test_compressor_torch.py

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

import copy
from unittest import TestCase, main
import numpy as np
import torch
import torch.nn.functional as F
import schema
import nni.algorithms.compression.pytorch.pruning as torch_pruner
import nni.algorithms.compression.pytorch.quantization as torch_quantizer
import math


class TorchModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(1, 5, 5, 1)
        self.bn1 = torch.nn.BatchNorm2d(5)
        self.conv2 = torch.nn.Conv2d(5, 10, 5, 1)
        self.bn2 = torch.nn.BatchNorm2d(10)
        self.fc1 = torch.nn.Linear(4 * 4 * 10, 100)
        self.fc2 = torch.nn.Linear(100, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4 * 4 * 10)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)


class CompressorTestCase(TestCase):
    def test_torch_quantizer_modules_detection(self):
        # test if modules can be detected
        model = TorchModel()
        config_list = [{
            'quant_types': ['weight'],
            'quant_bits': 8,
            'op_types': ['Conv2d', 'Linear']
        }, {
            'quant_types': ['output'],
            'quant_bits': 8,
            'quant_start_step': 0,
            'op_types': ['ReLU']
        }]

        model.relu = torch.nn.ReLU()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
        quantizer = torch_quantizer.QAT_Quantizer(model, config_list, optimizer)
        quantizer.compress()
        modules_to_compress = quantizer.get_modules_to_compress()
        modules_to_compress_name = [t[0].name for t in modules_to_compress]
        assert "conv1" in modules_to_compress_name
        assert "conv2" in modules_to_compress_name
        assert "fc1" in modules_to_compress_name
        assert "fc2" in modules_to_compress_name
        assert "relu" in modules_to_compress_name
        assert len(modules_to_compress_name) == 5

    def test_torch_level_pruner(self):
        model = TorchModel()
        configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
        torch_pruner.LevelPruner(model, configure_list).compress()

    def test_torch_naive_quantizer(self):
        model = TorchModel()
        configure_list = [{
            'quant_types': ['weight'],
            'quant_bits': {
                'weight': 8,
            },
            'op_types': ['Conv2d', 'Linear']
        }]
        torch_quantizer.NaiveQuantizer(model, configure_list).compress()

    def test_torch_fpgm_pruner(self):
        """
        With filters(kernels) weights defined as above (w), it is obvious that w[4] and w[5] is the Geometric Median
        which minimize the total geometric distance by defination of Geometric Median in this paper:
        Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration,
        https://arxiv.org/pdf/1811.00250.pdf

        So if sparsity is 0.2, the expected masks should mask out w[4] and w[5], this can be verified through:
        `all(torch.sum(masks, (1, 2, 3)).numpy() == np.array([125., 125., 125., 125., 0., 0., 125., 125., 125., 125.]))`

        If sparsity is 0.6, the expected masks should mask out w[2] - w[7], this can be verified through:
        `all(torch.sum(masks, (1, 2, 3)).numpy() == np.array([125., 125., 0., 0., 0., 0., 0., 0., 125., 125.]))`
        """
        w = np.array([np.ones((5, 5, 5)) * (i+1) for i in range(10)]).astype(np.float32)

        model = TorchModel()
        config_list = [{'sparsity': 0.6, 'op_types': ['Conv2d']}, {'sparsity': 0.2, 'op_types': ['Conv2d']}]
        pruner = torch_pruner.FPGMPruner(model, config_list)

        model.conv2.module.weight.data = torch.tensor(w).float()
        masks = pruner.calc_mask(model.conv2)
        assert all(torch.sum(masks['weight_mask'], (1, 2, 3)).numpy() == np.array([125., 125., 125., 125., 0., 0., 125., 125., 125., 125.]))

        model.conv2.module.weight.data = torch.tensor(w).float()
        model.conv2.if_calculated = False
        model.conv2.config = config_list[0]
        masks = pruner.calc_mask(model.conv2)
        assert all(torch.sum(masks['weight_mask'], (1, 2, 3)).numpy() == np.array([125., 125., 0., 0., 0., 0., 0., 0., 125., 125.]))

       
    def test_torch_l1filter_pruner(self):
        """
        Filters with the minimum sum of the weights' L1 norm are pruned in this paper:
        PRUNING FILTERS FOR EFFICIENT CONVNETS,
        https://arxiv.org/abs/1608.08710

        So if sparsity is 0.2 for conv1, the expected masks should mask out filter 0, this can be verified through:
        `all(torch.sum(mask1, (1, 2, 3)).numpy() == np.array([0., 25., 25., 25., 25.]))`

        If sparsity is 0.6 for conv2, the expected masks should mask out filter 0,1,2, this can be verified through:
        `all(torch.sum(mask2, (1, 2, 3)).numpy() == np.array([0., 0., 0., 0., 0., 0., 125., 125., 125., 125.]))`
        """
        w1 = np.array([np.ones((1, 5, 5))*i for i in range(5)]).astype(np.float32)
        w2 = np.array([np.ones((5, 5, 5))*i for i in range(10)]).astype(np.float32)

        model = TorchModel()
        config_list = [{'sparsity': 0.2, 'op_types': ['Conv2d'], 'op_names': ['conv1']},
                       {'sparsity': 0.6, 'op_types': ['Conv2d'], 'op_names': ['conv2']}]
        pruner = torch_pruner.L1FilterPruner(model, config_list)

        model.conv1.module.weight.data = torch.tensor(w1).float()
        model.conv2.module.weight.data = torch.tensor(w2).float()
        mask1 = pruner.calc_mask(model.conv1)
        mask2 = pruner.calc_mask(model.conv2)
        assert all(torch.sum(mask1['weight_mask'], (1, 2, 3)).numpy() == np.array([0., 25., 25., 25., 25.]))
        assert all(torch.sum(mask2['weight_mask'], (1, 2, 3)).numpy() == np.array([0., 0., 0., 0., 0., 0., 125., 125., 125., 125.]))

    def test_torch_slim_pruner(self):
        """
        Scale factors with minimum l1 norm in the BN layers are pruned in this paper:
        Learning Efficient Convolutional Networks through Network Slimming,
        https://arxiv.org/pdf/1708.06519.pdf

        So if sparsity is 0.2, the expected masks should mask out channel 0, this can be verified through:
        `all(mask1.numpy() == np.array([0., 1., 1., 1., 1.]))`
        `all(mask2.numpy() == np.array([0., 1., 1., 1., 1.]))`

        If sparsity is 0.6, the expected masks should mask out channel 0,1,2, this can be verified through:
        `all(mask1.numpy() == np.array([0., 0., 0., 1., 1.]))`
        `all(mask2.numpy() == np.array([0., 0., 0., 1., 1.]))`
        """
        w = np.array([0, 1, 2, 3, 4])
        model = TorchModel()
        config_list = [{'sparsity': 0.2, 'op_types': ['BatchNorm2d']}]
        model.bn1.weight.data = torch.tensor(w).float()
        model.bn2.weight.data = torch.tensor(-w).float()
        pruner = torch_pruner.SlimPruner(model, config_list, optimizer=None, trainer=None, criterion=None)

        mask1 = pruner.calc_mask(model.bn1)
        mask2 = pruner.calc_mask(model.bn2)
        assert all(mask1['weight_mask'].numpy() == np.array([0., 1., 1., 1., 1.]))
        assert all(mask2['weight_mask'].numpy() == np.array([0., 1., 1., 1., 1.]))
        assert all(mask1['bias_mask'].numpy() == np.array([0., 1., 1., 1., 1.]))
        assert all(mask2['bias_mask'].numpy() == np.array([0., 1., 1., 1., 1.]))

        model = TorchModel()
        config_list = [{'sparsity': 0.6, 'op_types': ['BatchNorm2d']}]
        model.bn1.weight.data = torch.tensor(w).float()
        model.bn2.weight.data = torch.tensor(w).float()
        pruner = torch_pruner.SlimPruner(model, config_list, optimizer=None, trainer=None, criterion=None)

        mask1 = pruner.calc_mask(model.bn1)
        mask2 = pruner.calc_mask(model.bn2)
        assert all(mask1['weight_mask'].numpy() == np.array([0., 0., 0., 1., 1.]))
        assert all(mask2['weight_mask'].numpy() == np.array([0., 0., 0., 1., 1.]))
        assert all(mask1['bias_mask'].numpy() == np.array([0., 0., 0., 1., 1.]))
        assert all(mask2['bias_mask'].numpy() == np.array([0., 0., 0., 1., 1.]))

    def test_torch_taylorFOweight_pruner(self):
        """
        Filters with the minimum importance approxiamtion based on the first order 
        taylor expansion on the weights (w*grad)**2 are pruned in this paper:
        Importance Estimation for Neural Network Pruning,
        http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf

        So if sparsity of conv1 is 0.2, the expected masks should mask out filter 0, this can be verified through:
        `all(torch.sum(mask1['weight_mask'], (1, 2, 3)).numpy() == np.array([0., 25., 25., 25., 25.]))`

        If sparsity of conv2 is 0.6, the expected masks should mask out filter 4,5,6,7,8,9 this can be verified through:
        `all(torch.sum(mask2['weight_mask'], (1, 2, 3)).numpy() == np.array([125., 125., 125., 125., 0., 0., 0., 0., 0., 0., ]))`
        """

        w1 = np.array([np.zeros((1, 5, 5)), np.ones((1, 5, 5)), np.ones((1, 5, 5)) * 2,
                      np.ones((1, 5, 5)) * 3, np.ones((1, 5, 5)) * 4])
        w2 = np.array([[[[i + 1] * 5] * 5] * 5 for i in range(10)[::-1]])

        grad1 = np.array([np.ones((1, 5, 5)) * -1, np.ones((1, 5, 5)) * 1, np.ones((1, 5, 5)) * -1,
                      np.ones((1, 5, 5)) * 1, np.ones((1, 5, 5)) * -1])

        grad2 = np.array([[[[(-1)**i] * 5] * 5] * 5 for i in range(10)])

        config_list = [{'sparsity': 0.2, 'op_types': ['Conv2d'], 'op_names': ['conv1']},
                       {'sparsity': 0.6, 'op_types': ['Conv2d'], 'op_names': ['conv2']}]

        model = TorchModel()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
        pruner = torch_pruner.TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer=None, criterion=None, sparsifying_training_batches=1)

        x = torch.rand((1, 1, 28, 28), requires_grad=True)
        model.conv1.module.weight.data = torch.tensor(w1).float()
        model.conv2.module.weight.data = torch.tensor(w2).float()

        y = model(x)
        y.backward(torch.ones_like(y))

        model.conv1.module.weight.grad.data = torch.tensor(grad1).float()
        model.conv2.module.weight.grad.data = torch.tensor(grad2).float()
        optimizer.step()

        mask1 = pruner.calc_mask(model.conv1)
        mask2 = pruner.calc_mask(model.conv2)
        assert all(torch.sum(mask1['weight_mask'], (1, 2, 3)).numpy() == np.array([0., 25., 25., 25., 25.]))
        assert all(torch.sum(mask2['weight_mask'], (1, 2, 3)).numpy() == np.array([125., 125., 125., 125., 0., 0., 0., 0., 0., 0., ]))

    def test_torch_taylorFOweight_pruner_global_sort(self):
        """
        After enabling global_sort, taylorFOweight pruner will calculate contributions and rank topk from all
        of the conv operators. Then it will prune low contribution filters depends on the global information.

        So if sparsity of conv operator is 0.4, the expected masks should mask out filter 0 and filter 1 together, 
        this can be verified through:
        `all(torch.sum(mask1['weight_mask'], (1, 2, 3)).numpy() == np.array([0., 0., 0, 0., 25.]))`
        `all(torch.sum(mask2['weight_mask'], (1, 2, 3)).numpy() == np.array([125., 125., 125., 125., 125., 125., 125., 0., 0., 0.]))`
        """

        w1 = np.array([np.zeros((1, 5, 5)), np.ones((1, 5, 5)), np.ones((1, 5, 5)) * 2,
                      np.ones((1, 5, 5)) * 3, np.ones((1, 5, 5)) * 4])
        w2 = np.array([[[[i + 1] * 5] * 5] * 5 for i in range(10)[::-1]])

        grad1 = np.array([np.ones((1, 5, 5)) * -1, np.ones((1, 5, 5)) * 1, np.ones((1, 5, 5)) * -1,
                      np.ones((1, 5, 5)) * 1, np.ones((1, 5, 5)) * -1])

        grad2 = np.array([[[[(-1)**i] * 5] * 5] * 5 for i in range(10)])

        config_list = [{'sparsity': 0.4, 'op_types': ['Conv2d']}]

        model = TorchModel()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
        pruner = torch_pruner.TaylorFOWeightFilterPruner(model, config_list, optimizer, trainer=None, criterion=None, sparsifying_training_batches=1, global_sort=True)

        x = torch.rand((1, 1, 28, 28), requires_grad=True)
        model.conv1.module.weight.data = torch.tensor(w1).float()
        model.conv2.module.weight.data = torch.tensor(w2).float()

        y = model(x)
        y.backward(torch.ones_like(y))

        model.conv1.module.weight.grad.data = torch.tensor(grad1).float()
        model.conv2.module.weight.grad.data = torch.tensor(grad2).float()
        optimizer.step()

        mask1 = pruner.calc_mask(model.conv1)
        mask2 = pruner.calc_mask(model.conv2)
        print(torch.sum(mask1['weight_mask'], (1, 2, 3)).numpy())
        print(torch.sum(mask2['weight_mask'], (1, 2, 3)).numpy())
        assert all(torch.sum(mask1['weight_mask'], (1, 2, 3)).numpy() == np.array([0., 0., 0, 0., 25.]))
        assert all(torch.sum(mask2['weight_mask'], (1, 2, 3)).numpy() == np.array([125., 125., 125., 125., 125., 125., 125., 0., 0., 0.]))

    def test_torch_observer_quantizer(self):
        model = TorchModel()
        # test invalid config
        # only support 8bit for now
        config_list = [{
            'quant_types': ['weight'],
            'quant_bits': 5,
            'op_types': ['Conv2d', 'Linear']
        }]
        with self.assertRaises(schema.SchemaError):
            torch_quantizer.ObserverQuantizer(model, config_list)

        # weight will not change for now
        model = TorchModel().eval()
        origin_parameters = copy.deepcopy(dict(model.named_parameters()))

        config_list = [{
            'quant_types': ['weight'],
            'quant_bits': 8,
            'op_types': ['Conv2d', 'Linear']
        }]
        quantizer = torch_quantizer.ObserverQuantizer(model, config_list)
        input = torch.randn(1, 1, 28, 28)
        model(input)
        quantizer.compress()
        buffers = dict(model.named_buffers())
        scales = {k: v for k, v in buffers.items() if 'scale' in k}
        model_path = "test_model.pth"
        calibration_path = "test_calibration.pth"
        calibration_config = quantizer.export_model(model_path, calibration_path)
        new_parameters = dict(model.named_parameters())
        for layer_name, v in calibration_config.items():
            scale_name = layer_name + '.module.weight_scale'
            weight_name = layer_name + '.weight'
            s = float(scales[scale_name])
            self.assertTrue(torch.allclose(origin_parameters[weight_name], new_parameters[weight_name], atol=0.5 * s))

        self.assertTrue(calibration_config is not None)
        self.assertTrue(len(calibration_config) == 4)

    def test_torch_quantizer_weight_type(self):
        quantizer_list = [
            torch_quantizer.QAT_Quantizer,
            torch_quantizer.LsqQuantizer,
            torch_quantizer.ObserverQuantizer,
            torch_quantizer.NaiveQuantizer,
            torch_quantizer.DoReFaQuantizer]
        for quantizer_type in quantizer_list:
            model = TorchModel().eval()
            config_list = [{
                'quant_types': ['weight'],
                'quant_bits': 8,
                'op_types': ['Conv2d', 'Linear']
            }]

            optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
            dummy = torch.randn(1, 1, 28, 28)
            if quantizer_type == torch_quantizer.QAT_Quantizer:
                quantizer_type(model, config_list, optimizer, dummy_input=dummy)
            else:
                quantizer_type(model, config_list, optimizer)

            self.assertFalse(isinstance(model.conv1.module.weight, torch.nn.Parameter))
            self.assertFalse(isinstance(model.conv2.module.weight, torch.nn.Parameter))
            self.assertFalse(isinstance(model.fc1.module.weight, torch.nn.Parameter))
            self.assertFalse(isinstance(model.fc2.module.weight, torch.nn.Parameter))

    def test_torch_QAT_quantizer(self):
        model = TorchModel()
        config_list = [{
            'quant_types': ['weight', 'input'],
            'quant_bits': 8,
            'op_types': ['Conv2d', 'Linear']
        }, {
            'quant_types': ['output'],
            'quant_bits': 8,
            'quant_start_step': 0,
            'op_types': ['ReLU']
        }]
        model.relu = torch.nn.ReLU()

        optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
        quantizer = torch_quantizer.QAT_Quantizer(model, config_list, optimizer)
        quantizer.compress()

        # test quantize
        # range not including 0
        eps = 1e-7
        input = torch.tensor([[1, 4], [2, 1]])
        weight = torch.tensor([[1, 2], [3, 5]]).float()
        model.conv2.module.weight.data = weight
        quantizer.quantize_weight(model.conv2, input_tensor=input)
        assert math.isclose(model.conv2.module.scale, 5 / 255, abs_tol=eps)
        assert model.conv2.module.zero_point == 0
        quantizer.quantize_input(input, model.conv2)
        self.assertTrue(torch.allclose(model.conv2.module.scale, torch.tensor([0.04 / 255])))
        self.assertTrue(torch.equal(model.conv2.module.zero_point, torch.tensor([0.])))
        # range including 0
        weight = torch.tensor([[-1, 2], [3, 5]]).float()
        model.conv2.module.weight = weight
        quantizer.quantize_weight(model.conv2, input_tensor=input)
        assert math.isclose(model.conv2.module.scale, 6 / 255, abs_tol=eps)
        assert model.conv2.module.zero_point in (42, 43)
        quantizer.quantize_input(input, model.conv2)
        self.assertTrue(torch.allclose(model.conv2.module.scale, torch.tensor([0.0796 / 255])))
        self.assertTrue(torch.equal(model.conv2.module.zero_point, torch.tensor([0.])))
        # test value of weight and bias after quantization
        weight = torch.tensor([[1.1287, 2.3456], [3.7814, 5.9723]])
        weight_valid = torch.tensor([[1.1242, 2.3421], [3.7707, 5.9723]])
        bias = torch.tensor([2.3432, 3.4342, 1.3414, 5.2341])
        bias_valid = torch.tensor([2.3432, 3.4342, 1.3414, 5.2341])
        model.conv2.module.weight = weight
        model.conv2.module.bias.data = bias
        quantizer.quantize_weight(model.conv2, input_tensor=input)
        assert torch.all(torch.isclose(model.conv2.module.weight.data, weight_valid, rtol=1e-4))
        assert torch.all(torch.isclose(model.conv2.module.bias.data, bias_valid, rtol=1e-7))

        # test ema
        eps = 1e-7
        x = torch.tensor([[-0.2, 0], [0.1, 0.2]])
        out = model.relu(x)
        assert math.isclose(model.relu.module.tracked_min_output, 0, abs_tol=eps)
        assert math.isclose(model.relu.module.tracked_max_output, 0.002, abs_tol=eps)

        quantizer.step_with_optimizer()
        x = torch.tensor([[0.2, 0.4], [0.6, 0.8]])
        out = model.relu(x)
        assert math.isclose(model.relu.module.tracked_min_output, 0.002, abs_tol=eps)
        assert math.isclose(model.relu.module.tracked_max_output, 0.00998, abs_tol=eps)

    def test_torch_quantizer_export(self):
        config_list_qat = [{
            'quant_types': ['weight'],
            'quant_bits': 8,
            'op_types': ['Conv2d', 'Linear']
        }, {
            'quant_types': ['output'],
            'quant_bits': 8,
            'quant_start_step': 0,
            'op_types': ['ReLU']
        }]
        config_list_dorefa = [{
            'quant_types': ['weight'],
            'quant_bits': {
                'weight': 8,
            }, # you can just use `int` here because all `quan_types` share same bits length, see config for `ReLu6` below.
            'op_types':['Conv2d', 'Linear']
        }]
        config_list_bnn = [{
            'quant_types': ['weight'],
            'quant_bits': 1,
            'op_types': ['Conv2d', 'Linear']
        }, {
            'quant_types': ['output'],
            'quant_bits': 1,
            'op_types': ['ReLU']
        }]
        config_set = [config_list_qat, config_list_dorefa, config_list_bnn]
        quantize_algorithm_set = [torch_quantizer.QAT_Quantizer, torch_quantizer.DoReFaQuantizer, torch_quantizer.BNNQuantizer]

        for config, quantize_algorithm in zip(config_set, quantize_algorithm_set):
            model = TorchModel()
            model.relu = torch.nn.ReLU()
            optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
            quantizer = quantize_algorithm(model, config, optimizer)
            quantizer.compress()

            x = torch.rand((1, 1, 28, 28), requires_grad=True)
            y = model(x)
            y.backward(torch.ones_like(y))

            model_path = "test_model.pth"
            calibration_path = "test_calibration.pth"
            onnx_path = "test_model.onnx"
            input_shape = (1, 1, 28, 28)
            device = torch.device("cpu")

            calibration_config = quantizer.export_model(model_path, calibration_path, onnx_path, input_shape, device)
            assert calibration_config is not None

    def test_quantizer_load_calibration_config(self):
        configure_list = [{
            'quant_types': ['weight', 'input'],
            'quant_bits': {'weight': 8, 'input': 8},
            'op_names': ['conv1', 'conv2']
        }, {
            'quant_types': ['output', 'weight', 'input'],
            'quant_bits': {'output': 8, 'weight': 8, 'input': 8},
            'op_names': ['fc1', 'fc2'],
        }]
        quantize_algorithm_set = [torch_quantizer.ObserverQuantizer, torch_quantizer.QAT_Quantizer, torch_quantizer.LsqQuantizer]
        calibration_config = None
        for quantize_algorithm in quantize_algorithm_set:
            model = TorchModel().eval()
            model.relu = torch.nn.ReLU()
            optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
            quantizer = quantize_algorithm(model, configure_list, optimizer)
            quantizer.compress()
            if calibration_config is not None:
                quantizer.load_calibration_config(calibration_config)

            model_path = "test_model.pth"
            calibration_path = "test_calibration.pth"
            onnx_path = "test_model.onnx"
            input_shape = (1, 1, 28, 28)
            device = torch.device("cpu")

            calibration_config = quantizer.export_model(model_path, calibration_path, onnx_path, input_shape, device)

    def test_torch_pruner_validation(self):
        # test bad configuraiton
        pruner_classes = [torch_pruner.__dict__[x] for x in \
            ['LevelPruner', 'SlimPruner', 'FPGMPruner', 'L1FilterPruner', 'L2FilterPruner', 'AGPPruner',\
            'ActivationMeanRankFilterPruner', 'ActivationAPoZRankFilterPruner']]

        bad_configs = [
            [
                {'sparsity': '0.2'},
                {'sparsity': 0.6 }
            ],
            [
                {'sparsity': 0.2},
                {'sparsity': 1.6 }
            ],
            [
                {'sparsity': 0.2, 'op_types': 'default'},
                {'sparsity': 0.6 }
            ],
            [
                {'sparsity': 0.2 },
                {'sparsity': 0.6, 'op_names': 'abc'}
            ]
        ]
        model = TorchModel()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
        for pruner_class in pruner_classes:
            for config_list in bad_configs:
                try:
                    kwargs = {}
                    if pruner_class in (torch_pruner.SlimPruner, torch_pruner.AGPPruner, torch_pruner.ActivationMeanRankFilterPruner, torch_pruner.ActivationAPoZRankFilterPruner):
                        kwargs = {'optimizer': None, 'trainer': None, 'criterion': None}

                    print('kwargs', kwargs)
                    pruner_class(model, config_list, **kwargs)      

                    print(config_list)
                    assert False, 'Validation error should be raised for bad configuration'
                except schema.SchemaError:
                    pass
                except:
                    print('FAILED:', pruner_class, config_list)
                    raise

    def test_torch_quantizer_validation(self):
        # test bad configuraiton
        quantizer_classes = [torch_quantizer.__dict__[x] for x in \
            ['NaiveQuantizer', 'QAT_Quantizer', 'DoReFaQuantizer', 'BNNQuantizer']]

        bad_configs = [
            [
                {'bad_key': 'abc'}
            ],
            [
                {'quant_types': 'abc'}
            ],
            [
                {'quant_bits': 34}
            ],
            [
                {'op_types': 'default'}
            ],
            [
                {'quant_bits': {'abc': 123}}
            ]
        ]
        model = TorchModel()
        optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
        for quantizer_class in quantizer_classes:
            for config_list in bad_configs:
                try:
                    quantizer_class(model, config_list, optimizer)
                    print(config_list)
                    assert False, 'Validation error should be raised for bad configuration'
                except schema.SchemaError:
                    pass
                except:
                    print('FAILED:', quantizer_class, config_list)
                    raise

if __name__ == '__main__':
    main()