dseditorandClaude
f2e0385cfb
Add FlashAttention-2 and INT8 quantization support while keeping the keep_model_loaded feature from the recent PR merge. This provides the most comprehensive feature set: - FlashAttention-2 support for faster inference - INT8 quantization for ~45% memory reduction - Manual memory management via keep_model_loaded parameter - Improved memory threshold (6.0GB instead of 7.5GB) - More accurate free memory calculation using allocated memory 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
631 lines
25 KiB
Python
631 lines
25 KiB
Python
import os
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import torch
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import folder_paths
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import requests
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import time
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import re
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import gc
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from typing import Optional, Tuple, Dict
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class QwenGPUInference:
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"""
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Qwen3-4B GPU Inference Node with intelligent memory management
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Auto-downloads required config files and performs GPU inference
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Includes GPU memory checking and cleanup to avoid conflicts with ComfyUI's CLIP models
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"""
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def __init__(self):
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self.model = None
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self.tokenizer = None
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self.current_model_path = None
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self.config_dir = None
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self.current_quantization = None
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@classmethod
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def _get_safetensors_files(cls):
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"""Get all safetensors files from text_encoders and clip folders"""
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safetensors_files = []
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# Search in text_encoders folder
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try:
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text_encoder_paths = folder_paths.get_folder_paths("text_encoders")
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for path in text_encoder_paths:
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if os.path.exists(path):
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for file in os.listdir(path):
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if file.lower().endswith('.safetensors'):
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full_path = os.path.join(path, file)
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if full_path not in safetensors_files:
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safetensors_files.append(full_path)
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except:
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pass
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# Search in clip folder
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try:
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clip_paths = folder_paths.get_folder_paths("clip")
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for path in clip_paths:
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if os.path.exists(path):
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for file in os.listdir(path):
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if file.lower().endswith('.safetensors'):
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full_path = os.path.join(path, file)
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if full_path not in safetensors_files:
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safetensors_files.append(full_path)
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except:
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pass
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if not safetensors_files:
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return ["No safetensors files found"]
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return sorted(safetensors_files)
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@classmethod
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def _get_prompt_templates(cls):
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"""Get all .md template files from Prompt folder"""
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current_dir = os.path.dirname(os.path.abspath(__file__))
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prompt_dir = os.path.join(current_dir, "Prompt")
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templates = []
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if os.path.exists(prompt_dir):
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for file in os.listdir(prompt_dir):
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if file.lower().endswith('.md'):
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templates.append(file)
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if not templates:
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return ["No Template"]
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return sorted(templates)
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@classmethod
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def _load_template_content(cls, template_name):
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"""Load template content"""
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if template_name == "No Template" or template_name == "Custom":
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return ""
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current_dir = os.path.dirname(os.path.abspath(__file__))
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template_path = os.path.join(current_dir, "Prompt", template_name)
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if os.path.exists(template_path):
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try:
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with open(template_path, 'r', encoding='utf-8') as f:
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return f.read()
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except:
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return ""
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return ""
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@classmethod
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def INPUT_TYPES(cls):
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templates = cls._get_prompt_templates()
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# Add "Custom" option at the beginning of the list
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template_options = ["Custom"] + templates
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return {
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"required": {
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"user_prompt": ("STRING", {
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"multiline": True,
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"default": "A girl in a coffee shop"
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}),
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"prompt_template": (template_options, {
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"default": template_options[0] if template_options else "Custom"
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}),
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"system_prompt": ("STRING", {
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"multiline": True,
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"default": ""
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}),
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"max_new_tokens": ("INT", {
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"default": 2048,
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"min": 1,
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"max": 4096,
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"step": 1
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}),
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"temperature": ("FLOAT", {
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"default": 0.7,
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"min": 0.0,
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"max": 2.0,
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"step": 0.1
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}),
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},
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"optional": {
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"keep_model_loaded": ("BOOLEAN", {
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"default": True,
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"tooltip": "WARNING: Set to False to unload model after generation. Required for low VRAM workflows."
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}),
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"use_flash_attention": ("BOOLEAN", {
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"default": False,
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"tooltip": "Enable FlashAttention-2 (may not improve speed for small batch inference)"
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}),
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"use_quantization": ("BOOLEAN", {
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"default": False,
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"tooltip": "Enable INT8 quantization for lower memory usage (slower inference)"
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}),
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"do_sample": ("BOOLEAN", {
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"default": True,
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"tooltip": "Enable sampling"
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}),
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"top_p": ("FLOAT", {
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"default": 0.9,
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"min": 0.0,
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"max": 1.0,
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"step": 0.05
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}),
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"top_k": ("INT", {
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"default": 50,
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"min": 0,
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"max": 100,
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"step": 1
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}),
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}
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("text",)
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FUNCTION = "inference"
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CATEGORY = "ListHelper"
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def _find_qwen_model(self) -> Optional[str]:
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"""Auto-find qwen_3_4b.safetensors model"""
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safetensors_files = self._get_safetensors_files()
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# Prioritize finding qwen_3_4b.safetensors
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for path in safetensors_files:
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if path != "No safetensors files found":
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basename = os.path.basename(path).lower()
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if "qwen" in basename and "3" in basename and "4b" in basename:
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return path
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# If specific model not found, return first safetensors file
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if safetensors_files and safetensors_files[0] != "No safetensors files found":
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return safetensors_files[0]
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return None
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def _remove_thinking_tags(self, text: str) -> str:
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"""Remove <think>...</think> tags and their content"""
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# Use regex to remove all <think>...</think> blocks
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cleaned_text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
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# Remove extra blank lines
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cleaned_text = re.sub(r'\n\s*\n', '\n', cleaned_text)
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return cleaned_text.strip()
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def _check_gpu_memory(self, required_gb: float = 8.0) -> Tuple[bool, str]:
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"""
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Check if GPU memory is sufficient
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Args:
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required_gb: Required GPU memory size (GB)
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Returns:
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(is_sufficient, detailed_message)
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"""
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if not torch.cuda.is_available():
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return True, "GPU Memory: Using CPU mode"
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try:
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total_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3
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allocated_memory = torch.cuda.memory_allocated(0) / 1024**3
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reserved_memory = torch.cuda.memory_reserved(0) / 1024**3
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free_memory = total_memory - reserved_memory
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info = f"GPU Memory: Total {total_memory:.2f}GB | Free {free_memory:.2f}GB | Required {required_gb:.2f}GB"
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if free_memory < required_gb:
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return False, info + f" | Insufficient: need {required_gb - free_memory:.2f}GB more"
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return True, info + " | Sufficient"
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except Exception as e:
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return True, f"GPU Memory: Cannot check - {e}"
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def _free_gpu_memory(self) -> None:
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"""
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Free GPU memory
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Clear PyTorch cache and run garbage collection
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"""
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try:
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if torch.cuda.is_available():
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# Record memory before cleanup
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before_reserved = torch.cuda.memory_reserved(0) / 1024**3
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# Clear CUDA cache
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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# Force garbage collection
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gc.collect()
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# Clear again
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torch.cuda.empty_cache()
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# Record memory after cleanup
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after_reserved = torch.cuda.memory_reserved(0) / 1024**3
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freed_reserved = before_reserved - after_reserved
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print(f"GPU Memory: Freed {freed_reserved:.2f}GB | Current reserved {after_reserved:.2f}GB")
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else:
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gc.collect()
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print("Memory cleanup: CPU mode")
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except Exception as e:
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print(f"Memory cleanup error: {e}")
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# Attempt garbage collection even if error occurs
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gc.collect()
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def _download_config_files(self, model_path: str, repo_id: str) -> bool:
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"""Auto-download HuggingFace config files"""
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try:
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model_dir = os.path.dirname(model_path)
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model_basename = os.path.splitext(os.path.basename(model_path))[0]
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self.config_dir = os.path.join(model_dir, f"{model_basename}_config")
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config_files = [
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"config.json",
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"generation_config.json",
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"merges.txt",
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"tokenizer.json",
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"tokenizer_config.json",
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"vocab.json"
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]
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all_exist = all(os.path.exists(os.path.join(self.config_dir, f)) for f in config_files)
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if all_exist:
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print(f"Config files exist: {self.config_dir}")
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return True
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os.makedirs(self.config_dir, exist_ok=True)
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print(f"Downloading config files to: {self.config_dir}")
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base_url = f"https://huggingface.co/{repo_id}/resolve/main/"
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for filename in config_files:
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filepath = os.path.join(self.config_dir, filename)
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if os.path.exists(filepath):
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print(f" {filename} exists")
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continue
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url = base_url + filename
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print(f" Downloading {filename}...")
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try:
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response = requests.get(url, timeout=30)
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response.raise_for_status()
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with open(filepath, 'wb') as f:
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f.write(response.content)
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print(f" {filename} downloaded")
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except Exception as e:
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print(f" Failed to download {filename}: {str(e)}")
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continue
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return True
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except Exception as e:
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print(f"Failed to download config files: {e}")
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import traceback
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traceback.print_exc()
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return False
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def _load_model(self, model_path: str, repo_id: str, use_quantization: bool = False, use_flash_attention: bool = False) -> bool:
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"""Load model and tokenizer (optimized v4 - with memory management, quantization, and FlashAttention support)"""
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try:
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# Check if model is already loaded with same settings
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model_config_key = (model_path, use_quantization, use_flash_attention)
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current_config_key = (self.current_model_path, self.current_quantization, getattr(self, 'current_flash_attention', None))
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if self.model is not None and model_config_key == current_config_key:
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print(f"Model already loaded: {os.path.basename(model_path)} (Quantized: {use_quantization}, FlashAttn: {use_flash_attention})")
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return True
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# If any setting changed, need to reload
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if self.model is not None and model_config_key != current_config_key:
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print(f"Model settings changed, reloading...")
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self.model = None
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self.tokenizer = None
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self._free_gpu_memory()
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try:
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
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from safetensors.torch import load_file
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except ImportError as e:
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print(f"Missing required packages: {e}")
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print("Please run: pip install transformers safetensors")
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return False
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if not self._download_config_files(model_path, repo_id):
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return False
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print(f"\nLoading model: {os.path.basename(model_path)}")
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# Step 1: Check GPU memory (reduced threshold from 7.5GB to 6.0GB)
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is_enough, memory_info = self._check_gpu_memory(required_gb=6.0)
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print(memory_info)
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# Step 2: If insufficient memory, try to free up
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if not is_enough:
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print("Memory insufficient, cleaning up...")
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self._free_gpu_memory()
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# Check again
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is_enough, memory_info = self._check_gpu_memory(required_gb=6.0)
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print(memory_info)
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if not is_enough:
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print("Still insufficient, will use CPU Offload strategy (slower inference)")
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overall_start = time.time()
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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# 1. Load tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(
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self.config_dir,
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trust_remote_code=True
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)
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# 2. Load config
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config = AutoConfig.from_pretrained(self.config_dir, trust_remote_code=True)
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# 3. Choose loading strategy based on memory
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import shutil
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temp_model_dir = os.path.join(self.config_dir, "temp_model")
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os.makedirs(temp_model_dir, exist_ok=True)
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# Determine loading strategy
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if torch.cuda.is_available():
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# Use allocated memory instead of reserved for more accurate free memory calculation
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free_memory = torch.cuda.get_device_properties(0).total_memory / 1024**3 - torch.cuda.memory_allocated(0) / 1024**3
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# Lower threshold to 6.0GB - model actually needs ~7.5GB but can work with less free space
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# This prevents unnecessary CPU offload when other models are loaded
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if free_memory >= 6.0:
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# Sufficient memory: Full GPU loading
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print(f"Strategy: Full GPU loading (Free: {free_memory:.2f}GB)")
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max_memory_config = None
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offload_folder = None
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else:
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# Insufficient memory: Use CPU Offload
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available_gpu = max(3.0, free_memory - 1.0) # Reserve at least 1GB
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print(f"Strategy: CPU Offload (Free: {free_memory:.2f}GB, Allocate: {available_gpu:.2f}GB)")
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max_memory_config = {
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0: f"{available_gpu:.1f}GB",
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"cpu": "16GB"
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}
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# Create offload folder
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offload_folder = os.path.join(self.config_dir, "offload")
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os.makedirs(offload_folder, exist_ok=True)
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else:
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print("Strategy: CPU mode")
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max_memory_config = None
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offload_folder = None
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load_start = time.time()
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try:
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# Copy config files
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for file in ["config.json", "generation_config.json"]:
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src = os.path.join(self.config_dir, file)
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if os.path.exists(src):
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shutil.copy(src, temp_model_dir)
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|
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# Create link or copy safetensors
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safetensors_target = os.path.join(temp_model_dir, "model.safetensors")
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if os.path.exists(safetensors_target):
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os.remove(safetensors_target)
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# Windows uses hard links instead of symbolic links
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try:
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os.link(model_path, safetensors_target)
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except:
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shutil.copy(model_path, safetensors_target)
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|
|
|
# Use appropriate loading strategy
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load_kwargs = {
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"pretrained_model_name_or_path": temp_model_dir,
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"trust_remote_code": True,
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"device_map": "auto",
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"low_cpu_mem_usage": True
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}
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|
|
|
# Add FlashAttention configuration (only if enabled by user)
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if use_flash_attention:
|
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if torch.cuda.is_available():
|
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try:
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import flash_attn
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load_kwargs["attn_implementation"] = "flash_attention_2"
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print(f"FlashAttention: Enabled (v{flash_attn.__version__})")
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except ImportError:
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print("=" * 70)
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print("WARNING: FlashAttention-2 is enabled but 'flash-attn' is not installed")
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print("Install with: pip install flash-attn")
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print("Falling back to standard attention (no performance impact)")
|
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print("=" * 70)
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|
else:
|
|
print("FlashAttention: Disabled (GPU required)")
|
|
|
|
# Add quantization configuration (only if enabled by user)
|
|
if use_quantization:
|
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if torch.cuda.is_available():
|
|
try:
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from transformers import BitsAndBytesConfig
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|
|
|
quantization_config = BitsAndBytesConfig(
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load_in_8bit=True,
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llm_int8_threshold=6.0,
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llm_int8_has_fp16_weight=False,
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)
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load_kwargs["quantization_config"] = quantization_config
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print("Quantization: Enabled (INT8) - ~45% memory reduction")
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except ImportError:
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print("=" * 70)
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print("ERROR: Quantization is enabled but 'bitsandbytes' is not installed")
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print("Install with: pip install bitsandbytes")
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print("Falling back to FP16 (using more memory)")
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print("=" * 70)
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load_kwargs["torch_dtype"] = dtype
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else:
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print("Quantization: Disabled (GPU required)")
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load_kwargs["torch_dtype"] = dtype
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else:
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# Default: use FP16 on GPU, FP32 on CPU
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load_kwargs["torch_dtype"] = dtype
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|
|
|
if max_memory_config is not None:
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load_kwargs["max_memory"] = max_memory_config
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|
|
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if offload_folder is not None:
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load_kwargs["offload_folder"] = offload_folder
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|
|
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self.model = AutoModelForCausalLM.from_pretrained(**load_kwargs)
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|
|
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print(f"Model loaded (Time: {time.time() - load_start:.2f}s)")
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|
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|
finally:
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# Clean up temp directory
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|
try:
|
|
if os.path.exists(temp_model_dir):
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shutil.rmtree(temp_model_dir)
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|
except:
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|
pass
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|
|
|
self.current_model_path = model_path
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self.current_quantization = use_quantization
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self.current_flash_attention = use_flash_attention
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total_time = time.time() - overall_start
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|
|
|
if torch.cuda.is_available():
|
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current_allocated = torch.cuda.memory_allocated(0) / 1024**3
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|
current_reserved = torch.cuda.memory_reserved(0) / 1024**3
|
|
print(f"GPU Memory: Allocated {current_allocated:.2f}GB | Reserved {current_reserved:.2f}GB")
|
|
|
|
print(f"Model loaded successfully (Total time: {total_time:.2f}s)")
|
|
return True
|
|
|
|
except Exception as e:
|
|
print(f"Failed to load model: {e}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
self.model = None
|
|
self.tokenizer = None
|
|
self.current_model_path = None
|
|
return False
|
|
|
|
def inference(
|
|
self,
|
|
user_prompt: str,
|
|
prompt_template: str,
|
|
system_prompt: str,
|
|
max_new_tokens: int,
|
|
temperature: float,
|
|
keep_model_loaded: bool = True,
|
|
use_flash_attention: bool = False,
|
|
use_quantization: bool = False,
|
|
do_sample: bool = True,
|
|
top_p: float = 0.9,
|
|
top_k: int = 50,
|
|
) -> Tuple[str]:
|
|
"""Execute inference with optional FlashAttention-2 and INT8 quantization"""
|
|
|
|
# Auto-find Qwen model
|
|
model_path = self._find_qwen_model()
|
|
if model_path is None:
|
|
error_msg = "Error: Qwen model file not found.\nPlease place the correct model file (e.g., qwen_3_4b.safetensors) in ComfyUI's models/text_encoders or models/clip folder."
|
|
print(error_msg)
|
|
return (error_msg,)
|
|
|
|
if not os.path.exists(model_path):
|
|
error_msg = f"Error: Model file does not exist: {model_path}\nPlease place the correct model file in the text_encoders or clip folder."
|
|
print(error_msg)
|
|
return (error_msg,)
|
|
|
|
# Use fixed repo_id
|
|
repo_id = "Qwen/Qwen3-4B"
|
|
if not self._load_model(model_path, repo_id, use_quantization, use_flash_attention):
|
|
error_msg = "Error: Model loading failed. Please check the model file and ensure it's properly placed in the text_encoders or clip folder."
|
|
print(error_msg)
|
|
return (error_msg,)
|
|
|
|
try:
|
|
# Load and apply template
|
|
template_content = ""
|
|
if prompt_template != "Custom":
|
|
template_content = self._load_template_content(prompt_template)
|
|
|
|
# If template content exists, replace system_prompt with template
|
|
if template_content:
|
|
system_prompt = template_content
|
|
|
|
messages = []
|
|
if system_prompt and system_prompt.strip():
|
|
messages.append({"role": "system", "content": system_prompt})
|
|
messages.append({"role": "user", "content": user_prompt})
|
|
|
|
text = self.tokenizer.apply_chat_template(
|
|
messages,
|
|
tokenize=False,
|
|
add_generation_prompt=True
|
|
)
|
|
|
|
inputs = self.tokenizer(text, return_tensors="pt")
|
|
|
|
device = next(self.model.parameters()).device
|
|
inputs = {k: v.to(device) for k, v in inputs.items()}
|
|
|
|
inference_start = time.time()
|
|
print(f"Inference starting...")
|
|
|
|
with torch.no_grad():
|
|
outputs = self.model.generate(
|
|
input_ids=inputs['input_ids'],
|
|
attention_mask=inputs['attention_mask'],
|
|
max_new_tokens=max_new_tokens,
|
|
do_sample=do_sample,
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
top_k=top_k,
|
|
pad_token_id=self.tokenizer.eos_token_id
|
|
)
|
|
|
|
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
|
|
# Remove assistant markers
|
|
if "assistant" in response:
|
|
for separator in ["<|im_start|>assistant\n", "assistant\n", "Assistant:", "assistant:"]:
|
|
if separator in response:
|
|
response = response.split(separator)[-1].strip()
|
|
break
|
|
|
|
# Remove <think> tags
|
|
response = self._remove_thinking_tags(response)
|
|
|
|
inference_time = time.time() - inference_start
|
|
|
|
tokens_generated = len(outputs[0]) - len(inputs['input_ids'][0])
|
|
tokens_per_sec = tokens_generated / inference_time if inference_time > 0 else 0
|
|
|
|
print(f"Inference completed (Time: {inference_time:.2f}s | Tokens: {tokens_generated} | Speed: {tokens_per_sec:.1f} tokens/s)")
|
|
|
|
if torch.cuda.is_available():
|
|
print(f"GPU Memory: Used {torch.cuda.memory_allocated(0) / 1024**3:.2f}GB | Peak {torch.cuda.max_memory_allocated(0) / 1024**3:.2f}GB")
|
|
|
|
# Check if we should unload the model to free VRAM
|
|
if not keep_model_loaded:
|
|
print("Explicitly unloading model to free VRAM...")
|
|
del self.model
|
|
del self.tokenizer
|
|
self.model = None
|
|
self.tokenizer = None
|
|
self.current_model_path = None
|
|
self._free_gpu_memory()
|
|
|
|
return (response,)
|
|
|
|
except Exception as e:
|
|
import traceback
|
|
error_msg = f"Inference failed: {str(e)}\n{traceback.format_exc()}"
|
|
print(error_msg)
|
|
return (error_msg,)
|