137 lines
6.1 KiB
Plaintext
137 lines
6.1 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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// Created by GS <sgazeos@gmail.com> on 4/6/2018.
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//
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#include <array/ResultSet.h>
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#include <ops/declarable/helpers/diag.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// diag functor cuda kernel
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// outputBuffer - output tensor buffer
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// outputShape - output tensor shape
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// inputBuffer - input tensor buffer - this tensor should be placed on diagonal position of output
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// inputShape - input tensor shape
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// inputLength - length for input tensor
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//
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template <typename T>
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static __global__ void diagFunctorKernel(void* outputBuffer, Nd4jLong* outputShape, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong inputLength) {
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__shared__ T *z;
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__shared__ T const* x;
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__shared__ Nd4jLong outputLength;
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if (threadIdx.x == 0) {
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z = reinterpret_cast<T*>(outputBuffer);
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x = reinterpret_cast<T const*>(inputBuffer);
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outputLength = shape::length(outputShape);
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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for (int t = tid; t < inputLength; t += step) { // for all vals in input, put all on diagonal position to output
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z[shape::getIndexOffset(t * (inputLength + 1), outputShape)] = x[shape::getIndexOffset(t, inputShape)]; //tX];
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}
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// diag part functor cuda kernel
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// outputBuffer - output tensor buffer - linear sequence of diagonal values
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// outputShape - output tensor shape
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// inputBuffer - input tensor buffer - this tensor should be placed on diagonal position of output
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// inputShape - input tensor shape
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// outputLength - given length of output
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// inputLength - given length for input tensor
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//
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template <typename T>
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static __global__ void diagPartFunctorKernel(void* outputBuffer, Nd4jLong* outputShape, void const* inputBuffer, Nd4jLong* inputShape, Nd4jLong outputLength, Nd4jLong inputLength) {
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__shared__ T *z;
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__shared__ T const* x;
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if (threadIdx.x == 0) {
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z = reinterpret_cast<T*>(outputBuffer);
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x = reinterpret_cast<T const*>(inputBuffer);
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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const auto step = gridDim.x * blockDim.x;
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Nd4jLong i = threadIdx.x * (outputLength + 1); // pos to diagonal value
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for (int t = tid; t < outputLength && i < inputLength; t += step) { // loop by output, but input matrix may not be square
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// put diagonal val from input onto output
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z[shape::getIndexOffset(t, outputShape)] = x[shape::getIndexOffset(i, inputShape)];
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i += outputLength + 1; // shift to next diagonal value
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}
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}
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//////////////////////////////////////////////////////////////////////////
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// Returns a batched matrix tensor with new batched diagonal values.
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// for detailed explanations please take a look on web page: https://www.tensorflow.org/api_docs/python/tf/matrix_set_diag
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template <typename T>
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static void _diagFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* output) {
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auto stream = context->getCudaStream();
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auto inputLength = input->lengthOf();
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dim3 launchDims(256, 512, 8192);
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if (!input->isActualOnDeviceSide())
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input->syncToDevice();
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diagFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), inputLength);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// diagFunctor - caller for diag functor processor
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void diagFunctor(sd::LaunchContext * context, const NDArray* input, NDArray* output) {
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auto xType = input->dataType();
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BUILD_SINGLE_SELECTOR(xType, _diagFunctor, (context, input, output), LIBND4J_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void _diagFunctor, (sd::LaunchContext * context, const NDArray* input, NDArray* output);, LIBND4J_TYPES);
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// diagPartFunctor - caller for diag part functor kernel
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template <typename T>
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void _diagPartFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output) {
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const int outLen = output->lengthOf();
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const int inLen = input->lengthOf();
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auto stream = context->getCudaStream();
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dim3 launchDims(256, 512, 8192);
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if (!input->isActualOnDeviceSide())
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input->syncToDevice();
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diagPartFunctorKernel<T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(output->specialBuffer(), output->specialShapeInfo(), input->getSpecialBuffer(), input->getSpecialShapeInfo(), outLen, inLen);
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}
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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// diagPartFunctor - caller for diag part functor processor
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void diagPartFunctor(sd::LaunchContext * context, NDArray const* input, NDArray* output) {
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auto zType = output->dataType();
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BUILD_SINGLE_SELECTOR(zType, _diagPartFunctor, (context, input, output), NUMERIC_TYPES);
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}
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}
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}
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} |