93 lines
3.9 KiB
C++
93 lines
3.9 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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 saudet
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// @author raver119@gmail.com
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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 <platform_boilerplate.h>
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#include <helpers/MKLDNNStream.h>
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#include "mkldnnUtils.h"
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#include <ops/declarable/helpers/convolutions.h>
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using namespace dnnl;
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namespace nd4j {
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namespace ops {
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namespace platforms {
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PLATFORM_IMPL(lrn, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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REQUIRE_TRUE(input->rankOf() == 4, 0, "lrn: Input rank of 4 expected, but got %i instead",
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input->rankOf());
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double alpha = T_ARG(1);
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double beta = T_ARG(2);
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double bias = T_ARG(0);
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int depth = INT_ARG(0);
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dnnl_memory_desc_t empty;
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dnnl::memory::desc lrn_src_md(empty), lrn_dst_md(empty), user_src_md(empty), user_dst_md(empty);
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mkldnnUtils::getMKLDNNMemoryDescLrn(input, nullptr, output, &lrn_src_md, nullptr, &lrn_dst_md,
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&user_src_md, nullptr, &user_dst_md, input->rankOf() - 1);
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auto lrn_desc = lrn_forward::desc(prop_kind::forward_inference, algorithm::lrn_across_channels,
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lrn_src_md, (2 * depth + 1), alpha * (2 * depth + 1), beta, bias);
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auto engine = mkldnnUtils::getEngine(LaunchContext::defaultContext()->engine());
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dnnl::stream stream(engine);
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auto lrn_prim_desc = lrn_forward::primitive_desc(lrn_desc, engine);
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auto user_src_memory = dnnl::memory(user_src_md, engine, input->buffer());
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auto user_dst_memory = dnnl::memory(user_dst_md, engine, output->buffer());
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auto lrn_src_memory = user_src_memory;
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if (lrn_prim_desc.src_desc() != user_src_memory.get_desc()) {
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lrn_src_memory = dnnl::memory(lrn_prim_desc.src_desc(), engine);
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reorder(user_src_memory, lrn_src_memory).execute(stream, user_src_memory, lrn_src_memory);
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}
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auto lrn_dst_memory = user_dst_memory;
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if (lrn_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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lrn_dst_memory = dnnl::memory(lrn_prim_desc.dst_desc(), engine);
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}
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lrn_forward(lrn_prim_desc).execute(stream, {{DNNL_ARG_SRC, lrn_src_memory},
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{DNNL_ARG_DST, lrn_dst_memory}});
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if (lrn_prim_desc.dst_desc() != user_dst_memory.get_desc()) {
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reorder(lrn_dst_memory, user_dst_memory).execute(stream, lrn_dst_memory, user_dst_memory);
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}
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stream.wait();
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return Status::OK();
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};
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PLATFORM_CHECK(lrn, ENGINE_CPU) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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return block.isUseMKLDNN() && nd4j::MKLDNNStream::isSupported({input, output});
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}
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}
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}
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} |