wehub-resource-sync
233 lines
12 KiB
C++
233 lines
12 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 deconv2TFdBpMKLDNN(NDArray* weights, NDArray* gradO, NDArray* gradI, const sd::LongType bS, const sd::LongType iC,
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const sd::LongType iH, const sd::LongType iW, const sd::LongType oC, const sd::LongType oH, const sd::LongType oW, const sd::LongType kH,
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const sd::LongType kW, const sd::LongType sH, const sd::LongType sW, const sd::LongType pH, const sd::LongType pW, const sd::LongType dH,
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const sd::LongType dW, const bool isNCHW, const int wFormat) {
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// gradI [bS, iH, iW, iC], mkl doesn't support ndhwc format
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// weights [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, iC, oC]
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// gradO [bS, oH, oW, oC]
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dnnl::memory::dims strides = {sH, sW};
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dnnl::memory::dims dilation = {dH - 1, dW - 1};
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dnnl::memory::dims padding = {pH, pW};
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dnnl::memory::dims padding_r = {(oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pW};
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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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dnnl::memory::format_tag xFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
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dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::oihw;
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dnnl::memory::dims xDims = {bS, iC, iH, iW};
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dnnl::memory::dims wDims = {oC, iC, kH, kW};
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dnnl::memory::dims zDims = {bS, oC, 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, gradOType, dnnl::memory::format_tag::any);
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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, {3, 2, 0, 1}); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
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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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auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// forward primitive description
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dnnl::convolution_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto,
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x_mkl_md, w_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
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dnnl::convolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
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// backward data primitive description
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dnnl::convolution_backward_data::desc op_data_bp_desc(dnnl::algorithm::convolution_auto, gradI_mkl_md, w_mkl_md,
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gradO_mkl_md, strides, dilation, padding, padding_r);
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dnnl::convolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, 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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// 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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onednnUtils::loadDataToMklStream(*gradO, engine, stream, gradO_user_md, op_data_bp_prim_desc.diff_dst_desc(),
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args[DNNL_ARG_DIFF_DST]);
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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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// run backward data calculations
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dnnl::convolution_backward_data(op_data_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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stream.wait();
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}
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//////////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(deconv2d_tf, ENGINE_CPU) {
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auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
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auto gradIShape = INPUT_VARIABLE(0); // [4] - shape of input of conv2d (that is shape of gradI)
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
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sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0)); // filter(kernel) height
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sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1)); // filter(kernel) width
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sd::LongType sH = INT_ARG(2); // strides height
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sd::LongType sW = INT_ARG(3); // strides width
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sd::LongType pH = INT_ARG(4); // paddings height
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sd::LongType pW = INT_ARG(5); // paddings width
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sd::LongType dH = INT_ARG(6); // dilations height
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sd::LongType dW = INT_ARG(7); // dilations width
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int isSameMode = INT_ARG(8); // 0-VALID, 1-SAME
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int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 1-NHWC, 0-NCHW
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int wFormat = block.getIArguments()->size() > 10
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? INT_ARG(10)
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: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
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const sd::LongType rank = gradO->rankOf();
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REQUIRE_TRUE(weights->rankOf() == rank, 0,
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"CUSTOM DECONV2D_TF MKLDNN OP: rank of weights array must be equal to 4, but got %i instead !",
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weights->rankOf());
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REQUIRE_TRUE(gradIShape->rankOf() == 1, 0,
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"CUSTOM DECONV2D_TF MKLDNN OP: rank of array with output shape must be equal to 1, but got %i instead !",
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gradIShape->rankOf());
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REQUIRE_TRUE(
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gradIShape->lengthOf() == rank, 0,
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"CUSTOM DECONV2D_TF MKLDNN OP: length of array with output shape must be equal to 4, but got %i instead !",
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gradIShape->lengthOf());
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int indIOioC, indIiH, indWoC(3), indOoH;
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if (!isNCHW) {
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indIOioC = 3;
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indIiH = 1;
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indOoH = 1;
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} else {
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indIOioC = 1;
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indIiH = 2;
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indOoH = 2;
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}
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std::vector<sd::LongType> gradIShapeVector = gradIShape->template asVectorT<sd::LongType>();
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const sd::LongType bS = gradIShapeVector[0]; // batch size
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const sd::LongType iH = gradIShapeVector[indIiH]; // input height
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const sd::LongType iW = gradIShapeVector[indIiH + 1]; // input width
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const sd::LongType iC = gradIShapeVector[indIOioC]; // input channels
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const sd::LongType oC = weights->sizeAt(indWoC); // output channels
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const sd::LongType oH = gradO->sizeAt(indOoH); // input height
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const sd::LongType oW = gradO->sizeAt(indOoH); // input width
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sd::LongType trueoH, trueoW; // true output height, width
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ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, isSameMode);
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std::vector<sd::LongType> expectedGradOShape =
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ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
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std::vector<sd::LongType> expectedWeightsShape = {kH, kW, iC, oC};
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REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
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"CUSTOM DECONV2D_TF MKLDNN OP: wrong shape of input array, basing on array with output shape expected "
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"is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
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REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
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"CUSTOM DECONV2D_TF MKLDNN OP: wrong shape of weights array, expected is %s, but got %s instead !",
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ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
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if (isSameMode) // SAME
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ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
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// // mkl supports only [oC, iC, kH, kW] for weights
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deconv2TFdBpMKLDNN(weights, gradO, gradI, bS, iC, iH, iW, oC, oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW,
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wFormat);
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return sd::Status::OK;
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}
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PLATFORM_CHECK(deconv2d_tf, ENGINE_CPU) {
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auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto gradO = INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
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auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI
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Requirements req("ONEDNN DECONV2d_TF OP");
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req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
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req.expectTrue(makeInfoVariable(
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[weights, gradI, gradO] {
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const DataType wType = weights->dataType();
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const DataType gradOType = gradO->dataType();
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const DataType gradIType = gradI->dataType();
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return ((wType == DataType::FLOAT32 || wType == DataType::BFLOAT16) &&
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(gradOType == DataType::FLOAT32 || gradOType == DataType::BFLOAT16) &&
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(gradIType == DataType::FLOAT32 || gradIType == DataType::BFLOAT16));
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},
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TYPECHECK_MSG),
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NO_MSG);
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req.logTheSuccess();
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return req;
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}
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} // namespace platforms
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} // namespace ops
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} // namespace sd
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