model.py

import math
import torch
import torch.nn as nn
from transformers import BertConfig
from torch.utils.checkpoint import checkpoint

class ConvBlock(nn.Module):
    def __init__(self, hidden_size, kernel_size=3, padding=1):
        super().__init__()
        self.conv_dw = nn.Conv1d(
            in_channels=hidden_size,
            out_channels=hidden_size,
            kernel_size=kernel_size,
            padding=padding,
            groups=hidden_size
        )
        self.conv_pw = nn.Conv1d(
            in_channels=hidden_size,
            out_channels=hidden_size,
            kernel_size=1
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.ffn = nn.Sequential(
            nn.Linear(hidden_size, hidden_size * 4),
            nn.GELU(),
            nn.Linear(hidden_size * 4, hidden_size)
        )
        self.norm2 = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(0.1)

    def forward(self, x):
        residual = x
        x_conv = x.transpose(1, 2)
        x_conv = self.conv_dw(x_conv)
        x_conv = self.conv_pw(x_conv)
        x_conv = x_conv.transpose(1, 2)
        x = self.norm1(residual + self.dropout(x_conv))
        residual = x
        x_ffn = self.ffn(x)
        x = self.norm2(residual + self.dropout(x_ffn))
        return x


class AttentionBlock(nn.Module):
    def __init__(self, hidden_size, num_heads):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(
            embed_dim=hidden_size,
            num_heads=num_heads,
            dropout=0.1,
            batch_first=True
        )
        self.norm1 = nn.LayerNorm(hidden_size)
        self.ffn = nn.Sequential(
            nn.Linear(hidden_size, hidden_size * 4),
            nn.GELU(),
            nn.Linear(hidden_size * 4, hidden_size)
        )
        self.norm2 = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(0.1)

    def forward(self, x, attention_mask=None):
        residual = x
        if attention_mask is not None:
            key_padding_mask = (attention_mask == 0)
        else:
            key_padding_mask = None
            
        attn_output, _ = self.self_attn(
            query=x,
            key=x,
            value=x,
            key_padding_mask=key_padding_mask,
            need_weights=False
        )
        x = self.norm1(residual + self.dropout(attn_output))
        residual = x
        x_ffn = self.ffn(x)
        x = self.norm2(residual + self.dropout(x_ffn))
        return x


class HCAEModel(nn.Module):
    def __init__(self, vocab_size=30522, hidden_size=384, max_seq_len=512, 
                 conv_layers=5, attn_layers=3, num_heads=12):
        super().__init__()
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.word_embeddings = nn.Embedding(vocab_size, hidden_size, padding_idx=0)
        self.position_embeddings = nn.Embedding(max_seq_len, hidden_size)
        self.LayerNorm = nn.LayerNorm(hidden_size)
        self.dropout = nn.Dropout(0.1)
        self.conv_blocks = nn.ModuleList([
            ConvBlock(hidden_size) for _ in range(conv_layers)
        ])
        self.attn_blocks = nn.ModuleList([
            AttentionBlock(hidden_size, num_heads) for _ in range(attn_layers)
        ])
        self.use_gradient_checkpointing = False

    def forward(self, input_ids, attention_mask=None):
        seq_length = input_ids.size(1)
        position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
        position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
        
        words_embeddings = self.word_embeddings(input_ids)
        position_embeddings = self.position_embeddings(position_ids)
        x = words_embeddings + position_embeddings
        x = self.LayerNorm(x)
        x = self.dropout(x)
        
        for i, block in enumerate(self.conv_blocks):
            if self.use_gradient_checkpointing and self.training:
                def create_custom_forward(module):
                    def custom_forward(*args):
                        return module(*args)
                    return custom_forward
                x = checkpoint(create_custom_forward(block), x, use_reentrant=False)
            else:
                x = block(x)
                
        for i, block in enumerate(self.attn_blocks):
            if self.use_gradient_checkpointing and self.training:
                def create_custom_forward(module):
                    def custom_forward(hidden_states, mask):
                        return module(hidden_states, attention_mask=mask)
                    return custom_forward
                x = checkpoint(create_custom_forward(block), x, attention_mask, use_reentrant=False)
            else:
                x = block(x, attention_mask=attention_mask)
        
        if attention_mask is not None:
            input_mask_expanded = attention_mask.unsqueeze(-1).expand(x.size()).float()
            sum_embeddings = torch.sum(x * input_mask_expanded, 1)
            sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)
            sentence_embeddings = sum_embeddings / sum_mask
        else:
            sentence_embeddings = x.mean(dim=1)
            
        return sentence_embeddings

if __name__ == "__main__":
    model = HCAEModel()
    total_params = sum(p.numel() for p in model.parameters())
    print(f"Total parameters: {total_params / 1e6:.2f} M")
    
    batch_size = 32
    seq_len = 128
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    
    dummy_input = torch.randint(0, 30522, (batch_size, seq_len)).to(device)
    dummy_mask = torch.ones((batch_size, seq_len)).to(device)
    
    model.use_gradient_checkpointing = True
    
    with torch.cuda.amp.autocast(dtype=torch.float16):
        output = model(dummy_input, attention_mask=dummy_mask)
        
    print(f"Output shape: {output.shape}")
    
    if torch.cuda.is_available():
        memory_allocated = torch.cuda.memory_allocated(device) / (1024 ** 2)
        memory_reserved = torch.cuda.memory_reserved(device) / (1024 ** 2)
        print(f"CUDA memory allocated: {memory_allocated:.2f} MB")
        print(f"CUDA memory reserved: {memory_reserved:.2f} MB")