pytorch/test/test_quantized_models.py

import torch
import torch.jit
from torch.testing._internal.common_utils import run_tests, TEST_WITH_UBSAN, IS_PPC
from torch.testing._internal.common_quantization import QuantizationTestCase, \
    ModelMultipleOps, ModelMultipleOpsNoAvgPool
from torch.testing._internal.common_quantized import override_quantized_engine

class ModelNumerics(QuantizationTestCase):
    def test_float_quant_compare_per_tensor(self):
        for qengine in ["fbgemm", "qnnpack"]:
            if qengine not in torch.backends.quantized.supported_engines:
                continue
            if qengine == 'qnnpack':
                if IS_PPC or TEST_WITH_UBSAN:
                    continue
            with override_quantized_engine(qengine):
                torch.manual_seed(42)
                my_model = ModelMultipleOps().to(torch.float32)
                my_model.eval()
                calib_data = torch.rand(1024, 3, 15, 15, dtype=torch.float32)
                eval_data = torch.rand(1, 3, 15, 15, dtype=torch.float32)
                out_ref = my_model(eval_data)
                qModel = torch.quantization.QuantWrapper(my_model)
                qModel.eval()
                qModel.qconfig = torch.quantization.default_qconfig
                torch.quantization.fuse_modules(qModel.module, [['conv1', 'bn1', 'relu1']], inplace=True)
                torch.quantization.prepare(qModel, inplace=True)
                qModel(calib_data)
                torch.quantization.convert(qModel, inplace=True)
                out_q = qModel(eval_data)
                SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_q))
                # Quantized model output should be close to floating point model output numerically
                # Setting target SQNR to be 30 dB so that relative error is 1e-3 below the desired
                # output
                self.assertGreater(SQNRdB, 30, msg='Quantized model numerics diverge from float, expect SQNR > 30 dB')

    def test_float_quant_compare_per_channel(self):
        # Test for per-channel Quant
        torch.manual_seed(67)
        my_model = ModelMultipleOps().to(torch.float32)
        my_model.eval()
        calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
        eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
        out_ref = my_model(eval_data)
        q_model = torch.quantization.QuantWrapper(my_model)
        q_model.eval()
        q_model.qconfig = torch.quantization.default_per_channel_qconfig
        torch.quantization.fuse_modules(q_model.module, [['conv1', 'bn1', 'relu1']], inplace=True)
        torch.quantization.prepare(q_model)
        q_model(calib_data)
        torch.quantization.convert(q_model)
        out_q = q_model(eval_data)
        SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_q))
        # Quantized model output should be close to floating point model output numerically
        # Setting target SQNR to be 35 dB
        self.assertGreater(SQNRdB, 35, msg='Quantized model numerics diverge from float, expect SQNR > 35 dB')

    def test_fake_quant_true_quant_compare(self):
        for qengine in ["fbgemm", "qnnpack"]:
            if qengine not in torch.backends.quantized.supported_engines:
                continue
            if qengine == 'qnnpack':
                if IS_PPC or TEST_WITH_UBSAN:
                    continue
            with override_quantized_engine(qengine):
                torch.manual_seed(67)
                my_model = ModelMultipleOpsNoAvgPool().to(torch.float32)
                calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
                eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
                my_model.eval()
                out_ref = my_model(eval_data)
                fq_model = torch.quantization.QuantWrapper(my_model)
                fq_model.train()
                fq_model.qconfig = torch.quantization.default_qat_qconfig
                torch.quantization.fuse_modules(fq_model.module, [['conv1', 'bn1', 'relu1']], inplace=True)
                torch.quantization.prepare_qat(fq_model)
                fq_model.eval()
                fq_model.apply(torch.quantization.disable_fake_quant)
                fq_model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
                fq_model(calib_data)
                fq_model.apply(torch.quantization.enable_fake_quant)
                fq_model.apply(torch.quantization.disable_observer)
                out_fq = fq_model(eval_data)
                SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_fq))
                # Quantized model output should be close to floating point model output numerically
                # Setting target SQNR to be 35 dB
                self.assertGreater(SQNRdB, 35, msg='Quantized model numerics diverge from float, expect SQNR > 35 dB')
                torch.quantization.convert(fq_model)
                out_q = fq_model(eval_data)
                SQNRdB = 20 * torch.log10(torch.norm(out_fq) / (torch.norm(out_fq - out_q) + 1e-10))
                self.assertGreater(SQNRdB, 60, msg='Fake quant and true quant numerics diverge, expect SQNR > 60 dB')

    # Test to compare weight only quantized model numerics and
    # activation only quantized model numerics with float
    def test_weight_only_activation_only_fakequant(self):
        for qengine in ["fbgemm", "qnnpack"]:
            if qengine not in torch.backends.quantized.supported_engines:
                continue
            if qengine == 'qnnpack':
                if IS_PPC or TEST_WITH_UBSAN:
                    continue
            with override_quantized_engine(qengine):
                torch.manual_seed(67)
                calib_data = torch.rand(2048, 3, 15, 15, dtype=torch.float32)
                eval_data = torch.rand(10, 3, 15, 15, dtype=torch.float32)
                qconfigset = set([torch.quantization.default_weight_only_qconfig,
                                  torch.quantization.default_activation_only_qconfig])
                SQNRTarget = [35, 45]
                for idx, qconfig in enumerate(qconfigset):
                    my_model = ModelMultipleOpsNoAvgPool().to(torch.float32)
                    my_model.eval()
                    out_ref = my_model(eval_data)
                    fq_model = torch.quantization.QuantWrapper(my_model)
                    fq_model.train()
                    fq_model.qconfig = qconfig
                    torch.quantization.fuse_modules(fq_model.module, [['conv1', 'bn1', 'relu1']], inplace=True)
                    torch.quantization.prepare_qat(fq_model)
                    fq_model.eval()
                    fq_model.apply(torch.quantization.disable_fake_quant)
                    fq_model.apply(torch.nn.intrinsic.qat.freeze_bn_stats)
                    fq_model(calib_data)
                    fq_model.apply(torch.quantization.enable_fake_quant)
                    fq_model.apply(torch.quantization.disable_observer)
                    out_fq = fq_model(eval_data)
                    SQNRdB = 20 * torch.log10(torch.norm(out_ref) / torch.norm(out_ref - out_fq))
                    self.assertGreater(SQNRdB, SQNRTarget[idx], msg='Quantized model numerics diverge from float')

if __name__ == "__main__":
    run_tests()