Softmax operation implementation for mkldnn (#286)
* 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>
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@ -22,6 +22,7 @@
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#ifndef DEV_TESTS_MKLDNNUTILS_H
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#define DEV_TESTS_MKLDNNUTILS_H
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#include <legacy/NativeOps.h>
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#include <array/NDArray.h>
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#include <dnnl.hpp>
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@ -86,6 +87,9 @@ namespace sd{
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DECLARE_PLATFORM(depthwise_conv2d_bp, ENGINE_CPU);
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DECLARE_PLATFORM(matmul, ENGINE_CPU);
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DECLARE_PLATFORM(softmax, ENGINE_CPU);
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}
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}
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183
libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp
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183
libnd4j/include/ops/declarable/platform/mkldnn/softmax.cpp
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@ -0,0 +1,183 @@
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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 softmaxMKLDNN(const NDArray* x, NDArray* z, const int axis) {
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const auto xRank = x->rankOf();
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const auto zRank = z->rankOf();
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std::vector<int64_t> dimsX(xRank), dimsZ(zRank);
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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; // 1 == xRank
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if (2 == xRank && 1 == axis) {
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format = dnnl::memory::format_tag::ab;
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}
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else if (2 == xRank && 0 == axis) {
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format = dnnl::memory::format_tag::ba;
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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 && 3 == axis) {
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format = dnnl::memory::format_tag::abcd;
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}
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else if (4 == xRank && 1 == axis && dimsX[2] * dimsX[3] > 1) {
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format = dnnl::memory::format_tag::acdb;
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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::data_type xType = dnnl::memory::data_type::f32;
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dnnl::memory::data_type zType = dnnl::memory::data_type::f32;
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dnnl::memory::desc x_mkl_md = dnnl::memory::desc(xShape, xType, format);
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dnnl::memory::desc x_user_md = dnnl::memory::desc(xShape, xType, 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, zType, dnnl::memory::format_tag::any);
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dnnl::memory::desc z_user_md = dnnl::memory::desc(zShape, zType, 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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// todo check this
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dnnl::softmax_forward::desc op_desc(dnnl::prop_kind::forward_inference, x_mkl_md, axis);
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dnnl::softmax_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::softmax_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(softmax, 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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int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
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if (dim < 0) {
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dim += rank;
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}
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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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// mkldnnSoftMax
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softmaxMKLDNN(input, output, dim);
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return Status::OK();
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}
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PLATFORM_CHECK(softmax, 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 > 2 && 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 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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@ -2921,8 +2921,10 @@ TEST_F(DeclarableOpsTests1, Test_Range_Integer_3) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test1) {
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auto input = NDArrayFactory::create<double>('c', {3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, 5});
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auto expOutput = NDArrayFactory::create<double>('c', {3, 3}, {1.14195199e-01, 8.43794734e-01, 4.20100661e-02, 2.68454951e-01, 1.80883523e-03, 7.29736214e-01, 9.02116571e-05, 2.68917160e-01, 7.30992629e-01});
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NDArray input('c', { 3, 3 }, { -1.f, 1.f, -2.f, 2.f, -3.f, 3.f, -4.f, 4.f, 5.f }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 3, 3 }, { 1.14195199e-01, 8.43794734e-01, 4.20100661e-02, 2.68454951e-01, 1.80883523e-03, 7.29736214e-01, 9.02116571e-05, 2.68917160e-01, 7.30992629e-01 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, {}, {});
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@ -2937,8 +2939,8 @@ TEST_F(DeclarableOpsTests1, softmax_test1) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test2) {
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auto input = NDArrayFactory::create<double>('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14});
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auto expOutput = NDArrayFactory::create<double>('c', {3, 3, 3}, {4.73142e-02,4.73847e-02,6.69062e-03, 9.50330e-01,8.67881e-04,9.92976e-01, 2.35563e-03,9.51747e-01,3.33106e-04, 4.74259e-02,2.26032e-06,4.74259e-02, 2.91395e-07,9.99998e-01,3.94360e-08, 9.52574e-01,1.12535e-07,9.52574e-01, 7.58256e-10,4.74259e-02,1.22325e-11, 1.00000e+00,1.32293e-11,1.19203e-01, 3.77513e-11,9.52574e-01,8.80797e-01});
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NDArray input('c', { 3, 3, 3 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 3, 3, 3 }, { 4.73142e-02,4.73847e-02,6.69062e-03, 9.50330e-01,8.67881e-04,9.92976e-01, 2.35563e-03,9.51747e-01,3.33106e-04, 4.74259e-02,2.26032e-06,4.74259e-02, 2.91395e-07,9.99998e-01,3.94360e-08, 9.52574e-01,1.12535e-07,9.52574e-01, 7.58256e-10,4.74259e-02,1.22325e-11, 1.00000e+00,1.32293e-11,1.19203e-01, 3.77513e-11,9.52574e-01,8.80797e-01 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, { 1 }, {});
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@ -2953,8 +2955,8 @@ TEST_F(DeclarableOpsTests1, softmax_test2) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test3) {
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auto input = NDArrayFactory::create<double>('c', {3, 3, 3}, {-1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14});
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auto expOutput = NDArrayFactory::create<double>('c', {3, 3, 3}, {2.47262e-03,1.23395e-04,3.35350e-04, 1.23395e-04,4.53979e-05,1.23395e-04, 6.14417e-06,1.23395e-04,5.56530e-09, 9.97527e-01,1.12521e-07,9.99665e-01, 1.52281e-08,9.99955e-01,2.06090e-09, 9.99994e-01,2.78912e-10,6.69285e-03, 3.05146e-07,9.99876e-01,4.13855e-08, 9.99877e-01,5.60254e-09,9.99877e-01, 7.58251e-10,9.99877e-01,9.93307e-01});
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NDArray input('c', { 3, 3, 3 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 3, 3, 3 }, { 2.47262e-03,1.23395e-04,3.35350e-04, 1.23395e-04,4.53979e-05,1.23395e-04, 6.14417e-06,1.23395e-04,5.56530e-09, 9.97527e-01,1.12521e-07,9.99665e-01, 1.52281e-08,9.99955e-01,2.06090e-09, 9.99994e-01,2.78912e-10,6.69285e-03, 3.05146e-07,9.99876e-01,4.13855e-08, 9.99877e-01,5.60254e-09,9.99877e-01, 7.58251e-10,9.99877e-01,9.93307e-01 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, { 0 }, {});
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@ -2969,8 +2971,8 @@ TEST_F(DeclarableOpsTests1, softmax_test3) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test4) {
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auto input = NDArrayFactory::create<double>('c', {1, 5}, {-1, 1, -2, 2, 3});
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auto expOutput = NDArrayFactory::create<double>('c', {1, 5}, {0.01198,0.08855,0.00441,0.24072,0.65434});
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NDArray input('c', { 1, 5 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 1, 5 }, { 0.01198,0.08855,0.00441,0.24072,0.65434 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, { 1 }, {});
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@ -2985,8 +2987,8 @@ TEST_F(DeclarableOpsTests1, softmax_test4) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test5) {
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auto input = NDArrayFactory::create<double>('c', {1, 5}, {-1, 1, -2, 2, 3});
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auto expOutput = NDArrayFactory::create<double>('c', {1, 5}, {1,1,1,1,1});
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NDArray input('c', { 1, 5 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 1, 5 }, { 1,1,1,1,1 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, { 0 });
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@ -3001,8 +3003,8 @@ TEST_F(DeclarableOpsTests1, softmax_test5) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test6) {
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auto input = NDArrayFactory::create<double>('c', {5, 1}, {-1, 1, -2, 2, 3});
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auto expOutput = NDArrayFactory::create<double>('c', {5, 1}, {0.01198,0.08855,0.00441,0.24072,0.65434});
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NDArray input('c', { 5, 1 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 5, 1 }, { 0.01198,0.08855,0.00441,0.24072,0.65434 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, { 0 }, {});
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@ -3017,8 +3019,8 @@ TEST_F(DeclarableOpsTests1, softmax_test6) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test7) {
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auto input = NDArrayFactory::create<double>('c', {5, 1}, {-1, 1, -2, 2, 3});
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auto expOutput = NDArrayFactory::create<double>('c', {5, 1}, {1,1,1,1,1});
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NDArray input('c', { 5, 1 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 5, 1 }, { 1,1,1,1,1 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, { 1 }, {});
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@ -3033,8 +3035,8 @@ TEST_F(DeclarableOpsTests1, softmax_test7) {
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test8) {
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auto input = NDArrayFactory::create<double>('c', {5}, {-1, 1, -2, 2, 3});
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auto expOutput = NDArrayFactory::create<double>('c', {5}, {0.01198,0.08855,0.00441,0.24072,0.65434});
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NDArray input('c', { 5 }, { -1, 1, -2, 2, 3 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 5 }, { 0.01198,0.08855,0.00441,0.24072,0.65434 }, sd::DataType::FLOAT32);
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sd::ops::softmax op;
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auto results = op.evaluate({ &input }, {}, {}, {});
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@ -3047,6 +3049,70 @@ TEST_F(DeclarableOpsTests1, softmax_test8) {
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delete results;
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}
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//////////////////////////////////////////////////////////////////////
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TEST_F(DeclarableOpsTests1, softmax_test9) {
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NDArray input('c', { 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8 }, sd::DataType::FLOAT32);
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NDArray expOutput('c', { 2, 2, 2, 2 }, { 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059 }, sd::DataType::FLOAT32);
|
||||
|
||||
sd::ops::softmax op;
|
||||
auto results = op.evaluate({ &input }, {}, { 2 }, {});
|
||||
auto z = results->at(0);
|
||||
|
||||
ASSERT_EQ(Status::OK(), results->status());
|
||||
ASSERT_TRUE(expOutput.isSameShape(z));
|
||||
ASSERT_TRUE(expOutput.equalsTo(z));
|
||||
|
||||
delete results;
|
||||
}
|
||||
//////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests1, softmax_test10) {
|
||||
NDArray input('c', { 2, 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14, -14, 15, -15, 16,-16 }, sd::DataType::FLOAT32);
|
||||
NDArray expOutput('c', { 2, 2, 2, 2, 2 }, { 0.119203, 0.880797, 0.017986, 0.982014, 0.002473, 0.997527, 0.000335, 0.999665, 0.000045, 0.999955, 0.000006, 0.999994, 0.000001, 0.999999, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 1.000000, 0.000000, 1.000000, 0.000000, 1.000000, 0.00000 }, sd::DataType::FLOAT32);
|
||||
|
||||
sd::ops::softmax op;
|
||||
auto results = op.evaluate({ &input }, {}, { 4 }, {});
|
||||
auto z = results->at(0);
|
||||
|
||||
ASSERT_EQ(Status::OK(), results->status());
|
||||
ASSERT_TRUE(expOutput.isSameShape(z));
|
||||
ASSERT_TRUE(expOutput.equalsTo(z));
|
||||
|
||||
delete results;
|
||||
}
|
||||
//////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests1, softmax_test11) {
|
||||
NDArray input('c', { 2, 2, 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14, -14, 15, -15, 16,-16, -2.1, 2.1, -2.2, 2.2, -2.3, 2.3, -2.4, 2.4, -2.5,2.5 ,-2.6,2.6, -2.7,2.7, -2.8,2.8, -2.9,2.9, -3.0,3.0, -3.1,3.1, -3.2,3.2, -3.3,3.3, 3.4, -3.4, 3.5, -3.5, 3.6,-3.6 }, sd::DataType::FLOAT32);
|
||||
NDArray expOutput('c', { 2, 2, 2, 2, 2, 2 }, { 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.731059, 0.268941, 0.268941, 0.731059, 0.000000, 1.000000, 1.000000, 0.000000, 0.268941, 0.731059, 0.731059, 0.268941, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.524979, 0.475021, 0.475021, 0.524979, 0.001229, 0.998771, 0.998771, 0.001229, 0.475021, 0.524979, 0.524979, 0.475021 }, sd::DataType::FLOAT32);
|
||||
|
||||
sd::ops::softmax op;
|
||||
auto results = op.evaluate({ &input }, {}, { 4 }, {});
|
||||
auto z = results->at(0);
|
||||
|
||||
ASSERT_EQ(Status::OK(), results->status());
|
||||
ASSERT_TRUE(expOutput.isSameShape(z));
|
||||
ASSERT_TRUE(expOutput.equalsTo(z));
|
||||
|
||||
delete results;
|
||||
}
|
||||
|
||||
//////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests1, softmax_test12) {
|
||||
NDArray input('f', { 2, 2, 2, 2, 2, 2 }, { -1, 1, -2, 2, -3, 3, -4, 4, -5,5 ,-6,6, -7,7, -8,8, -9,9, -10,10, -11,11, -12,12, -13,13, 14, -14, 15, -15, 16,-16, -2.1, 2.1, -2.2, 2.2, -2.3, 2.3, -2.4, 2.4, -2.5,2.5 ,-2.6,2.6, -2.7,2.7, -2.8,2.8, -2.9,2.9, -3.0,3.0, -3.1,3.1, -3.2,3.2, -3.3,3.3, 3.4, -3.4, 3.5, -3.5, 3.6,-3.6 }, sd::DataType::FLOAT32);
|
||||
NDArray exp('c', { 2, 2, 2, 2, 2, 2 }, { 0.982014, 0.598688, 0.982014, 0.598688, 0.017986, 0.401312, 0.017986, 0.401312, 0.982014, 0.598688, 0.000000, 0.001359, 0.017986, 0.401312, 1.000000, 0.998641, 0.982014, 0.598688, 0.000000, 0.001659, 0.017986, 0.401312, 1.000000, 0.998341, 0.982014, 0.598688, 0.000000, 0.001113, 0.017986, 0.401312, 1.000000, 0.998887, 0.017986, 0.401312, 0.017986, 0.401312, 0.982014, 0.598688, 0.982014, 0.598688, 0.017986, 0.401312, 1.000000, 0.998641, 0.982014, 0.598688, 0.000000, 0.001359, 0.017986, 0.401312, 1.000000, 0.998341, 0.982014, 0.598688, 0.000000, 0.001659, 0.017986, 0.401312, 1.000000, 0.998887, 0.982014, 0.598688, 0.000000, 0.001113 }, sd::DataType::FLOAT32);
|
||||
|
||||
auto expOutput = NDArray('f', { 2, 2, 2, 2, 2, 2 }, sd::DataType::FLOAT32);
|
||||
expOutput.assign(exp);
|
||||
|
||||
sd::ops::softmax op;
|
||||
auto results = op.evaluate({ &input }, {}, { 3 }, {});
|
||||
auto z = results->at(0);
|
||||
|
||||
ASSERT_EQ(Status::OK(), results->status());
|
||||
ASSERT_TRUE(expOutput.isSameShape(z));
|
||||
ASSERT_TRUE(expOutput.equalsTo(z));
|
||||
|
||||
delete results;
|
||||
}
|
||||
//////////////////////////////////////////////////////////////////////
|
||||
TEST_F(DeclarableOpsTests1, Reverse_1) {
|
||||
|
||||
@ -3436,4 +3502,3 @@ TEST_F(DeclarableOpsTests1, Test_Release) {
|
||||
auto x = NDArrayFactory::create<float>('c', { 8, 8 });
|
||||
// x.printShapeInfo("x shape");
|
||||
}
|
||||
|
||||
|
||||
@ -69,6 +69,8 @@ TEST_F(MklDnnTests, helpers_includer) {
|
||||
|
||||
sd::ops::platforms::PLATFORM_matmul_ENGINE_CPU matmul;
|
||||
|
||||
printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul});
|
||||
sd::ops::platforms::PLATFORM_softmax_ENGINE_CPU softmax;
|
||||
|
||||
printer({&conv2d, &conv2d_bp, &conv3d, &conv3d_bp, &avgpool2d, &avgpool2d_bp, &maxpool2d, &maxpool2d_bp, &avgpool3d, &avgpool3d_bp, &maxpool3d, &maxpool3d_bp, &lrn, &batchnorm, &matmul, &softmax });
|
||||
#endif
|
||||
}
|
||||
Loading…
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Reference in New Issue
Block a user