在人工智能与深度学习蓬勃发展的当下,预训练模型成为了推动自然语言处理、计算机视觉等众多领域取得突破性进展的关键力量。从最初的无监督预训练到如今复杂的多阶段预训练范式,每一次演进都为模型性能的提升和应用场景的拓展提供了新的可能。DeepSeek等先进系统通过创新的多阶段预训练技巧,在大规模数据上学习到丰富的特征表示,并在下游任务中展现出卓越的性能。
DeepSeek预训练框架的演进反映了大模型训练范式的三次革命:
阶段 | 时间范围 | 核心技术 | 训练数据量 | 典型模型 | 关键突破 |
---|---|---|---|---|---|
启蒙期 | 2018-2020 | 标准Transformer | 10-100GB | BERT/GPT-2 | 掩码语言建模 |
扩展期 | 2021-2022 | 混合专家(MoE) | 1-5TB | Switch-Transformer | 条件计算 |
专业期 | 2023-至今 | 多阶段课程学习 | 50TB+ | DeepSeek-67B | 动态数据调度 |
在训练67B参数模型时面临的核心问题:
# 传统单阶段训练的问题演示
def train(model, dataset):
optimizer = AdamW(model.parameters(), lr=5e-4)
for epoch in range(100):
for batch in dataset:
loss = model(batch)
loss.backward()
optimizer.step() # 持续的高学习率导致后期不稳定
optimizer.zero_grad()
问题分析:
DeepSeek提出"渐进式知识注入"的三阶段范式:
DeepSeek采用分阶段的训练策略,每个阶段专注于特定的训练任务和目标,逐步提升模型的能力。
阶段 | 训练目标 | 数据构成 | Batch Size | 学习率 | 持续时长 |
---|---|---|---|---|---|
筑基 | 语言建模 | 通用文本80%+代码20% | 4096 | 3e-4 | 30天 |
增强 | 多任务学习 | 领域文本50%+知识图谱30%+数学20% | 2048 | 1e-4 | 20天 |
精调 | 指令跟随 | 人工标注30%+合成数据70% | 1024 | 5e-5 | 10天 |
DeepSeek在预训练过程中,精心选择和动态更新训练数据,以提高模型的性能和泛化能力。
class CurriculumSampler:
def __init__(self, dataset, alpha=0.5):
self.scores = compute_difficulty(dataset) # 预计算样本难度
self.alpha = alpha # 课程进度系数
def sample(self, current_step):
# 动态调整采样概率
temp = 1.0 + self.alpha * current_step
probs = F.softmax(self.scores / temp, dim=0)
return torch.multinomial(probs, batch_size)
难度评分维度:
def hybrid_loss(batch):
text_loss = model(batch['text']) # 语言建模损失
kg_loss = model(batch['triples']) # 知识图谱三元组损失
math_loss = model(batch['equations']) # 数学公式推导损失
return 0.6*text_loss + 0.3*kg_loss + 0.1*math_loss
class DeepSeekScheduler:
def get_lr(self, step):
if step < warmup_steps: # 初始阶段
return base_lr * step / warmup_steps
elif step < transition_step: # 平稳阶段
return base_lr
else: # 衰减阶段
return base_lr * 0.5 ** ((step - transition_step) // decay_interval)
各阶段学习率曲线:
训练阶段 | 初始学习率 | 衰减策略 | 最终学习率 |
---|---|---|---|
筑基 | 3e-4 | 线性预热 | 3e-4 |
增强 | 1e-4 | 余弦衰减 | 5e-5 |
精调 | 5e-5 | 阶梯衰减 | 1e-5 |
def freeze_layers(model, current_stage):
# 筑基阶段:仅训练顶层
if current_stage == 1:
for param in model.base_layers.parameters():
param.requires_grad = False
# 增强阶段:解冻中间层
elif current_stage == 2:
for param in model.mid_layers.parameters():
param.requires_grad = True
# 精调阶段:全参数训练
else:
for param in model.parameters():
param.requires_grad = True
技术 | 筑基阶段 | 增强阶段 | 精调阶段 |
---|---|---|---|
Dropout率 | 0.1 | 0.3 | 0.5 |
权重衰减 | 0.01 | 0.05 | 0.1 |
梯度裁剪 | 1.0 | 2.0 | 5.0 |
标签平滑 | 0.05 | 0.1 | 0.2 |
DeepSeek对模型架构进行了一系列优化,以适应多阶段预训练的需求。
# 基于Megatron-LM的层内并行
from megatron.core import tensor_parallel
class ParallelMLP(nn.Module):
def __init__(self, hidden_size):
self.dense_h_to_4h = tensor_parallel.ColumnParallelLinear(
hidden_size, 4*hidden_size
)
self.dense_4h_to_h = tensor_parallel.RowParallelLinear(
4*hidden_size, hidden_size
)
def forward(self, x):
x = self.dense_h_to_4h(x)
x = gelu(x)
return self.dense_4h_to_h(x)
并行配置对比:
并行维度 | 筑基阶段 | 增强阶段 | 精调阶段 |
---|---|---|---|
数据并行 | 1024卡 | 512卡 | 256卡 |
流水并行 | 8 stages | 4 stages | 2 stages |
专家并行 | 32 experts | 64 experts | 128 experts |
def activation_checkpoint(module):
# 使用选择性激活检查点
from torch.utils.checkpoint import checkpoint_sequential
def forward(*inputs):
def custom_forward(*tensors):
return module(*tensors)
return checkpoint_sequential(
[custom_forward],
chunks=4,
input=inputs
)
return forward
显存节省效果:
优化技术 | 67B模型显存占用 | 节省比例 |
---|---|---|
基础配置 | 320GB | - |
+梯度检查点 | 245GB | 23% |
+CPU卸载 | 182GB | 43% |
+混合精度 | 97GB | 70% |
# 使用Torch Quantization
model_fp32 = load_pretrained_model()
model_fp32.eval()
# 量化配置
qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')
model_fp32.qconfig = qconfig
# 准备量化
model_int8 = torch.quantization.prepare_qat(model_fp32)
# 校准
for data in calibration_data:
model_int8(data)
# 转换
model_final = torch.quantization.convert(model_int8)
量化效果对比:
精度 | 模型大小 | 推理延迟 | 准确率 |
---|---|---|---|
FP32 | 260GB | 350ms | 82.3% |
FP16 | 130GB | 210ms | 82.1% |
INT8 | 65GB | 150ms | 81.7% |
# 使用Triton推理服务器配置
name: "deepseek_67b"
platform: "pytorch_libtorch"
max_batch_size: 16
input [
{ name: "input_ids", data_type: TYPE_INT64, dims: [ 2048 ] }
]
output [
{ name: "logits", data_type: TYPE_FP32, dims: [ 50257 ] }
]
instance_group [
{ count: 4, kind: KIND_GPU }
]
optimization {
cuda {
graphs: true
busy_wait_events: true
}
}
部署性能指标:
硬件配置 | 吞吐量(query/s) | P99延迟 | 服务成本($/M tokens) |
---|---|---|---|
4×A100 | 1240 | 210ms | 1.27 |
8×T4 | 680 | 450ms | 2.15 |
16×TPUv4 | 3840 | 95ms | 0.89 |
假设我们要在DeepSeek的多阶段预训练框架下,完成一个跨模态的情感分析任务,即根据图像和对应的文本描述,判断其中蕴含的情感倾向。
(一)环境准备
在进行多阶段预训练之前,需要准备相应的计算环境和数据资源。
(二)模型构建
使用PyTorch框架构建DeepSeek的多模态预训练模型。
import torch
import torch.nn as nn
from transformers import BertConfig, BertModel
from torchvision.models import resnet50
# 图像特征提取模块
class ImageFeatureExtractor(nn.Module):
def __init__(self):
super(ImageFeatureExtractor, self).__init__()
self.resnet = resnet50(pretrained=True)
self.features = nn.Sequential(*list(self.resnet.children())[:-1]) # 去掉最后一层全连接层
def forward(self, x):
x = self.features(x)
x = x.squeeze(-1).squeeze(-1) # 将二维特征图转换为一维特征向量
return x
# 文本特征提取模块
class TextFeatureExtractor(nn.Module):
def __init__(self, bert_model_name='bert-base-uncased'):
super(TextFeatureExtractor, self).__init__()
self.config = BertConfig.from_pretrained(bert_model_name)
self.bert = BertModel.from_pretrained(bert_model_name, config=self.config)
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
return outputs.last_hidden_state
# 多模态融合模块
class MultimodalFusion(nn.Module):
def __init__(self, image_feature_dim, text_feature_dim, fusion_dim):
super(MultimodalFusion, self).__init__()
self.image_fc = nn.Linear(image_feature_dim, fusion_dim)
self.text_fc = nn.Linear(text_feature_dim, fusion_dim)
self.activation = nn.ReLU()
def forward(self, image_features, text_features):
image_features = self.image_fc(image_features)
text_features = self.text_fc(text_features)
fused_features = image_features + text_features # 简单的加法融合,也可以尝试其他融合方式
fused_features = self.activation(fused_features)
return fused_features
# 完整的多模态预训练模型
class DeepSeekMultimodalModel(nn.Module):
def __init__(self, bert_model_name='bert-base-uncased'):
super(DeepSeekMultimodalModel, self).__init__()
self.image_extractor = ImageFeatureExtractor()
self.text_extractor = TextFeatureExtractor(bert_model_name)
self.fusion = MultimodalFusion(image_feature_dim=2048, text_feature_dim=768, fusion_dim=768)
self.classifier = nn.Linear(768, 2) # 假设为二分类任务
def forward(self, images, input_ids, attention_mask):
image_features = self.image_extractor(images)
text_features = self.text_extractor(input_ids, attention_mask)
text_features = text_features[:, 0, :] # 取[CLS] token的特征作为文本的整体特征
fused_features = self.fusion(image_features, text_features)
logits = self.classifier(fused_features)
return logits
(三)预训练阶段实现
第一阶段:基础特征预训练
# 图像特征预训练(以图像分类为例)
model = ImageFeatureExtractor()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 假设已经加载好图像数据和对应的分类标签
for epoch in range(num_epochs):
for images, labels in image_dataloader:
images = images.to(device)
labels = labels.to(device)
features = model(images)
logits = classifier(features) # classifier是一个简单的全连接分类层
loss = criterion(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 文本特征预训练(以Masked Language Model为例)
model = TextFeatureExtractor()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 假设已经加载好文本数据,并进行了Mask处理
for epoch in range(num_epochs):
for batch in text_dataloader:
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
labels = batch['labels'].to(device)
outputs = model(input_ids, attention_mask)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 跨模态关联预训练(以图像-文本匹配为例)
model = DeepSeekMultimodalModel()
criterion = nn.CosineEmbeddingLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 假设已经加载好图像和对应的文本数据,并生成了匹配和不匹配的样本对
for epoch in range(num_epochs):
for images, pos_texts, neg_texts in multimodal_dataloader:
images = images.to(device)
pos_input_ids = pos_texts['input_ids'].to(device)
pos_attention_mask = pos_texts['attention_mask'].to(device)
neg_input_ids = neg_texts['input_ids'].to(device)
neg_attention_mask = neg_texts['attention_mask'].to(device)
# 计算匹配对的特征
pos_text_features = model.text_extractor(pos_input_ids, pos_attention_mask)
pos_text_features = pos_text_features[:, 0, :]
image_features = model.image_extractor(images)
pos_similarity = torch.cosine_similarity(image_features, pos_text_features)
# 计算不匹配对的特征
neg_text_features = model.text_extractor(neg_input_ids, neg_attention_mask)
neg_text_features = neg_text_features[:, 0, :]
neg_similarity = torch.cosine_similarity(image_features, neg_text_features)
# 定义目标,匹配对的相似度应大于不匹配对
targets = torch.ones(images.size(0), dtype=torch.long).to(device)
loss = criterion(pos_similarity, neg_similarity, targets)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 多模态情感分析微调
model = DeepSeekMultimodalModel()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
# 假设已经加载好包含图像、文本和情感标签的数据
for epoch in range(num_epochs):
for images, input_ids, attention_mask, labels in sentiment_dataloader:
images = images.to(device)
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
labels = labels.to(device)
logits = model(images, input_ids, attention_mask)
loss = criterion(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
(四)模型评估与应用
在完成多阶段预训练和微调后,对模型进行评估和实际应用。
# 在测试集上评估模型性能
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, input_ids, attention_mask, labels in test_dataloader:
images = images.to(device)
input_ids = input_ids.to(device)
attention_mask = attention_mask.to(device)
labels = labels.to(device)
logits = model(images, input_ids, attention_mask)
_, predicted = torch.max(logits, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
accuracy = correct / total
print(f'Test Accuracy: {accuracy:.4f}')
# 保存模型
torch.save(model.state_dict(), 'deepseek_multimodal_sentiment_analysis.pth')
# 加载模型
model = DeepSeekMultimodalModel()
model.load_state_dict(torch.load('deepseek_multimodal_sentiment_analysis.pth'))
model.eval()
# 应用示例
def analyze_multimodal_sentiment(image_path, text):
# 加载和预处理图像
image = load_and_preprocess_image(image_path)
image_tensor = torch.tensor(image, dtype=torch.float32).unsqueeze(0).to(device)
# 预处理文本
text_inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
input_ids = text_inputs['input_ids'].to(device)
attention_mask = text_inputs['attention_mask'].to(device)
# 情感分析
logits = model(image_tensor, input_ids, attention_mask)
probabilities = torch.softmax(logits, dim=1)
predicted_class = torch.argmax(probabilities).item()
return predicted_class, probabilities.cpu().numpy()[0]
预训练范式从早期的无监督预训练到如今的多阶段预训练,经历了多次重要的演进。DeepSeek等系统通过分阶段训练策略、数据选择与动态更新、自监督与对比学习结合、模型架构优化等多方面的创新技巧,实现了在多模态等复杂任务中的卓越性能。
1 Liu Y, Ott M, Goyal N, et al. Roberta: A robustly optimized bert pretraining approachJ. arXiv preprint arXiv:1907.11692, 2019.
原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
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原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。
如有侵权,请联系 cloudcommunity@tencent.com 删除。