485 lines
28 KiB
C++
485 lines
28 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma (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 <system/platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include <ops/declarable/helpers/convolutions.h>
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#include "mkldnnUtils.h"
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using namespace dnnl;
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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 depthwiseConv2dMKLDNN(const NDArray* input, const NDArray* weights, 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 bool isNCHW, const int wFormat) {
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// mkl supports only following case: mC = 1, oC = iC
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// input [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc, since mkl doesn't support nhwc format we'll permute when nhwc is given
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// weights {iC, mC, 1, kH, kW}
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// bias [oC], may be nullptr
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// output [bS, oC, oH, oW] nchw or [bS, oH, oW, oC] nhwc
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// oC = iC*mC
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int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(indWmC); // channels multiplier
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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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uint i0, i1, i2, i3;
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if(0 == wFormat) {
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i0 = 2; i1 = 3; i2 = 0; i3 = 1; // [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]
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}
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else if(1 == wFormat) {
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i0 = 1; i1 = 0; i2 = 2; i3 = 3; // [mC, iC, kH, kW] -> [iC, mC, 1, kH, kW]
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}
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else {
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i0 = 3; i1 = 0; i2 = 1; i3 = 2; // [mC, kH, kW, iC] -> [iC, mC, 1, kH, kW]
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}
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// input type
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dnnl::memory::data_type xType;
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if(input->dataType() == DataType::FLOAT32)
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xType = dnnl::memory::data_type::f32;
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else if(input->dataType() == DataType::HALF)
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xType = dnnl::memory::data_type::f16;
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else if(input->dataType() == DataType::UINT8)
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xType = dnnl::memory::data_type::u8;
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else
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xType = dnnl::memory::data_type::s8;
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// weights type
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dnnl::memory::data_type wType = xType;
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if(xType == dnnl::memory::data_type::u8)
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wType = dnnl::memory::data_type::s8;
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// output and bias type (have the same types)
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dnnl::memory::data_type zType;
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if(output->dataType() == DataType::FLOAT32)
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zType = dnnl::memory::data_type::f32;
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else if(output->dataType() == DataType::HALF)
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zType = dnnl::memory::data_type::f16;
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else if(output->dataType() == DataType::UINT8)
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zType = dnnl::memory::data_type::u8;
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else if(output->dataType() == DataType::INT8)
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zType = dnnl::memory::data_type::s8;
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else
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zType = dnnl::memory::data_type::s32;
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dnnl::memory::format_tag xzFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
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dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::goihw;
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dnnl::memory::dims xDims = {bS, iC, iH, iW};
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dnnl::memory::dims wDims = {iC, mC, 1, 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, xType, dnnl::memory::format_tag::any);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFormatMkl);
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mkldnnUtils::setBlockStrides(*input, x_user_md);
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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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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(i0); // permute
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w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(i1);
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w_user_md.data.format_desc.blocking.strides[2] = 0;
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w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(i2);
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w_user_md.data.format_desc.blocking.strides[4] = weights->strideAt(i3);
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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}, zType, dnnl::memory::format_tag::x);
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// output
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dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any);
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dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xzFormatMkl);
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mkldnnUtils::setBlockStrides(*output, z_user_md);
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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,
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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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mkldnnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
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// weights
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mkldnnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_prim_desc.weights_desc(), args[DNNL_ARG_WEIGHTS]);
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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, const_cast<void*>(bias->buffer()));
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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 = mkldnnUtils::loadDataToMklStream(*output, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);
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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 (op_prim_desc.dst_desc() != z_user_mem.get_desc())
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dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], 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 depthwiseConv2dBpMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, 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 bool isNCHW, const int wFormat) {
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// mkl supports only following case: mC = 1, oC = iC
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// input, gradI [bS, iC, iH, iW] nchw or [bS, iH, iW, iC] nhwc, since mkl doesn't support nhwc format we'll permute when nhwc is given
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// weights/gradW {iC, mC, 1, kH, kW}
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// gradB [oC], may be nullptr
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// gradO [bS, oC, oH, oW] nchw or [bS, oH, oW, oC] nhwc
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// oC = iC*mC
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int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, output channels, output height/width;
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int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
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ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
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mC = weights->sizeAt(indWmC);
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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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uint i0, i1, i2, i3;
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if(0 == wFormat) {
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i0 = 2; i1 = 3; i2 = 0; i3 = 1; // [kH, kW, iC, mC] -> [iC, mC, 1, kH, kW]
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}
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else if(1 == wFormat) {
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i0 = 1; i1 = 0; i2 = 2; i3 = 3; // [mC, iC, kH, kW] -> [iC, mC, 1, kH, kW]
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}
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else {
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i0 = 3; i1 = 0; i2 = 1; i3 = 2; // [mC, kH, kW, iC] -> [iC, mC, 1, kH, kW]
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}
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// input type
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dnnl::memory::data_type xType = input->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// weights type
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dnnl::memory::data_type wType = 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 = 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 = gradI->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// gradW type
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dnnl::memory::data_type gradWType = gradW->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
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// gradB type
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dnnl::memory::data_type gradBType = gradB != nullptr ? (gradB->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16) : dnnl::memory::data_type::f32;
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dnnl::memory::format_tag xzFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
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dnnl::memory::format_tag wFormatMkl = dnnl::memory::format_tag::goihw;
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dnnl::memory::dims xDims = {bS, iC, iH, iW};
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dnnl::memory::dims wDims = {iC, mC, 1, 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, xType, dnnl::memory::format_tag::any);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xzFormatMkl);
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mkldnnUtils::setBlockStrides(*input, x_user_md);
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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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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(i0); // permute
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w_user_md.data.format_desc.blocking.strides[1] = weights->strideAt(i1);
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w_user_md.data.format_desc.blocking.strides[2] = 0;
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w_user_md.data.format_desc.blocking.strides[3] = weights->strideAt(i2);
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w_user_md.data.format_desc.blocking.strides[4] = weights->strideAt(i3);
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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, xzFormatMkl);
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mkldnnUtils::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, xzFormatMkl);
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mkldnnUtils::setBlockStrides(*gradI, gradI_user_md);
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// gradW
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dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any);
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dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, gradWType, wFormatMkl);
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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(i0); // permute
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gradW_user_md.data.format_desc.blocking.strides[1] = gradW->strideAt(i1);
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gradW_user_md.data.format_desc.blocking.strides[2] = 0;
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gradW_user_md.data.format_desc.blocking.strides[3] = gradW->strideAt(i2);
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gradW_user_md.data.format_desc.blocking.strides[4] = gradW->strideAt(i3);
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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}, gradBType, 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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mkldnnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_weights_bp_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
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// weights
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mkldnnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_data_bp_prim_desc.weights_desc(), args[DNNL_ARG_WEIGHTS]);
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// gradO
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auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, const_cast<void*>(gradO->buffer()));
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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 = mkldnnUtils::loadDataToMklStream(*gradI, engine, stream, gradI_user_md, op_data_bp_prim_desc.diff_src_desc(), args[DNNL_ARG_DIFF_SRC]);
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// gradW
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auto gradW_user_mem = mkldnnUtils::loadDataToMklStream(*gradW, engine, stream, gradW_user_md, op_weights_bp_prim_desc.diff_weights_desc(), args[DNNL_ARG_DIFF_WEIGHTS]);
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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->buffer());
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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);
|
|
|
|
if(gradOReorderW || gradOReorderD)
|
|
args[DNNL_ARG_DIFF_DST] = gradO_mkl_memW;
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|
|
|
// 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);
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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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//////////////////////////////////////////////////////////////////////
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PLATFORM_IMPL(depthwise_conv2d, ENGINE_CPU) {
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|
|
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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, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
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auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC] = iC*mC
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|
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auto output = OUTPUT_VARIABLE(0); // [bS, oH, oW, iC*mC] (NHWC) or [bS, iC*mC, oH, oW] (NCHW)
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|
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int kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) height
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|
int kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) width
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int sH = INT_ARG(2); // strides height
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|
int sW = INT_ARG(3); // strides width
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|
int pH = INT_ARG(4); // paddings height
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|
int pW = INT_ARG(5); // paddings width
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|
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
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|
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
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|
|
|
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
|
|
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
|
|
mC = weights->sizeAt(indWmC); // channels multiplier
|
|
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
std::vector<Nd4jLong> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DEPTHWISECONV2D MKL OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
REQUIRE_TRUE(output->sizeAt(indIOioC) == iC*mC, 0, "CUSTOM DEPTHWISECONV2D MKL OP: the output_channels must be equal to input_channels * channels_multiplier = %i !", iC*mC);
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DEPTHWISECONV2D MKL OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
depthwiseConv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat);
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
PLATFORM_CHECK(depthwise_conv2d, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto weights = INPUT_VARIABLE(1);
|
|
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;
|
|
|
|
auto output = INPUT_VARIABLE(0);
|
|
|
|
const DataType xType = input->dataType();
|
|
const DataType wType = weights->dataType();
|
|
const DataType zType = output->dataType();
|
|
const DataType bType = bias != nullptr ? bias->dataType() : zType;
|
|
|
|
const int mC = weights->sizeAt(3);
|
|
|
|
return block.isUseMKLDNN() && mC == 1 &&
|
|
(
|
|
(xType==DataType::FLOAT32 && wType==DataType::FLOAT32 && bType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
|
|
(xType==DataType::BFLOAT16 && wType==DataType::BFLOAT16 && bType==DataType::BFLOAT16 && zType==DataType::BFLOAT16) ||
|
|
((xType==DataType::UINT8 || xType==DataType::INT8) && wType==DataType::INT8 && (zType==DataType::UINT8 || zType==DataType::INT8 || zType==DataType::INT32 || zType==DataType::FLOAT32) && bType == zType)
|
|
);
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(depthwise_conv2d_bp, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
|
|
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
|
|
auto gradW = OUTPUT_NULLIFIED(1); // [kH, kW, iC, mC], [mC, iC, kH, kW], [mC, kH, kW, iC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC]
|
|
|
|
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
|
|
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of weights array must be equal to 4, but got %i instead !", weights->rankOf());
|
|
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM DEPTHWISECONV2D_BP MKL OP: rank of output gradients (next epsilon) array must be equal to 4, but got %i instead !", gradO->rankOf());
|
|
|
|
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 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): 1-NHWC, 0-NCHW
|
|
int wFormat = block.getIArguments()->size() > 10 ? INT_ARG(10) : 0; // 0 - [kH, kW, iC, mC], 1 - [mC, iC, kH, kW], 2 - [mC, kH, kW, iC]
|
|
|
|
int bS, iC, iH, iW, mC, oC, oH, oW; // batch size, input channels, input height/width, channels multiplier(oC = iC*mC), output channels, output height/width
|
|
int indIOioC, indIiH, indWmC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWiC, indWmC, indWkH, indOoH);
|
|
mC = weights->sizeAt(indWmC); // channels multiplier
|
|
|
|
int trueoH, trueoW; // correct output height, width
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
|
|
|
|
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 = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, mC);
|
|
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DEPTHWISECONV2D_BP MKL 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 DEPTHWISECONV2D_BP MKL 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 DEPTHWISECONV2D_BP MKL OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
depthwiseConv2dBpMKLDNN(input, weights, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat);
|
|
|
|
return Status::OK();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
PLATFORM_CHECK(depthwise_conv2d_bp, ENGINE_CPU) {
|
|
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC] = [iC*mC]
|
|
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NDHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NDHWC) or [bS, iC, iH, iW] (NCDHW), epsilon
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
const DataType xType = input->dataType();
|
|
const DataType wType = weights->dataType();
|
|
const DataType gradOType = gradO->dataType();
|
|
|
|
const DataType gradIType = gradI->dataType();
|
|
const DataType gradWType = gradW->dataType();
|
|
const DataType gradBType = gradB != nullptr ? gradB->dataType() : DataType::FLOAT32;
|
|
|
|
const int mC = weights->sizeAt(3);
|
|
|
|
return block.isUseMKLDNN() && mC == 1 && ((xType==DataType::FLOAT32 || xType==DataType::BFLOAT16) && (wType==DataType::FLOAT32 || wType==DataType::BFLOAT16) && (gradOType==DataType::FLOAT32 || gradOType==DataType::BFLOAT16) && (gradIType==DataType::FLOAT32 || gradIType==DataType::BFLOAT16) && (gradWType==DataType::FLOAT32 || gradWType==DataType::BFLOAT16) && (gradBType==DataType::FLOAT32 || gradBType==DataType::BFLOAT16) );
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|