Here are BitsFusion Script Quantisation without finetuned

[s9anus98a]

import torch
import copy
import numpy as np
from transformers import CLIPProcessor, CLIPModel
from diffusers import StableDiffusionPipeline
from scipy.cluster.vq import vq, kmeans2

# Konfigurasi
model_path = "path/to/your/sd-v1-5-fp16.safetensors"
output_path = "path/to/your/quantized_model.safetensors"
bits = [1, 2, 3, 4]  # Bit-width yang akan diuji
sensitivity_threshold = 0.05
size_factor = 0.5
clip_thresholds = [0.9, 0.95, 0.98]
time_steps = 50

# Fungsi untuk menghitung Mean Squared Error (MSE)
def calculate_mse(image1, image2):
  """Menghitung Mean Squared Error (MSE) antara dua gambar."""
  return ((image1 - image2) ** 2).mean()

# Fungsi untuk menghitung CLIP score
def calculate_clip_score(images, texts, clip_processor, clip_model):
  """Menghitung CLIP score untuk gambar yang dihasilkan dan teks prompt."""
  inputs = clip_processor(text=texts, images=images, return_tensors="pt", padding=True)
  outputs = clip_model(**inputs)
  logits_per_image = outputs.logits_per_image
  return logits_per_image.diag().mean().item()

# Fungsi untuk menganalisis sensitivitas layer
def analyze_layer_sensitivity(model, prompts, bits, sample_size=100):
  """Menganalisis sensitivitas layer terhadap kuantisasi."""
  clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
  clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
  results = {}
  for name, param in model.named_parameters():
    if "weight" in name:
      results[name] = {}
      for b in bits:
        quantized_model = copy.deepcopy(model)
        quantize_layer(quantized_model, name, b)
        # Generate gambar dengan model yang telah dikuantisasi (implementasi diperlukan)
        generated_images = generate_images(quantized_model, prompts, sample_size)
        # Hitung metrik
        results[name][b] = {
            "mse": calculate_mse(generated_images, generate_images(model, prompts, sample_size)),
            "clip_score": calculate_clip_score(generated_images, prompts, clip_processor, clip_model),
            "parameters": param.numel()
        }
  return results

# Fungsi untuk mengkuantisasi layer dengan Lloyd-Max dari SciPy
def quantize_layer(model, layer_name, bits):
  """Menerapkan kuantisasi pada layer tertentu menggunakan algoritma Lloyd-Max dari SciPy.

  Args:
    model: Model PyTorch yang mengandung layer yang akan di kuantisasikan.
    layer_name: Nama layer yang akan di kuantisasikan (string).
    bits: Jumlah bit yang digunakan untuk kuantisasi (integer).
  """

  # Dapatkan layer berdasarkan namanya
  layer = dict(model.named_modules())[layer_name]

  # Pastikan layer memiliki bobot
  if not hasattr(layer, 'weight'):
    print(f"Layer {layer_name} tidak memiliki bobot.")
    return

  # Dapatkan tensor bobot
  w = layer.weight.data

  # Lewati kuantisasi jika bobot sudah dalam tipe data integer
  if w.dtype not in [torch.float, torch.float16, torch.float32, torch.float64]:
    print(f"Layer {layer_name} sudah memiliki bobot integer.")
    return

  # Konversi tensor ke CPU untuk kuantisasi Lloyd-Max
  w = w.cpu().numpy()

  # Lakukan kuantisasi Lloyd-Max menggunakan kmeans2 dari SciPy
  centroids, labels = kmeans2(w.reshape(-1, 1), 2**bits, minit='points')
  quantized_w = centroids[labels].reshape(w.shape)

  # Konversi kembali ke tensor PyTorch dan simpan ke layer
  layer.weight.data = torch.from_numpy(quantized_w).to(layer.weight.device)

  # Hitung dan simpan faktor skala dan titik nol
  min_val = w.min()
  max_val = w.max()
  scale = (max_val - min_val) / (2**bits - 1)
  zero_point = torch.round(-min_val / scale).to(layer.weight.device)
  layer.quantization_scale = scale.to(layer.weight.device)
  layer.quantization_zero_point = zero_point

  # Ganti fungsi forward untuk melakukan dekuantisasi saat inferensi
  layer._forward_impl = layer.forward
  def quantized_forward(*args, **kwargs):
    # Dekuantized bobot sebelum operasi forward
    dequantized_w = (layer.weight.data - layer.quantization_zero_point) * layer.quantization_scale
    return layer._forward_impl(dequantized_w, *args[1:], **kwargs)
  layer.forward = quantized_forward

# Fungsi untuk menghasilkan gambar
def generate_images(model, prompts, sample_size):
  """Menghasilkan gambar menggunakan model Stable Diffusion."""
  # Implementasikan proses generasi gambar menggunakan pipeline diffusers
  # Gunakan `model` dan `prompts` sebagai input
  # ...

# Fungsi untuk menentukan strategi mixed-precision
def determine_mixed_precision(results, sensitivity_threshold, size_factor, clip_thresholds):
  """Menentukan bit-width optimal untuk setiap layer."""
  mixed_precision = {}
  for name, layer_results in results.items():
    sensitivity_scores = {
        b: layer_results[b]["mse"] / (layer_results[b]["parameters"] ** size_factor)
        for b in bits
    }
    optimal_bits = min(bits, key=lambda b: sensitivity_scores[b])
    if sensitivity_scores[optimal_bits] > sensitivity_threshold:
      optimal_bits = max(bits)  # Gunakan bit-width maksimum jika melebihi ambang batas
    clip_score_drop = layer_results[max(bits)]["clip_score"] - results[name][32]["clip_score"]
    for i, threshold in enumerate(clip_thresholds):
      if clip_score_drop > np.quantile(
          [results[n][32]["clip_score"] for n in results], threshold
      ):
        optimal_bits += i + 1
        break
    mixed_precision[name] = optimal_bits
  return mixed_precision

# Fungsi untuk mengkuantisasi model
def quantize_model(model, mixed_precision):
  """Menerapkan kuantisasi pada model berdasarkan strategi mixed-precision."""
  for name, param in model.named_parameters():
    if "weight" in name and name in mixed_precision:
      quantize_layer(model, name, mixed_precision[name])
  return model

# Fungsi untuk pre-komputasi dan caching time embedding
def precompute_time_embeddings(model, time_steps):
  """Menghitung dan menyimpan time embedding."""
  time_embeddings = {}
  for t in range(time_steps):
    time_embeddings[t] = model.time_embedding(torch.tensor([t]))
  return time_embeddings

# Memuat model Stable Diffusion
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
model = pipe.unet

# Analisis sensitivitas layer (implementasikan generate_images terlebih dahulu)
prompts = ["A photo of a cat"]  # Ganti dengan prompt yang Anda inginkan
results = analyze_layer_sensitivity(model, prompts, bits)

# Menentukan strategi mixed-precision
mixed_precision = determine_mixed_precision(
    results, sensitivity_threshold, size_factor, clip_thresholds
)

# Menerapkan kuantisasi pada model
quantized_model = quantize_model(model, mixed_precision)

# Pre-komputasi dan caching time embedding
time_embeddings = precompute_time_embeddings(quantized_model, time_steps)

# Menyimpan model yang telah dikuantisasi
torch.save(quantized_model.state_dict(), output_path)

[Shinyzenith]

Hi, did you implement this?

[charlesrwest]

Here's the same code with the comments translated. attached
commentsTranslated.txt
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[LukeLIN-web]

translated

import torch
import copy
import numpy as np
from transformers import CLIPProcessor, CLIPModel
from diffusers import StableDiffusionPipeline
from scipy.cluster.vq import kmeans2

# Configuration
model_path = "path/to/your/sd-v1-5-fp16.safetensors"
output_path = "path/to/your/quantized_model.safetensors"
bits = [1, 2, 3, 4]  # Bit-widths to be tested
sensitivity_threshold = 0.05
size_factor = 0.5
clip_thresholds = [0.9, 0.95, 0.98]
time_steps = 50

# Function to calculate Mean Squared Error (MSE)
def calculate_mse(image1, image2):
    """Calculate Mean Squared Error (MSE) between two images."""
    return ((image1 - image2) ** 2).mean()

# Function to calculate CLIP score
def calculate_clip_score(images, texts, clip_processor, clip_model):
    """Calculate CLIP score for generated images and text prompts."""
    inputs = clip_processor(text=texts, images=images, return_tensors="pt", padding=True)
    outputs = clip_model(**inputs)
    logits_per_image = outputs.logits_per_image
    return logits_per_image.diag().mean().item()

# Function to analyze layer sensitivity
def analyze_layer_sensitivity(model, prompts, bits, sample_size=100):
    """Analyze layer sensitivity to quantization."""
    clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
    clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
    results = {}
    for name, param in model.named_parameters():
        if "weight" in name:
            results[name] = {}
            for b in bits:
                quantized_model = copy.deepcopy(model)
                quantize_layer(quantized_model, name, b)
                # Generate images with the quantized model (implementation needed)
                generated_images = generate_images(quantized_model, prompts, sample_size)
                # Calculate metrics
                results[name][b] = {
                    "mse": calculate_mse(generated_images, generate_images(model, prompts, sample_size)),
                    "clip_score": calculate_clip_score(generated_images, prompts, clip_processor, clip_model),
                    "parameters": param.numel()
                }
    return results

# Function to quantize layer using Lloyd-Max from SciPy
def quantize_layer(model, layer_name, bits):
    """Apply quantization to a specific layer using the Lloyd-Max algorithm from SciPy.

    Args:
        model: PyTorch model containing the layer to be quantized.
        layer_name: Name of the layer to be quantized (string).
        bits: Number of bits used for quantization (integer).
    """

    # Get the layer by its name
    layer = dict(model.named_modules())[layer_name]

    # Ensure the layer has weights
    if not hasattr(layer, 'weight'):
        print(f"Layer {layer_name} does not have weights.")
        return

    # Get the weight tensor
    w = layer.weight.data

    # Skip quantization if weights are already in integer data type
    if w.dtype not in [torch.float, torch.float16, torch.float32, torch.float64]:
        print(f"Layer {layer_name} already has integer weights.")
        return

    # Convert tensor to CPU for Lloyd-Max quantization
    w = w.cpu().numpy()

    # Perform Lloyd-Max quantization using kmeans2 from SciPy
    centroids, labels = kmeans2(w.reshape(-1, 1), 2**bits, minit='points')
    quantized_w = centroids[labels].reshape(w.shape)

    # Convert back to PyTorch tensor and save to the layer
    layer.weight.data = torch.from_numpy(quantized_w).to(layer.weight.device)

    # Calculate and save scale factor and zero point
    min_val = w.min()
    max_val = w.max()
    scale = (max_val - min_val) / (2**bits - 1)
    zero_point = torch.round(-min_val / scale).to(layer.weight.device)
    layer.quantization_scale = scale.to(layer.weight.device)
    layer.quantization_zero_point = zero_point

    # Replace forward function to perform dequantization during inference
    layer._forward_impl = layer.forward
    def quantized_forward(*args, **kwargs):
        # Dequantize weights before forward operation
        dequantized_w = (layer.weight.data - layer.quantization_zero_point) * layer.quantization_scale
        return layer._forward_impl(dequantized_w, *args[1:], **kwargs)
    layer.forward = quantized_forward

# Function to generate images
def generate_images(model, prompts, sample_size):
    """Generate images using the Stable Diffusion model."""
    # Implement the image generation process using the diffusers pipeline
    # Use `model` and `prompts` as input
    # ...
    # TODO

# Function to determine mixed-precision strategy
def determine_mixed_precision(results, sensitivity_threshold, size_factor, clip_thresholds):
    """Determine optimal bit-width for each layer."""
    mixed_precision = {}
    for name, layer_results in results.items():
        sensitivity_scores = {
            b: layer_results[b]["mse"] / (layer_results[b]["parameters"] ** size_factor)
            for b in bits
        }
        optimal_bits = min(bits, key=lambda b: sensitivity_scores[b])
        if sensitivity_scores[optimal_bits] > sensitivity_threshold:
            optimal_bits = max(bits)  # Use maximum bit-width if exceeding threshold
        clip_score_drop = layer_results[max(bits)]["clip_score"] - results[name][32]["clip_score"]
        for i, threshold in enumerate(clip_thresholds):
            if clip_score_drop > np.quantile(
                [results[n][32]["clip_score"] for n in results], threshold
            ):
                optimal_bits += i + 1
                break
        mixed_precision[name] = optimal_bits
    return mixed_precision

# Function to quantize the model
def quantize_model(model, mixed_precision):
    """Apply quantization to the model based on mixed-precision strategy."""
    for name, param in model.named_parameters():
        if "weight" in name and name in mixed_precision:
            quantize_layer(model, name, mixed_precision[name])
    return model

# Function for pre-computation and caching of time embeddings
def precompute_time_embeddings(model, time_steps):
    """Calculate and store time embeddings."""
    time_embeddings = {}
    for t in range(time_steps):
        time_embeddings[t] = model.time_embedding(torch.tensor([t]))
    return time_embeddings

# Load Stable Diffusion model
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
model = pipe.unet

# Layer sensitivity analysis (implement generate_images first)
prompts = ["A photo of a cat"]  # Replace with your desired prompts
results = analyze_layer_sensitivity(model, prompts, bits)

# Determine mixed-precision strategy
mixed_precision = determine_mixed_precision(
    results, sensitivity_threshold, size_factor, clip_thresholds
)

# Apply quantization to the model
quantized_model = quantize_model(model, mixed_precision)

# Pre-computation and caching of time embeddings
time_embeddings = precompute_time_embeddings(quantized_model, time_steps)

# Save the quantized model
torch.save(quantized_model.state_dict(), output_path)