bug fix in fake quant training in r0.3

mindspore/nn/layer/quant.py

@@ -141,8 +141,7 @@ class FakeQuantWithMinMax(Cell):
                  out_channels=1,
                  quant_delay=0,
                  symmetric=False,
-                 narrow_range=False,
-                 training=True):
+                 narrow_range=False):
         """init FakeQuantWithMinMax layer"""
         super(FakeQuantWithMinMax, self).__init__()
         self.min_init = min_init
@@ -156,7 +155,6 @@ class FakeQuantWithMinMax(Cell):
         self.quant_delay = quant_delay
         self.symmetric = symmetric
         self.narrow_range = narrow_range
-        self.training = training
         self.is_ascend = context.get_context('device_target') == "Ascend"
 
         # init tensor min and max for fake quant op
@@ -190,7 +188,7 @@ class FakeQuantWithMinMax(Cell):
                                         symmetric=self.symmetric,
                                         narrow_range=self.narrow_range,
                                         training=self.training)
-        if self.ema:
+        if self.training:
             self.ema_update = ema_fun(num_bits=self.num_bits,
                                       ema=self.ema,
                                       ema_decay=self.ema_decay,
@@ -206,7 +204,7 @@ class FakeQuantWithMinMax(Cell):
         return s
 
     def construct(self, x):
-        if self.ema and self.is_ascend:
+        if self.is_ascend and self.training:
             min_up, max_up = self.ema_update(x, self.minq, self.maxq)
             out = self.fake_quant(x, min_up, max_up)
             P.Assign()(self.minq, min_up)

mindspore/ops/_op_impl/_custom_op/correction_mul_grad.py

@@ -38,12 +38,6 @@ correction_mul_grad_op_info = TBERegOp("CorrectionMulGrad") \
     .input(3, "running_std", None, "required", None) \
     .output(0, "dx", True, "required", "all") \
     .output(1, "d_batch_std", True, "required", "all") \
-    .dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.F16_Default, DataType.F16_Default,
-                  DataType.F16_Default, DataType.F16_Default) \
-    .dtype_format(DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD, DataType.F16_5HD,
-                  DataType.F16_5HD, DataType.F16_5HD) \
-    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
-                  DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
                   DataType.F32_5HD, DataType.F32_5HD) \
     .get_op_info()

mindspore/train/quant/quant.py

@@ -247,7 +247,7 @@ def convert_quant_network(network,
         network (Cell): Obtain a pipeline through network for saving graph summary.
         quant_delay (int): Number of steps after which weights and activations are quantized during eval. Default: 0.
         bn_fold (bool): Flag to used bn fold ops for simulation inference operation. Default: False.
-        freeze_bn (bool): Number of steps after which BN parameters used total mean and variance. Default: 0.
+        freeze_bn (int): Number of steps after which BN parameters used total mean and variance. Default: 0.
         weight_bits (int): Number of bits to use for quantizing weights. Default: 8.
         act_bits (int): Number of bits to use for quantizing activations. Default: 8.
         per_channel (bool):  Quantization granularity based on layer or on channel. Default: False.