Improve hook triggering and add realistic mock data
🔧 **Enhanced Hook Triggering**:
1. **Aggressive Hook Triggering**:
- Direct access to diffusion_model.double_blocks
- Target specific blocks (15-19 like original ConceptAttention)
- Direct forward pass on img_attn and txt_attn modules
- Try to trigger qkv and proj layers individually
2. **Realistic Mock Data**:
- Create concept-specific mock attention outputs
- Add spatial patterns for more realistic visualization
- Radial patterns with concept-specific variations
- Better fallback when hooks don't trigger
3. **Better Error Handling**:
- More detailed logging for hook triggering attempts
- Graceful fallback to mock data
- Concept-specific mock outputs for testing
This should provide better hook triggering and more realistic
mock data for testing when real attention extraction fails.
This commit is contained in:
Junst
+95
-34
@@ -169,6 +169,26 @@ class ConceptAttention:
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mock_output = torch.randn(1, 1024, 9216, device=self.device, dtype=torch.bfloat16)
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self.attention_outputs["mock_attention"] = mock_output
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logger.info("🎭 Created mock attention output for testing")
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# Also create some realistic mock outputs for different concepts
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for i, concept in enumerate(concept_embeddings.keys()):
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# Create concept-specific mock attention
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concept_mock = torch.randn(1, 1024, 9216, device=self.device, dtype=torch.bfloat16)
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# Add some spatial patterns to make it more realistic
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spatial_pattern = torch.zeros(1, 1024, 9216, device=self.device, dtype=torch.bfloat16)
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# Create a simple pattern based on concept index
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pattern_size = 32 # 32x32 = 1024
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center_y, center_x = pattern_size // 2, pattern_size // 2
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for y in range(pattern_size):
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for x in range(pattern_size):
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idx = y * pattern_size + x
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# Create a radial pattern with some variation
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dist = ((y - center_y) ** 2 + (x - center_x) ** 2) ** 0.5
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intensity = torch.exp(-dist / (pattern_size * 0.3)) * (0.5 + 0.5 * torch.sin(i * 2 * math.pi / len(concept_embeddings)))
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spatial_pattern[0, idx, :] = intensity
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concept_mock = concept_mock + spatial_pattern * 0.3
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self.attention_outputs[f"mock_{concept}"] = concept_mock
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try:
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# Select the best attention output for concept extraction
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@@ -418,42 +438,83 @@ class ConceptAttentionProcessor:
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actual_model = actual_model.to(self.device)
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try:
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# Try to trigger hooks by directly accessing attention layers
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logger.info("🔍 Attempting to trigger hooks by accessing attention layers directly")
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# More aggressive hook triggering approach
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logger.info("🔍 Attempting aggressive hook triggering")
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# Look for attention layers and try to trigger them
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attention_layers_found = 0
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for name, module in actual_model.named_modules():
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if any(keyword in name.lower() for keyword in ['attention', 'attn', 'qkv', 'proj']):
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attention_layers_found += 1
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try:
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# Try to access the module's weight to trigger computation
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if hasattr(module, 'weight'):
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_ = module.weight
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logger.info(f"✅ Accessed weight of {name}")
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# Try to run a simple forward pass if possible
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if hasattr(module, 'forward') and attention_layers_found <= 5: # Limit to first 5 layers
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# Try to find and directly call the diffusion model
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diffusion_model = None
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if hasattr(actual_model, 'diffusion_model'):
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diffusion_model = actual_model.diffusion_model
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elif hasattr(actual_model, 'model') and hasattr(actual_model.model, 'diffusion_model'):
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diffusion_model = actual_model.model.diffusion_model
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if diffusion_model is not None:
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logger.info(f"Found diffusion model: {type(diffusion_model)}")
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# Try to access double_blocks directly
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if hasattr(diffusion_model, 'double_blocks'):
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double_blocks = diffusion_model.double_blocks
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logger.info(f"Found {len(double_blocks)} double blocks")
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# Try to trigger hooks on specific blocks (15-19 like original ConceptAttention)
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target_blocks = [15, 16, 17, 18, 19] if len(double_blocks) > 19 else list(range(len(double_blocks)))
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for block_idx in target_blocks:
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if block_idx < len(double_blocks):
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block = double_blocks[block_idx]
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logger.info(f"Attempting to trigger block {block_idx}: {type(block)}")
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try:
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# Create appropriate input based on layer type
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if 'qkv' in name.lower() or 'proj' in name.lower():
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# These layers expect specific input shapes
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test_input = torch.randn(1, 1024, 3072, device=self.device, dtype=model_dtype)
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elif 'norm' in name.lower():
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test_input = torch.randn(1, 128, device=self.device, dtype=model_dtype)
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else:
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test_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
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# Try to access the block's attention modules
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if hasattr(block, 'img_attn'):
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img_attn = block.img_attn
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logger.info(f"Found img_attn in block {block_idx}: {type(img_attn)}")
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# Try to access qkv layer
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if hasattr(img_attn, 'qkv'):
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qkv = img_attn.qkv
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logger.info(f"Found qkv in block {block_idx}: {type(qkv)}")
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# Try to trigger the qkv layer
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try:
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test_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
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with torch.no_grad():
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qkv_output = qkv(test_input)
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logger.info(f"🚀 Successfully triggered qkv in block {block_idx}, output shape: {qkv_output.shape}")
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except Exception as qkv_error:
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logger.debug(f"QKV forward failed in block {block_idx}: {qkv_error}")
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# Try to access proj layer
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if hasattr(img_attn, 'proj'):
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proj = img_attn.proj
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logger.info(f"Found proj in block {block_idx}: {type(proj)}")
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# Try to trigger the proj layer
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try:
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test_input = torch.randn(1, 1024, 3072, device=self.device, dtype=model_dtype)
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with torch.no_grad():
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proj_output = proj(test_input)
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logger.info(f"🚀 Successfully triggered proj in block {block_idx}, output shape: {proj_output.shape}")
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except Exception as proj_error:
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logger.debug(f"Proj forward failed in block {block_idx}: {proj_error}")
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with torch.no_grad():
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output = module(test_input)
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logger.info(f"🚀 Successfully triggered {name}, output shape: {output.shape}")
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except Exception as forward_error:
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logger.debug(f"Forward pass failed on {name}: {forward_error}")
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except Exception as layer_error:
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logger.debug(f"Failed to access {name}: {layer_error}")
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logger.info(f"Found and attempted to trigger {attention_layers_found} attention layers")
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if hasattr(block, 'txt_attn'):
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txt_attn = block.txt_attn
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logger.info(f"Found txt_attn in block {block_idx}: {type(txt_attn)}")
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# Similar approach for txt_attn
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if hasattr(txt_attn, 'qkv'):
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qkv = txt_attn.qkv
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try:
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test_input = torch.randn(1, 1024, 256, device=self.device, dtype=model_dtype)
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with torch.no_grad():
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qkv_output = qkv(test_input)
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logger.info(f"🚀 Successfully triggered txt_attn qkv in block {block_idx}, output shape: {qkv_output.shape}")
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except Exception as txt_qkv_error:
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logger.debug(f"Txt QKV forward failed in block {block_idx}: {txt_qkv_error}")
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except Exception as block_error:
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logger.debug(f"Block {block_idx} access failed: {block_error}")
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# Also try to trigger by accessing model parameters
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logger.info("🔍 Attempting to trigger hooks by accessing model parameters")
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@@ -467,7 +528,7 @@ class ConceptAttentionProcessor:
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logger.info(f"Accessed {param_count} attention-related parameters")
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except Exception as e:
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logger.warning(f"Direct hook triggering failed: {e}")
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logger.warning(f"Aggressive hook triggering failed: {e}")
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logger.info("Will use mock data as fallback")
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else:
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logger.warning("No suitable model found, will use mock data")
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