forked from huawei/mindspore2022
1779 lines
87 KiB
Python
1779 lines
87 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0(the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http: // www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Operators for quantization."""
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from functools import partial
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import mindspore.context as context
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from ..._checkparam import Validator as validator
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from ..._checkparam import Rel
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from ..primitive import PrimitiveWithInfer, prim_attr_register
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from ...common import dtype as mstype
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if context.get_context('device_target') == "Ascend":
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import mindspore.ops._op_impl._custom_op
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__all__ = ["MinMaxUpdatePerLayer",
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"MinMaxUpdatePerChannel",
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"FakeLearnedScaleQuantPerLayer",
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"FakeLearnedScaleQuantPerLayerGrad",
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"FakeLearnedScaleQuantPerLayerGradD",
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"FakeLearnedScaleQuantPerLayerGradDReduce",
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"FakeLearnedScaleQuantPerChannel",
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"FakeLearnedScaleQuantPerChannelGrad",
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"FakeLearnedScaleQuantPerChannelGradD",
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"FakeLearnedScaleQuantPerChannelGradDReduce",
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"FakeQuantWithMinMaxVars",
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"FakeQuantWithMinMaxVarsGradient",
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"FakeQuantWithMinMaxVarsPerChannel",
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"FakeQuantWithMinMaxVarsPerChannelGradient",
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"FakeQuantPerLayer",
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"FakeQuantPerLayerGrad",
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"FakeQuantPerChannel",
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"FakeQuantPerChannelGrad",
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"BatchNormFold",
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"BatchNormFoldGrad",
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"CorrectionMul",
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"CorrectionMulGrad",
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"CorrectionMulGradReduce",
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"BatchNormFold2",
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"BatchNormFold2Grad",
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"BatchNormFoldD",
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"BatchNormFoldGradD",
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"BatchNormFold2D",
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"BatchNormFold2GradD",
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"BatchNormFold2GradReduce",
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"IFMR",
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"ActsULQ",
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"ActsULQInputGrad",
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"ActULQClampMinGrad",
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"ActULQClampMaxGrad",
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"WtsARQ"
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]
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class MinMaxUpdatePerLayer(PrimitiveWithInfer):
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r"""
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Updates min and max per layer.
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Args:
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ema (bool): Uses EMA algorithm update value min and max. Default: False.
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ema_decay (int) : EMA algorithm decay parameter. Default: 0.999.
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Inputs:
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- **x** (Tensor) : float32 Tensor representing the shape of the output tensor.
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- **min** (Tensor) : Value of the min range of the input data x.
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- **max** (Tensor) : Value of the max range of the input data x.
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Outputs:
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- Tensor: Simulates quantize tensor of x.
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Examples:
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>>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
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>>> min_tensor = Tensor(np.array([-6]), mstype.float32)
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>>> max_tensor = Tensor(np.array([6]), mstype.float32)
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>>> output_tensor = MinMaxUpdatePerLayer(num_bits=8)(input_tensor, min_tensor, max_tensor)
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"""
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support_quant_bit = [4, 7, 8]
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@prim_attr_register
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def __init__(self, ema=False, ema_decay=0.999):
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"""Initialize FakeQuantMinMaxPerLayerUpdate OP"""
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if context.get_context('device_target') == "Ascend":
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from mindspore.ops._op_impl._custom_op import minmax_update_perlayer
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if ema and not ema_decay:
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raise ValueError(
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f"For '{self.name}' attr \'ema\' and \'ema_decay\' should set together.")
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self.ema = validator.check_value_type('ema', ema, (bool,), self.name)
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self.ema_decay = validator.check_float_range(ema_decay, 0, 1, Rel.INC_BOTH, 'ema_decay', self.name)
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self.init_prim_io_names(inputs=['x', 'min', 'max'],
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outputs=['min_up', 'max_up'])
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def infer_shape(self, x_shape, min_shape, max_shape):
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validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
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validator.check("min shape", min_shape, "max shape",
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max_shape, Rel.EQ, self.name)
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validator.check_equal_int(len(min_shape), 1, "min shape", self.name)
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return min_shape, max_shape
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def infer_dtype(self, x_type, min_type, max_type):
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tuple(map(partial(validator.check_tensor_dtype_valid,
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valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
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("x", "min", "max"),
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(x_type, min_type, max_type)))
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return min_type, max_type
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class MinMaxUpdatePerChannel(PrimitiveWithInfer):
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r"""
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Updates min and max per channel.
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Args:
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ema (bool): Uses EMA algorithm update value min and max. Default: False.
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ema_decay (int) : EMA algorithm decay parameter. Default: 0.999.
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channel_axis (int): Quantization by channel axis. Ascend backend only supports 0 or 1. Default: 1.
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Inputs:
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- **x** (Tensor) : float32 Tensor representing the shape of the output tensor.
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- **min** (Tensor) : Value of the min range of the input data x.
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- **max** (Tensor) : Value of the max range of the input data x.
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Outputs:
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- Tensor: Simulates quantize tensor of x.
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Examples:
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>>> x = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
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>>> min_value = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32)
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>>> max_value = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32)
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>>> output_tensor = MinMaxUpdatePerChannel(num_bits=8)(x, min_value, max_value)
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"""
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support_quant_bit = [4, 7, 8]
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ascend_support_x_rank = [2, 4]
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@prim_attr_register
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def __init__(self, ema=False, ema_decay=0.999, channel_axis=1):
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"""Initialize FakeQuantPerChannelUpdate OP for Ascend"""
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self.is_ascend = context.get_context('device_target') == "Ascend"
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if self.is_ascend:
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from mindspore.ops._op_impl._custom_op import minmax_update_perchannel
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if ema and not ema_decay:
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raise ValueError(
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f"For '{self.name}' attr \'ema\' and \'ema_decay\' should set together.")
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self.ema = validator.check_value_type('ema', ema, (bool,), self.name)
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self.ema_decay = validator.check_float_range(ema_decay, 0, 1, Rel.INC_BOTH, 'ema_decay', self.name)
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if self.is_ascend:
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self.channel_axis = validator.check_int_range(channel_axis, 0, 1, Rel.INC_BOTH, 'channel_axis', self.name)
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else:
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self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel_axis', self.name)
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self.init_prim_io_names(
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inputs=['x', 'min', 'max'], outputs=['min_up', 'max_up'])
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def infer_shape(self, x_shape, min_shape, max_shape):
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if self.is_ascend and len(x_shape) not in self.ascend_support_x_rank:
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raise ValueError(f"For '{self.name}' x rank should be in '{self.ascend_support_x_rank}'")
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if not self.is_ascend:
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validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
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validator.check("min shape", min_shape, "max shape",
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max_shape, Rel.EQ, self.name)
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validator.check_equal_int(len(min_shape), 1, "min shape", self.name)
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return min_shape, max_shape
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def infer_dtype(self, x_type, min_type, max_type):
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tuple(map(partial(validator.check_tensor_dtype_valid,
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valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
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("x", "min", "max"),
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(x_type, min_type, max_type)))
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return min_type, max_type
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class FakeLearnedScaleQuantPerLayer(PrimitiveWithInfer):
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r"""
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Simulates the quantize and dequantize operations of the fake learned scale quant per-layer case in training time.
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Args:
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quant_delay (int): Quantilization delay parameter. Before delay step in training time not update
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simulate quantization aware function. After delay step in training time begin simulate the aware
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quantize function. Default: 0.
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neg_trunc (bool): Whether the quantization algorithm uses nagetive truncation or not. Default: False.
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training (bool): Training the network or not. Default: True.
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Inputs:
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- **input_x** (Tensor) : Input tensor that needs to be quantified.
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- **alpha** (Tensor) : Value of the max clipping range of the input data `input_x`.
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- **quant_max** (Tensor) : Value of the quantization range.
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Outputs:
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- Tensor: Simulates quantize tensor of `input_x`，with the same type and shape as the `input_x`.
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Examples:
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>>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
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>>> alpha_tensor = Tensor(np.array([6]), mstype.float32)
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>>> quant_max_tensor = Tensor(np.array([127]), mstype.float32)
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>>> output_tensor = FakeLearnedScaleQuantPerLayer()(input_tensor, alpha_tensor, quant_max_tensor)
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"""
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@prim_attr_register
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def __init__(self,
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quant_delay=0,
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neg_trunc=False,
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training=True):
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"""init FakeLearnedScaleQuantPerLayer OP"""
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if context.get_context('device_target') == "Ascend":
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from mindspore.ops._op_impl._custom_op import fake_learned_scale_quant_perlayer
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self.quant_delay = validator.check_non_negative_int(
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quant_delay, 'quant_delay', self.name)
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self.neg_trunc = validator.check_value_type(
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'neg_trunc', neg_trunc, (bool,), self.name)
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self.training = validator.check_value_type(
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'training', training, (bool,), self.name)
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self.init_prim_io_names(inputs=['input_x', 'alpha', 'quant_max'],
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outputs=['out'])
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def infer_shape(self, input_x_shape, alpha_shape, quant_max_shape):
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validator.check_int(len(input_x_shape), 1, Rel.GE, "input_x rank", self.name)
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validator.check_int(len(alpha_shape), 1, Rel.GE, "alpha rank", self.name)
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validator.check_int(len(quant_max_shape), 1, Rel.GE, "quant max rank", self.name)
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return input_x_shape
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def infer_dtype(self, input_x_type, alpha_type, quant_max_type):
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if context.get_context('device_target') == "GPU":
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valid_dtypes = (mstype.float32,)
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else:
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valid_dtypes = (mstype.float16, mstype.float32)
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tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=self.name),
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("input_x", "alpha", "quant_max"),
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(input_x_type, alpha_type, quant_max_type)))
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return input_x_type
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class FakeLearnedScaleQuantPerLayerGrad(PrimitiveWithInfer):
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r"""
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Performs grad of FakeLearnedScaleQuantPerLayer operation.
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Examples:
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>>> fake_learned_scale_grad = FakeLearnedScaleQuantPerLayerGrad()
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>>> dout = Tensor(np.array([[-2.3, 1.2], [5.7, 0.2]]), mindspore.float32)
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>>> input_x = Tensor(np.array([[18, -23], [0.2, 6]]), mindspore.float32)
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>>> _alpha = Tensor(np.array([6]), mindspore.float32)
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>>> _quant_max = Tensor(np.array([127]), mindspore.float32)
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>>> result = fake_learned_scale_grad(dout, input_x, _min, _max)
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"""
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@prim_attr_register
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def __init__(self,
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quant_delay=0,
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neg_trunc=False):
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self.quant_delay = validator.check_non_negative_int(
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quant_delay, 'quant_delay', self.name)
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self.neg_trunc = validator.check_value_type(
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'neg_trunc', neg_trunc, (bool,), self.name)
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self.init_prim_io_names(
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inputs=['dout', 'x', 'alpha', 'quant_max'], outputs=['dx', 'dalpha'])
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def infer_shape(self, dout_shape, x_shape, alpha_shape, quant_max_shape):
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validator.check("dout shape", dout_shape, "x_shape", x_shape, Rel.EQ, self.name)
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validator.check_int(len(alpha_shape), 1, Rel.GE, "alpha rank", self.name)
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validator.check_int(len(quant_max_shape), 1, Rel.GE, "quant max rank", self.name)
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return dout_shape, alpha_shape
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def infer_dtype(self, dout_type, x_type, alpha_type, quant_max_type):
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if context.get_context('device_target') == "GPU":
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valid_dtypes = (mstype.float32,)
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else:
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valid_dtypes = (mstype.float16, mstype.float32)
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tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=self.name),
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("dout", "x", "alpha", "quant_max"),
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(dout_type, x_type, alpha_type, quant_max_type)))
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return dout_type, alpha_type
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class FakeLearnedScaleQuantPerLayerGradD(PrimitiveWithInfer):
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r"""
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Performs input grad of FakeLearnedScaleQuantPerLayer operation.
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"""
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@prim_attr_register
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def __init__(self,
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neg_trunc=False):
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from mindspore.ops._op_impl._custom_op import fake_learned_scale_quant_perlayer_grad
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self.neg_trunc = validator.check_value_type(
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'neg_trunc', neg_trunc, (bool,), self.name)
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self.init_prim_io_names(
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inputs=['dout', 'x', 'alpha', 'quant_max'], outputs=['dx', 'dalpha'])
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def infer_shape(self, dout_shape, x_shape, alpha_shape, quant_max_shape):
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validator.check("dout shape", dout_shape, "x_shape", x_shape, Rel.EQ, self.name)
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validator.check_int(len(alpha_shape), 1, Rel.GE, "alpha rank", self.name)
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validator.check_int(len(quant_max_shape), 1, Rel.GE, "quant max rank", self.name)
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return dout_shape, dout_shape
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def infer_dtype(self, dout_type, x_type, alpha_type, quant_max_type):
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valid_dtypes = (mstype.float16, mstype.float32)
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tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=self.name),
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("dout", "x", "alpha", "quant_max"),
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(dout_type, x_type, alpha_type, quant_max_type)))
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return dout_type, dout_type
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class FakeLearnedScaleQuantPerLayerGradDReduce(PrimitiveWithInfer):
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r"""
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Performs alpha grad reduce of FakeLearnedScaleQuantPerLayer operation.
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"""
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@prim_attr_register
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def __init__(self):
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from mindspore.ops._op_impl._custom_op import fake_learned_scale_quant_perlayer_grad_reduce
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self.init_prim_io_names(
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inputs=['dout_alpha'], outputs=['dalpha'])
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def infer_shape(self, dout_alpha_shape):
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return (1,)
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def infer_dtype(self, dout_alpha_type):
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valid_dtypes = (mstype.float16, mstype.float32)
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validator.check_tensor_dtype_valid("dout_alpha", dout_alpha_type, valid_dtypes, self.name)
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return dout_alpha_type
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class FakeLearnedScaleQuantPerChannel(PrimitiveWithInfer):
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r"""
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Simulates the quantize and dequantize operations of the fake learned scale quant per-chnnel case in training time.
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Args:
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quant_delay (int): Quantilization delay parameter. Before delay step in training time not update
|
||
simulate quantization aware function. After delay step in training time begin simulate the aware
|
||
quantize function. Default: 0.
|
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neg_trunc (bool): Whether the quantization algorithm uses nagetive truncation or not. Default: False.
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training (bool): Training the network or not. Default: True.
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channel_axis (int): Quantization by channel axis. Ascend backend only supports 0 or 1. Default: 1.
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||
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Inputs:
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- **input_x** (Tensor) : Input tensor that needs to be quantified.
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||
- **alpha** (Tensor) : Value of the max clipping range of the input data `input_x`.
|
||
- **quant_max** (Tensor) : Value of the quantization range.
|
||
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||
Outputs:
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- Tensor: Simulates quantize tensor of `input_x`，with the same type and shape as the `input_x`.
|
||
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||
Examples:
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>>> input_tensor = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32)
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>>> alpha_tensor = Tensor(np.array([6]*3), mstype.float32)
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>>> quant_max_tensor = Tensor(np.array([127]), mstype.float32)
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>>> output_tensor = FakeLearnedScaleQuantPerChannel()(input_tensor, alpha_tensor, quant_max_tensor)
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"""
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ascend_support_x_rank = [2, 4]
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@prim_attr_register
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def __init__(self,
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quant_delay=0,
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neg_trunc=False,
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training=True,
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channel_axis=1):
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"""init FakeLearnedScaleQuantPerChannel OP"""
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if context.get_context('device_target') == "Ascend":
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from mindspore.ops._op_impl._custom_op import fake_learned_scale_quant_perchannel
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self.is_ascend = context.get_context('device_target') == "Ascend"
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self.quant_delay = validator.check_non_negative_int(
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quant_delay, 'quant_delay', self.name)
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self.neg_trunc = validator.check_value_type(
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'neg_trunc', neg_trunc, (bool,), self.name)
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self.training = validator.check_value_type(
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'training', training, (bool,), self.name)
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if self.is_ascend:
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self.channel_axis = validator.check_int_range(channel_axis, 0, 1, Rel.INC_BOTH, 'channel_axis', self.name)
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else:
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self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel_axis', self.name)
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self.init_prim_io_names(inputs=['input_x', 'alpha', 'quant_max'],
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outputs=['out'])
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def infer_shape(self, input_x_shape, alpha_shape, quant_max_shape):
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if self.is_ascend and len(input_x_shape) not in self.ascend_support_x_rank:
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raise ValueError(f"For '{self.name}' x rank should be in '{self.ascend_support_x_rank}'")
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if not self.is_ascend:
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validator.check_int(len(input_x_shape), 1, Rel.GE, "input_x rank", self.name)
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if len(input_x_shape) == 1:
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self.channel_axis = 0
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validator.check_equal_int(alpha_shape[0], input_x_shape[self.channel_axis], "alpha rank", self.name)
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validator.check_int(len(quant_max_shape), 1, Rel.GE, "quant max rank", self.name)
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return input_x_shape
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def infer_dtype(self, input_x_type, alpha_type, quant_max_type):
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if context.get_context('device_target') == "GPU":
|
||
valid_dtypes = (mstype.float32,)
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||
else:
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||
valid_dtypes = (mstype.float16, mstype.float32)
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||
tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=self.name),
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||
("input_x", "alpha", "quant_max"),
|
||
(input_x_type, alpha_type, quant_max_type)))
|
||
return input_x_type
|
||
|
||
|
||
class FakeLearnedScaleQuantPerChannelGrad(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of FakeLearnedScaleQuantPerChannel operation.
|
||
|
||
Examples:
|
||
>>> fake_learned_scale_grad = FakeLearnedScaleQuantPerChannelGrad()
|
||
>>> 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)
|
||
>>> _alpha = Tensor(np.array([6]*2), mindspore.float32)
|
||
>>> _quant_max = Tensor(np.array([127]), mindspore.float32)
|
||
>>> result = fake_learned_scale_grad(dout, input_x, _min, _max)
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
quant_delay=0,
|
||
neg_trunc=False,
|
||
channel_axis=1):
|
||
self.quant_delay = validator.check_non_negative_int(
|
||
quant_delay, 'quant_delay', self.name)
|
||
self.neg_trunc = validator.check_value_type(
|
||
'neg_trunc', neg_trunc, (bool,), self.name)
|
||
self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel axis', self.name)
|
||
self.init_prim_io_names(
|
||
inputs=['dout', 'x', 'alpha', 'quant_max'], outputs=['dx', 'dalpha'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape, alpha_shape, quant_max_shape):
|
||
validator.check("dout shape", dout_shape, "x_shape", x_shape, Rel.EQ, self.name)
|
||
return dout_shape, alpha_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, alpha_type, quant_max_type):
|
||
if context.get_context('device_target') == "GPU":
|
||
valid_dtypes = (mstype.float32,)
|
||
else:
|
||
valid_dtypes = (mstype.float16, mstype.float32)
|
||
tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=self.name),
|
||
("dout", "x", "alpha", "quant_max"),
|
||
(dout_type, x_type, alpha_type, quant_max_type)))
|
||
return dout_type, alpha_type
|
||
|
||
|
||
class FakeLearnedScaleQuantPerChannelGradD(PrimitiveWithInfer):
|
||
r"""
|
||
Performs input grad of FakeLearnedScaleQuantPerChannel operation.
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
neg_trunc=False,
|
||
channel_axis=1):
|
||
from mindspore.ops._op_impl._custom_op import fake_learned_scale_quant_perchannel_grad
|
||
self.neg_trunc = validator.check_value_type(
|
||
'neg_trunc', neg_trunc, (bool,), self.name)
|
||
self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel axis', self.name)
|
||
self.init_prim_io_names(
|
||
inputs=['dout', 'x', 'alpha', 'quant_max'], outputs=['dx', 'dalpha'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape, alpha_shape, quant_max_shape):
|
||
validator.check("dout shape", dout_shape, "x_shape", x_shape, Rel.EQ, self.name)
|
||
validator.check_int(len(alpha_shape), 1, Rel.GE, "alpha rank", self.name)
|
||
validator.check_int(len(quant_max_shape), 1, Rel.GE, "quant max rank", self.name)
|
||
return dout_shape, dout_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, alpha_type, quant_max_type):
|
||
valid_dtypes = (mstype.float16, mstype.float32)
|
||
tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=self.name),
|
||
("dout", "x", "alpha", "quant_max"),
|
||
(dout_type, x_type, alpha_type, quant_max_type)))
|
||
return dout_type, dout_type
|
||
|
||
|
||
class FakeLearnedScaleQuantPerChannelGradDReduce(PrimitiveWithInfer):
|
||
r"""
|
||
Performs alpha grad reduce of FakeLearnedScaleQuantPerChannel operation.
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, channel_axis=1):
|
||
from mindspore.ops._op_impl._custom_op import fake_learned_scale_quant_perchannel_grad_reduce
|
||
self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel axis', self.name)
|
||
self.init_prim_io_names(
|
||
inputs=['dout_alpha'], outputs=['dalpha'])
|
||
|
||
def infer_shape(self, dout_alpha_shape):
|
||
return (dout_alpha_shape[self.channel_axis],)
|
||
|
||
def infer_dtype(self, dout_alpha_type):
|
||
valid_dtypes = (mstype.float16, mstype.float32)
|
||
validator.check_tensor_dtype_valid("dout_alpha", dout_alpha_type, valid_dtypes, self.name)
|
||
return dout_alpha_type
|
||
|
||
|
||
class FakeQuantWithMinMaxVars(PrimitiveWithInfer):
|
||
r"""
|
||
Fake-quantize the input by min and max.
|
||
|
||
Args:
|
||
num_bits (int): Quantization bitwidth; between 2 and 16. Default: 8.
|
||
narrow_range (bool): Whether the quantization algorithm uses narrow range or not.
|
||
if True, the quantization range is [0, 2^num_bits-1]. Otherwise, the quantization
|
||
range is [1, 2^num_bits-1]. Default: False.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - Float32 tensor representing the shape of the output tensor.
|
||
- **min** (Tensor) - Value of the min range of the input data x.
|
||
- **max** (Tensor) - Value of the max range of the input data x.
|
||
|
||
Outputs:
|
||
- Tensor, the data type and shape of output tensor is the same as input x.
|
||
|
||
Examples:
|
||
>>> 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 = FakeQuantWithMinMaxVars(num_bits=8, narrow_range=False)(
|
||
... input_tensor, min_tensor, max_tensor)
|
||
>>> output_tensor # shape: (3, 16, 5, 5) data type: mstype.float32
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
narrow_range=False):
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.num_bits = validator.check_int_range(self.num_bits, 2, 16, Rel.INC_BOTH, 'num_bits', self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
|
||
def check_broadcast(self, min_shape, input_shape):
|
||
shape_val = 1
|
||
for shape in input_shape:
|
||
shape_val = shape_val * shape
|
||
if min_shape[0] > 1 and min_shape[0] != shape_val:
|
||
raise ValueError(f"For '{self.name}', the shape of \'min\' cannot broadcast to the shape of \'x\'.")
|
||
|
||
def infer_shape(self, x_shape, min_shape, max_shape):
|
||
validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
|
||
validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
|
||
validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
|
||
self.check_broadcast(min_shape, x_shape)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, min_type, max_type):
|
||
tuple(map(partial(validator.check_tensor_dtype_valid,
|
||
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
|
||
("x", "min", "max"),
|
||
(x_type, min_type, max_type)))
|
||
return x_type
|
||
|
||
|
||
class FakeQuantWithMinMaxVarsGradient(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of FakeQuantWithMinMaxVars operation.
|
||
|
||
Args:
|
||
num_bits (int): Quantization bitwidth; between 2 and 16, inclusive. Default: 8.
|
||
narrow_range (bool): Whether the quantization algorithm uses narrow range or not.
|
||
if True, the quantization range is [0, 2^num_bits-1]. Otherwise, the quantization
|
||
range is [1, 2^num_bits-1]. Default: False.
|
||
|
||
Inputs:
|
||
- **gradients** (Tensor) - The gradient above the FakeQuantWithMinMaxVars.
|
||
- **x** (Tensor) - Float32 tensor representing the shape of the output tensor.
|
||
- **min** (Tensor) - Value of the min range of the input data x.
|
||
- **max** (Tensor) - Value of the max range of the input data x.
|
||
|
||
Outputs:
|
||
- **backprops_wrt_x** (Tensor) - The gradient of input x, with the same shape and date type as input x.
|
||
- **backprops_wrt_min** (Tensor) - The gradient of input min, with the same shape and date type as input min.
|
||
- **backprops_wrt_max** (Tensor) - The gradient of input max, with the same shape and date type as input max.
|
||
|
||
Examples:
|
||
>>> gradients = 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)
|
||
>>> max_tensor = Tensor(np.array([6]), mstype.float32)
|
||
>>> x_gradient, min_gradient, max_gradient = FakeQuantWithMinMaxVarsGradient(num_bits=8,narrow_range=False)
|
||
... (gradients, input_tensor, min_tensor, max_tensor)
|
||
>>> x_gradient # shape: (3, 16, 5, 5) data type: mstype.float32
|
||
>>> min_gradient # shape: (1,) data type: mstype.float32
|
||
>>> max_gradient # shape: (1,) data type: mstype.float32
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
narrow_range=False):
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.num_bits = validator.check_int_range(self.num_bits, 2, 16, Rel.INC_BOTH, 'num_bits', self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
|
||
def check_broadcast(self, min_shape, input_shape):
|
||
shape_val = 1
|
||
for shape in input_shape:
|
||
shape_val = shape_val * shape
|
||
if min_shape[0] > 1 and min_shape[0] != shape_val:
|
||
raise ValueError(f"For '{self.name}', the shape of \'min\' cannot broadcast to the shape of \'x\'.")
|
||
|
||
def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
|
||
validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
|
||
validator.check("dout shape", dout_shape, "x shape", x_shape, Rel.EQ, self.name)
|
||
validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
|
||
validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
|
||
self.check_broadcast(min_shape, x_shape)
|
||
return x_shape, min_shape, max_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, min_type, max_type):
|
||
tuple(map(partial(validator.check_tensor_dtype_valid,
|
||
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
|
||
('dout', "x", "min", "max"),
|
||
(dout_type, x_type, min_type, max_type)))
|
||
return x_type, min_type, max_type
|
||
|
||
|
||
class FakeQuantWithMinMaxVarsPerChannel(PrimitiveWithInfer):
|
||
r"""
|
||
Fake-quantize the input and one of shape: [d], [b, d], [b, h, w, d] by per-channel min and max
|
||
|
||
Args:
|
||
num_bits (int): Quantization bitwidth; between 2 and 16, inclusive. Default: 8.
|
||
narrow_range (bool): Whether the quantization algorithm uses narrow range or not.
|
||
if True, the quantization range is [0, 2^num_bits-1]. Otherwise, the quantization
|
||
range is [1, 2^num_bits-1]. Default: False.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - Float32 tensor representing the shape of the output tensor.
|
||
- **min** (Tensor) - Value of the min range of the input data x.
|
||
- **max** (Tensor) - Value of the max range of the input data x.
|
||
|
||
Outputs:
|
||
- Tensor, the data type and shape of output tensor is the same as input x.
|
||
|
||
Examples:
|
||
>>> input_tensor = Tensor(np.random.rand(3, 16, 3, 4), mstype.float32)
|
||
>>> min_tensor = Tensor(np.array([-6, -1, -2, -3]), mstype.float32)
|
||
>>> max_tensor = Tensor(np.array([6, 1, 2, 3]), mstype.float32)
|
||
>>> output_tensor = FakeQuantWithMinMaxVars(num_bits=8, narrow_range=False)(
|
||
... input_tensor, min_tensor, max_tensor)
|
||
>>> output_tensor # shape: (3, 16, 3, 4) data type: mstype.float32
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
narrow_range=False):
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.num_bits = validator.check_int_range(self.num_bits, 2, 16, Rel.INC_BOTH, 'num_bits', self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
|
||
def infer_shape(self, x_shape, min_shape, max_shape):
|
||
validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
|
||
validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
|
||
validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
|
||
validator.check("min shape", min_shape[0], "x shape", x_shape[-1], Rel.EQ, self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, min_type, max_type):
|
||
tuple(map(partial(validator.check_tensor_dtype_valid,
|
||
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
|
||
("x", "min", "max"),
|
||
(x_type, min_type, max_type)))
|
||
return x_type
|
||
|
||
|
||
class FakeQuantWithMinMaxVarsPerChannelGradient(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of FakeQuantWithMinMaxVars operation.
|
||
|
||
Args:
|
||
num_bits (int): Quantization bitwidth; between 2 and 16, inclusive. Default: 8.
|
||
narrow_range (bool): Whether the quantization algorithm uses narrow range or not.
|
||
if True, the quantization range is [0, 2^num_bits-1]. Otherwise, the quantization
|
||
range is [1, 2^num_bits-1]. Default: False.
|
||
|
||
Inputs:
|
||
- **gradients** (Tensor) - The gradient above the FakeQuantWithMinMaxVars.
|
||
- **x** (Tensor) - Float32 tensor representing the shape of the output tensor.
|
||
- **min** (Tensor) - Value of the min range of the input data x.
|
||
- **max** (Tensor) - Value of the max range of the input data x.
|
||
|
||
Outputs:
|
||
- **backprops_wrt_x** (Tensor) - The gradient of input x, with the same shape and date type as input x.
|
||
- **backprops_wrt_min** (Tensor) - The gradient of input min, with the same shape and date type as input min.
|
||
- **backprops_wrt_max** (Tensor) - The gradient of input max, with the same shape and date type as input max.
|
||
|
||
Examples:
|
||
>>> gradients = Tensor(np.random.rand(3, 16, 3, 4), mstype.float32)
|
||
>>> input_tensor = Tensor(np.random.rand(3, 16, 3, 4), mstype.float32)
|
||
>>> min_tensor = Tensor(np.array([-6, -1, -2, -3]), mstype.float32)
|
||
>>> max_tensor = Tensor(np.array([6, 1, 2, 3]), mstype.float32)
|
||
>>> x_gradient, min_gradient, max_gradient = FakeQuantWithMinMaxVarsPerChannelGradient(
|
||
... num_bits=8, narrow_range=False)(
|
||
... gradients, input_tensor, min_tensor, max_tensor)
|
||
>>> x_gradient # shape: (3, 16, 3, 4) data type: mstype.float32
|
||
>>> min_gradient # shape: (4,) data type: mstype.float32
|
||
>>> max_gradient # shape: (4,) data type: mstype.float32
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
narrow_range=False):
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.num_bits = validator.check_int_range(self.num_bits, 2, 16, Rel.INC_BOTH, 'num_bits', self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
|
||
def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
|
||
validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
|
||
validator.check("dout shape", dout_shape, "x shape", x_shape, Rel.EQ, self.name)
|
||
validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
|
||
validator.check_int(len(min_shape), 1, Rel.EQ, "min shape", self.name)
|
||
validator.check("min shape", min_shape[0], "x shape", x_shape[-1], Rel.EQ, self.name)
|
||
return x_shape, min_shape, max_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, min_type, max_type):
|
||
tuple(map(partial(validator.check_tensor_dtype_valid,
|
||
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
|
||
("dout", "x", "min", "max"),
|
||
(dout_type, x_type, min_type, max_type)))
|
||
return x_type, min_type, max_type
|
||
|
||
|
||
def _fake_quant_per_infer_dtype(prim_name, x_type, min_type, max_type):
|
||
if context.get_context('device_target') == "GPU":
|
||
valid_dtypes = (mstype.float32,)
|
||
else:
|
||
valid_dtypes = (mstype.float16, mstype.float32)
|
||
tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=prim_name),
|
||
("x", "min", "max"),
|
||
(x_type, min_type, max_type)))
|
||
return x_type
|
||
|
||
|
||
def _fake_quant_per_grad_infer_dtype(prim_name, dout_type, x_type, min_type, max_type):
|
||
if context.get_context('device_target') == "GPU":
|
||
valid_dtypes = (mstype.float32,)
|
||
else:
|
||
valid_dtypes = (mstype.float16, mstype.float32)
|
||
tuple(map(partial(validator.check_tensor_dtype_valid, valid_dtypes=valid_dtypes, prim_name=prim_name),
|
||
("dout", "x", "min", "max"),
|
||
(dout_type, x_type, min_type, max_type)))
|
||
return dout_type
|
||
|
||
|
||
class FakeQuantPerLayer(PrimitiveWithInfer):
|
||
r"""
|
||
Simulates the quantize and dequantize operations in training time.
|
||
|
||
Args:
|
||
num_bits (int) : Number bits for quantization aware. Default: 8.
|
||
ema (bool): Uses EMA algorithm update value min and max. Default: False.
|
||
ema_decay (int) : EMA algorithm decay parameter. Default: 0.999.
|
||
quant_delay (int): Quantilization delay parameter. Before delay step in training time not update
|
||
simulate quantization aware function. After delay step in training time begin simulate the aware
|
||
quantize function. Default: 0.
|
||
symmetric (bool): Whether the quantization algorithm is symmetric or not. Default: False.
|
||
narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
|
||
training (bool): Training the network or not. Default: True.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) : float32 Tensor representing the shape of the output tensor.
|
||
- **min** (Tensor) : Value of the min range of the input data x.
|
||
- **max** (Tensor) : Value of the max range of the input data x.
|
||
|
||
Outputs:
|
||
- Tensor: Simulates quantize tensor of x.
|
||
|
||
Examples:
|
||
>>> 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 = FakeQuantPerLayer(num_bits=8)(input_tensor, min_tensor, max_tensor)
|
||
"""
|
||
support_quant_bit = [4, 7, 8]
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
ema=False,
|
||
ema_decay=0.999,
|
||
quant_delay=0,
|
||
symmetric=False,
|
||
narrow_range=False,
|
||
training=True):
|
||
"""Initialize FakeQuantPerLayer OP"""
|
||
if context.get_context('device_target') == "Ascend":
|
||
from mindspore.ops._op_impl._custom_op import fake_quant_perlayer
|
||
if num_bits not in self.support_quant_bit:
|
||
raise ValueError(
|
||
f"For '{self.name}' attr \'num_bits\' is not support.")
|
||
if ema and not ema_decay:
|
||
raise ValueError(
|
||
f"For '{self.name}' attr \'ema\' and \'ema_decay\' should set together.")
|
||
|
||
self.ema = validator.check_value_type('ema', ema, (bool,), self.name)
|
||
self.symmetric = validator.check_value_type(
|
||
'symmetric', symmetric, (bool,), self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
self.training = validator.check_value_type('training', training, (bool,), self.name)
|
||
self.ema_decay = validator.check_float_range(ema_decay, 0, 1, Rel.INC_BOTH, 'ema_decay', self.name)
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.quant_delay = validator.check_non_negative_int(quant_delay, 'quant_delay', self.name)
|
||
self.init_prim_io_names(inputs=['x', 'min', 'max'],
|
||
outputs=['out'])
|
||
|
||
def infer_shape(self, x_shape, min_shape, max_shape):
|
||
validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
|
||
validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
|
||
validator.check_equal_int(len(min_shape), 1, "min shape", self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, min_type, max_type):
|
||
return _fake_quant_per_infer_dtype(self.name, x_type, min_type, max_type)
|
||
|
||
|
||
class FakeQuantPerLayerGrad(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of FakeQuantPerLayer operation.
|
||
|
||
Examples:
|
||
>>> 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)
|
||
>>> _max = Tensor(np.array([2]), mindspore.float32)
|
||
>>> result = fake_min_max_grad(dout, input_x, _min, _max)
|
||
"""
|
||
support_quant_bit = [4, 7, 8]
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
quant_delay=0,
|
||
symmetric=False,
|
||
narrow_range=False):
|
||
if context.get_context('device_target') == "Ascend":
|
||
from mindspore.ops._op_impl._custom_op import fake_quant_perlayer_grad
|
||
if num_bits not in self.support_quant_bit:
|
||
raise ValueError(
|
||
f"For '{self.name}' attr \'num_bits\' is not support.")
|
||
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.quant_delay = validator.check_value_type(
|
||
'quant_delay', quant_delay, (int,), self.name)
|
||
self.symmetric = validator.check_value_type(
|
||
'symmetric', symmetric, (bool,), self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
self.init_prim_io_names(
|
||
inputs=['dout', 'x', 'min', 'max'], outputs=['dx'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
|
||
validator.check("dout shape", dout_shape, "x shape",
|
||
x_shape, Rel.EQ, self.name)
|
||
validator.check("min shape", min_shape, "max shape",
|
||
max_shape, Rel.EQ, self.name)
|
||
validator.check_equal_int(len(min_shape), 1, "min shape", self.name)
|
||
return dout_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, min_type, max_type):
|
||
return _fake_quant_per_grad_infer_dtype(self.name, dout_type, x_type, min_type, max_type)
|
||
|
||
|
||
class FakeQuantPerChannel(PrimitiveWithInfer):
|
||
r"""
|
||
Simulates the quantize and dequantize operations in training time base on per channel.
|
||
|
||
Args:
|
||
num_bits (int) : Number bits to quantilization. Default: 8.
|
||
ema (bool): Uses EMA algorithm update tensor min and tensor max. Default: False.
|
||
ema_decay (int) : EMA algorithm decay parameter. Default: 0.999.
|
||
quant_delay (int): Quantilization delay parameter. Before delay step in training time not
|
||
update the weight data to simulate quantize operation. After delay step in training time
|
||
begin simulate the quantize operation. Default: 0.
|
||
symmetric (bool): Whether the quantization algorithm is symmetric or not. Default: False.
|
||
narrow_range (bool): Whether the quantization algorithm uses narrow range or not. Default: False.
|
||
training (bool): Training the network or not. Default: True.
|
||
channel_axis (int): Quantization by channel axis. Ascend backend only supports 0 or 1. Default: 1.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) : 4-D float32 Tensor representing the shape of the output tensor.
|
||
- **min** (int, float) : Value of the min range of the input data.
|
||
- **max** (int, float) : Value of the max range of the input data.
|
||
|
||
Outputs:
|
||
- Tensor, has the same type as input.
|
||
|
||
Examples:
|
||
>>> 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)
|
||
>>> result = fake_quant(input_x, _min, _max)
|
||
"""
|
||
support_quant_bit = [4, 7, 8]
|
||
ascend_support_x_rank = [2, 4]
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
ema=False,
|
||
ema_decay=0.999,
|
||
quant_delay=0,
|
||
symmetric=False,
|
||
narrow_range=False,
|
||
training=True,
|
||
channel_axis=1):
|
||
"""Initialize FakeQuantPerChannel OP"""
|
||
self.is_ascend = context.get_context('device_target') == "Ascend"
|
||
if self.is_ascend:
|
||
from mindspore.ops._op_impl._custom_op import fake_quant_perchannel
|
||
if num_bits not in self.support_quant_bit:
|
||
raise ValueError(
|
||
f"For '{self.name}' Attr \'num_bits\' is not support.")
|
||
if ema and not ema_decay:
|
||
raise ValueError(
|
||
f"For '{self.name}' attr \'ema\' and \'ema_decay\' should set together.")
|
||
|
||
self.ema = validator.check_value_type('ema', ema, (bool,), self.name)
|
||
self.symmetric = validator.check_value_type(
|
||
'symmetric', symmetric, (bool,), self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
self.training = validator.check_value_type(
|
||
'training', training, (bool,), self.name)
|
||
self.ema_decay = validator.check_float_range(ema_decay, 0, 1, Rel.INC_BOTH, 'ema_decay', self.name)
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.quant_delay = validator.check_non_negative_int(quant_delay, 'quant_delay', self.name)
|
||
if self.is_ascend:
|
||
self.channel_axis = validator.check_int_range(channel_axis, 0, 1, Rel.INC_BOTH, 'channel_axis', self.name)
|
||
else:
|
||
self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel_axis', self.name)
|
||
self.init_prim_io_names(inputs=['x', 'min', 'max'], outputs=['out'])
|
||
|
||
def infer_shape(self, x_shape, min_shape, max_shape):
|
||
if self.is_ascend and len(x_shape) not in self.ascend_support_x_rank:
|
||
raise ValueError(f"For '{self.name}' x rank should be in '{self.ascend_support_x_rank}'")
|
||
if not self.is_ascend:
|
||
validator.check_int(len(x_shape), 1, Rel.GE, "x rank", self.name)
|
||
if len(x_shape) == 1:
|
||
self.channel_axis = 0
|
||
validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name)
|
||
validator.check_equal_int(min_shape[0], x_shape[self.channel_axis], "min shape", self.name)
|
||
validator.check_equal_int(max_shape[0], x_shape[self.channel_axis], "max shape", self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, min_type, max_type):
|
||
return _fake_quant_per_infer_dtype(self.name, x_type, min_type, max_type)
|
||
|
||
|
||
class FakeQuantPerChannelGrad(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of FakeQuantPerChannel operation.
|
||
|
||
Examples:
|
||
>>> 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)
|
||
>>> _max = Tensor(np.random.randint(-2, 8, (2, 3, 4)), mindspore.float32)
|
||
>>> result = fqmmpc_grad(dout, input_x, _min, _max)
|
||
"""
|
||
support_quant_bit = [4, 7, 8]
|
||
|
||
@prim_attr_register
|
||
def __init__(self,
|
||
num_bits=8,
|
||
quant_delay=0,
|
||
symmetric=False,
|
||
narrow_range=False,
|
||
channel_axis=1):
|
||
"""Initialize FakeQuantPerChannelGrad Fill"""
|
||
if context.get_context('device_target') == "Ascend":
|
||
from mindspore.ops._op_impl._custom_op import fake_quant_perchannel_grad
|
||
if num_bits not in self.support_quant_bit:
|
||
raise ValueError(
|
||
f"For '{self.name}' attr \'num_bits\' is not support.")
|
||
|
||
self.num_bits = validator.check_positive_int(num_bits, 'num_bits', self.name)
|
||
self.quant_delay = validator.check_value_type(
|
||
'quant_delay', quant_delay, (int,), self.name)
|
||
self.symmetric = validator.check_value_type(
|
||
'symmetric', symmetric, (bool,), self.name)
|
||
self.narrow_range = validator.check_value_type(
|
||
'narrow_range', narrow_range, (bool,), self.name)
|
||
self.channel_axis = validator.check_non_negative_int(channel_axis, 'channel axis', self.name)
|
||
self.init_prim_io_names(
|
||
inputs=['dout', 'x', 'min', 'max'], outputs=['dx'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape, min_shape, max_shape):
|
||
validator.check("dout shape", dout_shape, "x shape", x_shape)
|
||
validator.check("min shape", min_shape, "max shape", max_shape)
|
||
return dout_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, min_type, max_type):
|
||
return _fake_quant_per_grad_infer_dtype(self.name, dout_type, x_type, min_type, max_type)
|
||
|
||
|
||
class BatchNormFold(PrimitiveWithInfer):
|
||
"""
|
||
Batch Normalization folded.
|
||
|
||
Args:
|
||
momentum (float): Momentum value must be [0, 1]. Default: 0.9.
|
||
epsilon (float): A small float number to avoid dividing by 0. 1e-5 if dtype in
|
||
float32 else 1e-3. Default: 1e-5.
|
||
is_training (bool): In training mode set True, else set False. Default: True.
|
||
freeze_bn (int): Delay in steps at which computation switches from regular batch
|
||
norm to frozen mean and std. Default: 0.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - Tensor of shape :math:`(N, C)`.
|
||
- **mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **variance** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **global_step** (Tensor) - Tensor to record current global step.
|
||
|
||
Outputs:
|
||
Tuple of 4 Tensor, the normalized input and the updated parameters.
|
||
|
||
- **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
|
||
Examples:
|
||
>>> batch_norm_fold = P.BatchNormFold()
|
||
>>> input_x = Tensor(np.array([1, 2, -1, -2, -2, 1]).reshape(2, 3), mindspore.float32)
|
||
>>> mean = Tensor(np.array([0.5, -1, 1,]), mindspore.float32)
|
||
>>> variance = Tensor(np.array([0.36, 0.4, 0.49]), mindspore.float32)
|
||
>>> global_step = Tensor(np.arange(6), mindspore.int32)
|
||
>>> batch_mean, batch_std, running_mean, running_std = batch_norm_fold(input_x, mean, variance, global_step)
|
||
"""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, momentum=0.9, epsilon=1e-5, is_training=True, freeze_bn=0):
|
||
"""Initialize batch norm fold layer"""
|
||
self.momentum = validator.check_float_range(momentum, 0, 1, Rel.INC_BOTH, 'momentum', self.name)
|
||
self.epsilon = validator.check_positive_float(epsilon, 'epsilon', self.name)
|
||
self.is_training = validator.check_value_type('is_training', is_training, (bool,), self.name)
|
||
self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
|
||
|
||
self.init_prim_io_names(inputs=['x', 'mean', 'variance', 'global_step'],
|
||
outputs=['batch_mean', 'batch_std', 'running_mean', 'running_std'])
|
||
|
||
def infer_shape(self, x_shape, mean_shape, variance_shape, global_step_shape):
|
||
validator.check("mean shape", mean_shape, "gamma_shape", variance_shape, Rel.EQ, self.name)
|
||
validator.check("mean_shape[0]", mean_shape[0], "input channel", x_shape[self.channel_axis], Rel.EQ, self.name)
|
||
validator.check_equal_int(len(global_step_shape), 1, "global step shape len", self.name)
|
||
return mean_shape, mean_shape, mean_shape, mean_shape
|
||
|
||
def infer_dtype(self, x_type, mean_type, variance_type, global_step_type):
|
||
validator.check("input type", x_type, "mean type", mean_type)
|
||
validator.check("input type", x_type, "variance type", variance_type)
|
||
args = {"x": x_type, "mean": mean_type, "variance": variance_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
validator.check_tensor_dtype_valid("global_step", global_step_type, (mstype.int32,), self.name)
|
||
return x_type, x_type, x_type, x_type
|
||
|
||
|
||
class BatchNormFoldGrad(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of BatchNormFold operation.
|
||
|
||
Examples:
|
||
>>> batch_norm_fold_grad = ops.BatchNormFoldGrad()
|
||
>>> d_batch_mean = Tensor(np.random.randint(-2., 2., (1, 2, 2, 3)), mindspore.float32)
|
||
>>> d_batch_std = Tensor(np.random.randn(1, 2, 2, 3), mindspore.float32)
|
||
>>> input_x = Tensor(np.random.randint(0, 256, (4, 1, 4, 6)), mindspore.float32)
|
||
>>> batch_mean = Tensor(np.random.randint(-8., 8., (1, 2, 2, 3)), mindspore.float32)
|
||
>>> batch_std = Tensor(np.random.randint(0, 12, (1, 2, 2, 3)), mindspore.float32)
|
||
>>> global_step = Tensor([2], mindspore.int32)
|
||
>>> result = batch_norm_fold_grad(d_batch_mean, d_batch_std, input_x, batch_mean, batch_std, global_step)
|
||
"""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, epsilon=1e-5, is_training=True, freeze_bn=0):
|
||
"""Initialize BatchNormGrad layer"""
|
||
self.is_training = validator.check_value_type('is_training', is_training, (bool,), self.name)
|
||
self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
|
||
self.epsilon = validator.check_positive_float(epsilon, 'epsilon', self.name)
|
||
self.init_prim_io_names(inputs=['d_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std', 'global_step'],
|
||
outputs=['dx'])
|
||
|
||
def infer_shape(self, d_batch_mean_shape, d_batch_std_shape, x_shape, batch_mean_shape, batch_std_shape,
|
||
global_step_shape):
|
||
validator.check("d_batch_mean shape", d_batch_mean_shape,
|
||
"d_batch_std shape", d_batch_std_shape, Rel.EQ, self.name)
|
||
validator.check("d_batch_mean shape", d_batch_mean_shape,
|
||
"batch_mean shape", batch_mean_shape, Rel.EQ, self.name)
|
||
validator.check("d_batch_mean shape", d_batch_mean_shape,
|
||
"batch_std shape", batch_std_shape, Rel.EQ, self.name)
|
||
validator.check("d_batch_mean_shape[0]", d_batch_mean_shape[0],
|
||
"input channel", x_shape[self.channel_axis], Rel.EQ, self.name)
|
||
validator.check_equal_int(len(global_step_shape), 1, "global step shape len", self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, d_batch_mean_type, d_batch_std_type, x_type, batch_mean_type, batch_std_type,
|
||
global_step_type):
|
||
args = {"input": x_type, "d_batch_mean": d_batch_mean_type, "d_batch_std": d_batch_std_type,
|
||
"batch_mean": batch_mean_type, "batch_std": batch_std_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
validator.check_tensor_dtype_valid("global_step", global_step_type, (mstype.int32,), self.name)
|
||
return x_type
|
||
|
||
|
||
class CorrectionMul(PrimitiveWithInfer):
|
||
"""
|
||
Scales the weights with a correction factor to the long term statistics
|
||
prior to quantization. This ensures that there is no jitter in the quantized weights
|
||
due to batch to batch variation.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - Tensor of shape :math:`(N, C)`.
|
||
- **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
|
||
Outputs:
|
||
- **out** (Tensor) - Tensor has the same shape as x.
|
||
|
||
Examples:
|
||
>>> correction_mul = ops.CorrectionMul()
|
||
>>> input_x = Tensor(np.random.randint(-8, 12, (3, 4)), mindspore.float32)
|
||
>>> batch_std = Tensor(np.array([1.5, 3, 2]), mindspore.float32)
|
||
>>> running_std = Tensor(np.array([2, 1.2, 0.5]), mindspore.float32)
|
||
>>> out = correction_mul(input_x, batch_std, running_std)
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, channel_axis=0):
|
||
"""Initialize correction mul layer"""
|
||
if context.get_context('device_target') == "Ascend":
|
||
from mindspore.ops._op_impl._custom_op import correction_mul
|
||
self.channel_axis = channel_axis
|
||
self.init_prim_io_names(inputs=['x', 'batch_std', 'running_std'],
|
||
outputs=['out'])
|
||
|
||
def infer_shape(self, x_shape, batch_std_shape, running_std_shape):
|
||
validator.check("batch_std shape", batch_std_shape, "running_std shape", running_std_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std_shape[0]", batch_std_shape[0], "x_shape channel size", x_shape[self.channel_axis],
|
||
Rel.EQ, self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, batch_std_type, running_std_type):
|
||
args = {"x": x_type, "batch_std": batch_std_type, "running_std": running_std_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
return x_type
|
||
|
||
|
||
class CorrectionMulGrad(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of CorrectionMul operation.
|
||
|
||
Examples:
|
||
>>> correction_mul_grad = ops.CorrectionMulGrad()
|
||
>>> dout = Tensor(np.array([1.5, -2.2, 0.7, -3, 1.6, 2.8]).reshape(2, 1, 1, 3), mindspore.float32)
|
||
>>> input_x = Tensor(np.random.randint(0, 256, (2, 1, 1, 3)), mindspore.float32)
|
||
>>> gamma = Tensor(np.array([0.2, -0.2, 2.5, -1.]).reshape(2, 1, 2), mindspore.float32)
|
||
>>> running_std = Tensor(np.array([1.2, 0.1, 0.7, 2.3]).reshape(2, 1, 2), mindspore.float32)
|
||
>>> result = correction_mul_grad(dout, input_x, gamma, running_std)
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, channel_axis=0):
|
||
"""Initialize correction mul layer"""
|
||
if context.get_context('device_target') == "Ascend":
|
||
from mindspore.ops._op_impl._custom_op import correction_mul_grad
|
||
self.channel_axis = channel_axis
|
||
self.init_prim_io_names(inputs=['dout', 'x', 'gamma', 'running_std'],
|
||
outputs=['dx', 'mul_dx'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape, gamma_shape, running_std_shape):
|
||
validator.check("dout shape", dout_shape, "x_shape x", x_shape, Rel.EQ, self.name)
|
||
validator.check("gamma_shape[0]", gamma_shape[0], "dout channel size", dout_shape[self.channel_axis],
|
||
Rel.EQ, self.name)
|
||
validator.check("running_std_shape[0]", running_std_shape[0],
|
||
"dout channel size", dout_shape[self.channel_axis], Rel.EQ, self.name)
|
||
if context.get_context('device_target') == "Ascend":
|
||
return x_shape, x_shape
|
||
return x_shape, gamma_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, gamma_type, running_std_type):
|
||
args = {"dout": dout_type, "x": x_type, "gamma": gamma_type, "running_std": running_std_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
if context.get_context('device_target') == "Ascend":
|
||
return x_type, x_type
|
||
return x_type, gamma_type
|
||
|
||
|
||
class CorrectionMulGradReduce(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad reduce of CorrectionMul operation.
|
||
|
||
Examples:
|
||
>>> correction_mul_grad_rd = ops.CorrectionMulGradReduce()
|
||
>>> dout = Tensor(np.array([1.5, -2.2, 0.7, -3, 1.6, 2.8]).reshape(2, 1, 1, 3), mindspore.float32)
|
||
>>> input_x = Tensor(np.random.randint(0, 256, (2, 1, 1, 3)), mindspore.float32)
|
||
>>> gamma = Tensor(np.array([0.2, -0.2, 2.5, -1.]).reshape(2, 1, 2), mindspore.float32)
|
||
>>> running_std = Tensor(np.array([1.2, 0.1, 0.7, 2.3]).reshape(2, 1, 2), mindspore.float32)
|
||
>>> result = correction_mul_grad_rd(dout, input_x, gamma, running_std)
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, channel_axis=0):
|
||
"""Initialize correction mul reduce layer"""
|
||
if context.get_context('device_target') == "Ascend":
|
||
from mindspore.ops._op_impl._custom_op import correction_mul_grad
|
||
self.channel_axis = channel_axis
|
||
self.init_prim_io_names(inputs=['mul_dx'],
|
||
outputs=['d_gamma'])
|
||
|
||
def infer_shape(self, mul_dx_shape):
|
||
return [mul_dx_shape[self.channel_axis]]
|
||
|
||
def infer_dtype(self, mul_dx_type):
|
||
return mul_dx_type
|
||
|
||
|
||
class BatchNormFold2(PrimitiveWithInfer):
|
||
"""
|
||
Scales the bias with a correction factor to the long term statistics
|
||
prior to quantization. This ensures that there is no jitter in the quantized bias
|
||
due to batch to batch variation.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - Tensor of shape :math:`(N, C)`.
|
||
- **beta** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **gamma** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **global_step** (Tensor) - Tensor to record current global step.
|
||
|
||
Outputs:
|
||
- **y** (Tensor) - Tensor has the same shape as x.
|
||
|
||
Examples:
|
||
>>> batch_norm_fold2 = ops.BatchNormFold2()
|
||
>>> input_x = Tensor(np.random.randint(-6, 6, (4, 3)), mindspore.float32)
|
||
>>> beta = Tensor(np.array([0.2, -0.1, 0.25]), mindspore.float32)
|
||
>>> gamma = Tensor(np.array([-0.1, -0.25, 0.1]), mindspore.float32)
|
||
>>> batch_std = Tensor(np.array([0.1, 0.2, 0.1]), mindspore.float32)
|
||
>>> batch_mean = Tensor(np.array([0, 0.05, 0.2]), mindspore.float32)
|
||
>>> running_std = Tensor(np.array([0.1, 0.1, 0.3]), mindspore.float32)
|
||
>>> running_mean = Tensor(np.array([-0.1, 0, -0.1]), mindspore.float32)
|
||
>>> global_step = Tensor(np.random.randint(1, 8, (8, )), mindspore.int32)
|
||
>>> result = batch_norm_fold2(input_x, beta, gamma, batch_std, batch_mean,
|
||
>>> running_std, running_mean, global_step)
|
||
"""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, freeze_bn=0):
|
||
"""Initialize conv2d fold layer"""
|
||
self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
|
||
self.init_prim_io_names(inputs=['x', 'beta', 'gamma', 'batch_std', 'batch_mean',
|
||
'running_std', 'running_mean', 'global_step'],
|
||
outputs=['y'])
|
||
|
||
def infer_shape(self, x_shape, beta_shape, gamma_shape, batch_std_shape, running_std_shape, batch_mean_shape,
|
||
running_mean_shape, global_step_shape):
|
||
validator.check("batch_std shape", batch_std_shape, "running_std shape", running_std_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "batch_mean shape", batch_mean_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "beta shape", beta_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "running_mean shape", running_mean_shape,
|
||
Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "batch_mean shape", gamma_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std_shape[0]", batch_std_shape[0], "x_shape channel size", x_shape[self.channel_axis],
|
||
Rel.EQ, self.name)
|
||
validator.check_equal_int(len(global_step_shape), 1, "global step shape len", self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, beta_type, gamma_type, batch_std_type, running_std_type, batch_mean_type,
|
||
running_mean_type, global_step_type):
|
||
args = {"batch_std": batch_std_type, "running_std": running_std_type, "batch_mean": batch_mean_type,
|
||
"beta": beta_type, "running_mean": running_mean_type, "gamma": gamma_type, "x": x_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
validator.check_tensor_dtype_valid("global_step", global_step_type, (mstype.int32,), self.name)
|
||
return x_type
|
||
|
||
|
||
class BatchNormFold2Grad(PrimitiveWithInfer):
|
||
r"""
|
||
Performs grad of BatchNormFold2 operation.
|
||
|
||
Examples:
|
||
>>> bnf2_grad = ops.BatchNormFold2Grad()
|
||
>>> input_x = Tensor(np.arange(3*3*12*12).reshape(6, 3, 6, 12), mindspore.float32)
|
||
>>> dout = Tensor(np.random.randint(-32, 32, (6, 3, 6, 12)), mindspore.float32)
|
||
>>> gamma = Tensor(np.random.randint(-4, 4, (3, 1, 1, 2)), mindspore.float32)
|
||
>>> batch_std = Tensor(np.random.randint(0, 8, (3, 1, 1, 2)), mindspore.float32)
|
||
>>> batch_mean = Tensor(np.random.randint(-6, 6, (3, 1, 1, 2)), mindspore.float32)
|
||
>>> running_std = Tensor(np.linspace(0, 2, 6).reshape(3, 1, 1, 2), mindspore.float32)
|
||
>>> running_mean = Tensor(np.random.randint(-3, 3, (3, 1, 1, 2)), mindspore.float32)
|
||
>>> global_step = Tensor(np.array([-2]), mindspore.int32)
|
||
>>> result = bnf2_grad(dout, input_x, gamma, batch_std, batch_mean, running_std, running_mean, global_step)
|
||
"""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, freeze_bn=0):
|
||
"""Initialize MulFold layer"""
|
||
self.freeze_bn = freeze_bn
|
||
self.init_prim_io_names(inputs=['dout', 'x', 'gamma',
|
||
'batch_std', 'batch_mean',
|
||
'running_std', 'running_mean', 'global_step'],
|
||
outputs=['d_batch_std', 'd_batch_mean', 'd_beta', 'd_gamma', 'dx'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape, gamma_shape,
|
||
batch_std_shape, batch_mean_shape,
|
||
running_std_shape, running_mean_shape, global_step_shape):
|
||
validator.check("batch_std shape", batch_std_shape, "batch_mean shape", batch_mean_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "running_std shape", running_std_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "running_mean shape", running_mean_shape,
|
||
Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "gamma shape", gamma_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std size", batch_std_shape[0], "dout channel size", dout_shape[self.channel_axis],
|
||
Rel.EQ, self.name)
|
||
validator.check_equal_int(len(global_step_shape), 1, "global step shape len", self.name)
|
||
return gamma_shape, gamma_shape, gamma_shape, gamma_shape, x_shape
|
||
|
||
def infer_dtype(self, dout_type, x_type, gamma_type,
|
||
batch_std_type, batch_mean_type,
|
||
running_std_type, running_mean_type, global_step_type):
|
||
validator.check("batch_std type", batch_std_type,
|
||
"batch_mean type", batch_mean_type)
|
||
validator.check("batch_std type", batch_std_type,
|
||
"gamma type", gamma_type)
|
||
validator.check("batch_std type", batch_std_type,
|
||
"running_std type", running_std_type)
|
||
validator.check("batch_std type", batch_std_type,
|
||
"running_mean type", running_mean_type)
|
||
validator.check("batch_std_type", batch_std_type,
|
||
"dout type", dout_type)
|
||
args = {"batch_std": batch_std_type, "batch_mean": batch_mean_type, "gamma": gamma_type,
|
||
"running_std": running_std_type, "running_mean": running_mean_type, "dout": dout_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
validator.check_tensor_dtype_valid("global_step", global_step_type, (mstype.int32,), self.name)
|
||
return gamma_type, gamma_type, gamma_type, gamma_type, gamma_type
|
||
|
||
|
||
class BatchNormFoldD(PrimitiveWithInfer):
|
||
"""Performs grad of _BatchNormFold operation."""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, momentum=0.9, epsilon=1e-5, is_training=True, freeze_bn=0):
|
||
"""Initialize _BatchNormFold layer"""
|
||
from mindspore.ops._op_impl._custom_op import batchnorm_fold
|
||
self.momentum = validator.check_float_range(momentum, 0, 1, Rel.INC_BOTH, 'momentum', self.name)
|
||
self.epsilon = validator.check_positive_float(epsilon, 'epsilon', self.name)
|
||
self.is_training = validator.check_value_type('is_training', is_training, (bool,), self.name)
|
||
self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
|
||
self.data_format = "NCHW"
|
||
self.init_prim_io_names(inputs=['x', 'x_sum', 'x_square_sum', 'mean', 'variance'],
|
||
outputs=['batch_mean', 'batch_std', 'running_mean', 'running_std',
|
||
'mean_updated', 'variance_updated'])
|
||
|
||
def infer_shape(self, x_shape, x_sum_shape, x_square_sum_shape, mean_shape, variance_shape):
|
||
validator.check("mean shape", mean_shape, "gamma_shape", variance_shape, Rel.EQ, self.name)
|
||
validator.check("mean_shape[0]", mean_shape[0], "input channel", x_shape[1], Rel.EQ, self.name)
|
||
return x_shape, mean_shape, mean_shape, mean_shape, mean_shape, mean_shape, mean_shape
|
||
|
||
def infer_dtype(self, x_type, x_sum_type, x_square_sum_type, mean_type, variance_type):
|
||
validator.check("input type", x_type, "mean type", mean_type)
|
||
validator.check("input type", x_type, "variance type", variance_type)
|
||
args = {"x": x_type, "mean": mean_type, "variance": variance_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
return x_type, x_type, x_type, x_type, x_type, x_type, x_type
|
||
|
||
|
||
class BatchNormFoldGradD(PrimitiveWithInfer):
|
||
"""Performs grad of BatchNormFold operation."""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, epsilon=1e-5, is_training=True, freeze_bn=0):
|
||
"""Initialize _BatchNormFoldGrad layer"""
|
||
from mindspore.ops._op_impl._custom_op import batchnorm_fold_grad
|
||
self.epsilon = validator.check_positive_float(epsilon, 'epsilon', self.name)
|
||
self.is_training = validator.check_value_type('is_training', is_training, (bool,), self.name)
|
||
self.freeze_bn = validator.check_value_type('freeze_bn', freeze_bn, (int,), self.name)
|
||
self.init_prim_io_names(inputs=['d_batch_mean', 'd_batch_std', 'x', 'batch_mean', 'batch_std'],
|
||
outputs=['dx'])
|
||
|
||
def infer_shape(self, d_batch_mean_shape, d_batch_std_shape, x_shape, batch_mean_shape, batch_std_shape):
|
||
validator.check("d_batch_mean shape", d_batch_mean_shape, "d_batch_std shape", d_batch_std_shape)
|
||
validator.check("d_batch_mean shape", d_batch_mean_shape, "batch_mean shape", batch_mean_shape)
|
||
validator.check("d_batch_mean shape", d_batch_mean_shape, "batch_std shape", batch_std_shape)
|
||
validator.check("x_shape shape", d_batch_mean_shape[0], "input channel", x_shape[1])
|
||
return x_shape
|
||
|
||
def infer_dtype(self, d_batch_mean_type, d_batch_std_type, x_type, batch_mean_type, batch_std_type):
|
||
validator.check("input type", x_type, "d_batch_mean type", d_batch_mean_type)
|
||
validator.check("input type", x_type, "d_batch_std type", d_batch_std_type)
|
||
validator.check("input type", x_type, "batch_mean type", batch_mean_type)
|
||
validator.check("input type", x_type, "batch_std type", batch_std_type)
|
||
validator.check_tensor_dtype_valid("input type", x_type, (mstype.float16, mstype.float32), self.name)
|
||
return x_type
|
||
|
||
|
||
class BatchNormFold2D(PrimitiveWithInfer):
|
||
"""
|
||
Scales the bias with a correction factor to the long term statistics
|
||
prior to quantization. This ensures that there is no jitter in the quantized bias
|
||
due to batch to batch variation.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - Tensor of shape :math:`(N, C)`.
|
||
- **beta** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **gamma** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **batch_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **batch_mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_std** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **running_mean** (Tensor) - Tensor of shape :math:`(C,)`.
|
||
- **global_step** (Tensor) - Tensor to record current global step.
|
||
|
||
Outputs:
|
||
- **y** (Tensor) - Tensor has the same shape as x.
|
||
|
||
"""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, freeze_bn=0):
|
||
"""Initialize conv2d fold layer"""
|
||
from mindspore.ops._op_impl._custom_op import batchnorm_fold2
|
||
self.init_prim_io_names(inputs=['x', 'beta', 'gamma', 'batch_std', 'batch_mean', 'running_std'],
|
||
outputs=['y'])
|
||
|
||
def infer_shape(self, x_shape, beta_shape, gamma_shape, batch_std_shape, running_std_shape, batch_mean_shape):
|
||
validator.check("batch_std shape", batch_std_shape, "running_std shape", running_std_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "batch_mean shape", batch_mean_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "beta shape", beta_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "batch_mean shape", gamma_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std_shape[0]", batch_std_shape[0], "x_shape channel size", x_shape[self.channel_axis],
|
||
Rel.EQ, self.name)
|
||
return x_shape
|
||
|
||
def infer_dtype(self, x_type, beta_type, gamma_type, batch_std_type, running_std_type, batch_mean_type):
|
||
args = {"batch_std": batch_std_type, "running_std": running_std_type, "batch_mean": batch_mean_type,
|
||
"beta": beta_type, "gamma": gamma_type, "x": x_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
return x_type
|
||
|
||
|
||
class BatchNormFold2GradD(PrimitiveWithInfer):
|
||
"""Performs grad of BatchNormFold2 operation."""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, freeze_bn=False):
|
||
"""Initialize MulFold layer"""
|
||
from mindspore.ops._op_impl._custom_op import batchnorm_fold2_grad
|
||
self.freeze_bn = freeze_bn
|
||
self.init_prim_io_names(
|
||
inputs=['dout', 'dout_reduce', 'dout_x_reduce', 'gamma', 'batch_std', 'batch_mean', 'running_std'],
|
||
outputs=['d_batch_std', 'd_batch_mean', 'd_gamma', 'dx'])
|
||
|
||
def infer_shape(self, dout_shape, dout_reduce_shape, dout_x_reduce_shape, gamma_shape, batch_std_shape,
|
||
batch_mean_shape, running_std_shape):
|
||
validator.check("batch_std shape", batch_std_shape, "batch_mean shape", batch_mean_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "running_std shape", running_std_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std shape", batch_std_shape, "gamma shape", gamma_shape, Rel.EQ, self.name)
|
||
validator.check("batch_std size", batch_std_shape[0], "dout channel size", dout_shape[self.channel_axis],
|
||
Rel.EQ, self.name)
|
||
return gamma_shape, gamma_shape, gamma_shape, dout_shape
|
||
|
||
def infer_dtype(self, dout_type, dout_reduce_type, dout_x_reduce_type, gamma_type, batch_std_type,
|
||
batch_mean_type, running_std_type):
|
||
validator.check("batch_std type", batch_std_type,
|
||
"batch_mean type", batch_mean_type)
|
||
validator.check("batch_std type", batch_std_type,
|
||
"gamma type", gamma_type)
|
||
validator.check("batch_std type", batch_std_type,
|
||
"running_std type", running_std_type)
|
||
validator.check("batch_std_type", batch_std_type,
|
||
"dout type", dout_type)
|
||
args = {"batch_std": batch_std_type, "batch_mean": batch_mean_type, "gamma": gamma_type,
|
||
"running_std": running_std_type, "dout": dout_type}
|
||
validator.check_tensors_dtypes_same_and_valid(args, (mstype.float16, mstype.float32), self.name)
|
||
return gamma_type, gamma_type, gamma_type, gamma_type
|
||
|
||
|
||
class BatchNormFold2GradReduce(PrimitiveWithInfer):
|
||
"""Performs grad of CorrectionAddGrad operation."""
|
||
channel_axis = 1
|
||
|
||
@prim_attr_register
|
||
def __init__(self, freeze_bn=False):
|
||
"""Initialize MulFold layer"""
|
||
from mindspore.ops._op_impl._custom_op import batchnorm_fold2_grad_reduce
|
||
self.freeze_bn = freeze_bn
|
||
self.init_prim_io_names(inputs=['dout', 'x'],
|
||
outputs=['dout_reduce', 'dout_x_reduce'])
|
||
|
||
def infer_shape(self, dout_shape, x_shape):
|
||
validator.check("dout shape", dout_shape, "x shape", x_shape, Rel.EQ, self.name)
|
||
return (dout_shape[self.channel_axis],), (dout_shape[self.channel_axis],)
|
||
|
||
def infer_dtype(self, dout_type, x_type):
|
||
validator.check("dout type", dout_type, "x type", x_type)
|
||
return dout_type, dout_type
|
||
|
||
|
||
class ActsULQ(PrimitiveWithInfer):
|
||
"""
|
||
The ActsULQ(Activation universal learnable quantization).
|
||
|
||
Args:
|
||
fixed_min (bool): whether fix clamp min to zero.
|
||
num_bits (int): The bits num used for quantize.
|
||
|
||
Inputs:
|
||
- **x** (Tensor) - A Tensor of feature map. With float16 or float32 data type.
|
||
- **clamp_min** (Tensor) - A Tensor of clamp min with the same type as x.
|
||
- **clamp_max** (Tensor) - A Tensor of clamp max with the same type as x.
|
||
|
||
Outputs:
|
||
- **y** (Tensor) - A tensor of fake quant of feature map with the same type as `w`.
|
||
- **clamp_min** (Tensor) - A tensor of boolean masks if data in feature map >= clamp_min.
|
||
- **clamp_max** (Tensor) - A tensor of boolean masks if data in feature map <= clamp_max.
|
||
- **x_clamped_loss** (Tensor) - A tensor of clamped loss.
|
||
|
||
Examples:
|
||
>>> data_type = np.float32
|
||
>>> x= np.random.uniform(-10, 10, (32, 120)).astype(data_type)
|
||
>>> clamp_max = 0.7 * np.max(x)
|
||
>>> clamp_min = 0.7 * np.min(x)
|
||
>>> clamp_max = np.array([clamp_max], dtype=data_type)
|
||
>>> clamp_min = np.array([clamp_min], dtype=data_type)
|
||
>>> acts_ulq = Q.ActsULQ(fixed_mini=True, num_bits=8)
|
||
>>> quant_x, clamp_min_mask, clamp_max_mask, x_clamped_loss = acts_ulq(Tensor(x), Tensor( clamp_min),
|
||
Tensor(clamp_max))
|
||
"""
|
||
@prim_attr_register
|
||
def __init__(self, fixed_min=False, num_bits=8):
|
||
validator.check_value_type("fixed_min", fixed_min, [bool], self.name)
|
||
validator.check_value_type("num_bits", num_bits, [int], self.name)
|
||
validator.check_int(num_bits, 8, Rel.EQ, "value of num_bits", self.name)
|
||
|
||
def infer_shape(self, x_shape, clamp_min_shape, clamp_max_shape):
|
||
"""infer shape of primitive"""
|
||
validator.check_int(len(clamp_min_shape), len(x_shape), Rel.EQ, "dims of clamp_min", self.name)
|
||
validator.check_int(len(clamp_max_shape), len(x_shape), Rel.EQ, "dims of clamp_max", self.name)
|
||
|
||
x_shape_len = len(x_shape)
|
||
for i in range(x_shape_len):
|
||
validator.check_int(clamp_min_shape[i], 1, Rel.EQ, "dims of clamp_min", self.name)
|
||
validator.check_int(clamp_max_shape[i], 1, Rel.EQ, "dims of clamp_max", self.name)
|
||
|
||
return x_shape, x_shape, x_shape, x_shape
|
||
|
||
def infer_dtype(self, x_dtype, clamp_min_dtype, clamp_max_dtype):
|
||
"""infer dtype of primitive"""
|
||
valid_types = [mstype.float32, mstype.float16]
|
||
validator.check_tensor_dtype_valid("x", x_dtype, valid_types, self.name)
|
||
validator.check_tensor_dtype_valid("clamp_min", clamp_min_dtype, valid_types, self.name)
|
||
validator.check_tensor_dtype_valid("clamp_max", clamp_max_dtype, valid_types, self.name)
|
||
|
||
return x_dtype, mstype.bool_, mstype.bool_, x_dtype
|
||
|
||
|
||
class ActsULQInputGrad(PrimitiveWithInfer):
|
||
"""
|
||
The ActsULQInputGrad(grad of ActsULQ).
|
||
|
||
Inputs:
|
||
- **y_grad** (Tensor) - A Tensor of grad. With float16 or float32 data type.
|
||
|
||
Outputs:
|
||
- **x_grad** (Tensor) - A tensor of data grad with the same type as `y_grad`.
|
||
"""
|
||
@prim_attr_register
|
||
def __init__(self):
|
||
pass
|
||
|
||
def infer_shape(self, y_grad_shape, clamp_min_mask_shape, clamp_max_mask_shape):
|
||
return y_grad_shape
|
||
|
||
def infer_dtype(self, y_grad_type, clamp_min_mask_type, clamp_max_mask_type):
|
||
valid_types = [mstype.float32, mstype.float16]
|
||
validator.check_tensor_dtype_valid("y_grad", y_grad_type, valid_types, self.name)
|
||
return y_grad_type
|
||
|
||
|
||
class ActULQClampMinGrad(PrimitiveWithInfer):
|
||
"""
|
||
The ActULQClampMinGrad(Activation Universal Linear Quantization on Clamp Minimum Gradient)
|
||
|
||
Inputs:
|
||
- **y_grad** (Tensor) - A tensor of gradient, with float16 or float32 type.
|
||
- **clamp_min_mask** - A tensor of mask, only support int8 type.
|
||
- **x_clamped_loss** - A tensor of loss, with the same type as "y_grad".
|
||
|
||
Outputs:
|
||
- **clamp_min_grad** - A tensor of clamp minimum gradient, with the same type as "y_grad".
|
||
The length of tensor is 1.
|
||
|
||
Examples:
|
||
>>> data_type = np.float32
|
||
>>> y_grad = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
|
||
>>> clamp_min_mask = np.where(np.random.rand(32, 120) >= 0.5, 1, 0)
|
||
>>> x_clamped_loss = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
|
||
>>> act_ulq_clamp_min_grad = Q.ActULQClampMinGrad()
|
||
>>> clamp_min_grad = act_ulq_clamp_min_grad(Tensor(y_grad), Tensor(clamp_min_mask, mindspore.bool_),
|
||
Tensor(x_clamped_loss))
|
||
"""
|
||
@prim_attr_register
|
||
def __init__(self):
|
||
pass
|
||
|
||
def infer_shape(self, input_x, input_y, input_z):
|
||
input_x_len = len(input_x)
|
||
output_shape = []
|
||
for _ in range(input_x_len):
|
||
output_shape.append(1)
|
||
return tuple(output_shape)
|
||
|
||
def infer_dtype(self, input_x, input_y, input_z):
|
||
return mstype.float32
|
||
|
||
|
||
class ActULQClampMaxGrad(PrimitiveWithInfer):
|
||
"""
|
||
The ActULQClampMaxGrad(Activation Universal Linear Quantization on Clamp Maximum Gradient)
|
||
|
||
Inputs:
|
||
- **y_grad** (Tensor) - A tensor of gradient, with float16 or float32 type.
|
||
- **clamp_max_mask** - A tensor of mask, only support int8 type.
|
||
- **x_clamped_loss** - A tensor of loss, with the same type as "y_grad".
|
||
|
||
Outputs:
|
||
- **clamp_max_grad** - A tensor of clamp maximum gradient, with the same type as "y_grad".
|
||
The length of tensor is 1.
|
||
|
||
Examples:
|
||
>>> data_type = np.float32
|
||
>>> y_grad = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
|
||
>>> clamp_max_mask = np.where(np.random.rand(32, 120) >= 0.5, 1, 0)
|
||
>>> x_clamped_loss = np.random.uniform(-10, 10, (32, 120)).astype(data_type)
|
||
>>> act_ulq_clamp_max_grad = Q.ActULQClampMaxGrad()
|
||
>>> clamp_max_grad = act_ulq_clamp_max_grad(Tensor(y_grad), Tensor(clamp_max_mask, mindspore.bool_),
|
||
Tensor(x_clamped_loss))
|
||
"""
|
||
@prim_attr_register
|
||
def __init__(self):
|
||
pass
|
||
|
||
def infer_shape(self, input_x, input_y, input_z):
|
||
input_x_len = len(input_x)
|
||
output_shape = []
|
||
for _ in range(input_x_len):
|
||
output_shape.append(1)
|
||
return tuple(output_shape)
|
||
|
||
def infer_dtype(self, input_x, input_y, input_z):
|
||
return mstype.float32
|
||
|
||
|
||
class WtsARQ(PrimitiveWithInfer):
|
||
"""
|
||
The WtsARQ(Weights Adaptive Range Quantization).
|
||
|
||
Args:
|
||
num_bits (int): The bits num used for quantize.
|
||
offset_flag (bool): Whether use offset for quantize.
|
||
|
||
Inputs:
|
||
- **w** (Tensor) - A Tensor of weights. With float16 or float32 data type.
|
||
|
||
Outputs:
|
||
- **scale** (Tensor) - A tensor of optimal scale, has the same type as `w`.
|
||
- **offset** (Tensor) - A tensor of optimal offset, has the same type as `w`.
|
||
- If axis is [],
|
||
the shape of scale and offset is :math:`(1, )`.
|
||
- If axis is [0],
|
||
the shape of scale and offset is :math:`(w_1, )`.
|
||
- If axis is [1],
|
||
the shape of scale and offset is :math:`(w_2, )`.
|
||
- **y** (Tensor) - A tensor of fakequant weights, has the same type and shape as `w`.
|
||
|
||
Examples:
|
||
>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
|
||
>>> wts_arq = Q.WtsARQ(axes=[0], num_bits=8, offset_flag=False)
|
||
>>> scale, offset, y = wts_arq(data)
|
||
"""
|
||
@prim_attr_register
|
||
def __init__(self, num_bits, offset_flag):
|
||
validator.check_value_type("num_bits", num_bits, [int], self.name)
|
||
validator.check_int(num_bits, 8, Rel.EQ, "value of num_bits", self.name)
|
||
validator.check_value_type("offset_flag", offset_flag, [bool], self.name)
|
||
|
||
def infer_shape(self, w_shape, w_min_shape, w_max_shape):
|
||
validator.check_int(len(w_min_shape), len(w_shape), Rel.EQ, "dims of w_min", self.name)
|
||
validator.check_int(len(w_max_shape), len(w_shape), Rel.EQ, "dims of w_max", self.name)
|
||
return w_shape
|
||
|
||
def infer_dtype(self, w_dtype, w_min_dtype, w_max_dtype):
|
||
valid_types = [mstype.float32, mstype.float16]
|
||
validator.check_tensor_dtype_valid("w", w_dtype, valid_types, self.name)
|
||
validator.check_tensor_dtype_valid("w_min", w_min_dtype, valid_types, self.name)
|
||
validator.check_tensor_dtype_valid("w_max", w_max_dtype, valid_types, self.name)
|
||
return w_dtype
|
||
|
||
|
||
class IFMR(PrimitiveWithInfer):
|
||
"""
|
||
The TFMR(Input Feature Map Reconstruction).
|
||
|
||
Args:
|
||
min_percentile (float): Min init percentile. Default: 0.999999.
|
||
max_percentile (float): Max init percentile. Default: 0.999999.
|
||
search_range Union[list(float), tuple(float)]: Range of searching. Default: [0.7, 1.3].
|
||
search_step (float): Step size of searching. Default: 0.01.
|
||
with_offset (bool): Whether using offset. Default: True.
|
||
|
||
Inputs:
|
||
- **data** (Tensor) - A Tensor of feature map. With float16 or float32 data type.
|
||
- **data_min** (Tensor) - A Tensor of min value of feature map, the shape is :math:`(1)`.
|
||
With float16 or float32 data type.
|
||
- **data_max** (Tensor) - A Tensor of max value of feature map, the shape is :math:`(1)`.
|
||
With float16 or float32 data type.
|
||
- **cumsum** (Tensor) - A `1-D` Tensor of cumsum bin of data. With int32 data type.
|
||
|
||
Outputs:
|
||
- **scale** (Tensor) - A tensor of optimal scale, the shape is :math:`(1)`. Data dtype is float32.
|
||
- **offset** (Tensor) - A tensor of optimal offset, the shape is :math:`(1)`. Data dtype is float32.
|
||
|
||
Examples:
|
||
>>> data = Tensor(np.random.rand(1, 3, 6, 4).astype(np.float32))
|
||
>>> data_min = Tensor([0.1], mindspore.float32)
|
||
>>> data_max = Tensor([0.5], mindspore.float32)
|
||
>>> cumsum = Tensor(np.random.rand(4).astype(np.int32))
|
||
>>> ifmr = Q.IFMR(min_percentile=0.2, max_percentile=0.9, search_range=(1.0, 2.0),
|
||
... search_step=1.0, with_offset=False)
|
||
>>> output = ifmr(data, data_min, data_max, cumsum)
|
||
>>> print(output)
|
||
(Tensor(shape=[1], dtype=Float32, value= [7.87401572e-03]),
|
||
Tensor(shape=[1], dtype=Float32, value= [0.00000000e+00]))
|
||
"""
|
||
|
||
@prim_attr_register
|
||
def __init__(self, min_percentile=0.999999, max_percentile=0.999999, search_range=(0.7, 1.3), search_step=0.01,
|
||
with_offset=True):
|
||
validator.check_value_type("min_percentile", min_percentile, [float], self.name)
|
||
validator.check_value_type("max_percentile", max_percentile, [float], self.name)
|
||
validator.check_value_type("search_range", search_range, [list, tuple], self.name)
|
||
for item in search_range:
|
||
validator.check_positive_float(item, "item of search_range", self.name)
|
||
validator.check('search_range[1]', search_range[1], 'search_range[0]', search_range[0], Rel.GE, self.name)
|
||
validator.check_value_type("search_step", search_step, [float], self.name)
|
||
validator.check_value_type("offset_flag", with_offset, [bool], self.name)
|
||
|
||
def infer_shape(self, data_shape, data_min_shape, data_max_shape, cumsum_shape):
|
||
validator.check_equal_int(len(data_min_shape), 1, "dims of data_min", self.name)
|
||
validator.check_equal_int(data_min_shape[0], 1, "data_min[0]", self.name)
|
||
validator.check_equal_int(len(data_max_shape), 1, "dims of data_max", self.name)
|
||
validator.check_equal_int(data_max_shape[0], 1, "data_max[0]", self.name)
|
||
validator.check_equal_int(len(cumsum_shape), 1, "dims of cumsum", self.name)
|
||
return (1,), (1,)
|
||
|
||
def infer_dtype(self, data_dtype, data_min_dtype, data_max_dtype, cumsum_dtype):
|
||
tuple(map(partial(validator.check_tensor_dtype_valid,
|
||
valid_dtypes=(mstype.float16, mstype.float32), prim_name=self.name),
|
||
("input_value", "input_min", "input_max"),
|
||
(data_dtype, data_min_dtype, data_max_dtype)))
|
||
validator.check_tensor_dtype_valid("input_bins", cumsum_dtype, [mstype.int32], self.name)
|
||
return mstype.tensor_type(mstype.float32), mstype.tensor_type(mstype.float32)
|