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cavis/libnd4j/include/ops/declarable/platform/mkldnn/deconv2d.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 deconv2dMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* bias, NDArray* output,
const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW,
const int paddingMode) {
// input [bS, iC, iH, iW] nchw, mkl doesn't support format nhwc
// weights [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, oC, iC]
// bias [oC], may be nullptr
// output [bS, oC, oH, oW] nchw, mkl doesn't support format 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(true, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
dnnl::memory::dims strides = { sH, sW };
dnnl::memory::dims padding = { pH, pW };
dnnl::memory::dims padding_r = { (iH - 1) * sH - oH + kH - pH, (iW - 1) * sW - oW + kW - pW };
dnnl::memory::dims dilation = { 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::nchw; // isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::oihw;
dnnl::memory::dims xDims = {bS, iC, iH, iW};
dnnl::memory::dims wDims = {oC, iC, kH, kW};
dnnl::memory::dims zDims = {bS, oC, oH, oW};
// memory descriptors for arrays
// input
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, 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];
// 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];
// 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];
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 deconv2dBackPropMKLDNN(const NDArray* input, const NDArray* weights, const NDArray* gradO, NDArray* gradI, NDArray* gradW, NDArray* gradB,
const int kH, const int kW, const int sH, const int sW, const int pH, const int pW, const int dH, const int dW,
const int paddingMode) {
// input and gradI [bS, iC, iH, iW], mkl doesn't support ndhwc format
// weights and gradW [oC, iC, kH, kW] always, mkl doesn't support weights format [kH, kW, oC, iC]
// gradB [oC], may be nullptr
// gradO [bS, oC, oH, oW]
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(true, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
dnnl::memory::dims strides = { sH, sW };
dnnl::memory::dims padding = { pH, pW };
dnnl::memory::dims padding_r = { (iH - 1) * sH - oH + kH - pH, (iW - 1) * sW - oW + kW - pW };
dnnl::memory::dims dilation = { 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::nchw; // isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
dnnl::memory::format_tag wFormat = dnnl::memory::format_tag::oihw;
dnnl::memory::dims xDims = {bS, iC, iH, iW};
dnnl::memory::dims wDims = {oC, iC, kH, kW};
dnnl::memory::dims zDims = {bS, oC, oH, oW};
// memory descriptors for arrays
// input
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, 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];
// 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];
// 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];
// 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];
// gradW
dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, gradWType, dnnl::memory::format_tag::any);
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];
// 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(deconv2d, 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, oC, iC] 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)
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D_MKLDNN OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D_MKLDNN OP: rank of weights array must be equal to 4, but got %i instead !", weights->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): 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, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH);
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0, "CUSTOM DECONV2D_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 DECONV2D_MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !", oC, bias->rankOf(), bias->lengthOf());
if(paddingMode){ // SAME
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
}
// mkl supports only [oC, iC, kH, kW] format for weights
weights = new NDArray(weights->permute({2,3,0,1})); // [kH, kW, oC, iC] -> [oC, iC, kH, kW]
// mkl supports only NCHW
if(!isNCHW) {
input = new NDArray(input->permute({0,3,1,2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
output = new NDArray(output->permute({0,3,1,2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
}
deconv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode);
delete weights;
if(!isNCHW) {
delete input;
delete output;
}
return Status::OK();
}
PLATFORM_CHECK(deconv2d, ENGINE_CPU) {
// 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 dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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() && (dH <= 1 && dW <= 1 && !paddingMode) &&
(
(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(deconv2d_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [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, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, oC, iC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
REQUIRE_TRUE(input->rankOf() == 4, 0, "CUSTOM DECONV2D_MKLDNN_BP OP: rank of input array must be equal to 4, but got %i instead !", input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 4, 0, "CUSTOM DECONV2D_MKLDNN_BP OP: rank of weights array must be equal to 4 , but got %i instead !", weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 4, 0, "CUSTOM DECONV2D_MKLDNN_BP 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 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, indWoC, indWiC, indWkH, indOoH);
int trueoH, trueoW; // true output height, width
ConvolutionUtils::calcOutSizeDeconv2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
std::vector<Nd4jLong> expectedGradOShape = ShapeUtils::composeShapeUsingDimsAndIdx({bS,oC,trueoH,trueoW, 0,indIOioC,indOoH,indOoH+1});
std::vector<Nd4jLong> expectedWeightsShape = {kH, kW, oC, iC};
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0, "CUSTOM DECONV2D_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 DECONV2D_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 DECONV2D_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(paddingMode){ // SAME
//Note: we're intentionally swapping iH and oH, to calculated the padding for a"normal" conv (not deconv) forward pass
ConvolutionUtils::calcPadding2D(pH, pW, iH, iW, oH, oW, kH, kW, sH, sW, dH, dW);
}
// mkl supports only [oC, iC, kH, kW] for weights
weights = new NDArray(weights->permute({2,3,0,1})); // [kH, kW, oC, iC] -> [oC, iC, kH, kW]
gradW = new NDArray(gradW->permute({2,3,0,1})); // [kH, kW, oC, iC] -> [oC, iC, kH, kW]
// mkl supports NCHW format only
if(!isNCHW) {
input = new NDArray(input->permute({0,3,1,2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
gradI = new NDArray(gradI->permute({0,3,1,2})); // [bS, iH, iW, iC] -> [bS, iC, iH, iW]
gradO = new NDArray(gradO->permute({0,3,1,2})); // [bS, oH, oW, oC] -> [bS, oC, oH, oW]
}
deconv2dBackPropMKLDNN(input, weights, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode);
delete weights;
delete gradW;
if(!isNCHW) {
delete input;
delete gradI;
delete gradO;
}
return Status::OK();
}
PLATFORM_CHECK(deconv2d_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW)
auto weights = INPUT_VARIABLE(1); // [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, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCDHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCDHW), gradI
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, oC, iC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
int dH = INT_ARG(6); // dilations height
int dW = INT_ARG(7); // dilations width
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
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() && (dH <= 1 && dW <= 1 && !paddingMode) && ((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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