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cavis/libnd4j/include/ops/declarable/platform/mkldnn/deconv3d.cpp

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <platform_boilerplate.h>
#include <helpers/MKLDNNStream.h>
#include "mkldnnUtils.h"
#include <ops/declarable/helpers/convolutions.h>
namespace nd4j {
namespace ops {
namespace platforms {
//////////////////////////////////////////////////////////////////////////
static void deconv3dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output,
const int kD, const int kH, const int kW, const int sD, const int sH, const int sW,
const int pD, const int pH, const int pW, const int dD, const int dH, const int dW,
const int isSameMode) {
// input [bS, iD, iH, iW, iC] ncdhw, mkl doesn't support format ndhwc
// weights [oC, iC, kD, kH, kW] always, mkl doesn't support weights format [kD, kH, kW, oC, iC]
// bias [oC], may be nullptr
// output [bS, oD, oH, oW, oC] ncdhw, mkl doesn't support format ndhwc
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(true, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD);
dnnl::memory::dims strides = { sD, sH, sW };
dnnl::memory::dims padding = { pD, pH, pW };
dnnl::memory::dims padding_r = { (iD - 1) * sD - oD + kD - pD, (iH - 1) * sH - oH + kH - pH, (iW - 1) * sW - oW + kW - pW };
dnnl::memory::dims dilation = { dD-1, dH-1, dW-1 };
// input type
dnnl::memory::data_type xType;
if(input->dataType() == DataType::FLOAT32)
xType = dnnl::memory::data_type::f32;
else if(input->dataType() == DataType::HALF)
xType = dnnl::memory::data_type::f16;
else if(input->dataType() == DataType::UINT8)
xType = dnnl::memory::data_type::u8;
else
xType = dnnl::memory::data_type::s8;
// weights type
dnnl::memory::data_type wType = xType;
if(xType == dnnl::memory::data_type::u8)
wType = dnnl::memory::data_type::s8;
// output and bias type (have the same types)
dnnl::memory::data_type zType;
if(output->dataType() == DataType::FLOAT32)
zType = dnnl::memory::data_type::f32;
else if(output->dataType() == DataType::HALF)
zType = dnnl::memory::data_type::f16;
else if(output->dataType() == DataType::UINT8)
zType = dnnl::memory::data_type::u8;
else if(output->dataType() == DataType::INT8)
zType = dnnl::memory::data_type::s8;
else
zType = dnnl::memory::data_type::s32;
dnnl::memory::format_tag xFormat = dnnl::memory::format_tag::ncdhw;
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::oidhw;
dnnl::memory::dims xDims = {bS, iC, iD, iH, iW};
dnnl::memory::dims wDims = {oC, iC, kD, kH, kW};
dnnl::memory::dims zDims = {bS, oC, oD, oH, oW};
// memory descriptors for arrays
// input
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat);
x_user_md.data.format_kind = dnnl_blocked; // overrides format
x_user_md.data.format_desc.blocking.strides[0] = input->stridesOf()[0];
x_user_md.data.format_desc.blocking.strides[1] = input->stridesOf()[1];
x_user_md.data.format_desc.blocking.strides[2] = input->stridesOf()[2];
x_user_md.data.format_desc.blocking.strides[3] = input->stridesOf()[3];
x_user_md.data.format_desc.blocking.strides[4] = input->stridesOf()[4];
// weights
dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormat);
w_user_md.data.format_kind = dnnl_blocked; // overrides format
w_user_md.data.format_desc.blocking.strides[0] = weights->stridesOf()[0];
w_user_md.data.format_desc.blocking.strides[1] = weights->stridesOf()[1];
w_user_md.data.format_desc.blocking.strides[2] = weights->stridesOf()[2];
w_user_md.data.format_desc.blocking.strides[3] = weights->stridesOf()[3];
w_user_md.data.format_desc.blocking.strides[4] = weights->stridesOf()[4];
// bias
dnnl::memory::desc b_mkl_md;
if(bias != nullptr)
b_mkl_md = dnnl::memory::desc({oC}, zType, dnnl::memory::format_tag::x);
// output
dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, zType, dnnl::memory::format_tag::any);
dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, zType, xFormat);
z_user_md.data.format_kind = dnnl_blocked; // overrides format
z_user_md.data.format_desc.blocking.strides[0] = output->stridesOf()[0];
z_user_md.data.format_desc.blocking.strides[1] = output->stridesOf()[1];
z_user_md.data.format_desc.blocking.strides[2] = output->stridesOf()[2];
z_user_md.data.format_desc.blocking.strides[3] = output->stridesOf()[3];
z_user_md.data.format_desc.blocking.strides[4] = output->stridesOf()[4];
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
// operation primitive description
dnnl::deconvolution_forward::desc op_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::deconvolution_direct,
x_mkl_md, w_mkl_md, b_mkl_md, z_mkl_md, strides, dilation, padding, padding_r);
dnnl::deconvolution_forward::primitive_desc op_prim_desc(op_desc, engine);
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> args;
dnnl::stream stream(engine);
// provide memory buffers and check whether reorder is required
// input
auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
if (xReorder)
dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
args[DNNL_ARG_SRC] = x_mkl_mem;
// weights
auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
const bool wReorder = op_prim_desc.weights_desc() != w_user_mem.get_desc();
auto w_mkl_mem = wReorder ? dnnl::memory(op_prim_desc.weights_desc(), engine) : w_user_mem;
if (wReorder)
dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
// bias
if(bias != nullptr) {
auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, bias->getBuffer());
args[DNNL_ARG_BIAS] = b_mkl_mem;
}
// output
auto z_user_mem = dnnl::memory(z_user_md, engine, output->getBuffer());
const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
args[DNNL_ARG_DST] = z_mkl_mem;
// run calculations
dnnl::deconvolution_forward(op_prim_desc).execute(stream, args);
// reorder outputs if necessary
if (zReorder)
dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
stream.wait();
// shape::printArray(z_mkl_mem.map_data<float>(),8);
}
//////////////////////////////////////////////////////////////////////////
static void deconv3dBackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB,
const int kD, const int kH, const int kW, const int sD, const int sH, const int sW, const int pD, const int pH, const int pW, const int dD, const int dH, const int dW,
const int isSameMode) {
// input and gradI [bS, iD, iH, iW, iC], mkl doesn't support ndhwc format
// weights and gradW [oC, iC, kD, kH, kW] always, mkl doesn't support weights format [kD, kH, kW, oC, iC]
// gradB [oC], may be nullptr
// gradO [bS, oD, oH, oW, oC]
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(true, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD);
dnnl::memory::dims strides = { sD, sH, sW };
dnnl::memory::dims padding = { pD, pH, pW };
dnnl::memory::dims padding_r = { (iD - 1) * sD - oD + kD - pD, (iH - 1) * sH - oH + kH - pH, (iW - 1) * sW - oW + kW - pW };
dnnl::memory::dims dilation = { dD-1, dH-1, dW-1 };
// input type
dnnl::memory::data_type xType = input->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// weights type
dnnl::memory::data_type wType = weights->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradO type
dnnl::memory::data_type gradOType = gradO->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradI type
dnnl::memory::data_type gradIType = gradI->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradW type
dnnl::memory::data_type gradWType = gradW->dataType() == DataType::FLOAT32 ? dnnl::memory::data_type::f32 : dnnl::memory::data_type::bf16;
// gradB type
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;
dnnl::memory::format_tag xFormat = dnnl::memory::format_tag::ncdhw; // isNCDHW ? dnnl::memory::format_tag::ncdhw : dnnl::memory::format_tag::ndhwc;
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::oidhw;
dnnl::memory::dims xDims = {bS, iC, iD, iH, iW};
dnnl::memory::dims wDims = {oC, iC, kD, kH, kW};
dnnl::memory::dims zDims = {bS, oC, oD, oH, oW};
// memory descriptors for arrays
// input
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, xType, dnnl::memory::format_tag::any);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, xType, xFormat);
x_user_md.data.format_kind = dnnl_blocked; // overrides format
x_user_md.data.format_desc.blocking.strides[0] = input->stridesOf()[0];
x_user_md.data.format_desc.blocking.strides[1] = input->stridesOf()[1];
x_user_md.data.format_desc.blocking.strides[2] = input->stridesOf()[2];
x_user_md.data.format_desc.blocking.strides[3] = input->stridesOf()[3];
x_user_md.data.format_desc.blocking.strides[4] = input->stridesOf()[4];
// weights
dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, wType, dnnl::memory::format_tag::any);
dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, wType, wFormat);
w_user_md.data.format_kind = dnnl_blocked; // overrides format
w_user_md.data.format_desc.blocking.strides[0] = weights->stridesOf()[0];
w_user_md.data.format_desc.blocking.strides[1] = weights->stridesOf()[1];
w_user_md.data.format_desc.blocking.strides[2] = weights->stridesOf()[2];
w_user_md.data.format_desc.blocking.strides[3] = weights->stridesOf()[3];
w_user_md.data.format_desc.blocking.strides[4] = weights->stridesOf()[4];
// gradO
dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, gradOType, dnnl::memory::format_tag::any);
dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, gradOType, xFormat);
gradO_user_md.data.format_kind = dnnl_blocked; // overrides format
gradO_user_md.data.format_desc.blocking.strides[0] = gradO->stridesOf()[0];
gradO_user_md.data.format_desc.blocking.strides[1] = gradO->stridesOf()[1];
gradO_user_md.data.format_desc.blocking.strides[2] = gradO->stridesOf()[2];
gradO_user_md.data.format_desc.blocking.strides[3] = gradO->stridesOf()[3];
gradO_user_md.data.format_desc.blocking.strides[4] = gradO->stridesOf()[4];
// gradI
dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, gradIType, dnnl::memory::format_tag::any);
dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, gradIType, xFormat);
gradI_user_md.data.format_kind = dnnl_blocked; // overrides format
gradI_user_md.data.format_desc.blocking.strides[0] = gradI->stridesOf()[0];
gradI_user_md.data.format_desc.blocking.strides[1] = gradI->stridesOf()[1];
gradI_user_md.data.format_desc.blocking.strides[2] = gradI->stridesOf()[2];
gradI_user_md.data.format_desc.blocking.strides[3] = gradI->stridesOf()[3];
gradI_user_md.data.format_desc.blocking.strides[4] = gradI->stridesOf()[4];
// gradW
dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, wFormat);
dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, gradWType, wFormat);
gradW_user_md.data.format_kind = dnnl_blocked; // overrides format
gradW_user_md.data.format_desc.blocking.strides[0] = gradW->stridesOf()[0];
gradW_user_md.data.format_desc.blocking.strides[1] = gradW->stridesOf()[1];
gradW_user_md.data.format_desc.blocking.strides[2] = gradW->stridesOf()[2];
gradW_user_md.data.format_desc.blocking.strides[3] = gradW->stridesOf()[3];
gradW_user_md.data.format_desc.blocking.strides[4] = gradW->stridesOf()[4];
// gradB
dnnl::memory::desc gradB_mkl_md;
if(gradB != nullptr)
gradB_mkl_md = dnnl::memory::desc({oC}, gradBType, dnnl::memory::format_tag::x);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
// forward primitive description
dnnl::deconvolution_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::deconvolution_direct, x_mkl_md, w_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
dnnl::deconvolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
// backward data primitive description
dnnl::deconvolution_backward_data::desc op_data_bp_desc(dnnl::algorithm::deconvolution_direct, gradI_mkl_md, w_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
dnnl::deconvolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, op_ff_prim_desc);
// backward weights primitive description
dnnl::deconvolution_backward_weights::desc op_weights_bp_desc(dnnl::algorithm::deconvolution_direct, x_mkl_md, gradW_mkl_md, gradB_mkl_md, gradO_mkl_md, strides, dilation, padding, padding_r);
dnnl::deconvolution_backward_weights::primitive_desc op_weights_bp_prim_desc(op_weights_bp_desc, engine, op_ff_prim_desc);
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> args;
dnnl::stream stream(engine);
// provide memory buffers and check whether reorder is required
// input
auto x_user_mem = dnnl::memory(x_user_md, engine, input->getBuffer());
const bool xReorder = op_weights_bp_prim_desc.src_desc() != x_user_mem.get_desc();
auto x_mkl_mem = xReorder ? dnnl::memory(op_weights_bp_prim_desc.src_desc(), engine) : x_user_mem;
if (xReorder)
dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
args[DNNL_ARG_SRC] = x_mkl_mem;
// weights
auto w_user_mem = dnnl::memory(w_user_md, engine, weights->getBuffer());
const bool wReorder = op_data_bp_prim_desc.weights_desc() != w_user_mem.get_desc();
auto w_mkl_mem = wReorder ? dnnl::memory(op_data_bp_prim_desc.weights_desc(), engine) : w_user_mem;
if (wReorder)
dnnl::reorder(w_user_mem, w_mkl_mem).execute(stream, w_user_mem, w_mkl_mem);
args[DNNL_ARG_WEIGHTS] = w_mkl_mem;
// gradO
auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, gradO->getBuffer());
const bool gradOReorder = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
auto gradO_mkl_mem = gradOReorder ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
if (gradOReorder)
dnnl::reorder(gradO_user_mem, gradO_mkl_mem).execute(stream, gradO_user_mem, gradO_mkl_mem);
args[DNNL_ARG_DIFF_DST] = gradO_mkl_mem;
// gradI
auto gradI_user_mem = dnnl::memory(gradI_user_md, engine, gradI->getBuffer());
const bool gradIReorder = op_data_bp_prim_desc.diff_src_desc() != gradI_user_mem.get_desc();
auto gradI_mkl_mem = gradIReorder ? dnnl::memory(op_data_bp_prim_desc.diff_src_desc(), engine) : gradI_user_mem;
args[DNNL_ARG_DIFF_SRC] = gradI_mkl_mem;
// gradW
auto gradW_user_mem = dnnl::memory(gradW_user_md, engine, gradW->getBuffer());
const bool gradWReorder = op_weights_bp_prim_desc.diff_weights_desc() != gradW_user_mem.get_desc();
auto gradW_mkl_mem = gradWReorder ? dnnl::memory(op_weights_bp_prim_desc.diff_weights_desc(), engine) : gradW_user_mem;
args[DNNL_ARG_DIFF_WEIGHTS] = gradW_mkl_mem;
// gradB
if(gradB != nullptr) {
auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->getBuffer());
args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem;
}
// run backward data calculations
dnnl::deconvolution_backward_data(op_data_bp_prim_desc).execute(stream, args);
// run backward weights calculations
dnnl::deconvolution_backward_weights(op_weights_bp_prim_desc).execute(stream, args);
// reorder gradI if necessary
if (gradIReorder)
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);
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(deconv3d) {
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC] always
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto output = OUTPUT_VARIABLE(0); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW)
REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM DECONV3D_MKLDNN OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM DECONV3D_MKLDNN OP: rank of weights array must be equal to 5, but got %i instead !", weights->rankOf());
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0)); // filter(kernel) depth
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1)); // filter(kernel) height
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2)); // filter(kernel) width
int sD = INT_ARG(3); // strides depth
int sH = INT_ARG(4); // strides height
int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *output, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD);
std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, oC, iC};
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV3D_MKLDNN OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV3D_MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(isSameMode){ // SAME
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
}
// mkl supports only [oC, iC, kD, kH, kW] format for weights
weights = new NDArray(weights->permute({3,4,0,1,2})); // [kD, kH, kW, oC, iC] -> [oC, iC, kD, kH, kW]
// mkl supports only NCDHW
if(!isNCDHW) {
input = new NDArray(input->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
output = new NDArray(output->permute({0,4,1,2,3})); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
}
deconv3dMKLDNN(input, weights, bias, output, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode);
delete weights;
if(!isNCDHW) {
delete input;
delete output;
}
return Status::OK();
}
PLATFORM_CHECK(deconv3d) {
// we don't want to use mkldnn if cpu doesn't support avx/avx2
// if (::optimalLevel() < 2)
// return false;
auto input = INPUT_VARIABLE(0);
auto weights = INPUT_VARIABLE(1);
auto bias = block.width() > 2 ? INPUT_VARIABLE(2) : nullptr;
auto output = INPUT_VARIABLE(0);
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
const DataType xType = input->dataType();
const DataType wType = weights->dataType();
const DataType zType = output->dataType();
const DataType bType = bias != nullptr ? bias->dataType() : zType;
return block.isUseMKLDNN() && (dD <= 1 && dH <= 1 && dW <= 1 && !isSameMode) &&
(
(xType==DataType::FLOAT32 && wType==DataType::FLOAT32 && bType==DataType::FLOAT32 && zType==DataType::FLOAT32) ||
((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(deconv3d_bp) {
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC] always
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NDHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, oC, iC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
REQUIRE_TRUE(input->rankOf() == 5, 0, "CUSTOM DECONV3D_MKLDNN_BP OP: rank of input array must be equal to 5, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 5, 0, "CUSTOM DECONV3D_MKLDNN_BP OP: rank of weights array must be equal to 5 , but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 5, 0, "CUSTOM DECONV3D_MKLDNN_BP OP: rank of output gradients (next epsilon) array must be equal to 5, but got %i instead !", gradO->rankOf());
int kD = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<int>(weights->sizeAt(0));// filter(kernel) depth
int kH = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<int>(weights->sizeAt(1));// filter(kernel) height
int kW = INT_ARG(2) > 0 ? INT_ARG(2) : static_cast<int>(weights->sizeAt(2));// filter(kernel) width
int sD = INT_ARG(3); // strides depth
int sH = INT_ARG(4); // strides height
int sW = INT_ARG(5); // strides width
int pD = INT_ARG(6); // paddings depth
int pH = INT_ARG(7); // paddings height
int pW = INT_ARG(8); // paddings width
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
int isNCDHW = block.getIArguments()->size() > 13 ? !INT_ARG(13) : 1; // INT_ARG(13): 1-NDHWC, 0-NCDHW
int bS, iC, iD, iH, iW, oC, oD, oH, oW; // batch size, input channels, input depth/height/width, output channels, output depth/height/width;
int indIOioC, indIOioD, indWoC, indWiC, indWkD; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv3d(isNCDHW, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW, indIOioC, indIOioD, indWoC, indWiC, indWkD);
int trueoD, trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizeDeconv3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, iD, iH, iW, isSameMode);
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoD,trueoH,trueoW, 0,indIOioC,indIOioD,indIOioD+1,indIOioD+2});
std::vector<Nd4jLong> expectedWeightsShape = {kD, kH, kW, oC, iC};
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DECONV3D_MKLDNN_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV3D_MKLDNN_BP OP: wrong shape of weights array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
if(bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0, "CUSTOM DECONV3D_MKLDNN_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(isSameMode) // Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding3D(pD, pH, pW, iD, iH, iW, oD, oH, oW, kD, kH, kW, sD, sH, sW, dD, dH, dW);
// mkl supports only [oC, iC, kD, kH, kW] for weights
weights = new NDArray(weights->permute({3,4,0,1,2})); // [kD, kH, kW, oC, iC] -> [oC, iC, kD, kH, kW]
gradW = new NDArray(gradW->permute({3,4,0,1,2})); // [kD, kH, kW, oC, iC] -> [oC, iC, kD, kH, kW]
// mkl supports NCDHW format only
if(!isNCDHW) {
input = new NDArray(input->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
gradI = new NDArray(gradI->permute({0,4,1,2,3})); // [bS, iD, iH, iW, iC] -> [bS, iC, iD, iH, iW]
gradO = new NDArray(gradO->permute({0,4,1,2,3})); // [bS, oD, oH, oW, oC] -> [bS, oC, oD, oH, oW]
}
deconv3dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode);
delete weights;
delete gradW;
if(!isNCDHW) {
delete input;
delete gradI;
delete gradO;
}
return Status::OK();
}
PLATFORM_CHECK(deconv3d_bp) {
auto input = INPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NHWC) or [bS, iD, iC, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [kD, kH, kW, oC, iC] always
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
auto gradO = block.width() > 3 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(2); // [bS, oD, oH, oW, oC] (NHWC) or [bS, oC, oD, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iD, iH, iW, iC] (NHWC) or [bS, iC, iD, iH, iW] (NCDHW), gradI
auto gradW = OUTPUT_VARIABLE(1); // [kD, kH, kW, oC, iC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
int dD = INT_ARG(9); // dilations depth
int dH = INT_ARG(10); // dilations height
int dW = INT_ARG(11); // dilations width
int isSameMode = INT_ARG(12); // 0-SAME, 1-VALID
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;
return block.isUseMKLDNN() && (dD <= 1 && dH <= 1 && dW <= 1 && !isSameMode) && ((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) );
}
}
}
}
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