Source code for nni.algorithms.compression.pytorch.quantization.bnn_quantizer

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import logging
import torch
from schema import Schema, And, Or, Optional
from nni.compression.pytorch.utils.config_validation import QuantizerSchema
from nni.compression.pytorch.compressor import Quantizer, QuantGrad
from nni.compression.pytorch.quantization.literal import QuantType
from nni.compression.pytorch.quantization.utils import get_bits_length

logger = logging.getLogger(__name__)

class ClipGrad(QuantGrad):
    def quant_backward(tensor, grad_output, quant_type, scale, zero_point, qmin, qmax):
        if quant_type == QuantType.OUTPUT:
            grad_output[torch.abs(tensor) > 1] = 0
        return grad_output

[docs]class BNNQuantizer(Quantizer): r""" Binarized Neural Networks, as defined in: `Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 <>`__, .. We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During the forward pass, BNNs drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. Parameters ---------- model : torch.nn.Module Model to be quantized. config_list : List[Dict] List of configurations for quantization. Supported keys for dict: - quant_types : List[str] Type of quantization you want to apply, currently support 'weight', 'input', 'output'. - quant_bits : Union[int, Dict[str, int]] Bits length of quantization, key is the quantization type, value is the length, eg. {'weight': 8}, When the type is int, all quantization types share same bits length. - op_types : List[str] Types of nn.module you want to apply quantization, eg. 'Conv2d'. - op_names : List[str] Names of nn.module you want to apply quantization, eg. 'conv1'. - exclude : bool Set True then the layers setting by op_types and op_names will be excluded from quantization. optimizer : torch.optim.Optimizer Optimizer is required in `BNNQuantizer`, NNI will patch the optimizer and count the optimize step number. Examples -------- >>> from nni.algorithms.compression.pytorch.quantization import BNNQuantizer >>> model = ... >>> config_list = [{'quant_types': ['weight', 'input'], 'quant_bits': {'weight': 8, 'input': 8}, 'op_types': ['Conv2d']}] >>> optimizer = ... >>> quantizer = BNNQuantizer(model, config_list, optimizer) >>> quantizer.compress() >>> # Training Process... For detailed example please refer to :githublink:`examples/model_compress/quantization/ <examples/model_compress/quantization/>`. Notes ----- **Results** We implemented one of the experiments in `Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 <>`__, we quantized the **VGGNet** for CIFAR-10 in the paper. Our experiments results are as follows: .. list-table:: :header-rows: 1 :widths: auto * - Model - Accuracy * - VGGNet - 86.93% The experiments code can be found at :githublink:`examples/model_compress/quantization/ <examples/model_compress/quantization/>` """ def __init__(self, model, config_list, optimizer): assert isinstance(optimizer, torch.optim.Optimizer), "unrecognized optimizer type" super().__init__(model, config_list, optimizer) device = next(model.parameters()).device self.quant_grad = ClipGrad.apply modules_to_compress = self.get_modules_to_compress() for layer, config in modules_to_compress: if "weight" in config.get("quant_types", []): weight_bits = get_bits_length(config, 'weight') layer.module.register_buffer('weight_bits', torch.Tensor([int(weight_bits)])) def _del_simulated_attr(self, module): """ delete redundant parameters in quantize module """ del_attr_list = ['old_weight', 'weight_bits'] for attr in del_attr_list: if hasattr(module, attr): delattr(module, attr) def validate_config(self, model, config_list): """ Parameters ---------- model : torch.nn.Module Model to be pruned config_list : list of dict List of configurations """ schema = QuantizerSchema([{ Optional('quant_types'): Schema([lambda x: x in ['weight', 'output']]), Optional('quant_bits'): Or(And(int, lambda n: 0 < n < 32), Schema({ Optional('weight'): And(int, lambda n: 0 < n < 32), Optional('output'): And(int, lambda n: 0 < n < 32), })), Optional('op_types'): [str], Optional('op_names'): [str], Optional('exclude'): bool }], model, logger) schema.validate(config_list) def quantize_weight(self, wrapper, **kwargs): weight = wrapper.module.weight weight = torch.sign(weight) # remove zeros weight[weight == 0] = 1 wrapper.module.weight = weight wrapper.module.weight_bits = torch.Tensor([1.0]) return weight def quantize_output(self, output, wrapper, **kwargs): out = torch.sign(output) # remove zeros out[out == 0] = 1 return out def export_model(self, model_path, calibration_path=None, onnx_path=None, input_shape=None, device=None): """ Export quantized model weights and calibration parameters(optional) Parameters ---------- model_path : str path to save quantized model weight calibration_path : str (optional) path to save quantize parameters after calibration onnx_path : str (optional) path to save onnx model input_shape : list or tuple input shape to onnx model device : torch.device device of the model, used to place the dummy input tensor for exporting onnx file. the tensor is placed on cpu if ```device``` is None Returns ------- Dict """ assert model_path is not None, 'model_path must be specified' self._unwrap_model() calibration_config = {} for name, module in self.bound_model.named_modules(): if hasattr(module, 'weight_bits'): calibration_config[name] = {} calibration_config[name]['weight_bits'] = int(module.weight_bits) self._del_simulated_attr(module) self.export_model_save(self.bound_model, model_path, calibration_config, calibration_path, onnx_path, input_shape, device) return calibration_config