119_LLM训练的高效内存管理与优化技术：从ZeRO到Flash Attention

import torch
from torch.cuda.amp import autocast, GradScaler

# 初始化混合精度训练组件
scaler = GradScaler()

# 前向传播使用自动混合精度
with autocast():
    outputs = model(input_ids, attention_mask)
    loss = criterion(outputs, labels)

# 使用梯度缩放进行反向传播
scaler.scale(loss).backward()

# 优化器步骤
scaler.step(optimizer)
scaler.update()

# 清空梯度
optimizer.zero_grad()

# 自适应损失缩放示例
class AdaptiveLossScaler:
    def __init__(self, initial_scale=2**16, growth_factor=2.0, backoff_factor=0.5):
        self.scale = initial_scale
        self.growth_factor = growth_factor
        self.backoff_factor = backoff_factor
        self.good_steps = 0
        self.steps_since_rescale = 0
        
    def update(self, overflow):
        if overflow:
            self.scale *= self.backoff_factor
            self.good_steps = 0
        else:
            self.good_steps += 1
            if self.good_steps >= 2000:
                self.scale *= self.growth_factor
                self.good_steps = 0
        self.steps_since_rescale += 1

from torch.utils.checkpoint import checkpoint

# 定义一个需要检查点的模块
def create_custom_forward(module):
    def custom_forward(*inputs):
        return module(*inputs)
    return custom_forward

# 应用梯度检查点
outputs = checkpoint(create_custom_forward(model), input_ids, attention_mask)
loss = criterion(outputs, labels)
loss.backward()

class AdaptiveCheckpoint:
    def __init__(self, memory_threshold=0.8):
        self.memory_threshold = memory_threshold
        
    def should_checkpoint(self):
        # 检查当前GPU内存使用情况
        current_memory = torch.cuda.memory_allocated() / torch.cuda.max_memory_allocated()
        return current_memory > self.memory_threshold
    
    def apply(self, model, *inputs):
        if self.should_checkpoint():
            return checkpoint(model, *inputs)
        else:
            return model(*inputs)

accumulation_steps = 8  # 累积8个小批量
optimizer.zero_grad()

for i, (inputs, labels) in enumerate(dataloader):
    # 前向传播和计算损失
    with autocast():
        outputs = model(**inputs)
        loss = criterion(outputs.logits, labels) / accumulation_steps  # 缩放损失
    
    # 反向传播
    scaler.scale(loss).backward()
    
    # 每accumulation_steps步更新一次参数
    if (i + 1) % accumulation_steps == 0:
        scaler.step(optimizer)
        scaler.update()
        optimizer.zero_grad()

# 根据可用内存动态调整序列长度
def get_optimal_sequence_length(max_memory, batch_size, model_dim):
    # 简单的内存模型
    memory_per_sequence = model_dim * 4  # 粗略估计
    max_sequences = max_memory / memory_per_sequence
    optimal_length = max(1, int(max_sequences / batch_size))
    return optimal_length

# 使用动态序列长度
max_memory = torch.cuda.max_memory_allocated() * 0.8  # 保留20%内存余量
batch_size = 8
optimal_seq_len = get_optimal_sequence_length(max_memory, batch_size, model.config.hidden_size)
print(f"Optimal sequence length: {optimal_seq_len}")

class ChunkedSequenceProcessor:
    def __init__(self, model, chunk_size=512):
        self.model = model
        self.chunk_size = chunk_size
    
    def process_long_sequence(self, input_ids, attention_mask):
        # 将长序列分块处理
        seq_len = input_ids.size(1)
        outputs = []
        
        for i in range(0, seq_len, self.chunk_size):
            end = min(i + self.chunk_size, seq_len)
            chunk_ids = input_ids[:, i:end]
            chunk_mask = attention_mask[:, i:end]
            
            # 处理当前块
            with torch.no_grad():
                chunk_output = self.model(chunk_ids, attention_mask=chunk_mask)
            outputs.append(chunk_output.logits)
        
        # 合并结果
        return torch.cat(outputs, dim=1)

# DeepSpeed ZeRO-1配置
zero_config = {
    "zero_optimization": {
        "stage": 1,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True
        },
        "allgather_partitions": True,
        "allgather_bucket_size": 2e8,
        "overlap_comm": True,
        "reduce_scatter": True,
        "reduce_bucket_size": 2e8,
        "contiguous_gradients": True
    }
}

# 初始化DeepSpeed
model_engine, optimizer, train_dataloader, _ = deepspeed.initialize(
    model=model,
    optimizer=optimizer,
    model_parameters=model.parameters(),
    training_data=dataset,
    config=zero_config
)

# DeepSpeed ZeRO-2配置
zero_config = {
    "zero_optimization": {
        "stage": 2,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": True
        },
        "allgather_partitions": True,
        "allgather_bucket_size": 2e8,
        "overlap_comm": True,
        "reduce_scatter": True,
        "reduce_bucket_size": 2e8,
        "contiguous_gradients": True
    }
}

# 初始化DeepSpeed
model_engine, optimizer, train_dataloader, _ = deepspeed.initialize(
    model=model,
    optimizer=optimizer,
    model_parameters=model.parameters(),
    training_data=dataset,
    config=zero_config
)

# 2025年DeepSpeed ZeRO-3高级配置
zero_config = {
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": True
        },
        "overlap_comm": True,
        "contiguous_gradients": True,
        "sub_group_size": 1e9,  # 子组大小，用于分层通信
        "stage3_prefetch_bucket_size": 5e8,  # 预取桶大小
        "stage3_param_persistence_threshold": 1e6,  # 参数持久化阈值
        "stage3_max_live_parameters": 1e9,  # 最大活跃参数数
        "stage3_max_reuse_distance": 1e9,  # 最大重用距离
        "stage3_gather_16bit_weights_on_model_save": True  # 16位权重保存
    }
}

# ZeRO-Offload 3.0配置
zero_config = {
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True,
            "buffer_count": 4,  # 缓冲区数量
            "profile": True  # 启用性能分析
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": True,
            "num_param_persistence_threads": 1,  # 参数持久化线程数
            "profile": True  # 启用性能分析
        },
        "nvme_offload": {
            "device": "nvme",  # 启用NVMe卸载
            "path": "/path/to/nvme",  # NVMe路径
            "buffer_size": 1e9,  # 缓冲区大小
            "thread_count": 4  # NVMe线程数
        },
        "offload_optim_frac": 0.9,  # 卸载到CPU的优化器状态比例
        "offload_param_frac": 0.5,  # 卸载到CPU的参数比例
        "contiguous_gradients": True,
        "overlap_comm": True
    }
}

# ZeRO-Infinity配置示例
zero_config = {
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "auto",  # 自动选择卸载设备
            "pin_memory": True
        },
        "offload_param": {
            "device": "auto",  # 自动选择卸载设备
            "pin_memory": True
        },
        "nvme_offload": {
            "device": "nvme",
            "path": "/path/to/nvme",
            "buffer_size": 1e10,
            "thread_count": 8
        },
        "dynamic_granularity": True,  # 启用动态粒度
        "adaptive_prefetch": True,  # 启用自适应预取
        "auto_tune": True,  # 启用自动调优
        "compute_optimization": {
            "flash_attention": True,  # 启用Flash Attention
            "fused_kernels": True  # 启用融合内核
        }
    }
}

# 使用Flash Attention
from flash_attn import FlashAttention

# 替换标准注意力层
class FlashTransformerBlock(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout=0.1):
        super().__init__()
        self.self_attn = FlashAttention(
            dim=hidden_size,
            heads=num_heads,
            dropout=dropout,
            causal=True  # 自回归模型使用因果掩码
        )
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, 4 * hidden_size),
            nn.GELU(),
            nn.Linear(4 * hidden_size, hidden_size)
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        
    def forward(self, x):
        # 前向传播
        x = x + self.self_attn(self.norm1(x))
        x = x + self.feed_forward(self.norm2(x))
        return x

# 使用Flash Attention-2
from flash_attn_v2 import FlashAttention2

class EnhancedTransformerBlock(nn.Module):
    def __init__(self, hidden_size, num_heads, dropout=0.1):
        super().__init__()
        self.self_attn = FlashAttention2(
            dim=hidden_size,
            heads=num_heads,
            dropout=dropout,
            causal=True,
            fused_qkv=True,  # 融合QKV投影
            fused_softmax=True,  # 融合softmax
            return_residual=True  # 返回残差连接的中间结果
        )
        self.feed_forward = nn.Sequential(
            nn.Linear(hidden_size, 4 * hidden_size),
            nn.GELU(),
            nn.Linear(4 * hidden_size, hidden_size)
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.norm2 = nn.LayerNorm(hidden_size)
        
    def forward(self, x):
        # Flash Attention-2直接支持残差连接
        x = self.self_attn(self.norm1(x))
        x = x + self.feed_forward(self.norm2(x))
        return x

# 优化的稀疏注意力实现
class OptimizedSparseAttention(nn.Module):
    def __init__(self, hidden_size, num_heads, window_size=512, global_tokens=0):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.window_size = window_size
        self.global_tokens = global_tokens
        
        # QKV投影
        self.qkv = nn.Linear(hidden_size, 3 * hidden_size)
        # 输出投影
        self.out_proj = nn.Linear(hidden_size, hidden_size)
        
    def forward(self, x):
        batch_size, seq_len, _ = x.size()
        
        # 计算QKV
        qkv = self.qkv(x).reshape(batch_size, seq_len, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        # 构建稀疏注意力掩码
        # 本地窗口注意力 + 全局标记
        # 实现细节...
        
        # 高效的稀疏注意力计算
        # 利用Flash Attention优化的稀疏计算
        
        # 输出投影
        output = self.out_proj(attn_output)
        return output

# 简化的张量并行实现示例
class TensorParallelLinear(nn.Module):
    def __init__(self, in_features, out_features, world_size):
        super().__init__()
        self.world_size = world_size
        self.rank = torch.distributed.get_rank()
        
        # 分割权重矩阵
        self.out_features_per_rank = out_features // world_size
        self.weight = nn.Parameter(
            torch.empty(self.out_features_per_rank, in_features)
        )
        self.bias = nn.Parameter(
            torch.empty(self.out_features_per_rank)
        )
        
    def forward(self, x):
        # 本地计算
        output_local = F.linear(x, self.weight, self.bias)
        
        # 收集所有进程的输出
        output_list = [torch.zeros_like(output_local) for _ in range(self.world_size)]
        torch.distributed.all_gather(output_list, output_local)
        
        # 合并结果
        output = torch.cat(output_list, dim=-1)
        return output

# 2025年优化的流水线并行实现
class PipelineParallelStage(nn.Module):
    def __init__(self, layers, stage_id, num_stages, micro_batch_size=4):
        super().__init__()
        self.layers = nn.ModuleList(layers)
        self.stage_id = stage_id
        self.num_stages = num_stages
        self.micro_batch_size = micro_batch_size
        
    def forward(self, x, is_first_stage=False, is_last_stage=False):
        # 梯度检查点优化
        if self.stage_id % 2 == 1:
            with torch.utils.checkpoint.checkpoint_sequential(
                self.layers, chunks=len(self.layers) // 2
            ) as checkpointed_layers:
                x = checkpointed_layers(x)
        else:
            for layer in self.layers:
                x = layer(x)
        
        return x

# 序列并行实现示例
class SequenceParallelAttention(nn.Module):
    def __init__(self, hidden_size, num_heads):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_heads = num_heads
        self.world_size = torch.distributed.get_world_size()
        self.head_dim = hidden_size // num_heads
        
        # 本地投影层
        self.q_proj = nn.Linear(hidden_size, hidden_size)
        self.k_proj = nn.Linear(hidden_size, hidden_size)
        self.v_proj = nn.Linear(hidden_size, hidden_size)
        self.out_proj = nn.Linear(hidden_size, hidden_size)
        
    def forward(self, x):
        batch_size, seq_len, _ = x.size()
        
        # 分割序列维度
        local_seq_len = seq_len // self.world_size
        start_idx = local_seq_len * torch.distributed.get_rank()
        end_idx = start_idx + local_seq_len
        
        # 获取本地序列分片
        x_local = x[:, start_idx:end_idx]
        
        # 计算本地QKV
        q = self.q_proj(x_local)
        k = self.k_proj(x_local)
        v = self.v_proj(x_local)
        
        # 通信以获取完整的K和V（使用all_gather）
        # 实现细节...
        
        # 注意力计算和输出
        # 实现细节...
        
        return output

# 高效梯度压缩示例
class GradientCompressor:
    def __init__(self, compress_ratio=0.1, algorithm="topk"):
        self.compress_ratio = compress_ratio
        self.algorithm = algorithm
    
    def compress(self, gradient):
        if self.algorithm == "topk":
            # Top-k压缩：只传输绝对值最大的k%梯度
            num_elements = gradient.numel()
            k = int(num_elements * self.compress_ratio)
            values, indices = torch.topk(gradient.abs().flatten(), k)
            mask = torch.zeros_like(gradient.flatten())
            mask[indices] = 1
            compressed_gradient = gradient.flatten() * mask
            return compressed_gradient.view_as(gradient), indices
        elif self.algorithm == "threshold":
            # 阈值压缩：只传输绝对值超过阈值的梯度
            threshold = gradient.abs().mean() * 2
            mask = gradient.abs() > threshold
            compressed_gradient = gradient * mask
            return compressed_gradient, mask
        
    def decompress(self, compressed_gradient, metadata):
        # 解压缩梯度
        # 实现细节...
        return decompressed_gradient

# 简化的3D并行配置示例
class MixedParallelConfig:
    def __init__(self, tensor_parallel_size, pipeline_parallel_size, data_parallel_size):
        self.tensor_parallel_size = tensor_parallel_size
        self.pipeline_parallel_size = pipeline_parallel_size
        self.data_parallel_size = data_parallel_size
        self.global_size = tensor_parallel_size * pipeline_parallel_size * data_parallel_size
    
    def get_parallel_group(self, group_type):
        # 获取不同类型的并行分组
        # 实现细节...
        pass

# 2025年自动并行配置
mixed_config = {
    "tensor_parallel_size": 2,
    "pipeline_parallel_size": 4,
    "data_parallel_size": 8,
    "tensor_parallel_depth": "auto",  # 自动选择张量并行维度
    "pipeline_partition_style": "uniform",  # 均匀分割流水线
    "virtual_pipeline_chunks": 16,  # 虚拟流水线块
    "sequence_parallel": True,  # 启用序列并行
    "communication_dtype": "fp16"  # 通信数据类型
}

# 自动并行优化器示例
class AutoParallelOptimizer:
    def __init__(self, model, device_count, memory_per_device):
        self.model = model
        self.device_count = device_count
        self.memory_per_device = memory_per_device
        
    def analyze_model(self):
        # 分析模型结构、参数数量、计算量等
        # 实现细节...
        pass
    
    def recommend_parallel_strategy(self):
        # 基于模型分析和硬件约束推荐并行策略
        model_size = self.calculate_model_size()
        activation_size = self.estimate_activation_size()
        
        # 启发式规则或机器学习模型推荐最佳配置
        # 实现细节...
        
        return recommended_strategy
    
    def apply_strategy(self, strategy):
        # 应用推荐的并行策略
        # 实现细节...
        pass

# 自定义内存池示例
class GPUMemoryPool:
    def __init__(self, initial_capacity=1024**3):  # 初始1GB
        self.pool = torch.cuda.FloatTensor(initial_capacity)
        self.allocated = {}
        self.free_blocks = [(0, initial_capacity)]
    
    def allocate(self, size):
        # 首次适应算法分配内存
        for i, (start, block_size) in enumerate(self.free_blocks):
            if block_size >= size:
                # 分配内存
                self.allocated[(start, size)] = True
                # 更新空闲块
                self.free_blocks.pop(i)
                if block_size > size:
                    self.free_blocks.insert(i, (start + size, block_size - size))
                # 返回分配的内存视图
                return self.pool[start:start+size].view(-1, size)
        # 如果没有足够大的块，扩展内存池
        self._expand(size)
        return self.allocate(size)
    
    def free(self, tensor):
        # 释放内存并更新空闲块
        # 实现细节...
        pass
    
    def _expand(self, additional_size):
        # 扩展内存池
        # 实现细节...
        pass

# 激活值复用示例
class ActivationReuseOptimizer:
    def __init__(self, model):
        self.model = model
        self.activation_mapping = {}
        
    def register_hooks(self):
        # 为模型层注册前向和反向钩子
        for name, module in self.model.named_modules():
            if isinstance(module, (nn.Linear, nn.MultiheadAttention)):
                module.register_forward_pre_hook(self._forward_pre_hook)
                module.register_forward_hook(self._forward_hook)
    
    def _forward_pre_hook(self, module, input):
        # 前向传播前的钩子，尝试复用内存
        # 实现细节...
        pass
    
    def _forward_hook(self, module, input, output):
        # 前向传播后的钩子，记录激活值信息
        # 实现细节...
        pass

# 动态精度模型示例
class DynamicPrecisionModel(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        
        # 关键层使用高精度（如词嵌入、输出层）
        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, dtype=torch.float32)
        
        # 中间层使用低精度
        self.encoder_layers = nn.ModuleList([
            DynamicPrecisionLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)
        ])
        
        # 输出层使用高精度
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, dtype=torch.float32)
    
    def forward(self, input_ids):
        x = self.embeddings(input_ids)
        
        # 动态精度上下文管理器
        with precision_context(self.config.default_precision):
            for layer in self.encoder_layers:
                x = layer(x)
        
        logits = self.lm_head(x)
        return logits

# 结构感知优化器示例
class StructureAwareOptimizer:
    def __init__(self, model):
        self.model = model
        self.optimization_plan = self._analyze_and_plan()
    
    def _analyze_and_plan(self):
        # 分析模型结构，制定优化计划
        plan = {}
        
        # 识别大参数层
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Linear) and module.in_features * module.out_features > 1e6:
                plan[name] = {"gradient_checkpoint": True, "precision": "fp16"}
            
            # 识别计算密集型层
            elif isinstance(module, nn.MultiheadAttention):
                plan[name] = {"flash_attention": True, "sequence_parallel": True}
        
        return plan
    
    def apply_optimizations(self):
        # 应用优化计划
        # 实现细节...
        pass

# 使用torch.cuda.amp进行混合精度训练
def train_with_amp(model, dataloader, optimizer, epochs=10):
    scaler = torch.cuda.amp.GradScaler()
    
    for epoch in range(epochs):
        for inputs, targets in dataloader:
            inputs, targets = inputs.cuda(), targets.cuda()
            
            # 前向传播使用autocast
            with torch.cuda.amp.autocast():
                outputs = model(inputs)
                loss = criterion(outputs, targets)
            
            # 反向传播和优化器步骤
            optimizer.zero_grad()
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            scaler.step(optimizer)
            scaler.update()

# 使用梯度检查点优化内存使用
def create_checkpointed_model(model, checkpoint_ratio=0.5):
    # 确定需要应用检查点的层
    num_layers = len(model.encoder.layer)
    checkpoint_layers = int(num_layers * checkpoint_ratio)
    
    # 对前checkpoint_layers层应用检查点
    for i in range(checkpoint_layers):
        original_layer = model.encoder.layer[i]
        # 替换为检查点版本
        model.encoder.layer[i] = CheckpointFunction.apply(original_layer)
    
    return model

# 使用PyTorch 2025内存分析器
from torch.profiler import profile, record_function, ProfilerActivity
from torch.memory import MemoryProfiler

def analyze_memory_usage(model, sample_input):
    # 内存使用分析
    memory_profiler = MemoryProfiler()
    
    # 跟踪前向传播的内存使用
    with memory_profiler.trace() as trace:
        with torch.no_grad():
            output = model(sample_input)
    
    # 分析内存使用报告
    memory_report = memory_profiler.analyze(trace)
    
    # 打印内存使用热点
    print("内存使用热点：")
    for item in memory_report.top_memory_users(10):
        print(f"{item.name}: {item.memory_mb:.2f}MB")
    
    return memory_report

# DeepSpeed高级配置示例
deepspeed_config = {
    "train_batch_size": 512,
    "train_micro_batch_size_per_gpu": 8,
    "gradient_accumulation_steps": 64,
    
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": True
        },
        "overlap_comm": True,
        "contiguous_gradients": True,
        "sub_group_size": 1e9,
        "stage3_prefetch_bucket_size": 5e8,
        "stage3_param_persistence_threshold": 1e6,
        "stage3_max_live_parameters": 1e9,
        "stage3_max_reuse_distance": 1e9,
        "stage3_gather_16bit_weights_on_model_save": True
    },
    
    "fp16": {
        "enabled": "auto",
        "loss_scale": 0,
        "loss_scale_window": 1000,
        "initial_scale_power": 16,
        "hysteresis": 2,
        "min_loss_scale": 1
    },
    
    "gradient_clipping": 1.0,
    "wall_clock_breakdown": False,
    "activation_checkpointing": {
        "partition_activations": True,
        "cpu_checkpointing": True,
        "profile": True,
        "profile_dir": "./checkpoint_profile"
    }
}

# 使用DeepSpeed ZeRO-Offload
import deepspeed

def train_with_deepspeed_zero_offload(model, dataset, config):
    # 初始化DeepSpeed引擎
    model_engine, optimizer, train_dataloader, _ = deepspeed.initialize(
        model=model,
        optimizer=torch.optim.AdamW(model.parameters(), lr=1e-4),
        model_parameters=model.parameters(),
        training_data=dataset,
        config=config
    )
    
    # 训练循环
    for epoch in range(config["epochs"]):
        for batch in train_dataloader:
            # 将数据移至模型设备
            batch = {k: v.to(model_engine.device) for k, v in batch.items()}
            
            # 前向传播
            outputs = model_engine(**batch)
            loss = outputs.loss
            
            # 反向传播
            model_engine.backward(loss)
            
            # 优化器步骤
            model_engine.step()
    
    return model_engine.module  # 返回原始模型

# 使用Memory-Efficient Transformers库
from memory_efficient_transformers import (  # 假设的库名
    FlashAttentionLayer,
    MemoryEfficientLlama,
    ActivationReuse,
    MemoryOptimizer
)

def create_memory_efficient_model(model_name, **kwargs):
    # 创建内存优化的模型
    model = MemoryEfficientLlama.from_pretrained(
        model_name,
        memory_optimized=True,
        flash_attention=True,
        activation_reuse=True,
        **kwargs
    )
    
    # 应用额外的内存优化
    memory_optimizer = MemoryOptimizer()
    optimized_model = memory_optimizer.optimize(
        model,
        gradient_checkpoint_ratio=0.8,
        precision_policy="mixed",
        activation_compression="auto"
    )
    
    return optimized_model

# 使用AutoMemoryOptimizer
from auto_memory import AutoMemoryOptimizer

def optimize_model_memory(model, training_config):
    # 创建自动内存优化器
    optimizer = AutoMemoryOptimizer(
        target_memory_usage="80%",  # 目标内存使用率
        training_config=training_config,
        hardware_profile="auto"  # 自动检测硬件
    )
    
    # 执行优化
    optimized_model, best_config = optimizer.optimize(
        model,
        optimization_level="aggressive",  # 优化级别
        preserve_accuracy=True,  # 保持精度
        benchmark=True  # 运行基准测试
    )
    
    # 打印优化结果
    print("内存优化完成!")
    print(f"优化前内存使用: {optimizer.baseline_memory_mb:.2f}MB")
    print(f"优化后内存使用: {optimizer.optimized_memory_mb:.2f}MB")
    print(f"内存节省: {(1 - optimizer.optimized_memory_mb / optimizer.baseline_memory_mb) * 100:.2f}%")
    print(f"速度影响: {(optimizer.optimized_time / optimizer.baseline_time - 1) * 100:.2f}%")
    
    return optimized_model, best_config

# 受限资源环境的优化配置
offline_config = {
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": True
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": True
        },
        "contiguous_gradients": True,
        "stage3_prefetch_bucket_size": 2e8,
        "stage3_param_persistence_threshold": 1e5
    },
    "fp16": {
        "enabled": True,
        "loss_scale": 0,
        "loss_scale_window": 1000
    },
    "gradient_accumulation_steps": 32,
    "train_micro_batch_size_per_gpu": 1,
    "wall_clock_breakdown": True
}

# 综合内存优化示例
def create_ultra_optimized_model(model_config):
    # 1. 基础模型创建
    model = GPTNeoXForCausalLM.from_pretrained(
        model_config["base_model"],
        torch_dtype=torch.bfloat16  # 使用bfloat16减少内存
    )
    
    # 2. 替换注意力机制
    for i, layer in enumerate(model.gpt_neox.layers):
        # 替换为Flash Attention-2
        layer.attention = FlashAttention2(
            dim=model_config["hidden_size"],
            heads=model_config["num_attention_heads"],
            causal=True,
            fused_qkv=True,
            fused_softmax=True
        )
        
        # 应用梯度检查点
        model.gpt_neox.layers[i] = torch.utils.checkpoint.checkpoint_wrapper(layer)
    
    # 3. 优化前馈网络
    for layer in model.gpt_neox.layers:
        # 替换为低内存前馈网络实现
        layer.mlp = MemoryEfficientMLP(
            model_config["hidden_size"],
            model_config["intermediate_size"],
            activation=model_config["hidden_act"]
        )
    
    # 4. 激活值压缩
    apply_activation_compression(model, compression_ratio=0.5)
    
    return model