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remove _quant_ops.py from __init__.py

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pull/2282/head
chenzomi 5 years ago
parent 934bdce432
commit bbce6faff9
10 changed files with 49 additions and 48 deletions
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26
mindspore/nn/layer/quant.py
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@ -32,7 +32,7 @@ from .activation import get_activation
from ..cell import Cell from ..cell import Cell
from . import conv, basic from . import conv, basic
from ..._checkparam import ParamValidator as validator from ..._checkparam import ParamValidator as validator
from ...ops.operations import _quant_ops as Q
__all__ = [ __all__ = [
'Conv2dBnAct', 'Conv2dBnAct',
@ -242,11 +242,11 @@ class BatchNormFoldCell(Cell):
self.epsilon = epsilon self.epsilon = epsilon
self.is_gpu = context.get_context('device_target') == "GPU" self.is_gpu = context.get_context('device_target') == "GPU"
if self.is_gpu: if self.is_gpu:
self.bn_train = P.BatchNormFold(momentum, epsilon, is_training=True, freeze_bn=freeze_bn) self.bn_train = Q.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_infer = Q.BatchNormFold(momentum, epsilon, is_training=False, freeze_bn=freeze_bn)
else: else:
self.bn_reduce = P.BNTrainingReduce() 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): def construct(self, x, mean, variance, global_step):
if self.is_gpu: if self.is_gpu:
@ -337,11 +337,11 @@ class FakeQuantWithMinMax(Cell):
# init fake quant relative op # init fake quant relative op
if per_channel: if per_channel:
quant_fun = partial(P.FakeQuantPerChannel, channel_axis=self.channel_axis) quant_fun = partial(Q.FakeQuantPerChannel, channel_axis=self.channel_axis)
ema_fun = partial(P.FakeQuantMinMaxPerChannelUpdate, channel_axis=self.channel_axis) ema_fun = partial(Q.FakeQuantMinMaxPerChannelUpdate, channel_axis=self.channel_axis)
else: else:
quant_fun = P.FakeQuantPerLayer quant_fun = Q.FakeQuantPerLayer
ema_fun = P.FakeQuantMinMaxPerLayerUpdate ema_fun = Q.FakeQuantMinMaxPerLayerUpdate
if self.is_ascend: if self.is_ascend:
self.fake_quant = quant_fun(num_bits=self.num_bits, self.fake_quant = quant_fun(num_bits=self.num_bits,
@ -510,13 +510,13 @@ class Conv2dBatchNormQuant(Cell):
symmetric=symmetric, symmetric=symmetric,
narrow_range=narrow_range) narrow_range=narrow_range)
self.batchnorm_fold = BatchNormFoldCell(epsilon=eps, momentum=momentum, freeze_bn=freeze_bn) 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": if context.get_context('device_target') == "Ascend":
self.batchnorm_fold2_train = P.BatchNormFold2_D(freeze_bn=freeze_bn) self.batchnorm_fold2_train = Q.BatchNormFold2_D(freeze_bn=freeze_bn)
self.batchnorm_fold2_infer = P.BatchNormFold2_D(freeze_bn=0) self.batchnorm_fold2_infer = Q.BatchNormFold2_D(freeze_bn=0)
elif context.get_context('device_target') == "GPU": elif context.get_context('device_target') == "GPU":
self.batchnorm_fold2_train = P.BatchNormFold2(freeze_bn=freeze_bn) self.batchnorm_fold2_train = Q.BatchNormFold2(freeze_bn=freeze_bn)
self.batchnorm_fold2_infer = P.BatchNormFold2(freeze_bn=0) self.batchnorm_fold2_infer = Q.BatchNormFold2(freeze_bn=0)
else: else:
raise ValueError("Unsupported platform: {}".format(context.get_context('device_target'))) raise ValueError("Unsupported platform: {}".format(context.get_context('device_target')))
self.step = Parameter(initializer('normal', [1], dtype=mstype.int32), name='step', requires_grad=False) self.step = Parameter(initializer('normal', [1], dtype=mstype.int32), name='step', requires_grad=False)

37
mindspore/ops/_grad/grad_quant_ops.py
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@ -16,15 +16,16 @@
"""Generate bprop for aware quantization ops""" """Generate bprop for aware quantization ops"""
from .. import operations as P from .. import operations as P
from ..operations import _quant_ops as Q
from .grad_base import bprop_getters from .grad_base import bprop_getters
from ..composite.multitype_ops.zeros_like_impl import zeros_like from ..composite.multitype_ops.zeros_like_impl import zeros_like
from ... import context from ... import context
@bprop_getters.register(P.FakeQuantPerLayer) @bprop_getters.register(Q.FakeQuantPerLayer)
def get_bprop_fakequant_with_minmax(self): def get_bprop_fakequant_with_minmax(self):
"""Generate bprop for FakeQuantPerLayer for GPU and Ascend""" """Generate bprop for FakeQuantPerLayer for GPU and Ascend"""
op = P.FakeQuantPerLayerGrad( op = Q.FakeQuantPerLayerGrad(
num_bits=self.num_bits, quant_delay=self.quant_delay) num_bits=self.num_bits, quant_delay=self.quant_delay)
def bprop(x, x_min, x_max, out, dout): def bprop(x, x_min, x_max, out, dout):
@ -34,10 +35,10 @@ def get_bprop_fakequant_with_minmax(self):
return bprop return bprop
@bprop_getters.register(P.FakeQuantPerChannel) @bprop_getters.register(Q.FakeQuantPerChannel)
def get_bprop_fakequant_with_minmax_perchannel(self): def get_bprop_fakequant_with_minmax_perchannel(self):
"""Generate bprop for FakeQuantPerChannel""" """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, quant_delay=self.quant_delay,
symmetric=self.symmetric, symmetric=self.symmetric,
narrow_range=self.symmetric, narrow_range=self.symmetric,
@ -50,10 +51,10 @@ def get_bprop_fakequant_with_minmax_perchannel(self):
return bprop return bprop
@bprop_getters.register(P.BatchNormFold) @bprop_getters.register(Q.BatchNormFold)
def get_bprop_batchnorm_fold(self): def get_bprop_batchnorm_fold(self):
"""Generate bprop for BatchNormFold for GPU""" """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): def bprop(x, mean, variance, global_step, out, dout):
dx = op(dout[0], dout[1], x, out[0], out[1], global_step) 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 return bprop
@bprop_getters.register(P.CorrectionMul) @bprop_getters.register(Q.CorrectionMul)
def get_bprop_correction_mul(self): def get_bprop_correction_mul(self):
"""Generate bprop for CorrectionMul for Ascend and GPU""" """Generate bprop for CorrectionMul for Ascend and GPU"""
grad_dx = P.CorrectionMulGrad(self.channel_axis) grad_dx = Q.CorrectionMulGrad(self.channel_axis)
grad_d_batch_std = P.CorrectionMulGradReduce(self.channel_axis) grad_d_batch_std = Q.CorrectionMulGradReduce(self.channel_axis)
def bprop(x, batch_std, running_std, out, dout): def bprop(x, batch_std, running_std, out, dout):
dx, d_batch_std = grad_dx(dout, x, batch_std, running_std) dx, d_batch_std = grad_dx(dout, x, batch_std, running_std)
@ -83,10 +84,10 @@ def get_bprop_correction_mul(self):
return bprop return bprop
@bprop_getters.register(P.BatchNormFold2) @bprop_getters.register(Q.BatchNormFold2)
def get_bprop_batchnorm_fold2(self): def get_bprop_batchnorm_fold2(self):
"""Generate bprop for BatchNormFold2 for GPU""" """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): 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, 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 return bprop
@bprop_getters.register(P.BatchNormFoldD) @bprop_getters.register(Q.BatchNormFoldD)
def get_bprop_BatchNormFold(self): def get_bprop_BatchNormFold(self):
"""Generate bprop for BatchNormFold for Ascend""" """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): def bprop(x, x_sum, x_square_sum, mean, variance, out, dout):
dx = op(dout[1], dout[2], x, out[1], out[2]) dx = op(dout[1], dout[2], x, out[1], out[2])
@ -117,11 +118,11 @@ def get_bprop_BNTrainingReduce(self):
return bprop return bprop
@bprop_getters.register(P.BatchNormFold2_D) @bprop_getters.register(Q.BatchNormFold2_D)
def get_bprop_batchnorm_fold2_(self): def get_bprop_batchnorm_fold2_(self):
"""Generate bprop for BatchNormFold2 for Ascend""" """Generate bprop for BatchNormFold2 for Ascend"""
op_reduce = P.BatchNormFold2GradReduce(freeze_bn=self.freeze_bn) op_reduce = Q.BatchNormFold2GradReduce(freeze_bn=self.freeze_bn)
op_f = P.BatchNormFold2GradD(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): def bprop(x, beta, gamma, batch_std, batch_mean, running_std, out, dout):
dout_reduce, dout_x_reduce = op_reduce(dout, x) dout_reduce, dout_x_reduce = op_reduce(dout, x)
@ -132,7 +133,7 @@ def get_bprop_batchnorm_fold2_(self):
return bprop return bprop
@bprop_getters.register(P.FakeQuantMinMaxPerLayerUpdate) @bprop_getters.register(Q.FakeQuantMinMaxPerLayerUpdate)
def get_bprop_fakequant_with_minmax_per_layer_update(self): def get_bprop_fakequant_with_minmax_per_layer_update(self):
"""Generate bprop for FakeQuantMinMaxPerLayerUpdate for Ascend""" """Generate bprop for FakeQuantMinMaxPerLayerUpdate for Ascend"""
@ -142,7 +143,7 @@ def get_bprop_fakequant_with_minmax_per_layer_update(self):
return bprop return bprop
@bprop_getters.register(P.FakeQuantMinMaxPerChannelUpdate) @bprop_getters.register(Q.FakeQuantMinMaxPerChannelUpdate)
def get_bprop_fakequant_with_minmax_per_channel_update(self): def get_bprop_fakequant_with_minmax_per_channel_update(self):
"""Generate bprop for FakeQuantMinMaxPerChannelUpdate for Ascend""" """Generate bprop for FakeQuantMinMaxPerChannelUpdate for Ascend"""

1
mindspore/ops/operations/__init__.py
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@ -76,7 +76,6 @@ from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, Appl
ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK) ApplyRMSProp, ApplyCenteredRMSProp, BasicLSTMCell, InTopK)
from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode, from .other_ops import (Assign, IOU, BoundingBoxDecode, BoundingBoxEncode,
CheckValid, MakeRefKey, Partial, Depend, CheckBprop) CheckValid, MakeRefKey, Partial, Depend, CheckBprop)
from ._quant_ops import *
from .thor_ops import * from .thor_ops import *
__all__ = [ __all__ = [

12
mindspore/ops/operations/_quant_ops.py
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@ -69,7 +69,7 @@ class FakeQuantPerLayer(PrimitiveWithInfer):
>>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32) >>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
>>> min_tensor = Tensor(np.array([-6]), mstype.float32) >>> min_tensor = Tensor(np.array([-6]), mstype.float32)
>>> max_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] support_quant_bit = [4, 7, 8]
@ -129,7 +129,7 @@ class FakeQuantPerLayerGrad(PrimitiveWithInfer):
Performs grad of FakeQuantPerLayerGrad operation. Performs grad of FakeQuantPerLayerGrad operation.
Examples: 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) >>> 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) >>> input_x = Tensor(np.array([[18, -23], [0.2, 6]]), mindspore.float32)
>>> _min = Tensor(np.array([-4]), mindspore.float32) >>> _min = Tensor(np.array([-4]), mindspore.float32)
@ -206,7 +206,7 @@ class FakeQuantPerChannel(PrimitiveWithInfer):
- Tensor, has the same type as input. - Tensor, has the same type as input.
Examples: Examples:
>>> fake_quant = P.FakeQuantPerChannel() >>> fake_quant = FakeQuantPerChannel()
>>> input_x = Tensor(np.array([3, 4, 5, -2, -3, -1]).reshape(3, 2), mindspore.float32) >>> 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) >>> _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) >>> _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. Performs grad of FakeQuantPerChannelGrad operation.
Examples: Examples:
>>> fqmmpc_grad = P.FakeQuantPerChannelGrad() >>> fqmmpc_grad = FakeQuantPerChannelGrad()
>>> input_x = Tensor(np.random.randint(-4, 4, (2, 3, 4)), mindspore.float32) >>> 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) >>> dout = Tensor(np.random.randint(-2, 2, (2, 3, 4)), mindspore.float32)
>>> _min = Tensor(np.random.randint(-8, 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) >>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
>>> min_tensor = Tensor(np.array([-6]), mstype.float32) >>> min_tensor = Tensor(np.array([-6]), mstype.float32)
>>> max_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] support_quant_bit = [4, 7, 8]
@ -932,7 +932,7 @@ class FakeQuantMinMaxPerChannelUpdate(PrimitiveWithInfer):
>>> x = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32) >>> x = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
>>> min = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32) >>> min = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32)
>>> max = 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] support_quant_bit = [4, 7, 8]

2
mindspore/train/quant/quant.py
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@ -253,7 +253,7 @@ def convert_quant_network(network,
symmetric (bool): Quantization algorithm use symmetric or not. Default: False. symmetric (bool): Quantization algorithm use symmetric or not. Default: False.
narrow_range (bool): Quantization algorithm use narrow range 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. Cell, Network which has change to aware quantization training network.
""" """
net = ConvertToQuantNetwork( net = ConvertToQuantNetwork(

3
tests/st/ops/gpu/test_batchnorm_fold2_op.py
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@ -21,6 +21,7 @@ import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common.api import ms_function from mindspore.common.api import ms_function
from mindspore.ops import operations as P from mindspore.ops import operations as P
from mindspore.ops.operations import _quant_ops as Q
context.set_context(device_target='GPU') context.set_context(device_target='GPU')
@ -28,7 +29,7 @@ context.set_context(device_target='GPU')
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.op = P.BatchNormFold2(100000) self.op = Q.BatchNormFold2(100000)
@ms_function @ms_function
def construct(self, x, beta, gamma, batch_std, batch_mean, running_std, running_mean, current_step): def construct(self, x, beta, gamma, batch_std, batch_mean, running_std, running_mean, current_step):

4
tests/st/ops/gpu/test_batchnorm_fold_grad_op.py
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@ -20,7 +20,7 @@ import mindspore.context as context
import mindspore.nn as nn import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common.api import ms_function 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') context.set_context(device_target='GPU')
@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.op = P.BatchNormFoldGrad(freeze_bn=10) self.op = Q.BatchNormFoldGrad(freeze_bn=10)
@ms_function @ms_function
def construct(self, d_batch_mean, d_batch_std, x, batch_mean, batch_std, current_step): def construct(self, d_batch_mean, d_batch_std, x, batch_mean, batch_std, current_step):

4
tests/st/ops/gpu/test_batchnorm_fold_op.py
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@ -20,7 +20,7 @@ import mindspore.context as context
import mindspore.nn as nn import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common.api import ms_function 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') context.set_context(device_target='GPU')
@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() 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 @ms_function
def construct(self, x, mean, variance, current_step): def construct(self, x, mean, variance, current_step):

4
tests/st/ops/gpu/test_correction_mul_grad_op.py
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@ -20,7 +20,7 @@ import mindspore.context as context
import mindspore.nn as nn import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common.api import ms_function 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') context.set_context(device_target='GPU')
@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.op_w = P.CorrectionMulGrad() self.op_w = Q.CorrectionMulGrad()
@ms_function @ms_function
def construct(self, dy, x, batch_std, running_std): def construct(self, dy, x, batch_std, running_std):

4
tests/st/ops/gpu/test_correction_mul_op.py
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@ -20,7 +20,7 @@ import mindspore.context as context
import mindspore.nn as nn import mindspore.nn as nn
from mindspore import Tensor from mindspore import Tensor
from mindspore.common.api import ms_function 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') context.set_context(device_target='GPU')
@ -28,7 +28,7 @@ context.set_context(device_target='GPU')
class Net(nn.Cell): class Net(nn.Cell):
def __init__(self): def __init__(self):
super(Net, self).__init__() super(Net, self).__init__()
self.op = P.CorrectionMul() self.op = Q.CorrectionMul()
@ms_function @ms_function
def construct(self, x, batch_var, moving_var): def construct(self, x, batch_var, moving_var):

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