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Avoid relying on a default value for the Vectorizable template parameter of the EvalRange functor
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@ -75,7 +75,7 @@ class TensorExecutor<Expression, DefaultDevice, true>
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// Multicore strategy: the index space is partitioned and each partition is executed on a single core
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// Multicore strategy: the index space is partitioned and each partition is executed on a single core
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#ifdef EIGEN_USE_THREADS
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#ifdef EIGEN_USE_THREADS
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template <typename Evaluator, typename Index, bool Vectorizable = Evaluator::PacketAccess>
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template <typename Evaluator, typename Index, bool Vectorizable>
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struct EvalRange {
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struct EvalRange {
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static void run(Evaluator evaluator, const Index first, const Index last) {
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static void run(Evaluator evaluator, const Index first, const Index last) {
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eigen_assert(last > first);
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eigen_assert(last > first);
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@ -129,11 +129,11 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable>
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std::vector<Notification*> results;
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std::vector<Notification*> results;
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results.reserve(numblocks);
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results.reserve(numblocks);
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for (int i = 0; i < numblocks; ++i) {
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for (int i = 0; i < numblocks; ++i) {
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results.push_back(device.enqueue(&EvalRange<Evaluator, Index>::run, evaluator, i*blocksize, (i+1)*blocksize));
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results.push_back(device.enqueue(&EvalRange<Evaluator, Index, Vectorizable>::run, evaluator, i*blocksize, (i+1)*blocksize));
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}
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}
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if (numblocks * blocksize < size) {
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if (numblocks * blocksize < size) {
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EvalRange<Evaluator, Index>::run(evaluator, numblocks * blocksize, size);
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EvalRange<Evaluator, Index, Vectorizable>::run(evaluator, numblocks * blocksize, size);
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}
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}
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for (int i = 0; i < numblocks; ++i) {
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for (int i = 0; i < numblocks; ++i) {
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