560 lines
35 KiB
C++
560 lines
35 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2019 Skymind, Inc.
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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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* 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, created on 05.02.2018
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_conv3dnew)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include <MmulHelper.h>
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namespace nd4j {
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namespace ops {
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#ifdef HAVE_MKLDNN
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using namespace mkldnn;
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#endif
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CUSTOM_OP_IMPL(conv3dnew, 2, 1, false, 0, 13) {
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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, iC, oC] always
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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, "CUSTOM CONV3D OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if (bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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#ifdef HAVE_MKLDNN
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if (block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, output})) {
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std::vector<nd4j::MKLDNNStream>& streams = block.getMKLDNNStreams();
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if (streams.empty()) {
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streams.push_back(MKLDNNStream("conv3dnew"));
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}
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if (streams[0].checkAndReset({input, weights, bias}, {output}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNCDHW})) {
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mkldnn_memory_desc_t empty;
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mkldnn::memory::desc conv_src_md(empty), conv_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
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mkldnn::memory::desc user_src_md(empty), user_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
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mkldnn::memory::dims conv_strides, conv_padding, conv_padding_r;
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ConvolutionUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNCDHW,
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bS, iC, iD, iH, iW, oC, oD, oH, oW, input, nullptr, weights, nullptr, bias, output,
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&conv_src_md, nullptr, &conv_weights_md, nullptr, &conv_bias_md, &conv_dst_md,
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&user_src_md, nullptr, &user_weights_md, nullptr, &user_bias_md, &user_dst_md,
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conv_strides, conv_padding, conv_padding_r);
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auto conv_desc = bias != nullptr
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? convolution_forward::desc(prop_kind::forward,
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convolution_direct, conv_src_md, conv_weights_md, conv_bias_md,
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conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero)
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: convolution_forward::desc(prop_kind::forward,
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convolution_direct, conv_src_md, conv_weights_md,
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conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
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auto engine = streams[0].getEngine();
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auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine);
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auto user_src_memory = mkldnn::memory({user_src_md, engine}, const_cast<NDArray*>(input)->buffer());
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auto user_weights_memory = mkldnn::memory({user_weights_md, engine}, const_cast<NDArray*>(weights)->buffer());
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auto user_dst_memory = mkldnn::memory({user_dst_md, engine}, output->buffer());
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auto conv_src_memory = user_src_memory;
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streams[0].addMemory(user_src_memory);
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if (mkldnn::memory::primitive_desc(conv_prim_desc.src_primitive_desc())
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!= user_src_memory.get_primitive_desc()) {
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conv_src_memory = mkldnn::memory(conv_prim_desc.src_primitive_desc());
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streams[0].addMemory(conv_src_memory);
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streams[0].addOperation(reorder(user_src_memory, conv_src_memory));
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}
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auto conv_weights_memory = user_weights_memory;
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streams[0].addMemory(user_weights_memory);
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if (mkldnn::memory::primitive_desc(conv_prim_desc.weights_primitive_desc())
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!= user_weights_memory.get_primitive_desc()) {
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conv_weights_memory = mkldnn::memory(conv_prim_desc.weights_primitive_desc());
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streams[0].addMemory(conv_weights_memory);
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streams[0].addOperation(reorder(user_weights_memory, conv_weights_memory));
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}
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auto conv_dst_memory = user_dst_memory;
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streams[0].addMemory(user_dst_memory);
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if (mkldnn::memory::primitive_desc(conv_prim_desc.dst_primitive_desc())
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!= user_dst_memory.get_primitive_desc()) {
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conv_dst_memory = mkldnn::memory(conv_prim_desc.dst_primitive_desc());
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streams[0].addMemory(conv_dst_memory);
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}
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if (bias != nullptr) {
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auto conv_bias_memory = mkldnn::memory(conv_prim_desc.bias_primitive_desc(), bias->buffer());
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streams[0].addMemory(conv_bias_memory);
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streams[0].addOperation(convolution_forward(conv_prim_desc, conv_src_memory, conv_weights_memory, conv_bias_memory, conv_dst_memory));
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} else {
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streams[0].addOperation(convolution_forward(conv_prim_desc, conv_src_memory, conv_weights_memory, conv_dst_memory));
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}
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if (mkldnn::memory::primitive_desc(conv_prim_desc.dst_primitive_desc())
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!= user_dst_memory.get_primitive_desc()) {
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streams[0].addOperation(reorder(conv_dst_memory, user_dst_memory));
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}
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}
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streams[0].submitAndWait();
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return Status::OK();
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}
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#endif
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nd4j_debug("MKL-DNN is not used for conv3dnew!\n", 0);
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std::vector<int> permutForOutput;
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if (isNCDHW)
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permutForOutput = {0,2,3,4,1}; // [bS, oC, oD, oH, oW] -> [bS, oD, oH, oW, oC]
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else
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input = new NDArray(input->permute({0,4,1,2,3}));
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NDArray columns(input->ordering(), {bS, iC, kD, kH, kW, oD, oH, oW}, input->dataType(), block.launchContext());
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ConvolutionUtils::vol2col(block, *input, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
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// [bS, iC, kD, kH, kW, oD, oH, oW] x [kD, kH, kW, iC, oC] = [bS, oD, oH, oW, oC]
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MmulHelper::tensorDot(&columns, weights, output, {1,2,3,4}, {3,0,1,2}, permutForOutput);
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if(bias)
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output->applyBroadcast(broadcast::Add, {indIOioC}, bias);
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if(!isNCDHW)
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delete input;
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return Status::OK();
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}
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DECLARE_TYPES(conv3dnew) {
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getOpDescriptor()
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->setAllowedInputTypes(0, nd4j::DataType::ANY)
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(conv3dnew) {
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auto inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
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auto weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC] always
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auto biasShapeInfo = block.width() > 2 ? inputShape->at(2) : nullptr; // [oC]
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID;
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int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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const int rank = 5;
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REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
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REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
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int indIOioC, indIiD, indWoC(4);
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if(!isNCDHW) {
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indIOioC = 4; indIiD = 1;
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}
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else {
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indIOioC = 1; indIiD = 2;
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}
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int bS = inputShapeInfo[1]; // batch size
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int iD = inputShapeInfo[indIiD+1]; // input depth
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int iH = inputShapeInfo[indIiD+2]; // input height
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int iW = inputShapeInfo[indIiD+3]; // input width
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int iC = inputShapeInfo[indIOioC+1]; // input channels
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int oC = weightsShapeInfo[indWoC+1]; // output channels
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
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if (biasShapeInfo)
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REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
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int oD, oH, oW; // output depth, height, width
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ConvolutionUtils::calcOutSizePool3D(oD, oH, oW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
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Nd4jLong* outputShapeInfo = nullptr;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(inputShapeInfo), Nd4jLong);
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outputShapeInfo[0] = rank;
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outputShapeInfo[1] = bS;
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if (isNCDHW) {
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outputShapeInfo[2] = oC;
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outputShapeInfo[3] = oD;
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outputShapeInfo[4] = oH;
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outputShapeInfo[5] = oW;
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} else {
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outputShapeInfo[2] = oD;
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outputShapeInfo[3] = oH;
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outputShapeInfo[4] = oW;
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outputShapeInfo[5] = oC;
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}
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ShapeUtils::updateStridesAndType(outputShapeInfo, weightsShapeInfo, shape::order(inputShapeInfo));
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return SHAPELIST(CONSTANT(outputShapeInfo));
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}
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(conv3dnew_bp, 3, 2, false, 0, 13) {
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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, iC, oC] always
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auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
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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
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon
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auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC] always
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM CONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
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REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D_BP OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
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REQUIRE_TRUE(gradO->rankOf() == 5, 0, "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !", gradO->rankOf());
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int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
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int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
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int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
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int sD = INT_ARG(3); // strides depth
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int sH = INT_ARG(4); // strides height
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int sW = INT_ARG(5); // strides width
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int pD = INT_ARG(6); // paddings depth
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int pH = INT_ARG(7); // paddings height
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int pW = INT_ARG(8); // paddings width
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int dD = INT_ARG(9); // dilations depth
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int dH = INT_ARG(10); // dilations height
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int dW = INT_ARG(11); // dilations width
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int isSameMode = INT_ARG(12); // 1-SAME, 0-VALID
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int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
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int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
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int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv3d(isNDHWC, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWiC, indWoC, indWkD);
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int trueoD, trueoH, trueoW; // true output depth/height/width
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ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
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std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2}));
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std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
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REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if(bias)
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REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
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if(isSameMode) // SAME
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ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
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#ifdef HAVE_MKLDNN
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if (block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB})) {
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std::vector<nd4j::MKLDNNStream>& streams = block.getMKLDNNStreams();
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if (streams.empty()) {
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streams.push_back(MKLDNNStream("conv3dnew_bp_weights"));
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streams.push_back(MKLDNNStream("conv3dnew_bp_data"));
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}
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bool resetW = streams[0].checkAndReset({input, weights, bias, gradO}, {gradI, gradW, gradB}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNDHWC});
|
|
bool resetI = streams[1].checkAndReset({input, weights, bias, gradO}, {gradI, gradW, gradB}, {}, {kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNDHWC});
|
|
if (resetW || resetI) {
|
|
mkldnn_memory_desc_t empty;
|
|
mkldnn::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty),
|
|
conv_diff_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
|
|
mkldnn::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty),
|
|
user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
|
|
mkldnn::memory::dims conv_strides, conv_padding, conv_padding_r;
|
|
|
|
ConvolutionUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode, isNDHWC,
|
|
bS, iC, iD, iH, iW, oC, oD, oH, oW, input, gradI, weights, gradW, gradB, gradO,
|
|
&conv_src_md, &conv_diff_src_md, &conv_weights_md, &conv_diff_weights_md, &conv_bias_md, &conv_dst_md,
|
|
&user_src_md, &user_diff_src_md, &user_weights_md, &user_diff_weights_md, &user_bias_md, &user_dst_md,
|
|
conv_strides, conv_padding, conv_padding_r);
|
|
|
|
auto conv_desc = gradB != nullptr
|
|
? convolution_forward::desc(prop_kind::forward,
|
|
convolution_direct, conv_src_md, conv_weights_md, conv_bias_md,
|
|
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero)
|
|
: convolution_forward::desc(prop_kind::forward,
|
|
convolution_direct, conv_src_md, conv_weights_md,
|
|
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
|
|
|
|
auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, streams[0].getEngine());
|
|
|
|
if (gradW != nullptr) {
|
|
auto convW_desc = gradB != nullptr
|
|
? convolution_backward_weights::desc(
|
|
convolution_direct, conv_src_md, conv_diff_weights_md, conv_bias_md,
|
|
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero)
|
|
: convolution_backward_weights::desc(
|
|
convolution_direct, conv_src_md, conv_diff_weights_md,
|
|
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
|
|
|
|
auto engine = streams[0].getEngine();
|
|
auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine, conv_prim_desc);
|
|
auto userW_src_memory = mkldnn::memory({user_src_md, engine}, const_cast<NDArray*>(input)->buffer());
|
|
auto userW_weights_memory = mkldnn::memory({user_diff_weights_md, engine}, gradW->buffer());
|
|
auto userW_dst_memory = mkldnn::memory({user_dst_md, engine}, const_cast<NDArray*>(gradO)->buffer());
|
|
|
|
auto convW_src_memory = userW_src_memory;
|
|
streams[0].addMemory(userW_src_memory);
|
|
if (mkldnn::memory::primitive_desc(convW_prim_desc.src_primitive_desc())
|
|
!= userW_src_memory.get_primitive_desc()) {
|
|
convW_src_memory = mkldnn::memory(convW_prim_desc.src_primitive_desc());
|
|
streams[0].addMemory(convW_src_memory);
|
|
streams[0].addOperation(reorder(userW_src_memory, convW_src_memory));
|
|
}
|
|
|
|
auto convW_weights_memory = userW_weights_memory;
|
|
streams[0].addMemory(userW_weights_memory);
|
|
if (mkldnn::memory::primitive_desc(convW_prim_desc.diff_weights_primitive_desc())
|
|
!= userW_weights_memory.get_primitive_desc()) {
|
|
convW_weights_memory = mkldnn::memory(convW_prim_desc.diff_weights_primitive_desc());
|
|
streams[0].addMemory(convW_weights_memory);
|
|
}
|
|
|
|
auto convW_dst_memory = userW_dst_memory;
|
|
streams[0].addMemory(userW_dst_memory);
|
|
if (mkldnn::memory::primitive_desc(convW_prim_desc.diff_dst_primitive_desc())
|
|
!= userW_dst_memory.get_primitive_desc()) {
|
|
convW_dst_memory = mkldnn::memory(convW_prim_desc.diff_dst_primitive_desc());
|
|
streams[0].addMemory(convW_dst_memory);
|
|
streams[0].addOperation(reorder(userW_dst_memory, convW_dst_memory));
|
|
}
|
|
|
|
if (gradB != nullptr) {
|
|
auto convW_bias_memory = mkldnn::memory(convW_prim_desc.diff_bias_primitive_desc(), gradB->buffer());
|
|
streams[0].addMemory(convW_bias_memory);
|
|
streams[0].addOperation(convolution_backward_weights(convW_prim_desc, convW_src_memory, convW_dst_memory, convW_weights_memory, convW_bias_memory));
|
|
} else {
|
|
streams[0].addOperation(convolution_backward_weights(convW_prim_desc, convW_src_memory, convW_dst_memory, convW_weights_memory));
|
|
}
|
|
|
|
if (mkldnn::memory::primitive_desc(convW_prim_desc.diff_weights_primitive_desc())
|
|
!= userW_weights_memory.get_primitive_desc()) {
|
|
streams[0].addOperation(reorder(convW_weights_memory, userW_weights_memory));
|
|
}
|
|
}
|
|
|
|
if (gradI != nullptr) {
|
|
auto convI_desc =
|
|
convolution_backward_data::desc(
|
|
convolution_direct, conv_diff_src_md, conv_weights_md,
|
|
conv_dst_md, conv_strides, conv_padding, conv_padding_r, padding_kind::zero);
|
|
|
|
auto engine = streams[1].getEngine();
|
|
auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine, conv_prim_desc);
|
|
auto userI_src_memory = mkldnn::memory({user_diff_src_md, engine}, gradI->buffer());
|
|
auto userI_weights_memory = mkldnn::memory({user_weights_md, engine}, const_cast<NDArray*>(weights)->buffer());
|
|
auto userI_dst_memory = mkldnn::memory({user_dst_md, engine}, const_cast<NDArray*>(gradO)->buffer());
|
|
|
|
auto convI_src_memory = userI_src_memory;
|
|
streams[1].addMemory(userI_src_memory);
|
|
if (mkldnn::memory::primitive_desc(convI_prim_desc.diff_src_primitive_desc())
|
|
!= userI_src_memory.get_primitive_desc()) {
|
|
convI_src_memory = mkldnn::memory(convI_prim_desc.diff_src_primitive_desc());
|
|
streams[1].addMemory(convI_src_memory);
|
|
}
|
|
|
|
auto convI_weights_memory = userI_weights_memory;
|
|
streams[1].addMemory(userI_weights_memory);
|
|
if (mkldnn::memory::primitive_desc(convI_prim_desc.weights_primitive_desc())
|
|
!= userI_weights_memory.get_primitive_desc()) {
|
|
convI_weights_memory = mkldnn::memory(convI_prim_desc.weights_primitive_desc());
|
|
streams[1].addMemory(convI_weights_memory);
|
|
streams[1].addOperation(reorder(userI_weights_memory, convI_weights_memory));
|
|
}
|
|
|
|
auto convI_dst_memory = userI_dst_memory;
|
|
streams[1].addMemory(userI_dst_memory);
|
|
if (mkldnn::memory::primitive_desc(convI_prim_desc.diff_dst_primitive_desc())
|
|
!= userI_dst_memory.get_primitive_desc()) {
|
|
convI_dst_memory = mkldnn::memory(convI_prim_desc.diff_dst_primitive_desc());
|
|
streams[1].addMemory(convI_dst_memory);
|
|
streams[1].addOperation(reorder(userI_dst_memory, convI_dst_memory));
|
|
}
|
|
|
|
streams[1].addOperation(convolution_backward_data(convI_prim_desc, convI_dst_memory, convI_weights_memory, convI_src_memory));
|
|
|
|
if (mkldnn::memory::primitive_desc(convI_prim_desc.diff_src_primitive_desc())
|
|
!= userI_src_memory.get_primitive_desc()) {
|
|
streams[1].addOperation(reorder(convI_src_memory, userI_src_memory));
|
|
}
|
|
}
|
|
}
|
|
|
|
if (gradW != nullptr) {
|
|
streams[0].submitAndWait();
|
|
}
|
|
if (gradI != nullptr) {
|
|
streams[1].submitAndWait();
|
|
}
|
|
return Status::OK();
|
|
}
|
|
#endif
|
|
nd4j_debug("MKL-DNN is not used for conv3dnew_bp!\n", 0);
|
|
|
|
std::vector<int> gradOaxesForDot;
|
|
|
|
if(!isNDHWC) {
|
|
gradOaxesForDot = {0,1,2,3}; // bS, oD, oH, oW
|
|
input = new NDArray(input->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
|
|
gradI = new NDArray(gradI->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
|
|
}
|
|
else {
|
|
gradOaxesForDot = {0,2,3,4}; // bS, oD, oH, oW
|
|
}
|
|
|
|
// ----- calculation of gradW and gradB ----- //
|
|
NDArray columns(input->ordering(), {bS, iC, kD, kH, kW, oD, oH, oW}, input->dataType(), block.launchContext());
|
|
ConvolutionUtils::vol2col(block, *input, columns, sD, sH, sW, pD, pH, pW, dD, dH, dW); // [bS, iC, iD, iH, iW] is convoluted to [bS, iC, kD, kH, kW, oD, oH, oW]
|
|
MmulHelper::tensorDot(&columns, gradO, gradW, {0,5,6,7}, gradOaxesForDot, {3,0,1,2,4}); // [bS, iC, kD, kH, kW, oD, oH, oW] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [iC, kD, kH, kW, oC]
|
|
|
|
//----- calculation of gradO -----//
|
|
if(gradB) {
|
|
if(gradB->rankOf() == 2)
|
|
gradB = new NDArray(gradB->reshape(gradB->ordering(), {(int)gradB->lengthOf()}));
|
|
gradO->reduceAlongDimension(reduce::Sum, gradB, gradOaxesForDot); // sum over bS oD oH oW
|
|
if(gradB != OUTPUT_VARIABLE(2))
|
|
delete gradB;
|
|
}
|
|
|
|
//----- calculation of gradI -----//
|
|
MmulHelper::tensorDot(weights, gradO, &columns, {indWoC}, {indIOioC}, {2,3,4,1,0,5,6,7}); // [kD, kH, kW, iC, oC] x [bS, oD, oH, oW, oC]/[bS, oC, oD, oH, oW] = [kD, kH, kW, iC, bS, oD, oH, oW]
|
|
ConvolutionUtils::col2vol(block, columns, *gradI, sD, sH, sW, pD, pH, pW, dD, dH, dW); // columns [bS, iC, kD, kH, kW, oD, oH, oW] is de-convoluted to [bS, iC, iD, iH, iW]
|
|
|
|
if(!isNDHWC) {
|
|
delete input;
|
|
delete gradI;
|
|
}
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
DECLARE_TYPES(conv3dnew_bp) {
|
|
getOpDescriptor()
|
|
->setAllowedInputTypes(0, nd4j::DataType::ANY)
|
|
->setAllowedInputTypes(1, {ALL_FLOATS})
|
|
->setAllowedInputTypes(2, {ALL_FLOATS})
|
|
->setAllowedInputTypes(3, {ALL_FLOATS})
|
|
->setAllowedOutputTypes({ALL_FLOATS});
|
|
}
|
|
|
|
|
|
DECLARE_SHAPE_FN(conv3dnew_bp) {
|
|
|
|
Nd4jLong* inputShapeInfo = inputShape->at(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
|
|
Nd4jLong* weightsShapeInfo = inputShape->at(1); // [kD, kH, kW, iC, oC] always
|
|
Nd4jLong* biasShapeInfo = block.width() > 3 ? inputShape->at(2) : nullptr; // [oC]
|
|
Nd4jLong* gradOShapeInfo = block.width() > 3 ? inputShape->at(3) : inputShape->at(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
|
|
|
|
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 0));// filter(kernel) depth
|
|
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 1));// filter(kernel) height
|
|
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(shape::sizeAt(weightsShapeInfo, 2));// filter(kernel) width
|
|
int sD = INT_ARG(3); // strides depth
|
|
int sH = INT_ARG(4); // strides height
|
|
int sW = INT_ARG(5); // strides width
|
|
int pD = INT_ARG(6); // paddings depth
|
|
int pH = INT_ARG(7); // paddings height
|
|
int pW = INT_ARG(8); // paddings width
|
|
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); // 1-SAME, 0-VALID
|
|
int isNDHWC = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
|
|
|
|
const int rank = 5;
|
|
REQUIRE_TRUE(inputShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of input array must be equal to %i, but got %i instead !", rank, inputShapeInfo);
|
|
REQUIRE_TRUE(weightsShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of weights array must be equal to %i, but got %i instead !", rank, weightsShapeInfo);
|
|
REQUIRE_TRUE(gradOShapeInfo[0] == rank, 0, "CUSTOM CONV3D_BP OP: rank of output gradients (next epsilon) array must be equal to %i, but got %i instead !", rank, gradOShapeInfo);
|
|
|
|
int indIOioC, indIiD, indWoC(4);
|
|
if(!isNDHWC) {
|
|
indIOioC = 4; indIiD = 1;
|
|
}
|
|
else {
|
|
indIOioC = 1; indIiD = 2;
|
|
}
|
|
|
|
int bS = inputShapeInfo[1]; // batch size
|
|
int iD = inputShapeInfo[indIiD+1]; // input depth
|
|
int iH = inputShapeInfo[indIiD+2]; // input height
|
|
int iW = inputShapeInfo[indIiD+3]; // input width
|
|
int iC = inputShapeInfo[indIOioC+1]; // input channels
|
|
int oC = weightsShapeInfo[indWoC+1]; // output channels
|
|
|
|
int trueoD, trueoH, trueoW; // true output depth/height/width
|
|
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
|
|
|
|
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIiD,indIiD+1,indIiD+2}));
|
|
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
|
|
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradOShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradOShapeInfo).c_str());
|
|
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weightsShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weightsShapeInfo).c_str());
|
|
if(biasShapeInfo)
|
|
REQUIRE_TRUE(biasShapeInfo[0] <= 2 && oC == shape::length(biasShapeInfo), 0, "CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, biasShapeInfo[0], shape::length(biasShapeInfo));
|
|
|
|
auto gradIshapeInfo = ShapeBuilders::copyShapeInfoAndType(inputShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
auto gradWshapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
|
|
if(biasShapeInfo) {
|
|
auto gradBshapeInfo = ShapeBuilders::copyShapeInfoAndType(biasShapeInfo, gradOShapeInfo, false, block.getWorkspace());
|
|
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo), CONSTANT(gradBshapeInfo));
|
|
}
|
|
|
|
return SHAPELIST(CONSTANT(gradIshapeInfo), CONSTANT(gradWshapeInfo));
|
|
}
|
|
}
|
|
}
|
|
|
|
#endif
|