68 lines
2.9 KiB
C++
68 lines
2.9 KiB
C++
/* ******************************************************************************
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*
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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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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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 GS <sgazeos@gmail.com>
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//
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#include <ops/declarable/CustomOperations.h>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(relu_layer, 3, 1, false, 0, 0) {
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auto x = INPUT_VARIABLE(0);
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auto w = INPUT_VARIABLE(1);
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auto b = INPUT_VARIABLE(2);
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REQUIRE_TRUE(x->isMatrix(), 0, "relu_layer: x argument should be a 2D tensor, but got rank %i instead!", x->rankOf());
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REQUIRE_TRUE(w->isMatrix(), 0, "relu_layer: weights argument should be a 2D tensor, but got rank %i instead!", w->rankOf());
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REQUIRE_TRUE(b->isVector(), 0, "relu_layer: biases argument should be a 1D tensor, but got rank %i instead!", b->rankOf());
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REQUIRE_TRUE(b->lengthOf() == w->sizeAt(1), 0, "relu_layer: biases array length should match to columns of weights matrix, however got length = %i and columns = %i!", b->lengthOf(), w->sizeAt(1));
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REQUIRE_TRUE(x->sizeAt(1) == w->sizeAt(0), 0, "relu_layer: number of x columns should match to row number of weights matrix, but got x_columns = %i and weights_rows = %i!", x->sizeAt(1), w->sizeAt(0));
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auto output = OUTPUT_VARIABLE(0);
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sd::ops::xw_plus_b op;
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auto status = op.execute({x, w, b}, {output});
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REQUIRE_TRUE(Status::OK() == status, 0, "relu_layer: xw_plus_b op failed on input data.");
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auto scalar = block.numT() > 0 ? block.getTArguments()->at(0) : 0.0;
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output->applyScalar(sd::scalar::RELU, scalar, *output);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(relu_layer) {
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auto inShape = inputShape->at(0);
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auto weightsShape = inputShape->at(1);
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auto outputShape = ShapeUtils::matrixProductShape(inShape, weightsShape, false, false, ArrayOptions::dataType(inShape), block.getWorkspace());
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return SHAPELIST(outputShape);
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}
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DECLARE_TYPES(relu_layer) {
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getOpDescriptor()
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->setAllowedInputTypes(sd::DataType::ANY)
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// ->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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}
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}
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