libnd4j/include/ops/declarable/platform/vednn/logSoftmax.cpp
/*
* ******************************************************************************
* *
* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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
* *****************************************************************************
*/
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/helpers/convolutions.h>
#include <system/platform_boilerplate.h>
#include "vednnUtils.h"
namespace sd {
namespace ops {
namespace platforms {
PLATFORM_IMPL(log_softmax, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
const uint64_t inner_dim = input->sizeAt(rank - 1);
const uint64_t outer_dim = input->lengthOf() / inner_dim;
#if !defined(HAVE_VEDA)
auto ret = vednnSoftmaxForward(VEDNN_SOFTMAX_LOG, input->buffer(), output->buffer(), outer_dim, inner_dim);
return ret == VEDNN_SUCCESS ? sd::Status::OK : sd::Status::BAD_ARGUMENTS;
#else
VEDA_HANDLE& handle = VEDA::getInstance().getVEDA_HANDLE(0);
auto func = handle.getFunctionByConstPtrName("vedaVednnSoftmaxForward");
VEDAdeviceptr vIn, vO;
vIn = (VEDAdeviceptr)input->specialBuffer();
vO = (VEDAdeviceptr)output->specialBuffer();
VEDA_CALL_THROW(vedaLaunchKernel(func, 0, VEDNN_SOFTMAX_LOG, vIn, vO, outer_dim, inner_dim));
return sd::Status::OK;
#endif
}
PLATFORM_CHECK(log_softmax, ENGINE_CPU) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
const int rank = input->rankOf();
int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;
Requirements req("VEDNN LOG SOFTMAX OP");
req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT), DataType::FLOAT32) &&
req.expectEq(makeInfoVariable(output->dataType(), TYPE_MSG_OUTPUT), DataType::FLOAT32) &&
req.expectFalse(makeInfoVariable(input->isEmpty(), IS_EMPTY_MSG_INPUT), EXPECTED_FALSE) &&
req.expectIn(makeInfoVariable(dim, "The dimension would be performed on"), {-1, rank - 1}) &&
req.expectEq(makeInfoVariable(input->ordering(), ORDERING_MSG_INPUT), 'c') &&
req.expectEq(makeInfoVariable(output->ordering(), ORDERING_MSG_OUTPUT), 'c') &&
req.expectEq(makeInfoVariable(input->ews(), EWS_MSG_INPUT), 1) &&
req.expectEq(makeInfoVariable(output->ews(), EWS_MSG_OUTPUT), 1);
req.logTheSuccess();
return req;
}
} // namespace platforms
} // namespace ops
} // namespace sd