wehub-resource-sync
102 lines
4.0 KiB
Plaintext
102 lines
4.0 KiB
Plaintext
/* ******************************************************************************
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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 <execution/cuda/LaunchDims.h>
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#include <ops/declarable/helpers/sequence_mask.h>
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#include "helpers/DebugHelper.h"
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namespace sd {
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namespace ops {
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namespace helpers {
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template <typename I, typename B>
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static SD_KERNEL void sequenceMaskKernel(const void* inputBuf, const LongType* inputShape, void* outputBuf,
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const LongType* outputShape, int maxIndex) {
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__shared__ const I* input;
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__shared__ B* output;
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__shared__ LongType inputLen, outputLen;
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// Cache shape information
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__shared__ sd::LongType inputRank, outputRank;
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__shared__ const sd::LongType* inputShapePtr;
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__shared__ const sd::LongType* outputShapePtr;
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__shared__ const sd::LongType* inputStridePtr;
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__shared__ const sd::LongType* outputStridePtr;
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if (threadIdx.x == 0) {
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input = reinterpret_cast<const I*>(inputBuf);
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output = reinterpret_cast<B*>(outputBuf);
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inputLen = shape::length(inputShape);
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outputLen = shape::length(outputShape);
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// Cache shape information
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inputRank = shape::rank(inputShape);
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outputRank = shape::rank(outputShape);
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inputShapePtr = shape::shapeOf(inputShape);
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outputShapePtr = shape::shapeOf(outputShape);
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inputStridePtr = shape::stride(inputShape);
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outputStridePtr = shape::stride(outputShape);
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}
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__syncthreads();
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LongType inputCoords[SD_MAX_RANK];
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LongType outputCoords[SD_MAX_RANK];
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LongType inputOffset;
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LongType outputOffset;
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for (auto i = blockIdx.x; i < maxIndex; i += gridDim.x)
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for (auto k = threadIdx.x; k < inputLen; k += blockDim.x) {
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INDEX2COORDS(k, inputRank, inputShapePtr, inputCoords);
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COORDS2INDEX(inputRank, inputStridePtr, inputCoords, inputOffset);
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if (i < input[inputOffset]) {
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INDEX2COORDS(k * maxIndex + i, outputRank, outputShapePtr, outputCoords);
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COORDS2INDEX(outputRank, outputStridePtr, outputCoords, outputOffset);
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output[outputOffset] = B(true);
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}
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}
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}
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template <typename I, typename B>
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static void sequenceMask_(LaunchContext* context, NDArray* input, NDArray* output, int maxIndex) {
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dim3 launchDims = getSequenceMaskLaunchDims(maxIndex,*input);
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NDArray::prepareSpecialUse({output}, {input});
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auto stream = context->getCudaStream();
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sequenceMaskKernel<I, B><<<launchDims.y, launchDims.x, launchDims.z, *stream>>>(
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input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), maxIndex);
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sd::DebugHelper::checkErrorCode(stream, "sequenceMaskKernel failed");
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NDArray::registerSpecialUse({output}, {input});
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}
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void sequenceMask(LaunchContext* context, NDArray* input, NDArray* output, int maxIndex) {
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auto inputDType = input->dataType();
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auto outputDType = output->dataType();
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BUILD_DOUBLE_SELECTOR(input->dataType(), output->dataType(), sequenceMask_, (context, input, output, maxIndex),
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SD_INTEGER_TYPES, SD_COMMON_TYPES_EXTENDED);
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}
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BUILD_DOUBLE_TEMPLATE( void sequenceMask_,
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(sd::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex), SD_INTEGER_TYPES,
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SD_COMMON_TYPES_EXTENDED);
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} // namespace helpers
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} // namespace ops
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} // namespace sd
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