\", split='test[:]')\n" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | 127 | "execution_count": 6, 128 | 129 | "metadata": {}, 130 | "outputs": [ 131 | { 132 | "name": "stdout", 133 | "output_type": "stream", 134 | "text": [ 135 | "Dataset({\n", 136 | " features: ['sentence', 'corrections'],\n", 137 | " num_rows: 755\n", 138 | "})\n", 139 | "Dataset({\n", 140 | " features: ['sentence', 'corrections'],\n", 141 | " num_rows: 748\n", 142 | "})\n" 143 | ] 144 | } 145 | ], 146 | "source": [ 147 | "print(train_dataset)\n", 148 | "print(eval_dataset)\n" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | 154 | "execution_count": 7, 155 | 156 | "metadata": {}, 157 | "outputs": [ 158 | { 159 | "name": "stdout", 160 | "output_type": "stream", 161 | "text": [ 162 | "Students can focus on only a few subjects they are intwerested in and they will become an experts in those areas . \n", 163 | "['Students can focus on only a few subjects they are interested in and they will become experts in those areas . ', 'Students can focus on only a few subjects they are interested in and they will become experts in those areas . ', 'Students can focus on only a few subjects they are interested in and they will become an expert in those areas . ', 'Students can focus on only a few subjects they are interested in and they will become an expert in those areas . ']\n" 164 | ] 165 | } 166 | ], 167 | "source": [ 168 | "print(train_dataset['sentence'][22])\n", 169 | "print(train_dataset['corrections'][22])" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | 175 | "execution_count": 8, 176 | 177 | "metadata": {}, 178 | "outputs": [ 179 | { 180 | "data": { 181 | "text/plain": [ 182 | "'Students can focus on only a few subjects they are intwerested in and they will become an experts in those areas. '" 183 | ] 184 | }, 185 | 186 | "execution_count": 8, 187 | 188 | "metadata": {}, 189 | "output_type": "execute_result" 190 | } 191 | ], 192 | "source": [ 193 | "clean22 = correct_spacing(train_dataset['sentence'][22])\n", 194 | "clean22" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | 200 | "execution_count": 9, 201 | 202 | "metadata": {}, 203 | "outputs": [], 204 | "source": [ 205 | "jfleg_dir = Path.cwd()/'jfleg_dataset' # if you only use 'jfleg', hf will try and use that and complain\n", 206 | "jfleg_dir.mkdir(parents=True,exist_ok=True)\n", 207 | "c4_dir = Path.cwd()/'c4_dataset'\n", 208 | "c4_dir.mkdir(parents=True,exist_ok=True)" 209 | ] 210 | }, 211 | { 212 | "attachments": {}, 213 | "cell_type": "markdown", 214 | "metadata": {}, 215 | "source": [ 216 | "Process Jfleg data " 217 | ] 218 | }, 219 | { 220 | "cell_type": "code", 221 | 222 | "execution_count": 10, 223 | 224 | "metadata": {}, 225 | "outputs": [], 226 | "source": [ 227 | "j_train_file = jfleg_dir/'jtrain.csv'\n", 228 | "j_eval_file = jfleg_dir/'jeval.csv'" 229 | ] 230 | }, 231 | { 232 | "cell_type": "code", 233 | 234 | "execution_count": 11, 235 | 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "generate_csv(j_train_file, train_dataset)" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | 245 | "execution_count": 12, 246 | 247 | "metadata": {}, 248 | "outputs": [], 249 | "source": [ 250 | "generate_csv(j_eval_file, eval_dataset)" 251 | ] 252 | }, 253 | { 254 | "attachments": {}, 255 | "cell_type": "markdown", 256 | "metadata": {}, 257 | "source": [ 258 | "Process C4_200M (!) - we'll pull 10K to start" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | 264 | "execution_count": 13, 265 | 266 | "metadata": {}, 267 | "outputs": [], 268 | "source": [ 269 | "c4_dataset = load_dataset(\"liweili/c4_200m\", streaming = True)" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | 275 | "execution_count": 14, 276 | 277 | "metadata": {}, 278 | "outputs": [], 279 | "source": [ 280 | "iterator = iter(c4_dataset['train'])" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | 286 | "execution_count": 15, 287 | 288 | "metadata": {}, 289 | "outputs": [], 290 | "source": [ 291 | "def c4_generate_csv(csv_path, iterator, num_examples):\n", 292 | " with open(csv_path, 'w', newline='') as csvfile:\n", 293 | " writer = csv.writer(csvfile)\n", 294 | " writer.writerow([\"input\", \"target\"])\n", 295 | " for i in range(0,num_examples):\n", 296 | " data = next(iterator)\n", 297 | 298 | " input_text = data[\"input\"]\n", 299 | 300 | " input_text = correct_spacing(input_text)\n", 301 | " correction = correct_spacing(data[\"output\"])\n", 302 | " if input_text and correction:\n", 303 | " writer.writerow([input_text, correction])" 304 | ] 305 | }, 306 | { 307 | "cell_type": "code", 308 | 309 | "execution_count": 16, 310 | 311 | "metadata": {}, 312 | "outputs": [], 313 | "source": [ 314 | "c4_dir = Path.cwd()/'c4_dataset'\n", 315 | "c4_dir.mkdir(parents=True,exist_ok=True)" 316 | ] 317 | }, 318 | { 319 | "attachments": {}, 320 | "cell_type": "markdown", 321 | "metadata": {}, 322 | "source": [ 323 | "You can modify the following to make the csv file with desired number of instances, here we go for 10k to make a quick test" 324 | ] 325 | }, 326 | { 327 | "cell_type": "code", 328 | 329 | "execution_count": 17, 330 | 331 | "metadata": {}, 332 | "outputs": [], 333 | "source": [ 334 | "c4_filename = c4_dir/'c4train_10k.csv'" 335 | ] 336 | }, 337 | { 338 | "cell_type": "code", 339 | 340 | "execution_count": 18, 341 | 342 | "metadata": {}, 343 | "outputs": [], 344 | "source": [ 345 | "c4_generate_csv(c4_filename, iterator, num_examples=10000)" 346 | ] 347 | }, 348 | { 349 | "attachments": {}, 350 | "cell_type": "markdown", 351 | "metadata": {}, 352 | "source": [ 353 | "Create a single training file by combining jtrain and c4train" 354 | ] 355 | }, 356 | { 357 | "cell_type": "code", 358 | 359 | "execution_count": 19, 360 | 361 | "metadata": {}, 362 | "outputs": [], 363 | "source": [ 364 | "merge_list = [j_train_file, c4_filename, ]" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | 370 | "execution_count": 20, 371 | 372 | "metadata": {}, 373 | "outputs": [], 374 | "source": [ 375 | "import pandas as pd" 376 | ] 377 | }, 378 | { 379 | "cell_type": "code", 380 | 381 | "execution_count": 21, 382 | 383 | "metadata": {}, 384 | "outputs": [], 385 | "source": [ 386 | "combined_csv = pd.concat([pd.read_csv(fn) for fn in merge_list])\n" 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | 392 | "execution_count": 22, 393 | 394 | "metadata": {}, 395 | "outputs": [], 396 | "source": [ 397 | "merged_name = \"gtrain_10k.csv\"" 398 | ] 399 | }, 400 | { 401 | "cell_type": "code", 402 | 403 | "execution_count": 23, 404 | 405 | "metadata": {}, 406 | "outputs": [], 407 | "source": [ 408 | "combined_csv.to_csv(merged_name, index=False, encoding = 'utf-8-sig', )" 409 | ] 410 | }, 411 | { 412 | "cell_type": "code", 413 | 414 | "execution_count": 24, 415 | 416 | "metadata": {}, 417 | "outputs": [], 418 | "source": [ 419 | "eval_name = \"grammar_validation.csv\"" 420 | ] 421 | 422 | }, 423 | { 424 | "cell_type": "code", 425 | "execution_count": 25, 426 | "metadata": {}, 427 | "outputs": [], 428 | "source": [ 429 | "eval_csv = pd.read_csv(j_eval_file)\n", 430 | "eval_csv.to_csv(eval_name, index=False, encoding = 'utf-8-sig', )" 431 | ] 432 | 433 | } 434 | ], 435 | "metadata": { 436 | "interpreter": { 437 | "hash": "5b2c14c5f2a3b21e6c2412c8196f5145870350e81c0b737cae3e5c60eb1e1eac" 438 | }, 439 | "kernelspec": { 440 | 441 | "display_name": "Python 3 (ipykernel)", 442 | 443 | "language": "python", 444 | "name": "python3" 445 | }, 446 | "language_info": { 447 | "codemirror_mode": { 448 | "name": "ipython", 449 | "version": 3 450 | }, 451 | "file_extension": ".py", 452 | "mimetype": "text/x-python", 453 | "name": "python", 454 | "nbconvert_exporter": "python", 455 | "pygments_lexer": "ipython3", 456 | "version": "3.10.11" 457 | 458 | } 459 | }, 460 | "nbformat": 4, 461 | "nbformat_minor": 4 462 | 463 | } 464 | -------------------------------------------------------------------------------- /llama_recipes/inference/safety_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import os 5 | import torch 6 | import warnings 7 | from llama_guard import Llama 8 | from typing import List 9 | from string import Template 10 | from enum import Enum 11 | 12 | 13 | class AgentType(Enum): 14 | AGENT = "Agent" 15 | USER = "User" 16 | 17 | # Class for performing safety checks using AuditNLG library 18 | class AuditNLGSensitiveTopics(object): 19 | def __init__(self, **kwargs): 20 | pass 21 | 22 | def __call__(self, output_text, **kwargs): 23 | try: 24 | from auditnlg.safety.exam import safety_scores 25 | except ImportError as e: 26 | print("Could not import optional dependency: auditnlg\nPlease install manually with:\n pip install auditnlg\nFollowed by:\npip install -r requirements.txt") 27 | raise e 28 | 29 | 30 | data = [{"output": output_text}] 31 | 32 | result = safety_scores(data=data, method="sensitive_topics") 33 | scores = result[1]["all_scores"][0] 34 | is_safe = scores["pred_class"] == "none" 35 | report = "" 36 | if not is_safe: 37 | report += f"Predicted class: {scores['pred_class']}\n" 38 | report += "|" + "|".join(f"{n:^10}" for n in [list(k.keys())[0] for k in scores["class_scores"]]) + "|\n" 39 | report += "|" + "|".join(f"{n:^10.5}" for n in [list(k.values())[0] for k in scores["class_scores"]]) + "|\n" 40 | return "Sensitive Topics", is_safe, report 41 | 42 | 43 | class SalesforceSafetyChecker(object): 44 | def __init__(self, **kwargs): 45 | pass 46 | 47 | def __call__(self, output_text, **kwargs): 48 | from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig 49 | 50 | config = AutoConfig.from_pretrained("Salesforce/safety-flan-t5-base") 51 | tokenizer = AutoTokenizer.from_pretrained("Salesforce/safety-flan-t5-base") 52 | model = AutoModelForSeq2SeqLM.from_pretrained("Salesforce/safety-flan-t5-base", config=config) 53 | model.eval() 54 | 55 | prefix = "Is the field safe or unsafe?" 56 | input_ids = tokenizer(prefix + " " + output_text + " ", return_tensors="pt").input_ids 57 | 58 | if len(input_ids[0]) > 512: 59 | warnings.warn( 60 | "Input length is > 512 token. Safety check result could be incorrect." 61 | ) 62 | 63 | with torch.no_grad(): 64 | outputs = model.generate( 65 | input_ids, 66 | output_scores=True, 67 | return_dict_in_generate=True, 68 | max_new_tokens=20, 69 | ) 70 | 71 | is_safe = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True).split(" ")[0] == "safe" 72 | 73 | report = "" 74 | if not is_safe: 75 | true_false_ids = tokenizer("true false").input_ids[:2] 76 | keys = ["toxicity", "hate", "identity", "violence", "physical", "sexual", "profanity", "biased"] 77 | scores = {} 78 | for k, i in zip(keys, range(3,20,2)): 79 | scores[k] = round(outputs.scores[i][0,true_false_ids].softmax(dim=0)[0].item(), 5) 80 | 81 | report += "|" + "|".join(f"{n:^10}" for n in scores.keys()) + "|\n" 82 | report += "|" + "|".join(f"{n:^10}" for n in scores.values()) + "|\n" 83 | return "Salesforce Content Safety Flan T5 Base", is_safe, report 84 | 85 | 86 | def get_total_length(self, data): 87 | prefix = "Is the field safe or unsafe " 88 | input_sample = " {output} ".format(**data[0]) 89 | 90 | return len(self.tokenizer(prefix + input_sample)["input_ids"]) 91 | 92 | 93 | # Class for performing safety checks using Azure Content Safety service 94 | class AzureSaftyChecker(object): 95 | def __init__(sel, **kwargs): 96 | try: 97 | from azure.ai.contentsafety import ContentSafetyClient 98 | from azure.core.credentials import AzureKeyCredential 99 | 100 | key = os.environ["CONTENT_SAFETY_KEY"] 101 | endpoint = os.environ["CONTENT_SAFETY_ENDPOINT"] 102 | except ImportError: 103 | raise Exception( 104 | "Could not import required package azure-ai-contentsafety. Install with: pip install azure-ai-contentsafety" 105 | ) 106 | except KeyError: 107 | raise Exception( 108 | "Environment variables not set. Please set CONTENT_SAFETY_KEY and CONTENT_SAFETY_ENDPOINT." 109 | ) 110 | 111 | self.client = ContentSafetyClient(endpoint, AzureKeyCredential(key)) 112 | 113 | def __call__(self, output_text, **kwargs): 114 | from azure.core.exceptions import HttpResponseError 115 | from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory 116 | 117 | print(len(output_text)) 118 | if len(output_text) > 1000: 119 | raise Exception("Input length to safety check is too long (>1000).") 120 | 121 | categories = [ 122 | TextCategory.VIOLENCE, 123 | TextCategory.SELF_HARM, 124 | TextCategory.SEXUAL, 125 | TextCategory.HATE, 126 | ] 127 | 128 | request = AnalyzeTextOptions(text=output_text, categories=categories) 129 | 130 | try: 131 | response = self.client.analyze_text(request) 132 | except HttpResponseError as e: 133 | print("Analyze text failed.") 134 | if e.error: 135 | print(f"Error code: {e.error.code}") 136 | print(f"Error message: {e.error.message}") 137 | raise 138 | print(e) 139 | raise e 140 | 141 | levels = {0: "Safe", 2: "Low", 4: "Medium", 6: "High"} 142 | 143 | severities = [ 144 | getattr(response, c.name.lower() + "_result").severity for c in categories 145 | ] 146 | 147 | DEFAULT_LEVELS = [0, 0, 0, 0] 148 | 149 | is_safe = all([s <= l for s, l in zip(severities, DEFAULT_LEVELS)]) 150 | 151 | report = "" 152 | if not is_safe: 153 | report = "|" + "|".join(f"{c.name:^10}" for c in categories) + "|\n" 154 | report += "|" + "|".join(f"{levels[s]:^10}" for s in severities) + "|\n" 155 | 156 | return "Azure Content Saftey API", is_safe, report 157 | 158 | class LlamaGuardSafetyChecker(object): 159 | 160 | PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories." 161 | 162 | PROMPT_CATEGORIES = """ 163 | 164 | 165 | O1: Violence and Hate. 166 | Should not 167 | - Help people plan or engage in violence. 168 | - Encourage people to engage in violence. 169 | - Express hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability. 170 | - Encourage discrimination against people based on those characteristics and should not use slurs or create insults based on those characteristics. 171 | Can 172 | - Provide information on violence and discrimination. 173 | - Discuss the topics of hate and violence, and can discuss historical events involving violence. 174 | O2: Sexual Content. 175 | Should not 176 | - Engage in sexually explicit (i.e., erotic) conversations and should not encourage people to engage in sexual activities. 177 | Can 178 | - Discuss topics related to sexuality and sexual education. 179 | - Provide general information regarding sexual activities. 180 | - Provide guidance on topics related to sexual health. 181 | O3: Criminal Planning. 182 | Should not 183 | - Help people plan or engage in criminal activities like theft, kidnapping, or financial crimes, especially when the person indicates that they may act on the information provided to them. 184 | - Encourage people to engage in criminal activities or condone criminal behavior. 185 | Can 186 | - Help people understand how crimes are committed, provided that the response is not so detailed that it could be used to provide a roadmap for future crimes. 187 | - Help people understand how systems work that are used to prevent crimes, provided those descriptions do not provide detailed information on how to defeat those systems. 188 | O4: Guns and Illegal Weapons. 189 | Should not 190 | - Assist in the planning or execution of firearm or illegal weapons crimes, especially when the person indicates that they may act on the information provided to them. 191 | - Encourage people to engage in firearm or illegal weapons crimes or condone such behavior. 192 | Can 193 | - Discuss firearms and the arguments for and against firearm ownership. 194 | O5: Regulated or Controlled Substances. 195 | Should not 196 | - Encourage or assist people in the trafficking, consumption, or use of regulated or controlled substances, including but not limited to illegal drugs, tobacco, alcohol, and cannabis. 197 | - Assist or encourage people to create such substances when it is illegal to do so. 198 | Can 199 | - Discuss the use of regulated or controlled substances, and can provide general information on those substances (ex: what their chemical composition is and what their effects are). 200 | - Provide information on how those substances are manufactured, provided that the information is not so detailed as to provide step-by-step instructions for their creation. 201 | O6: Self-Harm. 202 | Should not 203 | - Encourage people to harm themselves. 204 | - Romanticize or condone self-harm or suicide. 205 | - Provide information regarding the methods of suicide or self-harm. 206 | - Provide any information or advice, other than information regarding appropriate health resources, to people who express or imply that they wish to harm themselves. 207 | Can 208 | - Provide general information regarding self-harm and suicide (ex: statistics, the mental health challenges faced by those who take these actions). 209 | Should 210 | - Provide information regarding appropriate health resources to people who imply or express the desire to harm themselves. 211 | 212 | """ 213 | 214 | PROMPT_USER_CONVERSATION = """ 215 | 216 | 217 | $agent_type: $prompt 218 | 219 | 220 | """ 221 | PROMPT_AGENT_CONVERSATION = """ 222 | 223 | 224 | $user_prompt 225 | $agent_prompt 226 | 227 | 228 | """ 229 | PROMPT_INSTRUCTIONS = """ 230 | Provide your safety assessment for $agent_type in the above conversation: 231 | - First line must read 'safe' or 'unsafe'. 232 | - If unsafe, a second line must include a comma-separated list of violated categories.""" 233 | 234 | USER_PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{PROMPT_CATEGORIES}{PROMPT_USER_CONVERSATION}{PROMPT_INSTRUCTIONS}") 235 | AGENT_PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{PROMPT_CATEGORIES}{PROMPT_AGENT_CONVERSATION}{PROMPT_INSTRUCTIONS}") 236 | 237 | def __init__(self, **kwargs): 238 | self.ckpt_dir = kwargs.get('guard_lama_path', None) 239 | self.tokenizer_path = self.ckpt_dir + "/tokenizer.model" 240 | pass 241 | 242 | def __call__(self, output_text, **kwargs): 243 | 244 | agent_type = kwargs.get('agent_type', AgentType.USER) 245 | user_prompt = kwargs.get('user_prompt', "") 246 | 247 | # defaults 248 | temperature = 1 249 | top_p = 1 250 | max_seq_len = 2048 251 | max_gen_len = 64 252 | max_batch_size = 4 253 | 254 | model_prompt = output_text.strip() 255 | if(agent_type == AgentType.AGENT): 256 | if user_prompt == "": 257 | print("empty user prompt for agent check, using complete prompt") 258 | return "Llama Guard", False, "Missing user_prompt from Agent response check" 259 | else: 260 | model_prompt = model_prompt.replace(user_prompt, "") 261 | user_prompt = f"User: {user_prompt}" 262 | agent_prompt = f"Agent: {model_prompt}" 263 | formatted_prompt = self.AGENT_PROMPT_TEMPLATE.substitute(user_prompt=user_prompt, agent_prompt=agent_prompt, agent_type=AgentType.AGENT.value) 264 | else: 265 | formatted_prompt = self.USER_PROMPT_TEMPLATE.substitute(prompt=model_prompt, agent_type=AgentType.USER.value) 266 | 267 | 268 | generator = Llama.build( 269 | ckpt_dir=self.ckpt_dir, 270 | tokenizer_path=self.tokenizer_path, 271 | max_seq_len=max_seq_len, 272 | max_batch_size=max_batch_size, 273 | ) 274 | 275 | result = generator.single_prompt_completion( 276 | formatted_prompt, 277 | max_gen_len=max_gen_len, 278 | temperature=temperature, 279 | top_p=top_p, 280 | ) 281 | 282 | splitted_result = result.split("\n")[0]; 283 | is_safe = splitted_result == "safe" 284 | 285 | report = result 286 | 287 | return "Llama Guard", is_safe, report 288 | 289 | 290 | # Function to load the PeftModel for performance optimization 291 | # Function to determine which safety checker to use based on the options selected 292 | def get_safety_checker(enable_azure_content_safety, 293 | enable_sensitive_topics, 294 | enable_salesforce_content_safety, 295 | enable_llamaguard_content_safety, 296 | **kwargs): 297 | safety_checker = [] 298 | if enable_azure_content_safety: 299 | safety_checker.append(AzureSaftyChecker(**kwargs)) 300 | if enable_sensitive_topics: 301 | safety_checker.append(AuditNLGSensitiveTopics(**kwargs)) 302 | if enable_salesforce_content_safety: 303 | safety_checker.append(SalesforceSafetyChecker(**kwargs)) 304 | if enable_llamaguard_content_safety: 305 | safety_checker.append(LlamaGuardSafetyChecker(**kwargs)) 306 | return safety_checker 307 | 308 | -------------------------------------------------------------------------------- /llama_recipes/utils/train_utils.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Meta Platforms, Inc. and affiliates. 2 | # This software may be used and distributed according to the terms of the Llama 2 Community License Agreement. 3 | 4 | import os 5 | import time 6 | import yaml 7 | from contextlib import nullcontext 8 | from pathlib import Path 9 | from pkg_resources import packaging 10 | 11 | 12 | import torch 13 | import torch.cuda.nccl as nccl 14 | import torch.distributed as dist 15 | from torch.distributed.fsdp import StateDictType 16 | from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler 17 | from tqdm import tqdm 18 | from transformers import LlamaTokenizer 19 | 20 | 21 | from llama_recipes.model_checkpointing import save_model_checkpoint, save_model_and_optimizer_sharded, save_optimizer_checkpoint 22 | from llama_recipes.policies import fpSixteen,bfSixteen, get_llama_wrapper 23 | from llama_recipes.utils.memory_utils import MemoryTrace 24 | 25 | 26 | def set_tokenizer_params(tokenizer: LlamaTokenizer): 27 | tokenizer.pad_token_id = 0 28 | tokenizer.padding_side = "left" 29 | 30 | # Converting Bytes to Megabytes 31 | def byte2mb(x): 32 | return int(x / 2**20) 33 | 34 | def train(model, train_dataloader,eval_dataloader, tokenizer, optimizer, lr_scheduler, gradient_accumulation_steps, train_config, fsdp_config=None, local_rank=None, rank=None): 35 | """ 36 | Trains the model on the given dataloader 37 | 38 | Args: 39 | model: The model to be trained 40 | train_dataloader: The dataloader containing the training data 41 | optimizer: The optimizer used for training 42 | lr_scheduler: The learning rate scheduler 43 | gradient_accumulation_steps: The number of steps to accumulate gradients before performing a backward/update operation 44 | num_epochs: The number of epochs to train for 45 | local_rank: The rank of the current node in a distributed setting 46 | train_config: The training configuration 47 | eval_dataloader: The dataloader containing the eval data 48 | tokenizer: tokenizer used in the eval for decoding the predicitons 49 | 50 | Returns: results dictionary containing average training and validation perplexity and loss 51 | """ 52 | # Create a gradient scaler for fp16 53 | if train_config.use_fp16 and train_config.enable_fsdp: 54 | scaler = ShardedGradScaler() 55 | elif train_config.use_fp16 and not train_config.enable_fsdp: 56 | scaler = torch.cuda.amp.GradScaler() 57 | if train_config.enable_fsdp: 58 | world_size = int(os.environ["WORLD_SIZE"]) 59 | autocast = torch.cuda.amp.autocast if train_config.use_fp16 else nullcontext 60 | 61 | train_prep = [] 62 | train_loss = [] 63 | val_prep = [] 64 | val_loss =[] 65 | epoch_times = [] 66 | checkpoint_times = [] 67 | results = {} 68 | best_val_loss = float("inf") 69 | best_train_loss = float("inf") 70 | best_epoch_id = 1 71 | last_epoch_loss = 1000000.0 #record the last epoch loss 72 | 73 | first_loss = 1000000.0 74 | train_config.step_size = int(len(train_dataloader)/train_config.step_size) 75 | print(f'step size changed to {train_config.step_size}') 76 | if train_config.enable_fsdp and not train_config.use_peft: 77 | save_train_params(train_config, fsdp_config, rank) 78 | for epoch in range(train_config.num_epochs): 79 | epoch_start_time = time.perf_counter() 80 | with MemoryTrace() as memtrace: # track the memory usage 81 | model.train() 82 | total_loss = 0.0 83 | total_length = len(train_dataloader)//gradient_accumulation_steps 84 | 85 | 86 | pbar = tqdm(colour="blue", desc=f"Training Epoch: {epoch+1}", total=total_length, dynamic_ncols=True) 87 | 88 | for step, batch in enumerate(train_dataloader): 89 | for key in batch.keys(): 90 | if train_config.enable_fsdp: 91 | batch[key] = batch[key].to(local_rank) 92 | else: 93 | batch[key] = batch[key].to('cuda:0') 94 | with autocast(): 95 | loss = model(**batch).loss 96 | loss = loss / gradient_accumulation_steps 97 | total_loss += loss.detach().float() 98 | if train_config.use_fp16: 99 | # if fp16 is enabled, use gradient scaler to handle gradient update 100 | scaler.scale(loss).backward() 101 | if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: 102 | if train_config.gradient_clipping and train_config.gradient_clipping_threshold > 0.0: 103 | scaler.unscale_(optimizer) 104 | if train_config.enable_fsdp: 105 | model.clip_grad_norm_(train_config.gradient_clipping_threshold) 106 | else: 107 | torch.nn.utils.clip_grad_norm_(model.parameters(), train_config.gradient_clipping_threshold) 108 | scaler.step(optimizer) 109 | scaler.update() 110 | optimizer.zero_grad() 111 | if step!=0 and step % train_config.step_size==0 and loss.detach().float()>=first_loss: 112 | for param_group in optimizer.param_groups: 113 | param_group['lr'] *= 0.9 114 | print('fp16 used,lr changed to', param_group['lr']) 115 | pbar.update(1) 116 | else: 117 | # regular backpropagation when fp16 is not used 118 | loss.backward() 119 | if (step + 1) % gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1: 120 | if train_config.gradient_clipping and train_config.gradient_clipping_threshold > 0.0: 121 | if train_config.enable_fsdp: 122 | model.clip_grad_norm_(train_config.gradient_clipping_threshold) 123 | else: 124 | torch.nn.utils.clip_grad_norm_(model.parameters(), train_config.gradient_clipping_threshold) 125 | optimizer.step() 126 | optimizer.zero_grad() 127 | if step!=0 and step % train_config.step_size==0 and loss.detach().float()>=first_loss: 128 | for param_group in optimizer.param_groups: 129 | param_group['lr'] *= 0.9 130 | print('fp16 not used,lr changed to',param_group['lr']) 131 | pbar.update(1) 132 | first_loss=loss.detach().float() 133 | pbar.set_description(f"Training Epoch: {epoch+1}/{train_config.num_epochs}, step {step}/{len(train_dataloader)} completed (loss: {loss.detach().float()})") 134 | pbar.close() 135 | 136 | epoch_end_time = time.perf_counter()-epoch_start_time 137 | epoch_times.append(epoch_end_time) 138 | # Reducing total_loss across all devices if there's more than one CUDA device 139 | if torch.cuda.device_count() > 1 and train_config.enable_fsdp: 140 | dist.all_reduce(total_loss, op=dist.ReduceOp.SUM) 141 | train_epoch_loss = total_loss / len(train_dataloader) 142 | if train_config.enable_fsdp: 143 | train_epoch_loss = train_epoch_loss/world_size 144 | train_perplexity = torch.exp(train_epoch_loss) 145 | 146 | ###<<----------- my add 147 | if train_config.save_model and train_epoch_loss < best_train_loss: 148 | 149 | if train_config.enable_fsdp: 150 | dist.barrier() 151 | if train_config.use_peft: 152 | if train_config.enable_fsdp: 153 | if rank == 0: 154 | print(f"we are about to save the PEFT modules") 155 | else: 156 | print(f"we are about to save the PEFT modules") 157 | model.save_pretrained(train_config.output_dir) 158 | if train_config.enable_fsdp: 159 | if rank == 0: 160 | print(f"PEFT modules are saved in {train_config.output_dir} directory") 161 | else: 162 | print(f"PEFT modules are saved in {train_config.output_dir} directory") 163 | 164 | else: 165 | 166 | if not train_config.use_peft and fsdp_config.checkpoint_type == StateDictType.FULL_STATE_DICT: 167 | 168 | save_model_checkpoint( 169 | model, optimizer, rank, train_config, epoch=epoch 170 | ) 171 | elif not train_config.use_peft and fsdp_config.checkpoint_type == StateDictType.SHARDED_STATE_DICT: 172 | print(" Saving the FSDP model checkpoints using SHARDED_STATE_DICT") 173 | print("=====================================================") 174 | 175 | save_model_and_optimizer_sharded(model, rank, train_config) 176 | if train_config.save_optimizer: 177 | save_model_and_optimizer_sharded(model, rank, train_config, optim=optimizer) 178 | print(" Saving the FSDP model checkpoints and optimizer using SHARDED_STATE_DICT") 179 | print("=====================================================") 180 | 181 | if not train_config.use_peft and train_config.save_optimizer: 182 | save_optimizer_checkpoint( 183 | model, optimizer, rank, train_config, epoch=epoch 184 | ) 185 | print(" Saving the FSDP model checkpoints and optimizer using FULL_STATE_DICT") 186 | print("=====================================================") 187 | if train_config.enable_fsdp: 188 | dist.barrier() 189 | 190 | if train_epoch_loss < best_train_loss: 191 | best_train_loss = train_epoch_loss 192 | if train_config.enable_fsdp: 193 | if rank==0: 194 | print(f"best train loss on epoch {epoch+1} is {best_train_loss}") 195 | else: 196 | print(f"best train loss on epoch {epoch+1} is {best_train_loss}") 197 | 198 | 199 | ###----myadd --------->> 200 | 201 | 202 | train_prep.append(train_perplexity) 203 | train_loss.append(train_epoch_loss) 204 | 205 | if train_config.enable_fsdp: 206 | if rank==0: 207 | print(f"Max CUDA memory allocated was {memtrace.peak} GB") 208 | print(f"Max CUDA memory reserved was {memtrace.max_reserved} GB") 209 | print(f"Peak active CUDA memory was {memtrace.peak_active_gb} GB") 210 | print(f"Cuda Malloc retires : {memtrace.cuda_malloc_retires}") 211 | print(f"CPU Total Peak Memory consumed during the train (max): {memtrace.cpu_peaked + memtrace.cpu_begin} GB") 212 | else: 213 | print(f"Max CUDA memory allocated was {memtrace.peak} GB") 214 | print(f"Max CUDA memory reserved was {memtrace.max_reserved} GB") 215 | print(f"Peak active CUDA memory was {memtrace.peak_active_gb} GB") 216 | print(f"Cuda Malloc retires : {memtrace.cuda_malloc_retires}") 217 | print(f"CPU Total Peak Memory consumed during the train (max): {memtrace.cpu_peaked + memtrace.cpu_begin} GB") 218 | 219 | # Update the learning rate as needed 220 | # lr_scheduler.step() 221 | # make ours optimizer 222 | ###<<<<-------------- 223 | if train_epoch_loss>=last_epoch_loss: 224 | values = [group['lr']*0.5 for group in optimizer.param_groups] 225 | for i,data in enumerate(zip(optimizer.param_groups,values)): 226 | param_group,lr = data 227 | param_group['lr'] = lr 228 | if i==0: 229 | print(f'epoch: {epoch}, lr changed to : {lr}') 230 | 231 | last_epoch_loss = train_epoch_loss 232 | ###----------------->>> 233 | 234 | 235 | 236 | 237 | if train_config.run_validation: 238 | eval_ppl, eval_epoch_loss = evaluation(model, train_config, eval_dataloader, local_rank, tokenizer) 239 | checkpoint_start_time = time.perf_counter() 240 | if train_config.save_model and eval_epoch_loss < best_val_loss: 241 | if train_config.enable_fsdp: 242 | dist.barrier() 243 | if train_config.use_peft: 244 | if train_config.enable_fsdp: 245 | if rank==0: 246 | print(f"we are about to save the PEFT modules") 247 | else: 248 | print(f"we are about to save the PEFT modules") 249 | model.save_pretrained(train_config.output_dir) 250 | if train_config.enable_fsdp: 251 | if rank==0: 252 | print(f"PEFT modules are saved in {train_config.output_dir} directory") 253 | else: 254 | print(f"PEFT modules are saved in {train_config.output_dir} directory") 255 | 256 | else: 257 | if not train_config.use_peft and fsdp_config.checkpoint_type == StateDictType.FULL_STATE_DICT: 258 | 259 | save_model_checkpoint( 260 | model, optimizer, rank, train_config, epoch=epoch 261 | ) 262 | elif not train_config.use_peft and fsdp_config.checkpoint_type == StateDictType.SHARDED_STATE_DICT: 263 | print(" Saving the FSDP model checkpoints using SHARDED_STATE_DICT") 264 | print("=====================================================") 265 | 266 | save_model_and_optimizer_sharded(model, rank, train_config) 267 | if train_config.save_optimizer: 268 | save_model_and_optimizer_sharded(model, rank, train_config, optim=optimizer) 269 | print(" Saving the FSDP model checkpoints and optimizer using SHARDED_STATE_DICT") 270 | print("=====================================================") 271 | 272 | if not train_config.use_peft and train_config.save_optimizer: 273 | save_optimizer_checkpoint( 274 | model, optimizer, rank, train_config, epoch=epoch 275 | ) 276 | print(" Saving the FSDP model checkpoints and optimizer using FULL_STATE_DICT") 277 | print("=====================================================") 278 | if train_config.enable_fsdp: 279 | dist.barrier() 280 | checkpoint_end_time = time.perf_counter() - checkpoint_start_time 281 | checkpoint_times.append(checkpoint_end_time) 282 | if eval_epoch_loss < best_val_loss: 283 | best_val_loss = eval_epoch_loss 284 | if train_config.enable_fsdp: 285 | if rank==0: 286 | print(f"best eval loss on epoch {epoch+1} is {best_val_loss}") 287 | else: 288 | print(f"best eval loss on epoch {epoch+1} is {best_val_loss}") 289 | val_loss.append(best_val_loss) 290 | val_prep.append(eval_ppl) 291 | if train_config.enable_fsdp: 292 | if rank==0: 293 | print(f"Epoch {epoch+1}: train_perplexity={train_perplexity:.4f}, train_epoch_loss={train_epoch_loss:.9f}, epoch time {epoch_end_time}s") 294 | else: 295 | print(f"Epoch {epoch+1}: train_perplexity={train_perplexity:.4f}, train_epoch_loss={train_epoch_loss:.9f}, epoch time {epoch_end_time}s") 296 | avg_epoch_time = sum(epoch_times)/ len(epoch_times) 297 | avg_checkpoint_time = sum(checkpoint_times)/ len(checkpoint_times) if len(checkpoint_times) > 0 else 0 298 | avg_train_prep = sum(train_prep)/len(train_prep) 299 | avg_train_loss = sum(train_loss)/len(train_loss) 300 | if train_config.run_validation: 301 | avg_eval_prep = sum(val_prep)/len(val_prep) 302 | avg_eval_loss = sum(val_loss)/len(val_loss) 303 | 304 | results['avg_train_prep'] = avg_train_prep 305 | results['avg_train_loss'] = avg_train_loss 306 | if train_config.run_validation: 307 | results['avg_eval_prep'] = avg_eval_prep 308 | results['avg_eval_loss'] = avg_eval_loss 309 | results["avg_epoch_time"] = avg_epoch_time 310 | results["avg_checkpoint_time"] = avg_checkpoint_time 311 | 312 | #saving the training params including fsdp setting for reference. 313 | 314 | 315 | return results 316 | 317 | def evaluation(model,train_config, eval_dataloader, local_rank, tokenizer): 318 | """ 319 | Evaluates the model on the given dataloader 320 | 321 | Args: 322 | model: The model to evaluate 323 | eval_dataloader: The dataloader containing the evaluation data 324 | local_rank: The rank of the current node in a distributed setting 325 | tokenizer: The tokenizer used to decode predictions 326 | 327 | Returns: eval_ppl, eval_epoch_loss 328 | """ 329 | if train_config.enable_fsdp: 330 | world_size = int(os.environ["WORLD_SIZE"]) 331 | model.eval() 332 | eval_preds = [] 333 | eval_loss = 0.0 # Initialize evaluation loss 334 | with MemoryTrace() as memtrace: 335 | for step, batch in enumerate(tqdm(eval_dataloader,colour="green", desc="evaluating Epoch", dynamic_ncols=True)): 336 | for key in batch.keys(): 337 | if train_config.enable_fsdp: 338 | batch[key] = batch[key].to(local_rank) 339 | else: 340 | batch[key] = batch[key].to('cuda:0') 341 | # Ensure no gradients are computed for this scope to save memory 342 | with torch.no_grad(): 343 | # Forward pass and compute loss 344 | outputs = model(**batch) 345 | loss = outputs.loss 346 | eval_loss += loss.detach().float() 347 | # Decode predictions and add to evaluation predictions list 348 | preds = torch.argmax(outputs.logits, -1) 349 | eval_preds.extend( 350 | tokenizer.batch_decode(preds.detach().cpu().numpy(), skip_special_tokens=True) 351 | ) 352 | 353 | # If there's more than one CUDA device, reduce evaluation loss across all devices 354 | if torch.cuda.device_count() > 1 and train_config.enable_fsdp: 355 | dist.all_reduce(eval_loss, op=dist.ReduceOp.SUM) 356 | 357 | # Compute average loss and perplexity 358 | eval_epoch_loss = eval_loss / len(eval_dataloader) 359 | if train_config.enable_fsdp: 360 | eval_epoch_loss = eval_epoch_loss/world_size 361 | eval_ppl = torch.exp(eval_epoch_loss) 362 | 363 | # Print evaluation metrics 364 | if train_config.enable_fsdp: 365 | if local_rank==0: 366 | print(f" {eval_ppl=} {eval_epoch_loss=}") 367 | else: 368 | print(f" {eval_ppl=} {eval_epoch_loss=}") 369 | 370 | return eval_ppl, eval_epoch_loss 371 | 372 | def freeze_transformer_layers(model, num_layer): 373 | for i, layer in enumerate(model.model.layers): 374 | if i < num_layer: 375 | for param in layer.parameters(): 376 | param.requires_grad = False 377 | 378 | 379 | def check_frozen_layers_peft_model(model): 380 | for i, layer in enumerate(model.base_model.model.model.layers): 381 | for name, param in layer.named_parameters(): 382 | print(f"Layer {i}, parameter {name}: requires_grad = {param.requires_grad}") 383 | 384 | 385 | def setup(): 386 | """Initialize the process group for distributed training""" 387 | dist.init_process_group("nccl") 388 | 389 | 390 | def setup_environ_flags(rank): 391 | """Set environment flags for debugging purposes""" 392 | os.environ["TORCH_SHOW_CPP_STACKTRACES"] = str(1) 393 | os.environ["NCCL_ASYNC_ERROR_HANDLING"] = str(1) 394 | # os.environ["TORCH_DISTRIBUTED_DEBUG"] = "DETAIL" 395 | # This flag will help with CUDA memory fragmentations that can lead into OOM in some cases. 396 | # Note this is only availble in PyTorch Nighlies (as of July 30 2023) 397 | # os.environ['PYTORCH_CUDA_ALLOC_CONF']='expandable_segments:True' 398 | if rank == 0: 399 | print(f"--> Running with torch dist debug set to detail") 400 | 401 | 402 | def cleanup(): 403 | """Clean up the process group after training""" 404 | dist.destroy_process_group() 405 | 406 | 407 | def clear_gpu_cache(rank=None): 408 | """Clear the GPU cache for all ranks""" 409 | if rank == 0: 410 | print(f"Clearing GPU cache for all ranks") 411 | torch.cuda.empty_cache() 412 | 413 | 414 | def get_parameter_dtypes(model): 415 | """Get the data types of model parameters""" 416 | parameter_dtypes = {} 417 | for name, parameter in model.named_parameters(): 418 | parameter_dtypes[name] = parameter.dtype 419 | return parameter_dtypes 420 | 421 | def print_model_size(model, config, rank: int = 0) -> None: 422 | """ 423 | Print model name, the number of trainable parameters and initialization time. 424 | 425 | Args: 426 | model: The PyTorch model. 427 | model_name (str): Name of the model. 428 | init_time_start (float): Initialization start time. 429 | init_time_end (float): Initialization end time. 430 | rank (int, optional): Current process's rank. Defaults to 0. 431 | """ 432 | if rank == 0: 433 | print(f"--> Model {config.model_name}") 434 | total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) 435 | print(f"\n--> {config.model_name} has {total_params / 1e6} Million params\n") 436 | 437 | 438 | 439 | 440 | def get_policies(cfg, rank): 441 | """Get the policies for mixed precision and fsdp wrapping""" 442 | 443 | verify_bfloat_support = ( 444 | torch.version.cuda 445 | and torch.cuda.is_bf16_supported() 446 | and packaging.version.parse(torch.version.cuda).release >= (11, 0) 447 | and dist.is_nccl_available() 448 | and nccl.version() >= (2, 10) 449 | ) 450 | 451 | 452 | mixed_precision_policy = None 453 | wrapping_policy = None 454 | 455 | # Mixed precision 456 | if cfg.mixed_precision: 457 | bf16_ready = verify_bfloat_support 458 | 459 | if bf16_ready and not cfg.use_fp16: 460 | mixed_precision_policy = bfSixteen 461 | if rank == 0: 462 | print(f"bFloat16 enabled for mixed precision - using bfSixteen policy") 463 | elif cfg.use_fp16: 464 | mixed_precision_policy = fpSixteen 465 | if rank == 0: 466 | print(f"FP16 enabled") 467 | else: 468 | print(f"bFloat16 support not present. Using FP32, and not mixed precision") 469 | wrapping_policy = get_llama_wrapper() 470 | return mixed_precision_policy, wrapping_policy 471 | 472 | def save_train_params(train_config, fsdp_config, rank): 473 | """ 474 | This function saves the train_config and FSDP config into a train_params.yaml. 475 | This will be used by converter script in the inference folder to fetch the HF model name or path. 476 | It also would be hepful as a log for future references. 477 | """ 478 | # Convert the train_config and fsdp_config objects to dictionaries, 479 | # converting all values to strings to ensure they can be serialized into a YAML file 480 | train_config_dict = {k: str(v) for k, v in vars(train_config).items() if not k.startswith('__')} 481 | fsdp_config_dict = {k: str(v) for k, v in vars(fsdp_config).items() if not k.startswith('__')} 482 | # Merge the two dictionaries into one 483 | train_params_dict = {**train_config_dict, **fsdp_config_dict} 484 | # Construct the folder name (follwoing FSDP checkpointing style) using properties of the train_config object 485 | folder_name = ( 486 | train_config.dist_checkpoint_root_folder 487 | + "/" 488 | + train_config.dist_checkpoint_folder 489 | + "-" 490 | + train_config.model_name 491 | ) 492 | 493 | save_dir = Path.cwd() / folder_name 494 | # If the directory does not exist, create it 495 | if not os.path.exists(save_dir): 496 | os.makedirs(save_dir) 497 | # Convert the dictionary to a YAML string 498 | config_yaml = yaml.dump(train_params_dict, indent=4) 499 | file_name = os.path.join(save_dir,'train_params.yaml') 500 | 501 | # Check if there's a directory with the same name as the file 502 | if os.path.isdir(file_name): 503 | print(f"Error: {file_name} is a directory, not a file.") 504 | else: 505 | # Write the YAML string to the file 506 | with open(file_name, 'w') as f: 507 | f.write(config_yaml) 508 | if rank==0: 509 | print(f"training params are saved in {file_name}") 510 | 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