650 lines
37 KiB
C++
650 lines
37 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 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 saudet
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// @author raver119@gmail.com
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// @author Yurii Shyrma (iuriish@yahoo.com)
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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using namespace dnnl;
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namespace nd4j {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////
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static void conv2dMKLDNN(const NDArray *input, const NDArray *weights,
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const NDArray *bias, NDArray *output,
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const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW,
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const int paddingMode, const int isNCHW) {
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// weights [kH, kW, iC, oC], we'll perform permutation since mkl support [oC, iC, kH, kW]
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == 1 for causal mode in conv1d
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dnnl::memory::dims strides = { sH, sW };
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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 - pWSame };
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dnnl::memory::dims dilation = { dH-1, dW-1};
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auto xzFrmat = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
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dnnl::memory::format_tag wFormat = 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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auto type = dnnl::memory::data_type::f32;
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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, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat);
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if(input->ews() != 1 || input->ordering() != 'c') {
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x_user_md.data.format_kind = dnnl_blocked; // overrides format
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x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0);
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x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1);
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x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2);
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x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3);
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}
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// weights
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dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormat);
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w_user_md.data.format_kind = dnnl_blocked; // overrides format
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w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(3); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
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w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(2);
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w_user_md.data.format_desc.blocking.strides[2] = weights->strideAt(0);
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w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(1);
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// bias
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dnnl::memory::desc b_mkl_md;
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if(bias != nullptr)
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b_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x);
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// output
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dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, type, xzFrmat);
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if(output->ews() != 1 || output->ordering() != 'c') {
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z_user_md.data.format_kind = dnnl_blocked; // overrides format
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z_user_md.data.format_desc.blocking.strides[0] = output->strideAt(0);
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z_user_md.data.format_desc.blocking.strides[1] = output->strideAt(1);
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z_user_md.data.format_desc.blocking.strides[2] = output->strideAt(2);
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z_user_md.data.format_desc.blocking.strides[3] = output->strideAt(3);
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}
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// operation primitive description
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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);
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dnnl::convolution_forward::primitive_desc op_prim_desc(op_desc, engine);
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// arguments (memory buffers) necessary for calculations
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std::unordered_map<int, dnnl::memory> args;
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dnnl::stream stream(engine);
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// provide memory buffers and check whether reorder is required
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// input
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auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
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const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
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auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
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if (xReorder)
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dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
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args[DNNL_ARG_SRC] = x_mkl_mem;
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// weights
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auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
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const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc();
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auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem;
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if (wReorder)
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dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
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args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
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// bias
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if(bias != nullptr) {
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auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, bias->getBuffer());
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args[DNNL_ARG_BIAS] = b_mkl_mem;
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}
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// output
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auto z_user_mem = dnnl::memory(z_user_md, engine, output->getBuffer());
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const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
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auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
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args[DNNL_ARG_DST] = z_mkl_mem;
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// run calculations
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dnnl::convolution_forward(op_prim_desc).execute(stream, args);
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// reorder outputs if necessary
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if (zReorder)
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dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
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stream.wait();
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// shape::printArray(z_mkl_mem.map_data<float>(),8);
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}
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//////////////////////////////////////////////////////////////////////
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static void conv2dBpMKLDNN(const NDArray *input, const NDArray *weights, const NDArray *bias, const NDArray *gradO,
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NDArray *gradI, NDArray *gradW, NDArray *gradB,
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const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW,
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const int paddingMode, const int isNCHW) {
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// weights/gradW [kH, kW, iC, oC], we'll perform permutation since mkl support [oC, iC, kH, kW]
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int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
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const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2 : pW; // dH == 1 for causal mode in conv1d
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dnnl::memory::dims strides = { sH, sW };
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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 - pWSame };
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dnnl::memory::dims dilation = { dH-1, dW-1};
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auto xzFrmat = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
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dnnl::memory::format_tag wFormat = 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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auto type = dnnl::memory::data_type::f32;
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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, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFrmat);
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if(input->ews() != 1 || input->ordering() != 'c') {
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x_user_md.data.format_kind = dnnl_blocked; // overrides format
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x_user_md.data.format_desc.blocking.strides[0] = input->strideAt(0);
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x_user_md.data.format_desc.blocking.strides[1] = input->strideAt(1);
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x_user_md.data.format_desc.blocking.strides[2] = input->strideAt(2);
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x_user_md.data.format_desc.blocking.strides[3] = input->strideAt(3);
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}
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// weights
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dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormat);
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w_user_md.data.format_kind = dnnl_blocked; // overrides format
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w_user_md.data.format_desc.blocking.strides[0] = weights->strideAt(3); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
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w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(2);
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w_user_md.data.format_desc.blocking.strides[2] = weights->strideAt(0);
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w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(1);
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// gradO
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dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, type, xzFrmat);
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if(gradO->ews() != 1 || gradO->ordering() != 'c') {
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gradO_user_md.data.format_kind = dnnl_blocked; // overrides format
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gradO_user_md.data.format_desc.blocking.strides[0] = gradO->strideAt(0);
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gradO_user_md.data.format_desc.blocking.strides[1] = gradO->strideAt(1);
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gradO_user_md.data.format_desc.blocking.strides[2] = gradO->strideAt(2);
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gradO_user_md.data.format_desc.blocking.strides[3] = gradO->strideAt(3);
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}
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// gradI
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dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, type, xzFrmat);
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if(gradI->ews() != 1 || gradI->ordering() != 'c') {
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gradI_user_md.data.format_kind = dnnl_blocked; // overrides format
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gradI_user_md.data.format_desc.blocking.strides[0] = gradI->strideAt(0);
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gradI_user_md.data.format_desc.blocking.strides[1] = gradI->strideAt(1);
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gradI_user_md.data.format_desc.blocking.strides[2] = gradI->strideAt(2);
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gradI_user_md.data.format_desc.blocking.strides[3] = gradI->strideAt(3);
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}
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// gradW
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dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, type, wFormat);
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gradW_user_md.data.format_kind = dnnl_blocked; // overrides format
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gradW_user_md.data.format_desc.blocking.strides[0] = gradW->strideAt(3); // permute [kH, kW, iC, oC] -> [oC, iC, kH, kW]
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gradW_user_md.data.format_desc.blocking.strides[1] = gradW->strideAt(2);
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gradW_user_md.data.format_desc.blocking.strides[2] = gradW->strideAt(0);
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gradW_user_md.data.format_desc.blocking.strides[3] = gradW->strideAt(1);
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// gradB
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dnnl::memory::desc gradB_mkl_md;
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if(gradB != nullptr)
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gradB_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x);
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auto engine = mkldnnUtils::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, x_mkl_md, w_mkl_md, gradB_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, 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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// backward weights primitive description
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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);
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dnnl::convolution_backward_weights::primitive_desc op_weights_bp_prim_desc(op_weights_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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// input
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auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
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const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc();
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auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem;
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if (xReorder)
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dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
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args[DNNL_ARG_SRC] = x_mkl_mem;
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// weights
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auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
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const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc();
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auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem;
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if (wReorder)
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dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
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args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
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// gradO
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auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer());
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const bool gradOReorderW = op_weights_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
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const bool gradOReorderD = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
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auto gradO_mkl_memW = gradOReorderW ? dnnl::memory(op_weights_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
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auto gradO_mkl_memD = gradOReorderD ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
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if (gradOReorderW)
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dnnl::reorder(gradO_user_mem, gradO_mkl_memW).execute(stream, gradO_user_mem, gradO_mkl_memW);
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if (gradOReorderD)
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dnnl::reorder(gradO_user_mem, gradO_mkl_memD).execute(stream, gradO_user_mem, gradO_mkl_memD);
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args[DNNL_ARG_DIFF_DST] = gradO_mkl_memD;
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// gradI
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auto gradI_user_mem = dnnl::memory(gradI_user_md, engine, gradI->getBuffer());
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const bool gradIReorder = op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc();
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auto gradI_mkl_mem = gradIReorder ? dnnl::memory(op_data_bp_prim_desc.diff_src_desc(), engine) : gradI_user_mem;
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args[DNNL_ARG_DIFF_SRC] = gradI_mkl_mem;
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// gradW
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auto gradW_user_mem = dnnl::memory(gradW_user_md, engine, gradW->getBuffer());
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const bool gradWReorder = op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc();
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auto gradW_mkl_mem = gradWReorder ? dnnl::memory(op_weights_bp_prim_desc.diff_weights_desc(), engine) : gradW_user_mem;
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args[DNNL_ARG_DIFF_WEIGHTS] = gradW_mkl_mem;
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// gradB
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if(gradB != nullptr) {
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auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->getBuffer());
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args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem;
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}
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// run backward data calculations
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dnnl::convolution_backward_data(op_data_bp_prim_desc).execute(stream, args);
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if(gradOReorderW || gradOReorderD)
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args[DNNL_ARG_DIFF_DST] = gradO_mkl_memW;
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// run backward weights calculations
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dnnl::convolution_backward_weights(op_weights_bp_prim_desc).execute(stream, args);
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// reorder gradI if necessary
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if (gradIReorder)
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|
dnnl::reorder(gradI_mkl_mem, gradI_user_mem).execute(stream, gradI_mkl_mem, gradI_user_mem);
|
|
if (gradWReorder)
|
|
dnnl::reorder(gradW_mkl_mem, gradW_user_mem).execute(stream, gradW_mkl_mem, gradW_user_mem);
|
|
|
|
stream.wait();
|
|
|
|
// shape::printArray(z_mkl_mem.map_data<float>(),8);
|
|
}
|
|
|
|
/*
|
|
//////////////////////////////////////////////////////////////////////
|
|
static void conv2dMKLDNN(nd4j::graph::Context &block, const NDArray *input, const NDArray *weights,
|
|
const NDArray *bias, NDArray *output, const int kH, const int kW, const int sH,
|
|
const int sW, int pH, int pW, const int dH, const int dW, const int paddingMode,
|
|
const int isNCHW) {
|
|
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
dnnl_memory_desc_t empty;
|
|
dnnl::memory::desc x_mkl_md(empty), w_mkl_md(empty), b_mkl_md(empty), z_mkl_md(empty);
|
|
dnnl::memory::desc x_user_md(empty), w_user_md(empty), b_user_md(empty), z_user_md(empty);
|
|
|
|
dnnl::memory::dims strides, padding, padding_r, dilation;
|
|
|
|
mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
|
|
bS, iC, iH, iW, oC, oH, oW, input, nullptr, weights, nullptr,
|
|
bias, output,
|
|
&x_mkl_md, nullptr, &w_mkl_md, nullptr,
|
|
&b_mkl_md, &z_mkl_md,
|
|
&x_user_md, nullptr, &w_user_md, nullptr,
|
|
&b_user_md, &z_user_md,
|
|
strides, padding, padding_r, dilation);
|
|
|
|
auto conv_desc = bias != nullptr ? convolution_forward::desc(prop_kind::forward,
|
|
algorithm::convolution_auto, x_mkl_md,
|
|
w_mkl_md, b_mkl_md,
|
|
z_mkl_md, strides, dilation, padding,
|
|
padding_r)
|
|
: convolution_forward::desc(prop_kind::forward,
|
|
algorithm::convolution_auto, x_mkl_md,
|
|
w_mkl_md,
|
|
z_mkl_md, strides, dilation, padding,
|
|
padding_r);
|
|
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
|
|
dnnl::stream stream(engine);
|
|
auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, engine);
|
|
auto user_src_memory = dnnl::memory(x_user_md, engine, const_cast<NDArray *>(input)->buffer());
|
|
auto user_weights_memory = dnnl::memory(w_user_md, engine,
|
|
const_cast<NDArray *>(weights)->buffer());
|
|
auto user_dst_memory = dnnl::memory(z_user_md, engine, output->buffer());
|
|
auto conv_src_memory = user_src_memory;
|
|
if (conv_prim_desc.src_desc() != user_src_memory.get_desc()) {
|
|
conv_src_memory = dnnl::memory(conv_prim_desc.src_desc(), engine);
|
|
reorder(user_src_memory, conv_src_memory).execute(stream, user_src_memory, conv_src_memory);
|
|
}
|
|
auto conv_weights_memory = user_weights_memory;
|
|
if (conv_prim_desc.weights_desc() != user_weights_memory.get_desc()) {
|
|
conv_weights_memory = dnnl::memory(conv_prim_desc.weights_desc(), engine);
|
|
reorder(user_weights_memory, conv_weights_memory).execute(stream, user_weights_memory,
|
|
conv_weights_memory);
|
|
}
|
|
auto conv_dst_memory = user_dst_memory;
|
|
if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
|
|
conv_dst_memory = dnnl::memory(conv_prim_desc.dst_desc(), engine);
|
|
}
|
|
if (bias != nullptr) {
|
|
auto conv_bias_memory = dnnl::memory(conv_prim_desc.bias_desc(), engine,
|
|
const_cast<NDArray *>(bias)->buffer());
|
|
convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC, conv_src_memory},
|
|
{DNNL_ARG_WEIGHTS, conv_weights_memory},
|
|
{DNNL_ARG_BIAS, conv_bias_memory},
|
|
{DNNL_ARG_DST, conv_dst_memory}});
|
|
} else {
|
|
convolution_forward(conv_prim_desc).execute(stream, {{DNNL_ARG_SRC, conv_src_memory},
|
|
{DNNL_ARG_WEIGHTS, conv_weights_memory},
|
|
{DNNL_ARG_DST, conv_dst_memory}});
|
|
}
|
|
if (conv_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
|
|
reorder(conv_dst_memory, user_dst_memory).execute(stream, conv_dst_memory, user_dst_memory);
|
|
}
|
|
stream.wait();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
static void conv2dBpMKLDNN(nd4j::graph::Context &block,
|
|
const NDArray *input, const NDArray *weights, const NDArray *bias, const NDArray *gradO,
|
|
NDArray *gradI, NDArray *gradW, NDArray *gradB,
|
|
const int kH, const int kW, const int sH,const int sW, int pH, int pW, const int dH, const int dW,
|
|
const int paddingMode, const int isNCHW) {
|
|
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
dnnl_memory_desc_t empty;
|
|
dnnl::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);
|
|
dnnl::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);
|
|
|
|
dnnl::memory::dims conv_strides, conv_padding, conv_padding_r, conv_dilation;
|
|
|
|
mkldnnUtils::getMKLDNNMemoryDescConv2d(kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
|
|
bS, iC, iH, iW, oC, 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, conv_dilation);
|
|
auto conv_desc = gradB != nullptr
|
|
? convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r)
|
|
: convolution_forward::desc(prop_kind::forward, algorithm::convolution_auto, conv_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);
|
|
|
|
auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, mkldnnUtils::getEngine( LaunchContext::defaultContext()->engine()));
|
|
|
|
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
|
|
dnnl::stream stream(engine);
|
|
|
|
if (gradW != nullptr) {
|
|
auto convW_desc = gradB != nullptr ? convolution_backward_weights::desc(algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_bias_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r)
|
|
: convolution_backward_weights::desc(algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);
|
|
|
|
|
|
auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine, conv_prim_desc);
|
|
|
|
auto userW_src_memory = dnnl::memory(user_src_md, engine, const_cast<NDArray *>(input)->buffer());
|
|
auto userW_weights_memory = dnnl::memory(user_diff_weights_md, engine, gradW->buffer());
|
|
auto userW_dst_memory = dnnl::memory(user_dst_md, engine,const_cast<NDArray *>(gradO)->buffer());
|
|
|
|
auto convW_src_memory = userW_src_memory;
|
|
|
|
if (convW_prim_desc.src_desc() != userW_src_memory.get_desc()) {
|
|
convW_src_memory = dnnl::memory(convW_prim_desc.src_desc(), engine);
|
|
reorder(userW_src_memory, convW_src_memory).execute(stream, userW_src_memory,convW_src_memory);
|
|
}
|
|
|
|
auto convW_weights_memory = userW_weights_memory;
|
|
if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
|
|
convW_weights_memory = dnnl::memory(convW_prim_desc.diff_weights_desc(), engine);
|
|
}
|
|
|
|
auto convW_dst_memory = userW_dst_memory;
|
|
if (convW_prim_desc.diff_dst_desc() != userW_dst_memory.get_desc()) {
|
|
convW_dst_memory = dnnl::memory(convW_prim_desc.diff_dst_desc(), engine);
|
|
reorder(userW_dst_memory, convW_dst_memory).execute(stream, userW_dst_memory, convW_dst_memory);
|
|
}
|
|
|
|
if (gradB != nullptr) {
|
|
auto convW_bias_memory = dnnl::memory(convW_prim_desc.diff_bias_desc(), engine, gradB->buffer());
|
|
|
|
convolution_backward_weights(convW_prim_desc).execute(stream,
|
|
{{DNNL_ARG_SRC, convW_src_memory},
|
|
{DNNL_ARG_DIFF_DST, convW_dst_memory},
|
|
{DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory},
|
|
{DNNL_ARG_DIFF_BIAS, convW_bias_memory}});
|
|
}
|
|
else {
|
|
convolution_backward_weights(convW_prim_desc).execute(stream,
|
|
{{DNNL_ARG_SRC, convW_src_memory},
|
|
{DNNL_ARG_DIFF_DST, convW_dst_memory},
|
|
{DNNL_ARG_DIFF_WEIGHTS, convW_weights_memory}});
|
|
}
|
|
|
|
if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
|
|
reorder(convW_weights_memory, userW_weights_memory).execute(stream, convW_weights_memory,
|
|
userW_weights_memory);
|
|
}
|
|
|
|
stream.wait();
|
|
}
|
|
|
|
if (gradI != nullptr) {
|
|
|
|
auto convI_desc = convolution_backward_data::desc(algorithm::convolution_auto, conv_diff_src_md, conv_weights_md, conv_dst_md, conv_strides, conv_dilation, conv_padding, conv_padding_r);
|
|
|
|
|
|
auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine, conv_prim_desc);
|
|
auto userI_src_memory = dnnl::memory(user_diff_src_md, engine, gradI->buffer());
|
|
auto userI_weights_memory = dnnl::memory(user_weights_md, engine,const_cast<NDArray *>(weights)->buffer());
|
|
auto userI_dst_memory = dnnl::memory(user_dst_md, engine, const_cast<NDArray *>(gradO)->buffer());
|
|
|
|
auto convI_src_memory = userI_src_memory;
|
|
if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
|
|
convI_src_memory = dnnl::memory(convI_prim_desc.diff_src_desc(), engine);
|
|
}
|
|
|
|
auto convI_weights_memory = userI_weights_memory;
|
|
if (convI_prim_desc.weights_desc() != userI_weights_memory.get_desc()) {
|
|
convI_weights_memory = dnnl::memory(convI_prim_desc.weights_desc(), engine);
|
|
reorder(userI_weights_memory, convI_weights_memory).execute(stream, userI_weights_memory, convI_weights_memory);
|
|
}
|
|
|
|
auto convI_dst_memory = userI_dst_memory;
|
|
if (convI_prim_desc.diff_dst_desc() != userI_dst_memory.get_desc()) {
|
|
convI_dst_memory = dnnl::memory(convI_prim_desc.diff_dst_desc(), engine);
|
|
reorder(userI_dst_memory, convI_dst_memory).execute(stream, userI_dst_memory, convI_dst_memory);
|
|
}
|
|
|
|
convolution_backward_data(convI_prim_desc).execute(stream,
|
|
{{DNNL_ARG_DIFF_DST, convI_dst_memory},
|
|
{DNNL_ARG_WEIGHTS, convI_weights_memory},
|
|
{DNNL_ARG_DIFF_SRC, convI_src_memory}});
|
|
|
|
if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
|
|
reorder(convI_src_memory, userI_src_memory).execute(stream, convI_src_memory, userI_src_memory);
|
|
}
|
|
|
|
stream.wait();
|
|
}
|
|
}
|
|
|
|
*/
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(conv2d, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW)
|
|
|
|
int sH = INT_ARG(2); // strides height
|
|
int sW = INT_ARG(3); // strides width
|
|
int pH = INT_ARG(4); // paddings height
|
|
int pW = INT_ARG(5); // paddings width
|
|
int dH = INT_ARG(6); // dilations height
|
|
int dW = INT_ARG(7); // dilations width
|
|
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
|
|
|
int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) height
|
|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) width
|
|
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, oC};
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CONV2D 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, "CONV2D MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
conv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
|
|
PLATFORM_CHECK(conv2d, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto weights = INPUT_VARIABLE(1);
|
|
|
|
// conv2d is only available for float32 dtype
|
|
return block.isUseMKLDNN() && input->dataType() == nd4j::DataType::FLOAT32 &&
|
|
weights->dataType() == nd4j::DataType::FLOAT32;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(conv2d_bp, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
int kH = INT_ARG(0); // filter(kernel) height
|
|
int kW = INT_ARG(1); // filter(kernel) width
|
|
int sH = INT_ARG(2); // strides height
|
|
int sW = INT_ARG(3); // strides width
|
|
int pH = INT_ARG(4); // paddings height
|
|
int pW = INT_ARG(5); // paddings width
|
|
int dH = INT_ARG(6); // dilations height
|
|
int dW = INT_ARG(7); // dilations width
|
|
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
int isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
|
|
|
int bS, iC, iH, iW, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
int indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
int trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
|
|
|
|
if(paddingMode) // SAME
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
|
|
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, iC, oC};
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CONV2D_BP MKLDNN 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, "CONV2D_BP 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, "CONV2D_BP MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
conv2dBpMKLDNN(input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW);
|
|
|
|
return Status::OK();
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}
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PLATFORM_CHECK(conv2d_bp, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
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auto weights = INPUT_VARIABLE(1); // [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, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
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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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auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always
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auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
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|
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return block.isUseMKLDNN() &&
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nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB});
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}
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|
|
|
|
|
|
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}
|
|
}
|
|
}
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