!2282 remove _quant_op.py from __init__.py

Merge pull request !2282 from chenzhongming/master

mindspore/nn/layer/quant.py

@@ -32,7 +32,7 @@ from .activation import get_activation
 from ..cell import Cell
 from . import conv, basic
 from ..._checkparam import ParamValidator as validator
-
+from ...ops.operations import _quant_ops as Q
 
 __all__ = [
     'Conv2dBnAct',
@@ -242,11 +242,11 @@ class BatchNormFoldCell(Cell):
         self.epsilon = epsilon
         self.is_gpu = context.get_context('device_target') == "GPU"
         if self.is_gpu:
-            self.bn_train = P.BatchNormFold(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
-            self.bn_infer = P.BatchNormFold(momentum, epsilon, is_training=False, freeze_bn=freeze_bn)
+            self.bn_train = Q.BatchNormFold(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
+            self.bn_infer = Q.BatchNormFold(momentum, epsilon, is_training=False, freeze_bn=freeze_bn)
         else:
             self.bn_reduce = P.BNTrainingReduce()
-            self.bn_update = P.BatchNormFoldD(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
+            self.bn_update = Q.BatchNormFoldD(momentum, epsilon, is_training=True, freeze_bn=freeze_bn)
 
     def construct(self, x, mean, variance, global_step):
         if self.is_gpu:
@@ -337,11 +337,11 @@ class FakeQuantWithMinMax(Cell):
 
         # init fake quant relative op
         if per_channel:
-            quant_fun = partial(P.FakeQuantPerChannel, channel_axis=self.channel_axis)
-            ema_fun = partial(P.FakeQuantMinMaxPerChannelUpdate, channel_axis=self.channel_axis)
+            quant_fun = partial(Q.FakeQuantPerChannel, channel_axis=self.channel_axis)
+            ema_fun = partial(Q.FakeQuantMinMaxPerChannelUpdate, channel_axis=self.channel_axis)
         else:
-            quant_fun = P.FakeQuantPerLayer
-            ema_fun = P.FakeQuantMinMaxPerLayerUpdate
+            quant_fun = Q.FakeQuantPerLayer
+            ema_fun = Q.FakeQuantMinMaxPerLayerUpdate
 
         if self.is_ascend:
             self.fake_quant = quant_fun(num_bits=self.num_bits,
@@ -510,13 +510,13 @@ class Conv2dBatchNormQuant(Cell):
                                                      symmetric=symmetric,
                                                      narrow_range=narrow_range)
         self.batchnorm_fold = BatchNormFoldCell(epsilon=eps, momentum=momentum, freeze_bn=freeze_bn)
-        self.correct_mul = P.CorrectionMul(channel_axis)
+        self.correct_mul = Q.CorrectionMul(channel_axis)
         if context.get_context('device_target') == "Ascend":
-            self.batchnorm_fold2_train = P.BatchNormFold2_D(freeze_bn=freeze_bn)
-            self.batchnorm_fold2_infer = P.BatchNormFold2_D(freeze_bn=0)
+            self.batchnorm_fold2_train = Q.BatchNormFold2_D(freeze_bn=freeze_bn)
+            self.batchnorm_fold2_infer = Q.BatchNormFold2_D(freeze_bn=0)
         elif context.get_context('device_target') == "GPU":
-            self.batchnorm_fold2_train = P.BatchNormFold2(freeze_bn=freeze_bn)
-            self.batchnorm_fold2_infer = P.BatchNormFold2(freeze_bn=0)
+            self.batchnorm_fold2_train = Q.BatchNormFold2(freeze_bn=freeze_bn)
+            self.batchnorm_fold2_infer = Q.BatchNormFold2(freeze_bn=0)
         else:
             raise ValueError("Unsupported platform: {}".format(context.get_context('device_target')))
         self.step = Parameter(initializer('normal', [1], dtype=mstype.int32), name='step', requires_grad=False)

mindspore/ops/_grad/grad_quant_ops.py

@@ -16,15 +16,16 @@
 """Generate bprop for aware quantization ops"""
 
 from .. import operations as P
+from ..operations import _quant_ops as Q
 from .grad_base import bprop_getters
 from ..composite.multitype_ops.zeros_like_impl import zeros_like
 from ... import context
 
 
-@bprop_getters.register(P.FakeQuantPerLayer)
+@bprop_getters.register(Q.FakeQuantPerLayer)
 def get_bprop_fakequant_with_minmax(self):
     """Generate bprop for FakeQuantPerLayer for GPU and Ascend"""
-    op = P.FakeQuantPerLayerGrad(
+    op = Q.FakeQuantPerLayerGrad(
         num_bits=self.num_bits, quant_delay=self.quant_delay)
 
     def bprop(x, x_min, x_max, out, dout):
@@ -34,10 +35,10 @@ def get_bprop_fakequant_with_minmax(self):
     return bprop
 
 
-@bprop_getters.register(P.FakeQuantPerChannel)
+@bprop_getters.register(Q.FakeQuantPerChannel)
 def get_bprop_fakequant_with_minmax_perchannel(self):
     """Generate bprop for FakeQuantPerChannel"""
-    op = P.FakeQuantPerChannelGrad(num_bits=self.num_bits,
+    op = Q.FakeQuantPerChannelGrad(num_bits=self.num_bits,
                                    quant_delay=self.quant_delay,
                                    symmetric=self.symmetric,
                                    narrow_range=self.symmetric,
@@ -50,10 +51,10 @@ def get_bprop_fakequant_with_minmax_perchannel(self):
     return bprop
 
 
-@bprop_getters.register(P.BatchNormFold)
+@bprop_getters.register(Q.BatchNormFold)
 def get_bprop_batchnorm_fold(self):
     """Generate bprop for BatchNormFold for GPU"""
-    op = P.BatchNormFoldGrad(self.epsilon, self.is_training, self.freeze_bn)
+    op = Q.BatchNormFoldGrad(self.epsilon, self.is_training, self.freeze_bn)
 
     def bprop(x, mean, variance, global_step, out, dout):
         dx = op(dout[0], dout[1], x, out[0], out[1], global_step)
@@ -62,11 +63,11 @@ def get_bprop_batchnorm_fold(self):
     return bprop
 
 
-@bprop_getters.register(P.CorrectionMul)
+@bprop_getters.register(Q.CorrectionMul)
 def get_bprop_correction_mul(self):
     """Generate bprop for CorrectionMul for Ascend and GPU"""
-    grad_dx = P.CorrectionMulGrad(self.channel_axis)
-    grad_d_batch_std = P.CorrectionMulGradReduce(self.channel_axis)
+    grad_dx = Q.CorrectionMulGrad(self.channel_axis)
+    grad_d_batch_std = Q.CorrectionMulGradReduce(self.channel_axis)
 
     def bprop(x, batch_std, running_std, out, dout):
         dx, d_batch_std = grad_dx(dout, x, batch_std, running_std)
@@ -83,10 +84,10 @@ def get_bprop_correction_mul(self):
     return bprop
 
 
-@bprop_getters.register(P.BatchNormFold2)
+@bprop_getters.register(Q.BatchNormFold2)
 def get_bprop_batchnorm_fold2(self):
     """Generate bprop for BatchNormFold2 for GPU"""
-    op_f = P.BatchNormFold2Grad(freeze_bn=self.freeze_bn)
+    op_f = Q.BatchNormFold2Grad(freeze_bn=self.freeze_bn)
 
     def bprop(x, beta, gamma, batch_std, batch_mean, running_std, running_mean, global_step, out, dout):
         d_batch_std, d_batch_mean, d_beta, d_gamma, d_x = op_f(dout, x, gamma, batch_std, batch_mean, running_std,
@@ -97,10 +98,10 @@ def get_bprop_batchnorm_fold2(self):
     return bprop
 
 
-@bprop_getters.register(P.BatchNormFoldD)
+@bprop_getters.register(Q.BatchNormFoldD)
 def get_bprop_BatchNormFold(self):
     """Generate bprop for BatchNormFold for Ascend"""
-    op = P.BatchNormFoldGradD(self.epsilon, self.is_training, self.freeze_bn)
+    op = Q.BatchNormFoldGradD(self.epsilon, self.is_training, self.freeze_bn)
 
     def bprop(x, x_sum, x_square_sum, mean, variance, out, dout):
         dx = op(dout[1], dout[2], x, out[1], out[2])
@@ -117,11 +118,11 @@ def get_bprop_BNTrainingReduce(self):
     return bprop
 
 
-@bprop_getters.register(P.BatchNormFold2_D)
+@bprop_getters.register(Q.BatchNormFold2_D)
 def get_bprop_batchnorm_fold2_(self):
     """Generate bprop for BatchNormFold2 for Ascend"""
-    op_reduce = P.BatchNormFold2GradReduce(freeze_bn=self.freeze_bn)
-    op_f = P.BatchNormFold2GradD(freeze_bn=self.freeze_bn)
+    op_reduce = Q.BatchNormFold2GradReduce(freeze_bn=self.freeze_bn)
+    op_f = Q.BatchNormFold2GradD(freeze_bn=self.freeze_bn)
 
     def bprop(x, beta, gamma, batch_std, batch_mean, running_std, out, dout):
         dout_reduce, dout_x_reduce = op_reduce(dout, x)
@@ -132,7 +133,7 @@ def get_bprop_batchnorm_fold2_(self):
     return bprop
 
 
-@bprop_getters.register(P.FakeQuantMinMaxPerLayerUpdate)
+@bprop_getters.register(Q.FakeQuantMinMaxPerLayerUpdate)
 def get_bprop_fakequant_with_minmax_per_layer_update(self):
     """Generate bprop for FakeQuantMinMaxPerLayerUpdate for Ascend"""
 
@@ -142,7 +143,7 @@ def get_bprop_fakequant_with_minmax_per_layer_update(self):
     return bprop
 
 
-@bprop_getters.register(P.FakeQuantMinMaxPerChannelUpdate)
+@bprop_getters.register(Q.FakeQuantMinMaxPerChannelUpdate)
 def get_bprop_fakequant_with_minmax_per_channel_update(self):
     """Generate bprop for FakeQuantMinMaxPerChannelUpdate for Ascend"""
 

mindspore/ops/operations/__init__.py

@@ -76,7 +76,6 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
                      ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
 from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode,
                         CheckValid, MakeRefKey, Partial, Depend, CheckBprop)
-from ._quant_ops import *
 from .thor_ops import *
 
 __all__ = [

mindspore/ops/operations/_quant_ops.py

@@ -69,7 +69,7 @@ class FakeQuantPerLayer(PrimitiveWithInfer):
         >>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
         >>> min_tensor = Tensor(np.array([-6]), mstype.float32)
         >>> max_tensor = Tensor(np.array([6]), mstype.float32)
-        >>> output_tensor = P.FakeQuantPerLayer(num_bits=8)(input_tensor, min_tensor, max_tensor)
+        >>> output_tensor = FakeQuantPerLayer(num_bits=8)(input_tensor, min_tensor, max_tensor)
     """
     support_quant_bit = [4, 7, 8]
 
@@ -129,7 +129,7 @@ class FakeQuantPerLayerGrad(PrimitiveWithInfer):
     Performs grad of FakeQuantPerLayerGrad operation.
 
     Examples:
-        >>> fake_min_max_grad = P.FakeQuantPerLayerGrad()
+        >>> fake_min_max_grad = FakeQuantPerLayerGrad()
         >>> dout = Tensor(np.array([[-2.3, 1.2], [5.7, 0.2]]), mindspore.float32)
         >>> input_x = Tensor(np.array([[18, -23], [0.2, 6]]), mindspore.float32)
         >>> _min = Tensor(np.array([-4]), mindspore.float32)
@@ -206,7 +206,7 @@ class FakeQuantPerChannel(PrimitiveWithInfer):
         - Tensor, has the same type as input.
 
     Examples:
-        >>> fake_quant = P.FakeQuantPerChannel()
+        >>> fake_quant = FakeQuantPerChannel()
         >>> input_x = Tensor(np.array([3, 4, 5, -2, -3, -1]).reshape(3, 2), mindspore.float32)
         >>> _min = Tensor(np.linspace(-2, 2, 12).reshape(3, 2, 2), mindspore.float32)
         >>> _max = Tensor(np.linspace(8, 12, 12).reshape(3, 2, 2), mindspore.float32)
@@ -275,7 +275,7 @@ class FakeQuantPerChannelGrad(PrimitiveWithInfer):
     Performs grad of FakeQuantPerChannelGrad operation.
 
     Examples:
-        >>> fqmmpc_grad = P.FakeQuantPerChannelGrad()
+        >>> fqmmpc_grad = FakeQuantPerChannelGrad()
         >>> input_x = Tensor(np.random.randint(-4, 4, (2, 3, 4)), mindspore.float32)
         >>> dout = Tensor(np.random.randint(-2, 2, (2, 3, 4)), mindspore.float32)
         >>> _min = Tensor(np.random.randint(-8, 2, (2, 3, 4)), mindspore.float32)
@@ -858,7 +858,7 @@ class FakeQuantMinMaxPerLayerUpdate(PrimitiveWithInfer):
         >>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
         >>> min_tensor = Tensor(np.array([-6]), mstype.float32)
         >>> max_tensor = Tensor(np.array([6]), mstype.float32)
-        >>> output_tensor = P.FakeQuantWithMinMax(num_bits=8)(input_tensor, min_tensor, max_tensor)
+        >>> output_tensor = FakeQuantWithMinMax(num_bits=8)(input_tensor, min_tensor, max_tensor)
     """
     support_quant_bit = [4, 7, 8]
 
@@ -932,7 +932,7 @@ class FakeQuantMinMaxPerChannelUpdate(PrimitiveWithInfer):
         >>> x = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
         >>> min = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32)
         >>> max = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32)
-        >>> output_tensor = P.FakeQuantWithMinMax(num_bits=8)(x, min, max)
+        >>> output_tensor = FakeQuantWithMinMax(num_bits=8)(x, min, max)
     """
     support_quant_bit = [4, 7, 8]
 

mindspore/train/quant/quant.py

@@ -253,7 +253,7 @@ def convert_quant_network(network,
         symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
         narrow_range (bool): Quantization algorithm use narrow range or not. Default: False.
 
-    returns:
+    Returns:
         Cell, Network which has change to aware quantization training network.
     """
     net = ConvertToQuantNetwork(

tests/st/ops/gpu/test_batchnorm_fold2_op.py

@@ -21,6 +21,7 @@ import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
 from mindspore.ops import operations as P
+from mindspore.ops.operations import _quant_ops as Q
 
 context.set_context(device_target='GPU')
 
@@ -28,7 +29,7 @@ context.set_context(device_target='GPU')
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
-        self.op = P.BatchNormFold2(100000)
+        self.op = Q.BatchNormFold2(100000)
 
     @ms_function
     def construct(self, x, beta, gamma, batch_std, batch_mean, running_std, running_mean, current_step):

tests/st/ops/gpu/test_batchnorm_fold_grad_op.py

@@ -20,7 +20,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
-from mindspore.ops import operations as P
+from mindspore.ops.operations import _quant_ops as Q
 
 context.set_context(device_target='GPU')
 
@@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
-        self.op = P.BatchNormFoldGrad(freeze_bn=10)
+        self.op = Q.BatchNormFoldGrad(freeze_bn=10)
 
     @ms_function
     def construct(self, d_batch_mean, d_batch_std, x, batch_mean, batch_std, current_step):

tests/st/ops/gpu/test_batchnorm_fold_op.py

@@ -20,7 +20,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
-from mindspore.ops import operations as P
+from mindspore.ops.operations import _quant_ops as Q
 
 context.set_context(device_target='GPU')
 
@@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
-        self.op = P.BatchNormFold(momentum=0.9, freeze_bn=10)
+        self.op = Q.BatchNormFold(momentum=0.9, freeze_bn=10)
 
     @ms_function
     def construct(self, x, mean, variance, current_step):

tests/st/ops/gpu/test_correction_mul_grad_op.py

@@ -20,7 +20,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
-from mindspore.ops import operations as P
+from mindspore.ops.operations import _quant_ops as Q
 
 context.set_context(device_target='GPU')
 
@@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
-        self.op_w = P.CorrectionMulGrad()
+        self.op_w = Q.CorrectionMulGrad()
 
     @ms_function
     def construct(self, dy, x, batch_std, running_std):

tests/st/ops/gpu/test_correction_mul_op.py

@@ -20,7 +20,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common.api import ms_function
-from mindspore.ops import operations as P
+from mindspore.ops.operations import _quant_ops as Q
 
 context.set_context(device_target='GPU')
 
@@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
 class Net(nn.Cell):
     def __init__(self):
         super(Net, self).__init__()
-        self.op = P.CorrectionMul()
+        self.op = Q.CorrectionMul()
 
     @ms_function
     def construct(self, x, batch_var, moving_var):