Npu megatron adapt

cookbook/megatron/npu/tp_lora_npu.py

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+import os
+
+from peft import LoraConfig
+
+import twinkle
+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
+
+# Build a device mesh for the verified NPU LoRA smoke.
+MODEL_ID = os.environ.get('TWINKLE_LOCAL_MODEL_DIR', 'ms://Qwen/Qwen3-4B')
+DATASET_PATH = os.environ.get(
+    'TWINKLE_LOCAL_DATASET_PATH',
+    'ms://swift/self-cognition',
+)
+MAX_STEPS = int(os.environ.get('TWINKLE_MAX_STEPS', '10'))
+TRAIN_SAMPLES = int(os.environ.get('TWINKLE_TRAIN_SAMPLE_LIMIT', '160'))
+BATCH_SIZE = int(os.environ.get('TWINKLE_BATCH_SIZE', '16'))
+
+# 8 cards: dp=2, tp=2, pp=2
+device_mesh = DeviceMesh.from_sizes(dp_size=2, tp_size=2, pp_size=2)
+twinkle.initialize(mode='local', global_device_mesh=device_mesh)
+
+logger = get_logger()
+
+
+def build_dataloader() -> DataLoader:
+    dataset = Dataset(dataset_meta=DatasetMeta(DATASET_PATH, data_slice=range(TRAIN_SAMPLES)))
+    dataset.set_template('Template', model_id=MODEL_ID)
+    dataset.map(SelfCognitionProcessor('twinkle大模型', 'ModelScope社区'))
+    dataset.encode()
+    return DataLoader(dataset=dataset, batch_size=BATCH_SIZE)
+
+
+def train():
+    dataloader = build_dataloader()
+
+    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)
+
+    # Keep the scheduler compatible with the shortened smoke run.
+    lr_decay_steps = max(MAX_STEPS, 2)
+    model.set_lr_scheduler(
+        scheduler_cls='default',
+        lr_warmup_steps=1,
+        lr_decay_steps=lr_decay_steps,
+    )
+
+    logger.info(get_device_placement())
+    logger.info(model.get_train_configs())
+    logger.info(
+        'LoRA NPU smoke config: '
+        f'model_id={MODEL_ID}, dataset={DATASET_PATH}, batch_size={BATCH_SIZE}, '
+        f'train_samples={TRAIN_SAMPLES}, max_steps={MAX_STEPS}'
+    )
+    logger.info(f'dataloader_steps={len(dataloader)}')
+
+    for step, batch in enumerate(dataloader):
+        model.forward_backward(inputs=batch)
+        model.clip_grad_and_step()
+        metric = model.calculate_metric(is_training=True)
+        logger.info(f'step={step} metric={metric}')
+        if step + 1 >= MAX_STEPS:
+            break
+
+    model.save('last-checkpoint')
+
+
+if __name__ == '__main__':
+    train()

cookbook/megatron/npu/tp_lora_npu.sh

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+#!/usr/bin/env bash
+
+MEGATRON_LM_PATH=${MEGATRON_LM_PATH:-/path/to/Megatron-LM}
+ASCEND_RT_VISIBLE_DEVICES=${ASCEND_RT_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}
+
+export PYTHONPATH="${MEGATRON_LM_PATH}:${PYTHONPATH:-}"
+
+ASCEND_RT_VISIBLE_DEVICES="${ASCEND_RT_VISIBLE_DEVICES}" \
+torchrun --nproc_per_node=8 cookbook/megatron/npu/tp_lora_npu.py

cookbook/megatron/npu/tp_moe_lora_npu.py

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+import os
+
+from peft import LoraConfig
+
+import twinkle
+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
+
+# Build a device mesh for the verified NPU MoE LoRA smoke.
+# Expert LoRA currently only supports ETP=1, so we keep TP at 1 here.
+MODEL_ID = os.environ.get(
+    'TWINKLE_LOCAL_MODEL_DIR',
+    'ms://Qwen/Qwen3-30B-A3B-Instruct-2507',
+)
+DATASET_PATH = os.environ.get(
+    'TWINKLE_LOCAL_DATASET_PATH',
+    'ms://swift/self-cognition',
+)
+MAX_STEPS = int(os.environ.get('TWINKLE_MAX_STEPS', '10'))
+TRAIN_SAMPLES = int(os.environ.get('TWINKLE_TRAIN_SAMPLE_LIMIT', '80'))
+BATCH_SIZE = int(os.environ.get('TWINKLE_BATCH_SIZE', '8'))
+DP_SIZE = int(os.environ.get('TWINKLE_DP_SIZE', '8'))
+TP_SIZE = int(os.environ.get('TWINKLE_TP_SIZE', '1'))
+EP_SIZE = int(os.environ.get('TWINKLE_EP_SIZE', '2'))
+PP_SIZE = int(os.environ.get('TWINKLE_PP_SIZE', '1'))
+CP_SIZE = int(os.environ.get('TWINKLE_CP_SIZE', '1'))
+LR = float(os.environ.get('TWINKLE_LR', '1e-4'))
+
+# 8 cards: dp=8, tp=1, ep=2, pp=1, cp=1
+device_mesh = DeviceMesh.from_sizes(
+    dp_size=DP_SIZE,
+    tp_size=TP_SIZE,
+    pp_size=PP_SIZE,
+    cp_size=CP_SIZE,
+    ep_size=EP_SIZE,
+)
+twinkle.initialize(mode='local', global_device_mesh=device_mesh)
+
+logger = get_logger()
+
+
+def build_dataloader() -> DataLoader:
+    dataset = Dataset(dataset_meta=DatasetMeta(DATASET_PATH, data_slice=range(TRAIN_SAMPLES)))
+    dataset.set_template('Template', model_id=MODEL_ID)
+    dataset.map(SelfCognitionProcessor('twinkle大模型', 'ModelScope社区'))
+    dataset.encode()
+    return DataLoader(dataset=dataset, batch_size=BATCH_SIZE)
+
+
+def _to_loss_value(outputs) -> float:
+    loss = outputs['loss'] if isinstance(outputs, dict) else outputs.loss
+    return float(loss.detach().cpu()) if hasattr(loss, 'detach') else float(loss)
+
+
+def train():
+    dataloader = build_dataloader()
+
+    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=LR)
+
+    # Keep the scheduler compatible with the shortened smoke run.
+    lr_decay_steps = max(MAX_STEPS, 2)
+    model.set_lr_scheduler(
+        scheduler_cls='default',
+        lr_warmup_steps=1,
+        lr_decay_steps=lr_decay_steps,
+    )
+
+    logger.info(get_device_placement())
+    logger.info(model.get_train_configs())
+    logger.info(
+        'MoE LoRA NPU smoke config: '
+        f'model_id={MODEL_ID}, dataset={DATASET_PATH}, batch_size={BATCH_SIZE}, '
+        f'train_samples={TRAIN_SAMPLES}, max_steps={MAX_STEPS}, '
+        f'dp={DP_SIZE}, tp={TP_SIZE}, ep={EP_SIZE}, pp={PP_SIZE}, cp={CP_SIZE}'
+    )
+    logger.info(f'dataloader_steps={len(dataloader)}')
+
+    for step, batch in enumerate(dataloader):
+        outputs = model.forward_backward(inputs=batch)
+        model.clip_grad_and_step()
+        logger.info(f'step={step} loss={_to_loss_value(outputs)}')
+        if step + 1 >= MAX_STEPS:
+            break
+
+    model.save('last-checkpoint')
+
+
+if __name__ == '__main__':
+    train()

cookbook/megatron/npu/tp_moe_lora_npu.sh

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+#!/usr/bin/env bash
+
+MEGATRON_LM_PATH=${MEGATRON_LM_PATH:-/path/to/Megatron-LM}
+ASCEND_RT_VISIBLE_DEVICES=${ASCEND_RT_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}
+
+export PYTHONPATH="${MEGATRON_LM_PATH}:${PYTHONPATH:-}"
+
+ASCEND_RT_VISIBLE_DEVICES="${ASCEND_RT_VISIBLE_DEVICES}" \
+torchrun --nproc_per_node=8 cookbook/megatron/npu/tp_moe_lora_npu.py