[ColossalChat] Add PP support (#6001)

* support pp training

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* update rm

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* refactor

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* update test case

* fix

* change to 4

* fix eval

* test

* add pp

* hotfix

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* support pp training

* update rm

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* refactor

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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* update test case

* fix

* change to 4

* fix eval

* test

* add pp

* hotfix

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* update

* skip pp eval

* update all reduce

* update sft

* update ignore

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* update no cache

* add eval

* remove fi

* remove debug

* remove parentheses to avoid warning

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* Revert "add eval"

This reverts commit 3ab2f6fa32.

* add all reduce

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/run_chatgpt_examples.yml

@@ -31,18 +31,18 @@ jobs:
 
       - name: Install Colossal-AI
         run: |
-          BUILD_EXT=1 pip install -v -e .
+          BUILD_EXT=1 pip install --no-cache-dir -v -e .
 
       - name: Install ChatGPT
         run: |
           cd applications/ColossalChat
-          pip install -v .
+          pip install --no-cache-dir -v .
           export BUILD_EXT=1
-          pip install -r examples/requirements.txt
+          pip install --no-cache-dir -r examples/requirements.txt
 
       - name: Install Transformers
         run: |
-          pip install transformers==4.36.2
+          pip install --no-cache-dir transformers==4.36.2
 
       - name: Execute Examples
         run: |

applications/ColossalChat/.gitignore

@@ -161,3 +161,9 @@ applications/ColossalChat/sft_data
 applications/ColossalChat/prompt_data
 applications/ColossalChat/preference_data
 applications/ColossalChat/temp
+
+# Testing data
+/kto_data/
+/preference_data/
+/prompt_data/
+/sft_data/

applications/ColossalChat/coati/trainer/base.py

@@ -16,7 +16,7 @@ from coati.experience_buffer import NaiveExperienceBuffer
 from coati.experience_maker import Experience
 from torch.optim import Optimizer
 
-from colossalai.booster import Booster
+from colossalai.booster import Booster, Plugin
 
 from .utils import is_rank_0
 
@@ -38,6 +38,7 @@ class SLTrainer(ABC):
         max_epochs: int,
         model: nn.Module,
         optimizer: Optimizer,
+        plugin: Plugin,
         start_epoch: int = 0,
     ) -> None:
         super().__init__()
@@ -45,6 +46,7 @@ class SLTrainer(ABC):
         self.max_epochs = max_epochs
         self.model = model
         self.optimizer = optimizer
+        self.plugin = plugin
         self.start_epoch = start_epoch
 
     @abstractmethod

applications/ColossalChat/coati/trainer/dpo.py

@@ -16,7 +16,7 @@ from torch.utils.data import DataLoader
 from tqdm import trange
 from transformers import PreTrainedTokenizerBase
 
-from colossalai.booster import Booster
+from colossalai.booster import Booster, Plugin
 from colossalai.cluster import DistCoordinator
 from colossalai.utils import get_current_device
 
@@ -50,6 +50,7 @@ class DPOTrainer(SLTrainer):
         ref_model: Any,
         booster: Booster,
         actor_optim: Optimizer,
+        plugin: Plugin,
         actor_lr_scheduler: _LRScheduler,
         tokenizer: PreTrainedTokenizerBase,
         max_epochs: int = 1,
@@ -63,7 +64,9 @@ class DPOTrainer(SLTrainer):
         save_dir: str = None,
         coordinator: DistCoordinator = None,
     ) -> None:
-        super().__init__(booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, start_epoch=start_epoch)
+        super().__init__(
+            booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, plugin=plugin, start_epoch=start_epoch
+        )
         self.ref_model = ref_model
         self.actor_scheduler = actor_lr_scheduler
         self.tokenizer = tokenizer

applications/ColossalChat/coati/trainer/kto.py

@@ -17,7 +17,7 @@ from torch.utils.data import DataLoader
 from tqdm import trange
 from transformers import PreTrainedTokenizerBase
 
-from colossalai.booster import Booster
+from colossalai.booster import Booster, Plugin
 from colossalai.cluster import DistCoordinator
 from colossalai.utils import get_current_device
 
@@ -53,6 +53,7 @@ class KTOTrainer(SLTrainer):
         ref_model: Any,
         booster: Booster,
         actor_optim: Optimizer,
+        plugin: Plugin,
         actor_lr_scheduler: _LRScheduler,
         tokenizer: PreTrainedTokenizerBase,
         max_epochs: int = 1,
@@ -66,7 +67,9 @@ class KTOTrainer(SLTrainer):
         save_dir: str = None,
         coordinator: DistCoordinator = None,
     ) -> None:
-        super().__init__(booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, start_epoch=start_epoch)
+        super().__init__(
+            booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, plugin=plugin, start_epoch=start_epoch
+        )
         self.ref_model = ref_model
         self.actor_scheduler = actor_lr_scheduler
         self.tokenizer = tokenizer

applications/ColossalChat/coati/trainer/orpo.py

@@ -16,7 +16,7 @@ from torch.utils.data import DataLoader
 from tqdm import trange
 from transformers import PreTrainedTokenizerBase
 
-from colossalai.booster import Booster
+from colossalai.booster import Booster, Plugin
 from colossalai.cluster import DistCoordinator
 from colossalai.utils import get_current_device
 
@@ -48,6 +48,7 @@ class ORPOTrainer(SLTrainer):
         actor: Any,
         booster: Booster,
         actor_optim: Optimizer,
+        plugin: Plugin,
         actor_lr_scheduler: _LRScheduler,
         tokenizer: PreTrainedTokenizerBase,
         max_epochs: int = 1,
@@ -59,7 +60,9 @@ class ORPOTrainer(SLTrainer):
         save_dir: str = None,
         coordinator: DistCoordinator = None,
     ) -> None:
-        super().__init__(booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, start_epoch=start_epoch)
+        super().__init__(
+            booster, max_epochs=max_epochs, model=actor, optimizer=actor_optim, plugin=plugin, start_epoch=start_epoch
+        )
         self.actor_scheduler = actor_lr_scheduler
         self.tokenizer = tokenizer
         self.odds_ratio_loss_fn = OddsRatioLoss()

applications/ColossalChat/coati/trainer/rm.py

@@ -15,7 +15,7 @@ from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader
 from transformers import PreTrainedTokenizerBase
 
-from colossalai.booster import Booster
+from colossalai.booster import Booster, Plugin
 from colossalai.cluster import DistCoordinator
 from colossalai.utils import get_current_device
 
@@ -48,6 +48,7 @@ class RewardModelTrainer(SLTrainer):
         model: Any,
         booster: Booster,
         optimizer: Optimizer,
+        plugin: Plugin,
         lr_scheduler: _LRScheduler,
         tokenizer: PreTrainedTokenizerBase,
         loss_fn: Optional[Callable] = None,
@@ -59,7 +60,9 @@ class RewardModelTrainer(SLTrainer):
         save_dir: str = None,
         coordinator: DistCoordinator = None,
     ) -> None:
-        super().__init__(booster, max_epochs=max_epochs, model=model, optimizer=optimizer, start_epoch=start_epoch)
+        super().__init__(
+            booster, max_epochs=max_epochs, model=model, optimizer=optimizer, plugin=plugin, start_epoch=start_epoch
+        )
         self.actor_scheduler = lr_scheduler
         self.tokenizer = tokenizer
         self.loss_fn = loss_fn if loss_fn is not None else LogSigLoss(beta=beta)

applications/ColossalChat/coati/trainer/sft.py

@@ -6,14 +6,16 @@ import os
 from typing import Optional
 
 import torch
+import torch.distributed as dist
 from coati.trainer.utils import all_reduce_mean
 from coati.utils import AccumulativeMeanMeter, save_checkpoint
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader
-from tqdm import trange
+from tqdm import tqdm, trange
 
 from colossalai.booster import Booster
+from colossalai.booster.plugin import HybridParallelPlugin, Plugin
 from colossalai.cluster import DistCoordinator
 
 from .base import SLTrainer
@@ -40,6 +42,7 @@ class SFTTrainer(SLTrainer):
         optim: Optimizer,
         lr_scheduler: _LRScheduler,
         max_epochs: int = 2,
+        plugin: Plugin = None,
         accumulation_steps: int = 8,
         apply_loss_mask: bool = True,
         start_epoch=0,
@@ -47,7 +50,7 @@ class SFTTrainer(SLTrainer):
         save_dir: str = None,
         coordinator: Optional[DistCoordinator] = None,
     ) -> None:
-        super().__init__(booster, max_epochs, model, optim, start_epoch=start_epoch)
+        super().__init__(booster, max_epochs, model, optim, plugin, start_epoch=start_epoch)
 
         self.accumulation_steps = accumulation_steps
         self.scheduler = lr_scheduler
@@ -94,60 +97,85 @@ class SFTTrainer(SLTrainer):
 
     def _train(self, epoch: int):
         self.model.train()
-        step_bar = trange(
+        if isinstance(self.plugin, HybridParallelPlugin) and self.plugin.pp_size > 1:
-            len(self.train_dataloader) // self.accumulation_steps,
+            data_iter = iter(self.train_dataloader)
-            desc=f"Epoch {epoch + 1}/{self.max_epochs}",
+            step_bar = tqdm(
-            disable=not is_rank_0(),
+                range(len(self.train_dataloader)),
-        )
+                desc="Step",
-        for i, batch in enumerate(self.train_dataloader):
+                disable=not (dist.get_rank() == dist.get_world_size() - 1),
-            batch = to_device(batch, torch.cuda.current_device())
-            batch_size = batch["input_ids"].size(0)
-            outputs = self.model(
-                batch["input_ids"],
-                attention_mask=batch["attention_mask"],
-                labels=batch["labels"] if self.apply_loss_mask else batch["input_ids"],
             )
-            loss = outputs.loss
+            for step in step_bar:
+                outputs = self.booster.execute_pipeline(
+                    data_iter,
+                    self.model,
+                    criterion=lambda outputs, inputs: outputs[0],
+                    optimizer=self.optimizer,
+                    return_loss=True,
+                )
+                loss = outputs["loss"]
 
-            self.booster.backward(loss=loss, optimizer=self.optimizer)
+                if self.booster.plugin.stage_manager.is_last_stage():
+                    global_loss = all_reduce_mean(loss, self.plugin)
+                    if dist.get_rank() == dist.get_world_size() - 1:
+                        step_bar.set_postfix({"train/loss": global_loss.item()})
 
-            loss_mean = all_reduce_mean(tensor=loss)
-            self.accumulative_meter.add("loss", loss_mean.to(torch.float16).item())
-
-            # Gradient accumulation
-            if (i + 1) % self.accumulation_steps == 0:
                 self.optimizer.step()
                 self.optimizer.zero_grad()
-                self.scheduler.step()
+        else:
+            step_bar = trange(
+                len(self.train_dataloader) // self.accumulation_steps,
+                desc=f"Epoch {epoch + 1}/{self.max_epochs}",
+                disable=not is_rank_0(),
+            )
+            for i, batch in enumerate(self.train_dataloader):
+                batch = to_device(batch, torch.cuda.current_device())
+                batch_size = batch["input_ids"].size(0)
+                outputs = self.model(
+                    batch["input_ids"],
+                    attention_mask=batch["attention_mask"],
+                    labels=batch["labels"] if self.apply_loss_mask else batch["input_ids"],
+                )
+                loss = outputs.loss
 
-                step_bar.set_postfix({"train/loss": self.accumulative_meter.get("loss")})
+                self.booster.backward(loss=loss, optimizer=self.optimizer)
-                if self.writer:
-                    self.writer.add_scalar("train/loss", self.accumulative_meter.get("loss"), self.num_train_step)
-                    self.writer.add_scalar("train/lr", self.scheduler.get_last_lr()[0], self.num_train_step)
-                self.num_train_step += 1
-                self.accumulative_meter.reset()
-                step_bar.update()
 
-                # Save checkpoint
+                loss_mean = all_reduce_mean(tensor=loss)
-                if (
+                self.accumulative_meter.add("loss", loss_mean.to(torch.float16).item())
-                    self.save_dir is not None
+
-                    and self.save_interval is not None
+                # Gradient accumulation
-                    and (self.num_train_step + 1) % self.save_interval == 0
+                if (i + 1) % self.accumulation_steps == 0:
-                ):
+                    self.optimizer.step()
-                    save_checkpoint(
+                    self.optimizer.zero_grad()
-                        save_dir=self.save_dir,
+                    self.scheduler.step()
-                        booster=self.booster,
+
-                        model=self.model,
+                    step_bar.set_postfix({"train/loss": self.accumulative_meter.get("loss")})
-                        optimizer=self.optimizer,
+                    if self.writer:
-                        lr_scheduler=self.scheduler,
+                        self.writer.add_scalar("train/loss", self.accumulative_meter.get("loss"), self.num_train_step)
-                        epoch=epoch,
+                        self.writer.add_scalar("train/lr", self.scheduler.get_last_lr()[0], self.num_train_step)
-                        step=self.num_train_step + 1,
+                    self.num_train_step += 1
-                        batch_size=batch_size,
+                    self.accumulative_meter.reset()
-                        coordinator=self.coordinator,
+                    step_bar.update()
-                    )
+
-                    self.coordinator.print_on_master(
+            # Save checkpoint
-                        f"Saved checkpoint at epoch {epoch} step {self.num_train_step} at folder {self.save_dir}"
+            if (
-                    )
+                self.save_dir is not None
+                and self.save_interval is not None
+                and (self.num_train_step + 1) % self.save_interval == 0
+            ):
+                save_checkpoint(
+                    save_dir=self.save_dir,
+                    booster=self.booster,
+                    model=self.model,
+                    optimizer=self.optimizer,
+                    lr_scheduler=self.scheduler,
+                    epoch=epoch,
+                    step=self.num_train_step + 1,
+                    batch_size=batch_size,
+                    coordinator=self.coordinator,
+                )
+                self.coordinator.print_on_master(
+                    f"Saved checkpoint at epoch {epoch} step {self.num_train_step} at folder {self.save_dir}"
+                )
         step_bar.close()
 
     def _eval(self, epoch: int):
@@ -157,27 +185,64 @@ class SFTTrainer(SLTrainer):
         self.accumulative_meter.reset()
         self.model.eval()
         with torch.no_grad():
-            step_bar = trange(
+            if isinstance(self.plugin, HybridParallelPlugin) and self.plugin.pp_size > 1:
-                len(self.eval_dataloader),
+                data_iter = iter(self.eval_dataloader)
-                desc=f"Epoch {epoch + 1}/{self.max_epochs}",
+                step_bar = tqdm(
-                disable=not is_rank_0(),
+                    range(len(self.eval_dataloader)),
-            )
+                    desc="Step",
-            for batch in self.eval_dataloader:
+                    disable=not (dist.get_rank() == dist.get_world_size() - 1),
-                batch = to_device(batch, torch.cuda.current_device())
-                outputs = self.model(
-                    batch["input_ids"],
-                    attention_mask=batch["attention_mask"],
-                    labels=batch["labels"] if self.apply_loss_mask else batch["input_ids"],
                 )
-                loss_mean = all_reduce_mean(tensor=outputs.loss)
+                for step in step_bar:
-                self.accumulative_meter.add("loss", loss_mean.item(), count_update=batch["input_ids"].size(0))
+                    outputs = self.booster.execute_pipeline(
-                step_bar.update()
+                        data_iter,
-            loss_mean = self.accumulative_meter.get("loss")
+                        self.model,
-            msg = "Evaluation Result:\n"
+                        criterion=lambda outputs, inputs: outputs[0],
-            for tag in ["loss"]:
+                        optimizer=self.optimizer,
-                msg = msg + f"{tag}: {self.accumulative_meter.get(tag)}\n"
+                        return_loss=True,
-            self.coordinator.print_on_master(msg)
+                    )
-            os.makedirs(self.save_dir, exist_ok=True)
+                    loss = outputs["loss"]
-            with open(os.path.join(self.save_dir, f"eval_result_epoch{epoch}.txt"), "w") as f:
+                    if self.booster.plugin.stage_manager.is_last_stage():
-                f.write(msg)
+                        global_loss = all_reduce_mean(loss, self.plugin)
-            step_bar.close()
+                        if dist.get_rank() == dist.get_world_size() - 1:
+                            step_bar.set_postfix({"eval/loss": global_loss.item()})
+                            self.accumulative_meter.add("loss", global_loss.item())
+
+                if dist.get_rank() == dist.get_world_size() - 1:
+                    loss_mean = self.accumulative_meter.get("loss")
+                    msg = "Evaluation Result:\n"
+                    for tag in ["loss"]:
+                        msg = msg + f"{tag}: {self.accumulative_meter.get(tag)}\n"
+                    print(msg)
+                    if self.save_dir is not None:
+                        os.makedirs(self.save_dir, exist_ok=True)
+                        with open(os.path.join(self.save_dir, f"eval_result_epoch{epoch}.txt"), "w") as f:
+                            f.write(msg)
+                        step_bar.close()
+
+            else:
+                step_bar = trange(
+                    len(self.eval_dataloader),
+                    desc=f"Epoch {epoch + 1}/{self.max_epochs}",
+                    disable=not is_rank_0(),
+                )
+                for batch in self.eval_dataloader:
+                    batch = to_device(batch, torch.cuda.current_device())
+                    outputs = self.model(
+                        batch["input_ids"],
+                        attention_mask=batch["attention_mask"],
+                        labels=batch["labels"] if self.apply_loss_mask else batch["input_ids"],
+                    )
+                    loss_mean = all_reduce_mean(tensor=outputs.loss)
+                    self.accumulative_meter.add("loss", loss_mean.item(), count_update=batch["input_ids"].size(0))
+                    step_bar.update()
+
+                loss_mean = self.accumulative_meter.get("loss")
+                msg = "Evaluation Result:\n"
+                for tag in ["loss"]:
+                    msg = msg + f"{tag}: {self.accumulative_meter.get(tag)}\n"
+                self.coordinator.print_on_master(msg)
+                if self.save_dir is not None:
+                    os.makedirs(self.save_dir, exist_ok=True)
+                    with open(os.path.join(self.save_dir, f"eval_result_epoch{epoch}.txt"), "w") as f:
+                        f.write(msg)
+                    step_bar.close()

applications/ColossalChat/coati/trainer/utils.py

@@ -9,6 +9,8 @@ import torch.distributed as dist
 from torch.utils._pytree import tree_map
 from torch.utils.data import DataLoader
 
+from colossalai.booster import Plugin
+
 
 class CycledDataLoader:
     """
@@ -85,7 +87,7 @@ def to_device(x: Any, device: torch.device) -> Any:
     return tree_map(_to, x)
 
 
-def all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:
+def all_reduce_mean(tensor: torch.Tensor, plugin: Plugin = None) -> torch.Tensor:
     """
     Perform all-reduce operation on the given tensor and compute the mean across all processes.
 
@@ -95,8 +97,13 @@ def all_reduce_mean(tensor: torch.Tensor) -> torch.Tensor:
     Returns:
         torch.Tensor: The reduced tensor with mean computed across all processes.
     """
-    dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
+    # All reduce mean across DP group
-    tensor.div_(dist.get_world_size())
+    if plugin is not None:
+        dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM, group=plugin.dp_group)
+        tensor.div_(plugin.dp_size)
+    else:
+        dist.all_reduce(tensor=tensor, op=dist.ReduceOp.SUM)
+        tensor.div_(dist.get_world_size())
     return tensor
 
 

applications/ColossalChat/examples/training_scripts/train_dpo.py

@@ -267,6 +267,7 @@ def train(args):
         ref_model=ref_model,
         booster=booster,
         actor_optim=optim,
+        plugin=plugin,
         actor_lr_scheduler=lr_scheduler,
         tokenizer=tokenizer,
         max_epochs=args.max_epochs,

applications/ColossalChat/examples/training_scripts/train_kto.py

@@ -286,6 +286,7 @@ def train(args):
         ref_model=ref_model,
         booster=booster,
         actor_optim=optim,
+        plugin=plugin,
         actor_lr_scheduler=lr_scheduler,
         tokenizer=tokenizer,
         max_epochs=args.max_epochs,

applications/ColossalChat/examples/training_scripts/train_orpo.py

@@ -250,6 +250,7 @@ def train(args):
         actor=model,
         booster=booster,
         actor_optim=optim,
+        plugin=plugin,
         actor_lr_scheduler=lr_scheduler,
         tokenizer=tokenizer,
         max_epochs=args.max_epochs,

applications/ColossalChat/examples/training_scripts/train_rm.py

@@ -262,6 +262,7 @@ def train(args):
         model,
         booster,
         optim,
+        plugin,
         lr_scheduler,
         tokenizer,
         loss_fn=loss_fn,

applications/ColossalChat/examples/training_scripts/train_sft.py

@@ -114,7 +114,7 @@ def train(args):
             parallel_output=False,
             max_norm=args.grad_clip,
             precision=args.mixed_precision,
-            microbatch_size=args.batch_size,
+            microbatch_size=args.microbatch_size,
         )
     else:
         raise ValueError(f"Unknown plugin {args.plugin}")
@@ -269,6 +269,7 @@ def train(args):
         model=model,
         booster=booster,
         optim=optim,
+        plugin=plugin,
         lr_scheduler=lr_scheduler,
         max_epochs=args.max_epochs,
         accumulation_steps=args.accumulation_steps,
@@ -344,6 +345,7 @@ if __name__ == "__main__":
     parser.add_argument("--use_wandb", default=False, action="store_true")
     parser.add_argument("--grad_checkpoint", default=False, action="store_true")
     parser.add_argument("--use_flash_attn", default=False, action="store_true")
+    parser.add_argument("--microbatch_size", type=int, default=1)
     args = parser.parse_args()
     if args.config_file is not None:
         os.makedirs(os.path.dirname(args.config_file), exist_ok=True)

applications/ColossalChat/tests/test_lora.py

@@ -61,7 +61,7 @@ def test_overfit():
     _, predicted = torch.max(outputs.data, 1)
     total = labels.size(0)
     correct = (predicted == Y).sum().item()
-    assert (correct / total > 0.95, "The model has not overfitted to the synthesized dataset")
+    assert correct / total > 0.95
     assert (weight_to_compare - model.fc1.weight).sum() < 0.01
 
 

applications/ColossalChat/tests/test_train.sh

@@ -15,7 +15,7 @@ set_n_least_used_CUDA_VISIBLE_DEVICES() {
     echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
 }
 
-set_n_least_used_CUDA_VISIBLE_DEVICES 2
+set_n_least_used_CUDA_VISIBLE_DEVICES 4
 
 set -xu
 
@@ -30,7 +30,7 @@ MODEL_SAVE_PATH=$TEMP_DIR/rlhf_models
 MODELS_DIR=$TEMP_DIR/models_config
 # Skip those tests due to CI tests timeout
 MODELS=('llama')
-ADVANCED_PLUGINS=('zero2' 'sp_split_gather' 'sp_ring' 'sp_all_to_all' 'tp_zero2' '3d' 'gemini' 'gemini_auto' 'zero2_cpu')  # pp is still buggy
+ADVANCED_PLUGINS=('zero2' 'sp_split_gather' 'sp_ring' 'sp_all_to_all' 'tp_zero2' '3d' 'gemini' 'gemini_auto' 'zero2_cpu' 'pp' 'tp_pp')
 PLUGINS=('zero2' '3d' 'gemini' 'gemini_auto' 'zero2_cpu')
 LORA_RANK=('0')  # skip to reduce CI execution time, can pass all locally
 LORA_CONFIG_ENABLE="--lora_config $BASE_DIR/examples/training_scripts/lora_config.json"
@@ -91,7 +91,7 @@ SKIPPED_TESTS=(
     llama-gemini_auto-20  # gemini_auto plugin doesn't support lora
     llama-gemini-20 # gemini doesn't support lora
 )
-
+skip_eval=false
 GRAD_CKPTS=('--grad_checkpoint')
 for lora_rank in ${LORA_RANK[@]}; do
     for model in ${MODELS[@]}; do
@@ -129,15 +129,18 @@ for lora_rank in ${LORA_RANK[@]}; do
                 plugin='3d'
             fi
             if [[ $plugin == "tp_pp" ]]; then
+                echo "Here"
                 tp='2'
                 bs='8'
                 pp='2'
                 plugin='3d'
+                skip_eval=true
             fi
             if [[ $plugin == "pp" ]]; then
                 bs='8'
                 pp='2'
                 plugin='3d'
+                skip_eval=true
             fi
             if [[ $plugin == "sp_split_gather" ]]; then
                 enable_sequence_parallelism='--enable_sequence_parallelism'
@@ -175,28 +178,53 @@ for lora_rank in ${LORA_RANK[@]}; do
                 for split in $(seq -f "%05g" 0 0); do
                     dataset+=("$TEMP_DIR/rlhf_data/tokenized_${model}_sft/arrow/part-$split")
                 done
-                colossalai run --nproc_per_node 2 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_sft.py \
+
-                    --pretrain $pretrain \
+                if [[ $skip_eval ]]; then
-                    --tokenizer_dir $tokenizer_dir \
+                    colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_sft.py \
-                    --dataset ${dataset[@]} \
+                        --pretrain $pretrain \
-                    --eval_dataset ${dataset[@]} \
+                        --tokenizer_dir $tokenizer_dir \
-                    --save_path $MODEL_SAVE_PATH \
+                        --dataset ${dataset[@]} \
-                    --config_file $MODELS_DIR/config.jsonl \
+                        --save_path $MODEL_SAVE_PATH \
-                    $lora_config \
+                        --config_file $MODELS_DIR/config.jsonl \
-                    --plugin $plugin \
+                        $lora_config \
-                    --batch_size $bs \
+                        --plugin $plugin \
-                    --max_epochs 1 \
+                        --batch_size $bs \
-                    --accumulation_steps $grad_accu \
+                        --max_epochs 1 \
-                    --tp $tp \
+                        --accumulation_steps $grad_accu \
-                    --pp $pp \
+                        --tp $tp \
-                    --zero_stage $zero_stage \
+                        --pp $pp \
-                    --sp $sp \
+                        --zero_stage $zero_stage \
-                    --sp_mode $sp_mode \
+                        --sp $sp \
-                    $enable_sequence_parallelism \
+                        --sp_mode $sp_mode \
-                    --lr 2e-5 \
+                        $enable_sequence_parallelism \
-                    $grad_ckpt \
+                        --lr 2e-5 \
-                    --max_len 400 \
+                        $grad_ckpt \
-                    --use_flash_attn
+                        --max_len 400 \
+                        --use_flash_attn
+                else
+                    colossalai run --nproc_per_node 4 --master_port 31332 $EXAMPLES_DIR/training_scripts/train_sft.py \
+                        --pretrain $pretrain \
+                        --tokenizer_dir $tokenizer_dir \
+                        --dataset ${dataset[@]} \
+                        --eval_dataset ${dataset[@]} \
+                        --save_path $MODEL_SAVE_PATH \
+                        --config_file $MODELS_DIR/config.jsonl \
+                        $lora_config \
+                        --plugin $plugin \
+                        --batch_size $bs \
+                        --max_epochs 1 \
+                        --accumulation_steps $grad_accu \
+                        --tp $tp \
+                        --pp $pp \
+                        --zero_stage $zero_stage \
+                        --sp $sp \
+                        --sp_mode $sp_mode \
+                        $enable_sequence_parallelism \
+                        --lr 2e-5 \
+                        $grad_ckpt \
+                        --max_len 400 \
+                        --use_flash_attn
+                fi
                 passed=$?
                 if [ $passed -eq 0 ]; then
                     rm -rf ${MODEL_SAVE_PATH:?}/*