add out scale for quantization (#272)

* add out scale for quantization

* update quant_aware unittest

* update quant_aware unittest

paddleslim/quant/__init__.py

@@ -25,6 +25,6 @@ try:
 except Exception as e:
     _logger.warning(
         "If you want to use training-aware and post-training quantization, "
-        "please use Paddle >= 1.7.0 or develop version")
+        "please use Paddle >= 2.0.0 or develop version")
 
 from .quant_embedding import quant_embedding

paddleslim/quant/quanter.py

@@ -24,6 +24,8 @@ from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
 from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
 from paddle.fluid.contrib.slim.quantization import PostTrainingQuantization
 from paddle.fluid.contrib.slim.quantization import AddQuantDequantPass
+from paddle.fluid.contrib.slim.quantization import OutScaleForTrainingPass
+from paddle.fluid.contrib.slim.quantization import OutScaleForInferencePass
 from paddle.fluid import core
 from paddle.fluid.contrib.slim.quantization import WeightQuantization
 
@@ -220,6 +222,10 @@ def quant_aware(program, place, config=None, scope=None, for_test=False):
             quantizable_op_type=quant_dequant_ops)
         quant_dequant_pass.apply(main_graph)
 
+    out_scale_training_pass = OutScaleForTrainingPass(
+        scope=scope, place=place, moving_rate=config['moving_rate'])
+    out_scale_training_pass.apply(main_graph)
+
     if for_test:
         quant_program = main_graph.to_program()
     else:
@@ -363,9 +369,11 @@ def convert(program, place, config=None, scope=None, save_int8=False):
         assert isinstance(config, dict), "config must be dict"
         config = _parse_configs(config)
     _logger.info("convert config {}".format(config))
-
     test_graph = IrGraph(core.Graph(program.desc), for_test=True)
 
+    out_scale_infer_pass = OutScaleForInferencePass(scope=scope)
+    out_scale_infer_pass.apply(test_graph)
+
     # Freeze the graph after training by adjusting the quantize
     # operators' order for the inference.
     freeze_pass = QuantizationFreezePass(
@@ -379,10 +387,7 @@ def convert(program, place, config=None, scope=None, save_int8=False):
     freezed_program = test_graph.to_program()
 
     if save_int8:
-        convert_int8_pass = ConvertToInt8Pass(
-            scope=fluid.global_scope(),
-            place=place,
-            quantizable_op_type=support_op_types)
+        convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
         convert_int8_pass.apply(test_graph)
         freezed_program_int8 = test_graph.to_program()
         return freezed_program, freezed_program_int8

tests/test_quant_aware.py

@@ -132,7 +132,7 @@ class TestQuantAwareCase2(unittest.TestCase):
         def test(program):
             iter = 0
             result = [[], [], []]
-            for data in train_reader():
+            for data in eval_reader():
                 cost, top1, top5 = exe.run(
                     program,
                     feed=feeder.feed(data),
@@ -161,7 +161,8 @@ class TestQuantAwareCase2(unittest.TestCase):
             main_prog, place, config, for_test=False)
         quant_eval_prog = quant_aware(val_prog, place, config, for_test=True)
         train(quant_train_prog)
-        quant_eval_prog = convert(quant_eval_prog, place, config)
+        quant_eval_prog, int8_prog = convert(
+            quant_eval_prog, place, config, save_int8=True)
         top1_2, top5_2 = test(quant_eval_prog)
         # values before quantization and after quantization should be close
         print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1))