Added an option to use external FP32 model in QAT comparison test (#22858)

* Added an option to use external FP32 model in QAT comparison test

python/paddle/fluid/contrib/slim/tests/qat_int8_nlp_comparison.py

@@ -48,9 +48,11 @@ def parse_args():
     parser.add_argument(
         '--qat_model', type=str, default='', help='A path to a QAT model.')
     parser.add_argument(
-        '--save_model',
+        '--fp32_model',
-        action='store_true',
+        type=str,
-        help='If used, the QAT model will be saved after all transformations')
+        default='',
+        help='A path to an FP32 model. If empty, the QAT model will be used for FP32 inference.'
+    )
     parser.add_argument('--infer_data', type=str, default='', help='Data file.')
     parser.add_argument(
         '--labels', type=str, default='', help='File with labels.')
@@ -240,7 +242,10 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
             return
 
         qat_model_path = test_case_args.qat_model
+        assert qat_model_path, 'The QAT model path cannot be empty. Please, use the --qat_model option.'
+        fp32_model_path = test_case_args.fp32_model if test_case_args.fp32_model else qat_model_path
         data_path = test_case_args.infer_data
+        assert data_path, 'The dataset path cannot be empty. Please, use the --infer_data option.'
         labels_path = test_case_args.labels
         batch_size = test_case_args.batch_size
         batch_num = test_case_args.batch_num
@@ -251,6 +256,7 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
 
         _logger.info('QAT FP32 & INT8 prediction run.')
         _logger.info('QAT model: {0}'.format(qat_model_path))
+        _logger.info('FP32 model: {0}'.format(fp32_model_path))
         _logger.info('Dataset: {0}'.format(data_path))
         _logger.info('Labels: {0}'.format(labels_path))
         _logger.info('Batch size: {0}'.format(batch_size))
@@ -263,11 +269,12 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
             self._reader_creator(data_path, labels_path), batch_size=batch_size)
         fp32_acc, fp32_pps, fp32_lat = self._predict(
             val_reader,
-            qat_model_path,
+            fp32_model_path,
             batch_size,
             batch_num,
             skip_batch_num,
             transform_to_int8=False)
+        _logger.info('FP32: avg accuracy: {0:.6f}'.format(fp32_acc))
         _logger.info('--- QAT INT8 prediction start ---')
         val_reader = paddle.batch(
             self._reader_creator(data_path, labels_path), batch_size=batch_size)
@@ -278,6 +285,7 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
             batch_num,
             skip_batch_num,
             transform_to_int8=True)
+        _logger.info('INT8: avg accuracy: {0:.6f}'.format(int8_acc))
 
         self._summarize_performance(fp32_pps, fp32_lat, int8_pps, int8_lat)
         self._compare_accuracy(fp32_acc, int8_acc, acc_diff_threshold)