wehub-resource-sync
547 lines
29 KiB
C++
547 lines
29 KiB
C++
/* ******************************************************************************
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*
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <helpers/MKLDNNStream.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <system/platform_boilerplate.h>
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#include "mkldnnUtils.h"
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namespace sd {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////////
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static void deconv3dMKLDNN(NDArray* input, NDArray* weights, NDArray* bias, NDArray* output,
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const sd::LongType kD, const sd::LongType kH, const sd::LongType kW, const sd::LongType sD, const sd::LongType sH, const sd::LongType sW,
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const sd::LongType pD, const sd::LongType pH, const sd::LongType pW, const sd::LongType dD, const sd::LongType dH, const sd::LongType dW,
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const bool isNCDHW, const int wFormat) {
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// mkl supports weights in [oC, iC, kD, kH, kW] only
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sd::LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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sd::LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWoC, indWiC, indWkD);
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dnnl::memory::dims strides = {sD, sH, sW};
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dnnl::memory::dims padding = {pD, pH, pW};
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dnnl::memory::dims padding_r = {(iD - 1) * sD - oD + kD - pD, (iH - 1) * sH - oH + kH - pH,
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(iW - 1) * sW - oW + kW - pW};
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dnnl::memory::dims dilation = {dD - 1, dH - 1, dW - 1};
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std::vector<int> permut;
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if (0 == wFormat)
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permut = {3, 4, 0, 1, 2}; // [kD, kH, kW, oC, iC] -> [oC, iC, kD, kH, kW]
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else if (1 == wFormat)
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permut = {1, 0, 2, 3, 4}; // [iC, oC, kD, kH, kW] -> [oC, iC, kD, kH, kW]
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else
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permut = {4, 0, 1, 2, 3}; // [iC, kD, kH, kW, oC] -> [oC, iC, kD, kH, kW]
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// input type
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dnnl::memory::data_type xType;
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if (input->dataType() == DataType::FLOAT32)
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xType = dnnl::memory::data_type::f32;
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else if (input->dataType() == DataType::HALF)
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xType = dnnl::memory::data_type::f16;
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else if (input->dataType() == DataType::UINT8)
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xType = dnnl::memory::data_type::u8;
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else
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xType = dnnl::memory::data_type::s8;
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// weights type
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dnnl::memory::data_type wType = xType;
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if (xType == dnnl::memory::data_type::u8) wType = dnnl::memory::data_type::s8;
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// output and bias type (have the same types)
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dnnl::memory::data_type zType;
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if (output->dataType() == DataType::FLOAT32)
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zType = dnnl::memory::data_type::f32;
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else if (output->dataType() == DataType::HALF)
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zType = dnnl::memory::data_type::f16;
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else if (output->dataType() == DataType::UINT8)
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zType = dnnl::memory::data_type::u8;
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else if (output->dataType() == DataType::INT8)
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zType = dnnl::memory::data_type::s8;
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else
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zType = dnnl::memory::data_type::s32;
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dnnl::memory::format_tag xFormatMkl = isNCDHW ? dnnl::memory::format_tag::ncdhw : dnnl::memory::format_tag::ndhwc;
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dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::oidhw;
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dnnl::memory::dims xDims = {bS, iC, iD, iH, iW};
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dnnl::memory::dims wDims = {oC, iC, kD, kH, kW};
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dnnl::memory::dims zDims = {bS, oC, oD, oH, oW};
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// memory descriptors for arrays
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// input
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dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormatMkl);
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onednnUtils::setBlockStrides(*input, x_user_md);
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// weights
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dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
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dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormatMkl);
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onednnUtils::setBlockStrides(*weights, w_user_md, permut);
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// bias
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dnnl::memory::desc b_mkl_md;
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if (bias != nullptr) b_mkl_md = dnnl::memory::desc({oC}, zType, dnnl::memory::format_tag::x);
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// output
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dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any);
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dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xFormatMkl);
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onednnUtils::setBlockStrides(*output, z_user_md);
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auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// operation primitive description
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dnnl::deconvolution_forward::desc op_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::deconvolution_direct,
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x_mkl_md, w_mkl_md, b_mkl_md, z_mkl_md, strides, dilation, padding,
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padding_r);
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dnnl::deconvolution_forward::primitive_desc op_prim_desc(op_desc, engine);
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// arguments (memory buffers) necessary for calculations
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std::unordered_map<int, dnnl::memory> args;
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dnnl::stream stream(engine);
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// provide memory buffers and check whether reorder is required
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// input
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onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
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// weights
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onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_prim_desc.weights_desc(),
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args[DNNL_ARG_WEIGHTS]);
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// bias
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if (bias != nullptr) {
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auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, const_cast<void*>(bias->buffer()));
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args[DNNL_ARG_BIAS] = b_mkl_mem;
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}
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// output
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auto z_user_mem =
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onednnUtils::loadDataToMklStream(*output, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);
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// run calculations
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dnnl::deconvolution_forward(op_prim_desc).execute(stream, args);
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// reorder outputs if necessary
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if (op_prim_desc.dst_desc() != z_user_mem.get_desc())
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dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], z_user_mem);
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stream.wait();
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}
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//////////////////////////////////////////////////////////////////////////
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static void deconv3dBackPropMKLDNN(NDArray* input, NDArray* weights, NDArray* gradO, NDArray* gradI,
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NDArray* gradW, NDArray* gradB, const sd::LongType kD, const sd::LongType kH, const sd::LongType kW,
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const sd::LongType sD, const sd::LongType sH, const sd::LongType sW, const sd::LongType pD, const sd::LongType pH, const sd::LongType pW,
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const sd::LongType dD, const sd::LongType dH, const int dW, const bool isNCDHW, const int wFormat) {
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// mkl supports weights/gradW in [oC, iC, kD, kH, kW] format only
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sd::LongType bS, iC, iD, iH, iW, oC, oD, oH,
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oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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sd::LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
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indIOioC, indIOioD, indWoC, indWiC, indWkD);
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dnnl::memory::dims strides = {sD, sH, sW};
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dnnl::memory::dims padding = {pD, pH, pW};
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dnnl::memory::dims padding_r = {(iD - 1) * sD - oD + kD - pD, (iH - 1) * sH - oH + kH - pH,
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(iW - 1) * sW - oW + kW - pW};
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dnnl::memory::dims dilation = {dD - 1, dH - 1, dW - 1};
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std::vector<int> permut;
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if (0 == wFormat)
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permut = {3, 4, 0, 1, 2}; // [kD, kH, kW, oC, iC] -> [oC, iC, kD, kH, kW]
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else if (1 == wFormat)
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permut = {1, 0, 2, 3, 4}; // [iC, oC, kD, kH, kW] -> [oC, iC, kD, kH, kW]
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else
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permut = {4, 0, 1, 2, 3}; // [iC, kD, kH, kW, oC] -> [oC, iC, kD, kH, kW]
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// input type
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dnnl::memory::data_type xType =
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input->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// weights type
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dnnl::memory::data_type wType =
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weights->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// gradO type
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dnnl::memory::data_type gradOType =
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gradO->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// gradI type
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dnnl::memory::data_type gradIType =
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gradI->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// gradW type
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dnnl::memory::data_type gradWType =
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gradW->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// gradB type
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dnnl::memory::data_type gradBType =
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gradB != nullptr
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? (gradB->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16)
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: dnnl::memory::data_type::f32;
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dnnl::memory::format_tag xFormatMkl = isNCDHW ? dnnl::memory::format_tag::ncdhw : dnnl::memory::format_tag::ndhwc;
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dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::oidhw;
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dnnl::memory::dims xDims = {bS, iC, iD, iH, iW};
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dnnl::memory::dims wDims = {oC, iC, kD, kH, kW};
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dnnl::memory::dims zDims = {bS, oC, oD, oH, oW};
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// memory descriptors for arrays
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// input
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dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormatMkl);
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onednnUtils::setBlockStrides(*input, x_user_md);
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// weights
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dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
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dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormatMkl);
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onednnUtils::setBlockStrides(*weights, w_user_md, permut);
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// gradO
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dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormatMkl);
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onednnUtils::setBlockStrides(*gradO, gradO_user_md);
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// gradI
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dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormatMkl);
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onednnUtils::setBlockStrides(*gradI, gradI_user_md);
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// gradW
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dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, gradWType, wFormatMkl);
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onednnUtils::setBlockStrides(*gradW, gradW_user_md, permut);
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// gradB
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dnnl::memory::desc gradB_mkl_md;
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if (gradB != nullptr) gradB_mkl_md = dnnl::memory::desc({oC}, gradBType, dnnl::memory::format_tag::x);
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auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// forward primitive description
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dnnl::deconvolution_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference,
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dnnl::algorithm::deconvolution_direct, x_mkl_md, w_mkl_md, gradB_mkl_md,
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gradO_mkl_md, strides, dilation, padding, padding_r);
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dnnl::deconvolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
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// backward data primitive description
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dnnl::deconvolution_backward_data::desc op_data_bp_desc(dnnl::algorithm::deconvolution_direct, gradI_mkl_md, w_mkl_md,
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gradO_mkl_md, strides, dilation, padding, padding_r);
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dnnl::deconvolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, op_ff_prim_desc);
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// backward weights primitive description
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dnnl::deconvolution_backward_weights::desc op_weights_bp_desc(dnnl::algorithm::deconvolution_direct, x_mkl_md,
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gradW_mkl_md, gradB_mkl_md, gradO_mkl_md, strides,
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dilation, padding, padding_r);
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dnnl::deconvolution_backward_weights::primitive_desc op_weights_bp_prim_desc(op_weights_bp_desc, engine,
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op_ff_prim_desc);
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// arguments (memory buffers) necessary for calculations
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std::unordered_map<int, dnnl::memory> args;
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dnnl::stream stream(engine);
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// provide memory buffers and check whether reorder is required
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// input
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onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_weights_bp_prim_desc.src_desc(),
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args[DNNL_ARG_SRC]);
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// weights
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onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_data_bp_prim_desc.weights_desc(),
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args[DNNL_ARG_WEIGHTS]);
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// gradO
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auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, const_cast<void*>(gradO->buffer()));
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const bool gradOReorderW = op_weights_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
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const bool gradOReorderD = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
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auto gradO_mkl_memW = gradOReorderW ? dnnl::memory(op_weights_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
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auto gradO_mkl_memD = gradOReorderD ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
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if (gradOReorderW) dnnl::reorder(gradO_user_mem, gradO_mkl_memW).execute(stream, gradO_user_mem, gradO_mkl_memW);
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if (gradOReorderD) dnnl::reorder(gradO_user_mem, gradO_mkl_memD).execute(stream, gradO_user_mem, gradO_mkl_memD);
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args[DNNL_ARG_DIFF_DST] = gradO_mkl_memD;
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// gradI
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auto gradI_user_mem = onednnUtils::loadDataToMklStream(*gradI, engine, stream, gradI_user_md,
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op_data_bp_prim_desc.diff_src_desc(), args[DNNL_ARG_DIFF_SRC]);
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// gradW
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auto gradW_user_mem = onednnUtils::loadDataToMklStream(
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*gradW, engine, stream, gradW_user_md, op_weights_bp_prim_desc.diff_weights_desc(), args[DNNL_ARG_DIFF_WEIGHTS]);
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// gradB
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if (gradB != nullptr) {
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auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->buffer());
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args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem;
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}
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// run backward data calculations
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dnnl::deconvolution_backward_data(op_data_bp_prim_desc).execute(stream, args);
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if (gradOReorderW || gradOReorderD) args[DNNL_ARG_DIFF_DST] = gradO_mkl_memW;
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// run backward weights calculations
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dnnl::deconvolution_backward_weights(op_weights_bp_prim_desc).execute(stream, args);
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// reorder gradI if necessary
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if (op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc())
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dnnl::reorder(args[DNNL_ARG_DIFF_SRC], gradI_user_mem).execute(stream, args[DNNL_ARG_DIFF_SRC], gradI_user_mem);
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if (op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc())
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dnnl::reorder(args[DNNL_ARG_DIFF_WEIGHTS], gradW_user_mem)
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.execute(stream, args[DNNL_ARG_DIFF_WEIGHTS], gradW_user_mem);
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stream.wait();
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(deconv3d, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
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REQUIRE_TRUE(input->rankOf() == 5, 0,
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"CUSTOM DECONV3D_MKLDNN OP: rank of input array must be equal to 5, but got %i instead !",
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input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0,
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"CUSTOM DECONV3D_MKLDNN OP: rank of weights array must be equal to 5, but got %i instead !",
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weights->rankOf());
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|
|
sd::LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0)); // filter(kernel) depth
|
|
sd::LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1)); // filter(kernel) height
|
|
sd::LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<sd::LongType>(weights->sizeAt(2)); // filter(kernel) width
|
|
sd::LongType sD = INT_ARG(3); // strides depth
|
|
sd::LongType sH = INT_ARG(4); // strides height
|
|
sd::LongType sW = INT_ARG(5); // strides width
|
|
sd::LongType pD = INT_ARG(6); // paddings depth
|
|
sd::LongType pH = INT_ARG(7); // paddings height
|
|
sd::LongType pW = INT_ARG(8); // paddings width
|
|
sd::LongType dD = INT_ARG(9); // dilations depth
|
|
sd::LongType dH = INT_ARG(10); // dilations height
|
|
sd::LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]
|
|
|
|
sd::LongType bS, iC, iD, iH, iW, oC, oD, oH,
|
|
oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
sd::LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW,
|
|
indIOioC, indIOioD, indWoC, indWiC, indWkD);
|
|
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CUSTOM DECONV3D_MKLDNN OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
|
|
"CUSTOM DECONV3D_MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got "
|
|
"%i, %i instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
if (isSameMode) { // SAME
|
|
// Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward
|
|
// pass
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
|
|
}
|
|
|
|
deconv3dMKLDNN(input, weights, bias, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isNCDHW, wFormat);
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
PLATFORM_CHECK(deconv3d, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto weights = INPUT_VARIABLE(1);
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;
|
|
|
|
auto output = INPUT_VARIABLE(0);
|
|
|
|
sd::LongType dD = INT_ARG(9); // dilations depth
|
|
sd::LongType dH = INT_ARG(10); // dilations height
|
|
sd::LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
|
|
Requirements req("ONEDNN DECONV3d OP");
|
|
req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
|
|
req.expectLessEq(makeInfoVariable(dD, "Dilation depth"), 1) &&
|
|
req.expectLessEq(makeInfoVariable(dH, "Dilation height"), 1) &&
|
|
req.expectLessEq(makeInfoVariable(dW, "Dilation width"), 1) &&
|
|
req.expectFalse(makeInfoVariable(isSameMode, "isSameMode")) &&
|
|
req.expectTrue(makeInfoVariable(
|
|
[input, weights, bias, output] {
|
|
const DataType xType = input->dataType();
|
|
const DataType wType = weights->dataType();
|
|
const DataType zType = output->dataType();
|
|
const DataType bType = bias != nullptr ? bias->dataType() : zType;
|
|
return (xType == DataType::FLOAT32 && wType == DataType::FLOAT32 &&
|
|
bType == DataType::FLOAT32 && zType == DataType::FLOAT32) ||
|
|
((xType == DataType::UINT8 || xType == DataType::INT8) && wType == DataType::INT8 &&
|
|
(zType == DataType::UINT8 || zType == DataType::INT8 || zType == DataType::INT32 ||
|
|
zType == DataType::FLOAT32) &&
|
|
bType == zType);
|
|
},
|
|
TYPECHECK_MSG),
|
|
NO_MSG);
|
|
req.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(deconv3d_bp, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D_MKLDNN_BP OP: rank of input array must be equal to 5, but got %i instead !",
|
|
input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D_MKLDNN_BP OP: rank of weights array must be equal to 5 , but got %i instead !",
|
|
weights->rankOf());
|
|
REQUIRE_TRUE(gradO->rankOf() == 5, 0,
|
|
"CUSTOM DECONV3D_MKLDNN_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but "
|
|
"got %i instead !",
|
|
gradO->rankOf());
|
|
|
|
sd::LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0)); // filter(kernel) depth
|
|
sd::LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1)); // filter(kernel) height
|
|
sd::LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<sd::LongType>(weights->sizeAt(2)); // filter(kernel) width
|
|
sd::LongType sD = INT_ARG(3); // strides depth
|
|
sd::LongType sH = INT_ARG(4); // strides height
|
|
sd::LongType sW = INT_ARG(5); // strides width
|
|
sd::LongType pD = INT_ARG(6); // paddings depth
|
|
sd::LongType pH = INT_ARG(7); // paddings height
|
|
sd::LongType pW = INT_ARG(8); // paddings width
|
|
sd::LongType dD = INT_ARG(9); // dilations depth
|
|
sd::LongType dH = INT_ARG(10); // dilations height
|
|
sd::LongType dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
int wFormat = block.getIArguments()->size() > 14
|
|
? INT_ARG(14)
|
|
: 0; // 0 - [kD, kH, kW, oC, iC], 1 - [iC, oC, kD, kH, kW], 2 - [iC, kD, kH, kW, oC]
|
|
|
|
sd::LongType bS, iC, iD, iH, iW, oC, oD, oH,
|
|
oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
|
|
sd::LongType indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, wFormat, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
|
|
indIOioC, indIOioD, indWoC, indWiC, indWkD);
|
|
|
|
sd::LongType trueoD, trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH,
|
|
iW, isSameMode);
|
|
|
|
std::vector<sd::LongType> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx(
|
|
{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2});
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, oC, iC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
|
|
"CUSTOM DECONV3D_MKLDNN_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, "
|
|
"but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CUSTOM DECONV3D_MKLDNN_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
|
|
"CUSTOM DECONV3D_MKLDNN_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but "
|
|
"got %i, %i instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
if (isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not
|
|
// deconv) forward pass
|
|
ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
|
|
|
|
deconv3dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW,
|
|
isNCDHW, wFormat);
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
PLATFORM_CHECK(deconv3d_bp, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NHWC) or [bS, iD, iC, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, oC, iC], [iC, oC, kD, kH, kW], [iC, kD, kH, kW, oC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
int dD = INT_ARG(9); // dilations depth
|
|
int dH = INT_ARG(10); // dilations height
|
|
int dW = INT_ARG(11); // dilations width
|
|
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
|
|
|
|
Requirements req("ONEDNN DECONV3d_BP OP");
|
|
req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
|
|
req.expectLessEq(makeInfoVariable(dD, "Dilation depth"), 1) &&
|
|
req.expectLessEq(makeInfoVariable(dH, "Dilation height"), 1) &&
|
|
req.expectLessEq(makeInfoVariable(dW, "Dilation width"), 1) &&
|
|
req.expectFalse(makeInfoVariable(isSameMode, "isSameMode")) &&
|
|
req.expectTrue(makeInfoVariable(
|
|
[input, weights, gradO, gradI, gradW, gradB] {
|
|
const DataType xType = input->dataType();
|
|
const DataType wType = weights->dataType();
|
|
const DataType gradOType = gradO->dataType();
|
|
|
|
const DataType gradIType = gradI->dataType();
|
|
const DataType gradWType = gradW->dataType();
|
|
const DataType gradBType = gradB != nullptr ? gradB->dataType() : DataType::FLOAT32;
|
|
return ((xType == DataType::FLOAT32 || xType == DataType::BFLOAT16) &&
|
|
(wType == DataType::FLOAT32 || wType == DataType::BFLOAT16) &&
|
|
(gradOType == DataType::FLOAT32 || gradOType == DataType::BFLOAT16) &&
|
|
(gradIType == DataType::FLOAT32 || gradIType == DataType::BFLOAT16) &&
|
|
(gradWType == DataType::FLOAT32 || gradWType == DataType::BFLOAT16) &&
|
|
(gradBType == DataType::FLOAT32 || gradBType == DataType::BFLOAT16));
|
|
},
|
|
TYPECHECK_MSG),
|
|
NO_MSG);
|
|
req.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
} // namespace platforms
|
|
} // namespace ops
|
|
} // namespace sd
|