Tanh mkldnn implementation (#296)
* libnd4j first step of softmax mkldnn implementation Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j raw implementation of mkldnn softmax Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j merge master and added softmax to MklDnnTests Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j some corrections for softmax mkldnn Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j merge branch, fixed problem with negative axis, fixed dnnl::memory::format_tag selection, test cases added Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j minor corrections to avoid risk connected with negative axis usage Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed windows builds, added switcher to use mkldnn sofmax version only for 3D, 4D, 5D, 6D arrays Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed dataType selection per request Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fix for mac and windows builds Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j builds fix Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j first spet of elementwize tanh implementation on mkldnn Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed typo in error message for softmax MKLDNN, test case added, implementation of tanh on MKLDNN, need supported DataType testing Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j several fixes for tanh and temporary performance test added Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed mkldnn platform loader for tanh Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j MklDnn tanh removed unsupported data types, removed performance test case, added more appropriate equivalence test case, code clean up Signed-off-by: Oleg <oleg.semeniv@gmail.com> * libnd4j fixed problem with empty input case for MklDnn tanh and softmax Signed-off-by: Oleg <oleg.semeniv@gmail.com>
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@ -90,6 +90,8 @@ namespace sd {
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DECLARE_PLATFORM(softmax, ENGINE_CPU);
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DECLARE_PLATFORM(tanh, ENGINE_CPU);
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}
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}
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@ -152,7 +152,7 @@ namespace sd {
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REQUIRE_TRUE(dim < rank && dim >= 0, 0, "SOFTMAX_MKLDNN OP: the value of input integer parameter (dimension) must be less than input array rank %i, but got dimension = %i instead !", rank, dim);
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REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 4, but got rank = %i instead !", rank);
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REQUIRE_TRUE(rank <= 6, 0, "SOFTMAX_MKLDNN OP: the rank of input must be less or qual 6, but got rank = %i instead !", rank);
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// mkldnnSoftMax
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softmaxMKLDNN(input, output, dim);
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@ -174,7 +174,7 @@ namespace sd {
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Source Destination
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f32 f32
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*/
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return block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
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return !x->isEmpty() && block.isUseMKLDNN() && bSupportedRanks && (xType == DataType::FLOAT32 && zType == DataType::FLOAT32);
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}
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158
libnd4j/include/ops/declarable/platform/mkldnn/tanh.cpp
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158
libnd4j/include/ops/declarable/platform/mkldnn/tanh.cpp
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@ -0,0 +1,158 @@
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/*******************************************************************************
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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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std::vector<int64_t> dimsX(xRank), dimsZ(xRank);
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for (auto i = 0; i < xRank; i++) {
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dimsX[i] = x->sizeAt(i);
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dimsZ[i] = z->sizeAt(i);
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}
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dnnl::memory::dims xShape = dnnl::memory::dims(dimsX);
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dnnl::memory::dims zShape = dnnl::memory::dims(dimsZ);
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dnnl::memory::format_tag format = dnnl::memory::format_tag::a;
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if (2 == xRank) {
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format = dnnl::memory::format_tag::ab;
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}
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else if (3 == xRank) {
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format = dnnl::memory::format_tag::abc;
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}
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else if (4 == xRank) {
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format = dnnl::memory::format_tag::abcd;
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}
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else if (5 == xRank) {
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format = dnnl::memory::format_tag::abcde;
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}
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else if (6 == xRank) {
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format = dnnl::memory::format_tag::abcdef;
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}
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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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if (x->ews() != 1 || x->ordering() != 'c') {
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x_user_md.data.format_kind = dnnl_blocked; // overrides format
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for (auto i = 0; i < xRank; ++i) {
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x_user_md.data.format_desc.blocking.strides[i] = x->strideAt(i);
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}
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}
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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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if (z->ews() != 1 || z->ordering() != 'c') {
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z_user_md.data.format_kind = dnnl_blocked; // overrides format
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for (auto i = 0; i < xRank; ++i) {
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z_user_md.data.format_desc.blocking.strides[i] = z->strideAt(i);
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}
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}
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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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auto x_user_mem = dnnl::memory(x_user_md, engine, x->getBuffer());
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const bool xReorder = op_prim_desc.src_desc() != x_user_mem.get_desc();
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auto x_mkl_mem = xReorder ? dnnl::memory(op_prim_desc.src_desc(), engine) : x_user_mem;
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if (xReorder)
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dnnl::reorder(x_user_mem, x_mkl_mem).execute(stream, x_user_mem, x_mkl_mem);
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args[DNNL_ARG_SRC] = x_mkl_mem;
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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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File diff suppressed because one or more lines are too long
@ -71,6 +71,8 @@ TEST_F(MklDnnTests, helpers_includer) {
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sd::ops::platforms::PLATFORM_softmax_ENGINE_CPU softmax;
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printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax });
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sd::ops::platforms::PLATFORM_tanh_ENGINE_CPU tanh;
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printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax, &tanh });
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#endif
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}
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