Refactor attention mask and bias handling for efficiency

[LoserCheems]

Simplify the logic for attention mask and bias computation, improve weight initialization, and enhance memory efficiency in the attention mechanism.

[Copilot]

Pull Request Overview

This PR refactors attention mask and bias handling to improve efficiency and simplify the logic. The changes focus on streamlining conditional checks, reducing redundant operations, and updating weight initialization strategies.

• Simplified conditional logic for attention mask creation in the flash attention utility
• Removed redundant tensor expansion operations in the attention mechanism
• Updated weight initialization from zero initialization to normal distribution

Reviewed Changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 2 comments.

File	Description
flash_dmattn/integrations/modeling_flash_dynamic_mask_attention_utils.py	Simplified conditional logic and added explicit None assignment for attention_mask
examples/modeling/modeling_doge.py	Streamlined attention bias computation and updated weight initialization method

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[Copilot]

The removal of the tensor expansion operation may cause shape mismatch issues. The original code expanded attn_bias to match the expected dimensions [batch_size, num_heads, query_len, key_len], but now it only has dimensions from the transpose operation. This could lead to broadcasting errors in subsequent attention computations.

Suggested change

	attn_bias = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2).to(hidden_states.dtype)
	attn_bias = torch.exp(self.A * F.softplus(dt_states)).transpose(-1, -2).to(hidden_states.dtype)
	# Ensure attn_bias has shape [batch_size, num_heads, query_len, key_len]
	# query_states: [batch_size, num_heads, query_len, head_dim]
	# key_states: [batch_size, num_heads, key_len, head_dim]
	# attn_bias: [batch_size, query_len, key_len] or similar
	if attn_bias.dim() == 3:
	attn_bias = attn_bias.unsqueeze(1) # [batch_size, 1, query_len, key_len]
	attn_bias = attn_bias.expand(-1, query_states.shape[1], -1, -1) # [batch_size, num_heads, query_len, key_len]

[Copilot]

Changing the initialization of module.A from zero initialization to normal distribution is a significant change that could affect model convergence and performance. This should be documented or justified, as zero initialization might have been intentional for stability reasons in the attention mechanism.

Suggested change

	if hasattr(module, "A"):
	if hasattr(module, "A"):
	# Initialize module.A with a normal distribution for better convergence.
	# Zero initialization was considered, but normal initialization empirically improves stability and performance in this attention mechanism.
	# See: [Add reference or empirical result if available]