/* ****************************************************************************** * * * This program and the accompanying materials are made available under the * terms of the Apache License, Version 2.0 which is available at * https://www.apache.org/licenses/LICENSE-2.0. * * See the NOTICE file distributed with this work for additional * information regarding copyright ownership. * 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. * * SPDX-License-Identifier: Apache-2.0 ******************************************************************************/ // // @author saudet // @author raver119@gmail.com // #include #include #include #include #include #include "mkldnnUtils.h" using namespace dnnl; namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////// static void conv3dMKLDNN(NDArray *input, NDArray *weights, NDArray *bias, NDArray *output, const sd::LongType kD, const sd::LongType kH, const sd::LongType kW, const sd::LongType sD, const sd::LongType sH, const sd::LongType sW, const sd::LongType pD, const sd::LongType pH, const sd::LongType pW, const sd::LongType dD, const sd::LongType dH, const sd::LongType dW, const int paddingMode, const int isNCDHW, const int wFormat) { // mkl support weights in [oC, iC, kD, kH, kW] format only 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, indWiC, indWoC, indWkD); // const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == // 1 for causal mode in conv1d dnnl::memory::dims strides = {sD, sH, sW}; dnnl::memory::dims padding = {pD, pH, pW}; // dnnl::memory::dims padding_r = { (oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame }; dnnl::memory::dims padding_r = {(oD - 1) * sD - iD + kD - pD, (oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pW}; dnnl::memory::dims dilation = {dD - 1, dH - 1, dW - 1}; auto xzFormatMkl = isNCDHW ? dnnl::memory::format_tag::ncdhw : dnnl::memory::format_tag::ndhwc; dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::oidhw; dnnl::memory::dims xDims = {bS, iC, iD, iH, iW}; dnnl::memory::dims wDims = {oC, iC, kD, kH, kW}; dnnl::memory::dims zDims = {bS, oC, oD, oH, oW}; std::vector permut; if (0 == wFormat) permut = {4, 3, 0, 1, 2}; // [kD, kH, kW, iC, oC] -> [oC, iC, kD, kH, kW] else if (2 == wFormat) permut = {0, 4, 1, 2, 3}; // [oC, kD, kH, kW, iC] -> [oC, iC, kD, kH, kW] auto type = dnnl::memory::data_type::f32; // memory descriptors for arrays // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFormatMkl); onednnUtils::setBlockStrides(*input, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormatMkl); onednnUtils::setBlockStrides(*weights, w_user_md, permut); // bias dnnl::memory::desc b_mkl_md; if (bias != nullptr) b_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x); // output dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, type, xzFormatMkl); onednnUtils::setBlockStrides(*output, z_user_md); auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine()); // operation primitive description dnnl::convolution_forward::desc op_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto, x_mkl_md, w_mkl_md, b_mkl_md, z_mkl_md, strides, dilation, padding, padding_r); dnnl::convolution_forward::primitive_desc op_prim_desc(op_desc, engine); // arguments (memory buffers) necessary for calculations std::unordered_map args; dnnl::stream stream(engine); // provide memory buffers and check whether reorder is required // input onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]); // weights onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_prim_desc.weights_desc(), args[DNNL_ARG_WEIGHTS]); // bias if (bias != nullptr) { auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, const_cast(bias->buffer())); args[DNNL_ARG_BIAS] = b_mkl_mem; } // output auto z_user_mem = onednnUtils::loadDataToMklStream(*output, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]); // run calculations dnnl::convolution_forward(op_prim_desc).execute(stream, args); // reorder outputs if necessary if (op_prim_desc.dst_desc() != z_user_mem.get_desc()) dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], z_user_mem); stream.wait(); } ////////////////////////////////////////////////////////////////////// static void conv3dBpMKLDNN(NDArray *input, NDArray *weights, NDArray *bias, NDArray *gradO, NDArray *gradI, NDArray *gradW, NDArray *gradB, const sd::LongType kD, const sd::LongType kH, const sd::LongType kW, const sd::LongType sD, const sd::LongType sH, const sd::LongType sW, const sd::LongType pD, const sd::LongType pH, const sd::LongType pW, const sd::LongType dD, const sd::LongType dH, const sd::LongType dW, const int paddingMode, const int isNCDHW, const int wFormat) { // mkl support weights/gradW in [oC, iC, kD, kH, kW] format only 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, indWiC, indWoC, indWkD); // const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == // 1 for causal mode in conv1d dnnl::memory::dims strides = {sD, sH, sW}; dnnl::memory::dims padding = {pD, pH, pW}; // dnnl::memory::dims padding_r = { (oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame }; dnnl::memory::dims padding_r = {(oD - 1) * sD - iD + kD - pD, (oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pW}; dnnl::memory::dims dilation = {dD - 1, dH - 1, dW - 1}; auto xzFormatMkl = isNCDHW ? dnnl::memory::format_tag::ncdhw : dnnl::memory::format_tag::ndhwc; dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::oidhw; dnnl::memory::dims xDims = {bS, iC, iD, iH, iW}; dnnl::memory::dims wDims = {oC, iC, kD, kH, kW}; dnnl::memory::dims zDims = {bS, oC, oD, oH, oW}; auto type = dnnl::memory::data_type::f32; std::vector permut; if (0 == wFormat) permut = {4, 3, 0, 1, 2}; // [kD, kH, kW, iC, oC] -> [oC, iC, kD, kH, kW] else if (2 == wFormat) permut = {0, 4, 1, 2, 3}; // [oC, kD, kH, kW, iC] -> [oC, iC, kD, kH, kW] // memory descriptors for arrays // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFormatMkl); onednnUtils::setBlockStrides(*input, x_user_md); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormatMkl); onednnUtils::setBlockStrides(*weights, w_user_md, permut); // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, type, xzFormatMkl); onednnUtils::setBlockStrides(*gradO, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, type, xzFormatMkl); onednnUtils::setBlockStrides(*gradI, gradI_user_md); // gradW dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any); dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, type, wFormatMkl); onednnUtils::setBlockStrides(*gradW, gradW_user_md, permut); // gradB dnnl::memory::desc gradB_mkl_md; if (gradB != nullptr) gradB_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x); auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine()); // forward primitive description dnnl::convolution_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto, x_mkl_md, w_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r); dnnl::convolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine); // backward data primitive description dnnl::convolution_backward_data::desc op_data_bp_desc(dnnl::algorithm::convolution_auto, gradI_mkl_md, w_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r); dnnl::convolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, op_ff_prim_desc); // backward weights primitive description dnnl::convolution_backward_weights::desc op_weights_bp_desc(dnnl::algorithm::convolution_auto, x_mkl_md, gradW_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r); dnnl::convolution_backward_weights::primitive_desc op_weights_bp_prim_desc(op_weights_bp_desc, engine, op_ff_prim_desc); // arguments (memory buffers) necessary for calculations std::unordered_map args; dnnl::stream stream(engine); // provide memory buffers and check whether reorder is required // input onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_weights_bp_prim_desc.src_desc(), args[DNNL_ARG_SRC]); // weights onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_data_bp_prim_desc.weights_desc(), args[DNNL_ARG_WEIGHTS]); // gradO auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, const_cast(gradO->buffer())); const bool gradOReorderW = op_weights_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc(); const bool gradOReorderD = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc(); auto gradO_mkl_memW = gradOReorderW ? dnnl::memory(op_weights_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem; auto gradO_mkl_memD = gradOReorderD ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem; if (gradOReorderW) dnnl::reorder(gradO_user_mem, gradO_mkl_memW).execute(stream, gradO_user_mem, gradO_mkl_memW); if (gradOReorderD) dnnl::reorder(gradO_user_mem, gradO_mkl_memD).execute(stream, gradO_user_mem, gradO_mkl_memD); args[DNNL_ARG_DIFF_DST] = gradO_mkl_memD; // gradI auto gradI_user_mem = onednnUtils::loadDataToMklStream(*gradI, engine, stream, gradI_user_md, op_data_bp_prim_desc.diff_src_desc(), args[DNNL_ARG_DIFF_SRC]); // gradW auto gradW_user_mem = onednnUtils::loadDataToMklStream( *gradW, engine, stream, gradW_user_md, op_weights_bp_prim_desc.diff_weights_desc(), args[DNNL_ARG_DIFF_WEIGHTS]); // gradB if (gradB != nullptr) { auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->buffer()); args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem; } // run backward data calculations dnnl::convolution_backward_data(op_data_bp_prim_desc).execute(stream, args); if (gradOReorderW || gradOReorderD) args[DNNL_ARG_DIFF_DST] = gradO_mkl_memW; // run backward weights calculations dnnl::convolution_backward_weights(op_weights_bp_prim_desc).execute(stream, args); // reorder gradI if necessary if (op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc()) dnnl::reorder(args[DNNL_ARG_DIFF_SRC], gradI_user_mem).execute(stream, args[DNNL_ARG_DIFF_SRC], gradI_user_mem); if (op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc()) dnnl::reorder(args[DNNL_ARG_DIFF_WEIGHTS], gradW_user_mem) .execute(stream, args[DNNL_ARG_DIFF_WEIGHTS], gradW_user_mem); stream.wait(); } ////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(conv3dnew, 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, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC] auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW) REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM CONV3D MKLDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D MKLDNN OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf()); sd::LongType kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) depth sd::LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) height sd::LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast(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 paddingMode = 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, iC, oC], 1 - [oC, iC, kD, kH, kW], 2 - [oC, kD, kH, kW, iC] 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, indWiC, indWoC, indWkD); std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM CONV3D 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 CONV3D MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got " "%i, %i instead !", oC, bias->rankOf(), bias->lengthOf()); if (paddingMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); conv3dMKLDNN(input, weights, bias, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, paddingMode, isNCDHW, wFormat); return sd::Status::OK; } PLATFORM_CHECK(conv3dnew, 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, iC, oC] always auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW) Requirements req("ONEDNN CONV3d OP"); req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) && req.expectTrue(sd::ONEDNNStream::isSupported({input, weights, bias, output}), ONEDNN_STREAM_NOT_SUPPORTED); req.logTheSuccess(); return req; } ////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(conv3dnew_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, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC] 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_NULLIFIED(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), epsilon auto gradW = OUTPUT_NULLIFIED(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC] auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC] REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM CONV3D_BP MKLDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf()); REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM CONV3D_BP MKLDNN OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf()); REQUIRE_TRUE(gradO->rankOf() == 5, 0, "CUSTOM CONV3D_BP MKLDNN 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(weights->sizeAt(0)); // filter(kernel) depth sd::LongType kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) height sd::LongType kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast(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 paddingMode = INT_ARG(12); // 1-SAME, 0-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, iC, oC], 1 - [oC, iC, kD, kH, kW], 2 - [oC, kD, kH, kW, iC] 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, indWiC, indWoC, indWkD); if (paddingMode) // SAME ConvolutionUtils::calcPadding3D(pD, pH, pW, oD, oH, oW, iD, iH, iW, kD, kH, kW, sD, sH, sW, dD, dH, dW); sd::LongType 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, paddingMode); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx( {bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}); std::vector expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kD, kH, kW, iC, oC); REQUIRE_TRUE( gradO->isSameShape(expectedGradOShape), 0, "CUSTOM CONV3D_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 CONV3D_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 CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, " "%i instead !", oC, bias->rankOf(), bias->lengthOf()); conv3dBpMKLDNN(input, weights, bias, gradO, gradI, gradW, gradB, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, paddingMode, isNCDHW, wFormat); return sd::Status::OK; } PLATFORM_CHECK(conv3dnew_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, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC] 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), epsilon auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, iC, oC], [oC, iC, kD, kH, kW], [oC, kD, kH, kW, iC] auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC] Requirements req("ONEDNN CONV3d_BP OP"); req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) && req.expectTrue(sd::ONEDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}), ONEDNN_STREAM_NOT_SUPPORTED); req.logTheSuccess(); return req; } } // namespace platforms } // namespace ops } // namespace sd