TensorFlow量化调优：如何实现对称幂2范围的8位权重量化？

Absolutely, you can adjust the older tf.contrib.quantize workflow (including create_eval_graph()) to mirror the SCALED mode behavior you want—symmetric 8-bit scaling, exact zero representation, and power-of-two bounded ranges optimized for DSPs. Here's how to do it step by step:

Key Adjustments Needed #

Your requirements (symmetry, exact 0, power-of-two ranges like -31 to 31) don’t come out of the box with the default tf.contrib.quantize setup, but we can customize the quantization logic to match:

1. For Post-Training Quantization (PTQ) #

After calling create_eval_graph(), you’ll need to manually override the quantization ranges for weight nodes to enforce your desired symmetric, power-of-two bounds:

import tensorflow as tf

# Build your base model first
# ... (model definition code) ...

# Initialize the default quantization eval graph
tf.contrib.quantize.create_eval_graph()

# Access the graph and modify weight quantization nodes
graph = tf.get_default_graph()
for node in graph.as_graph_def().node:
    # Target weight quantization nodes (adjust name matching to your model)
    if node.op == "QuantizeV2" and "weights" in node.name.lower():
        # Set symmetric power-of-two range (e.g., -31 to 31 = -(2^5-1) to 2^5-1)
        for attr in node.attr:
            if attr == "min":
                node.attr["min"].f = -31.0
            elif attr == "max":
                node.attr["max"].f = 31.0
        # Ensure 8-bit signed output with exact zero support
        node.attr["T"].type = tf.int8.as_datatype_enum

2. For Quantization-Aware Training (QAT) #

If you need to apply this logic during training (to preserve accuracy better), create a custom QuantizeConfig to enforce symmetric quantization with your preferred ranges:

import tensorflow as tf
from tensorflow.contrib.quantize.python import quant_ops
from tensorflow.contrib.quantize.python import quantize_config

class DSPSymmetricQuantizeConfig(quantize_config.QuantizeConfig):
    def get_weights_and_quantizers(self):
        # Apply symmetric quantization to all trainable weights
        return [(var, quant_ops.MovingAvgQuantizer(
            num_bits=8,
            symmetric=True,  # Enforce symmetric range
            narrow_range=True,  # Use [-127, 127] instead of [-128, 127] for cleaner power-of-two mapping
            vars_collection=tf.GraphKeys.MOVING_AVERAGE_VARIABLES))
                for var in tf.trainable_variables() if "weights" in var.name.lower()]

    def get_activations_and_quantizers(self):
        # Skip activation quantization if not needed, or mirror the same logic here
        return []

    def get_output_quantizers(self):
        return []

    def get_quantization_vars(self):
        return []

# Apply the custom config when creating the training quantization graph
tf.contrib.quantize.create_training_graph(quantize_config=DSPSymmetricQuantizeConfig())

Why This Works #

• Symmetric Ranges: The symmetric=True flag ensures quantization bounds are mirrored around 0, which guarantees an exact representation of 0 (critical for DSP efficiency).
• Power-of-Two Bounds: Manually setting min/max to values like -31 to 31 (a power-of-two minus one) aligns with DSP hardware optimizations, which often handle these ranges with faster, lower-power operations.
• 8-bit Precision: Explicitly setting the output type to tf.int8 ensures weights are scaled to the 8-bit format you need.

Notes to Keep in Mind #

• Adjust the range values (e.g., -63 to 63 instead of -31 to 31) based on your model’s weight distribution—just ensure they’re symmetric and power-of-two derived.
• Test accuracy after quantization: Restricting ranges to power-of-two bounds may cause minor precision loss, but this is usually acceptable for DSP-targeted deployments.
• Match node name patterns to your model: The example uses "weights" to target weight nodes, but you may need to tweak this if your model uses different naming conventions.

内容的提问来源于stack exchange，提问作者Anton Krug