中国 AI 芯片生态全景：从寒武纪到昇腾的自主突围之路

# 不同架构的计算特性对比（概念演示）
# 展示 GPU vs NPU vs DSA 的编程模型差异

import numpy as np

# GPU 模式：CUDA 风格的通用并行计算
def gpu_matmul(A, B, threads=1024):
    """GPU 通过大量线程并行执行矩阵乘法"""
    C = np.zeros((A.shape[0], B.shape[1]))
    # 每个线程计算一个输出元素
    for i in range(A.shape[0]):  # 可并行化
        for j in range(B.shape[1]):  # 可并行化
            C[i][j] = np.dot(A[i], B[:, j])
    return C  # O(n³) 但高度并行

# NPU 模式：专用矩阵乘法单元
def npu_matmul(A, B):
    """NPU 硬件内置矩阵乘法器，无需软件调度"""
    # 硬件直接执行 C = A × B，无需线程管理
    # NPU 内部有专用的 Systolic Array（脉动阵列）
    return np.matmul(A, B)  # 硬件加速

# DSA 模式：针对特定网络结构的定制计算
def dsa_conv2d(input_tensor, kernel):
    """DSA 为卷积操作设计了专用数据流架构"""
    # 硬件自动处理数据搬运、复用和累加
    # 不需要软件层面的循环展开
    # DSA 内部有专用的卷积加速器（Convolution Accelerator）
    output_h = input_tensor.shape[0] - kernel.shape[0] + 1
    output_w = input_tensor.shape[1] - kernel.shape[1] + 1
    output = np.zeros((output_h, output_w))
    for i in range(output_h):
        for j in range(output_w):
            output[i][j] = np.sum(
                input_tensor[i:i+kernel.shape[0], j:j+kernel.shape[1]] * kernel
            )
    return output

print("GPU: 通用但需要 CUDA 编程")
print("NPU: 矩阵乘法极快但仅支持神经网络")
print("DSA: 特定操作极致快但灵活性最低")

# 芯片算力评估基准：TOPS（Tera Operations Per Second）
# 不同芯片在同一负载下的性能对比

def calculate_tops(clock_ghz, cores, ops_per_cycle):
    """计算理论峰值算力（TOPS）"""
    tops = clock_ghz * cores * ops_per_cycle / 1e3
    return round(tops, 1)

# 典型 AI 芯片参数对比（2026 年数据）
chips = {
    "NVIDIA H100": {"clock": 1.83, "cores": 16896, "ops": 2},  # FP16 Tensor Core
    "华为昇腾 910B": {"clock": 1.2, "cores": 8192, "ops": 4},    # Da Vinci 架构
    "寒武纪 MLU370-X8": {"clock": 1.0, "cores": 4096, "ops": 8}, # MLUarch03
    "摩尔线程 MTT S4000": {"clock": 1.6, "cores": 12288, "ops": 2}, # MUSA 架构
}

print("=== AI 芯片理论算力对比（TOPS）===")
for name, params in chips.items():
    tops = calculate_tops(**params)
    print(f"{name}: {tops} TOPS")

# 注意：理论算力 ≠ 实际性能
# 实际性能受限于内存带宽、软件栈、通信效率等

# 昇腾 CANN + MindSpore 训练示例
# 展示从 PyTorch 迁移到昇腾平台的代码对比

# === PyTorch 原生代码 ===
import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 10)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)

model = SimpleNet().cuda()  # GPU
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# === 迁移到昇腾平台 ===
import mindspore as ms
from mindspore import nn, Model

class SimpleNetMS(nn.Cell):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Dense(784, 256)
        self.fc2 = nn.Dense(256, 10)
    
    def construct(self, x):
        x = ms.ops.relu(self.fc1(x))
        return self.fc2(x)

# 关键差异：MindSpore 原生支持自动并行
ms.set_context(mode=ms.GRAPH_MODE, device_target="Ascend")
model = SimpleNetMS()

# 自动分布式训练配置（MindSpore 原生支持）
ms.set_auto_parallel_context(
    parallel_mode=ms.ParallelMode.SEMI_AUTO_PARALLEL,
    gradients_mean=True,
    device_num=8  # 8 卡昇腾 910B
)

optimizer = nn.Adam(model.trainable_params(), learning_rate=0.001)
net_with_loss = nn.WithLossCell(model, nn.SoftmaxCrossEntropyWithLogits())
train_net = nn.TrainOneStepCell(net_with_loss, optimizer)

# 寒武纪 MLU 上的模型部署示例
# 使用 PyTorch 插件 + MagicMind 推理引擎

import torch
import torch_mlu  # 寒武纪 PyTorch 插件

# 1. 模型定义（标准 PyTorch 代码，无需修改）
class TransformerBlock(nn.Module):
    def __init__(self, dim=768, heads=12):
        super().__init__()
        self.attention = nn.MultiheadAttention(dim, heads)
        self.ffn = nn.Sequential(
            nn.Linear(dim, dim * 4),
            nn.GELU(),
            nn.Linear(dim * 4, dim)
        )
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
    
    def forward(self, x):
        attn_out, _ = self.attention(x, x, x)
        x = self.norm1(x + attn_out)
        ffn_out = self.ffn(x)
        return self.norm2(x + ffn_out)

# 2. 迁移到 MLU（仅需修改设备名）
model = TransformerBlock().to('mlu')  # 'cuda' → 'mlu'

# 3. 使用 MagicMind 进行推理优化
from cnmagicmind import MMBuilder

builder = MMBuilder()
builder.set_device('MLU370')  # 指定目标芯片
builder.build_from_torch(model, 
    input_shape=(1, 512, 768),
    precision='int8',  # INT8 量化
    optimize_level='high'  # 高级优化
)

# 4. 量化后的模型在 MLU 上运行
quantized_model = builder.export()
output = quantized_model(input_tensor)  # INT8 推理

# 摩尔线程 MUSA：CUDA 兼容代码示例
# 展示 CUDA 代码如何迁移到 MUSA 平台

# === 原始 CUDA 代码（NVIDIA GPU）===
import torch

def train_step(model, data, labels):
    # CUDA 原生训练循环
    model.cuda()
    optimizer = torch.optim.Adam(model.parameters())
    
    with torch.cuda.amp.autocast():  # 自动混合精度
        output = model(data)
        loss = torch.nn.functional.cross_entropy(output, labels)
    
    loss.backward()
    optimizer.step()
    return loss.item()

# === 迁移到 MUSA（摩尔线程）===
# 关键变化：仅修改设备名和导入
import torch_musa  # MUSA 的 PyTorch 后端

def train_step_musa(model, data, labels):
    model.musa()  # .cuda() → .musa()
    optimizer = torch.optim.Adam(model.parameters())
    
    with torch.musa.amp.autocast():  # MUSA 也支持自动混合精度
        output = model(data)
        loss = torch.nn.functional.cross_entropy(output, labels)
    
    loss.backward()
    optimizer.step()
    return loss.item()

# 大多数 CUDA 代码只需做以下替换：
# torch.cuda → torch.musa
# .cuda() → .musa()
# import torch_musa  # 额外的导入

# 复杂场景：自定义 CUDA kernel 需要重写为 MUSA kernel
# MUSA 提供 musa_kernel 装饰器，语法与 CUDA __global__ 类似

# AI 芯片选型评估工具
# 根据项目需求自动推荐最合适的国产芯片方案

from dataclasses import dataclass
from typing import List, Optional

@dataclass
class ProjectRequirements:
    workload: str       # "training" 或 "inference"
    model_type: str     # "llm", "cnn", "transformer", "recommendation"
    batch_size: int     # 批量大小
    max_latency_ms: Optional[float] = None  # 最大延迟要求
    max_power_w: Optional[float] = None      # 最大功耗限制
    framework: str      # "pytorch", "tensorflow", "mindspore"
    cuda_dependency: bool = True  # 是否依赖 CUDA 代码

@dataclass
class ChipCandidate:
    name: str
    type: str           # "gpu", "npu", "dsa"
    training: bool
    inference: bool
    cuda_compat: float  # 0-1 兼容性评分
    framework_support: List[str]
    best_for: List[str]
    estimated_perf: str
    risk_level: str     # "low", "medium", "high"

def recommend_chip(req: ProjectRequirements) -> List[ChipCandidate]:
    """根据项目需求推荐芯片方案"""
    
    candidates = []
    
    # 训练场景 + PyTorch + CUDA 依赖 → 摩尔线程
    if req.workload == "training" and req.cuda_dependency:
        candidates.append(ChipCandidate(
            name="摩尔线程 MTT S4000",
            type="gpu",
            training=True, inference=True,
            cuda_compat=0.8,
            framework_support=["pytorch", "tensorflow"],
            best_for=["大模型训练", "CV 训练"],
            estimated_perf="约 H100 的 40-60%",
            risk_level="medium"
        ))
    
    # 训练场景 + MindSpore → 昇腾
    if req.workload == "training" or req.framework == "mindspore":
        candidates.append(ChipCandidate(
            name="华为昇腾 910B",
            type="npu",
            training=True, inference=True,
            cuda_compat=0.3,
            framework_support=["mindspore", "pytorch", "tensorflow"],
            best_for=["全场景训练", "政企云", "大模型训练"],
            estimated_perf="约 H100 的 50-70%",
            risk_level="low"
        ))
    
    # 推理场景 + CNN → 寒武纪
    if req.workload == "inference" and req.model_type == "cnn":
        candidates.append(ChipCandidate(
            name="寒武纪 MLU370-X8",
            type="dsa",
            training=False, inference=True,
            cuda_compat=0.6,
            framework_support=["pytorch", "tensorflow"],
            best_for=["图像推理", "视频分析", "智能安防"],
            estimated_perf="T4 的 1.5-2 倍（CNN 场景）",
            risk_level="medium"
        ))
    
    # 推荐系统推理 → 平头哥
    if req.model_type == "recommendation":
        candidates.append(ChipCandidate(
            name="平头哥含光 800",
            type="npu",
            training=False, inference=True,
            cuda_compat=0.2,
            framework_support=["tensorflow"],
            best_for=["推荐系统", "搜索广告"],
            estimated_perf="定制化优化，特定场景领先",
            risk_level="medium"
        ))
    
    # 按风险等级排序（低风险优先）
    risk_order = {"low": 0, "medium": 1, "high": 2}
    candidates.sort(key=lambda c: risk_order[c.risk_level])
    
    return candidates

# 使用示例
req = ProjectRequirements(
    workload="training",
    model_type="llm",
    batch_size=32,
    framework="pytorch",
    cuda_dependency=True
)
results = recommend_chip(req)
for chip in results:
    print(f"推荐: {chip.name} | 风险: {chip.risk_level} | 性能: {chip.estimated_perf}")