Creating Dynamic Quantized Linear Layers in PyTorch: Alternatives to torch.ao.nn.quantized.dynamic.Linear.from_reference()

PyTorch Quantization and Dynamic Linear Layers

Quantization is an optimization technique that reduces the size and computational cost of a deep learning model by converting its weights and activations from floating-point numbers (e.g., 32-bit floats) to lower-precision formats (e.g., 8-bit integers). This can significantly improve model inference speed and memory footprint, especially on resource-constrained devices.

torch.ao.nn.quantized.dynamic.Linear is a class in PyTorch's quantization API that represents a dynamically quantized linear layer. A dynamic quantized layer operates on floating-point tensors as inputs and outputs, but its weights are quantized for efficiency. Quantization parameters (scale and zero point) are dynamically computed during the forward pass based on the input distribution. This approach balances performance gains from quantization with the flexibility of floating-point arithmetic.

torch.ao.nn.quantized.dynamic.Linear.from_reference() Function

• ref_qlinear: This is a reference quantized linear module, which can be either:
  • Produced by PyTorch's quantization utilities (like torch.ao.quantization.quantize)
  • Provided by the user (a pre-quantized linear layer)

Functionality

1. • The method extracts the scale and zero-point values from the reference quantized linear layer (ref_qlinear). These parameters are crucial for converting the floating-point weights of the new layer into a quantized representation.

2. Creating the Dynamic Quantized Linear Layer
   

   • Using the extracted scale and zero-point, the method constructs a new torch.ao.nn.quantized.dynamic.Linear instance. This new layer inherits the structure (input and output features) from the reference layer but operates dynamically with floating-point inputs and outputs.

Benefits

• Dynamic Flexibility
  Since the new layer works dynamically, it maintains the flexibility of floating-point arithmetic during the forward pass. This can be advantageous in certain scenarios where fixed-point quantization might introduce slight accuracy issues.
• Leveraging Existing Quantization
  This method allows you to reuse the quantization parameters from a pre-quantized layer, potentially saving time and computational resources compared to requantizing weights from scratch.

When to Use torch.ao.nn.quantized.dynamic.Linear.from_reference()

• As part of a more complex quantization workflow where you might be combining different quantization strategies or reusing pre-quantized components.
• If you have a pre-quantized linear layer and want to create a new dynamic quantized linear layer with the same structure and parameters.

Additional Considerations

• If you prioritize maximum efficiency and your use case allows for it, consider exploring static quantization techniques.
• While dynamic quantization offers flexibility, it might not provide the same level of performance improvement as static quantization (where both weights and activations are quantized).

---

import torch
from torch import nn
from torch.ao.nn.quantization import quantize

# Create a reference quantized linear layer (assuming it's already quantized)
ref_qlinear = nn.quantized.dynamic.Linear(10, 20)  # Replace with your pre-quantized layer

# Get the scale and zero point from the reference layer
scale = ref_qlinear.scale
zero_point = ref_qlinear.zero_point

# Create a new dynamic quantized linear layer using the reference parameters
new_qlinear = torch.ao.nn.quantized.dynamic.Linear.from_reference(ref_qlinear, scale, zero_point)

# Example usage (assuming you have your input tensor `x`)
output = new_qlinear(x)

In this example:

1. We first create a reference quantized linear layer (ref_qlinear). Replace this with your actual pre-quantized layer that you want to use as a reference.
2. We extract the scale and zero_point values from the reference layer. These are essential for the quantization process.
3. We call torch.ao.nn.quantized.dynamic.Linear.from_reference() to create a new dynamic quantized linear layer (new_qlinear). We provide the reference layer, scale, and zero point as arguments.
4. The newly created new_qlinear has the same structure (input and output features) as the reference layer but operates dynamically with floating-point inputs and outputs.
5. Finally, we demonstrate how to use the new layer by passing an input tensor (x) through it, resulting in a quantized output.

---

torch.ao.nn.quantized.dynamic.Linear.from_float()

This method creates a dynamic quantized linear layer from a floating-point linear layer (nn.Linear). It performs the quantization process itself, determining the optimal scale and zero point values based on the input distribution during the first forward pass. This approach is more self-contained but might require additional calibration runs to ensure optimal quantization parameters.

# Create a floating-point linear layer
float_linear = nn.Linear(10, 20)

# Convert it to a dynamic quantized linear layer
new_qlinear = torch.ao.nn.quantized.dynamic.Linear.from_float(float_linear)

# Use the new layer
output = new_qlinear(x)

Manual Quantization with Dynamic Range

For more control, you can manually quantize a floating-point linear layer. This involves calculating the scale and zero point based on the input distribution and then quantizing the weights using these parameters.

# ... (Define your floating-point linear layer)

# Calculate scale and zero point (example)
min_val, max_val = torch.min(x), torch.max(x)
scale = (max_val - min_val) / 255.0  # Adjust divisor based on your desired range
zero_point = int(min_val)

# Quantize weights (example)
qweight = torch.quantize_per_tensor(float_linear.weight, scale, zero_point, dtype=torch.qint8)

# Create a custom dynamic quantized linear layer (example)
class MyDynamicLinear(nn.Module):
  def __init__(self, in_features, out_features, scale, zero_point):
    super(MyDynamicLinear, self).__init__()
    self.weight = nn.Parameter(qweight)
    self.bias = float_linear.bias  # Keep bias as float

  def forward(self, x):
    quantized_x = torch.quantize_per_tensor(x, self.scale, self.zero_point, dtype=torch.qint8)
    return torch.dequantize(torch.nn.functional.linear(quantized_x, self.weight, self.bias))

new_qlinear = MyDynamicLinear(10, 20, scale, zero_point)

# Use the custom layer
output = new_qlinear(x)

• torch.ao.nn.quantized.dynamic.Linear.from_reference()
  Use this when you already have a pre-quantized layer and want to create a new one with the same parameters.
• Manual Quantization
  Offers the most flexibility but requires more effort.
• torch.ao.nn.quantized.dynamic.Linear.from_float()
  Good balance between convenience and control.