wehub-resource-sync
440 lines
22 KiB
C++
440 lines
22 KiB
C++
/* ******************************************************************************
|
|
*
|
|
*
|
|
* 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.
|
|
*
|
|
* See the NOTICE file distributed with this work for additional
|
|
* information regarding copyright ownership.
|
|
* 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 saudet
|
|
// @author raver119@gmail.com
|
|
// @author Yurii Shyrma (iuriish@yahoo.com)
|
|
//
|
|
#include <helpers/MKLDNNStream.h>
|
|
#include <ops/declarable/OpRegistrator.h>
|
|
#include <ops/declarable/PlatformHelper.h>
|
|
#include <ops/declarable/helpers/convolutions.h>
|
|
#include <system/platform_boilerplate.h>
|
|
|
|
#include "mkldnnUtils.h"
|
|
|
|
using namespace dnnl;
|
|
|
|
namespace sd {
|
|
namespace ops {
|
|
namespace platforms {
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
static void conv2dMKLDNN(NDArray *input, NDArray *weights, NDArray *bias, NDArray *output,
|
|
const sd::LongType kH, const sd::LongType kW, const sd::LongType sH, const sd::LongType sW, const sd::LongType pH, const sd::LongType pW,
|
|
const sd::LongType dH, const sd::LongType dW, const int paddingMode, const int isNCHW, const int wFormat) {
|
|
// mkl support weights in [oC, iC, kH, kW] format only
|
|
|
|
sd::LongType bS, iC, iH, iW, oC, oH,
|
|
oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
sd_debug("Running conv2d onednn with strides: %d %d padding: %d %d dilation: %d %d paddingMode %d weightFormat %d\n",sH,sW,pH,pW,dH,dW,paddingMode,wFormat);
|
|
const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2
|
|
: pW; // dH == 1 for causal mode in conv1d
|
|
|
|
dnnl::memory::dims strides = {sH, sW};
|
|
dnnl::memory::dims padding = {pH, pW};
|
|
dnnl::memory::dims padding_r = {(oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame};
|
|
dnnl::memory::dims dilation = {dH - 1, dW - 1};
|
|
|
|
auto xzFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
|
|
dnnl::memory::format_tag wFormatMkl = 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};
|
|
|
|
auto type = dnnl::memory::data_type::f32;
|
|
|
|
std::vector<int> permut;
|
|
if (0 == wFormat)
|
|
permut = {3, 2, 0, 1}; // [kH, kW, iC, oC] -> [oC, iC, kH, kW]
|
|
else if (2 == wFormat)
|
|
permut = {0, 3, 1, 2}; // [oC, kH, kW, iC] -> [oC, iC, kH, kW]
|
|
|
|
// memory descriptors for arrays
|
|
|
|
sd_debug("Creating input descriptor\n",0);
|
|
// input
|
|
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFormatMkl);
|
|
onednnUtils::setBlockStrides(*input, x_user_md);
|
|
|
|
sd_debug("Creating weight descriptor\n",0);
|
|
|
|
// weights
|
|
dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormatMkl);
|
|
onednnUtils::setBlockStrides(*weights, w_user_md, permut);
|
|
|
|
sd_debug("Creating bias descriptor\n",0);
|
|
|
|
// bias
|
|
dnnl::memory::desc b_mkl_md;
|
|
if (bias != nullptr) b_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x);
|
|
|
|
sd_debug("Creating output\n",0);
|
|
|
|
// output
|
|
dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc z_user_md = dnnl::memory::desc(zDims, type, xzFormatMkl);
|
|
onednnUtils::setBlockStrides(*output, z_user_md);
|
|
|
|
auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
|
|
|
|
sd_debug("Creating op descriptor\n",0);
|
|
|
|
// operation primitive description
|
|
dnnl::convolution_forward::desc op_desc(dnnl::prop_kind::forward_inference, dnnl::algorithm::convolution_auto,
|
|
x_mkl_md, w_mkl_md, b_mkl_md, z_mkl_md, strides, dilation, padding,
|
|
padding_r);
|
|
|
|
sd_debug("Creating prim descriptor\n",0);
|
|
|
|
dnnl::convolution_forward::primitive_desc op_prim_desc(op_desc, engine);
|
|
sd_debug("Created engine\n",0);
|
|
|
|
// 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
|
|
onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
|
|
|
|
// weights
|
|
onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_prim_desc.weights_desc(),
|
|
args[DNNL_ARG_WEIGHTS]);
|
|
|
|
// bias
|
|
if (bias != nullptr) {
|
|
auto b_mkl_mem = dnnl::memory(b_mkl_md, engine, const_cast<void *>(bias->buffer()));
|
|
args[DNNL_ARG_BIAS] = b_mkl_mem;
|
|
}
|
|
|
|
// output
|
|
auto z_user_mem =
|
|
onednnUtils::loadDataToMklStream(*output, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);
|
|
|
|
// run calculations
|
|
dnnl::convolution_forward(op_prim_desc).execute(stream, args);
|
|
|
|
// reorder outputs if necessary
|
|
if (op_prim_desc.dst_desc() != z_user_mem.get_desc())
|
|
dnnl::reorder(args[DNNL_ARG_DST], z_user_mem).execute(stream, args[DNNL_ARG_DST], z_user_mem);
|
|
|
|
stream.wait();
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
static void conv2dBpMKLDNN(NDArray *input, NDArray *weights, NDArray *bias, 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,
|
|
const int isNCHW, const int wFormat) {
|
|
// mkl support weights/gradW in [oC, iC, kH, kW] format only
|
|
|
|
sd::LongType bS, iC, iH, iW, oC, oH,
|
|
oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
const int pWSame = (paddingMode == 2 && dW > 1) ? ((oW - 1) * sW + (kW - 1) * dW + 1 - iW) / 2
|
|
: pW; // dH == 1 for causal mode in conv1d
|
|
|
|
dnnl::memory::dims strides = {sH, sW};
|
|
dnnl::memory::dims padding = {pH, pW};
|
|
dnnl::memory::dims padding_r = {(oH - 1) * sH - iH + kH - pH, (oW - 1) * sW - iW + kW - pWSame};
|
|
dnnl::memory::dims dilation = {dH - 1, dW - 1};
|
|
|
|
auto xzFormatMkl = isNCHW ? dnnl::memory::format_tag::nchw : dnnl::memory::format_tag::nhwc;
|
|
dnnl::memory::format_tag wFormatMkl = 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};
|
|
|
|
auto type = dnnl::memory::data_type::f32;
|
|
|
|
std::vector<int> permut;
|
|
if (0 == wFormat)
|
|
permut = {3, 2, 0, 1}; // [kH, kW, iC, oC] -> [oC, iC, kH, kW]
|
|
else if (2 == wFormat)
|
|
permut = {0, 3, 1, 2}; // [oC, kH, kW, iC] -> [oC, iC, kH, kW]
|
|
|
|
// memory descriptors for arrays
|
|
|
|
// input
|
|
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc x_user_md = dnnl::memory::desc(xDims, type, xzFormatMkl);
|
|
onednnUtils::setBlockStrides(*input, x_user_md);
|
|
|
|
// weights
|
|
dnnl::memory::desc w_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc w_user_md = dnnl::memory::desc(wDims, type, wFormatMkl);
|
|
onednnUtils::setBlockStrides(*weights, w_user_md, permut);
|
|
|
|
// gradO
|
|
dnnl::memory::desc gradO_mkl_md = dnnl::memory::desc(zDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc gradO_user_md = dnnl::memory::desc(zDims, type, xzFormatMkl);
|
|
onednnUtils::setBlockStrides(*gradO, gradO_user_md);
|
|
|
|
// gradI
|
|
dnnl::memory::desc gradI_mkl_md = dnnl::memory::desc(xDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc gradI_user_md = dnnl::memory::desc(xDims, type, xzFormatMkl);
|
|
onednnUtils::setBlockStrides(*gradI, gradI_user_md);
|
|
|
|
// gradW
|
|
dnnl::memory::desc gradW_mkl_md = dnnl::memory::desc(wDims, type, dnnl::memory::format_tag::any);
|
|
dnnl::memory::desc gradW_user_md = dnnl::memory::desc(wDims, type, wFormatMkl);
|
|
onednnUtils::setBlockStrides(*gradW, gradW_user_md, permut);
|
|
|
|
// gradB
|
|
dnnl::memory::desc gradB_mkl_md;
|
|
if (gradB != nullptr) gradB_mkl_md = dnnl::memory::desc({oC}, type, dnnl::memory::format_tag::x);
|
|
|
|
auto engine = onednnUtils::getEngine(LaunchContext::defaultContext()->engine());
|
|
|
|
// forward primitive description
|
|
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);
|
|
dnnl::convolution_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
|
|
|
|
// backward data primitive description
|
|
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);
|
|
dnnl::convolution_backward_data::primitive_desc op_data_bp_prim_desc(op_data_bp_desc, engine, op_ff_prim_desc);
|
|
|
|
// backward weights primitive description
|
|
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);
|
|
dnnl::convolution_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
|
|
onednnUtils::loadDataToMklStream(*input, engine, stream, x_user_md, op_weights_bp_prim_desc.src_desc(),
|
|
args[DNNL_ARG_SRC]);
|
|
|
|
// weights
|
|
onednnUtils::loadDataToMklStream(*weights, engine, stream, w_user_md, op_data_bp_prim_desc.weights_desc(),
|
|
args[DNNL_ARG_WEIGHTS]);
|
|
|
|
// gradO
|
|
auto gradO_user_mem = dnnl::memory(gradO_user_md, engine, const_cast<void *>(gradO->buffer()));
|
|
const bool gradOReorderW = op_weights_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
|
|
const bool gradOReorderD = op_data_bp_prim_desc.diff_dst_desc() != gradO_user_mem.get_desc();
|
|
auto gradO_mkl_memW = gradOReorderW ? dnnl::memory(op_weights_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
|
|
auto gradO_mkl_memD = gradOReorderD ? dnnl::memory(op_data_bp_prim_desc.diff_dst_desc(), engine) : gradO_user_mem;
|
|
if (gradOReorderW) dnnl::reorder(gradO_user_mem, gradO_mkl_memW).execute(stream, gradO_user_mem, gradO_mkl_memW);
|
|
if (gradOReorderD) dnnl::reorder(gradO_user_mem, gradO_mkl_memD).execute(stream, gradO_user_mem, gradO_mkl_memD);
|
|
args[DNNL_ARG_DIFF_DST] = gradO_mkl_memD;
|
|
|
|
// gradI
|
|
auto gradI_user_mem = onednnUtils::loadDataToMklStream(*gradI, engine, stream, gradI_user_md,
|
|
op_data_bp_prim_desc.diff_src_desc(), args[DNNL_ARG_DIFF_SRC]);
|
|
|
|
// gradW
|
|
auto gradW_user_mem = onednnUtils::loadDataToMklStream(
|
|
*gradW, engine, stream, gradW_user_md, op_weights_bp_prim_desc.diff_weights_desc(), args[DNNL_ARG_DIFF_WEIGHTS]);
|
|
|
|
// gradB
|
|
if (gradB != nullptr) {
|
|
auto gradB_mkl_mem = dnnl::memory(gradB_mkl_md, engine, gradB->buffer());
|
|
args[DNNL_ARG_DIFF_BIAS] = gradB_mkl_mem;
|
|
}
|
|
|
|
// run backward data calculations
|
|
dnnl::convolution_backward_data(op_data_bp_prim_desc).execute(stream, args);
|
|
|
|
if (gradOReorderW || gradOReorderD) args[DNNL_ARG_DIFF_DST] = gradO_mkl_memW;
|
|
|
|
// 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);
|
|
|
|
stream.wait();
|
|
|
|
}
|
|
|
|
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(conv2d, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
|
|
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)
|
|
|
|
sd::LongType sH = INT_ARG(2); // strides height
|
|
sd::LongType sW = INT_ARG(3); // strides width
|
|
sd::LongType pH = INT_ARG(4); // paddings height
|
|
sd::LongType pW = INT_ARG(5); // paddings width
|
|
sd::LongType dH = INT_ARG(6); // dilations height
|
|
sd::LongType dW = INT_ARG(7); // dilations width
|
|
int paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
|
|
bool isNCHW = block.getIArguments()->size() > 9 ? !INT_ARG(9) : 1; // INT_ARG(9): 0-NCHW, 1-NHWC
|
|
int wFormat = block.getIArguments()->size() > 10
|
|
? INT_ARG(10)
|
|
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
|
|
|
|
sd::LongType kH = INT_ARG(0) > 0 ? INT_ARG(0) : static_cast<sd::LongType>(weights->sizeAt(0)); // filter(kernel) height
|
|
sd::LongType kW = INT_ARG(1) > 0 ? INT_ARG(1) : static_cast<sd::LongType>(weights->sizeAt(1)); // filter(kernel) width
|
|
|
|
sd::LongType bS, iC, iH, iW, oC, oH,
|
|
oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *output, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CONV2D MKLDNN OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(
|
|
bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
|
|
"CONV2D MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
conv2dMKLDNN(input, weights, bias, output, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW, wFormat);
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
PLATFORM_CHECK(conv2d, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0);
|
|
auto weights = INPUT_VARIABLE(1);
|
|
|
|
// conv2d is only available for float32 dtype
|
|
Requirements req("ONEDNN CONV2d OP");
|
|
req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
|
|
req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0), sd::DataType::FLOAT32) &&
|
|
req.expectEq(makeInfoVariable(weights->dataType(), TYPE_MSG_INPUT1), sd::DataType::FLOAT32);
|
|
req.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
//////////////////////////////////////////////////////////////////////
|
|
PLATFORM_IMPL(conv2d_bp, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_NULLIFIED(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
|
|
auto gradW = OUTPUT_NULLIFIED(1); // [kH, kW, iC, oC], [oC, iC, kH, kW], [oC, kH, kW, iC]
|
|
auto gradB = block.width() > 3 ? OUTPUT_NULLIFIED(2) : nullptr; // [oC]
|
|
|
|
sd::LongType kH = INT_ARG(0); // filter(kernel) height
|
|
sd::LongType kW = INT_ARG(1); // filter(kernel) width
|
|
sd::LongType sH = INT_ARG(2); // strides height
|
|
sd::LongType sW = INT_ARG(3); // strides width
|
|
sd::LongType pH = INT_ARG(4); // paddings height
|
|
sd::LongType pW = INT_ARG(5); // paddings width
|
|
sd::LongType dH = INT_ARG(6); // dilations height
|
|
sd::LongType 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 wFormat = block.getIArguments()->size() > 10
|
|
? INT_ARG(10)
|
|
: 0; // 0 - [kH, kW, iC, oC], 1 - [oC, iC, kH, kW], 2 - [oC, kH, kW, iC]
|
|
|
|
sd::LongType bS, iC, iH, iW, oC, oH,
|
|
oW; // batch size, input channels, input height/width, output channels, output height/width;
|
|
sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
|
|
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, wFormat, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC,
|
|
indIiH, indWiC, indWoC, indWkH, indOoH);
|
|
|
|
sd::LongType trueoH, trueoW; // true output height, width
|
|
ConvolutionUtils::calcOutSizePool2D(trueoH, trueoW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
|
|
|
|
if (paddingMode) // SAME
|
|
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW, paddingMode);
|
|
|
|
std::vector<sd::LongType> expectedGradOShape =
|
|
ShapeUtils::composeShapeUsingDimsAndIdx({bS, oC, trueoH, trueoW, 0, indIOioC, indOoH, indOoH + 1});
|
|
std::vector<sd::LongType> expectedWeightsShape = ConvolutionUtils::expectWeightsShape(wFormat, kH, kW, iC, oC);
|
|
REQUIRE_TRUE(
|
|
gradO->isSameShape(expectedGradOShape), 0,
|
|
"CONV2D_BP MKLDNN OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
|
|
REQUIRE_TRUE(weights->isSameShape(expectedWeightsShape), 0,
|
|
"CONV2D_BP MKLDNN OP: wrong shape of weights array, expected is %s, but got %s instead !",
|
|
ShapeUtils::shapeAsString(expectedWeightsShape).c_str(), ShapeUtils::shapeAsString(weights).c_str());
|
|
if (bias)
|
|
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
|
|
"CONV2D_BP MKLDNN OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, "
|
|
"%i instead !",
|
|
oC, bias->rankOf(), bias->lengthOf());
|
|
|
|
conv2dBpMKLDNN(input, weights, bias, gradO, gradI, gradW, gradB, kH, kW, sH, sW, pH, pW, dH, dW, paddingMode, isNCHW,
|
|
wFormat);
|
|
|
|
return sd::Status::OK;
|
|
}
|
|
|
|
PLATFORM_CHECK(conv2d_bp, ENGINE_CPU) {
|
|
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
|
|
auto weights = INPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
|
auto bias = block.width() > 3 ? INPUT_VARIABLE(2) : nullptr; // [oC]
|
|
auto gradO = block.width() > 3
|
|
? INPUT_VARIABLE(3)
|
|
: INPUT_VARIABLE(2); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
|
|
|
|
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
|
|
auto gradW = OUTPUT_VARIABLE(1); // [kH, kW, iC, oC] always
|
|
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
|
|
|
|
Requirements req("ONEDNN CONV2d_BP OP");
|
|
req.expectTrue(block.isUseONEDNN(), IS_USE_ONEDNN_MSG) &&
|
|
req.expectTrue(sd::ONEDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB}),
|
|
ONEDNN_STREAM_NOT_SUPPORTED);
|
|
req.logTheSuccess();
|
|
return req;
|
|
}
|
|
|
|
} // namespace platforms
|
|
} // namespace ops
|
|
} // namespace sd
|