update trainers

Former-commit-id: 8c77b1091296e204dc3c8c1f157c288ca5b236bd

README.md

@@ -72,9 +72,9 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
 
 [24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See `examples/fsdp_qlora` for usage.
 
-[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. Try `loraplus_lr_ratio=16.0` to enable LoRA+ algorithm.
+[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See `examples/extras/loraplus` for usage.
 
-[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. Try `--use_galore` to use the memory-efficient optimizer.
+[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See `examples/extras/galore` for usage.
 
 [24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `--infer_backend vllm` to enjoy **270%** inference speed. (LoRA is not yet supported, merge it first.)
 

README_zh.md

@@ -72,9 +72,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
 
 [24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 **FSDP+QLoRA**。详细用法请参照 `examples/fsdp_qlora`。
 
-[24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。请使用 `loraplus_lr_ratio=16.0` 参数开启 LoRA+ 方法。
+[24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。详细用法请参照 `examples/extras/loraplus`。
 
-[24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。请使用 `--use_galore` 参数切换显存高效的优化器。
+[24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。详细用法请参照 `examples/extras/galore`。
 
 [24/03/07] 我们集成了 **[vLLM](https://github.com/vllm-project/vllm)** 以实现极速并发推理。请使用 `--infer_backend vllm` 来获得 **270%** 的推理速度。（尚不支持 LoRA，请先合并权重。）
 

examples/extras/galore/adamw.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
-    --stage sft \
-    --do_train \
-    --model_name_or_path meta-llama/Llama-2-7b-hf \
-    --dataset alpaca_gpt4_en,glaive_toolcall \
-    --dataset_dir ../../../data \
-    --template default \
-    --finetuning_type full \
-    --output_dir ../../../saves/LLaMA2-7B/galore/sft \
-    --overwrite_cache \
-    --overwrite_output_dir \
-    --cutoff_len 1024 \
-    --preprocessing_num_workers 16 \
-    --per_device_train_batch_size 1 \
-    --per_device_eval_batch_size 1 \
-    --gradient_accumulation_steps 1 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --warmup_steps 20 \
-    --save_steps 100 \
-    --eval_steps 100 \
-    --evaluation_strategy steps \
-    --load_best_model_at_end \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --max_samples 3000 \
-    --val_size 0.1 \
-    --plot_loss \
-    --fp16

examples/extras/galore/adamw_8bit_bf16.sh

@@ -1,32 +0,0 @@
-#!/bin/bash
-
-CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
-    --stage sft \
-    --do_train \
-    --model_name_or_path meta-llama/Llama-2-7b-hf \
-    --dataset alpaca_gpt4_en,glaive_toolcall \
-    --dataset_dir ../../../data \
-    --template default \
-    --finetuning_type full \
-    --optim adamw_8bit \
-    --output_dir ../../../saves/LLaMA2-7B/galore/sft \
-    --overwrite_cache \
-    --overwrite_output_dir \
-    --cutoff_len 1024 \
-    --preprocessing_num_workers 16 \
-    --per_device_train_batch_size 1 \
-    --per_device_eval_batch_size 1 \
-    --gradient_accumulation_steps 1 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --warmup_steps 20 \
-    --save_steps 100 \
-    --eval_steps 100 \
-    --evaluation_strategy steps \
-    --load_best_model_at_end \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --max_samples 3000 \
-    --val_size 0.1 \
-    --plot_loss \
-    --pure_bf16

examples/extras/galore/galore_adamw_8bit_bf16.sh

@@ -1,36 +0,0 @@
-#!/bin/bash
-
-CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
-    --stage sft \
-    --do_train \
-    --model_name_or_path meta-llama/Llama-2-7b-hf \
-    --dataset alpaca_gpt4_en,glaive_toolcall \
-    --dataset_dir ../../../data \
-    --template default \
-    --finetuning_type full \
-    --optim adamw_8bit \
-    --use_galore \
-    --galore_layerwise \
-    --galore_target mlp,self_attn \
-    --galore_rank 128 \
-    --output_dir ../../../saves/LLaMA2-7B/galore/sft \
-    --overwrite_cache \
-    --overwrite_output_dir \
-    --cutoff_len 1024 \
-    --preprocessing_num_workers 16 \
-    --per_device_train_batch_size 1 \
-    --per_device_eval_batch_size 1 \
-    --gradient_accumulation_steps 1 \
-    --lr_scheduler_type cosine \
-    --logging_steps 10 \
-    --warmup_steps 20 \
-    --save_steps 100 \
-    --eval_steps 100 \
-    --evaluation_strategy steps \
-    --load_best_model_at_end \
-    --learning_rate 5e-5 \
-    --num_train_epochs 3.0 \
-    --max_samples 3000 \
-    --val_size 0.1 \
-    --plot_loss \
-    --pure_bf16

examples/extras/galore/sft.sh

@@ -32,4 +32,4 @@ CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
     --max_samples 3000 \
     --val_size 0.1 \
     --plot_loss \
-    --fp16
+    --pure_bf16

src/llmtuner/model/utils.py

@@ -47,6 +47,8 @@ def find_all_linear_modules(model: "PreTrainedModel") -> List[str]:
     output_layer_names = ["lm_head"]
     if model.config.model_type == "chatglm":
         output_layer_names.append("output_layer")
+    elif model.config.model_type == "internlm2":
+        output_layer_names.append("output")
 
     module_names = set()
     for name, module in model.named_modules():

src/llmtuner/train/dpo/trainer.py

@@ -8,7 +8,7 @@ from trl import DPOTrainer
 from trl.trainer.utils import disable_dropout_in_model
 
 from ...extras.constants import IGNORE_INDEX
-from ..utils import create_custom_optimzer
+from ..utils import create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
@@ -63,12 +63,16 @@ class CustomDPOTrainer(DPOTrainer):
             else:
                 self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
 
-    def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
+    def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:
-            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
+            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
 
-        self.create_optimizer()
+    def create_scheduler(
-        self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "torch.optim.lr_scheduler.LRScheduler":
+        create_custom_scheduler(self.args, num_training_steps, optimizer)
+        return super().create_scheduler(num_training_steps, optimizer)
 
     def sft_loss(self, chosen_logits: torch.FloatTensor, chosen_labels: torch.LongTensor) -> torch.Tensor:
         r"""

src/llmtuner/train/ppo/workflow.py

@@ -13,7 +13,7 @@ from ...extras.callbacks import FixValueHeadModelCallback
 from ...extras.misc import fix_valuehead_checkpoint
 from ...extras.ploting import plot_loss
 from ...model import load_model, load_tokenizer
-from ..utils import create_custom_optimzer, create_ref_model, create_reward_model
+from ..utils import create_custom_optimzer, create_custom_scheduler, create_ref_model, create_reward_model
 from .trainer import CustomPPOTrainer
 
 
@@ -70,7 +70,8 @@ def run_ppo(
         total_train_batch_size = backward_batch_size * finetuning_args.ppo_buffer_size * training_args.world_size
         num_training_steps = training_args.num_train_epochs * math.ceil(len(dataset) / total_train_batch_size)
 
-    optimizer = create_custom_optimzer(model, training_args, finetuning_args, num_training_steps)
+    optimizer = create_custom_optimzer(model, training_args, finetuning_args)
+    create_custom_scheduler(training_args, num_training_steps, optimizer)
     if optimizer is None:
         optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=training_args.learning_rate)
 

src/llmtuner/train/pt/trainer.py

@@ -1,12 +1,14 @@
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Optional
 
 from transformers import Trainer
 
 from ...extras.logging import get_logger
-from ..utils import create_custom_optimzer
+from ..utils import create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
+    import torch
+
     from ...hparams import FinetuningArguments
 
 
@@ -22,9 +24,13 @@ class CustomTrainer(Trainer):
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
 
-    def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
+    def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:
-            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
+            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
 
-        self.create_optimizer()
+    def create_scheduler(
-        self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "torch.optim.lr_scheduler.LRScheduler":
+        create_custom_scheduler(self.args, num_training_steps, optimizer)
+        return super().create_scheduler(num_training_steps, optimizer)

src/llmtuner/train/rm/trainer.py

@@ -1,12 +1,12 @@
 import json
 import os
-from typing import TYPE_CHECKING, Dict, List, Tuple, Union
+from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
 
 import torch
 from transformers import Trainer
 
 from ...extras.logging import get_logger
-from ..utils import create_custom_optimzer
+from ..utils import create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
@@ -29,12 +29,16 @@ class PairwiseTrainer(Trainer):
         self.finetuning_args = finetuning_args
         self.can_return_loss = True  # override property to return eval_loss
 
-    def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
+    def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:
-            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
+            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
 
-        self.create_optimizer()
+    def create_scheduler(
-        self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "torch.optim.lr_scheduler.LRScheduler":
+        create_custom_scheduler(self.args, num_training_steps, optimizer)
+        return super().create_scheduler(num_training_steps, optimizer)
 
     def compute_loss(
         self, model: "PreTrainedModel", inputs: Dict[str, torch.Tensor], return_outputs: bool = False

src/llmtuner/train/sft/trainer.py

@@ -8,7 +8,7 @@ from transformers import Seq2SeqTrainer
 
 from ...extras.constants import IGNORE_INDEX
 from ...extras.logging import get_logger
-from ..utils import create_custom_optimzer
+from ..utils import create_custom_optimzer, create_custom_scheduler
 
 
 if TYPE_CHECKING:
@@ -29,12 +29,16 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
         super().__init__(**kwargs)
         self.finetuning_args = finetuning_args
 
-    def create_optimizer_and_scheduler(self, num_training_steps: int) -> None:
+    def create_optimizer(self) -> "torch.optim.Optimizer":
         if self.optimizer is None:
-            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args, num_training_steps)
+            self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
+        return super().create_optimizer()
 
-        self.create_optimizer()
+    def create_scheduler(
-        self.create_scheduler(num_training_steps=num_training_steps, optimizer=self.optimizer)
+        self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
+    ) -> "torch.optim.lr_scheduler.LRScheduler":
+        create_custom_scheduler(self.args, num_training_steps, optimizer)
+        return super().create_scheduler(num_training_steps, optimizer)
 
     def prediction_step(
         self,

src/llmtuner/train/utils.py

@@ -5,6 +5,7 @@ from transformers import Trainer
 from transformers.optimization import get_scheduler
 from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
 from transformers.trainer_pt_utils import get_parameter_names
+from transformers.utils.versions import require_version
 
 from ..extras.logging import get_logger
 from ..extras.packages import is_galore_available
@@ -28,7 +29,13 @@ logger = get_logger(__name__)
 
 
 class DummyOptimizer(torch.optim.Optimizer):
-    def __init__(self, lr: float = 1e-3, optimizer_dict: Optional[dict] = None, *args, **kwargs) -> None:
+    r"""
+    A dummy optimizer used for the GaLore algorithm.
+    """
+
+    def __init__(
+        self, lr: float = 1e-3, optimizer_dict: Optional[Dict["torch.nn.Parameter", "torch.optim.Optimizer"]] = None
+    ) -> None:
         dummy_tensor = torch.randn(1, 1)
         self.optimizer_dict = optimizer_dict
         super().__init__([dummy_tensor], {"lr": lr})
@@ -155,8 +162,9 @@ def _create_galore_optimizer(
     model: "PreTrainedModel",
     training_args: "Seq2SeqTrainingArguments",
     finetuning_args: "FinetuningArguments",
-    max_steps: int,
 ) -> "torch.optim.Optimizer":
+    require_version("galore_torch", "To fix: pip install galore_torch")
+
     if len(finetuning_args.galore_target) == 1 and finetuning_args.galore_target[0] == "all":
         galore_targets = find_all_linear_modules(model)
     else:
@@ -211,29 +219,19 @@ def _create_galore_optimizer(
         for param in decay_params:
             param_groups = [dict(params=[param], weight_decay=training_args.weight_decay)]
             optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
-        for param in galore_params:
+        for param in galore_params:  # galore params have weight decay
             param_groups = [dict(params=[param], weight_decay=training_args.weight_decay, **galore_kwargs)]
             optimizer_dict[param] = optim_class(param_groups, **optim_kwargs)
 
-        scheduler_dict: Dict["torch.Tensor", "torch.optim.lr_scheduler.LRScheduler"] = {}
+        def optimizer_hook(param: "torch.nn.Parameter"):
-        for param in trainable_params:
-            scheduler_dict[param] = get_scheduler(
-                training_args.lr_scheduler_type,
-                optimizer=optimizer_dict[param],
-                num_warmup_steps=training_args.get_warmup_steps(max_steps) * 2,
-                num_training_steps=max_steps * 2,
-            )
-
-        def optimizer_hook(param: "torch.Tensor"):
             if param.grad is not None:
                 optimizer_dict[param].step()
                 optimizer_dict[param].zero_grad()
-                scheduler_dict[param].step()
 
         for param in trainable_params:
             param.register_post_accumulate_grad_hook(optimizer_hook)
 
-        optimizer = DummyOptimizer(lr=training_args.learning_rate)  # display scheduler result
+        optimizer = DummyOptimizer(lr=training_args.learning_rate, optimizer_dict=optimizer_dict)
     else:
         param_groups = [
             dict(params=nodecay_params),
@@ -292,10 +290,34 @@ def create_custom_optimzer(
     model: "PreTrainedModel",
     training_args: "Seq2SeqTrainingArguments",
     finetuning_args: "FinetuningArguments",
-    max_steps: int,
 ) -> Optional["torch.optim.Optimizer"]:
     if finetuning_args.use_galore:
-        return _create_galore_optimizer(model, training_args, finetuning_args, max_steps)
+        return _create_galore_optimizer(model, training_args, finetuning_args)
 
     if finetuning_args.loraplus_lr_ratio is not None:
         return _create_loraplus_optimizer(model, training_args, finetuning_args)
+
+
+def create_custom_scheduler(
+    training_args: "Seq2SeqTrainingArguments",
+    num_training_steps: int,
+    optimizer: Optional["torch.optim.Optimizer"] = None,
+) -> None:
+    if optimizer is not None and isinstance(optimizer, DummyOptimizer):
+        optimizer_dict = optimizer.optimizer_dict
+        scheduler_dict: Dict["torch.nn.Parameter", "torch.optim.lr_scheduler.LRScheduler"] = {}
+
+        for param in optimizer_dict.keys():
+            scheduler_dict[param] = get_scheduler(
+                training_args.lr_scheduler_type,
+                optimizer=optimizer_dict[param],
+                num_warmup_steps=training_args.get_warmup_steps(num_training_steps) * 2,
+                num_training_steps=num_training_steps * 2,
+            )
+
+        def scheduler_hook(param: "torch.nn.Parameter"):
+            if param.grad is not None:
+                scheduler_dict[param].step()
+
+        for param in optimizer_dict.keys():
+            param.register_post_accumulate_grad_hook(scheduler_hook)