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

159 lines
6.4 KiB
C++

/*******************************************************************************
* 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) {
const auto xRank = x->rankOf();
std::vector<int64_t> dimsX(xRank), dimsZ(xRank);
for (auto i = 0; i < xRank; i++) {
dimsX[i] = x->sizeAt(i);
dimsZ[i] = z->sizeAt(i);
}
dnnl::memory::dims xShape = dnnl::memory::dims(dimsX);
dnnl::memory::dims zShape = dnnl::memory::dims(dimsZ);
dnnl::memory::format_tag format = dnnl::memory::format_tag::a;
if (2 == xRank) {
format = dnnl::memory::format_tag::ab;
}
else if (3 == xRank) {
format = dnnl::memory::format_tag::abc;
}
else if (4 == xRank) {
format = dnnl::memory::format_tag::abcd;
}
else if (5 == xRank) {
format = dnnl::memory::format_tag::abcde;
}
else if (6 == xRank) {
format = dnnl::memory::format_tag::abcdef;
}
dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
if (x->ews() != 1 || x->ordering() != 'c') {
x_user_md.data.format_kind = dnnl_blocked; // overrides format
for (auto i = 0; i < xRank; ++i) {
x_user_md.data.format_desc.blocking.strides[i] = x->strideAt(i);
}
}
// z
dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format);
dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format);
if (z->ews() != 1 || z->ordering() != 'c') {
z_user_md.data.format_kind = dnnl_blocked; // overrides format
for (auto i = 0; i < xRank; ++i) {
z_user_md.data.format_desc.blocking.strides[i] = z->strideAt(i);
}
}
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
auto x_user_mem = dnnl::memory(x_user_md, engine, x->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;
// z
auto z_user_mem = dnnl::memory(z_user_md, engine, z->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::eltwise_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();
}
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 = xRank < 7;
/*
Source Destination
f32 f32
*/
return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
}
}
}
}