Demystifying Per-Channel Quantization in PyTorch: The Role of torch.Tensor.q_per_channel_scales

Context: Post-Training Quantization (PTQ)

In PyTorch, PTQ is a technique to optimize pre-trained floating-point models for deployment on resource-constrained devices. It reduces the model size and improves inference speed by converting weights and activations from floating-point (e.g., float32) to lower-precision integer formats (e.g., int8).

Per-Channel Affine Quantization

This is a specific type of PTQ where each channel of a weight tensor has its own scaling factor (scale) and zero-point (zero_point) for the quantization process. These values help map the floating-point range of the channel to the integer range of the quantized representation.

torch.Tensor.q_per_channel_scales Method

• Return Value
  

  • It returns a one-dimensional tensor containing the scaling factors for each channel. The length of this tensor matches the dimension specified by q_per_channel_axis during the quantization process (usually 1 for weights and the channel dimension for activations).

• Arguments
  

  • It takes a single argument, which is the quantized tensor itself.
• • This method is used on a quantized tensor to access its per-channel scaling factors.
  • These scales are crucial for converting the quantized values back to floating-point during the dequantization process, allowing for computations or comparisons with other floating-point tensors.

Example

import torch

# Assuming a quantized weight tensor 'quantized_weights'
scales = quantized_weights.q_per_channel_scales()
print(scales)  # Output: tensor([1.2345, 5.6789, ...])  # Example scales for each channel

Key Points

• It provides access to the information used for dequantization, which is essential for recovering the original floating-point values from the quantized representation.
• q_per_channel_scales is only applicable to tensors quantized with per-channel affine quantization.

---

import torch
import torch.nn as nn
import torch.quantization as quant

# Define a small model (replace with your actual model)
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(4, 8)
        self.fc2 = nn.Linear(8, 2)

    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

# Create a model instance
model = MyModel()

# Prepare for quantization
model.eval()  # Set the model to evaluation mode (recommended for PTQ)
qconfig = torch.quantization.get_default_qconfig('qnnpack')  # Choose a quantization configuration
quant_prepare = quant.prepare(model, qconfig)

# Simulate some data
dummy_input = torch.randn(1, 4)

# Calibrate the quantizer (optional, but recommended for better accuracy)
quant_prepare.calibrate(dummy_input)

# Convert the model to a quantized version
model_quantized = quant.convert(quant_prepare)

# Run inference with the quantized model
quantized_output = model_quantized(dummy_input)

# Access per-channel scales for the first layer (assuming weights)
scales = model_quantized.fc1._packed_params.q_per_channel_scales()

# Print the scales (example output)
print(scales)  # tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000])

1. We define a simple model MyModel with two linear layers.
2. We import necessary modules and create a model instance.
3. We set the model to evaluation mode and choose a quantization configuration (qnnpack).
4. We create a quant.prepare object to configure the model for quantization.
5. We create dummy input data for calibration (optional but recommended).
6. We call quant_prepare.calibrate to gather statistics about the activation ranges.
7. We convert the prepared model to a quantized version using quant.convert.
8. We run inference with the quantized model on the dummy input.
9. We access the q_per_channel_scales attribute of the first layer's packed parameters (_packed_params) to retrieve the scaling factors.
10. We print the scaling factors for each channel of the weights in the first layer.

• This is a simplified example for demonstration purposes. In a real-world scenario, you'd likely use a more complex model, a representative dataset for calibration, and handle potential issues like accuracy degradation due to quantization.

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Manual Calculation (if applicable)

• If you know the quantization parameters (scale and zero_point) used during the quantization process, you can potentially calculate the per-channel scales yourself. This involves manipulating the quantized values and the known parameters. However, this approach can be error-prone and less convenient compared to q_per_channel_scales.

Information from Quantization Configuration

• The quantization configuration used for the model (e.g., qnnpack) might hold information about the quantization scheme. If it's a symmetric quantization (same scale for all channels), you wouldn't need per-channel scales. However, this is usually not the case for per-channel affine quantization.

Debugging and Analysis

• If your goal is to analyze or debug the quantization process, you might use other techniques like:
  • Printing the quantized values along with their corresponding original values before quantization. This can help visualize how the per-channel scales affect the representation.
  • Using tools like torch.quantization.quantize_per_tensor or torch.quantization.quantize_per_channel to manually quantize a tensor with specific scales and observe the behavior.

Alternative Quantization Schemes

• Consider exploring different quantization schemes that might not require per-channel scales:
  • Symmetric Quantization
    This uses a single scale factor for all channels, simplifying access to scaling information. However, it might be less accurate than per-channel quantization.
  • Quantization-Aware Training (QAT)
    This method trains the model while considering the quantization constraints, potentially reducing the need for post-training adjustments like per-channel scales.

• Using alternatives without a clear understanding of the specific quantization scheme and its impact on accuracy might lead to unexpected results. It's generally recommended to stick with q_per_channel_scales when dealing with per-channel affine quantization for accurate dequantization.