/*
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */

/*!
 *  Lower allreduce to device implementable ir.
 * \file lower_thread_allreduce.cc
 */
#include <tvm/arith/analyzer.h>
#include <tvm/ffi/reflection/registry.h>
#include <tvm/target/target.h>
#include <tvm/tir/builtin.h>
#include <tvm/tir/expr.h>
#include <tvm/tir/stmt_functor.h>
#include <tvm/tir/transform.h>

#include <unordered_set>
#include <utility>

#include "runtime/thread_storage_scope.h"
#include "tir/transforms/ir_utils.h"
#include "tir/transforms/update_pointer_storage_scope.h"

namespace tvm {
namespace tl {
using namespace tir;

using runtime::StorageRank;
using runtime::StorageScope;

/*!
 * \brief collect the mapping from the buffer var to its allocate
 */
class AllocateCollector : public StmtExprVisitor {

private:
  bool IsDynamicSharedMemory(Var buffer_var) {
    StorageScope storage_scope = runtime::StorageScope::Create(
        GetPtrStorageScope(std::move(buffer_var)));
    return storage_scope.rank == runtime::StorageRank::kShared &&
           storage_scope.tag == ".dyn";
  }

  bool IsStaticSharedMemory(Var buffer_var) {
    StorageScope storage_scope = runtime::StorageScope::Create(
        GetPtrStorageScope(std::move(buffer_var)));
    return storage_scope.rank == runtime::StorageRank::kShared &&
           storage_scope.tag.empty();
  }

public:
  void VisitStmt_(const AllocateNode *op) final {
    if (IsDynamicSharedMemory(op->buffer_var)) {
      dyn_shmem_allocs_[op->buffer_var.get()] = op;
    } else if (IsStaticSharedMemory(op->buffer_var)) {
      static_shmem_allocs_[op->buffer_var.get()] = op;
    }
    StmtExprVisitor::VisitStmt_(op);
  }
  // The dynamic mapping from the original buffer var to its allocate
  std::unordered_map<const VarNode *, const AllocateNode *> dyn_shmem_allocs_;
  // The static mapping from the original buffer var to its allocate
  std::unordered_map<const VarNode *, const AllocateNode *>
      static_shmem_allocs_;
};

class ThreadAllreduceBuilder final : public StmtExprMutator {
public:
  explicit ThreadAllreduceBuilder(const TargetNode *target,
                                  bool is_dynamic = false)
      : target_(target),
        warp_size_(
            target->GetAttr<Integer>("thread_warp_size", 1).value().IntValue()),
        max_num_threads_(target->GetAttr<Integer>("max_num_threads", -1)
                             .value()
                             .IntValue()) {
    if (is_dynamic) {
      shared_scope = "shared.dyn";
    }
  }

  Stmt VisitStmt_(const AttrStmtNode *op) final {
    if (op->attr_key == tir::attr::thread_extent) {
      thread_extents_.push_back(op);
      Stmt ret = StmtExprMutator::VisitStmt_(op);
      thread_extents_.pop_back();
      return ret;
    } else if (op->attr_key == tir::attr::reduce_scope) {
      const CommReducerNode *combiner = op->node.as<CommReducerNode>();
      ICHECK(combiner);
      reduce_combiner_.push_back(combiner);
      Stmt ret = StmtExprMutator::VisitStmt_(op);
      reduce_combiner_.pop_back();
      return ret;
    } else {
      return StmtExprMutator::VisitStmt_(op);
    }
  }
  Stmt VisitStmt_(const EvaluateNode *op) final {
    Stmt stmt = StmtExprMutator::VisitStmt_(op);
    op = stmt.as<EvaluateNode>();
    const CallNode *call = op->value.as<CallNode>();
    if (call && call->op.same_as(builtin::tvm_thread_allreduce())) {
      return MakeAllreduce(call);
    } else {
      return stmt;
    }
  }
  Stmt VisitStmt_(const AllocateNode *op) final {
    auto node = Downcast<Allocate>(StmtExprMutator::VisitStmt_(op));

    if (auto it = alloc_remap_.find(node->buffer_var.get());
        it != alloc_remap_.end()) {
      Buffer buf = Downcast<Buffer>(it->second);
      auto write_ptr = node.CopyOnWrite();
      write_ptr->buffer_var = buf->data;
      write_ptr->dtype = buf->dtype;
      write_ptr->extents = buf->shape;
      write_ptr->condition = const_true(buf->dtype.lanes());

      if (buf.scope() == shared_scope) {
        // Use volatile access to shared buffer.
        write_ptr->body =
            AttrStmt(buf->data, tir::attr::volatile_scope, 1, write_ptr->body);
      }
    }
    return std::move(node);
  }

  Optional<Buffer> GetRemappedBuffer(const Buffer &buf) {
    if (auto it = buf_remap_.find(buf.get()); it != buf_remap_.end()) {
      return it->second;
    }

    if (auto it = var_remap_.find(buf->data.get()); it != var_remap_.end()) {
      Buffer new_buf = buf;
      new_buf.CopyOnWrite()->data = it->second;
      buf_remap_[buf.get()] = new_buf;
      return new_buf;
    }

    return std::nullopt;
  }

  Stmt VisitStmt_(const DeclBufferNode *op) final {
    auto node = Downcast<DeclBuffer>(StmtExprMutator::VisitStmt_(op));
    if (auto buf = GetRemappedBuffer(node->buffer)) {
      node.CopyOnWrite()->buffer = buf.value();
    }
    return std::move(node);
  }

  PrimExpr VisitExpr_(const BufferLoadNode *op) final {
    if (auto it = load_remap_.find(op->buffer->data.get());
        it != load_remap_.end()) {
      for (const auto &index : op->indices) {
        ICHECK(is_zero(index))
            << "The index of buffer " << op->buffer << " is " << index;
      }
      return it->second;
    }

    BufferLoad load = Downcast<BufferLoad>(StmtExprMutator::VisitExpr_(op));

    if (auto opt = GetRemappedBuffer(load->buffer)) {
      load.CopyOnWrite()->buffer = opt.value();
    }
    return std::move(load);
  }

  Stmt VisitStmt_(const BufferStoreNode *op) final {
    BufferStore store = Downcast<BufferStore>(StmtExprMutator::VisitStmt_(op));

    if (auto opt = GetRemappedBuffer(store->buffer)) {
      store.CopyOnWrite()->buffer = opt.value();
    }
    return std::move(store);
  }

private:
  // Thread entry
  struct ThreadEntry {
    runtime::ThreadScope scope;
    IterVar iv;
    int extent{};
    // comparator
    bool operator<(const ThreadEntry &other) const {
      return scope.dim_index < other.scope.dim_index;
    }
  };

  // make allreduce.
  Stmt MakeAllreduce(const CallNode *call) {
    ICHECK(!reduce_combiner_.empty());
    const CommReducerNode *combiner = reduce_combiner_.back();
    size_t size = combiner->result.size();

    const IntImmNode *size_of_args = call->args[0].as<IntImmNode>();
    ICHECK(size_of_args) << call->args[0]->GetTypeKey();
    ICHECK_EQ(size, size_of_args->value);
    Array<PrimExpr> inits = combiner->identity_element;
    std::vector<PrimExpr> values(size);
    std::vector<DataType> types(size);
    PrimExpr cond = call->args[size + 1];
    for (size_t idx = 0; idx < size; ++idx) {
      values[idx] = call->args[1 + idx];
      if (!is_one(cond)) {
        values[idx] = Select(cond, values[idx], inits[idx]);
      }
      types[idx] = values[idx].dtype();
    }
    std::vector<Buffer> buffers(size);
    for (size_t idx = 0; idx < size; ++idx) {
      PrimExpr arg = call->args[2 + size + idx];
      // Loads from boolean buffers may have cast nodes inserted by
      // earlier passes.
      if (auto cast = arg.as<CastNode>()) {
        arg = cast->value;
      }
      buffers[idx] = Downcast<BufferLoad>(arg)->buffer;
    }

    std::unordered_set<const VarNode *> reduce_set;
    for (size_t i = 2 + 2 * size; i < call->args.size(); ++i) {
      const VarNode *v = call->args[i].as<VarNode>();
      // The simply optimization replace a iteration variable with a constant
      // when extent of the iteration is 1. As threaded IterVar always started
      // from 0, we can just ignore this variable in this case.
      if (v) {
        reduce_set.insert(v);
      } else {
        ICHECK(call->args[i].as<IntImmNode>() &&
               call->args[i].as<IntImmNode>()->value == 0)
            << "arg" << i << "should be a VarNode or IntImmNode "
            << "while it is " << call->args[i];
      }
    }

    size_t nmatch = 0;
    std::vector<ThreadEntry> vred, vpar;
    int reduce_dim_index = -1;
    for (const AttrStmtNode *attr : thread_extents_) {
      ThreadEntry e;
      IterVar iv = Downcast<IterVar>(attr->node);
      e.scope = runtime::ThreadScope::Create(iv->thread_tag);
      e.iv = iv;
      ICHECK_LE(e.scope.rank, 1);
      ICHECK_GE(e.scope.dim_index, 0)
          << "vthread do not work with cross thread reduction";
      if (e.scope.rank == 1) {
        const auto *ptr = attr->value.as<IntImmNode>();
        ICHECK(ptr) << "Need constant extent for reduce set " << iv;
        e.extent = static_cast<int>(ptr->value);
        // ignore variables equal to 0
        if (e.extent == 1) {
          continue;
        }

        if (reduce_set.count(iv->var.get())) {
          bool already_exists = false;
          for (const auto &entry : vred) {
            if (entry.scope.dim_index == e.scope.dim_index) {
              already_exists = true;
              break;
            }
          }
          if (!already_exists) {
            vred.push_back(e);
            ++nmatch;
            reduce_dim_index = e.scope.dim_index;
          }
        } else {
          bool already_exists = false;
          for (const auto &entry : vpar) {
            if (entry.scope.dim_index == e.scope.dim_index) {
              already_exists = true;
              break;
            }
          }
          if (!already_exists) {
            vpar.push_back(e);
          }
        }
      }
    }

    // remove reduce thread from parallel thread
    if (reduce_dim_index != -1) {
      for (size_t i = 0; i < vpar.size(); ++i) {
        if (vpar[i].scope.dim_index == reduce_dim_index) {
          vpar.erase(vpar.begin() + i);
          break;
        }
      }
    }

    ICHECK_EQ(nmatch, reduce_set.size())
        << "Not all reduce index are presented in the context";
    std::sort(vred.begin(), vred.end());
    std::sort(vpar.begin(), vpar.end());
    // the size of each index.
    int reduce_extent, group_extent;
    PrimExpr reduce_index = FlattenThread(vred, &reduce_extent);
    PrimExpr group_index = FlattenThread(vpar, &group_extent);

    // the longest contiguous reduce extent after flattening
    int contiguous_reduce_extent = 1;
    std::vector<std::tuple<int, int, bool>>
        block_threads; // tuple(dim_index, extent, is_reduce)
    for (const ThreadEntry &thr : vred) {
      if (thr.scope.rank == 1) { // threadIdx
        block_threads.emplace_back(thr.scope.dim_index, thr.extent, true);
      }
    }
    for (const ThreadEntry &thr : vpar) {
      if (thr.scope.rank == 1) { // threadIdx
        block_threads.emplace_back(thr.scope.dim_index, thr.extent, false);
      }
    }
    // sort according to dim_index
    std::sort(block_threads.begin(), block_threads.end());
    for (auto &&thr_attr : block_threads) {
      auto [dim_index, extent, is_reduce] = thr_attr;
      (void)dim_index; // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81767
      if (is_reduce) {
        contiguous_reduce_extent *= extent;
      } else {
        break;
      }
    }

    std::vector<Stmt> seq;
    std::vector<Buffer> new_alloc_bufs;
    //
    // This is an optimization. For small reduction sizes, it may be beneficial
    // for a single warp to performance the entire reduction. No trips to shared
    // memory and no cross warp synchronizations are required.
    // The following code emits the reduction as follows:
    //
    // Allocate reduction vars v[i], i = 0..size-1
    //
    // for offset from WARP_SIZE to 1 by 2
    //
    //   a    <- load(v[i])
    //   b    <- shuffle_down(load(v[i], offset))
    //   v[i] <- reduction(a, b)
    //
    // broadcast results from lane 0 to all other lanes and store
    // the final reduction result to the proper location.
    //
    // When the thread extent is multiple of warp size, we can use a two-stage
    // warp-level reduction to optimize. This is implemented by applying the
    // algorithm above twice.
    //
    // For example, suppose we want to use 512 threads to reduce 512 elements
    // and the warp size is 32. In this case there are (512 / 32) = 16 warps.
    // In the first stage, each of the 16 warps reduces 32 elements. So after
    // the stage, we have 16 remaining elements to be reduced, one for each
    // warp. We store the 16 elements in shared memory, and start the second
    // stage. In the second stage we use the first 16 lanes of the first warp to
    // reduce the remaining elements, and this reduction can also be optimized
    // by shuffle_down warp-level primitives.
    PrimExpr zero_index = make_const(reduce_index->dtype, 0);

    if (IsWarpReduction(types, group_extent, reduce_extent,
                        contiguous_reduce_extent)) {
      std::vector<PrimExpr> reduce_results;
      DataType mask_dtype = DataType::UInt(32);
      PrimExpr mask = Call(mask_dtype, builtin::tvm_warp_activemask(), {});

      if (reduce_extent <= warp_size_) {
        std::tie(reduce_results, new_alloc_bufs) = MakeWarpAllreduce(
            values, types, combiner, reduce_index, reduce_extent, group_index,
            mask, std::nullopt, &seq);

        // Broadcast the reduction result from lane 0 to all other lanes.
        // This avoids to emit predicated stores, as all threads are
        // uniformly writing the same result.
        for (size_t i = 0; i < size; ++i) {
          Buffer buf = Downcast<BufferLoad>(reduce_results[i])->buffer;
          PrimExpr val = BufferLoad(buf, {zero_index});
          ICHECK_EQ(val->dtype, types[i]);
          PrimExpr splat =
              WarpShuffle(builtin::tvm_warp_shuffle(), new_alloc_bufs.back(),
                          val, reduce_extent * group_index);
          seq.push_back(BufferStore(buf, splat, {zero_index}));
        }
      } else {
        int n_warps = reduce_extent / warp_size_;
        std::vector<Buffer> local_bufs;

        // 1. Create the staging buffer in shared memory.
        std::vector<Buffer> staging_shared_bufs;
        staging_shared_bufs.reserve(size);
        for (size_t i = 0; i < size; ++i) {
          Buffer staging_shared_buf = decl_buffer(
              /*shape=*/{make_const(reduce_index->dtype,
                                    n_warps * group_extent)},
              /*dtype=*/buffers[i]->dtype, /*name=*/"red_buf_staging",
              /*storage_scope=*/shared_scope);
          staging_shared_bufs.push_back(staging_shared_buf);
          new_alloc_bufs.push_back(staging_shared_buf);
        }

        // 2. First round of allreduce.
        std::tie(reduce_results, local_bufs) =
            MakeWarpAllreduce(values, types, combiner, reduce_index, warp_size_,
                              group_index, mask, std::nullopt, &seq);
        new_alloc_bufs.insert(new_alloc_bufs.end(), local_bufs.begin(),
                              local_bufs.end());

        // 3. Write allreduce results to staging buffer.
        std::vector<Stmt> write_staging_buf;
        write_staging_buf.reserve(size);
        for (size_t i = 0; i < size; ++i) {
          new_alloc_bufs.push_back(
              Downcast<BufferLoad>(reduce_results[i])->buffer);
          write_staging_buf.push_back(BufferStore(
              /*buffer=*/staging_shared_bufs[i],
              /*value=*/reduce_results[i],
              /*indices=*/
              {group_index * n_warps + floordiv(reduce_index, warp_size_)}));
        }
        PrimExpr cond = floormod(reduce_index, warp_size_) == zero_index;
        seq.push_back(IfThenElse(cond, SeqStmt::Flatten(write_staging_buf)));
        seq.push_back(SyncThread(shared_scope));

        // 4. Load staging buffer.
        //    Second round of allreduce.
        for (size_t i = 0; i < size; ++i) {
          values[i] =
              BufferLoad(/*buffer=*/staging_shared_bufs[i],
                         /*indices=*/{group_index * n_warps + reduce_index});
        }
        std::tie(reduce_results, local_bufs) = MakeWarpAllreduce(
            values, types, combiner, reduce_index, n_warps, group_index, mask,
            /*predicate=*/reduce_index <
                make_const(reduce_index->dtype, n_warps),
            &seq);
        new_alloc_bufs.insert(new_alloc_bufs.end(), local_bufs.begin(),
                              local_bufs.end());

        // 5. Create shared memory buffer(s) of `group_extent` elements, storing
        // the allreduce results so each thread can access.
        std::vector<Stmt> write_result;
        write_result.reserve(size);
        for (size_t i = 0; i < size; ++i) {
          new_alloc_bufs.push_back(
              Downcast<BufferLoad>(reduce_results[i])->buffer);
          Buffer broadcast_shared_buf = decl_buffer(
              /*shape=*/{make_const(reduce_index->dtype, group_extent)},
              /*dtype=*/buffers[i]->dtype, /*name=*/"red_result",
              /*storage_scope=*/shared_scope);
          write_result.push_back(BufferStore(broadcast_shared_buf,
                                             reduce_results[i], {group_index}));
          // Update `reduce_results`, pointing to the value loaded from the
          // shared memory buffer.
          reduce_results[i] = BufferLoad(broadcast_shared_buf, {group_index});
        }
        seq.push_back(IfThenElse(reduce_index == zero_index,
                                 SeqStmt::Flatten(write_result)));
        seq.push_back(SyncThread(shared_scope));
      }

      // Write back allreduce results and update existing allocations.
      for (size_t i = 0; i < size; ++i) {
        ICHECK(!load_remap_.count(buffers[i]->data.get()));
        PrimExpr pred = const_true(types[i].lanes());
        Buffer buf = Downcast<BufferLoad>(reduce_results[i])->buffer;
        ICHECK_EQ(reduce_results[i]->dtype, types[i]);
        load_remap_[buffers[i]->data.get()] = reduce_results[i];

        auto node =
            Allocate(buf->data, types[i], buf->shape, pred, Evaluate(0));
        alloc_remap_[buffers[i]->data.get()] = buf;
        var_remap_[buffers[i]->data.get()] = buf->data;
        buf_remap_[buffers[i].get()] = buf;
      }
    } else {
      std::vector<Buffer> shared_bufs(size);
      if (reduce_extent == 1) {
        // special case, no reduction is needed.
        std::vector<Stmt> stores;
        stores.reserve(size);
        for (size_t i = 0; i < size; ++i) {
          stores.emplace_back(BufferStore(buffers[i], values[i], {0}));
        }
        return SeqStmt::Flatten(stores);
      }
      // This sync is necessary because there might be incomplete read of
      // previous iteration on the same buffer.
      seq.emplace_back(SyncThread(shared_scope));
      for (size_t idx = 0; idx < size; ++idx) {
        shared_bufs[idx] = decl_buffer(
            {IntImm(group_index->dtype, group_extent * reduce_extent)},
            types[idx], "red_buf" + std::to_string(idx), shared_scope);
        seq.emplace_back(
            BufferStore(shared_bufs[idx], values[idx],
                        {BufIndex(reduce_index, group_index, reduce_extent)}));
      }
      seq.emplace_back(SyncThread(shared_scope));
      seq.emplace_back(MakeBufAllreduce(
          combiner, types, shared_bufs, reduce_index, group_index,
          reduce_extent, group_extent, contiguous_reduce_extent));
      for (size_t idx = 0; idx < size; ++idx) {
        ICHECK(!load_remap_.count(buffers[idx]->data.get()));
        PrimExpr pred = const_true(types[idx].lanes());
        BufferLoad load(shared_bufs[idx],
                        {BufIndex(make_zero(reduce_index.dtype()), group_index,
                                  reduce_extent)});
        ICHECK_EQ(load->dtype, types[idx]);
        load_remap_[buffers[idx]->data.get()] = load;
        alloc_remap_[buffers[idx]->data.get()] = shared_bufs[idx];
        var_remap_[buffers[idx]->data.get()] = shared_bufs[idx]->data;
        buf_remap_[buffers[idx].get()] = shared_bufs[idx];
      }
    }

    // Fix all local allocations as all statements are built.
    Stmt body = SeqStmt::Flatten(seq);
    for (const Buffer &buf : new_alloc_bufs) {
      body = DeclBuffer(buf, body);
      body = Allocate(buf->data, buf->dtype, buf->shape,
                      const_true(buf->dtype.lanes()), body);
    }

    return body;
  }

  std::pair<std::vector<PrimExpr>, std::vector<Buffer>>
  MakeWarpAllreduce(std::vector<PrimExpr> src_values,                //
                    std::vector<DataType> dtypes,                    //
                    const CommReducerNode *combiner,                 //
                    const PrimExpr &reduce_index, int reduce_extent, //
                    const PrimExpr &group_index,                     //
                    const PrimExpr &mask,
                    const Optional<PrimExpr> &predicate, //
                    std::vector<Stmt> *seq) {
    int n_buffers = src_values.size();

    std::vector<Buffer> shared_bufs;
    std::vector<Buffer> local_bufs;
    shared_bufs.reserve(n_buffers);

    // This is the index to the reduction variable, one reduction
    // variable per warp. Local scope seems easier to reason without
    // relying on a pattern match pass to fix it later.
    Array<PrimExpr> zero_indices = {0};
    Array<PrimExpr> shape = {1};

    std::vector<Stmt> load_values;
    load_values.reserve(n_buffers);
    for (int idx = 0; idx < n_buffers; ++idx) {
      shared_bufs.push_back(decl_buffer(
          shape, dtypes[idx], "red_buf" + std::to_string(idx), "local"));
      load_values.push_back(
          BufferStore(shared_bufs[idx], src_values[idx], zero_indices));

      // Uses a local variable to store the shuffled data.  Later
      // on, an allocation will be built for this local variable.
      local_bufs.push_back(
          decl_buffer(shape, dtypes[idx], "t" + std::to_string(idx), "local"));
    }

    if (predicate.defined()) {
      seq->push_back(
          IfThenElse(predicate.value(), SeqStmt::Flatten(load_values)));
    } else {
      seq->insert(seq->end(), load_values.begin(), load_values.end());
    }

    // The mask for this reducer, as this reducer may sit inside
    // a divergent control flow. Here it uses a variable to cache the current
    // active channels.
    Optional<Buffer> mask_buffer;
    if (need_warp_shuffle_mask_) {
      mask_buffer = decl_buffer(shape, mask->dtype, "mask", "local");
      seq->emplace_back(BufferStore(mask_buffer.value(), mask, zero_indices));
      // Push the buffer description.  Later this will have an
      // allocation built for it.
      local_bufs.push_back(mask_buffer.value());
    }

    // Emit reductions within a warp.
    int start_offset = 1;
    while (start_offset * 2 < reduce_extent) {
      start_offset *= 2;
    }
    for (int offset = start_offset; offset > 0; offset /= 2) {
      // Load reduction values, no synchronization needed.
      Array<PrimExpr> a, b;
      for (int i = 0; i < n_buffers; ++i) {
        const Buffer &shared_buf = shared_bufs[i];
        BufferLoad val(shared_buf, zero_indices);
        ICHECK_EQ(val->dtype, dtypes[i]);
        a.push_back(val);

        // __shfl_*sync calls shall not appear in if_then_else expressions
        // as this is causing extra divergency. E.g.
        //
        // v1 = (v2 < v3) ? v3 : __shfl_sync(mask, v1, 0);
        //
        // behaves differently from
        //
        // int t = __shfl_sync(mask, v1, 0);
        // v1 = (v2 < v3) ? v3 : t;
        //
        // The former may cause dead lock as there is a divergent
        // branch with a warp sync call inside.
        PrimExpr other = WarpShuffle(builtin::tvm_warp_shuffle_down(),
                                     mask_buffer, val, offset);
        const Buffer &local_buf = local_bufs[i];
        Stmt s = BufferStore(local_buf, other, zero_indices);
        seq->push_back(s);

        BufferLoad load = BufferLoad(local_buf, zero_indices);
        ICHECK_EQ(load->dtype, dtypes[i]);
        b.push_back(load);
      }

      // Do reductions.
      Array<PrimExpr> ret = (*combiner)(a, b);

      // Store the reduction result to itself.
      std::vector<Stmt> stores;
      stores.reserve(n_buffers);
      for (int i = 0; i < n_buffers; ++i) {
        const Buffer &buf = shared_bufs[i];
        stores.push_back(BufferStore(buf, ret[i], zero_indices));
      }

      // During the sub-warp reduction, values from inactive threads could be
      // read, which is an undefined behavior according to the cuda document.
      //
      // In practice, the return value are usually 0, which does no harm to sum
      // reduction. However, the result can be incorrect in max or prod
      // reduction. Therefore an additional range check has to be performed to
      // ensure the correctness.
      if (offset * 2 > reduce_extent) {
        PrimExpr cond = reduce_index + offset < reduce_extent;
        seq->push_back(IfThenElse(cond, SeqStmt::Flatten(stores)));
      } else {
        seq->push_back(SeqStmt::Flatten(stores));
      }
    }

    std::vector<PrimExpr> reduce_results;
    reduce_results.reserve(n_buffers);
    for (int i = 0; i < n_buffers; ++i) {
      reduce_results.push_back(BufferLoad(shared_bufs[i], zero_indices));
    }

    return {reduce_results, local_bufs};
  }

  // make allreduce.
  Stmt MakeBufAllreduce(const CommReducerNode *combiner,
                        const std::vector<DataType> &types,
                        const Array<Buffer> &shared_bufs, PrimExpr reduce_index,
                        PrimExpr group_index, int reduce_extent,
                        int group_extent, int contiguous_reduce_extent) {
    // Get next power of two
    int reduce_align = 1;
    while (reduce_extent > reduce_align) {
      reduce_align = reduce_align << 1;
    }
    ICHECK_GT(reduce_align, 1);
    std::vector<Stmt> seq;

    size_t size = shared_bufs.size();
    PrimExpr buf_index = BufIndex(reduce_index, group_index, reduce_extent);
    // make reduction
    auto fload = [&](int offset) {
      Array<PrimExpr> a, b;
      for (size_t i = 0; i < size; ++i) {
        BufferLoad b_load(
            shared_bufs[i],
            {BufIndex(reduce_index + offset, group_index, reduce_extent)});
        ICHECK_EQ(b_load->dtype, types[i]);
        b.push_back(b_load);

        BufferLoad a_load(shared_bufs[i], {buf_index});
        ICHECK_EQ(a_load->dtype, types[i]);
        a.push_back(a_load);
      }
      Array<PrimExpr> ret = (*combiner)(a, b);
      return ret;
    };
    auto fstore = [&](const Array<PrimExpr> &ret) {
      std::vector<Stmt> stores(size);
      for (size_t i = 0; i < size; ++i) {
        stores[i] = BufferStore(shared_bufs[i], ret[i], {buf_index});
      }
      return SeqStmt::Flatten(stores);
    };
    auto freduce = [&](int offset) {
      auto ret = fload(offset);
      return fstore(ret);
    };
    // Step one, check for
    if (reduce_align > reduce_extent) {
      // reduction with the boundary condition
      reduce_align = reduce_align >> 1;
      PrimExpr cond = reduce_index < (reduce_extent - reduce_align);
      seq.emplace_back(IfThenElse(cond, freduce(reduce_align)));
      seq.emplace_back(SyncThread(shared_scope));
    }

    // normal synchronization
    bool warp_align =
        group_extent == 1 || contiguous_reduce_extent % warp_size_ == 0;
    while (reduce_align > contiguous_reduce_extent ||
           reduce_align > warp_size_ || !warp_align) {
      if (reduce_align == 1) {
        break;
      }
      reduce_align = reduce_align >> 1;
      PrimExpr cond = reduce_index < reduce_align;
      seq.emplace_back(IfThenElse(cond, freduce(reduce_align)));
      seq.emplace_back(SyncThread(shared_scope));
    }
    // in warp synchronization.
    if (reduce_align > 1) {
      PrimExpr in_warp_cond = reduce_index < (reduce_align >> 1);

      std::vector<Stmt> in_warp_seq;

      while (reduce_align > 1) {
        reduce_align = reduce_align >> 1;

        // freduce can read/write to the same memory location.  For
        // example, with reduce_align of 4, threadIdx 3 reads from
        // memory location 7 as threadIdx 7 is writing to it.
        // Therefore, we need to separate out the load from the store
        // with a memory barrier in-between.  This isn't necessary for
        // the earlier normal synchronization, because those are each
        // protected by an if-statement.  The if-statement is avoided
        // here to reduce thread divergence.
        auto loads = fload(reduce_align);

        Array<Var> in_warp_local_vars;
        for (auto expr : loads) {
          Var var("w_" + std::to_string(reduce_align) + "_" +
                      std::to_string(in_warp_local_vars.size()),
                  expr->dtype);
          in_warp_local_vars.push_back(var);
        }

        std::vector<Stmt> in_let_statement;
        in_let_statement.emplace_back(SyncThread("warp"));
        in_let_statement.emplace_back(
            fstore({in_warp_local_vars.begin(), in_warp_local_vars.end()}));
        in_let_statement.emplace_back(SyncThread("warp"));

        Stmt body = SeqStmt::Flatten(in_let_statement);
        for (size_t i = 0; i < size; i++) {
          body = LetStmt(in_warp_local_vars[i], loads[i], body);
        }
        in_warp_seq.push_back(body);
      }

      Stmt warp_body = SeqStmt::Flatten(in_warp_seq);

      seq.emplace_back(IfThenElse(in_warp_cond, warp_body));
      seq.emplace_back(SyncThread(shared_scope));
    }
    return SeqStmt::Flatten(seq);
  }
  // Flatten the thread index.
  // Also return a warp number,
  PrimExpr FlattenThread(const std::vector<ThreadEntry> &tvec,
                         int *out_total_extent) {
    int &total_extent = *out_total_extent;
    total_extent = 1;
    if (tvec.empty()) {
      return make_zero(DataType::Int(32));
    }

    PrimExpr ret;
    for (const ThreadEntry &e : tvec) {
      if (ret.defined()) {
        ret = ret + e.iv->var * total_extent;
      } else {
        ICHECK_EQ(total_extent, 1);
        ret = e.iv->var;
      }
      total_extent *= e.extent;
    }
    return ret;
  }
  // The local buffer index.
  PrimExpr BufIndex(PrimExpr reduce_index, const PrimExpr &group_index,
                    int reduce_extent) {
    if (!is_zero(group_index)) {
      return analyzer_.Simplify(group_index * reduce_extent + reduce_index);
    } else {
      return reduce_index;
    }
  }
  // sync thread op.
  static Stmt SyncThread(const std::string &sync) {
    return Evaluate(Call(DataType::Int(32), builtin::tvm_storage_sync(),
                         {StringImm(sync)}));
  }

  // Emit warp shuffle  calls.
  PrimExpr WarpShuffle(const Op &op, const Optional<Buffer> &mask_buffer,
                       const PrimExpr &val, PrimExpr delta_or_lane) {
    Array<PrimExpr> indices = {0};
    PrimExpr mask;
    if (mask_buffer.defined()) {
      mask = BufferLoad(mask_buffer.value(), indices);
    } else {
      mask = IntImm(DataType::Int(32), 0);
    }
    PrimExpr width = IntImm(DataType::Int(32), warp_size_);
    Array<PrimExpr> args{mask, val, std::move(delta_or_lane), width, width};
    return Call(val.dtype(), op, args);
  }

  // Check if we can use warp level reduction.
  //
  // Note: The ROCm backend will only have warp reductions for now.
  // Also, the warp/wavefront size differs (64 on rocm, 32 on cuda and metal).
  bool IsWarpReduction(const std::vector<DataType> &types, int group_extent,
                       int reduce_extent, int contiguous_reduce_extent) {
    if ((target_->kind->name != "cuda") && (target_->kind->name != "rocm") &&
        (target_->kind->name != "metal")) {
      return false;
    }

    need_warp_shuffle_mask_ = target_->kind->name != "metal";

    // rocm only supports 32 bit operands for shuffling at the moment
    if ((target_->kind->name == "rocm") &&
        (std::any_of(types.begin(), types.end(), [](DataType ty) {
          if (ty.is_fixed_length_vector())
            return ty.bits() * ty.lanes() != 32;
          return ty.bits() != 32;
        }))) {
      return false;
    }

    // Supported types:
    // {u}int, {u}long, {u}long long, float, double, half/half2
    if (std::any_of(types.begin(), types.end(), [](DataType ty) {
          if (ty.is_float16())
            return ty.lanes() > 2;
          if (ty.is_fixed_length_vector())
            return true;
          return ty.bytes() < 4 || ty.bytes() > 8;
        })) {
      return false;
    }
    if (thread_extents_.empty()) {
      return false;
    }

    // reduce region must be contiguous.
    if (contiguous_reduce_extent != reduce_extent) {
      return false;
    }

    // whether reduce_extent and group_extent are valid for warp reduction.
    if (target_->kind->name == "rocm") {
      return reduce_extent == warp_size_;
    } else {
      if (reduce_extent == 1) {
        return false; // no need to warp reduce
      } else {
        bool is_subwarp_reduction = warp_size_ % reduce_extent == 0;
        bool is_multiwarp_reduction =
            max_num_threads_ != -1 &&
            max_num_threads_ <= warp_size_ * warp_size_ &&
            reduce_extent % warp_size_ == 0;
        if (is_subwarp_reduction || is_multiwarp_reduction) {
          return true;
        } else {
          return group_extent == 1 && reduce_extent <= warp_size_;
        }
      }
    }
  }

  // The target.
  const TargetNode *target_ = nullptr;
  // The shared scope.
  String shared_scope = "shared";
  // The warp size of the device.
  int warp_size_{1};
  // The maximum number of threads of the device. "-1" denotes unknown.
  int max_num_threads_{-1};
  // A boolean indicating if the target supports warp-level masking.
  bool need_warp_shuffle_mask_{};

  // surrounding scope of thread extent.
  std::vector<const AttrStmtNode *> thread_extents_;
  std::vector<const CommReducerNode *> reduce_combiner_;
  // The load remap
  std::unordered_map<const VarNode *, PrimExpr> load_remap_;
  // Allocate remap
  std::unordered_map<const VarNode *, Buffer> alloc_remap_;
  // BufferVar remap
  std::unordered_map<const VarNode *, Var> var_remap_;
  // Buffer remap
  std::unordered_map<const BufferNode *, Buffer> buf_remap_;
  // Internal analyzer
  arith::Analyzer analyzer_;
};

namespace transform {
using namespace tir::transform;

tvm::transform::Pass LowerThreadAllreduce() {
  auto pass_func = [](PrimFunc f, const IRModule &m, const PassContext &ctx) {
    AllocateCollector collector;
    collector(f->body);
    bool is_dynamic = collector.dyn_shmem_allocs_.size() > 1;

    auto *n = f.CopyOnWrite();
    auto target = f->GetAttr<Target>(tvm::attr::kTarget);
    ICHECK(target.defined())
        << "LowerThreadAllreduce: Require the target attribute";
    const TargetNode *target_node = target.as<TargetNode>();
    ThreadAllreduceBuilder thread_all_reduce(target_node, is_dynamic);
    n->body = thread_all_reduce(n->body);
    return f;
  };
  return CreatePrimFuncPass(pass_func, 0, "tl.LowerThreadAllreduce", {});
}

TVM_FFI_STATIC_INIT_BLOCK({
  namespace refl = tvm::ffi::reflection;
  refl::GlobalDef().def("tl.transform.LowerThreadAllreduce",
                        LowerThreadAllreduce);
});

} // namespace transform
} // namespace tl
} // namespace tvm