国产算力与低比特大模型实战教程 #

# 传统量化: W ∈ {-127, ..., 127} (INT8)
# BitCPM: W ∈ {-1, 0, 1} (1.58-bit)

def ternary_quantization(weight_tensor):
    """
    三值量化: 将连续值量化为 {-1, 0, 1}
    """
    # 计算量化阈值
    threshold = 0.7 * torch.mean(torch.abs(weight_tensor))
    
    # 量化
    quantized = torch.zeros_like(weight_tensor)
    quantized[weight_tensor > threshold] = 1
    quantized[weight_tensor < -threshold] = -1
    # 其余为 0
    
    return quantized

class JointTrainQuantModel(nn.Module):
    def __init__(self, model):
        super().__init__()
        self.fp_model = model  # 全精度模型（用于梯度传播）
        self.quant_model = TernaryModel(model)  # 三值模型（用于推理）
    
    def forward(self, x):
        # 前向传播：使用三值模型
        if self.training:
            # 训练时：全精度 + Straight-Through Estimator
            fp_output = self.fp_model(x)
            
            # STE: 三值梯度的近似
            with torch.no_grad():
                quant_output = self.quant_model(x)
            
            # 损失 = 三值模型输出 + 全精度模型正则化
            loss = F.mse_loss(quant_output, fp_output.detach())
            loss.backward()
            
            return quant_output
        else:
            # 推理时：直接使用三值模型
            return self.quant_model(x)

class TernaryActivation(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        # 前向：三值化
        return torch.sign(x) * torch.clamp(torch.abs(x), 0, 1)
    
    @staticmethod
    def backward(ctx, grad_output):
        # 反向：直通估计器（梯度直接传递，不做三值化）
        return grad_output

def qat_training(model, dataloader, epochs=10):
    """
    量化感知训练：在训练时模拟量化误差
    """
    for epoch in range(epochs):
        for batch in dataloader:
            # 1. 前向传播（带量化模拟）
            model.train()
            with QATWrapper(model):  # 自动在每层加入量化噪声
                output = model(batch['input'])
                loss = criterion(output, batch['target'])
            
            # 2. 反向传播（使用STE）
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            # 3. 权重裁剪（保证量化后不溢出）
            for param in model.parameters():
                param.data = torch.clamp(param.data, -1, 1)

import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor, ops
import numpy as np

# 1. 定义三值量化层
class TernaryLinear(nn.Cell):
    def __init__(self, in_features, out_features):
        super().__init__()
        # 全精度权重（用于训练）
        self.weight_fp = ms.Parameter(
            ms.Tensor(np.random.randn(out_features, in_features) * 0.02, ms.float32)
        )
        # 三值权重（用于推理）
        self.weight_ternary = None
    
    def construct(self, x):
        if self.training:
            # 训练时：使用全精度权重 + STE
            output = ops.matmul(x, self.weight_fp.T)
            return output
        else:
            # 推理时：使用三值权重
            if self.weight_ternary is None:
                self.quantize_weights()
            output = ops.matmul(x, self.weight_ternary.T)
            return output
    
    def quantize_weights(self):
        """将全精度权重量化为三值"""
        threshold = 0.7 * ops.abs(self.weight_fp).mean()
        
        ternary = ops.zeros_like(self.weight_fp)
        ternary = ops.where(self.weight_fp > threshold, 
                          ops.ones_like(self.weight_fp), 
                          ternary)
        ternary = ops.where(self.weight_fp < -threshold, 
                          -ops.ones_like(self.weight_fp), 
                          ternary)
        
        self.weight_ternary = ms.Parameter(ternary, requires_grad=False)

# 2. 定义BitCPM模型
class BitCPM(nn.Cell):
    def __init__(self, vocab_size=32000, hidden_size=4096, num_layers=32):
        super().__init__()
        
        self.embeddings = nn.Embedding(vocab_size, hidden_size)
        
        self.layers = nn.CellList([
            nn.TransformerEncoderLayer(hidden_size, 32)
            for _ in range(num_layers)
        ])
        
        self.lm_head = TernaryLinear(hidden_size, vocab_size)  # 使用三值线性层
    
    def construct(self, input_ids):
        x = self.embeddings(input_ids)
        
        for layer in self.layers:
            x = layer(x)
        
        logits = self.lm_head(x)
        return logits

# 3. 训练脚本
def train_bitcpm():
    # 初始化MindSpore（指定昇腾NPU）
    ms.set_context(device_target="Ascend", device_id=0)
    
    # 加载模型
    model = BitCPM()
    
    # 损失函数和优化器
    loss_fn = nn.CrossEntropyLoss()
    optimizer = nn.Adam(model.trainable_params(), learning_rate=1e-4)
    
    # 训练数据
    dataset = create_dataset("data/train.txt", batch_size=8)
    
    # 训练循环
    for epoch in range(10):
        model.set_train()
        
        for batch, (input_ids, labels) in enumerate(dataset):
            # 前向传播
            logits = model(input_ids)
            loss = loss_fn(logits.view(-1, logits.shape[-1]), labels.view(-1))
            
            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            if batch % 100 == 0:
                print(f"Epoch {epoch}, Batch {batch}, Loss: {loss.asnumpy():.4f}")
        
        # 每个epoch后保存模型
        ms.save_checkpoint(model, f"checkpoints/bitcpm_epoch{epoch}.ckpt")

if __name__ == "__main__":
    train_bitcpm()

# 导出脚本
import mindspore as ms
from mindspore import export

# 加载训练好的模型
model = BitCPM()
ms.load_checkpoint("checkpoints/bitcpm_epoch9.ckpt", model)
model.set_train(False)

# 导出为MINDIR格式（跨平台）
input_ids = Tensor(np.ones((1, 128)), ms.int32)
export(model, input_ids, file_name="bitcpm_8b", file_format="MINDIR")

# 转换为昇腾离线模型（OM格式）
# 使用ATC工具
!atc --model=bitcpm_8b.mindir \
     --framework=1 \
     --output=bitcpm_8b \
     --soc_version=Ascend910 \
     --input_format=ND \
     --input_shape="input_ids:1,128"

import acl
import numpy as np

# 初始化ACL（Ascend Computing Language）
acl.init()
model_id, _ = acl.mdl.load_from_file("bitcpm_8b.om")

# 准备输入
input_ids = np.ones((1, 128), dtype=np.int32)
input_data = acl.util.numpy_to_ptr(input_ids)

# 推理
input_dataset = acl.mdl.create_dataset()
input_buffer = acl.create_data_buffer(input_data, input_ids.nbytes)
acl.mdl.add_dataset_buffer(input_dataset, input_buffer)

output_dataset = acl.mdl.create_dataset()
output_buffer = acl.create_data_buffer(np.empty((1, 128, 32000), dtype=np.float32))
acl.mdl.add_dataset_buffer(output_dataset, output_buffer)

acl.mdl.execute(model_id, input_dataset, output_dataset)

# 获取输出
output_ptr = acl.mdl.get_dataset_buffer(output_dataset, 0)
output_data = acl.util.ptr_to_numpy(output_ptr, (1, 128, 32000))
logits = output_data

# 后处理（生成文本）
generated_ids = np.argmax(logits[0], axis=-1)
print("Generated IDs:", generated_ids)

# 清理
acl.mdl.unload(model_id)
acl.finalize()

import tensorflow as tf

# 从MindSpore转换为ONNX，再转换为TFLite
# 1. MindSpore → ONNX
python scripts/convert_ms_to_onnx.py \
    --input checkpoint/bitcpm_8b.mindir \
    --output bitcpm_8b.onnx

# 2. ONNX → TFLite
import tf2onnx

onnx_model = onnx.load("bitcpm_8b.onnx")
tflite_model = tf2onnx.convert.from_onnx(onnx_model)

with open("bitcpm_8b.tflite", "wb") as f:
    f.write(tflite_model)

# 不同层使用不同量化位宽
class MixedPrecisionBitCPM(nn.Module):
    def __init__(self):
        # Embedding层：高精度（INT8）
        self.embeddings = nn.Embedding(vocab_size, hidden_size)
        self.embeddings_quant = INT8Quantization()
        
        # Attention层：三值（1.58-bit）
        self.attention = TernaryAttention()
        
        # FFN层：三值（1.58-bit）
        self.ffn = TernaryFFN()
        
        # LM Head：高精度（INT4）
        self.lm_head = INT4QuantizedLinear(hidden_size, vocab_size)

# 在训练时模拟量化误差
class QATWrapper(nn.Module):
    def __init__(self, module):
        super().__init__()
        self.module = module
        
    def forward(self, x):
        # 训练时：加入量化噪声
        if self.training:
            x = x + torch.randn_like(x) * 0.01  # 模拟量化噪声
        
        # 量化
        x_quant = ternary_quantize(x)
        
        # 前向传播
        output = self.module(x_quant)
        
        return output

# 动态批处理（Continuous Batching）
class DynamicBatching:
    def __init__(self, max_batch_size=32):
        self.pending_requests = []
        self.max_batch_size = max_batch_size
    
    def add_request(self, request):
        self.pending_requests.append(request)
        
        if len(self.pending_requests) >= self.max_batch_size:
            return self.process_batch()
    
    def process_batch(self):
        # 将多个请求拼接成一个batch
        batch_input = pad_and_concat(self.pending_requests)
        
        # 一次推理处理整个batch
        batch_output = model(batch_input)
        
        # 拆分结果
        outputs = split_batch(batch_output, self.pending_requests)
        
        self.pending_requests = []
        return outputs

# 训练时：不保存中间激活值，反向传播时重新计算
# 节省内存 2~3x，速度慢 20%

model = BitCPM()

# 启用激活值重计算
model.gradient_checkpointing_enable()

# 训练
output = model(input_ids)
loss = loss_fn(output, labels)
loss.backward()  # 反向传播时会重新计算激活值

# 将模型切分到多个NPU上
import mindspore as ms
from mindspore.parallel import Pipeline

# 定义流水线并行策略
class PipelineBitCPM(nn.Cell):
    def __init__(self, num_stages=4):
        super().__init__()
        
        # 第1个NPU：Embedding + 前8层
        self.stage1 = nn.CellList([BitCPMBlock() for _ in range(8)])
        
        # 第2个NPU：中间8层
        self.stage2 = nn.CellList([BitCPMBlock() for _ in range(8)])
        
        # 第3个NPU：中间8层
        self.stage3 = nn.CellList([BitCPMBlock() for _ in range(8)])
        
        # 第4个NPU：最后8层 + LM Head
        self.stage4 = nn.CellList([BitCPMBlock() for _ in range(8)])
        self.lm_head = TernaryLinear(hidden_size, vocab_size)
    
    def construct(self, x):
        # 流水线执行
        x = Pipeline.send(self.stage1, x)
        x = Pipeline.send(self.stage2, x)
        x = Pipeline.send(self.stage3, x)
        x = Pipeline.send(self.stage4, x)
        return x