ENH Extend usage for OLoRA finetune script (huggingface#2308)

- allow DDP
- make it work on CPU
- set seed and dtype

Related: dequantize_bnb_weight is updated not to move to cuda if not
available.
---------

Signed-off-by: jiqing-feng <[email protected]>

Author: jiqing-feng

examples/olora_finetuning/README.md

@@ -39,6 +39,19 @@ OLoRA also supports quantization. To use 4-bit quantization try:
 ```bash
 python3 examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m --quantize
 ```
+or you can just pass a quantized model without the quantize flag.
+
+If you want to run DDP by [accelerate](https://huggingface.co/docs/accelerate/en/index), please run `accelerate config` to set your ddp config, and run:
+```bash
+accelerate launch examples/olora_finetuning/olora_finetuning.py --base_model facebook/opt-350m
+```
+please add `--device_map cpu` if you want to run finetune on CPU.
+
+If you want to train a quantized model like AWQ and GPTQ which do not support olora init method, please pass `--init_lora_weights gaussian`. For example:
+```bash
+python3 examples/olora_finetuning/olora_finetuning.py --base_model hugging-quants/Meta-Llama-3.1-8B-Instruct-AWQ-INT4 --init_lora_weights gaussian
+
+```
 
 
 ## Use the model

examples/olora_finetuning/olora_finetuning.py

@@ -13,12 +13,13 @@
 # limitations under the License.
 
 
-from typing import List
+import os
+from typing import List, Optional
 
 import torch
 import transformers
 from datasets import load_dataset
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed
 
 from peft import (
     LoraConfig,
@@ -43,23 +44,33 @@ def train(
     lora_alpha: int = 16,
     lora_dropout: float = 0.05,
     lora_target_modules: List[str] = None,
+    torch_dtype: str = "float16",
     init_lora_weights="olora",
+    seed: Optional[int] = None,
 ):
-    model = AutoModelForCausalLM.from_pretrained(
-        base_model,
-        device_map=device_map,
-        quantization_config=BitsAndBytesConfig(
+    # Set device_map to the right place when enabling DDP.
+    world_size = int(os.environ.get("WORLD_SIZE", 0)) or int(os.environ.get("PMI_SIZE", 0))
+    if world_size > 1 and device_map != "cpu":
+        from accelerate import Accelerator
+
+        device_map = {"": Accelerator().process_index}
+    # Set seed
+    if seed is not None:
+        set_seed(seed)
+    model_kwargs = {"torch_dtype": getattr(torch, torch_dtype), "device_map": device_map}
+    if quantize:
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(
             load_in_4bit=True,
             bnb_4bit_compute_dtype=torch.bfloat16,
             bnb_4bit_use_double_quant=True,
             bnb_4bit_quant_type="nf4",
         )
-        if quantize
-        else None,
-        torch_dtype=torch.float16,
-    )
+    model = AutoModelForCausalLM.from_pretrained(base_model, **model_kwargs)
 
     tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
+    # For some tokenizer with no pad token like llama
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
 
     def tokenize(prompt, add_eos_token=True):
         result = tokenizer(
@@ -112,7 +123,6 @@ def generate_and_tokenize_prompt(example):
             warmup_steps=100,
             num_train_epochs=num_epochs,
             learning_rate=learning_rate,
-            fp16=True,
             logging_steps=100,
             optim="adamw_torch",
             evaluation_strategy="steps",
@@ -122,6 +132,7 @@ def generate_and_tokenize_prompt(example):
             output_dir=output_dir,
             save_total_limit=3,
             load_best_model_at_end=True,
+            ddp_find_unused_parameters=False if world_size > 1 else None,
         ),
         data_collator=transformers.DataCollatorForSeq2Seq(
             tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
@@ -159,7 +170,9 @@ def generate_prompt(example):
     parser.add_argument("--lora_alpha", type=int, default=16)
     parser.add_argument("--lora_dropout", type=float, default=0.05)
     parser.add_argument("--lora_target_modules", type=str, default=None)
+    parser.add_argument("--torch_dtype", type=str, default="float16")
     parser.add_argument("--init_lora_weights", type=str, default="olora")
+    parser.add_argument("--seed", type=int, default=None)
 
     args = parser.parse_args()
 
@@ -180,5 +193,7 @@ def generate_prompt(example):
         lora_alpha=args.lora_alpha,
         lora_dropout=args.lora_dropout,
         lora_target_modules=args.lora_target_modules,
+        torch_dtype=args.torch_dtype,
         init_lora_weights=args.init_lora_weights,
+        seed=args.seed,
     )

src/peft/utils/integrations.py

@@ -88,7 +88,7 @@ def dequantize_bnb_weight(weight: torch.nn.Parameter, state=None):
     # BNB requires CUDA weights
     device = weight.device
     is_cpu = device.type == torch.device("cpu").type
-    if is_cpu:
+    if is_cpu and torch.cuda.is_available():
         weight = weight.to(torch.device("cuda"))
 
     cls_name = weight.__class__.__name__