CANN生成式AI与大模型优化

一起养小猫

377人浏览 · 2026-02-07 00:05:50

一起养小猫 · 2026-02-07 00:05:50 发布

CANN生成式AI与大模型优化

CANN组织链接：https://atomgit.com/cann
CANN community仓库链接：https://atomgit.com/cann/community

一、生成式AI概述

1.1 生成式AI简介

生成式AI是指能够生成新内容的AI技术，包括文本、图像、音频、视频等。

1.1.1 主要技术

GPT系列：文本生成
Stable Diffusion：图像生成
Whisper：语音生成
MusicLM：音乐生成
VideoGAN：视频生成

1.1.2 应用场景

内容创作：文章、图像、音乐
数据增强：扩充训练数据
虚拟现实：生成虚拟场景
辅助设计：自动生成设计

1.2 CANN在生成式AI中的优势

高性能计算加速
大内存支持
多模态融合
端侧部署能力

二、大模型推理优化

2.1 模型量化

import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoTokenizer

class LLMQuantizer:
    def __init__(self, model_path):
        """大模型量化器"""
        self.model_path = model_path
        self.model = None
        self.tokenizer = None

    def load_model(self):
        """加载模型"""
        # 加载tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)

        # 加载模型
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_path,
            torch_dtype=torch.float16,
            device_map="auto"
        )

        return self.model

    def quantize_dynamic(self):
        """动态量化"""
        import torch.quantization as quant

        # 配置量化
        self.model.qconfig = quant.get_default_qconfig('fbgemm')

        # 准备量化
        quant.prepare(self.model, inplace=True)

        # 转换
        quantized_model = quant.convert(self.model, inplace=True)

        return quantized_model

    def quantize_static(self, calibration_data):
        """静态量化"""
        from transformers import GPTQConfig

        # GPTQ量化
        quantization_config = GPTQConfig(
            bits=4,
            group_size=128,
            damp_percent=0.01
        )

        # 量化模型
        quantized_model = AutoModelForCausalLM.from_pretrained(
            self.model_path,
            quantization_config=quantization_config,
            device_map="auto"
        )

        return quantized_model

class QuantizedLLMInference:
    def __init__(self, quantized_model, tokenizer, device_id=0):
        """量化大模型推理"""
        self.device = torch.device(f"npu:{device_id}")
        self.model = quantized_model.to(self.device)
        self.tokenizer = tokenizer

    def generate(self, prompt, max_length=100):
        """生成文本"""
        # 编码输入
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)

        # 生成
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_length=max_length,
                do_sample=True,
                top_p=0.95,
                top_k=50
            )

        # 解码输出
        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

        return generated_text

2.2 Flash Attention实现

class FlashAttention(nn.Module):
    def __init__(self, embed_dim, num_heads, dropout=0.0):
        """Flash Attention"""
        super(FlashAttention, self).__init__()

        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads

        self.qkv = nn.Linear(embed_dim, embed_dim * 3)
        self.out = nn.Linear(embed_dim, embed_dim)

        self.dropout = nn.Dropout(dropout)

    def forward(self, x, mask=None):
        """前向传播"""
        batch_size, seq_len, _ = x.size()

        # 计算Q、K、V
        qkv = self.qkv(x)
        qkv = qkv.reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim)
        qkv = qkv.permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]

        # 缩放点积注意力
        scale = self.head_dim ** -0.5
        scores = torch.matmul(q, k.transpose(-2, -1)) * scale

        # 应用mask
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e9)

        # Softmax
        attn = torch.softmax(scores, dim=-1)
        attn = self.dropout(attn)

        # 应用注意力
        out = torch.matmul(attn, v)
        out = out.transpose(1, 2).contiguous()
        out = out.reshape(batch_size, seq_len, -1)

        # 输出投影
        out = self.out(out)

        return out

class OptimizedTransformerBlock(nn.Module):
    def __init__(self, embed_dim, num_heads, ffn_dim, dropout=0.1):
    """优化的Transformer块"""
        super(OptimizedTransformerBlock, self).__init__()

        # Flash Attention
        self.attention = FlashAttention(embed_dim, num_heads, dropout)

        # FFN
        self.ffn = nn.Sequential(
            nn.Linear(embed_dim, ffn_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(ffn_dim, embed_dim),
            nn.Dropout(dropout)
        )

        # Layer Norm
        self.norm1 = nn.LayerNorm(embed_dim)
        self.norm2 = nn.LayerNorm(embed_dim)

    def forward(self, x, mask=None):
        """前向传播"""
        # Self-attention with residual
        attn_out = self.attention(x, mask)
        x = x + attn_out
        x = self.norm1(x)

        # FFN with residual
        ffn_out = self.ffn(x)
        x = x + ffn_out
        x = self.norm2(x)

        return x

三、LoRA微调

3.1 LoRA实现

class LoRALayer(nn.Module):
    def __init__(self, in_features, out_features, rank=8, alpha=16):
        """LoRA层"""
        super(LoRALayer, self).__init__()

        self.rank = rank
        self.alpha = alpha

        # LoRA参数
        self.lora_A = nn.Parameter(torch.zeros(in_features, rank))
        self.lora_B = nn.Parameter(torch.zeros(rank, out_features))

        # 初始化
        nn.init.kaiming_uniform_(self.lora_A)
        nn.init.zeros_(self.lora_B)

        # 缩放因子
        self.scaling = alpha / rank

    def forward(self, x):
        """前向传播"""
        # LoRA变换: B * A * x
        lora_out = x @ self.lora_A @ self.lora_B

        return lora_out * self.scaling

class LoRALinear(nn.Module):
    def __init__(self, linear_layer, rank=8, alpha=16):
        """带LoRA的线性层"""
        super(LoRALinear, self).__init__()

        self.linear = linear_layer
        self.lora = LoRALinear(
            linear_layer.in_features,
            linear_layer.out_features,
            rank,
            alpha
        )

        # 冻结原始层参数
        for param in self.linear.parameters():
            param.requires_grad = False

    def forward(self, x):
        """前向传播"""
        # 原始线性变换
        linear_out = self.linear(x)

        # LoRA增量
        lora_out = self.lora(x)

        return linear_out + lora_out

def apply_lora_to_model(model, target_modules=["q_proj", "v_proj"], rank=8):
    """对模型应用LoRA"""
    for name, module in model.named_modules():
        # 检查是否是目标模块
        if any(target in name for target in target_modules):
            if isinstance(module, nn.Linear):
                # 替换为LoRA版本
                lora_linear = LoRALinear(module, rank=rank)
                parent_name = name.rsplit('.', 1)[0]
                parent_name = parent_name if '.' in name else ''

                if parent_name:
                    parent = model.get_submodule(parent_name)
                    setattr(parent, name.split('.')[-1], lora_linear)
                else:
                    setattr(model, name, lora_linear)

    return model

class LLMFineTuner:
    def __init__(self, model_path, lora_rank=8, device_id=0):
        """大模型微调器"""
        self.device = torch.device(f"npu:{device_id}")

        # 加载基础模型
        from transformers import AutoModelForCausalLM, AutoTokenizer

        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            device_map="auto"
        )

        self.tokenizer = AutoTokenizer.from_pretrained(model_path)

        # 应用LoRA
        self.model = apply_lora_to_model(self.model, rank=lora_rank)

        # 只训练LoRA参数
        self.lora_params = [
            param for name, param in self.model.named_parameters()
            if "lora" in name.lower()
        ]

        # 优化器
        self.optimizer = torch.optim.AdamW(self.lora_params, lr=1e-4)

    def fine_tune(self, train_dataset, num_epochs=3):
        """微调模型"""
        self.model.train()

        for epoch in range(num_epochs):
            for batch in train_dataset:
                # 前向传播
                outputs = self.model(
                    input_ids=batch["input_ids"].to(self.device),
                    attention_mask=batch["attention_mask"].to(self.device),
                    labels=batch["labels"].to(self.device)
                )

                # 计算损失
                loss = outputs.loss

                # 反向传播（只更新LoRA参数）
                self.optimizer.zero_grad()
                loss.backward()

                # 梯度裁剪
                torch.nn.utils.clip_grad_norm_(self.lora_params, 1.0)

                self.optimizer.step()

                print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

    def save_lora_weights(self, path):
        """保存LoRA权重"""
        lora_state_dict = {}

        for name, param in self.model.named_parameters():
            if "lora" in name.lower():
                lora_state_dict[name] = param.cpu().data

        torch.save(lora_state_dict, path)

四、Prefix Tuning

4.1 Prefix微调

class PrefixTuning(nn.Module):
    def __init__(self, model, prefix_length=10):
        """Prefix Tuning"""
        super(PrefixTuning, self).__init__()

        self.model = model
        self.prefix_length = prefix_length

        # Prefix参数
        embed_dim = model.config.hidden_size
        self.prefix_embeddings = nn.Parameter(
            torch.randn(prefix_length, embed_dim)
        )

        # 冻结模型参数
        for param in self.model.parameters():
            param.requires_grad = False

        # 只训练prefix
        self.prefix_embeddings.requires_grad = True

    def forward(self, input_ids, attention_mask):
        """前向传播"""
        batch_size = input_ids.size(0)

        # 扩展prefix到batch
        prefix = self.prefix_embeddings.unsqueeze(0).expand(batch_size, -1, -1)

        # 拼接到输入
        input_ids = torch.cat([prefix, input_ids], dim=1)

        # 更新attention mask
        prefix_mask = torch.ones(batch_size, self.prefix_length).to(input_ids.device)
        attention_mask = torch.cat([prefix_mask, attention_mask], dim=1)

        # 前向传播
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask
        )

        return outputs

class PrefixTuner:
    def __init__(self, model_path, prefix_length=10, device_id=0):
        """Prefix微调器"""
        self.device = torch.device(f"npu:{device_id}")

        # 加载模型
        from transformers import AutoModelForCausalLM, AutoTokenizer

        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            device_map="auto"
        )

        # 应用Prefix Tuning
        self.model = PrefixTuning(self.model, prefix_length).to(self.device)

        # 优化器
        self.optimizer = torch.optim.AdamW(
            self.model.parameters(),
            lr=1e-4
        )

    def fine_tune(self, train_dataset, num_epochs=3):
        """微调"""
        self.model.train()

        for epoch in range(num_epochs):
            for batch in train_dataset:
                # 前向传播
                outputs = self.model(
                    input_ids=batch["input_ids"].to(self.device),
                    attention_mask=batch["attention_mask"].to(self.device),
                    labels=batch["labels"].to(self.device)
                )

                # 计算损失
                loss = outputs.loss

                # 反向传播
                self.optimizer.zero_grad()
                loss.backward()
                self.optimizer.step()

                print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

五、Prompt优化

5.1 Prompt模板

class PromptTemplate:
    def __init__(self, template):
        """Prompt模板"""
        self.template = template
        self.inputs = []

    def format(self, **kwargs):
        """格式化Prompt"""
        return self.template.format(**kwargs)

class PromptOptimizer:
    def __init__(self, model, tokenizer):
        """Prompt优化器"""
        self.model = model
        self.tokenizer = tokenizer

        # Prompt模板库
        self.templates = [
            "请根据以下内容{task_type}：{content}",
            "任务：{task_type}\n内容：{content}\n要求：生成{output_type}",
            "输入：{content}\n指令：{instruction}"
        ]

    def optimize_prompt(self, task, content, num_samples=10):
        """优化Prompt"""
        best_prompt = None
        best_score = -1

        # 尝试不同模板
        for template_str in self.templates:
            template = PromptTemplate(template_str)

            # 生成Prompt
            prompt = template.format(
                task_type=task,
                content=content[:100],
                output_type="文本"
            )

            # 评估Prompt质量
            score = self._evaluate_prompt(prompt)

            if score > best_score:
                best_score = score
                best_prompt = prompt

        return best_prompt

    def _evaluate_prompt(self, prompt):
        """评估Prompt质量"""
        # 使用模型评估
        # 这里简化实现
        return len(prompt) * 0.1

class PromptEngineeringSystem:
    def __init__(self, model_path, device_id=0):
        """Prompt工程系统"""
        self.device = torch.device(f"npu:{device_id}")

        # 加载模型
        from transformers import AutoModelForCausalLM, AutoTokenizer

        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            device_map="auto"
        )

        self.tokenizer = AutoTokenizer.from_pretrained(model_path)

        # Prompt优化器
        self.optimizer = PromptOptimizer(self.model, self.tokenizer)

    def generate_with_prompt(self, task, content):
        """使用Prompt生成"""
        # 优化Prompt
        prompt = self.optimizer.optimize_prompt(task, content)

        # 编码
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)

        # 生成
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_length=200,
                do_sample=True,
                top_p=0.95,
                temperature=0.8
            )

        # 解码
        generated_text = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

        return {
            "prompt": prompt,
            "generated_text": generated_text
        }

六、推理加速

6.1 KV Cache优化

class KVCacheManager:
    def __init__(self, model, max_batch_size=1, max_seq_length=2048):
        """KV Cache管理器"""
        self.model = model
        self.max_batch_size = max_batch_size
        self.max_seq_length = max_seq_length

        # 初始化KV Cache
        self.kv_cache = None
        self.current_length = 0

    def initialize_cache(self, batch_size, seq_length, hidden_dim, num_heads):
        """初始化Cache"""
        # 预分配KV Cache
        shape = (
            batch_size,
            num_heads,
            max_seq_length,
            hidden_dim // num_heads
        )

        self.kv_cache = {
            "key": torch.zeros(shape),
            "value": torch.zeros(shape)
        }

        self.current_length = seq_length

    def update_cache(self, new_key, new_value):
        """更新Cache"""
        batch_size, seq_len, num_heads, head_dim = new_key.shape

        # 将新的key/value添加到cache
        start_idx = self.current_length
        end_idx = start_idx + seq_len

        self.kv_cache["key"][:, :, start_idx:end_idx, :] = new_key
        self.kv_cache["value"][:, :, start_idx:end_idx, :] = new_value

        self.current_length = end_idx

    def get_cached_kv(self):
        """获取缓存的KV"""
        if self.kv_cache is None:
            return None, None

        # 截取到当前长度
        cached_key = self.kv_cache["key"][:, :, :self.current_length, :]
        cached_value = self.kv_cache["value"][:, :, :self.current_length, :]

        return cached_key, cached_value

class FastLLMInference:
    def __init__(self, model_path, device_id=0):
        """快速LLM推理"""
        self.device = torch.device(f"npu:{device_id}")

        # 加载模型
        from transformers import AutoModelForCausalLM, AutoTokenizer

        self.model = AutoModelForCausalLM.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            device_map="auto"
        )

        self.tokenizer = AutoTokenizer.from_pretrained(model_path)

        # KV Cache管理器
        self.cache_manager = KVCacheManager(self.model)

    def generate_fast(self, prompt, max_length=100):
        """快速生成"""
        # 编码prompt
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)

        # 初始化cache
        batch_size = inputs["input_ids"].size(0)
        seq_length = inputs["input_ids"].size(1)

        self.cache_manager.initialize_cache(
            batch_size,
            seq_length,
            self.model.config.hidden_size,
            self.model.config.num_attention_heads
        )

        # 第一步：处理prompt
        with torch.no_grad():
            outputs = self.model(
                input_ids=inputs["input_ids"],
                attention_mask=inputs["attention_mask"],
                use_cache=False
            )

        # 逐token生成
        generated_tokens = [outputs.logits.argmax(-1)[-1]]

        for _ in range(max_length):
            # 获取缓存的KV
            cached_key, cached_value = self.cache_manager.get_cached_kv()

            # 生成下一个token
            with torch.no_grad():
                outputs = self.model(
                    input_ids=generated_tokens[-1:].unsqueeze(0),
                    use_cache=True,
                    past_key_values=(cached_key, cached_value)
                )

            new_token = outputs.logits.argmax(-1)[-1]

            # 更新cache
            self.cache_manager.update_cache(
                outputs.past_key_values[0],
                outputs.past_key_values[1]
            )

            generated_tokens.append(new_token)

            # 检查结束条件
            if new_token.item() == self.tokenizer.eos_token_id:
                break

        # 解码
        generated_text = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)

        return generated_text

七、多模态生成

7.1 文本生成图像

class TextToImageGenerator:
    def __init__(self, model_path, device_id=0):
        """文本生成图像"""
        self.device = torch.device(f"npu:{device_id}")

        # 加载模型
        from diffusers import StableDiffusionPipeline

        self.pipe = StableDiffusionPipeline.from_pretrained(
            model_path,
            torch_dtype=torch.float16,
            safety_checker=None
        ).to(self.device)

    def generate(self, prompt, negative_prompt="", num_inference_steps=20):
        """生成图像"""
        # 生成
        with torch.no_grad():
            image = self.pipe(
                prompt=prompt,
                negative_prompt=negative_prompt,
                num_inference_steps=num_inference_steps,
                guidance_scale=7.5
).images[0]

        # 转换为PIL
        from PIL import Image
        import numpy as np

        image = image.cpu().numpy()
        image = (image * 255).astype(np.uint8)
        image = Image.fromarray(image)

        return image