/* ****************************************************************************** * * * 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 Yurii Shyrma (iuriish@yahoo.com) // #include #include #include #include #include #include "mkldnnUtils.h" namespace sd { namespace ops { namespace platforms { ////////////////////////////////////////////////////////////////////////// static void deconv2TFdBpMKLDNN(NDArray* weights, NDArray* gradO, NDArray* gradI, const sd::LongType bS, const sd::LongType iC, const sd::LongType iH, const sd::LongType iW, const sd::LongType oC, const sd::LongType oH, const sd::LongType oW, const sd::LongType kH, const sd::LongType kW, const sd::LongType sH, const sd::LongType sW, const sd::LongType pH, const sd::LongType pW, const sd::LongType dH, const sd::LongType dW, const bool isNCHW, const int wFormat) { // gradI [bS, iH, iW, iC], mkl doesn't support ndhwc format // weights [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, iC, oC] // gradO [bS, oH, oW, oC] dnnl::memory::dims strides = {sH, sW}; dnnl::memory::dims dilation = {dH - 1, dW - 1}; dnnl::memory::dims padding = {pH, pW}; dnnl::memory::dims padding_r = {(oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pW}; // weights type dnnl::memory::data_type wType = weights->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16; // gradO type dnnl::memory::data_type gradOType = gradO->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16; // gradI type dnnl::memory::data_type gradIType = gradI->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16; dnnl::memory::format_tag xFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc; dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::oihw; dnnl::memory::dims xDims = {bS, iC, iH, iW}; dnnl::memory::dims wDims = {oC, iC, kH, kW}; dnnl::memory::dims zDims = {bS, oC, oH, oW}; // memory descriptors for arrays // input dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, gradOType, dnnl::memory::format_tag::any); // weights dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any); dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormatMkl); onednnUtils::setBlockStrides(*weights, w_user_md, {3, 2, 0, 1}); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW] // gradO dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any); dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormatMkl); onednnUtils::setBlockStrides(*gradO, gradO_user_md); // gradI dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any); dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormatMkl); onednnUtils::setBlockStrides(*gradI, gradI_user_md); 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, 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); // arguments (memory buffers) necessary for calculations std::unordered_map args; dnnl::stream stream(engine); // provide memory buffers and check whether reorder is required // weights onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_data_bp_prim_desc.weights_desc(), args[DNNL_ARG_WEIGHTS]); // gradO onednnUtils::loadDataToMklStream(*gradO, engine, stream, gradO_user_md, op_data_bp_prim_desc.diff_dst_desc(), args[DNNL_ARG_DIFF_DST]); // 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]); // run backward data calculations dnnl::convolution_backward_data(op_data_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); stream.wait(); } ////////////////////////////////////////////////////////////////////////// PLATFORM_IMPL(deconv2d_tf, ENGINE_CPU) { auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC] auto gradIShape = INPUT_VARIABLE(0); // [4] - shape of input of conv2d (that is shape of gradI) auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast(weights->sizeAt(0)); // filter(kernel) height sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast(weights->sizeAt(1)); // filter(kernel) width sd::LongType sH = INT_ARG(2); // strides height sd::LongType sW = INT_ARG(3); // strides width sd::LongType pH = INT_ARG(4); // paddings height sd::LongType pW = INT_ARG(5); // paddings width sd::LongType dH = INT_ARG(6); // dilations height sd::LongType dW = INT_ARG(7); // dilations width int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC] const sd::LongType rank = gradO->rankOf(); REQUIRE_TRUE(weights->rankOf() == rank, 0, "CUSTOM DECONV2D_TF MKLDNN OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf()); REQUIRE_TRUE(gradIShape->rankOf() == 1, 0, "CUSTOM DECONV2D_TF MKLDNN OP: rank of array with output shape must be equal to 1, but got %i instead !", gradIShape->rankOf()); REQUIRE_TRUE( gradIShape->lengthOf() == rank, 0, "CUSTOM DECONV2D_TF MKLDNN OP: length of array with output shape must be equal to 4, but got %i instead !", gradIShape->lengthOf()); int indIOioC, indIiH, indWoC(3), indOoH; if (!isNCHW) { indIOioC = 3; indIiH = 1; indOoH = 1; } else { indIOioC = 1; indIiH = 2; indOoH = 2; } std::vector gradIShapeVector = gradIShape->template asVectorT(); const sd::LongType bS = gradIShapeVector[0]; // batch size const sd::LongType iH = gradIShapeVector[indIiH]; // input height const sd::LongType iW = gradIShapeVector[indIiH + 1]; // input width const sd::LongType iC = gradIShapeVector[indIOioC]; // input channels const sd::LongType oC = weights->sizeAt(indWoC); // output channels const sd::LongType oH = gradO->sizeAt(indOoH); // input height const sd::LongType oW = gradO->sizeAt(indOoH); // input width sd::LongType trueoH, trueoW; // true output height, width ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode); std::vector expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1}); std::vector expectedWeightsShape = {kH, kW, iC, oC}; REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DECONV2D_TF MKLDNN OP: wrong shape of input array, basing on array with output shape expected " "is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str()); REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV2D_TF 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 (isSameMode) // SAME ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW); // // mkl supports only [oC, iC, kH, kW] for weights deconv2TFdBpMKLDNN(weights, gradO, gradI, bS, iC, iH, iW, oC, oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW, wFormat); return sd::Status::OK; } PLATFORM_CHECK(deconv2d_tf, ENGINE_CPU) { auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI Requirements req("ONEDNN DECONV2d_TF OP"); req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) && req.expectTrue(makeInfoVariable( [weights, gradI, gradO] { const DataType wType = weights->dataType(); const DataType gradOType = gradO->dataType(); const DataType gradIType = gradI->dataType(); return ((wType == DataType::FLOAT32 || wType == DataType::BFLOAT16) && (gradOType == DataType::FLOAT32 || gradOType == DataType::BFLOAT16) && (gradIType == DataType::FLOAT32 || gradIType == DataType::BFLOAT16)); }, TYPECHECK_MSG), NO_MSG); req.logTheSuccess(); return req; } } // namespace platforms } // namespace ops } // namespace sd