/*******************************************************************************
* 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 saudet
// @author raver119@gmail.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>
using namespace mkldnn;
namespace nd4j {
namespace ops {
namespace platforms {
PLATFORM_IMPL(conv3dnew_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, 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, 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), epsilon
auto gradW = OUTPUT_VARIABLE(
1); // [kD, kH, kW, iC, oC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
REQUIRE_TRUE(input->rankOf() == 5, 0,
"CUSTOM CONV3D_BP OP: rank of input array must be equal to 5, but got %i instead !",
input->rankOf());
REQUIRE_TRUE(weights->rankOf() == 5, 0,
"CUSTOM CONV3D_BP OP: rank of weights array must be equal to 5, but got %i instead !",
weights->rankOf());
REQUIRE_TRUE(gradO->rankOf() == 5, 0,
"CUSTOM CONV3D_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); // 1-SAME, 0-VALID
int isNDHWC =
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(isNDHWC, *input, *gradO, bS, iC, iD, iH, iW, oC, oD, oH, oW,
indIOioC, indIOioD, indWiC, indWoC, indWkD);
int trueoD, trueoH, trueoW; // true output depth/height/width
ConvolutionUtils::calcOutSizePool3D(trueoD, trueoH, trueoW, kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH,
dW, iD, iH, iW, isSameMode);
std::string expectedGradOShape = ShapeUtils::shapeAsString(ShapeUtils::composeShapeUsingDimsAndIdx(
{bS, oC, trueoD, trueoH, trueoW, 0, indIOioC, indIOioD, indIOioD + 1, indIOioD + 2}));
std::string expectedWeightsShape = ShapeUtils::shapeAsString({kD, kH, kW, iC, oC});
REQUIRE_TRUE(expectedGradOShape == ShapeUtils::shapeAsString(gradO), 0,
"CUSTOM CONV3D_BP OP: wrong shape of output gradients (next epsilon) array, expected is %s, but got %s instead !",
expectedGradOShape.c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(expectedWeightsShape == ShapeUtils::shapeAsString(weights), 0,
"CUSTOM CONV3D_BP OP: wrong shape of weights array, expected is %s, but got %s instead !",
expectedWeightsShape.c_str(), ShapeUtils::shapeAsString(weights).c_str());
if (bias)
REQUIRE_TRUE(bias->rankOf() <= 2 && oC == bias->lengthOf(), 0,
"CUSTOM CONV3D_BP OP: wrong shape of array with biases, expected rank, length: <=2, %i, but got %i, %i instead !",
oC, bias->rankOf(), bias->lengthOf());
mkldnn_memory_desc_t empty;
mkldnn::memory::desc conv_src_md(empty), conv_diff_src_md(empty), conv_weights_md(empty),
conv_diff_weights_md(empty), conv_bias_md(empty), conv_dst_md(empty);
mkldnn::memory::desc user_src_md(empty), user_diff_src_md(empty), user_weights_md(empty),
user_diff_weights_md(empty), user_bias_md(empty), user_dst_md(empty);
mkldnn::memory::dims conv_strides, conv_padding, conv_padding_r;
mkldnnUtils::getMKLDNNMemoryDescConv3d(kD, kH, kW, sD, sH, sW, pD, pH, pW, dD, dH, dW, isSameMode,
isNDHWC,
bS, iC, iD, iH, iW, oC, oD, oH, oW, input, gradI, weights,
gradW, gradB, gradO,
&conv_src_md, &conv_diff_src_md, &conv_weights_md,
&conv_diff_weights_md, &conv_bias_md, &conv_dst_md,
&user_src_md, &user_diff_src_md, &user_weights_md,
&user_diff_weights_md, &user_bias_md, &user_dst_md,
conv_strides, conv_padding, conv_padding_r);
auto conv_desc = gradB != nullptr
? convolution_forward::desc(prop_kind::forward,
algorithm::convolution_auto, conv_src_md,
conv_weights_md, conv_bias_md,
conv_dst_md, conv_strides, conv_padding,
conv_padding_r)
: convolution_forward::desc(prop_kind::forward,
algorithm::convolution_auto, conv_src_md,
conv_weights_md,
conv_dst_md, conv_strides, conv_padding,
conv_padding_r);
auto conv_prim_desc = convolution_forward::primitive_desc(conv_desc, mkldnnUtils::getEngine(
LaunchContext::defaultContext()->engine()));
if (gradW != nullptr) {
auto convW_desc = gradB != nullptr
? convolution_backward_weights::desc(
algorithm::convolution_auto, conv_src_md, conv_diff_weights_md, conv_bias_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r)
: convolution_backward_weights::desc(
algorithm::convolution_auto, conv_src_md, conv_diff_weights_md,
conv_dst_md, conv_strides, conv_padding, conv_padding_r);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
mkldnn::stream stream(engine);
auto convW_prim_desc = convolution_backward_weights::primitive_desc(convW_desc, engine,
conv_prim_desc);
auto userW_src_memory = mkldnn::memory(user_src_md, engine,
const_cast<NDArray *>(input)->buffer());
auto userW_weights_memory = mkldnn::memory(user_diff_weights_md, engine, gradW->buffer());
auto userW_dst_memory = mkldnn::memory(user_dst_md, engine,
const_cast<NDArray *>(gradO)->buffer());
auto convW_src_memory = userW_src_memory;
if (convW_prim_desc.src_desc() != userW_src_memory.get_desc()) {
convW_src_memory = mkldnn::memory(convW_prim_desc.src_desc(), engine);
reorder(userW_src_memory, convW_src_memory).execute(stream, userW_src_memory,
convW_src_memory);
}
auto convW_weights_memory = userW_weights_memory;
if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
convW_weights_memory = mkldnn::memory(convW_prim_desc.diff_weights_desc(), engine);
}
auto convW_dst_memory = userW_dst_memory;
if (convW_prim_desc.diff_dst_desc() != userW_dst_memory.get_desc()) {
convW_dst_memory = mkldnn::memory(convW_prim_desc.diff_dst_desc(), engine);
reorder(userW_dst_memory, convW_dst_memory).execute(stream, userW_dst_memory,
convW_dst_memory);
}
if (gradB != nullptr) {
auto convW_bias_memory = mkldnn::memory(convW_prim_desc.diff_bias_desc(), engine,
gradB->buffer());
convolution_backward_weights(convW_prim_desc).execute(stream,
{{MKLDNN_ARG_SRC, convW_src_memory},
{MKLDNN_ARG_DIFF_DST, convW_dst_memory},
{MKLDNN_ARG_DIFF_WEIGHTS, convW_weights_memory},
{MKLDNN_ARG_DIFF_BIAS, convW_bias_memory}});
} else {
convolution_backward_weights(convW_prim_desc).execute(stream,
{{MKLDNN_ARG_SRC, convW_src_memory},
{MKLDNN_ARG_DIFF_DST, convW_dst_memory},
{MKLDNN_ARG_DIFF_WEIGHTS, convW_weights_memory}});
}
if (convW_prim_desc.diff_weights_desc() != userW_weights_memory.get_desc()) {
reorder(convW_weights_memory, userW_weights_memory).execute(stream, convW_weights_memory,
userW_weights_memory);
}
stream.wait();
}
if (gradI != nullptr) {
auto convI_desc = convolution_backward_data::desc(algorithm::convolution_auto,
conv_diff_src_md, conv_weights_md,
conv_dst_md, conv_strides, conv_padding,
conv_padding_r);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
mkldnn::stream stream(engine);
auto convI_prim_desc = convolution_backward_data::primitive_desc(convI_desc, engine,
conv_prim_desc);
auto userI_src_memory = mkldnn::memory(user_diff_src_md, engine, gradI->buffer());
auto userI_weights_memory = mkldnn::memory(user_weights_md, engine,
const_cast<NDArray *>(weights)->buffer());
auto userI_dst_memory = mkldnn::memory(user_dst_md, engine,
const_cast<NDArray *>(gradO)->buffer());
auto convI_src_memory = userI_src_memory;
if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
convI_src_memory = mkldnn::memory(convI_prim_desc.diff_src_desc(), engine);
}
auto convI_weights_memory = userI_weights_memory;
if (convI_prim_desc.weights_desc() != userI_weights_memory.get_desc()) {
convI_weights_memory = mkldnn::memory(convI_prim_desc.weights_desc(), engine);
reorder(userI_weights_memory, convI_weights_memory).execute(stream, userI_weights_memory,
convI_weights_memory);
}
auto convI_dst_memory = userI_dst_memory;
if (convI_prim_desc.diff_dst_desc() != userI_dst_memory.get_desc()) {
convI_dst_memory = mkldnn::memory(convI_prim_desc.diff_dst_desc(), engine);
reorder(userI_dst_memory, convI_dst_memory).execute(stream, userI_dst_memory,
convI_dst_memory);
}
convolution_backward_data(convI_prim_desc).execute(stream,
{{MKLDNN_ARG_DIFF_DST, convI_dst_memory},
{MKLDNN_ARG_WEIGHTS, convI_weights_memory},
{MKLDNN_ARG_DIFF_SRC, convI_src_memory}});
if (convI_prim_desc.diff_src_desc() != userI_src_memory.get_desc()) {
reorder(convI_src_memory, userI_src_memory).execute(stream, convI_src_memory,
userI_src_memory);
}
stream.wait();
}
return Status::OK();
}
PLATFORM_CHECK(conv3dnew_bp) {
// we don't want to use mkldnn if cpu doesn't support avx/avx2
if (::optimalLevel() < 2)
return false;
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, 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, 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), epsilon
auto gradW = OUTPUT_VARIABLE(
1); // [kD, kH, kW, iC, oC] always
auto gradB = block.width() > 3 ? OUTPUT_VARIABLE(2) : nullptr; // [oC]
return block.isUseMKLDNN() &&
nd4j::MKLDNNStream::isSupported({input, weights, bias, gradO, gradI, gradW, gradB});
}
}
}
}