/** * 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: A required list or tuple. 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: An optional list or tuple. 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: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. 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. * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropFilter. * @li Compatible with the Caffe operator DepthwiseConv2DBackpropFilter. */ 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})) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .REQUIRED_ATTR(pads, ListInt) .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: A required list or tuple. Shape of filter. * @li strides: A required list or tuple. 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: An optional list or tuple. 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: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. 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. * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropFilter. * @li Compatible with the Caffe operator DepthwiseConv2DBackpropFilter. */ REG_OP(DepthwiseConv2DBackpropFilterD) .INPUT(input, TensorType({float16})) .INPUT(out_backprop, TensorType({float16})) .OUTPUT(filter_grad, TensorType({float32})) .REQUIRED_ATTR(filter_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .REQUIRED_ATTR(pads, ListInt) .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: A required list or tuple. 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: An optional list or tuple. 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: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. 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 * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropInput. * @li Compatible with the Caffe operator DepthwiseConv2DBackpropInput. */ 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})) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .REQUIRED_ATTR(pads, ListInt) .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: A required list or tuple. The origin shape of input. * @li strides: A required list or tuple. 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: An optional list or tuple. 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: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. 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 * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator DepthwiseConv2DBackpropInput. * @li Compatible with the Caffe operator DepthwiseConv2DBackpropInput. */ REG_OP(DepthwiseConv2DBackpropInputD) .INPUT(filter, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) .OUTPUT(input_grad, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .REQUIRED_ATTR(pads, ListInt) .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 float16 or int32 * @li offset_w: An optional float16 or int8, used for quantized inference * @par Attributes: * @li strides: A required list or tuple. 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: An optional list or tuple. 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: A required list or tuple. Padding added to each dimension of the * input. * @li data_format: An optional string. Input data format, either "NHWC" or * "NCHW". Defaults to "NHWC". * @li offset_x: An optional int. Input offset, used for quantized inference. * Defaults to 0. * @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 * @par Quantization supported or not * Yes * @par Third-party framework compatibility * @li Compatible with the TensorFlow operator DepthwiseConv2D. * @li Compatible with the Caffe operator DepthwiseConv2D. */ REG_OP(DepthwiseConv2D) .INPUT(x, TensorType({DT_FLOAT16, DT_INT8})) .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_FLOAT16, DT_INT8})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32})) .REQUIRED_ATTR(strides, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .REQUIRED_ATTR(pads, ListInt) .ATTR(data_format, String, "NHWC") .ATTR(offset_x, 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 NumberType. *@par Attributes: *data_format: Data format. Defaults to "NHWC". *@par Outputs: *y: A Tensor.Has the same type as "x". *@par Third-party framework compatibility * Compatible with the TensorFlow operator BiasAddGrad. */ 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_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 filter: A Tensor. Must be one of the following types: float16, float32, float64. * 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: * Five 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] * @li groups: Number of blocked connections from input channels to output channels. * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as filter,and has same format as input_size *@par Third-party framework compatibility * Compatible with Tensorflow's conv2d_backprop_input */ REG_OP(Conv2DBackpropInput) .INPUT(input_size, TensorType({DT_INT32})) .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NHWC") .OP_END_FACTORY_REG(Conv2DBackpropInput) /** *@brief Computes the gradients of convolution with respect to the input. *@par Inputs: * Two inputs: * @li filter: 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: * Six 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] * @li groups: Number of blocked connections from input channels to output channels. * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as filter,4-D tensor [batch, height, width, channels] or [batch, channels, height, width]. *@par Third-party framework compatibility * Compatible with Tensorflow's conv2d_backprop_input */ REG_OP(Conv2DBackpropInputD) .INPUT(filter, TensorType({DT_FLOAT16, DT_INT8})) .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_INT8})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_INT32})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NHWC") .OP_END_FACTORY_REG(Conv2DBackpropInputD) /** *@brief Computes the Deconvolution with respect to the input. *@par Inputs: * Three inputs: * @li x: A Tensor. Must have the same type as "filter". 4D with shape * [batch, out_channels, out_height, out_width]. Gradients with respect * to the output of the convolution. * @li filter: A Tensor of type float16. * 4D with shape [out_channels, in_channel, filter_height, filter_width].\n * Two optional inputs: * @li bias: An optional tensor of type float16 * @li offset_w: An optional 1D tensor for quantized deconvolution. Reserved.\n *@par Attributes: * Six attributes: * @li strides: A tuple or list of 2 integers. The stride of the sliding window * for H/W dimension. * @li pads: A tuple or list of 4 integers. The [top, bottom, left, right] * padding on the feature map * @li dilations: A tuple or list of 4 integers. The dilation factor for each * dimension of input. Must be [1, 1, 1, 1]. * @li groups: Number of blocked connections from input channels to * output channels. * @li data_format: An optional string from: "NCHW". Defaults to "NCHW".\n Specify the data format of the input and output data. * @li offset_x: An optional integer for quantized deconvolution. *@par Outputs: * y: A Tensor. Has the same type as "filter". 4D tensor with shape * [batch, channels, height, width]. */ REG_OP(Deconvolution) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8})) .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8})) .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32})) .ATTR(strides, ListInt, {1, 1}) .ATTR(pads, ListInt, {0, 0, 0, 0}) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NCHW") .ATTR(offset_x, Int, 0) .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, float32, float64. * 4-D with shape [batch, in_height, in_width, in_channels] or [batch, in_channels, in_height, in_width]. * @li filter_size: 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: * Five 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]. * @li groups: Number of blocked connections from input channels to output channels. * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as x *@par Third-party framework compatibility * Compatible with Tensorflow's conv2d_backprop_filter */ REG_OP(Conv2DBackpropFilter) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .INPUT(filter_size, TensorType({DT_INT32})) .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NHWC") .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: * Six attributes: * @li filter_size: 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]. * @li groups: Number of blocked connections from input channels to output channels. * @li data_format: An optional string from: "NHWC", "NCHW". Defaults to "NHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as x *@par Third-party framework compatibility * Compatible with Tensorflow's conv2d_backprop_filter */ REG_OP(Conv2DBackpropFilterD) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) .OUTPUT(y, TensorType({DT_FLOAT})) .REQUIRED_ATTR(filter_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NHWC") .OP_END_FACTORY_REG(Conv2DBackpropFilterD) /** *@brief Computes a 2D convolution given 4D "x" and "filter" tensors. *@par Inputs: * @li x: A 4D tensor of input images. * @li filter: A 4D tensor of filters. * @li bias: An optional 1D tensor. * @li offset_w: An optional 1D tensor for quantized convolution. Reserved. * * The input and output tensor attributes are listed as follows: * @verbatim Tensor | x | filter | bias | offset_w | y -----------|---------|---------|---------|----------|-------- Data Type | float16 | float16 | float16 | _ | float16 |---------|---------|---------|----------|-------- | float32 | float32 | float32 | _ | float32 |---------|---------|---------|----------|-------- | int8 | int8 | int32 | int8 | int32 -----------|---------|---------|---------|----------|-------- Format | NCHW | NCHW | ND | ND | NCHW | NHWC | NHWC | | | NHWC | | HWCN | | | @endverbatim * It should be noted that the data types must correspond to each other, but the * format does not need to. *@par Attributes: * @li strides: A list of 4 integers. Specifying the strides of the * convolution along the height and width. The dimension order is determined * by the data format of "x". By default the N and C dimensions are set to 1. * @li pads: A list of 4 integers. Specifying the top, bottom, left and right * padding. * @li dilations: A list of 4 integers. Specifying the dilation rate to use * for dilated convolution. Has the same dimension order and value as "strides". * @li groups: Number of blocked connections from input channels to output * channels. Input channels and output channels must both be divisible by * "groups". * @li offset_x: An optional integer for quantized convolution. * @li data_format: An optional string from: "NHWC", "NCHW". Specifying the * data format of the input and output images. Reserved. *@par Outputs: * @li y: A 4D Tensor of output images. *@attention * @li The parameter scope is listed as follows: * @verbatim Name | Field | Scope ------------------|--------------|---------- Input Image Size | H dimension | [1, 4096] | W dimension | [1, 4096] ------------------|--------------|---------- Filter Size | H dimension | [1, 255] | W dimension | [1, 255] ------------------|--------------|---------- Stride Size | H dimension | [1, 63] | W dimension | [1, 63] ------------------|--------------|---------- Padding Size | top side | [0, 255] | bottom side | [0, 255] | left side | [0, 255] | right side | [0, 255] ------------------|--------------|---------- Dilation Size | H dimension | [1, 255] | W dimension | [1, 255] @endverbatim * @li There are restrictions for certain scenarios: * @verbatim Output | Restrictions ------------------|---------------------------------------------- W dimension == 1 | HxW(input) == HxW(filter) H dimension == 1 | ------------------|---------------------------------------------- W dimension == 1 | Not supported H dimension != 1 | @endverbatim * As shown above, "HxW(input)" indicates the image size after padding and * "HxW(filter)" indicates the filter size after dilation. *@par Quantization supported or not * Yes *@par Third-party framework compatibility *@li Compatible with the TensorFlow operator "conv2d". *@li Compatible with the Caffe operator 2D "Convolution". */ REG_OP(Conv2D) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8})) .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT8})) .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_INT32})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NHWC") .ATTR(offset_x, Int, 0) .OP_END_FACTORY_REG(Conv2D) REG_OP(Conv2DCompress) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8})) .INPUT(filter_compress, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT8})) .INPUT(compress_index, TensorType({DT_INT8})) .OPTIONAL_INPUT(bias, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32})) .OPTIONAL_INPUT(offset_w, TensorType({DT_INT8})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NHWC") .ATTR(offset_x, Int, 0) .OP_END_FACTORY_REG(Conv2DCompress) /** *@brief Computes a 3D convolution given 5D "x" and "filter" tensors. *@par Inputs: *@li x: A 5D tensor. Must be one of the following types: float16, float32, float64. The format is NCDHW or NDHWC. *@li filter: A 5D tensor of the same type as "x". The format is NCDHW, NDHWC or DHWCN. *@li bias: An optional 1D tensor of the same type as "x". *@par Attributes: *@li strides: A list of 5 ints. Specifies the stride of the sliding window for each dimension of "x". The N and C dimensions must be 1. Has the same format as "x". *@li pads: A list of 6 ints. Supports only padding along the D, H and W dimensions in sequence of head, tail, top, bottom, left and right. *@li data_format: An optional string from: "NDHWC", "NCDHW". Defaults to "NDHWC". Specify the data format of the input and output data. *@li dilations: A list of 5 ints. Specifies the dilation factor for each dimension of "x". The N and C dimensions must be 1. Has the same format as "x". *@par Outputs: *y: A Tensor. Has the same type as "x". *@attention Constraints:\n *The image size after padding is greater than the filter size.\n *@par Third-party framework compatibility *@li Compatible with the TensorFlow operator conv3d. *@li Compatible with the Caffe operator Convolution. */ REG_OP(Conv3D) .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, 1}) .ATTR(pads, ListInt, {0, 0, 0, 0, 0, 0}) .ATTR(data_format, String, "NDHWC") .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .OP_END_FACTORY_REG(Conv3D) /** *@brief Computes the gradients of convolution 3d with respect to the input. *@par Inputs: * Three inputs: * @li input_size: A Tensor of type int32, int64. An integer vector representing the shape of input, * where input is a 5-D tensor [batch, depth, height, width, channels] or [batch, channels, depth, height, width]. * @li filter: A Tensor. Must be one of the following types: float16, float32, float64. * @li grads: A Tensor. Must have the same type as filter. 5-D with shape [batch, depth, out_height, out_width, out_channels] * or [batch, out_channels, depth, out_height, out_width]. Gradients with respect to the output of the convolution. *@par Attributes: * Four attributes: * @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension. * @li pads: A tuple/list of 6 integers * @li dilations: A tuple/list of 6 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1] * @li data_format: An optional string from: "NDHWC", "NCHWD". Defaults to "NDHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as filter,and has same format as input_size *@par Third-party framework compatibility * Compatible with Tensorflow's conv3d_backprop_input */ REG_OP(Conv3DBackpropInput) .INPUT(input_size, TensorType({DT_INT32, DT_INT64})) .INPUT(filter, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .INPUT(grads, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .REQUIRED_ATTR(strides, ListInt) .ATTR(pads, ListInt, {0, 0, 0, 0, 0, 0}) .ATTR(data_format, String, "NDHWC") .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .OP_END_FACTORY_REG(Conv3DBackpropInput) /** *@brief Computes the gradients of convolution 3d with respect to the input. *@par Inputs: * Two inputs: * @li filter: A Tensor. Types is float16. * @li grads: A Tensor. Must have the same type as filter. *@par Attributes: * Five attributes: * @li input_size A Tensor of type int32. An integer vector representing the shape of input, * @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension. * @li pads: A tuple/list of 4 integers * @li dilations: A tuple/list of 5 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1] * @li data_format: An optional string from: "NDHWC", "NCHWD". Defaults to "NDHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as filter *@par Third-party framework compatibility * Compatible with Tensorflow's conv3d_backprop_input */ REG_OP(Conv3DBackpropInputD) .INPUT(filter, TensorType({DT_FLOAT16})) .INPUT(grads, TensorType({DT_FLOAT16})) .OUTPUT(y, TensorType({DT_FLOAT16})) .REQUIRED_ATTR(input_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .ATTR(pads, ListInt, {0, 0, 0, 0, 0, 0}) .ATTR(data_format, String, "NDHWC") .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .OP_END_FACTORY_REG(Conv3DBackpropInputD) REG_OP(LSTM) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(cont, TensorType({DT_FLOAT32,DT_FLOAT16})) .INPUT(w_x, TensorType({DT_FLOAT16})) .INPUT(bias, TensorType({DT_FLOAT16,DT_FLOAT32,DT_INT16,DT_INT32})) .INPUT(w_h, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(x_static, TensorType({DT_FLOAT16})) .OPTIONAL_INPUT(h_0, TensorType({DT_FLOAT16,DT_FLOAT32})) .OPTIONAL_INPUT(c_0, TensorType({DT_FLOAT16,DT_FLOAT32})) .OPTIONAL_INPUT(w_x_static, TensorType({DT_FLOAT16})) .OUTPUT(h, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(h_t, TensorType({DT_FLOAT16, DT_FLOAT})) .OUTPUT(c_t, TensorType({DT_FLOAT16, DT_FLOAT})) .ATTR(num_output, Int, 0) .ATTR(expose_hidden, Bool, false) .OP_END_FACTORY_REG(LSTM) /** *@brief Computes the gradients of convolution3D with respect to the filter *@par Inputs: * Three inputs: * @li x: A Tensor. Must be one of the following types: float16 * 5-D with shape [batch, in_depth, in_height, in_width, in_channels] or [batch, in_depth, in_channels, in_height, in_width]. * @li filter_size: A Tensor of type int32. An integer vector representing the tensor shape of filter, * where filter is a 5-D tensor [filter_depth, filter_height, filter_width, in_channels, out_channels] * or [out_channels, filter_depth, filter_height, filter_width, in_channels] or [out_channels, filter_depth, in_channel, filter_height, filter_width]. * @li out_backprop: A Tensor. Must have the same type as x. 5-D with shape [batch, out_depth, out_height, out_width, out_channels] * or [batch, out_depth, 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 3 integers. The stride of the sliding window for D/H/W dimension. * @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right] pads on feature map. * @li dilations: A tuple/list of 5 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1]. * @li groups: Number of blocked connections from input channels to output channels. * @li data_format: An optional string from: "NDHWC", "NDCHW". Defaults to "NDHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as x *@par Third-party framework compatibility * Compatible with Tensorflow's conv3d_backprop_filter */ REG_OP(Conv3DBackpropFilter) .INPUT(x, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .INPUT(filter_size, TensorType({DT_INT32})) .INPUT(out_backprop, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .OUTPUT(y, TensorType({DT_FLOAT16, DT_FLOAT, DT_DOUBLE})) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NDHWC") .OP_END_FACTORY_REG(Conv3DBackpropFilter) /** *@brief Computes the gradients of convolution with respect to the filter. *@par Inputs: * Two inputs: * @li x: A Tensor. Type is float16. * 5-D with shape [batch, in_depth, in_height, in_width, in_channels] or [batch, in_depth, in_channels, in_height, in_width]. * @li out_backprop: A Tensor. Must have the same type as x. 5-D with shape [batch, out_depth, out_height, out_width, out_channels] * or [batch, out_depth, out_channels, out_height, out_width]. Gradients with respect to the output of the convolution. *@par Attributes: * Four attributes: * @li filter_size: A Tensor of type integers. An integer vector representing the tensor shape of filter, * where filter is a 5-D tensor [filter_depth, filter_height, filter_width, in_channels, out_channels] * or [out_channels, filter_depth, filter_height, filter_width, in_channels] or [out_channels, filter_depth, in_channel, filter_height, filter_width]. * @li strides: A tuple/list of 3 integers. The stride of the sliding window for D/H/W dimension. * @li pads: A tuple/list of 6 integers, [front, back, top, bottom, left, right] pads on feature map * @li dilations: A tuple/list of 5 integers, The dilation factor for each dimension of input, now only support [1,1,1,1,1]. * @li groups: Number of blocked connections from input channels to output channels. * @li data_format: An optional string from: "NDHWC", "NDCHW". Defaults to "NDHWC". Specify the data format of the input and output data. *@par Outputs: * y: A Tensor. Has the same type as x *@par Third-party framework compatibility * Compatible with Tensorflow's conv3d_backprop_filter */ REG_OP(Conv3DBackpropFilterD) .INPUT(x, TensorType({DT_FLOAT16})) .INPUT(out_backprop, TensorType({DT_FLOAT16})) .OUTPUT(y, TensorType({DT_FLOAT})) .REQUIRED_ATTR(filter_size, ListInt) .REQUIRED_ATTR(strides, ListInt) .REQUIRED_ATTR(pads, ListInt) .ATTR(dilations, ListInt, {1, 1, 1, 1, 1}) .ATTR(groups, Int, 1) .ATTR(data_format, String, "NDHWC") .OP_END_FACTORY_REG(Conv3DBackpropFilterD) } // namespace ge #endif // GE_OP_NN_CALCULATION_OPS_H