feat(cookbook): add single controller SP example and reorganize transformers cookbook

- Add new single_controller_sp.py example demonstrating FSDP + SP validation over 4 GPUs
- Move legacy single_controller_sp.py to transformers/sp_fsdp_dense.py
- Add shell script sp_fsdp_dense.sh for running the example
- Update imports and structure to use twinkle framework components

Author: meichangsu1

cookbook/transformers/single_controller_sp.py

@@ -0,0 +1,90 @@
+from functools import partial
+import numpy as np
+import torch
+from peft import LoraConfig
+
+import twinkle
+from twinkle import get_logger, DeviceGroup, Platform, DeviceMesh
+from twinkle.dataloader import DataLoader
+from twinkle.dataset import Dataset, DatasetMeta
+from twinkle.model import TransformersModel
+from twinkle.preprocessor import SelfCognitionProcessor
+
+logger = get_logger()
+MODEL_ID = 'ms://Qwen/Qwen2.5-7B-Instruct'
+
+device_group = [
+    DeviceGroup(
+        name="default",
+        ranks=[0, 1, 2, 3],
+        device_type=Platform.get_platform().device_prefix(),
+    )
+]
+
+# FSDP + SP validation over 4 GPUs: dp=2, fsdp=2 (SP only affects input slicing)
+device_mesh = DeviceMesh(
+    device_type="cuda",
+    mesh=np.arange(4).reshape(2, 2),
+    mesh_dim_names=("dp", "fsdp"),
+    ulysses_size=2,
+)
+
+twinkle.initialize(
+    mode="local",
+    nproc_per_node=4,
+    global_device_mesh=device_mesh,
+    lazy_collect=False,
+)
+
+
+def create_dataset(data_slice=None):
+    dataset = Dataset(
+        dataset_meta=DatasetMeta("ms://swift/self-cognition", data_slice=data_slice)
+    )
+    dataset.set_template(
+        "Template",
+        model_id=MODEL_ID,
+        truncation_strategy="left",
+        max_length=256,
+    )
+    dataset.map(SelfCognitionProcessor("twinkle模型", "twinkle团队"))
+    dataset.encode(batched=True)
+    return dataset
+
+
+
+def train():
+    dataloader = DataLoader(
+        dataset=partial(create_dataset, data_slice=None),
+        batch_size=4,
+        device_mesh=device_mesh,
+    )
+
+    model = TransformersModel(
+        model_id=MODEL_ID,
+        device_mesh=device_mesh,
+        strategy="native_fsdp",
+    )
+
+    lora_config = LoraConfig(target_modules="all-linear")
+    model.add_adapter_to_model("default", lora_config, gradient_accumulation_steps=1)
+    model.set_optimizer("AdamW", lr=1e-4, adapter_name="default")
+
+    for step, batch in enumerate(dataloader):
+        model.forward_backward(inputs=batch, adapter_name='default')
+        model.clip_grad_and_step(adapter_name='default')
+        if step % 1 == 0:
+            metric = model.calculate_metric(is_training=True, adapter_name='default')
+            _metrics = {}
+            for key, value in metric.items():
+                try:
+                    value = float(value)
+                    _metrics[key] = value
+                except:
+                    pass
+            logger.info(f'Current is step {step} of {len(dataloader)}, metric: {metric}')
+    model.save(f'last-checkpoint', interval=1)
+
+
+if __name__ == "__main__":
+    train()

cookbook/legacy/single_controller_sp.py cookbook/transformers/sp_fsdp_dense.pycookbook/legacy/single_controller_sp.py renamed to cookbook/transformers/sp_fsdp_dense.py

@@ -0,0 +1 @@
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 sp_fsdp_dense.py