graphengine/third_party/fwkacllib/inc/ops/nn_calculation_ops.h

/**
 * Copyright 2019-2020 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.
 */

#ifndef GE_OP_NN_CALCULATION_OPS_H
#define GE_OP_NN_CALCULATION_OPS_H

#include "../graph/operator_reg.h"

namespace ge {
/**
* @brief Computes the gradients of depthwise convolution with respect to the filter.

* @par Inputs:
* Three inputs include: \n
* @li input: 4D origin shape of input tensor [N, C, H, W] or [N, H, W, C], support float16, float32, double
* @li filter_size: A 4D tensor of type int32, with shape [H, W, C, K]
* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C]. Must be one of the following types: float16, float32, double.

* @par Attributes:
* @li strides: The stride of the sliding window for height and width of input "x" of the convolution.
* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height, stride_width, 1].
* @li dilations: The dilation factor for each dimension of input "x". If set to k > 1, there will be k-1 skipped cells between each
*               filter element on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] or [1, dilation_height, dilation_width, 1].
* @li pads: Padding added to each dimension of the input.
* @li data_format: Input data format, either "NHWC" or "NCHW".

* @par Outputs:
* filter_grad: Gradient of the deep convolution relative to the filter with shape [H, W, C, K]. Must be one of the following types: float16, float32, double.

* @attention Constraints:\n
* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape [C1, Hf, Wf, K, Co, C0],
* where K is fixed at 1, and Co and C0 are 16.\n
* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the data is 5D with shape [N, C1, Ho, Wo, C0],
* where C is the same as that of the feature map and C0 is 16.\n
* Limited by Tiling and L1 / L0 buffer memory: 512 * ceil(Wo, 16) + (480 * stride_h + 32 * filter_h) * ceil(Wi, 16) ≤ l1_size and Hf * Wf ≤ l0b_size/512.\n
*/
REG_OP(DepthwiseConv2DBackpropFilter)
    .INPUT(input, TensorType({float16}))
    .INPUT(filter_size, TensorType({DT_INT32, DT_INT64}))
    .INPUT(out_backprop, TensorType({float16}))
    .OUTPUT(filter_grad, TensorType({float32}))
    .ATTR(strides, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .ATTR(pads, ListInt, {0, 0, 0, 0})
    .ATTR(data_format, String, "NHWC")
    .OP_END_FACTORY_REG(DepthwiseConv2DBackpropFilter)

/**
* @brief Computes the gradients of depthwise convolution with respect to the filter.

* @par Inputs:
* Two inputs include: \n
* @li input: 4D tensor with shape [N, C, H, W] or [N, H, W, C], of type float16
* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C], of type float16

* @par Attributes:
* @li filter_size: Shape of filter.
* @li strides: The stride of the sliding window for height and width of input "x" of the convolution.
* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height, stride_width, 1].
* @li dilations: The dilation factor for each dimension of input "x". If set to k > 1, there will be k-1 skipped cells between each
*               filter element on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] or [1, dilation_height, dilation_width, 1].
* @li pads: Padding added to each dimension of the input.
* @li data_format: Input data format, either "NHWC" or "NCHW".

* @par Outputs:
* filter_grad: Gradient of the deep convolution relative to the filter with shape [H, W, C, K]. Must be of type float32.

* @attention Constraints:\n
* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape [C1, Hf, Wf, K, Co, C0],
* where K is fixed at 1, and Co and C0 are 16.\n
* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the data is 5D with shape [N, C1, Ho, Wo, C0],
* where C is the same as that of the feature map and C0 is 16.\n
* Limited by Tiling and L1 / L0 buffer memory: 512 * ceil(Wo, 16) + (480 * stride_h + 32 * filter_h) * ceil(Wi, 16) ≤ l1_size and Hf * Wf ≤ l0b_size/512.\n
*/
REG_OP(DepthwiseConv2DBackpropFilterD)
    .INPUT(input, TensorType({float16}))
    .INPUT(out_backprop, TensorType({float16}))
    .OUTPUT(filter_grad, TensorType({float32}))
    .ATTR(filter_size, ListInt, {1, 1, 1, 1})
    .ATTR(strides, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .ATTR(pads, ListInt, {0, 0, 0, 0})
    .ATTR(data_format, String, "NHWC")
    .OP_END_FACTORY_REG(DepthwiseConv2DBackpropFilterD)

/**
* @brief Computes the gradients of depthwise convolution with respect to the input.

* @par Inputs:
* Three inputs include: \n
* @li input_size: 4D shape of input tensor [N, C, H, W] or [N, H, W, C], support int32
* @li filter: 4D filter tensor with shape of [H, W, C, K], support float16, float32, double
* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C]. Must be one of the following types: float16, float32, double.

* @par Attributes:
* @li strides: The stride of the sliding window for height and width of input "x" of the convolution.
* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height, stride_width, 1].
* @li dilations: The dilation factor for each dimension of input "x". If set to k > 1, there will be k-1 skipped cells between each
*               filter element on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] or [1, dilation_height, dilation_width, 1].
* @li pads: Padding added to each dimension of the input.
* @li data_format: Input data format, either "NHWC" or "NCHW".

* @par Outputs:
* input_grad: Gradient of the deep convolution relative to the input with shape [N, C, H, W] or [N, H, W, C] Must be one of the following types: float16, float32, double.

* @attention Constraints:\n
* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape [C1, Hf, Wf, K, Co, C0],
* where K is fixed at 1, and Co and C0 are 16.\n
* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the data is 5D with shape [N, C1, Ho, Wo, C0],
* where C is the same as that of the feature map and C0 is 16.\n
* Limited by Tiling: max_h_in_l1 ≥ C0, where max_h_in_l1 = (l1_size - Hf*Wf*C0*C0*2) / (2* Wo *C0).\n
*/
REG_OP(DepthwiseConv2DBackpropInput)
    .INPUT(input_size, TensorType({DT_INT32, DT_INT64}))
    .INPUT(filter, TensorType({DT_FLOAT16}))
    .INPUT(out_backprop, TensorType({DT_FLOAT16}))
    .OUTPUT(input_grad, TensorType({DT_FLOAT16}))
    .ATTR(strides, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .ATTR(pads, ListInt, {0, 0, 0, 0})
    .ATTR(data_format, String, "NHWC")
    .OP_END_FACTORY_REG(DepthwiseConv2DBackpropInput)

/**
* @brief Computes the gradients of depthwise convolution with respect to the input.

* @par Inputs:
* Two inputs include: \n
* @li filter: A 4D tensor of type float16, with shape [H, W, C, K]
* @li out_backprop: 4D tensor with shape [N, C, H, W] or [N, H, W, C], of type float16

* @par Attributes:
* @li input_size: The origin shape of input.
* @li strides: The stride of the sliding window for height and width of input "x" of the convolution.
* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height, stride_width, 1].
* @li dilations: The dilation factor for each dimension of input "x". If set to k > 1, there will be k-1 skipped cells between each
*               filter element on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] or [1, dilation_height, dilation_width, 1].
* @li pads: Padding added to each dimension of the input.
* @li data_format: Input data format, either "NHWC" or "NCHW".

* @par Outputs:
* input_grad: Gradient of the deep convolution relative to the input with shape [N, C, H, W] or [N, H, W, C]. Must be of type float16.

* @attention Constraints:\n
* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape [C1, Hf, Wf, K, Co, C0],
* where K is fixed at 1, and Co and C0 are 16.\n
* Output backprop is 4D with shape [N, C, Ho, Wo] or [N, Ho, Wo, C], but the data is 5D with shape [N, C1, Ho, Wo, C0],
* where C is the same as that of the feature map and C0 is 16.\n
* Limited by Tiling: max_h_in_l1 ≥ C0, where max_h_in_l1 = (l1_size - Hf*Wf*C0*C0*2) / (2* Wo *C0).\n
*/
REG_OP(DepthwiseConv2DBackpropInputD)
    .INPUT(filter, TensorType({DT_FLOAT16}))
    .INPUT(out_backprop, TensorType({DT_FLOAT16}))
    .OUTPUT(input_grad, TensorType({DT_FLOAT16}))
    .ATTR(input_size, ListInt, {1, 1, 1, 1})
    .ATTR(strides, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .ATTR(pads, ListInt, {0, 0, 0, 0})
    .ATTR(data_format, String, "NHWC")
    .OP_END_FACTORY_REG(DepthwiseConv2DBackpropInputD)

/**
*@brief Computes a 2D deep convolution given a 4D input tensor and a filter tensor.

*@par Inputs:
*Two required inputs and two optional inputs, including: \n
* @li x: A 4D tensor of type float16, with shape [N, C, H, W] or [N, H, W, C]
* @li filter: A 4D tensor of type float16, with shape [H, W, C, K]
* @li bias: An optional tensor of type int8
* @li offset_w: An optional float16, used for quantized inference

* @par Attributes:
* @li strides: The stride of the sliding window for height and width of input "x" of the convolution.
* Must be with shape [1, 1, stride_height, stride_width] or [1, stride_height, stride_width, 1].
* @li dilations: The dilation factor for each dimension of input "x". If set to k > 1, there will be k-1 skipped cells between each
*               filter element on that dimension. Must be with shape [1, 1, dilation_height, dilation_width] or [1, dilation_height, dilation_width, 1].
* @li pads: Padding added to each dimension of the input.
* @li data_format: Input data format, either "NHWC" or "NCHW".
* @li offset_a: Input offset, used for quantized inference.

* @par Outputs:
* y: 4D tensor of type float16, with shape [N, C, H, W] or [N, H, W, C]

* @attention Constraints:\n
* The feature map is 4D with shape [N, C, Hi, Wi] or [N, Hi, Wi, C], but
* the data is 5D with shape [N, C1, Hi, Wi, C0], where C0 is 16.\n
* The filter is 4D with shape [Hf, Wf, C, K], but the data is 6D with shape [C1, Hf, Wf, K, Co, C0],
* where K is fixed at 1, and Co and C0 are 16.\n
* Limited by the size of L1 buffer memory: \n
* (l1_size - filter_h*filter_w*BLOCK_SIZE*BLOCK_SIZE*data_size) // (Wi*BLOCK_SIZE*data_size) >= (BLOCK_SIZE*strides_h + filter_h - strides_h).\n
*/
REG_OP(DepthwiseConv2D)
    .INPUT(x, TensorType({DT_FLOAT16}))
    .INPUT(filter, TensorType({DT_FLOAT16}))
    .OPTIONAL_INPUT(bias, TensorType({DT_INT8}))
    .OPTIONAL_INPUT(offset_w, TensorType({DT_FLOAT16}))
    .OUTPUT(y, TensorType({DT_FLOAT16}))
    .ATTR(strides, ListInt, {})
    .ATTR(dilations, ListInt, {})
    .ATTR(pads, ListInt, {0, 0, 0, 0})
    .ATTR(data_format, String, "NHWC")
    .ATTR(offset_a, Int, 0)
    .OP_END_FACTORY_REG(DepthwiseConv2D)

REG_OP(Conv2DCCE)
    .INPUT(x, TensorType{DT_FLOAT})              // The input tensor
    .INPUT(w, TensorType({DT_FLOAT, DT_INT8}))   // The weight tensor ,If QuantType =1 ,shall use type""tensor(int8)
    .OPTIONAL_INPUT(b, TensorType{DT_FLOAT})     // Optional 1D bias to be added to the convolution, has size of M.
    .OUTPUT(y, TensorType{DT_FLOAT})             // The output tensor
    .ATTR(mode, Int, 1)
    .ATTR(group, Int, 1)               // number of groups input channels and output channels are divided into
    .ATTR(num_output, Int, 0)          // number of output tensor
    .ATTR(pad, ListInt, {0, 0, 0, 0}) // Padding for the beginning and ending along each axis
    .ATTR(kernel, ListInt, {0, 0})
    .ATTR(stride, ListInt, {1, 1})    // Stride along each axis.
    .ATTR(dilation, ListInt, {1, 1})  // dilation value along each axis of the filter.
    .ATTR(pad_mode, Int, 0)            // pad mode, 0:NOTSET, 1:SAME_UPPER, SAME_LOWER or 2:VALID.defaul default value is 0:NOTSET
    .ATTR(algo, Int, 2)
    .OP_END_FACTORY_REG(Conv2DCCE)

REG_OP(Conv2DBackpropFilterCCE)
    .INPUT(x, TensorType{DT_FLOAT})
    .INPUT(filter_sizes, TensorType{DT_INT8})
    .INPUT(out_backprop, TensorType{DT_FLOAT})
    .OUTPUT(y, TensorType{DT_FLOAT})
    .ATTR(conv_grad_filter_output_shape, ListInt, {0, 0, 0, 0})
    .ATTR(mode, Int, 1)
    .ATTR(group, Int, 1)
    .ATTR(pad, ListInt, {0, 0, 0, 0})
    .ATTR(stride, ListInt, {1, 1})
    .ATTR(dilation, ListInt, {1, 1})
    .ATTR(padding, Int, 0)   //pad_mode:same valid
    .ATTR(algo, Int, 0)
    .OP_END_FACTORY_REG(Conv2DBackpropFilterCCE)

REG_OP(Conv2DBackpropInputCCE)
   .INPUT(input_sizes, TensorType{DT_INT8})
   .INPUT(filter, TensorType{DT_FLOAT})
   .INPUT(out_backprop, TensorType{DT_FLOAT})
   .OUTPUT(output, TensorType{DT_FLOAT})
   .ATTR(conv_grad_input_output_shape, ListInt, {0, 0, 0, 0})
   .ATTR(mode, Int, 1)
   .ATTR(format, Int, 0)
   .ATTR(group, Int, 1)
   .ATTR(pad_mode, Int, 0)
   .ATTR(stride, ListInt, {1, 1})
   .ATTR(dilation, ListInt, {1, 1})
   .ATTR(pad, ListInt, {0, 0, 0, 0})
   .ATTR(algo, Int, 0)
   .OP_END_FACTORY_REG(Conv2DBackpropInputCCE)

/**
*@brief Performs the the backward operation for "BiasAdd" on the "bias" tensor.
*        It accumulates all the values from out_backprop into the feature
*        dimension. For NHWC data format, the feature dimension is the last.
*        For NCHW data format, the feature dimension is the third-to-last.

*@par Inputs:
*x: A Tensor of type TensorType::NumberType().

*@par Attributes:
*data_format: Data format. Defaults to "NHWC".

*@par Outputs:
*y: A Tensor.Has the same type as "x".
*/
REG_OP(BiasAddGrad)
    .INPUT(x, TensorType::NumberType())
    .OUTPUT(y, TensorType::NumberType())
    .ATTR(data_format, String, "NHWC")
    .OP_END_FACTORY_REG(BiasAddGrad)

/**
*@brief Computes the gradients of convolution with respect to the input.
*@par Inputs:
 * Three inputs:
 * @li input_sizes: A Tensor of type int32. An integer vector representing the shape of input,
 * where input is a 4-D tensor [batch, height, width, channels] or [batch, channels, height, width].
 * @li filters: A Tensor. Must be one of the following types: float16.
 * 4-D with shape [filter_height, filter_width, in_channels, out_channels]
 * or [out_channels, filter_height, filter_width, in_channels] or [out_channels, in_channel, filter_height, filter_width].
 * @li out_backprop: A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]
 * or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
*@par Attributes:
 * Three attributes:
 * @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
 * @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map
 * @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1]
*@par Outputs:
 * y: A Tensor. Has the same type as filter,and has same format as input_size
*/
REG_OP(Conv2DBackpropInput)
    .INPUT(input_sizes, TensorType({DT_INT32, DT_INT64}))
    .INPUT(filters, TensorType{DT_FLOAT16})
    .INPUT(out_backprop, TensorType{DT_FLOAT16})
    .OUTPUT(y, TensorType{DT_FLOAT16})
    .REQUIRED_ATTR(strides, ListInt)
    .ATTR(pads, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .OP_END_FACTORY_REG(Conv2DBackpropInput)

/**
*@brief Computes the gradients of convolution with respect to the input.
*@par Inputs:
 * Two inputs:
 * @li filters: A Tensor. Types is float16.
 * 4-D with shape [filter_height, filter_width, in_channels, out_channels] or [out_channels, filter_height, filter_width, in_channels]
 * or [out_channels, in_channel, filter_height, filter_width].
 * @li out_backprop: A Tensor. Must have the same type as filter. 4-D with shape [batch, out_height, out_width, out_channels]
 * or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
*@par Attributes:
 * Four attributes:
 * @li input_size A Tensor of type int32. An integer vector representing the shape of input,
 * where input is a 4-D tensor [batch, height, width, channels] or [batch, channels, height, width].
 * @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
 * @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map
 * @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1]
*@par Outputs:
 * y: A Tensor. Has the same type as filter,4-D tensor [batch, height, width, channels] or [batch, channels, height, width].
*/
REG_OP(Conv2DBackpropInputD)
    .INPUT(filters, TensorType{DT_FLOAT16})
    .INPUT(out_backprop, TensorType{DT_FLOAT16})
    .OUTPUT(y, TensorType{DT_FLOAT16})
    .REQUIRED_ATTR(input_sizes, ListInt)
    .REQUIRED_ATTR(strides, ListInt)
    .ATTR(pads, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .OP_END_FACTORY_REG(Conv2DBackpropInputD)

REG_OP(Deconvolution)
    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE))
    .ATTR(strides, ListInt, {1, 1, 1, 1})
    .ATTR(pads, ListInt, {0, 0, 0, 0})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .OP_END_FACTORY_REG(Deconvolution)
/**
*@brief Computes the gradients of convolution with respect to the filter
*@par Inputs:
 * Three inputs:
 * @li x: A Tensor. Must be one of the following types: float16.
 * 4-D with shape [batch, in_height, in_width, in_channels] or [batch, in_channels, in_height, in_width].
 * @li filter_sizes: A Tensor of type int32. An integer vector representing the tensor shape of filter,
 * where filter is a 4-D tensor [filter_height, filter_width, in_channels, out_channels]
 * or [out_channels, filter_height, filter_width, in_channels] or [out_channels, in_channel, filter_height, filter_width].
 * @li out_backprop: A Tensor. Must have the same type as x. 4-D with shape [batch, out_height, out_width, out_channels]
 * or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
*@par Attributes:
 * Three attributes:
 * @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
 * @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map.
 * @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1].
*@par Outputs:
 * y: A Tensor. Has the same type as x
*/
REG_OP(Conv2DBackpropFilter)
    .INPUT(x, TensorType{DT_FLOAT16})
    .INPUT(filter_sizes, TensorType({DT_INT32, DT_INT64}))
    .INPUT(out_backprop, TensorType{DT_FLOAT16})
    .OUTPUT(y, TensorType{DT_FLOAT})
    .REQUIRED_ATTR(strides, ListInt)
    .ATTR(pads, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .OP_END_FACTORY_REG(Conv2DBackpropFilter)

/**
*@brief Computes the gradients of convolution with respect to the filter.
*@par Inputs:
 * Two inputs:
 * @li x: A Tensor. Type is float16.
 * 4-D with shape [batch, in_height, in_width, in_channels] or [batch, in_channels, in_height, in_width].
 * @li out_backprop: A Tensor. Must have the same type as x. 4-D with shape [batch, out_height, out_width, out_channels]
 * or [batch, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution.
*@par Attributes:
 * Four attributes:
 * @li filter_sizes: A Tensor of type integers. An integer vector representing the tensor shape of filter,
 * where filter is a 4-D tensor [filter_height, filter_width, in_channels, out_channels]
 * or [out_channels, filter_height, filter_width, in_channels] or [out_channels, in_channel, filter_height, filter_width].
 * @li strides: A tuple/list of 2 integers. The stride of the sliding window for H/W dimension.
 * @li pads: A tuple/list of 4 integers, [top, bottom, left, right] pads on feature map
 * @li dilations: A tuple/list of 4 integers, The dilation factor for each dimension of input, now only support [1,1,1,1].
*@par Outputs:
 * y: A Tensor. Has the same type as x
*/
REG_OP(Conv2DBackpropFilterD)
    .INPUT(x, TensorType{DT_FLOAT16})
    .INPUT(out_backprop, TensorType{DT_FLOAT16})
    .OUTPUT(y, TensorType{DT_FLOAT})
    .REQUIRED_ATTR(filter_sizes, ListInt)
    .REQUIRED_ATTR(strides, ListInt)
    .ATTR(pads, ListInt, {1, 1, 1, 1})
    .ATTR(dilations, ListInt, {1, 1, 1, 1})
    .OP_END_FACTORY_REG(Conv2DBackpropFilterD)

REG_OP(Conv2D)
    .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8}))                // the featrue map tensor
    .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8}))           // the filter tensor
    .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32}))    // optional 1D bias to be added to the conv2d
    .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8}))
    .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32}))               // the output tensor
    .ATTR(strides, ListInt, {1, 1, 1, 1})         // stride on H\W, format sensitive
    .ATTR(pads, ListInt, {0, 0, 0, 0})            // top, bottom, left and right pads on feature map
    .ATTR(dilations, ListInt, {1, 1, 1, 1})       // dilation on H\W, format sensitive
    .ATTR(offset_a, Int, 0)
    .OP_END_FACTORY_REG(Conv2D)

}  // namespace ge
#endif  // GE_OP_NN_CALCULATION_OPS_H