123 lines
4.8 KiB
C++
123 lines
4.8 KiB
C++
/*******************************************************************************
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* Copyright (c) 2019-2020 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Oleg Semeniv <oleg.semeniv@gmail.com>
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//
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//
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#include <ops/declarable/PlatformHelper.h>
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#include <ops/declarable/OpRegistrator.h>
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#include <system/platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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using namespace dnnl;
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namespace sd {
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namespace ops {
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namespace platforms {
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//////////////////////////////////////////////////////////////////////
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static void tanhMKLDNN(const NDArray* x, NDArray* z) {
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const auto xRank = x->rankOf();
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dnnl::memory::dims xShape, zShape;
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mkldnnUtils::getDims(x, xRank, xShape);
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mkldnnUtils::getDims(z, xRank, zShape);
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dnnl::memory::format_tag format = mkldnnUtils::getFormat(xRank);
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dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, dnnl::memory::data_type::f32, format);
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mkldnnUtils::setBlockStrides(x, xRank, x_user_md);
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// z
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dnnl::memory::desc z_mkl_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format);
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dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, dnnl::memory::data_type::f32, format);
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mkldnnUtils::setBlockStrides(z, xRank, z_user_md);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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// Create attributes (to handle alpha and beta if necessary)
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dnnl::primitive_attr attr; // it is empty since we have usual values for alpha (=1) and beta (=0)
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// operation primitive description
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dnnl::eltwise_forward::desc op_desc(dnnl::prop_kind::forward_inference, algorithm::eltwise_tanh, x_mkl_md, 0, 0);
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dnnl::eltwise_forward::primitive_desc op_prim_desc(op_desc, attr, engine);
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// arguments (memory buffers) necessary for calculations
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std::unordered_map<int, dnnl::memory> args;
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dnnl::stream stream(engine);
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// provide memory buffers and check whether reorder is required
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// input
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mkldnnUtils::loadDataToMklStream(x, engine, stream, args, x_user_md, op_prim_desc.src_desc(), DNNL_ARG_SRC);
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// z
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auto z_user_mem = dnnl::memory(z_user_md, engine, z->getBuffer());
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const bool zReorder = op_prim_desc.dst_desc() != z_user_mem.get_desc();
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auto z_mkl_mem = zReorder ? dnnl::memory(op_prim_desc.dst_desc(), engine) : z_user_mem;
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args[DNNL_ARG_DST] = z_mkl_mem;
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// run calculations
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dnnl::eltwise_forward(op_prim_desc).execute(stream, args);
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// reorder outputs if necessary
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if (zReorder)
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dnnl::reorder(z_mkl_mem, z_user_mem).execute(stream, z_mkl_mem, z_user_mem);
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stream.wait();
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}
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PLATFORM_IMPL(tanh, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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const int rank = input->rankOf();
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REQUIRE_TRUE(rank <= 6, 0, "TANH_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
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// mkldnnTanh
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tanhMKLDNN(input, output);
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return Status::OK();
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}
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PLATFORM_CHECK(tanh, ENGINE_CPU) {
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auto x = INPUT_VARIABLE(0);
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auto z = OUTPUT_VARIABLE(0);
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const DataType xType = x->dataType();
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const DataType zType = z->dataType();
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const int xRank = x->rankOf();
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bool bSupportedRanks = xRank < 7;
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/*
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Source Destination
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f32 f32
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*/
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return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
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}
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}
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}
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}
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