migrate to latest main for hf transformers.

Author: lxuechen

README.md

@@ -15,6 +15,7 @@ This is the repo for the Stanford Alpaca project, which aims to build and share
 - The [52K data](#data-release) used for fine-tuning the model.
 - The code for [generating the data](#data-generation-process).
 - The code for [fine-tuning the model](#fine-tuning).
+- The code for [recovering Alpaca-7B weights from our released weight diff](#recovering-alpaca-weights).
 
 Note: We thank the community for feedback on Stanford-Alpaca and supporting our research. Our live demo is suspended until further notice.
 
@@ -115,10 +116,7 @@ We fine-tune LLaMA-7B and LLaMA-13B with the following hyperparameters:
 | Max length     | 512      | 512       |
 | Weight decay   | 0        | 0         |
 
-We have also fine-tuned larger variants of LLaMA and are in the process of evaluating those models.
-
-Given Hugging Face hasn't officially supported the LLaMA models, we fine-tuned LLaMA with Hugging Face's transformers library by installing it from a particular fork (i.e. this [PR](https://github.com/huggingface/transformers/pull/21955) to be merged).
-The hash of the specific commit we installed was `68d640f7c368bcaaaecfc678f11908ebbd3d6176`.
+We have also fine-tuned larger variants of LLaMA and performed subsequent RLHF and are in the process of evaluating those models.
 
 To reproduce our fine-tuning runs for LLaMA, first install the requirements
 
@@ -153,20 +151,10 @@ torchrun --nproc_per_node=4 --master_port=<your_random_port> train.py \
     --lr_scheduler_type "cosine" \
     --logging_steps 1 \
     --fsdp "full_shard auto_wrap" \
-    --fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \
+    --fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
     --tf32 True
 ```
 
-### Warning
-
-`fsdp_transformer_layer_cls_to_wrap` must be set to the name of the specific decoder layer.
-The LLaMA Hugging Face PR is not stable.
-Earlier commits used the name `LLaMADecoderLayer` for their decoder layer (the commit hash our code is based on this).
-More recent commits use `LlamaDecoderLayer` (notice the small case difference).
-Not setting `fsdp_transformer_layer_cls_to_wrap` to the correct name will lead to drastic slowdowns in training.
-
-### Side notes
-
 The same script also works for OPT fine-tuning. Here's an example for fine-tuning OPT-6.7B
 
 ```bash
@@ -196,6 +184,58 @@ torchrun --nproc_per_node=4 --master_port=<your_random_port> train.py \
 Note the given training script is meant to be simple and easy to use, and is not particularly optimized.
 To run on more gpus, you may prefer to turn down `gradient_accumulation_steps` to keep a global batch size of 128. Global batch size has not been tested for optimality.
 
+### Addressing OOM
+
+Naively, fine-tuning a 7B model requires about 7 x 4 x 4 = 112 GB of VRAM. Commands given above enable parameter sharding, so no redundant model copy is stored on any GPU.
+If you'd like to further reduce the memory footprint, here are some options:
+
+- Turn on CPU offload for FSDP with `--fsdp "full_shard auto_wrap offload"`. This saves VRAM at the cost longer runtime.
+- In our experience, DeepSpeed stage-3 (with offload) can at times be more memory efficient than FSDP. Here's an example to use DeepSpeed stage-3 with 4 GPUs with both parameter and optimizer offload:
+    ```bash
+    pip install deepspeed
+    torchrun --nproc_per_node=4 --master_port=<your_random_port> train.py \
+        --model_name_or_path <your_path_to_hf_converted_llama_ckpt_and_tokenizer> \
+        --data_path ./alpaca_data.json \
+        --bf16 True \
+        --output_dir <your_output_dir> \
+        --num_train_epochs 3 \
+        --per_device_train_batch_size 4 \
+        --per_device_eval_batch_size 4 \
+        --gradient_accumulation_steps 8 \
+        --evaluation_strategy "no" \
+        --save_strategy "steps" \
+        --save_steps 2000 \
+        --save_total_limit 1 \
+        --learning_rate 2e-5 \
+        --weight_decay 0. \
+        --warmup_ratio 0.03 \
+        --deepspeed "./configs/default_offload_opt_param.json" \
+        --tf32 True
+    ```
+  - The DeepSpeed library also provides some [helpful functions](https://deepspeed.readthedocs.io/en/latest/memory.html) to estimate memory usage. 
+- [LoRA](https://arxiv.org/abs/2106.09685) fine-tunes low-rank slices of the query, key, and value embeddings. This can reduce the total memory footprint from 112GB to about 7x4=28GB. We may release our re-implemention of this in the future, but for now the [peft](https://github.com/huggingface/peft) codebase can be a useful resource.
+
+## Recovering Alpaca Weights
+
+The weight diff between Alpaca-7B and LLaMA-7B is located [here](https://huggingface.co/tatsu-lab/alpaca-7b-wdiff/tree/main).
+To recover the original Alpaca-7B weights, follow these steps:
+```text
+1. Convert Meta's released weights into huggingface format. Follow this guide:
+    https://huggingface.co/docs/transformers/main/model_doc/llama
+2. Make sure you cloned the released weight diff into your local machine. The weight diff is located at:
+    https://huggingface.co/tatsu-lab/alpaca-7b/tree/main
+3. Run this function with the correct paths. E.g.,
+    python weight_diff.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir> --path_tuned <path_to_store_recovered_weights>
+```
+
+Once step 3 completes, you should have a directory with the recovered weights, from which you can load the model like the following
+
+```python
+import transformers
+alpaca_model = transformers.AutoModelForCausalLM.from_pretrained("<path_to_store_recovered_weights>")
+alpaca_tokenizer = transformers.AutoTokenizer.from_pretrained("<path_to_store_recovered_weights>")
+```
+
 ### Authors
 
 All grad students below contributed equally and the order is determined by random draw.

configs/default_offload_opt_param.json

@@ -0,0 +1,49 @@
+{
+  "bf16": {
+    "enabled": "auto"
+  },
+  "optimizer": {
+    "type": "AdamW",
+    "params": {
+      "lr": "auto",
+      "betas": "auto",
+      "eps": "auto",
+      "weight_decay": "auto"
+    }
+  },
+  "scheduler": {
+    "type": "WarmupDecayLR",
+    "params": {
+      "total_num_steps": "auto",
+      "warmup_min_lr": "auto",
+      "warmup_max_lr": "auto",
+      "warmup_num_steps": "auto"
+    }
+  },
+  "zero_optimization": {
+    "stage": 3,
+    "offload_optimizer": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "offload_param": {
+      "device": "cpu",
+      "pin_memory": true
+    },
+    "overlap_comm": true,
+    "contiguous_gradients": true,
+    "sub_group_size": 1e9,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 1e9,
+    "stage3_max_reuse_distance": 1e9,
+    "stage3_gather_16bit_weights_on_model_save": false
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "steps_per_print": 5,
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

requirements.txt

@@ -2,8 +2,8 @@ numpy
 rouge_score
 fire
 openai
-transformers>=4.26.1
+transformers>=4.28.1
 torch
 sentencepiece
-tokenizers==0.12.1
+tokenizers>=0.13.3
 wandb

train.py

@@ -15,20 +15,19 @@
 import copy
 import logging
 from dataclasses import dataclass, field
-from typing import Optional, Dict, Sequence
+from typing import Dict, Optional, Sequence
 
 import torch
 import transformers
+import utils
 from torch.utils.data import Dataset
 from transformers import Trainer
 
-import utils
-
 IGNORE_INDEX = -100
 DEFAULT_PAD_TOKEN = "[PAD]"
 DEFAULT_EOS_TOKEN = "</s>"
-DEFAULT_BOS_TOKEN = "</s>"
-DEFAULT_UNK_TOKEN = "</s>"
+DEFAULT_BOS_TOKEN = "<s>"
+DEFAULT_UNK_TOKEN = "<unk>"
 PROMPT_DICT = {
     "prompt_input": (
         "Below is an instruction that describes a task, paired with an input that provides further context. "
@@ -63,15 +62,6 @@ class TrainingArguments(transformers.TrainingArguments):
     )
 
 
-def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
-    """Collects the state dict and dump to disk."""
-    state_dict = trainer.model.state_dict()
-    if trainer.args.should_save:
-        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
-        del state_dict
-        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa
-
-
 def smart_tokenizer_and_embedding_resize(
     special_tokens_dict: Dict,
     tokenizer: transformers.PreTrainedTokenizer,
@@ -205,26 +195,27 @@ def train():
         padding_side="right",
         use_fast=False,
     )
+    special_tokens_dict = dict()
     if tokenizer.pad_token is None:
-        smart_tokenizer_and_embedding_resize(
-            special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
-            tokenizer=tokenizer,
-            model=model,
-        )
-    if "llama" in model_args.model_name_or_path:
-        tokenizer.add_special_tokens(
-            {
-                "eos_token": DEFAULT_EOS_TOKEN,
-                "bos_token": DEFAULT_BOS_TOKEN,
-                "unk_token": DEFAULT_UNK_TOKEN,
-            }
-        )
+        special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN
+    if tokenizer.eos_token is None:
+        special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN
+    if tokenizer.bos_token is None:
+        special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN
+    if tokenizer.unk_token is None:
+        special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN
+
+    smart_tokenizer_and_embedding_resize(
+        special_tokens_dict=special_tokens_dict,
+        tokenizer=tokenizer,
+        model=model,
+    )
 
     data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
     trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)
     trainer.train()
     trainer.save_state()
-    safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)
+    trainer.save_model(output_dir=training_args.output_dir)
 
 
 if __name__ == "__main__":