# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0(the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http: // www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Operators for quantization.""" import mindspore.context as context from ..._checkparam import Validator as validator from ..._checkparam import Rel from ..primitive import PrimitiveWithInfer, prim_attr_register from ...common import dtype as mstype __all__ = ["MinMaxUpdatePerLayer", "MinMaxUpdatePerChannel", "FakeQuantPerLayer", "FakeQuantPerLayerGrad", "FakeQuantPerChannel", "FakeQuantPerChannelGrad", "BatchNormFold", "BatchNormFoldGrad", "CorrectionMul", "CorrectionMulGrad", "CorrectionMulGradReduce", "BatchNormFold2", "BatchNormFold2Grad", "BatchNormFoldD", "BatchNormFoldGradD", "BatchNormFold2_D", "BatchNormFold2GradD", "BatchNormFold2GradReduce" ] class MinMaxUpdatePerLayer(PrimitiveWithInfer): r""" Updates min and max per layer. Args: ema (bool): Uses EMA algorithm update value min and max. Default: False. ema_decay (int) : EMA algorithm decay parameter. Default: 0.999. 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 = MinMaxUpdatePerLayer(num_bits=8)(input_tensor, min_tensor, max_tensor) """ support_quant_bit = [4, 7, 8] @prim_attr_register def __init__(self, ema=False, ema_decay=0.999): """Initialize FakeQuantMinMaxPerLayerUpdate OP""" if context.get_context('device_target') == "Ascend": from mindspore.ops._op_impl._custom_op import minmax_update_perlayer 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.ema_decay = validator.check_number_range( 'ema_decay', ema_decay, 0, 1, Rel.INC_BOTH, self.name) self.init_prim_io_names(inputs=['x', 'min', 'max'], outputs=['min_up', 'max_up']) def infer_shape(self, x_shape, min_shape, max_shape): validator.check_integer("x rank", len(x_shape), 1, Rel.GE, self.name) validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name) validator.check_integer("min shape", len( min_shape), 1, Rel.EQ, self.name) return min_shape, max_shape def infer_dtype(self, x_type, min_type, max_type): valid_types = (mstype.float16, mstype.float32) validator.check_tensor_type_same({"x": x_type}, valid_types, self.name) validator.check_tensor_type_same( {"min": min_type}, valid_types, self.name) validator.check_tensor_type_same( {"max": max_type}, valid_types, self.name) return min_type, max_type class MinMaxUpdatePerChannel(PrimitiveWithInfer): r""" Updates min and max per channel. Args: ema (bool): Uses EMA algorithm update value min and max. Default: False. ema_decay (int) : EMA algorithm decay parameter. Default: 0.999. channel_axis (int): Quantization by channel axis. Ascend backend only supports 0 or 1. Default: 1. 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: >>> x = Tensor(np.random.rand(3, 16, 5, 5), mstype.float32) >>> min = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32) >>> max = Tensor(np.random.uniform(-1, 1, size=16), mstype.float32) >>> output_tensor = MinMaxUpdatePerChannel(num_bits=8)(x, min, max) """ support_quant_bit = [4, 7, 8] ascend_support_x_rank = [2, 4] @prim_attr_register def __init__(self, ema=False, ema_decay=0.999, channel_axis=1): """Initialize FakeQuantPerChannelUpdate OP for Ascend""" self.is_ascend = context.get_context('device_target') == "Ascend" if self.is_ascend: from mindspore.ops._op_impl._custom_op import minmax_update_perchannel 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.ema_decay = validator.check_number_range( 'ema_decay', ema_decay, 0, 1, Rel.INC_BOTH, self.name) if self.is_ascend: self.channel_axis = validator.check_int_range('channel_axis', channel_axis, 0, 1, Rel.INC_BOTH, 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=['min_up', 'max_up']) 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_integer("x rank", len(x_shape), 1, Rel.GE, self.name) validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name) validator.check_integer("min shape", len( min_shape), 1, Rel.EQ, self.name) return min_shape, max_shape def infer_dtype(self, x_type, min_type, max_type): valid_types = (mstype.float16, mstype.float32) validator.check_tensor_type_same( {"x": x_type}, valid_types, self.name) validator.check_tensor_type_same( {"min": min_type}, valid_types, self.name) validator.check_tensor_type_same( {"max": max_type}, valid_types, self.name) return min_type, max_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 funcion. After delay step in training time begin simulate the aware quantize funcion. 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_number_range( 'ema_decay', ema_decay, 0, 1, Rel.INC_BOTH, 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_integer("x rank", len(x_shape), 1, Rel.GE, self.name) validator.check("min shape", min_shape, "max shape", max_shape, Rel.EQ, self.name) validator.check_integer("min shape", len(min_shape), 1, Rel.EQ, self.name) return x_shape def infer_dtype(self, x_type, min_type, max_type): valid_types = (mstype.float16, mstype.float32) validator.check_tensor_type_same({"x": x_type}, valid_types, self.name) validator.check_tensor_type_same( {"min": min_type}, valid_types, self.name) validator.check_tensor_type_same( {"max": max_type}, valid_types, self.name) return x_type class FakeQuantPerLayerGrad(PrimitiveWithInfer): r""" Performs grad of FakeQuantPerLayerGrad 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_integer("min shape", len( min_shape), 1, Rel.EQ, self.name) return dout_shape def infer_dtype(self, dout_type, x_type, min_type, max_type): valid_types = (mstype.float16, mstype.float32) validator.check_tensor_type_same( {"dout": dout_type}, valid_types, self.name) validator.check_tensor_type_same({"x": x_type}, valid_types, self.name) validator.check_tensor_type_same( {"min": min_type}, valid_types, self.name) validator.check_tensor_type_same( {"max": max_type}, valid_types, self.name) return dout_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_number_range( 'ema_decay', ema_decay, 0, 1, Rel.INC_BOTH, 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', channel_axis, 0, 1, Rel.INC_BOTH, 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_integer("x rank", len(x_shape), 1, Rel.GE, 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_integer( "min shape", min_shape[0], x_shape[self.channel_axis], Rel.EQ, self.name) validator.check_integer( "max shape", max_shape[0], x_shape[self.channel_axis], Rel.EQ, self.name) return x_shape def infer_dtype(self, x_type, min_type, max_type): valid_types = (mstype.float16, mstype.float32) validator.check_tensor_type_same({"x": x_type}, valid_types, self.name) validator.check_tensor_type_same( {"min": min_type}, valid_types, self.name) validator.check_tensor_type_same( {"max": max_type}, valid_types, self.name) return x_type class FakeQuantPerChannelGrad(PrimitiveWithInfer): r""" Performs grad of FakeQuantPerChannelGrad 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): valid_types = (mstype.float16, mstype.float32) validator.check_tensor_type_same( {"dout": dout_type}, valid_types, self.name) validator.check_tensor_type_same({"x": x_type}, valid_types, self.name) validator.check_tensor_type_same( {"min": min_type}, valid_types, self.name) validator.check_tensor_type_same( {"max": max_type}, valid_types, self.name) return dout_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_number_range('momentum', momentum, 0, 1, Rel.INC_BOTH, 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_integer("global step shape len", len(global_step_shape), 1, Rel.EQ, 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_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) validator.check_tensor_type_same({"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 = P.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_integer("global step shape len", len(global_step_shape), 1, Rel.EQ, 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_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) validator.check_tensor_type_same({"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 = P.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_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) return x_type class CorrectionMulGrad(PrimitiveWithInfer): r""" Performs grad of CorrectionMul operation. Examples: >>> correction_mul_grad = P.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_tensor_type_same(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 = P.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 = P.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_integer("global step shape len", len(global_step_shape), 1, 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, 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_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) validator.check_tensor_type_same({"global_step": global_step_type}, (mstype.int32,), self.name) return x_type class BatchNormFold2Grad(PrimitiveWithInfer): r""" Performs grad of CorrectionAddGrad operation. Examples: >>> bnf2_grad = P.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_integer("global step shape len", len(global_step_shape), 1, Rel.EQ, 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_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) validator.check_tensor_type_same({"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_number_range('momentum', momentum, 0, 1, Rel.INC_BOTH, 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_tensor_type_same(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 _BatchNormFoldGrad 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) args = {"input type": x_type} validator.check_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) return x_type class BatchNormFold2_D(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_tensor_type_same(args, (mstype.float16, mstype.float32), self.name) return x_type class BatchNormFold2GradD(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 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_tensor_type_same(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