refactor trainer setup to account for deepspeed integration

scripts/finetune.py

@@ -16,7 +16,7 @@ from peft import (
     LoraConfig,
     get_peft_model,
     prepare_model_for_int8_training,
-    get_peft_model_state_dict, PeftModel,
+    PeftModel,
 )
 from torch import nn
 from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM, LlamaTokenizer
@@ -214,6 +214,89 @@ def choose_config(path: Path):
     return chosen_file
 
 
+def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
+    total_num_steps = int(
+        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
+    )
+    save_steps = eval_steps = min(int(0.05 * total_num_steps), 200)
+
+    training_arguments_kwargs = {}
+
+    if not cfg.deepspeed:
+        warmup_steps = min(int(0.03 * total_num_steps), 100)
+        logging_steps = min(int(0.005 * total_num_steps), 10)
+
+        training_arguments_kwargs["warmup_steps"] = warmup_steps
+        training_arguments_kwargs["logging_steps"] = logging_steps
+        training_arguments_kwargs["logging_steps"] = logging_steps
+        training_arguments_kwargs["bf16"] = cfg.bf16
+        training_arguments_kwargs["tf32"] = cfg.tf32
+
+    training_args = transformers.TrainingArguments(
+        per_device_train_batch_size=cfg.micro_batch_size,
+        gradient_accumulation_steps=cfg.gradient_accumulation_steps,
+        num_train_epochs=cfg.num_epochs,
+        learning_rate=cfg.learning_rate,
+        evaluation_strategy="steps" if cfg.val_set_size > 0 else "no",
+        save_strategy="steps",
+        eval_steps=eval_steps if cfg.val_set_size > 0 else None,
+        save_steps=save_steps,
+        output_dir=cfg.output_dir,
+        save_total_limit=3,
+        load_best_model_at_end=True if cfg.val_set_size > 0 else False,
+        ddp_find_unused_parameters=False if cfg.ddp else None,
+        group_by_length=cfg.group_by_length,
+        report_to="wandb" if cfg.use_wandb else None,
+        run_name=cfg.wandb_run_name if cfg.use_wandb else None,
+        **training_arguments_kwargs,
+    )
+
+    trainer_kwargs = {}
+
+    if not cfg.deepspeed:
+        decay_parameters = get_parameter_names(model, [nn.LayerNorm])
+        decay_parameters = [name for name in decay_parameters if "bias" not in name]
+        optimizer_grouped_parameters = [
+            {
+                "params": [p for n, p in model.named_parameters() if n in decay_parameters],
+                "weight_decay": training_args.weight_decay,
+            },
+            {
+                "params": [
+                    p for n, p in model.named_parameters() if n not in decay_parameters
+                ],
+                "weight_decay": 0.0,
+            },
+        ]
+
+        adam_bnb_optim = bnb.optim.Adam8bit(
+            optimizer_grouped_parameters,
+            betas=(training_args.adam_beta1, training_args.adam_beta2),
+            eps=training_args.adam_epsilon,
+            lr=training_args.learning_rate,
+        )
+
+        lr_scheduler = transformers.get_cosine_schedule_with_warmup(
+            adam_bnb_optim,
+            training_args.warmup_steps,
+            total_num_steps,
+        )
+        trainer_kwargs["optimizers"] = (adam_bnb_optim, lr_scheduler)
+
+
+    trainer = transformers.Trainer(
+        model=model,
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        args=training_args,
+        data_collator=transformers.DataCollatorForSeq2Seq(
+            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
+        ),
+        **trainer_kwargs,
+    )
+
+    return trainer
+
 def train(
     config: Path = Path("configs/"),
     **kwargs,
@@ -308,73 +391,8 @@ def train(
             tokenizer,
         )
 
-    total_num_steps = int(
-        math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
-    )
-    warmup_steps = min(int(0.03 * total_num_steps), 100)
-    logging_steps = min(int(0.005 * total_num_steps), 10)
-    save_steps = eval_steps = min(int(0.05 * total_num_steps), 200)
+    trainer = setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer)
 
-    training_args = transformers.TrainingArguments(
-        per_device_train_batch_size=cfg.micro_batch_size,
-        gradient_accumulation_steps=cfg.gradient_accumulation_steps,
-        warmup_steps=warmup_steps,
-        num_train_epochs=cfg.num_epochs,
-        learning_rate=cfg.learning_rate,
-        bf16=cfg.bf16,
-        tf32=cfg.tf32,
-        logging_steps=logging_steps,
-        evaluation_strategy="steps" if cfg.val_set_size > 0 else "no",
-        save_strategy="steps",
-        eval_steps=eval_steps if cfg.val_set_size > 0 else None,
-        save_steps=save_steps,
-        output_dir=cfg.output_dir,
-        save_total_limit=3,
-        load_best_model_at_end=True if cfg.val_set_size > 0 else False,
-        ddp_find_unused_parameters=False if cfg.ddp else None,
-        group_by_length=cfg.group_by_length,
-        report_to="wandb" if cfg.use_wandb else None,
-        run_name=cfg.wandb_run_name if cfg.use_wandb else None,
-    )
-
-    decay_parameters = get_parameter_names(model, [nn.LayerNorm])
-    decay_parameters = [name for name in decay_parameters if "bias" not in name]
-    optimizer_grouped_parameters = [
-        {
-            "params": [p for n, p in model.named_parameters() if n in decay_parameters],
-            "weight_decay": training_args.weight_decay,
-        },
-        {
-            "params": [
-                p for n, p in model.named_parameters() if n not in decay_parameters
-            ],
-            "weight_decay": 0.0,
-        },
-    ]
-
-    adam_bnb_optim = bnb.optim.Adam8bit(
-        optimizer_grouped_parameters,
-        betas=(training_args.adam_beta1, training_args.adam_beta2),
-        eps=training_args.adam_epsilon,
-        lr=training_args.learning_rate,
-    )
-
-    lr_scheduler = transformers.get_cosine_schedule_with_warmup(
-        adam_bnb_optim,
-        training_args.warmup_steps,
-        total_num_steps,
-    )
-
-    trainer = transformers.Trainer(
-        model=model,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        args=training_args,
-        optimizers=(adam_bnb_optim, lr_scheduler),
-        data_collator=transformers.DataCollatorForSeq2Seq(
-            tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
-        ),
-    )
     model.config.use_cache = False
 
     if torch.__version__ >= "2" and sys.platform != "win32":
@@ -391,6 +409,7 @@ def train(
 
     trainer.train(resume_from_checkpoint=cfg.resume_from_checkpoint)
 
+    # TODO do we need this fix? https://huggingface.co/docs/accelerate/usage_guides/fsdp#saving-and-loading
     model.save_pretrained(cfg.output_dir)