1. First, let's calculate how much memory your model might be using:

The error shows you're trying to allocate ~69GB (69271363584 bytes), which is far too large for most GPUs or CPUs

2. Potential causes of the memory issue:

a) Input Dimensions:

You haven't shared the dimensions of your input data, but this could be a key issue Check your batch size, sequence length, and d_model values The memory usage grows quadratically with sequence length due to attention mechanisms

b) Positional Encoding Implementation:

self.encoding = torch.zeros(max_len, d_model)

• Your current implementation creates a large tensor of size (max_len, d_model)
• max_len=5000 might be unnecessarily large for your use case

3. Suggestions for fixing the issue:

   class PositionalEncoding(nn.Module): def init(self, d_model, max_len=5000, dropout=0.1): super(PositionalEncoding, self).init() self.dropout = nn.Dropout(p=dropout)

        # Create more memory-efficient positional encoding
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len).unsqueeze(1).float()
        div_term = torch.exp(
            torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
        )
   
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
   
        # Register buffer instead of parameter
        self.register_buffer('pe', pe)
   
    def forward(self, x):
        x = x + self.pe[:, :x.size(1)]
        return self.dropout(x)
   

4. Additional optimization suggestions:

   class TransformerDecoder(nn.Module): def init(self, vocab_size, d_model, num_heads, num_layers, max_seq_length): super(TransformerDecoder, self).init() self.embedding = nn.Embedding(vocab_size, d_model) self.positional_encoding = PositionalEncoding(d_model, max_seq_length)

        # Add dropout
        self.dropout = nn.Dropout(0.1)
   
        # Memory-efficient decoder layer
        decoder_layer = nn.TransformerDecoderLayer(
            d_model=d_model,
            nhead=num_heads,
            dim_feedforward=4*d_model,  # Standard size
            dropout=0.1,
            batch_first=True  # Avoid permute operations
        )
        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)
        self.fc_out = nn.Linear(d_model, vocab_size)
   
    def forward(self, target_seqs, memory, target_mask):
        embedded = self.embedding(target_seqs)
        embedded = self.positional_encoding(embedded)
        embedded = self.dropout(embedded)
   
        # No need for permute if batch_first=True
        output = self.transformer_decoder(embedded, memory, target_mask)
        return self.fc_out(output)
   

5. To debug this issue:

a) Print the shapes of your tensors:

def forward(self, target_seqs, memory, target_mask):
    print(f"Target seqs shape: {target_seqs.shape}")
    print(f"Memory shape: {memory.shape}")
    print(f"Target mask shape: {target_mask.shape}")
    # ... rest of the forward method

b) Try reducing these parameters:

• Batch size
• Sequence length (max_seq_length)
• Model dimension (d_model)
• Number of heads (num_heads)
• Number of layers (num_layers)

c) Add gradient checkpointing more strategically: from torch.utils.checkpoint import checkpoint

class TransformerDecoder(nn.Module):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.use_checkpointing = True  # Add a flag for checkpointing

    def forward(self, target_seqs, memory, target_mask):
        if self.use_checkpointing and self.training:
            return checkpoint(self._forward, target_seqs, memory, target_mask)
        return self._forward(target_seqs, memory, target_mask)

    def _forward(self, target_seqs, memory, target_mask):
        # Original forward pass code here
        pass

Could you share:

1. What are the dimensions of your input data?
2. The values you're using for d_model, num_heads, and num_layers?
3. Your batch size?

This would help pinpoint the exact cause of the memory issue.