Validate quantization parameters carefully

When implementing quantized operations in ML models, thoroughly validate quantization parameters to ensure correctness and prevent subtle numerical errors. Key validation points:

copy reviewer prompt

Prompt

Reviewer Prompt
Deploy Agent

When implementing quantized operations in ML models, thoroughly validate quantization parameters to ensure correctness and prevent subtle numerical errors. Key validation points:

  1. For int16 quantized operators, verify zero points are exactly 0
  2. For quantized tensor operations, ensure consistent scale factors between input and output
  3. When one tensor is quantized, verify all related tensors use compatible quantization schemes

Example:

// Good: Proper quantization parameter validation
if (input->type == kTfLiteInt16) {
  TF_LITE_ENSURE_EQ(context, input->params.zero_point, 0);
  TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0);
}

// For quantized operations, validate scale consistency
if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
  TF_LITE_ENSURE_EQ(context, input->params.scale, output->params.scale);
}

// Ensure quantization scheme compatibility
if (lhs.IsQuantized()) {
  if (!rhs.IsQuantized() || !output.IsQuantized()) {
    return absl::FailedPreconditionError(
        "If one tensor is quantized, all must be quantized");
  }
}

Source discussions