tiny fix about badam

.github/workflows/tests.yml

@@ -34,7 +34,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          python -m pip install .[torch,dev]
+          python -m pip install ".[torch,dev]"
 
       - name: Check quality
         run: |

.gitignore

@@ -160,8 +160,8 @@ cython_debug/
 .idea/
 
 # custom .gitignore
-user.config
-saves/
 cache/
-wandb
-ds_badam_exp
+config/
+saves/
+output/
+wandb/

examples/extras/badam/llama3_full_sft.yaml

@@ -6,6 +6,7 @@ stage: sft
 do_train: true
 finetuning_type: full
 use_badam: true
+badam_mode: layer
 badam_switch_mode: ascending
 badam_switch_interval: 50
 badam_verbose: 2
@@ -32,7 +33,6 @@ learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1
-pure_bf16: true
 
 ### eval
 val_size: 0.1

examples/extras/badam/llama3_full_sft_ds3.yaml

@@ -6,9 +6,11 @@ stage: sft
 do_train: true
 finetuning_type: full
 use_badam: true
+badam_mode: layer
 badam_switch_mode: ascending
 badam_switch_interval: 50
 badam_verbose: 2
+deepspeed: examples/deepspeed/ds_z3_config.json
 
 ### dataset
 dataset: identity,alpaca_en_demo
@@ -28,7 +30,7 @@ overwrite_output_dir: true
 ### train
 per_device_train_batch_size: 1
 gradient_accumulation_steps: 8
-learning_rate: 1.0e-6
+learning_rate: 1.0e-4
 num_train_epochs: 3.0
 lr_scheduler_type: cosine
 warmup_ratio: 0.1

examples/extras/badam/train_single_gpu.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-export CUDA_VISIBLE_DEVICES=0
-
-cd ../../..
-
-llamafactory-cli train \
-    --stage sft \
-    --do_train True \
-    --model_name_or_path meta-llama/Llama-2-13b-hf \
-    --preprocessing_num_workers 16 \
-    --finetuning_type full \
-    --template default \
-    --flash_attn auto \
-    --dataset_dir data \
-    --dataset alpaca_en_demo \
-    --cutoff_len 1024 \
-    --learning_rate 1e-6 \
-    --num_train_epochs 3.0 \
-    --max_samples 100000 \
-    --per_device_train_batch_size 1 \
-    --gradient_accumulation_steps 8 \
-    --lr_scheduler_type cosine \
-    --max_grad_norm 1.0 \
-    --logging_steps 5 \
-    --save_steps 100 \
-    --warmup_steps 0 \
-    --optim adamw_torch \
-    --packing False \
-    --report_to none \
-    --use_badam True \
-    --output_dir saves/LLaMA2-13B/full/BAdam \
-    --plot_loss True \
-    --ddp_timeout 180000000 \
-    --include_num_input_tokens_seen True \
-    --badam_mode layer \
-    --badam_switch_mode ascending \
-    --badam_switch_interval 50

examples/extras/badam/train_zero3.sh

@@ -1,39 +0,0 @@
-#!/bin/bash
-export CUDA_VISIBLE_DEVICES=0,1,2,3
-
-cd ../../..
-
-llamafactory-cli train \
-    --stage sft \
-    --do_train True \
-    --model_name_or_path meta-llama/Llama-2-13b-hf \
-    --preprocessing_num_workers 16 \
-    --finetuning_type full \
-    --template default \
-    --flash_attn auto \
-    --dataset_dir data \
-    --dataset alpaca_en_demo \
-    --cutoff_len 1024 \
-    --learning_rate 1e-6 \
-    --num_train_epochs 3.0 \
-    --max_samples 100000 \
-    --per_device_train_batch_size 8 \
-    --gradient_accumulation_steps 2 \
-    --lr_scheduler_type cosine \
-    --max_grad_norm 1.0 \
-    --logging_steps 5 \
-    --save_steps 100 \
-    --warmup_steps 0 \
-    --optim adamw_torch \
-    --packing False \
-    --report_to none \
-    --use_badam True \
-    --output_dir saves/LLaMA2-13B/full/BAdam \
-    --fp16 True \
-    --plot_loss True \
-    --ddp_timeout 180000000 \
-    --include_num_input_tokens_seen True \
-    --badam_mode layer \
-    --badam_switch_mode ascending \
-    --badam_switch_interval 50 \
-    --deepspeed cache/ds_z3_config.json 

setup.py

@@ -41,7 +41,7 @@ extra_require = {
     "bitsandbytes": ["bitsandbytes>=0.39.0"],
     "vllm": ["vllm>=0.4.3"],
     "galore": ["galore-torch"],
-    "badam": ["badam"],
+    "badam": ["badam>=1.2.1"],
     "gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
     "awq": ["autoawq"],
     "aqlm": ["aqlm[gpu]>=1.1.0"],

src/llamafactory/hparams/parser.py

@@ -121,7 +121,7 @@ def _check_extra_dependencies(
         require_version("galore_torch", "To fix: pip install galore_torch")
 
     if finetuning_args.use_badam:
-        require_version("badam", "To fix: pip install badam")
+        require_version("badam>=1.2.1", "To fix: pip install badam>=1.2.1")
 
     if finetuning_args.plot_loss:
         require_version("matplotlib", "To fix: pip install matplotlib")
@@ -214,15 +214,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
 
     if (
         finetuning_args.use_badam
-        and training_args.parallel_mode.value == "distributed"
+        and training_args.parallel_mode == ParallelMode.DISTRIBUTED
     ):
         if finetuning_args.badam_mode == "ratio":
-            raise ValueError("Ratio-wise BAdam does not yet support distributed training, use layer-wise BAdam: --badam_mode layer")
-        if finetuning_args.badam_mode == "layer" and (not is_deepspeed_zero3_enabled()):
-            raise ValueError(f"Layer-wise BAdam only supports DeepSpeed ZeRO 3 stage.")
+            raise ValueError("Radio-based BAdam does not yet support distributed training, use layer-wise BAdam.")
+        elif not is_deepspeed_zero3_enabled():
+            raise ValueError("Layer-wise BAdam only supports DeepSpeed ZeRO-3 training.")
 
-    if (finetuning_args.use_galore) and training_args.deepspeed is not None:
-        raise ValueError("GaLore are incompatible with DeepSpeed yet.")
+    if finetuning_args.use_galore and training_args.deepspeed is not None:
+        raise ValueError("GaLore is incompatible with DeepSpeed yet.")
 
     if model_args.infer_backend == "vllm":
         raise ValueError("vLLM backend is only available for API, CLI and Web.")

src/llamafactory/train/dpo/trainer.py

@@ -96,7 +96,8 @@ class CustomDPOTrainer(DPOTrainer):
             self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
 
         if finetuning_args.use_badam:
-            from badam import clip_grad_norm_old_version, BAdamCallback
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.callback_handler.add_callback(BAdamCallback)
 

src/llamafactory/train/kto/trainer.py

@@ -91,7 +91,8 @@ class CustomKTOTrainer(KTOTrainer):
                 self.ref_model.eval()
 
         if finetuning_args.use_badam:
-            from badam import clip_grad_norm_old_version, BAdamCallback
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.callback_handler.add_callback(BAdamCallback)
 

src/llamafactory/train/ppo/trainer.py

@@ -166,7 +166,8 @@ class CustomPPOTrainer(PPOTrainer, Trainer):
                 self.reward_model = self.accelerator.prepare_model(self.reward_model, evaluation_mode=True)
 
         if finetuning_args.use_badam:
-            from badam import clip_grad_norm_old_version, BAdamCallback
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.callback_handler.add_callback(BAdamCallback)
 

src/llamafactory/train/pt/trainer.py

@@ -48,7 +48,8 @@ class CustomTrainer(Trainer):
             self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
 
         if finetuning_args.use_badam:
-            from badam import clip_grad_norm_old_version, BAdamCallback
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.callback_handler.add_callback(BAdamCallback)
 

src/llamafactory/train/rm/trainer.py

@@ -72,7 +72,8 @@ class PairwiseTrainer(Trainer):
         self.processor = processor
         self.can_return_loss = True  # override property to return eval_loss
         if finetuning_args.use_badam:
-            from badam import clip_grad_norm_old_version, BAdamCallback
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.callback_handler.add_callback(BAdamCallback)
 

src/llamafactory/train/sft/trainer.py

@@ -56,7 +56,8 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
             self.save_model(os.path.join(self.args.output_dir, "pissa_init"))
 
         if finetuning_args.use_badam:
-            from badam import clip_grad_norm_old_version, BAdamCallback
+            from badam import BAdamCallback, clip_grad_norm_old_version
+
             self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
             self.callback_handler.add_callback(BAdamCallback)
 

src/llamafactory/train/trainer_utils.py

@@ -23,6 +23,7 @@ from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
 import torch
 from peft import PeftModel
 from transformers import Trainer
+from transformers.integrations import is_deepspeed_zero3_enabled
 from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
@@ -372,9 +373,6 @@ def _create_badam_optimizer(
         dict(params=decay_params, weight_decay=training_args.weight_decay),
     ]
 
-    from transformers.integrations import is_deepspeed_zero3_enabled
-    ds_zero3_enabled = is_deepspeed_zero3_enabled()
-
     if finetuning_args.badam_mode == "layer":
         from badam import BlockOptimizer
 
@@ -387,7 +385,7 @@ def _create_badam_optimizer(
             start_block=finetuning_args.badam_start_block,
             switch_mode=finetuning_args.badam_switch_mode,
             verbose=finetuning_args.badam_verbose,
-            ds_zero3_enabled=ds_zero3_enabled
+            ds_zero3_enabled=is_deepspeed_zero3_enabled(),
         )
         logger.info(
             f"Using BAdam optimizer with layer-wise update, switch mode is {finetuning_args.badam_switch_mode}, "
@@ -398,7 +396,6 @@ def _create_badam_optimizer(
     elif finetuning_args.badam_mode == "ratio":
         from badam import BlockOptimizerRatio
 
-        assert not ds_zero3_enabled, "BAdam with ratio-based update does not support Deepspeed ZeRO-3 yet, use layer-wise update instead: --badam_mode layer."
         assert finetuning_args.badam_update_ratio > 1e-6
         optimizer = BlockOptimizerRatio(
             param_groups=param_groups,