NPU 与专用 AI 芯片

"""
华为昇腾 Kubernetes 集成
"""
from typing import Dict, List, Optional
from dataclasses import dataclass
import yaml


@dataclass
class AscendDeviceConfig:
    """昇腾设备配置"""
    device_id: int
    chip_type: str  # "910B", "310P"
    memory_size: int  # GB
    health_status: str


class AscendDevicePlugin:
    """昇腾设备插件管理"""

    def __init__(self, namespace: str = "kube-system"):
        self.namespace = namespace

    def generate_device_plugin_daemonset(self) -> str:
        """生成设备插件 DaemonSet"""
        return f"""
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: ascend-device-plugin
  namespace: {self.namespace}
spec:
  selector:
    matchLabels:
      name: ascend-device-plugin
  template:
    metadata:
      labels:
        name: ascend-device-plugin
    spec:
      tolerations:
      - key: huawei.com/Ascend910
        operator: Exists
        effect: NoSchedule
      - key: huawei.com/Ascend310
        operator: Exists
        effect: NoSchedule
      priorityClassName: system-node-critical
      containers:
      - name: device-plugin
        image: ascendhub.huawei.com/public-ascendhub/ascend-k8sdeviceplugin:v5.0.0
        imagePullPolicy: Always
        command: ["/bin/bash", "-c", "--"]
        args: ["ascend-k8sdeviceplugin -useAscendDocker=true"]
        securityContext:
          privileged: true
        env:
        - name: NODE_NAME
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        volumeMounts:
        - name: device-plugin
          mountPath: /var/lib/kubelet/device-plugins
        - name: hiai-driver
          mountPath: /usr/local/Ascend/driver
        - name: log-npu
          mountPath: /var/log/devicePlugin
      volumes:
      - name: device-plugin
        hostPath:
          path: /var/lib/kubelet/device-plugins
      - name: hiai-driver
        hostPath:
          path: /usr/local/Ascend/driver
      - name: log-npu
        hostPath:
          path: /var/log/devicePlugin
          type: DirectoryOrCreate
"""

    def generate_pod_spec(
        self,
        name: str,
        image: str,
        npu_count: int = 1,
        chip_type: str = "910B"
    ) -> Dict:
        """生成使用昇腾 NPU 的 Pod 规格"""
        resource_name = f"huawei.com/Ascend{chip_type}"

        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {
                "name": name,
                "annotations": {
                    # 昇腾特定注解
                    "ascend.huawei.com/driver-version": "latest"
                }
            },
            "spec": {
                "containers": [{
                    "name": "npu-workload",
                    "image": image,
                    "resources": {
                        "limits": {
                            resource_name: str(npu_count)
                        }
                    },
                    "volumeMounts": [{
                        "name": "ascend-driver",
                        "mountPath": "/usr/local/Ascend/driver"
                    }, {
                        "name": "hccl-config",
                        "mountPath": "/user/config/hccl.json",
                        "subPath": "hccl.json"
                    }],
                    "env": [{
                        "name": "ASCEND_VISIBLE_DEVICES",
                        "value": ",".join(str(i) for i in range(npu_count))
                    }]
                }],
                "volumes": [{
                    "name": "ascend-driver",
                    "hostPath": {
                        "path": "/usr/local/Ascend/driver"
                    }
                }, {
                    "name": "hccl-config",
                    "configMap": {
                        "name": "hccl-config"
                    }
                }],
                "nodeSelector": {
                    f"accelerator/{chip_type.lower()}": "true"
                }
            }
        }

    def generate_hccl_config(
        self,
        server_count: int,
        device_count_per_server: int = 8,
        rank_table_file: str = None
    ) -> Dict:
        """生成 HCCL 通信配置"""
        config = {
            "server_count": str(server_count),
            "server_list": []
        }

        for server_id in range(server_count):
            server = {
                "server_id": f"server_{server_id}",
                "device": []
            }
            for device_id in range(device_count_per_server):
                device = {
                    "device_id": str(device_id),
                    "device_ip": f"192.168.{server_id}.{device_id + 1}",
                    "rank_id": str(server_id * device_count_per_server + device_id)
                }
                server["device"].append(device)
            config["server_list"].append(server)

        return {
            "apiVersion": "v1",
            "kind": "ConfigMap",
            "metadata": {
                "name": "hccl-config"
            },
            "data": {
                "hccl.json": yaml.dump(config)
            }
        }


class MindSporeJob:
    """MindSpore 训练任务"""

    def __init__(self, name: str, namespace: str = "default"):
        self.name = name
        self.namespace = namespace

    def generate_training_job(
        self,
        image: str,
        command: List[str],
        worker_count: int = 1,
        npu_per_worker: int = 8,
        chip_type: str = "910B"
    ) -> Dict:
        """生成分布式训练 Job"""
        resource_name = f"huawei.com/Ascend{chip_type}"

        return {
            "apiVersion": "mindspore.gitee.com/v1",
            "kind": "MSJob",
            "metadata": {
                "name": self.name,
                "namespace": self.namespace
            },
            "spec": {
                "runPolicy": {
                    "cleanPodPolicy": "None"
                },
                "replicaSpecs": {
                    "Worker": {
                        "replicas": worker_count,
                        "template": {
                            "spec": {
                                "containers": [{
                                    "name": "mindspore",
                                    "image": image,
                                    "command": command,
                                    "resources": {
                                        "limits": {
                                            resource_name: str(npu_per_worker),
                                            "memory": "256Gi"
                                        }
                                    },
                                    "env": [{
                                        "name": "RANK_SIZE",
                                        "value": str(worker_count * npu_per_worker)
                                    }, {
                                        "name": "DEVICE_NUM",
                                        "value": str(npu_per_worker)
                                    }],
                                    "volumeMounts": [{
                                        "name": "ascend-driver",
                                        "mountPath": "/usr/local/Ascend/driver"
                                    }, {
                                        "name": "data",
                                        "mountPath": "/data"
                                    }, {
                                        "name": "output",
                                        "mountPath": "/output"
                                    }]
                                }],
                                "volumes": [{
                                    "name": "ascend-driver",
                                    "hostPath": {
                                        "path": "/usr/local/Ascend/driver"
                                    }
                                }, {
                                    "name": "data",
                                    "persistentVolumeClaim": {
                                        "claimName": "training-data-pvc"
                                    }
                                }, {
                                    "name": "output",
                                    "persistentVolumeClaim": {
                                        "claimName": "training-output-pvc"
                                    }
                                }]
                            }
                        }
                    }
                }
            }
        }


# 使用示例
if __name__ == "__main__":
    # 设备插件
    plugin = AscendDevicePlugin()
    print(plugin.generate_device_plugin_daemonset())

    # Pod 示例
    pod_spec = plugin.generate_pod_spec(
        name="ascend-inference",
        image="ascendhub.huawei.com/public-ascendhub/mindspore:2.2.0",
        npu_count=1,
        chip_type="910B"
    )
    print(yaml.dump(pod_spec))

    # 训练任务
    job = MindSporeJob("llm-training")
    training_job = job.generate_training_job(
        image="ascendhub.huawei.com/public-ascendhub/mindspore:2.2.0",
        command=["python", "train.py"],
        worker_count=4,
        npu_per_worker=8
    )
    print(yaml.dump(training_job))

"""
Intel Gaudi Kubernetes 集成
"""
from typing import Dict, List, Optional
from dataclasses import dataclass
import yaml
import json


class GaudiDevicePlugin:
    """Gaudi 设备插件管理"""

    def __init__(self, namespace: str = "habana-system"):
        self.namespace = namespace

    def generate_device_plugin_daemonset(self) -> str:
        """生成 Habana 设备插件 DaemonSet"""
        return f"""
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: habanalabs-device-plugin
  namespace: {self.namespace}
spec:
  selector:
    matchLabels:
      name: habanalabs-device-plugin
  template:
    metadata:
      labels:
        name: habanalabs-device-plugin
    spec:
      tolerations:
      - key: habana.ai/gaudi
        operator: Exists
        effect: NoSchedule
      priorityClassName: system-node-critical
      containers:
      - name: habanalabs-device-plugin
        image: vault.habana.ai/gaudi-docker/1.14.0/habanalabs/habana-k8s-device-plugin:latest
        imagePullPolicy: Always
        resources:
          limits:
            memory: 500Mi
          requests:
            cpu: 50m
            memory: 100Mi
        securityContext:
          privileged: true
        volumeMounts:
        - name: device-plugin
          mountPath: /var/lib/kubelet/device-plugins
        - name: habana-container
          mountPath: /var/run/habana
      volumes:
      - name: device-plugin
        hostPath:
          path: /var/lib/kubelet/device-plugins
      - name: habana-container
        hostPath:
          path: /var/run/habana

---
# Habana Runtime ConfigMap
apiVersion: v1
kind: ConfigMap
metadata:
  name: habana-runtime-config
  namespace: {self.namespace}
data:
  habana.json: |
    {{
      "hl_init_disable_topology_check": "1",
      "hl_init_enable_huge_pages": "1"
    }}
"""

    def generate_pod_spec(
        self,
        name: str,
        image: str,
        gaudi_count: int = 1,
        memory: str = "128Gi"
    ) -> Dict:
        """生成使用 Gaudi 的 Pod 规格"""
        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {
                "name": name
            },
            "spec": {
                "containers": [{
                    "name": "gaudi-workload",
                    "image": image,
                    "resources": {
                        "limits": {
                            "habana.ai/gaudi": str(gaudi_count),
                            "memory": memory
                        },
                        "requests": {
                            "habana.ai/gaudi": str(gaudi_count),
                            "memory": memory
                        }
                    },
                    "env": [{
                        "name": "HABANA_VISIBLE_DEVICES",
                        "value": "all"
                    }, {
                        "name": "OMPI_MCA_btl_vader_single_copy_mechanism",
                        "value": "none"
                    }],
                    "volumeMounts": [{
                        "name": "habana-driver",
                        "mountPath": "/usr/lib/habanalabs"
                    }]
                }],
                "volumes": [{
                    "name": "habana-driver",
                    "hostPath": {
                        "path": "/usr/lib/habanalabs"
                    }
                }],
                "nodeSelector": {
                    "accelerator/gaudi": "true"
                }
            }
        }


class GaudiTrainingJob:
    """Gaudi 训练任务"""

    def __init__(self, name: str, namespace: str = "default"):
        self.name = name
        self.namespace = namespace

    def generate_pytorch_job(
        self,
        image: str,
        script: str,
        worker_count: int = 1,
        gaudi_per_worker: int = 8,
        use_deepspeed: bool = True
    ) -> Dict:
        """生成 PyTorch 分布式训练 Job"""
        env_vars = [{
            "name": "WORLD_SIZE",
            "value": str(worker_count * gaudi_per_worker)
        }, {
            "name": "LOCAL_WORLD_SIZE",
            "value": str(gaudi_per_worker)
        }, {
            "name": "MASTER_ADDR",
            "value": f"{self.name}-worker-0"
        }, {
            "name": "MASTER_PORT",
            "value": "29500"
        }]

        if use_deepspeed:
            env_vars.extend([{
                "name": "USE_DEEPSPEED",
                "value": "1"
            }, {
                "name": "DEEPSPEED_HPU_ZERO3_SYNC_MARK_STEP_REQUIRED",
                "value": "1"
            }])

        return {
            "apiVersion": "kubeflow.org/v1",
            "kind": "PyTorchJob",
            "metadata": {
                "name": self.name,
                "namespace": self.namespace
            },
            "spec": {
                "pytorchReplicaSpecs": {
                    "Worker": {
                        "replicas": worker_count,
                        "restartPolicy": "OnFailure",
                        "template": {
                            "spec": {
                                "containers": [{
                                    "name": "pytorch",
                                    "image": image,
                                    "command": [
                                        "python",
                                        "-u",
                                        script
                                    ],
                                    "resources": {
                                        "limits": {
                                            "habana.ai/gaudi": str(gaudi_per_worker),
                                            "memory": "512Gi",
                                            "hugepages-2Mi": "95000Mi"
                                        }
                                    },
                                    "env": env_vars,
                                    "volumeMounts": [{
                                        "name": "shm",
                                        "mountPath": "/dev/shm"
                                    }, {
                                        "name": "data",
                                        "mountPath": "/data"
                                    }]
                                }],
                                "volumes": [{
                                    "name": "shm",
                                    "emptyDir": {
                                        "medium": "Memory",
                                        "sizeLimit": "32Gi"
                                    }
                                }, {
                                    "name": "data",
                                    "persistentVolumeClaim": {
                                        "claimName": "training-data"
                                    }
                                }],
                                "nodeSelector": {
                                    "accelerator/gaudi": "true"
                                },
                                "tolerations": [{
                                    "key": "habana.ai/gaudi",
                                    "operator": "Exists",
                                    "effect": "NoSchedule"
                                }]
                            }
                        }
                    }
                }
            }
        }

    def generate_llm_training_config(
        self,
        model_name: str,
        batch_size: int,
        gradient_accumulation: int,
        learning_rate: float
    ) -> Dict:
        """生成 LLM 训练配置"""
        return {
            "model": {
                "name": model_name,
                "dtype": "bf16"
            },
            "training": {
                "batch_size": batch_size,
                "gradient_accumulation_steps": gradient_accumulation,
                "learning_rate": learning_rate,
                "warmup_steps": 100,
                "max_steps": 10000
            },
            "gaudi_config": {
                "use_habana": True,
                "use_lazy_mode": True,
                "use_hpu_graphs": True,
                "throughput_warmup_steps": 3
            },
            "deepspeed": {
                "train_batch_size": batch_size * gradient_accumulation,
                "fp16": {
                    "enabled": False
                },
                "bf16": {
                    "enabled": True
                },
                "zero_optimization": {
                    "stage": 3,
                    "offload_optimizer": {
                        "device": "cpu",
                        "pin_memory": True
                    },
                    "overlap_comm": True,
                    "contiguous_gradients": True,
                    "reduce_scatter": True
                }
            }
        }


# 使用示例
if __name__ == "__main__":
    # 设备插件
    plugin = GaudiDevicePlugin()
    print(plugin.generate_device_plugin_daemonset())

    # 训练任务
    job = GaudiTrainingJob("llm-finetune", "ml-training")
    pytorch_job = job.generate_pytorch_job(
        image="vault.habana.ai/gaudi-docker/1.14.0/pytorch:latest",
        script="train_llm.py",
        worker_count=4,
        gaudi_per_worker=8,
        use_deepspeed=True
    )
    print(yaml.dump(pytorch_job))

"""
Google TPU GKE 集成
"""
from typing import Dict, List, Optional
from dataclasses import dataclass
import yaml


@dataclass
class TPUConfig:
    """TPU 配置"""
    tpu_type: str  # "v5e-8", "v5e-16", "v5p-8", etc.
    topology: str  # "2x2x2", "4x4", etc.
    accelerator_count: int


class TPUGKEManager:
    """TPU GKE 管理器"""

    # TPU 类型映射
    TPU_TYPES = {
        "v5e-1": {"chips": 1, "topology": "1x1", "hbm_gb": 16},
        "v5e-4": {"chips": 4, "topology": "2x2", "hbm_gb": 64},
        "v5e-8": {"chips": 8, "topology": "2x4", "hbm_gb": 128},
        "v5e-16": {"chips": 16, "topology": "4x4", "hbm_gb": 256},
        "v5e-64": {"chips": 64, "topology": "8x8", "hbm_gb": 1024},
        "v5e-256": {"chips": 256, "topology": "16x16", "hbm_gb": 4096},
        "v5p-8": {"chips": 8, "topology": "2x2x2", "hbm_gb": 768},
        "v5p-16": {"chips": 16, "topology": "2x2x4", "hbm_gb": 1536},
        "v5p-128": {"chips": 128, "topology": "4x4x8", "hbm_gb": 12288},
    }

    def __init__(self, project: str, zone: str):
        self.project = project
        self.zone = zone

    def create_tpu_node_pool(
        self,
        cluster_name: str,
        node_pool_name: str,
        tpu_type: str,
        node_count: int = 1,
        spot: bool = False
    ) -> Dict:
        """创建 TPU 节点池配置"""
        tpu_info = self.TPU_TYPES.get(tpu_type)
        if not tpu_info:
            raise ValueError(f"Unknown TPU type: {tpu_type}")

        config = {
            "name": node_pool_name,
            "config": {
                "machineType": f"ct5lp-hightpu-{tpu_info['chips']}t",
                "accelerators": [{
                    "acceleratorCount": tpu_info['chips'],
                    "acceleratorType": f"tpu-{tpu_type}",
                    "gpuPartitionSize": None
                }],
                "spot": spot,
                "reservationAffinity": {
                    "consumeReservationType": "NO_RESERVATION"
                }
            },
            "initialNodeCount": node_count,
            "autoscaling": {
                "enabled": True,
                "minNodeCount": 0,
                "maxNodeCount": node_count * 2
            }
        }

        return config

    def generate_tpu_workload(
        self,
        name: str,
        image: str,
        tpu_type: str,
        command: List[str],
        namespace: str = "default"
    ) -> Dict:
        """生成 TPU 工作负载"""
        tpu_info = self.TPU_TYPES.get(tpu_type)
        chip_count = tpu_info['chips'] if tpu_info else 1

        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {
                "name": name,
                "namespace": namespace
            },
            "spec": {
                "containers": [{
                    "name": "tpu-container",
                    "image": image,
                    "command": command,
                    "resources": {
                        "limits": {
                            "google.com/tpu": str(chip_count)
                        }
                    },
                    "env": [{
                        "name": "TPU_NAME",
                        "valueFrom": {
                            "fieldRef": {
                                "fieldPath": "metadata.name"
                            }
                        }
                    }, {
                        "name": "TPU_CHIPS_PER_HOST",
                        "value": str(chip_count)
                    }],
                    "securityContext": {
                        "privileged": True
                    }
                }],
                "nodeSelector": {
                    "cloud.google.com/gke-tpu-accelerator": tpu_type,
                    "cloud.google.com/gke-tpu-topology": tpu_info['topology']
                },
                "tolerations": [{
                    "key": "google.com/tpu",
                    "operator": "Equal",
                    "value": "present",
                    "effect": "NoSchedule"
                }],
                "restartPolicy": "Never"
            }
        }

    def generate_jax_training_job(
        self,
        name: str,
        image: str,
        tpu_type: str,
        script_path: str,
        worker_count: int = 1,
        namespace: str = "default"
    ) -> Dict:
        """生成 JAX 分布式训练 Job"""
        tpu_info = self.TPU_TYPES.get(tpu_type)
        chips_per_host = tpu_info['chips'] if tpu_info else 8

        return {
            "apiVersion": "batch/v1",
            "kind": "Job",
            "metadata": {
                "name": name,
                "namespace": namespace
            },
            "spec": {
                "parallelism": worker_count,
                "completions": worker_count,
                "completionMode": "Indexed",
                "template": {
                    "spec": {
                        "subdomain": f"{name}-svc",
                        "containers": [{
                            "name": "jax-trainer",
                            "image": image,
                            "command": [
                                "python",
                                script_path
                            ],
                            "resources": {
                                "limits": {
                                    "google.com/tpu": str(chips_per_host)
                                }
                            },
                            "env": [{
                                "name": "JAX_COORDINATOR_ADDRESS",
                                "value": f"{name}-svc-0.{name}-svc:8080"
                            }, {
                                "name": "JAX_PROCESS_COUNT",
                                "value": str(worker_count)
                            }, {
                                "name": "JAX_PROCESS_ID",
                                "valueFrom": {
                                    "fieldRef": {
                                        "fieldPath": "metadata.annotations['batch.kubernetes.io/job-completion-index']"
                                    }
                                }
                            }, {
                                "name": "TPU_CHIPS_PER_PROCESS_BOUNDS",
                                "value": str(chips_per_host)
                            }],
                            "ports": [{
                                "containerPort": 8080,
                                "name": "jax-coord"
                            }]
                        }],
                        "nodeSelector": {
                            "cloud.google.com/gke-tpu-accelerator": tpu_type
                        },
                        "tolerations": [{
                            "key": "google.com/tpu",
                            "operator": "Exists",
                            "effect": "NoSchedule"
                        }],
                        "restartPolicy": "Never"
                    }
                }
            }
        }


class TPUSliceManager:
    """TPU Slice 管理"""

    def create_tpu_slice(
        self,
        name: str,
        tpu_type: str,
        accelerator_type: str = "v5e",
        runtime_version: str = "tpu-ubuntu2204-base"
    ) -> Dict:
        """创建 TPU Slice (Queued Resource)"""
        return {
            "apiVersion": "cloud.google.com/v1alpha1",
            "kind": "QueuedResource",
            "metadata": {
                "name": name
            },
            "spec": {
                "tpu": {
                    "nodeSpec": [{
                        "parent": "projects/{project}/locations/{zone}",
                        "node": {
                            "acceleratorType": tpu_type,
                            "runtimeVersion": runtime_version
                        }
                    }]
                },
                "queueingPolicy": {
                    "validUntilDuration": "3600s"
                }
            }
        }


# JAX 训练脚本示例
JAX_TRAINING_SCRIPT = """
import jax
import jax.numpy as jnp
from jax import random
from flax import linen as nn
from flax.training import train_state
import optax

# 初始化 JAX 分布式
jax.distributed.initialize()

print(f"Process {jax.process_index()} of {jax.process_count()}")
print(f"Local devices: {jax.local_devices()}")
print(f"Global devices: {jax.devices()}")

# 定义简单模型
class SimpleMLP(nn.Module):
    hidden_dim: int = 256
    output_dim: int = 10

    @nn.compact
    def __call__(self, x):
        x = nn.Dense(self.hidden_dim)(x)
        x = nn.relu(x)
        x = nn.Dense(self.output_dim)(x)
        return x

# 创建模型和优化器
model = SimpleMLP()
key = random.PRNGKey(0)
params = model.init(key, jnp.ones((1, 784)))

# 分布式训练
@jax.pmap
def train_step(state, batch):
    def loss_fn(params):
        logits = state.apply_fn(params, batch['image'])
        return optax.softmax_cross_entropy_with_integer_labels(
            logits, batch['label']
        ).mean()

    grads = jax.grad(loss_fn)(state.params)
    grads = jax.lax.pmean(grads, axis_name='devices')
    state = state.apply_gradients(grads=grads)
    return state

# 训练循环
print("Training started...")
# ... 训练代码 ...
"""


# 使用示例
if __name__ == "__main__":
    manager = TPUGKEManager(project="my-project", zone="us-central2-b")

    # 创建节点池配置
    node_pool = manager.create_tpu_node_pool(
        cluster_name="my-cluster",
        node_pool_name="tpu-v5e-pool",
        tpu_type="v5e-8",
        node_count=2
    )
    print(yaml.dump(node_pool))

    # 生成训练 Job
    job = manager.generate_jax_training_job(
        name="llm-training",
        image="gcr.io/my-project/jax-training:latest",
        tpu_type="v5e-16",
        script_path="/app/train.py",
        worker_count=4
    )
    print(yaml.dump(job))

"""
多种 AI 芯片统一管理抽象
"""
from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Any
from dataclasses import dataclass
from enum import Enum
import yaml


class AcceleratorType(Enum):
    """加速器类型"""
    NVIDIA_GPU = "nvidia_gpu"
    AMD_GPU = "amd_gpu"
    INTEL_GAUDI = "intel_gaudi"
    HUAWEI_ASCEND = "huawei_ascend"
    GOOGLE_TPU = "google_tpu"
    AWS_TRAINIUM = "aws_trainium"
    AWS_INFERENTIA = "aws_inferentia"


@dataclass
class AcceleratorSpec:
    """加速器规格"""
    type: AcceleratorType
    model: str
    memory_gb: int
    compute_capability: str
    count: int = 1
    topology: Optional[str] = None


@dataclass
class AcceleratorStatus:
    """加速器状态"""
    id: str
    type: AcceleratorType
    utilization: float
    memory_used: int
    memory_total: int
    temperature: int
    power_usage: int
    healthy: bool


class AcceleratorPlugin(ABC):
    """加速器插件抽象基类"""

    @abstractmethod
    def get_resource_name(self) -> str:
        """获取 Kubernetes 资源名称"""
        pass

    @abstractmethod
    def generate_device_plugin_config(self) -> Dict:
        """生成设备插件配置"""
        pass

    @abstractmethod
    def generate_pod_spec(
        self,
        name: str,
        image: str,
        accelerator_count: int,
        **kwargs
    ) -> Dict:
        """生成 Pod 规格"""
        pass

    @abstractmethod
    def get_status(self) -> List[AcceleratorStatus]:
        """获取加速器状态"""
        pass


class NVIDIAGPUPlugin(AcceleratorPlugin):
    """NVIDIA GPU 插件"""

    def get_resource_name(self) -> str:
        return "nvidia.com/gpu"

    def generate_device_plugin_config(self) -> Dict:
        return {
            "version": "v1",
            "flags": {
                "migStrategy": "mixed"
            }
        }

    def generate_pod_spec(
        self,
        name: str,
        image: str,
        accelerator_count: int,
        **kwargs
    ) -> Dict:
        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {"name": name},
            "spec": {
                "containers": [{
                    "name": "main",
                    "image": image,
                    "resources": {
                        "limits": {
                            self.get_resource_name(): str(accelerator_count)
                        }
                    }
                }]
            }
        }

    def get_status(self) -> List[AcceleratorStatus]:
        # 实际实现会调用 NVML
        return []


class IntelGaudiPlugin(AcceleratorPlugin):
    """Intel Gaudi 插件"""

    def get_resource_name(self) -> str:
        return "habana.ai/gaudi"

    def generate_device_plugin_config(self) -> Dict:
        return {
            "version": "v1",
            "habana": {
                "enabled": True
            }
        }

    def generate_pod_spec(
        self,
        name: str,
        image: str,
        accelerator_count: int,
        **kwargs
    ) -> Dict:
        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {"name": name},
            "spec": {
                "containers": [{
                    "name": "main",
                    "image": image,
                    "resources": {
                        "limits": {
                            self.get_resource_name(): str(accelerator_count),
                            "hugepages-2Mi": "95000Mi"
                        }
                    },
                    "env": [{
                        "name": "HABANA_VISIBLE_DEVICES",
                        "value": "all"
                    }]
                }],
                "tolerations": [{
                    "key": "habana.ai/gaudi",
                    "operator": "Exists",
                    "effect": "NoSchedule"
                }]
            }
        }

    def get_status(self) -> List[AcceleratorStatus]:
        return []


class HuaweiAscendPlugin(AcceleratorPlugin):
    """华为昇腾插件"""

    def __init__(self, chip_type: str = "910B"):
        self.chip_type = chip_type

    def get_resource_name(self) -> str:
        return f"huawei.com/Ascend{self.chip_type}"

    def generate_device_plugin_config(self) -> Dict:
        return {
            "version": "v1",
            "ascend": {
                "chipType": self.chip_type
            }
        }

    def generate_pod_spec(
        self,
        name: str,
        image: str,
        accelerator_count: int,
        **kwargs
    ) -> Dict:
        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {"name": name},
            "spec": {
                "containers": [{
                    "name": "main",
                    "image": image,
                    "resources": {
                        "limits": {
                            self.get_resource_name(): str(accelerator_count)
                        }
                    },
                    "volumeMounts": [{
                        "name": "ascend-driver",
                        "mountPath": "/usr/local/Ascend/driver"
                    }]
                }],
                "volumes": [{
                    "name": "ascend-driver",
                    "hostPath": {
                        "path": "/usr/local/Ascend/driver"
                    }
                }]
            }
        }

    def get_status(self) -> List[AcceleratorStatus]:
        return []


class UnifiedAcceleratorManager:
    """统一加速器管理器"""

    def __init__(self):
        self.plugins: Dict[AcceleratorType, AcceleratorPlugin] = {
            AcceleratorType.NVIDIA_GPU: NVIDIAGPUPlugin(),
            AcceleratorType.INTEL_GAUDI: IntelGaudiPlugin(),
            AcceleratorType.HUAWEI_ASCEND: HuaweiAscendPlugin()
        }

    def register_plugin(
        self,
        acc_type: AcceleratorType,
        plugin: AcceleratorPlugin
    ):
        """注册加速器插件"""
        self.plugins[acc_type] = plugin

    def get_plugin(self, acc_type: AcceleratorType) -> AcceleratorPlugin:
        """获取加速器插件"""
        if acc_type not in self.plugins:
            raise ValueError(f"Unknown accelerator type: {acc_type}")
        return self.plugins[acc_type]

    def generate_workload(
        self,
        name: str,
        image: str,
        accelerator_type: AcceleratorType,
        accelerator_count: int,
        **kwargs
    ) -> Dict:
        """生成工作负载配置"""
        plugin = self.get_plugin(accelerator_type)
        return plugin.generate_pod_spec(
            name=name,
            image=image,
            accelerator_count=accelerator_count,
            **kwargs
        )

    def generate_multi_accelerator_workload(
        self,
        name: str,
        specs: List[Dict[str, Any]]
    ) -> Dict:
        """
        生成多加速器工作负载

        specs: [
            {"type": "nvidia_gpu", "count": 2, "image": "..."},
            {"type": "intel_gaudi", "count": 4, "image": "..."}
        ]
        """
        containers = []
        tolerations = []
        volumes = []
        volume_mounts = []

        for i, spec in enumerate(specs):
            acc_type = AcceleratorType(spec["type"])
            plugin = self.get_plugin(acc_type)
            resource_name = plugin.get_resource_name()

            container = {
                "name": f"container-{i}",
                "image": spec.get("image", ""),
                "resources": {
                    "limits": {
                        resource_name: str(spec["count"])
                    }
                }
            }
            containers.append(container)

            # 添加特定加速器的 tolerations
            if acc_type == AcceleratorType.INTEL_GAUDI:
                tolerations.append({
                    "key": "habana.ai/gaudi",
                    "operator": "Exists",
                    "effect": "NoSchedule"
                })
            elif acc_type == AcceleratorType.HUAWEI_ASCEND:
                tolerations.append({
                    "key": f"huawei.com/Ascend",
                    "operator": "Exists",
                    "effect": "NoSchedule"
                })

        return {
            "apiVersion": "v1",
            "kind": "Pod",
            "metadata": {"name": name},
            "spec": {
                "containers": containers,
                "tolerations": tolerations,
                "volumes": volumes
            }
        }


# 芯片选择器
class AcceleratorSelector:
    """加速器选择器 - 根据工作负载特性推荐最佳芯片"""

    # 芯片能力矩阵
    CAPABILITY_MATRIX = {
        AcceleratorType.NVIDIA_GPU: {
            "training": 0.95,
            "inference": 0.90,
            "flexibility": 1.0,
            "ecosystem": 1.0,
            "cost_efficiency": 0.6
        },
        AcceleratorType.INTEL_GAUDI: {
            "training": 0.85,
            "inference": 0.80,
            "flexibility": 0.7,
            "ecosystem": 0.6,
            "cost_efficiency": 0.85
        },
        AcceleratorType.HUAWEI_ASCEND: {
            "training": 0.88,
            "inference": 0.85,
            "flexibility": 0.65,
            "ecosystem": 0.5,
            "cost_efficiency": 0.8
        },
        AcceleratorType.GOOGLE_TPU: {
            "training": 0.92,
            "inference": 0.70,
            "flexibility": 0.5,
            "ecosystem": 0.55,
            "cost_efficiency": 0.9
        }
    }

    def recommend(
        self,
        workload_type: str,  # "training" | "inference"
        priority: str = "performance",  # "performance" | "cost" | "ecosystem"
        constraints: Optional[Dict] = None
    ) -> List[AcceleratorType]:
        """
        推荐最佳加速器

        Args:
            workload_type: 工作负载类型
            priority: 优先考虑因素
            constraints: 约束条件
        """
        scores = {}

        priority_weights = {
            "performance": {"training": 0.4, "inference": 0.3, "flexibility": 0.2, "ecosystem": 0.1},
            "cost": {"cost_efficiency": 0.6, "training": 0.2, "inference": 0.2},
            "ecosystem": {"ecosystem": 0.5, "flexibility": 0.3, "training": 0.1, "inference": 0.1}
        }

        weights = priority_weights.get(priority, priority_weights["performance"])

        for acc_type, capabilities in self.CAPABILITY_MATRIX.items():
            # 检查约束
            if constraints:
                if constraints.get("exclude_types") and acc_type in constraints["exclude_types"]:
                    continue
                if constraints.get("require_types") and acc_type not in constraints["require_types"]:
                    continue

            score = 0
            for factor, weight in weights.items():
                if factor in capabilities:
                    score += capabilities[factor] * weight
                elif factor == workload_type:
                    score += capabilities.get(factor, 0.5) * weight

            scores[acc_type] = score

        # 排序并返回
        sorted_types = sorted(scores.items(), key=lambda x: x[1], reverse=True)
        return [t[0] for t in sorted_types]


# 使用示例
if __name__ == "__main__":
    # 统一管理器
    manager = UnifiedAcceleratorManager()

    # 生成 NVIDIA GPU 工作负载
    nvidia_pod = manager.generate_workload(
        name="nvidia-training",
        image="pytorch/pytorch:latest",
        accelerator_type=AcceleratorType.NVIDIA_GPU,
        accelerator_count=4
    )
    print("NVIDIA GPU Pod:")
    print(yaml.dump(nvidia_pod))

    # 生成 Gaudi 工作负载
    gaudi_pod = manager.generate_workload(
        name="gaudi-training",
        image="vault.habana.ai/gaudi-docker/1.14.0/pytorch:latest",
        accelerator_type=AcceleratorType.INTEL_GAUDI,
        accelerator_count=8
    )
    print("\nIntel Gaudi Pod:")
    print(yaml.dump(gaudi_pod))

    # 芯片推荐
    selector = AcceleratorSelector()
    recommended = selector.recommend(
        workload_type="training",
        priority="cost"
    )
    print(f"\nRecommended accelerators for cost-optimized training: {recommended}")