Npu adapt megatron

cookbook/megatron/ascend/tp_moe_cp_npu.py

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+import twinkle
+from peft import LoraConfig
+
+from twinkle import DeviceMesh, get_device_placement, get_logger
+from twinkle.dataloader import DataLoader
+from twinkle.dataset import Dataset, DatasetMeta
+from twinkle.model import MegatronModel
+from twinkle.preprocessor import SelfCognitionProcessor
+
+MODEL_ID = 'ms://Qwen/Qwen3-30B-A3B'
+DATASET_ID = 'ms://swift/self-cognition'
+DATASET_SLICE = range(128)
+BATCH_SIZE = 2
+MAX_STEPS = 10
+
+# Keep the original 8-card MoE + CP layout so we can verify the default
+# megatron_cp_algo path after repatching TEDotProductAttention back to the
+# older MindSpeedCPDotProductAttention.
+device_mesh = DeviceMesh.from_sizes(dp_size=1, tp_size=2, pp_size=2, cp_size=2, ep_size=2, device_type='npu')
+twinkle.initialize(mode='local', global_device_mesh=device_mesh)
+
+logger = get_logger()
+
+
+def build_dataset():
+    dataset = Dataset(dataset_meta=DatasetMeta(DATASET_ID, data_slice=DATASET_SLICE))
+    dataset.set_template('Template', model_id=MODEL_ID, max_length=512)
+    dataset.map(SelfCognitionProcessor('twinkle大模型', 'ModelScope社区'))
+    dataset.encode()
+    return dataset
+
+
+def build_model(total_steps: int):
+    model = MegatronModel(model_id=MODEL_ID)
+    lora_config = LoraConfig(r=8, lora_alpha=32, target_modules='all-linear')
+    model.add_adapter_to_model('default', lora_config)
+    model.set_optimizer(optimizer_cls='default', lr=1e-4)
+    model.set_lr_scheduler(scheduler_cls='default', lr_warmup_steps=2, lr_decay_steps=total_steps)
+    return model
+
+
+def train():
+    dataset = build_dataset()
+    dataloader = DataLoader(dataset=dataset, batch_size=BATCH_SIZE, num_workers=0)
+    model = build_model(len(dataloader))
+
+    logger.info(get_device_placement())
+    logger.info(model.get_train_configs())
+    logger.info(f'Total steps: {len(dataloader)}, validating {MAX_STEPS} steps')
+
+    for step, batch in enumerate(dataloader):
+        if step >= MAX_STEPS:
+            break
+        model.forward_backward(inputs=batch)
+        model.clip_grad_and_step()
+        metric = model.calculate_metric(is_training=True)
+        logger.info(f'[MoE CP NPU smoke] step {step + 1}/{MAX_STEPS}, metric: {metric}')
+
+
+if __name__ == '__main__':
+    train()

cookbook/megatron/ascend/tp_moe_cp_npu.sh

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+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 tp_moe_cp_npu.py

cookbook/megatron/ascend/tp_moe_npu.py

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+import twinkle
+from peft import LoraConfig
+
+from twinkle import DeviceMesh, get_device_placement, get_logger
+from twinkle.dataloader import DataLoader
+from twinkle.dataset import Dataset, DatasetMeta
+from twinkle.model import MegatronModel
+from twinkle.preprocessor import SelfCognitionProcessor
+
+MODEL_ID = 'ms://Qwen/Qwen3-30B-A3B'
+DATASET_ID = 'ms://swift/self-cognition'
+DATASET_SLICE = range(128)
+BATCH_SIZE = 2
+MAX_STEPS = 10
+
+# Run the MoE smoke without context parallelism so we can isolate the MoE path
+# itself on the same 8-card topology.
+device_mesh = DeviceMesh.from_sizes(dp_size=2, tp_size=2, pp_size=2, cp_size=1, ep_size=2, device_type='npu')
+twinkle.initialize(mode='local', global_device_mesh=device_mesh)
+
+logger = get_logger()
+
+
+def build_dataset():
+    dataset = Dataset(dataset_meta=DatasetMeta(DATASET_ID, data_slice=DATASET_SLICE))
+    dataset.set_template('Template', model_id=MODEL_ID, max_length=512)
+    dataset.map(SelfCognitionProcessor('twinkle大模型', 'ModelScope社区'))
+    dataset.encode()
+    return dataset
+
+
+def build_model(total_steps: int):
+    model = MegatronModel(model_id=MODEL_ID)
+    lora_config = LoraConfig(r=8, lora_alpha=32, target_modules='all-linear')
+    model.add_adapter_to_model('default', lora_config)
+    model.set_optimizer(optimizer_cls='default', lr=1e-4)
+    model.set_lr_scheduler(scheduler_cls='default', lr_warmup_steps=2, lr_decay_steps=total_steps)
+    return model
+
+
+def train():
+    dataset = build_dataset()
+    dataloader = DataLoader(dataset=dataset, batch_size=BATCH_SIZE, num_workers=0)
+    model = build_model(len(dataloader))
+
+    logger.info(get_device_placement())
+    logger.info(model.get_train_configs())
+    logger.info(f'Total steps: {len(dataloader)}, validating {MAX_STEPS} steps')
+
+    for step, batch in enumerate(dataloader):
+        if step >= MAX_STEPS:
+            break
+        model.forward_backward(inputs=batch)
+        model.clip_grad_and_step()
+        metric = model.calculate_metric(is_training=True)
+        logger.info(f'[MoE NPU smoke] step {step + 1}/{MAX_STEPS}, metric: {metric}')
+
+
+if __name__ == '__main__':
+    train()

cookbook/megatron/ascend/tp_moe_npu.sh

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+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 tp_moe_npu.py

cookbook/megatron/ascend/tp_npu.py

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+import twinkle
+from peft import LoraConfig
+
+from twinkle import DeviceMesh, get_device_placement, get_logger
+from twinkle.dataloader import DataLoader
+from twinkle.dataset import Dataset, DatasetMeta
+from twinkle.model import MegatronModel
+from twinkle.preprocessor import SelfCognitionProcessor
+
+MODEL_ID = 'ms://Qwen/Qwen3-4B'
+DATASET_ID = 'ms://swift/self-cognition'
+DATASET_SLICE = range(256)
+BATCH_SIZE = 8
+MAX_STEPS = 10
+
+# Keep the same 8-card TP/PP/DP layout as the GPU reference script, but run it
+# through the NPU backend to validate Megatron + MindSpeed integration.
+device_mesh = DeviceMesh.from_sizes(dp_size=2, tp_size=2, pp_size=2, device_type='npu')
+twinkle.initialize(mode='local', global_device_mesh=device_mesh)
+
+logger = get_logger()
+
+
+def build_dataset():
+    dataset = Dataset(dataset_meta=DatasetMeta(DATASET_ID, data_slice=DATASET_SLICE))
+    # Qwen3-4B is a text-only model, so use the base template instead of the VL template.
+    dataset.set_template('Template', model_id=MODEL_ID, max_length=512)
+    dataset.map(SelfCognitionProcessor('twinkle大模型', 'ModelScope社区'))
+    dataset.encode()
+    return dataset
+
+
+def build_model(total_steps: int):
+    model = MegatronModel(model_id=MODEL_ID)
+    lora_config = LoraConfig(r=8, lora_alpha=32, target_modules='all-linear')
+    model.add_adapter_to_model('default', lora_config)
+    model.set_optimizer(optimizer_cls='default', lr=1e-4)
+    model.set_lr_scheduler(scheduler_cls='default', lr_warmup_steps=2, lr_decay_steps=total_steps)
+    return model
+
+
+def train():
+    dataset = build_dataset()
+    dataloader = DataLoader(dataset=dataset, batch_size=BATCH_SIZE, num_workers=0)
+    model = build_model(len(dataloader))
+
+    logger.info(get_device_placement())
+    logger.info(model.get_train_configs())
+    logger.info(f'Total steps: {len(dataloader)}, validating {MAX_STEPS} steps')
+
+    for step, batch in enumerate(dataloader):
+        if step >= MAX_STEPS:
+            break
+        model.forward_backward(inputs=batch)
+        model.clip_grad_and_step()
+        metric = model.calculate_metric(is_training=True)
+        logger.info(f'[NPU smoke] step {step + 1}/{MAX_STEPS}, metric: {metric}')
+
+
+if __name__ == '__main__':
+    train()

cookbook/megatron/ascend/tp_npu.sh

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+ASCEND_RT_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 torchrun --nproc_per_node=8 tp_npu.py