平头哥开源T-Head SAIL：真武AI芯片软件栈开源，AI芯片算力解放运动深度解析

// SAIL框架兼容性适配层核心接口
package sail

import (
	"context"
	"errors"
)

// 计算图节点类型
type OpType int

const (
	OpMatMul   OpType = iota // 矩阵乘法
	OpConv                   // 卷积
	OpAttention              // Attention
	OpSoftmax                // Softmax
	OpLayerNorm              // LayerNorm
	OpRelu                   // ReLU激活
	OpGelu                   // GELU激活
	OpReshape                // Reshape
	OpTranspose              // 转置
	OpAdd                    // 加法
)

// 计算图IR（中间表示）
type GraphIR struct {
	Nodes []NodeIR
	Edges []EdgeIR
}

type NodeIR struct {
	ID       int
	Op       OpType
	Inputs   []int  // 输入张量ID列表
	Outputs  []int  // 输出张量ID列表
	Attrs    map[string]interface{} // 算子属性
}

type EdgeIR struct {
	SrcNode   int
	SrcOutput int
	DstNode   int
	DstInput  int
}

// 张量描述
type TensorDesc struct {
	Shape   []int64
	Dtype   string // float32, float16, int8, bf16
	Stride  []int64
	Offset  int64
}

// SAIL编译器的核心接口
type SAILCompiler interface {
	// 从PyTorch/TensorFlow等框架的计算图编译为真武芯片指令
	Compile(ctx context.Context, graph *GraphIR) (Executable, error)
	
	// 查询算子是否支持
	IsOpSupported(op OpType) bool
	
	// 获取算子性能预估（毫秒）
	EstimateOpLatency(op OpType, inputShapes [][]int64) float64
}

// 可执行程序
type Executable interface {
	// 执行编译后的计算图
	Execute(ctx context.Context, inputs map[int]*Tensor) (map[int]*Tensor, error)
	
	// 获取执行统计信息
	Stats() ExecutionStats
}

type ExecutionStats struct {
	TotalLatencyMs float64
	OpLatencies    map[string]float64
	MemoryUsageMB  int64
	PowerDrawW     float64
}

// 框架适配器接口 - 每个框架需要实现
type FrameworkAdapter interface {
	// 将框架原生计算图转换为SAIL IR
	ConvertToIR(model interface{}) (*GraphIR, error)
	
	// 框架名称
	Name() string
	
	// 支持的版本
	SupportedVersions() []string
}

"""
SAIL编译器核心 - 图优化与代码生成
"""
from typing import Dict, List, Tuple, Optional, Set
from dataclasses import dataclass, field
from enum import Enum
import numpy as np

class OpType(Enum):
    MATMUL = "matmul"
    ATTENTION = "attention"
    SOFTMAX = "softmax"
    LAYERNORM = "layernorm"
    GELU = "gelu"
    RESHAPE = "reshape"
    ADD = "add"
    CONV2D = "conv2d"

@dataclass
class Tensor:
    id: int
    shape: List[int]
    dtype: str
    data: Optional[np.ndarray] = None

@dataclass
class Operator:
    op_type: OpType
    inputs: List[int]
    outputs: List[int]
    attrs: Dict = field(default_factory=dict)

@dataclass
class ComputeGraph:
    operators: Dict[int, Operator]
    tensors: Dict[int, Tensor]
    entry_points: List[int]
    exit_points: List[int]

class GraphOptimizer:
    """
    计算图优化器
    实现算子融合、内存优化等关键pass
    """
    
    def fuse_matmul_activation(self, graph: ComputeGraph) -> ComputeGraph:
        """
        Pass 1: 融合 MatMul + Activation
        MatMul + GELU → FusedMatMulGELU
        MatMul + ReLU → FusedMatMulReLU
        """
        fused_ops = {}
        remove_ops = set()
        
        for op_id, op in graph.operators.items():
            if op.op_type == OpType.MATMUL:
                # 检查后继节点是否为activation
                for next_op_id, next_op in graph.operators.items():
                    if next_op_id == op_id:
                        continue
                    if set(op.outputs) & set(next_op.inputs):
                        if next_op.op_type in [OpType.GELU, OpType.RELU]:
                            # 融合
                            fused_type = f"fused_matmul_{next_op.op_type.value}"
                            fused_op = Operator(
                                op_type=OpType(fused_type),
                                inputs=op.inputs,
                                outputs=next_op.outputs,
                                attrs={**op.attrs, **next_op.attrs}
                            )
                            new_id = max(graph.operators.keys()) + 1
                            fused_ops[new_id] = fused_op
                            remove_ops.add(op_id)
                            remove_ops.add(next_op_id)
        
        # 重建计算图
        new_operators = {
            oid: op for oid, op in graph.operators.items() 
            if oid not in remove_ops
        }
        new_operators.update(fused_ops)
        graph.operators = new_operators
        return graph
    
    def fuse_attention(self, graph: ComputeGraph) -> ComputeGraph:
        """
        Pass 2: 融合 Attention 计算链
        QKV投影 → Attention Score → Softmax → Attention Output
        """
        # 查找Attention模式
        qkv_ops = []
        for op_id, op in graph.operators.items():
            if op.op_type == OpType.MATMUL and 'qkv' in str(op.attrs.get('name', '')).lower():
                qkv_ops.append(op_id)
        
        if len(qkv_ops) != 3:
            return graph
        
        # 如果有3个MatMul分别对应Q/K/V投影，且后面跟着attention score计算
        # 融合为单个FusedAttention算子
        attention_start = min(qkv_ops)
        attention_end = max(qkv_ops)
        
        # 查找attention score和后续操作
        all_attention_ops = set()
        visited = {attention_start, attention_end}
        
        def dfs(op_id):
            if op_id in visited:
                return
            visited.add(op_id)
            op = graph.operators[op_id]
            all_attention_ops.add(op_id)
            for output_id in op.outputs:
                for next_oid, next_op in graph.operators.items():
                    if output_id in next_op.inputs:
                        dfs(next_oid)
        
        for op_id in qkv_ops:
            dfs(op_id)
        
        if len(all_attention_ops) < 5:
            return graph
        
        # 融合为单算子
        new_op = Operator(
            op_type=OpType.ATTENTION,
            inputs=graph.operators[attention_start].inputs,
            outputs=graph.operators[max(all_attention_ops)].outputs,
            attrs={"fused_ops": len(all_attention_ops)}
        )
        new_id = max(graph.operators.keys()) + 1
        
        new_operators = {
            oid: op for oid, op in graph.operators.items() 
            if oid not in all_attention_ops
        }
        new_operators[new_id] = new_op
        graph.operators = new_operators
        return graph
    
    def optimize_memory_plan(self, graph: ComputeGraph, 
                              memory_limit_mb: int) -> ComputeGraph:
        """
        Pass 3: 内存优化 - 张量生命周期分析与共享内存分配
        核心思想：在保证正确性的前提下，最大化重用中间张量的内存
        """
        # 计算每个张量的生命周期
        tensor_lifetime: Dict[int, Tuple[int, int]] = {}
        for op_id, op in graph.operators.items():
            for t_id in op.inputs:
                if t_id not in tensor_lifetime:
                    tensor_lifetime[t_id] = (op_id, op_id)
                else:
                    start, end = tensor_lifetime[t_id]
                    tensor_lifetime[t_id] = (start, max(end, op_id))
            for t_id in op.outputs:
                tensor_lifetime[t_id] = (op_id, op_id)
        
        # 贪心内存分配
        allocated = {}  # tensor_id -> memory_offset
        free_blocks = [(0, memory_limit_mb * 1024 * 1024)]  # (offset, size)
        
        # 按创建时间排序
        sorted_tensors = sorted(tensor_lifetime.items(), 
                               key=lambda x: x[1][0])
        
        for t_id, (create_at, destroy_at) in sorted_tensors:
            tensor = graph.tensors.get(t_id)
            if not tensor:
                continue
            
            # 计算所需内存
            tensor_size = int(np.prod(tensor.shape)) * 4  # 假设float32
            if tensor.dtype == 'float16':
                tensor_size //= 2
            
            # 释放已过生命周期的张量
            for alloc_tid, (offset, size) in list(allocated.items()):
                t_create, t_destroy = tensor_lifetime.get(alloc_tid, (0, 0))
                if t_destroy < create_at:
                    free_blocks.append((offset, size))
                    del allocated[alloc_tid]
            
            free_blocks.sort()
            
            # 找到合适的空闲块
            for i, (offset, size) in enumerate(free_blocks):
                if size >= tensor_size:
                    allocated[t_id] = (offset, tensor_size)
                    remaining = size - tensor_size
                    if remaining > 0:
                        free_blocks[i] = (offset + tensor_size, remaining)
                    else:
                        free_blocks.pop(i)
                    break
        
        return graph
    
    def optimize(self, graph: ComputeGraph) -> ComputeGraph:
        """
        完整优化管线
        """
        graph = self.fuse_matmul_activation(graph)
        graph = self.fuse_attention(graph)
        graph = self.optimize_memory_plan(graph, 80 * 1024)  # 80GB HBM
        return graph


class CodeGenerator:
    """
    代码生成器 - 将优化后的计算图转换为真武芯片指令
    """
    
    def __init__(self, chip_name: str = "Zhenwu-M890"):
        self.chip_name = chip_name
        self.instructions: List[str] = []
    
    def generate(self, graph: ComputeGraph) -> str:
        """生成可执行指令序列"""
        self.instructions = []
        
        # 生成加载指令
        for t_id, tensor in graph.tensors.items():
            if tensor.data is not None:
                self._emit(f"LOAD t{t_id} [{tensor.shape}] {tensor.dtype}")
        
        # 按拓扑序生成算子指令
        sorted_ops = self._topological_sort(graph)
        for op_id in sorted_ops:
            op = graph.operators[op_id]
            self._generate_op(op)
        
        # 生成存储指令
        for t_id in graph.exit_points:
            self._emit(f"STORE t{t_id}")
        
        return "\n".join(self.instructions)
    
    def _topological_sort(self, graph: ComputeGraph) -> List[int]:
        """拓扑排序"""
        in_degree = {oid: 0 for oid in graph.operators}
        adj = {oid: [] for oid in graph.operators}
        
        for oid, op in graph.operators.items():
            for output_tid in op.outputs:
                for next_oid, next_op in graph.operators.items():
                    if output_tid in next_op.inputs:
                        adj[oid].append(next_oid)
                        in_degree[next_oid] = in_degree.get(next_oid, 0) + 1
        
        queue = [oid for oid, deg in in_degree.items() if deg == 0]
        result = []
        
        while queue:
            node = queue.pop(0)
            result.append(node)
            for neighbor in adj[node]:
                in_degree[neighbor] -= 1
                if in_degree[neighbor] == 0:
                    queue.append(neighbor)
        
        return result
    
    def _generate_op(self, op: Operator):
        """生成单个算子的指令"""
        if op.op_type == OpType.MATMUL:
            m, k, n = [op.attrs.get(d, 1) for d in ['m', 'k', 'n']]
            self._emit(f"MATMUL t{op.inputs[0]} t{op.inputs[1]} -> t{op.outputs[0]} [{m}x{k}x{n}]")
        elif op.op_type == OpType.ATTENTION:
            self._emit(f"ATTENTION t{op.inputs[0]} -> t{op.outputs[0]} fused={op.attrs.get('fused_ops', 0)}")
        elif op.op_type == OpType.LAYERNORM:
            self._emit(f"LAYERNORM t{op.inputs[0]} -> t{op.outputs[0]} eps={op.attrs.get('eps', 1e-5)}")
        else:
            self._emit(f"{op.op_type.value.upper()} t{op.inputs[0]} -> t{op.outputs[0]}")
    
    def _emit(self, instr: str):
        self.instructions.append(instr)

"""
真武M890训推一体模式切换
"""
from enum import Enum

class ComputeMode(Enum):
    TRAINING = "training"    # 训练模式：BF16/FP32
    INFERENCE = "inference"  # 推理模式：INT8/FP8
    MIXED = "mixed"          # 混合模式

class TensorCoreConfig:
    """张量核配置 - 决定计算精度和吞吐"""
    def __init__(self, mode: ComputeMode):
        self.mode = mode
        self.configs = {
            ComputeMode.TRAINING: {
                "compute_precision": "bf16",
                "accumulation_precision": "fp32",
                "tile_shape_m": 128,
                "tile_shape_n": 128,
                "tile_shape_k": 64,
                "warp_count": 4,
            },
            ComputeMode.INFERENCE: {
                "compute_precision": "int8",
                "accumulation_precision": "int32",
                "tile_shape_m": 256,
                "tile_shape_n": 256,
                "tile_shape_k": 128,
                "warp_count": 8,
            },
            ComputeMode.MIXED: {
                "compute_precision": "fp8",
                "accumulation_precision": "fp16",
                "tile_shape_m": 128,
                "tile_shape_n": 256,
                "tile_shape_k": 64,
                "warp_count": 6,
            }
        }
    
    def theoretical_throughput(self, flops_per_tc: float) -> float:
        """理论吞吐量 (TFLOPS/TOPS)"""
        config = self.configs[self.mode]
        warp_factor = config["warp_count"] / 4.0  # 基准为4 warp
        tile_efficiency = (config["tile_shape_m"] * config["tile_shape_n"]) / (128 * 128)
        return flops_per_tc * warp_factor * tile_efficiency


class SAILRuntime:
    """
    SAIL运行时 - 负责训推模式切换和任务调度
    """
    def __init__(self, chip_count: int = 8):
        self.chip_count = chip_count
        self.mode_configs: Dict[str, ComputeMode] = {}
        self.active_streams: List[ComputeStream] = []
    
    def create_stream(self, mode: ComputeMode) -> 'ComputeStream':
        """创建计算流，指定模式"""
        stream = ComputeStream(mode)
        self.active_streams.append(stream)
        return stream
    
    def mixed_precision_training(self, model, optimizer, 
                                  train_loader, epochs: int):
        """
        混合精度训练循环
        训练时前向用FP8，反向用BF16，权重更新用FP32
        """
        for epoch in range(epochs):
            for batch_idx, (data, target) in enumerate(train_loader):
                # 前向传播 - FP8精度
                train_stream = self.create_stream(ComputeMode.INFERENCE)
                output = train_stream.execute(model, data)
                
                # 损失计算 - FP32
                loss = self._compute_loss(output, target)
                
                # 反向传播 - BF16
                backward_stream = self.create_stream(ComputeMode.TRAINING)
                backward_stream.backward(model, loss)
                
                # 权重更新 - FP32主权重
                optimizer.step()
                
                if batch_idx % 100 == 0:
                    print(f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item():.4f}")
    
    def _compute_loss(self, output, target):
        """损失计算"""
        import torch
        criterion = torch.nn.CrossEntropyLoss()
        return criterion(output, target)

// SAIL性能对比基准测试
package benchmark

import (
	"testing"
	"time"
)

type BenchmarkResult struct {
	ModelName    string
	BatchSize    int
	LatencyMs    float64
	Throughput   float64 // tokens/s
	MemoryMB     int64
	PowerW       float64
}

func RunBenchmark(model string, batchSize int, 
                  optimizationLevel string) BenchmarkResult {
	// 模拟SAIL在不同优化级别下的性能
	baseLatency := 100.0 // 基准延迟（ms）
	
	switch optimizationLevel {
	case "quick_migrate":
		// 快速迁移：零代码修改，性能约85%
		return BenchmarkResult{
			ModelName:  model,
			BatchSize:  batchSize,
			LatencyMs:  baseLatency / 0.85,
			Throughput: float64(batchSize) / (baseLatency / 0.85 / 1000),
			MemoryMB:   1024 * 8,
			PowerW:     350,
		}
	case "deep_optimize":
		// 深度优化：算子融合+内存规划，性能约100%
		return BenchmarkResult{
			ModelName:  model,
			BatchSize:  batchSize,
			LatencyMs:  baseLatency,
			Throughput: float64(batchSize) / (baseLatency / 1000),
			MemoryMB:   1024 * 6, // 内存优化后更省
			PowerW:     320,      // 功耗优化
		}
	default:
		return BenchmarkResult{}
	}
}

func BenchmarkLLaMA70B(b *testing.B) {
	models := []struct {
		name string
		opt  string
	}{
		{"LLaMA-70B-quick", "quick_migrate"},
		{"LLaMA-70B-deep", "deep_optimize"},
		{"LLaMA-70B-reference-NVIDIA", "reference"},
	}
	
	for _, m := range models {
		b.Run(m.name, func(b *testing.B) {
			for i := 0; i < b.N; i++ {
				result := RunBenchmark(m.name, 1, m.opt)
				_ = result
			}
		})
	}
}