415 lines
16 KiB
Python
Executable File
415 lines
16 KiB
Python
Executable File
"""
|
|
AICAS 2026 - Participant Core Modification File
|
|
|
|
Participants should modify the VLMModel class to implement optimizations.
|
|
|
|
Note:
|
|
- Benchmark directly calls self.model.generate() for performance testing.
|
|
- Your optimizations should modify self.model or its operators in __init__ via Monkey Patch.
|
|
- The generate() method is optional and mainly for debugging.
|
|
"""
|
|
from typing import Dict
|
|
try:
|
|
from PIL import Image
|
|
except ImportError:
|
|
# For testing without PIL
|
|
class Image:
|
|
pass
|
|
import torch
|
|
from transformers import AutoModelForImageTextToText, AutoProcessor
|
|
|
|
|
|
class VLMModel:
|
|
"""
|
|
Participant optimization class - modify this to implement optimizations.
|
|
|
|
Optimization Architecture:
|
|
- Split optimizations into separate methods for isolation and testing
|
|
- Enable/disable each optimization independently in __init__
|
|
- Each optimization method can be tested individually
|
|
|
|
Important Notes:
|
|
1. Benchmark directly calls self.model.generate() for performance testing.
|
|
2. Your optimizations should modify self.model or its operators via Monkey Patch.
|
|
3. All optimizations are applied in __init__ by calling optimization methods.
|
|
"""
|
|
|
|
def __init__(self, model_path: str, device: str = "cuda:0"):
|
|
"""
|
|
Initialize model and apply optimizations.
|
|
|
|
Args:
|
|
model_path: Qwen3-VL-2B-Instruct model path
|
|
device: CUDA device, e.g., "cuda:0"
|
|
"""
|
|
self._device = device
|
|
self.model_path = model_path
|
|
|
|
# Load processor
|
|
print(f"[VLMModel] Loading processor from {model_path}...")
|
|
self._processor = AutoProcessor.from_pretrained(model_path)
|
|
|
|
# Load model
|
|
print(f"[VLMModel] Loading model with FP16...")
|
|
self._model = AutoModelForImageTextToText.from_pretrained(
|
|
model_path,
|
|
torch_dtype=torch.float16,
|
|
device_map=device
|
|
)
|
|
self._model.eval()
|
|
|
|
# Track applied optimizations
|
|
self._optimizations_applied = []
|
|
|
|
# ================================================================
|
|
# Participant Optimization Area - Enable/disable optimizations here
|
|
# Uncomment the optimization methods you want to apply
|
|
# ================================================================
|
|
|
|
# 1. Vision Encoder Acceleration
|
|
# self._optimize_vision_encoder()
|
|
|
|
# 2. KV Cache Management
|
|
# self._optimize_kv_cache()
|
|
|
|
# 3. Cross-modal Connector Optimization
|
|
# self._optimize_cross_modal_connector()
|
|
|
|
# 4. Flash Attention Optimization
|
|
# self._enable_flash_attention()
|
|
|
|
# 5. Quantization
|
|
# self._apply_quantization()
|
|
|
|
# Optional: Explore model structure before optimization
|
|
# self._explore_model_structure()
|
|
|
|
# ================================================================
|
|
|
|
print(f"[VLMModel] Model loaded successfully on {device}")
|
|
if self._optimizations_applied:
|
|
print(f"[VLMModel] Applied optimizations: {', '.join(self._optimizations_applied)}")
|
|
|
|
# ================================================================
|
|
# Optimization Methods - Implement your optimizations here
|
|
# ================================================================
|
|
|
|
def _explore_model_structure(self):
|
|
"""
|
|
Helper method to explore model structure.
|
|
|
|
Use this to understand the model architecture before implementing optimizations.
|
|
This helps identify where to apply monkey patches.
|
|
"""
|
|
print("=" * 60)
|
|
print("Model Structure Exploration")
|
|
print("=" * 60)
|
|
|
|
# Explore vision model structure
|
|
if hasattr(self._model, 'vision_model'):
|
|
print(f"Vision Model: {type(self._model.vision_model)}")
|
|
if hasattr(self._model.vision_model, 'encoder'):
|
|
if hasattr(self._model.vision_model.encoder, 'layers'):
|
|
print(f" Vision Encoder Layers: {len(self._model.vision_model.encoder.layers)}")
|
|
# Show first layer structure
|
|
if len(self._model.vision_model.encoder.layers) > 0:
|
|
print(f" First Layer Type: {type(self._model.vision_model.encoder.layers[0])}")
|
|
else:
|
|
print("Vision Model: Not found (model structure may differ)")
|
|
|
|
# Explore language model structure
|
|
if hasattr(self._model, 'model'):
|
|
print(f"Language Model: {type(self._model.model)}")
|
|
if hasattr(self._model.model, 'layers'):
|
|
print(f" Language Model Layers: {len(self._model.model.layers)}")
|
|
else:
|
|
print("Language Model: Not found (model structure may differ)")
|
|
|
|
# Explore cross-modal components
|
|
cross_modal_attrs = ['connector', 'cross_attn', 'cross_attention', 'proj', 'projector']
|
|
found_components = []
|
|
for attr in cross_modal_attrs:
|
|
if hasattr(self._model, attr):
|
|
found_components.append(attr)
|
|
if found_components:
|
|
print(f"Cross-modal Components: {', '.join(found_components)}")
|
|
else:
|
|
print("Cross-modal Components: Explore manually (structure may vary)")
|
|
|
|
print("=" * 60)
|
|
print("Tip: Use print(self._model) to see full model structure")
|
|
print("=" * 60)
|
|
|
|
def _optimize_vision_encoder(self):
|
|
"""
|
|
Optimize Vision Encoder for high-resolution image inputs.
|
|
|
|
Optimization Directions:
|
|
1. Patch embedding convolution optimization
|
|
2. Vision Transformer attention mechanism optimization
|
|
3. Layer normalization optimization
|
|
4. Memory-efficient image processing
|
|
|
|
Implementation Steps:
|
|
1. Inspect model structure: call self._explore_model_structure()
|
|
2. Identify bottlenecks using profiling tools (PyTorch Profiler, nsys, etc.)
|
|
3. Implement optimized operators (Triton/CUDA kernels)
|
|
4. Replace original operators via monkey patch
|
|
|
|
Target Components:
|
|
- self._model.vision_model (if exists)
|
|
- Vision encoder layers and attention mechanisms
|
|
- Convolution operations in patch embedding
|
|
"""
|
|
# TODO: Implement your Vision Encoder optimization here
|
|
#
|
|
# Example workflow:
|
|
# 1. from your_optimization import optimized_attention, optimized_conv
|
|
# 2. Inspect: print(self._model.vision_model) to find target layers
|
|
# 3. Replace: layer.self_attn.forward = optimized_attention
|
|
# 4. Test: Run benchmark to verify improvement
|
|
import types
|
|
from triton_layer_norm import path_forward
|
|
|
|
for i in range(len(self._model.model.visual.blocks)):
|
|
norm1 = self._model.model.visual.blocks[i].norm1
|
|
norm1.forward = types.MethodType(path_forward, norm1)
|
|
norm2 = self._model.model.visual.blocks[i].norm2
|
|
norm2.forward = types.MethodType(path_forward, norm2)
|
|
|
|
if 'vision_encoder' not in self._optimizations_applied:
|
|
self._optimizations_applied.append('vision_encoder')
|
|
|
|
def _optimize_kv_cache(self):
|
|
"""
|
|
Optimize KV Cache management to reduce memory fragmentation.
|
|
|
|
Optimization Directions:
|
|
1. Memory layout optimization (contiguous memory allocation)
|
|
2. Fragmentation-free allocation strategies
|
|
3. Efficient cache reuse patterns
|
|
4. Dynamic cache sizing
|
|
|
|
Implementation Steps:
|
|
1. Understand current KV cache implementation in model layers
|
|
2. Design memory-efficient cache allocation strategy
|
|
3. Implement custom KV cache allocator if needed
|
|
4. Apply optimizations via monkey patch or config modification
|
|
|
|
Target Components:
|
|
- self._model.config (cache configuration)
|
|
- Attention layers (KV cache allocation)
|
|
- Generation loop (cache management)
|
|
"""
|
|
# Enable KV Cache first
|
|
self._model.config.use_cache = True
|
|
if hasattr(self._model.config, 'pad_token_id'):
|
|
if self._model.config.pad_token_id is None:
|
|
self._model.config.pad_token_id = self._model.config.eos_token_id
|
|
|
|
# TODO: Implement advanced KV Cache optimizations here
|
|
#
|
|
# Example workflow:
|
|
# 1. from your_optimization import FragmentationFreeKVCache
|
|
# 2. for layer in self._model.model.layers:
|
|
# 3. layer.attention.custom_kv_cache = FragmentationFreeKVCache()
|
|
# 4. Test: Monitor memory usage and generation speed
|
|
|
|
if 'kv_cache' not in self._optimizations_applied:
|
|
self._optimizations_applied.append('kv_cache')
|
|
|
|
def _optimize_cross_modal_connector(self):
|
|
"""
|
|
Optimize Cross-modal Connector computation efficiency.
|
|
|
|
Optimization Directions:
|
|
1. Cross-attention mechanism optimization
|
|
2. Vision-to-language projection optimization
|
|
3. Multi-modal fusion layer efficiency
|
|
4. Feature alignment and transformation optimization
|
|
|
|
Implementation Steps:
|
|
1. Identify cross-modal components using self._explore_model_structure()
|
|
2. Profile cross-modal operations to find bottlenecks
|
|
3. Implement optimized cross-attention or projection kernels
|
|
4. Replace original operations via monkey patch
|
|
|
|
Note: Qwen3-VL's cross-modal structure may vary.
|
|
Use model exploration to identify actual component names and locations.
|
|
"""
|
|
# TODO: Implement your Cross-modal Connector optimization here
|
|
#
|
|
# Example workflow:
|
|
# 1. Explore: self._explore_model_structure() to find connector components
|
|
# 2. from your_optimization import optimized_cross_attention
|
|
# 3. Identify: Inspect model to find cross-attention layers
|
|
# 4. Replace: connector.cross_attention.forward = optimized_cross_attention
|
|
# 5. Test: Verify accuracy and performance improvements
|
|
|
|
from my_patch import patch_forward
|
|
self._model.model.__class__.forward = patch_forward
|
|
|
|
if 'cross_modal' not in self._optimizations_applied:
|
|
self._optimizations_applied.append('cross_modal')
|
|
|
|
def _enable_flash_attention(self):
|
|
"""
|
|
Enable or implement Flash Attention optimization.
|
|
|
|
Implementation Approaches:
|
|
|
|
Approach 1: Enable PyTorch's Built-in Flash Attention (Simple)
|
|
- Uses torch.backends.cuda.enable_flash_sdp(True)
|
|
- Easy to enable but limited customization
|
|
- May not work for all attention patterns in Qwen3-VL
|
|
|
|
Approach 2: Implement Custom Flash Attention (Advanced, Recommended)
|
|
- Write custom Triton/CUDA kernels for attention computation
|
|
- Replace torch.nn.functional.scaled_dot_product_attention
|
|
- Full control over attention computation and memory layout
|
|
- Better performance potential but requires more implementation effort
|
|
|
|
Recommended: Implement Approach 2 for better performance gains.
|
|
Use profiling to identify which attention operations benefit most from optimization.
|
|
"""
|
|
# TODO: Choose and implement your Flash Attention approach
|
|
|
|
# Approach 1: Simple (enable PyTorch built-in)
|
|
# torch.backends.cuda.enable_flash_sdp(True)
|
|
|
|
# Approach 2: Advanced (custom implementation - recommended)
|
|
# from your_optimization import custom_flash_attention
|
|
# torch.nn.functional.scaled_dot_product_attention = custom_flash_attention
|
|
#
|
|
# Or replace at layer level:
|
|
# for layer in self._model.model.layers:
|
|
# layer.self_attn.forward = custom_attention_with_flash
|
|
|
|
if 'flash_attention' not in self._optimizations_applied:
|
|
self._optimizations_applied.append('flash_attention')
|
|
|
|
def _apply_quantization(self):
|
|
"""
|
|
Apply quantization to reduce model size and speed up inference.
|
|
|
|
Optimization Directions:
|
|
1. INT8 quantization (8-bit integer)
|
|
2. FP8 quantization (8-bit floating point)
|
|
3. Mixed precision quantization
|
|
4. Dynamic vs static quantization
|
|
|
|
Implementation Steps:
|
|
1. Choose quantization strategy based on accuracy/performance trade-off
|
|
2. Use quantization libraries (BitsAndBytes, TensorRT, etc.)
|
|
3. Calibrate quantized model on validation data
|
|
4. Verify accuracy preservation
|
|
|
|
Note: Quantization may require reloading the model with quantization config.
|
|
Consider applying quantization before other optimizations if model reload is needed.
|
|
"""
|
|
# TODO: Implement your quantization here
|
|
#
|
|
# Example workflow:
|
|
# 1. from transformers import BitsAndBytesConfig
|
|
# 2. quantization_config = BitsAndBytesConfig(load_in_8bit=True)
|
|
# 3. Note: May need to reload model with quantization config
|
|
# 4. Test: Verify accuracy and performance improvements
|
|
|
|
if 'quantization' not in self._optimizations_applied:
|
|
self._optimizations_applied.append('quantization')
|
|
|
|
# Required properties for benchmark
|
|
@property
|
|
def processor(self):
|
|
"""
|
|
Required by benchmark for input processing.
|
|
|
|
Benchmark uses this to prepare inputs with unified tokenizer.
|
|
"""
|
|
return self._processor
|
|
|
|
@property
|
|
def model(self):
|
|
"""
|
|
Required by benchmark for direct model.generate() calls.
|
|
|
|
Benchmark directly calls self.model.generate() for performance testing.
|
|
Your optimizations should modify this model object or its operators.
|
|
"""
|
|
return self._model
|
|
|
|
@property
|
|
def device(self):
|
|
"""
|
|
Required by benchmark for device information.
|
|
"""
|
|
return self._device
|
|
|
|
def generate(
|
|
self,
|
|
image: Image.Image,
|
|
question: str,
|
|
max_new_tokens: int = 128
|
|
) -> Dict:
|
|
"""
|
|
Generate answer (optional method, mainly for debugging).
|
|
|
|
Note: Benchmark uses self.model.generate() directly for performance testing.
|
|
This method is provided for convenience and debugging purposes.
|
|
|
|
Args:
|
|
image: PIL Image object
|
|
question: Question text
|
|
max_new_tokens: Maximum tokens to generate
|
|
|
|
Returns:
|
|
Dict: {
|
|
"text": str, # Generated text answer
|
|
"token_count": int # Generated token count
|
|
}
|
|
"""
|
|
# Build Qwen3-VL message format
|
|
messages = [{
|
|
"role": "user",
|
|
"content": [
|
|
{"type": "image", "image": image},
|
|
{"type": "text", "text": question}
|
|
]
|
|
}]
|
|
|
|
# Process inputs
|
|
inputs = self._processor.apply_chat_template(
|
|
messages,
|
|
tokenize=True,
|
|
add_generation_prompt=True,
|
|
return_dict=True,
|
|
return_tensors="pt"
|
|
).to(self._device)
|
|
|
|
# Generate
|
|
with torch.no_grad():
|
|
output_ids = self._model.generate(
|
|
**inputs,
|
|
max_new_tokens=max_new_tokens,
|
|
do_sample=False,
|
|
temperature=0.0,
|
|
top_p=1.0,
|
|
use_cache=True
|
|
)
|
|
|
|
# Extract generated tokens (remove input part)
|
|
input_len = inputs.input_ids.shape[1]
|
|
generated_ids = output_ids[0][input_len:]
|
|
|
|
# Decode
|
|
text = self._processor.tokenizer.decode(
|
|
generated_ids,
|
|
skip_special_tokens=True,
|
|
clean_up_tokenization_spaces=False
|
|
)
|
|
|
|
return {
|
|
"text": text,
|
|
"token_count": len(generated_ids)
|
|
}
|