/*******************************************************************************
* Copyright (c) 2019-2020 Konduit K.K.
*
* 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 Oleg Semeniv <oleg.semeniv@gmail.com>
//
//
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/OpRegistrator.h>
#include <system/platform_boilerplate.h>
#include <helpers/MKLDNNStream.h>
#include "mkldnnUtils.h"
using namespace dnnl;
namespace sd {
namespace ops {
namespace platforms {
//////////////////////////////////////////////////////////////////////
static void tanhMKLDNN(const NDArray* x, NDArray* z) {
dnnl::memory::dims shape = x->getShapeAsFlatVector();
dnnl::memory::desc x_mkl_md, x_user_md, z_mkl_md, z_user_md;
x_user_md = x_mkl_md = dnnl::memory::desc(shape, dnnl::memory::data_type::f32, mkldnnUtils::getFormat(*x));
mkldnnUtils::setBlockStrides(*x, x_user_md);
// z
z_user_md = z_mkl_md = dnnl::memory::desc(shape, dnnl::memory::data_type::f32, mkldnnUtils::getFormat(*z));
mkldnnUtils::setBlockStrides(*z, z_user_md);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
// Create attributes (to handle alpha and beta if necessary)
dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
// operation primitive description
dnnl::eltwise_forward::desc op_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0);
dnnl::eltwise_forward::primitive_desc op_prim_desc(op_desc, attr, 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
mkldnnUtils::loadDataToMklStream(*x, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
// z
auto z_user_mem = mkldnnUtils::loadDataToMklStream(*z, engine, stream, z_user_md, op_prim_desc.dst_desc(), args[DNNL_ARG_DST]);
// run calculations
dnnl::eltwise_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();
}
PLATFORM_IMPL(tanh, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
REQUIRE_TRUE(rank <= 6, 0, "TANH_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
// mkldnnTanh
tanhMKLDNN(input, output);
return Status::OK();
}
PLATFORM_CHECK(tanh, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto z = OUTPUT_VARIABLE(0);
const DataType xType = x->dataType();
const DataType zType = z->dataType();
const int xRank = x->rankOf();
bool bSupportedRanks = !x->isEmpty() && xRank < 7 && xRank > 0 && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
/*
Source Destination
f32 f32
*/
return block.isUseMKLDNN() && bSupportedRanks;
}
//////////////////////////////////////////////////////////////////////
static void tanhBpMKLDNN(const NDArray* x, const NDArray* dLdz, NDArray* dLdx) {
dnnl::memory::dims shape = x->getShapeAsFlatVector();
dnnl::memory::desc x_mkl_md, x_user_md, dLdx_mkl_md, dLdx_user_md, dLdz_mkl_md, dLdz_user_md;
// x
x_user_md = x_mkl_md = dnnl::memory::desc(shape, dnnl::memory::data_type::f32, mkldnnUtils::getFormat(*x));
mkldnnUtils::setBlockStrides(*x, x_user_md);
// dLdz
dLdz_user_md = dLdz_mkl_md = dnnl::memory::desc(shape, dnnl::memory::data_type::f32, mkldnnUtils::getFormat(*dLdz));
mkldnnUtils::setBlockStrides(*dLdz, dLdz_user_md);
// dLdx
dLdx_user_md = dLdx_mkl_md = dnnl::memory::desc(shape, dnnl::memory::data_type::f32, mkldnnUtils::getFormat(*dLdx));
mkldnnUtils::setBlockStrides(*dLdx, dLdx_user_md);
auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
// arguments (memory buffers) necessary for calculations
std::unordered_map<int, dnnl::memory> args;
dnnl::stream stream(engine);
// operation primitive description
// forward
dnnl::eltwise_forward::desc op_ff_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0);
dnnl::eltwise_forward::primitive_desc op_ff_prim_desc(op_ff_desc, engine);
// backward description
dnnl::eltwise_backward::desc op_desc(algorithm::eltwise_tanh, dLdz_mkl_md, x_mkl_md, 0, 0);
dnnl::eltwise_backward::primitive_desc op_prim_desc(op_desc, engine, op_ff_prim_desc);
// provide memory buffers and check whether reorder is required for forward
// input
mkldnnUtils::loadDataToMklStream(*x, engine, stream, x_user_md, op_prim_desc.src_desc(), args[DNNL_ARG_SRC]);
// dLdz
mkldnnUtils::loadDataToMklStream(*dLdz, engine, stream, dLdz_user_md, op_prim_desc.diff_dst_desc(), args[DNNL_ARG_DIFF_DST]);
// dLdx
auto dLdx_user_mem = mkldnnUtils::loadDataToMklStream(*dLdx, engine, stream, dLdx_user_md, op_prim_desc.diff_src_desc(), args[DNNL_ARG_DIFF_SRC]);
// run calculations backward
dnnl::eltwise_backward(op_prim_desc).execute(stream, args);
// reorder outputs if necessary
if (op_prim_desc.diff_src_desc() != dLdx_user_mem.get_desc())
dnnl::reorder(args[DNNL_ARG_DIFF_SRC], dLdx_user_mem).execute(stream, args[DNNL_ARG_DIFF_SRC], dLdx_user_mem);
stream.wait();
}
PLATFORM_IMPL(tanh_bp, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto dLdz = INPUT_VARIABLE(1);
auto dLdx = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
const int dLdzRank = dLdz->rankOf();
REQUIRE_TRUE(rank <= 6 && dLdzRank <= 6, 0, "TANH_BP_MKLDNN OP: the rank of input and dLdz must be less or qual 6, but got input rank = %i and dLdz rank rank = %i instead !", rank, dLdzRank);
// mkldnnSoftMax
tanhBpMKLDNN(input, dLdz, dLdx);
return Status::OK();
}
PLATFORM_CHECK(tanh_bp, ENGINE_CPU) {
auto x = INPUT_VARIABLE(0);
auto dLdz = INPUT_VARIABLE(1);
auto dLdx = OUTPUT_VARIABLE(0);
const DataType xType = x->dataType();
const DataType dLdzType = dLdz->dataType();
const DataType dLdxType = dLdx->dataType();
const int xRank = x->rankOf();
const int dLdzRank = dLdz->rankOf();
bool bSupportedRanks = xRank < 7 && xRank > 0 && dLdzRank == xRank && (!x->isEmpty() && !dLdz->isEmpty());
bSupportedRanks &= (xType == DataType::FLOAT32 && dLdzType == DataType::FLOAT32 && dLdxType == DataType::FLOAT32);
if (bSupportedRanks) {
for (int i = 0; i < xRank; i++) {
if (x->sizeAt(i) != dLdz->sizeAt(i)) {
bSupportedRanks = false;
break;
}
}
}
//Source Destination
//f32 f32
return block.isUseMKLDNN() && bSupportedRanks;
}
}
}
}