| import torch |
| import torch.jit |
| import unittest |
| from common_utils import run_tests |
| from common_quantization import QuantizationTestCase, ModelMultipleOps, ModelMultipleOpsNoAvgPool |
| |
| @unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines, |
| "Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs" |
| " with instruction set support avx2 or newer.") |
| class ModelNumerics(QuantizationTestCase): |
| def test_float_quant_compare_per_tensor(self): |
| 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) |
| q_model = torch.quantization.QuantWrapper(my_model) |
| q_model.eval() |
| q_model.qconfig = torch.quantization.default_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 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): |
| torch.manual_seed(67) |
| myModel = 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) |
| myModel.eval() |
| out_ref = myModel(eval_data) |
| fqModel = torch.quantization.QuantWrapper(myModel) |
| fqModel.train() |
| fqModel.qconfig = torch.quantization.default_qat_qconfig |
| torch.quantization.fuse_modules(fqModel.module, [['conv1', 'bn1', 'relu1']], inplace=True) |
| torch.quantization.prepare_qat(fqModel) |
| fqModel.eval() |
| fqModel.apply(torch.quantization.disable_fake_quant) |
| fqModel.apply(torch.nn._intrinsic.qat.freeze_bn_stats) |
| fqModel(calib_data) |
| fqModel.apply(torch.quantization.enable_fake_quant) |
| fqModel.apply(torch.quantization.disable_observer) |
| out_fq = fqModel(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(fqModel) |
| out_q = fqModel(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): |
| 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_quant_qconfig, |
| torch.quantization.default_activation_only_quant_qconfig]) |
| SQNRTarget = [35, 45] |
| for idx, qconfig in enumerate(qconfigset): |
| myModel = ModelMultipleOpsNoAvgPool().to(torch.float32) |
| myModel.eval() |
| out_ref = myModel(eval_data) |
| fqModel = torch.quantization.QuantWrapper(myModel) |
| fqModel.train() |
| fqModel.qconfig = qconfig |
| torch.quantization.fuse_modules(fqModel.module, [['conv1', 'bn1', 'relu1']], inplace=True) |
| torch.quantization.prepare_qat(fqModel) |
| fqModel.eval() |
| fqModel.apply(torch.quantization.disable_fake_quant) |
| fqModel.apply(torch.nn._intrinsic.qat.freeze_bn_stats) |
| fqModel(calib_data) |
| fqModel.apply(torch.quantization.enable_fake_quant) |
| fqModel.apply(torch.quantization.disable_observer) |
| out_fq = fqModel(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() |
