Remove SimpleThreadPool and always use {NonBlocking}ThreadPool

Author: ezhulenev

unsupported/Eigen/CXX11/ThreadPool

@@ -55,21 +55,8 @@
 #include "src/ThreadPool/RunQueue.h"
 #include "src/ThreadPool/ThreadPoolInterface.h"
 #include "src/ThreadPool/ThreadEnvironment.h"
-#include "src/ThreadPool/SimpleThreadPool.h"
 #include "src/ThreadPool/NonBlockingThreadPool.h"
 
-
-// Use the more efficient NonBlockingThreadPool by default.
-namespace Eigen {
-#ifndef EIGEN_USE_SIMPLE_THREAD_POOL
-template <typename Env> using ThreadPoolTempl = NonBlockingThreadPoolTempl<Env>;
-typedef NonBlockingThreadPool ThreadPool;
-#else
-template <typename Env> using ThreadPoolTempl = SimpleThreadPoolTempl<Env>;
-typedef SimpleThreadPool ThreadPool;
-#endif
-}  // namespace Eigen
-
 #endif
 
 #include <Eigen/src/Core/util/ReenableStupidWarnings.h>

unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h

@@ -15,47 +15,6 @@
 
 namespace Eigen {
 
-#ifdef EIGEN_USE_SIMPLE_THREAD_POOL
-namespace internal {
-
-template<typename LhsScalar, typename LhsMapper, typename Index>
-struct packLhsArg {
-  LhsScalar* blockA;
-  const LhsMapper& lhs;
-  const Index m_start;
-  const Index k_start;
-  const Index mc;
-  const Index kc;
-};
-
-template<typename LhsScalar, typename RhsScalar, typename RhsMapper, typename OutputMapper, typename Index>
-struct packRhsAndKernelArg {
-  const MaxSizeVector<LhsScalar*>* blockAs;
-  RhsScalar* blockB;
-  const RhsMapper& rhs;
-  OutputMapper& output;
-  const Index m;
-  const Index k;
-  const Index n;
-  const Index mc;
-  const Index kc;
-  const Index nc;
-  const Index num_threads;
-  const Index num_blockAs;
-  const Index max_m;
-  const Index k_block_idx;
-  const Index m_block_idx;
-  const Index n_block_idx;
-  const Index m_blocks;
-  const Index n_blocks;
-  MaxSizeVector<Notification*>* kernel_notifications;
-  const MaxSizeVector<Notification*>* lhs_notifications;
-  const bool need_to_pack;
-};
-
-}  // end namespace internal
-#endif  // EIGEN_USE_SIMPLE_THREAD_POOL
-
 template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
 struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> :
     public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> > {
@@ -112,7 +71,6 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
   TensorEvaluator(const XprType& op, const Device& device) :
       Base(op, device) {}
 
-#ifndef EIGEN_USE_SIMPLE_THREAD_POOL
   template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous,
             bool rhs_inner_dim_reordered, int Alignment>
   void evalProduct(Scalar* buffer) const {
@@ -763,288 +721,6 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
     return 0;
   }
 
-#else  // EIGEN_USE_SIMPLE_THREAD_POOL
-  // TODO(ezhulenev): SimpleThreadPool will be removed in the future, and seems
-  // like it's not worth adding output kernel support here.
-  static_assert(std::is_same<OutputKernelType, const NoOpOutputKernel>::value,
-                "SimpleThreadPool does not support contraction output kernels.");
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
-  void evalProduct(Scalar* buffer) const {
-    if (this->m_j_size == 1) {
-      this->template evalGemv<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
-      return;
-    }
-
-    evalGemm<lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Alignment>(buffer);
-  }
-
-  template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
-  void evalGemm(Scalar* buffer) const {
-    // columns in left side, rows in right side
-    const Index k = this->m_k_size;
-
-    // rows in left side
-    const Index m = this->m_i_size;
-
-    // columns in right side
-    const Index n = this->m_j_size;
-
-    // zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
-    this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
-
-
-    const int lhs_packet_size = internal::unpacket_traits<typename LeftEvaluator::PacketReturnType>::size;
-    const int rhs_packet_size = internal::unpacket_traits<typename RightEvaluator::PacketReturnType>::size;
-
-    typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
-                                                   LeftEvaluator, left_nocontract_t,
-                                                   contract_t, lhs_packet_size,
-                                                   lhs_inner_dim_contiguous,
-                                                   false, Unaligned> LhsMapper;
-
-    typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
-                                                   RightEvaluator, right_nocontract_t,
-                                                   contract_t, rhs_packet_size,
-                                                   rhs_inner_dim_contiguous,
-                                                   rhs_inner_dim_reordered, Unaligned> RhsMapper;
-
-    typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
-
-    // TODO: packing could be faster sometimes if we supported row major tensor mappers
-    typedef internal::gemm_pack_lhs<LhsScalar, Index, typename LhsMapper::SubMapper, Traits::mr,
-                                    Traits::LhsProgress, ColMajor> LhsPacker;
-    typedef internal::gemm_pack_rhs<RhsScalar, Index, typename RhsMapper::SubMapper, Traits::nr, ColMajor> RhsPacker;
-
-    // TODO: replace false, false with conjugate values?
-    typedef internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper,
-                                  Traits::mr, Traits::nr, false, false> GebpKernel;
-
-    typedef internal::packLhsArg<LhsScalar, LhsMapper, Index> packLArg;
-    typedef internal::packRhsAndKernelArg<LhsScalar, RhsScalar, RhsMapper, OutputMapper, Index> packRKArg;
-
-    // initialize data mappers
-    LhsMapper lhs(this->m_leftImpl, this->m_left_nocontract_strides, this->m_i_strides,
-                  this->m_left_contracting_strides, this->m_k_strides);
-
-    RhsMapper rhs(this->m_rightImpl, this->m_right_nocontract_strides, this->m_j_strides,
-                  this->m_right_contracting_strides, this->m_k_strides);
-
-    OutputMapper output(buffer, m);
-
-    // compute block sizes (which depend on number of threads)
-    const Index num_threads = this->m_device.numThreads();
-    internal::TensorContractionBlocking<LhsMapper, RhsMapper, Index, internal::ShardByCol> blocking(k, m, n, num_threads);
-    Index mc = blocking.mc();
-    Index nc = blocking.nc();
-    Index kc = blocking.kc();
-    eigen_assert(mc <= m);
-    eigen_assert(nc <= n);
-    eigen_assert(kc <= k);
-
-#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
-    const Index k_blocks = CEIL_DIV(k, kc);
-    const Index n_blocks = CEIL_DIV(n, nc);
-    const Index m_blocks = CEIL_DIV(m, mc);
-    const Index sizeA = mc * kc;
-    const Index sizeB = kc * nc;
-
-    /*    cout << "m: " << m << " n: " << n << " k: " << k << endl;
-    cout << "mc: " << mc << " nc: " << nc << " kc: " << kc << endl;
-    cout << "m_blocks: " << m_blocks << " n_blocks: " << n_blocks << " k_blocks: " << k_blocks << endl;
-    cout << "num threads: " << num_threads << endl;
-    */
-
-    // note: m_device.allocate should return 16 byte aligned pointers, but if blockA and blockB
-    //       aren't 16 byte aligned segfaults will happen due to SIMD instructions
-    // note: You can get away with allocating just a single blockA and offsets and meet the
-    //       the alignment requirements with the assumption that
-    //       (Traits::mr * sizeof(ResScalar)) % 16 == 0
-    const Index numBlockAs = numext::mini(num_threads, m_blocks);
-    MaxSizeVector<LhsScalar *> blockAs(num_threads);
-    for (int i = 0; i < num_threads; i++) {
-      blockAs.push_back(static_cast<LhsScalar *>(this->m_device.allocate(sizeA * sizeof(LhsScalar))));
-    }
-
-    // To circumvent alignment issues, I'm just going to separately allocate the memory for each thread
-    // TODO: is this too much memory to allocate? This simplifies coding a lot, but is wasteful.
-    //       Other options: (1) reuse memory when a thread finishes. con: tricky
-    //                      (2) allocate block B memory in each thread. con: overhead
-    MaxSizeVector<RhsScalar *> blockBs(n_blocks);
-    for (int i = 0; i < n_blocks; i++) {
-      blockBs.push_back(static_cast<RhsScalar *>(this->m_device.allocate(sizeB * sizeof(RhsScalar))));
-    }
-
-    // lhs_notifications starts with all null Notifications
-    MaxSizeVector<Notification*> lhs_notifications(num_threads, nullptr);
-
-    // this should really be numBlockAs * n_blocks;
-    const Index num_kernel_notifications = num_threads * n_blocks;
-    MaxSizeVector<Notification*> kernel_notifications(num_kernel_notifications,
-                                                    nullptr);
-
-    for (Index k_block_idx = 0; k_block_idx < k_blocks; k_block_idx++) {
-      const Index k_start = k_block_idx * kc;
-      // make sure we don't overshoot right edge of left matrix
-      const Index actual_kc = numext::mini(k_start + kc, k) - k_start;
-
-      for (Index m_block_idx = 0; m_block_idx < m_blocks; m_block_idx += numBlockAs) {
-        const Index num_blocks = numext::mini(m_blocks-m_block_idx, numBlockAs);
-
-        for (Index mt_block_idx = m_block_idx; mt_block_idx < m_block_idx+num_blocks; mt_block_idx++) {
-          const Index m_start = mt_block_idx * mc;
-          const Index actual_mc = numext::mini(m_start + mc, m) - m_start;
-          eigen_assert(actual_mc > 0);
-
-          Index blockAId = (k_block_idx * m_blocks + mt_block_idx) % num_threads;
-
-          for (int i = 0; i < n_blocks; ++i) {
-            Index notification_id = (blockAId * n_blocks + i);
-            // Wait for any current kernels using this slot to complete
-            // before using it.
-            if (kernel_notifications[notification_id]) {
-              wait_until_ready(kernel_notifications[notification_id]);
-              delete kernel_notifications[notification_id];
-            }
-            kernel_notifications[notification_id] = new Notification();
-          }
-          const packLArg arg = {
-            blockAs[blockAId], // blockA
-            lhs,        // lhs
-            m_start,    // m
-            k_start,    // k
-            actual_mc,  // mc
-            actual_kc,  // kc
-          };
-
-          // Delete any existing notification since we may be
-          // replacing it.  The algorithm should ensure that there are
-          // no existing waiters on this notification.
-          delete lhs_notifications[blockAId];
-          lhs_notifications[blockAId] =
-          this->m_device.enqueue(&Self::packLhs<packLArg, LhsPacker>, arg);
-        }
-
-        // now start kernels.
-        const Index m_base_start = m_block_idx * mc;
-        const bool need_to_pack = m_block_idx == 0;
-
-        for (Index n_block_idx = 0; n_block_idx < n_blocks; n_block_idx++) {
-          const Index n_start = n_block_idx * nc;
-          const Index actual_nc = numext::mini(n_start + nc, n) - n_start;
-
-          // first make sure the previous kernels are all done before overwriting rhs. Also wait if
-          // we're going to start new k. In both cases need_to_pack is true.
-          if (need_to_pack) {
-            for (Index i = num_blocks; i < num_threads; ++i) {
-              Index blockAId = (k_block_idx * m_blocks + i + m_block_idx) % num_threads;
-              Index future_id = (blockAId * n_blocks + n_block_idx);
-              wait_until_ready(kernel_notifications[future_id]);
-            }
-          }
-
-          packRKArg arg = {
-            &blockAs, // blockA
-            blockBs[n_block_idx], // blockB
-            rhs,          // rhs
-            output,       // output
-            m_base_start, // m
-            k_start,      // k
-            n_start,      // n
-            mc,           // mc
-            actual_kc,    // kc
-            actual_nc,    // nc
-            num_threads,
-            numBlockAs,
-            m,
-            k_block_idx,
-            m_block_idx,
-            n_block_idx, // n_block_idx
-            m_blocks, // m_blocks
-            n_blocks, // n_blocks
-            &kernel_notifications, // kernel notifications
-            &lhs_notifications,    // lhs notifications
-            need_to_pack, // need_to_pack
-          };
-
-          // We asynchronously kick off this function, which ends up
-          // notifying the appropriate kernel_notifications objects,
-          // which this thread waits on before exiting.
-          this->m_device.enqueueNoNotification(&Self::packRhsAndKernel<packRKArg, RhsPacker, GebpKernel>, arg);
-        }
-      }
-    }
-
-    // Make sure all the kernels are done.
-    for (size_t i = 0; i < kernel_notifications.size(); ++i) {
-      wait_until_ready(kernel_notifications[i]);
-      delete kernel_notifications[i];
-    }
-
-    // No need to wait for lhs notifications since they should have
-    // already been waited on.  Just clean them up.
-    for (size_t i = 0; i < lhs_notifications.size(); ++i) {
-      delete lhs_notifications[i];
-    }
-
-    // deallocate all of the memory for both A and B's
-    for (size_t i = 0; i < blockAs.size(); i++) {
-      this->m_device.deallocate(blockAs[i]);
-    }
-    for (size_t i = 0; i < blockBs.size(); i++) {
-      this->m_device.deallocate(blockBs[i]);
-    }
-
-#undef CEIL_DIV
-  }
-
-  /*
-   * Packs a LHS block of size (mt, kc) starting at lhs(m, k). Before packing
-   * the LHS block, check that all of the kernels that worked on the same
-   * mt_block_idx in the previous m_block are done.
-   */
-  template <typename packLArg, typename LhsPacker>
-  static void packLhs(const packLArg arg) {
-    // perform actual packing
-    LhsPacker pack_lhs;
-    pack_lhs(arg.blockA, arg.lhs.getSubMapper(arg.m_start, arg.k_start), arg.kc, arg.mc);
-  }
-
-  /*
-   * Packs a RHS block of size (kc, nc) starting at (k, n) after checking that
-   * all kernels in the previous block are done.
-   * Then for each LHS future, we wait on the future and then call GEBP
-   * on the area packed by the future (which starts at
-   * blockA + future_idx * mt * kc) on the LHS and with the full packed
-   * RHS block.
-   * The output of this GEBP is written to output(m + i * mt, n).
-   */
-  template <typename packRKArg, typename RhsPacker, typename GebpKernel>
-  static void packRhsAndKernel(packRKArg arg) {
-    if (arg.need_to_pack) {
-      RhsPacker pack_rhs;
-      pack_rhs(arg.blockB, arg.rhs.getSubMapper(arg.k, arg.n), arg.kc, arg.nc);
-    }
-
-    GebpKernel gebp;
-    for (Index mt_block_idx = 0; mt_block_idx < arg.num_blockAs; mt_block_idx++) {
-      const Index m_base_start = arg.m + arg.mc*mt_block_idx;
-      if (m_base_start < arg.max_m) {
-        Index blockAId = (arg.k_block_idx * arg.m_blocks + mt_block_idx + arg.m_block_idx) % arg.num_threads;
-        wait_until_ready((*arg.lhs_notifications)[blockAId]);
-        const Index actual_mc = numext::mini(m_base_start + arg.mc, arg.max_m) - m_base_start;
-        gebp(arg.output.getSubMapper(m_base_start, arg.n),
-             (*arg.blockAs)[blockAId], arg.blockB,
-             actual_mc, arg.kc, arg.nc, Scalar(1), -1, -1, 0, 0);
-
-        // Notify that the kernel is done.
-        const Index set_idx = blockAId * arg.n_blocks + arg.n_block_idx;
-        (*arg.kernel_notifications)[set_idx]->Notify();
-      }
-    }
-  }
-#endif  // EIGEN_USE_SIMPLE_THREAD_POOL
-
   TensorOpCost contractionCost(Index m, Index n, Index bm, Index bn, Index bk,
                                bool shard_by_col, bool prepacked) const {
     const int packed_size = std::min<int>(PacketType<LhsScalar, Device>::size,

unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h

@@ -150,13 +150,6 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> {
     if (needs_assign)
     {
       const Index size = array_prod(evaluator.dimensions());
-#if !defined(EIGEN_USE_SIMPLE_THREAD_POOL)
-      device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
-                         EvalRange<Evaluator, Index, Vectorizable>::alignBlockSize,
-                         [&evaluator](Index first, Index last) {
-                           EvalRange<Evaluator, Index, Vectorizable>::run(&evaluator, first, last);
-                         });
-#else
       size_t num_threads = device.numThreads();
       if (num_threads > 1) {
         num_threads = TensorCostModel<ThreadPoolDevice>::numThreads(
@@ -182,7 +175,6 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> {
         }
         barrier.Wait();
       }
-#endif  // defined(!EIGEN_USE_SIMPLE_THREAD_POOL)
     }
     evaluator.cleanup();
   }