[Feature] Add CUDA support for `min` and `max` reducer in heterogeneous API...

[Feature] Add CUDA support for `min` and `max` reducer in heterogeneous API for unary message functions (#3566)

* CUDA support max/min reducer on forward pass

* docstring

* concised UpdateGradMinMax_hetero

* reorganized UpdateGradMinMax_hetero

* CUDA kernels for max/min reducer

* variable name

* lint check

* changed CUDA 2D thread mapping to 1D

* removed legacy cusparse for min/max reducer

* git CI issue

* restarting git CI

* adding namespace std
Co-authored-by: Israt Nisa <nisisrat@amazon.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>

src/array/cpu/segment_reduce.h

@@ -105,6 +105,7 @@ void ScatterAdd(NDArray feat, NDArray idx, NDArray out) {
 }
 
 /*!
+ * \brief CPU kernel to update gradients for reduce op max/min
  * \param graph The input heterogeneous graph.
  * \param op The binary operator, could be `copy_u`, `copy_e'.
  * \param list_feat List of the input tensors.
@@ -117,61 +118,35 @@ void UpdateGradMinMax_hetero(HeteroGraphPtr graph,
                        const std::string& op,
                        const std::vector<NDArray>& list_feat,
                        const std::vector<NDArray>& list_idx,
-                       const std::vector<NDArray>& list_idx_ntypes,
+                       const std::vector<NDArray>& list_idx_types,
                        std::vector<NDArray>* list_out) {
-  if (op == "copy_lhs") {
-    std::vector<std::vector<dgl_id_t>> dst_src_ntids(graph->NumVertexTypes(),
+  if (op == "copy_lhs" || op == "copy_rhs") {
+    std::vector<std::vector<dgl_id_t>> src_dst_ntypes(graph->NumVertexTypes(),
     std::vector<dgl_id_t>());
+
     for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
       auto pair = graph->meta_graph()->FindEdge(etype);
-      const dgl_id_t dst_id = pair.first;  // graph is reversed
-      const dgl_id_t src_id = pair.second;
-      dst_src_ntids[dst_id].push_back(src_id);  // can have duplicates. Use Hashtable to optimize.
-    }
-    std::vector<bool> updated(graph->NumVertexTypes());
-    for (int dst_id = 0; dst_id < dst_src_ntids.size(); ++dst_id) {
-      std::fill(updated.begin(), updated.end(), false);
-      for (int j = 0; j < dst_src_ntids[dst_id].size(); ++j) {
-        int src_id = dst_src_ntids[dst_id][j];
-        if (updated[src_id]) continue;
-        const DType* feat_data = list_feat[dst_id].Ptr<DType>();
-        const IdType* idx_data = list_idx[dst_id].Ptr<IdType>();
-        const IdType* idx_ntype_data = list_idx_ntypes[dst_id].Ptr<IdType>();
-        DType* out_data = (*list_out)[src_id].Ptr<DType>();
-        int dim = 1;
-        for (int i = 1; i < (*list_out)[src_id]->ndim; ++i)
-          dim *= (*list_out)[src_id]->shape[i];
-        int n = list_feat[dst_id]->shape[0];
-#pragma omp parallel for
-        for (int i = 0; i < n; ++i) {
-          for (int k = 0; k < dim; ++k) {
-            if (src_id == idx_ntype_data[i * dim + k]) {
-              const int write_row = idx_data[i * dim + k];
-#pragma omp atomic
-              out_data[write_row * dim + k] += feat_data[i * dim + k];  // feat = dZ
-            }
-          }
-        }
-        updated[src_id] = true;
-      }
-    }
-  } else if (op == "copy_rhs") {
-    for (dgl_type_t etid = 0; etid < graph->NumEdgeTypes(); ++etid) {
-      auto pair = graph->meta_graph()->FindEdge(etid);
-      const dgl_id_t dst_id = pair.first;  // graph is reversed
-      const dgl_id_t src_id = pair.second;
-      const DType* feat_data = list_feat[dst_id].Ptr<DType>();
-      const IdType* idx_data = list_idx[dst_id].Ptr<IdType>();
-      const IdType* idx_ntype_data = list_idx_ntypes[dst_id].Ptr<IdType>();
-      DType* out_data = (*list_out)[etid].Ptr<DType>();
+      const dgl_id_t dst_ntype = pair.first;  // graph is reversed
+      const dgl_id_t src_ntype = pair.second;
+      auto same_src_dst_ntype = std::find(std::begin(src_dst_ntypes[dst_ntype]),
+        std::end(src_dst_ntypes[dst_ntype]), src_ntype);
+      // if op is "copy_lhs", relation type with same src and dst node type will be updated once
+      if (op == "copy_lhs" && same_src_dst_ntype != std::end(src_dst_ntypes[dst_ntype]))
+        continue;
+      src_dst_ntypes[dst_ntype].push_back(src_ntype);
+      const DType* feat_data = list_feat[dst_ntype].Ptr<DType>();
+      const IdType* idx_data = list_idx[dst_ntype].Ptr<IdType>();
+      const IdType* idx_type_data = list_idx_types[dst_ntype].Ptr<IdType>();
+      int type = (op == "copy_lhs") ? src_ntype : etype;
+      DType* out_data = (*list_out)[type].Ptr<DType>();
       int dim = 1;
-      for (int i = 1; i < (*list_out)[etid]->ndim; ++i)
-        dim *= (*list_out)[etid]->shape[i];
-      int n = list_feat[dst_id]->shape[0];
+      for (int i = 1; i < (*list_out)[type]->ndim; ++i)
+        dim *= (*list_out)[type]->shape[i];
+      int n = list_feat[dst_ntype]->shape[0];
 #pragma omp parallel for
       for (int i = 0; i < n; ++i) {
         for (int k = 0; k < dim; ++k) {
-          if (etid == idx_ntype_data[i * dim + k]) {
+          if (type == idx_type_data[i * dim + k]) {
             const int write_row = idx_data[i * dim + k];
 #pragma omp atomic
             out_data[write_row * dim + k] += feat_data[i * dim + k];  // feat = dZ

src/array/cpu/spmm.h

@@ -319,12 +319,19 @@ void SpMMCmpCsr(const BcastOff& bcast, const CSRMatrix& csr, NDArray ufeat,
  * \param argu Arg-Min/Max on source nodes, which refers the source node indices
  *        correspond to the minimum/maximum values of reduction result on
  *        destination nodes. It's useful in computing gradients of Min/Max
- * reducer. \param arge Arg-Min/Max on edges. which refers the source node
- * indices correspond to the minimum/maximum values of reduction result on
+ *        reducer.
+ * \param arge Arg-Min/Max on edges. which refers the source node
+ *        indices correspond to the minimum/maximum values of reduction result on
  *        destination nodes. It's useful in computing gradients of Min/Max
- * reducer. \note It uses node parallel strategy, different threads are
- * responsible for the computation of different nodes. \note The result will
- * contain infinity for zero-degree nodes.
+ *        reducer.
+ * \param argu_ntype Node type of the arg-Min/Max on source nodes, which refers the
+ *        source node types correspond to the minimum/maximum values of reduction result
+ *        on destination nodes. It's useful in computing gradients of Min/Max reducer.
+ * \param arge_etype Edge-type of the arg-Min/Max on edges. which refers the source
+ *        node indices correspond to the minimum/maximum values of reduction result on
+ *        destination nodes. It's useful in computing gradients of Min/Max reducer.
+ * \param src_type Node type of the source nodes of an etype
+ * \param etype Edge type
  */
 template <typename IdType, typename DType, typename Op, typename Cmp>
 void SpMMCmpCsrHetero(const BcastOff& bcast, const CSRMatrix& csr, NDArray ufeat,

src/array/cuda/segment_reduce.cu

@@ -58,7 +58,7 @@ void UpdateGradMinMax_hetero(const HeteroGraphPtr& g,
                 const std::vector<NDArray>& idx_etype,
                 std::vector<NDArray>* out) {
   SWITCH_BITS(bits, DType, {
-    LOG(FATAL) << "Not implemented. Please use CPU version.";
+    cuda::UpdateGradMinMax_hetero<IdType, DType>(g, op, feat, idx, idx_etype, out);
   });
 }
 

src/array/cuda/segment_reduce.cuh

@@ -63,6 +63,34 @@ __global__ void ScatterAddKernel(
   }
 }
 
+/*!
+ * \brief CUDA kernel to update gradients for reduce op max/min
+ * \note each WARP (group of 32 threads) is responsible for adding a row in
+ * feature tensor to a target row in output tensor.
+ */
+
+template <typename IdType, typename DType>
+__global__ void UpdateGradMinMaxHeteroKernel(
+    const DType *feat, const IdType *idx, const IdType *idx_type, DType *out,
+    int64_t n, int64_t dim, int type) {
+  unsigned int tId = threadIdx.x;
+  unsigned int laneId = tId & 31;
+  unsigned int gId = blockIdx.x * blockDim.x + threadIdx.x;
+  unsigned int warpId = gId >> 5;
+  unsigned int warp_size = 32;
+  unsigned int row = warpId;
+
+  while (row < n) {
+    for(unsigned int col = laneId; col < dim; col += warp_size) {
+      if (type == idx_type[row * dim + col]) {
+        const int write_row = idx[row * dim + col];
+        cuda::AtomicAdd(out + write_row * dim + col, feat[row * dim + col]);
+      }
+    }
+    row += blockDim.x * gridDim.x;
+  }
+}
+
 /*!
  * \brief CUDA kernel of backward phase in segment min/max.
  * \note each blockthread is responsible for writing a row in the
@@ -155,6 +183,58 @@ void ScatterAdd(
                    n, dim);
 }
 
+/*!
+ * \brief CUDA implementation to update gradients for reduce op max/min
+ * \param graph The input heterogeneous graph.
+ * \param op The binary operator, could be `copy_u`, `copy_e'.
+ * \param list_feat List of the input tensors.
+ * \param list_idx  List of the indices tensors.
+ * \param list_idx_etype List of the node- or edge-type tensors.
+ * \param list_out List of the output tensors.
+ */
+template <typename IdType, typename DType>
+void UpdateGradMinMax_hetero(const HeteroGraphPtr& graph,
+                const std::string& op,
+                const std::vector<NDArray>& list_feat,
+                const std::vector<NDArray>& list_idx,
+                const std::vector<NDArray>& list_idx_types,
+                std::vector<NDArray>* list_out) {
+  if (op == "copy_lhs" || op == "copy_rhs") {
+    std::vector<std::vector<dgl_id_t>> src_dst_ntypes(graph->NumVertexTypes(),
+    std::vector<dgl_id_t>());
+    for (dgl_type_t etype = 0; etype < graph->NumEdgeTypes(); ++etype) {
+      auto pair = graph->meta_graph()->FindEdge(etype);
+      const dgl_id_t dst_ntype = pair.first;  // graph is reversed
+      const dgl_id_t src_ntype = pair.second;
+      auto same_src_dst_ntype = std::find(std::begin(src_dst_ntypes[dst_ntype]),
+        std::end(src_dst_ntypes[dst_ntype]), src_ntype);
+      // if op is "copy_lhs", relation type with same src and dst node type will be updated once
+      if (op == "copy_lhs" && same_src_dst_ntype != std::end(src_dst_ntypes[dst_ntype]))
+        continue;
+      src_dst_ntypes[dst_ntype].push_back(src_ntype);
+      const DType* feat_data = list_feat[dst_ntype].Ptr<DType>();
+      const IdType* idx_data = list_idx[dst_ntype].Ptr<IdType>();
+      const IdType* idx_type_data = list_idx_types[dst_ntype].Ptr<IdType>();
+      int type = (op == "copy_lhs") ? src_ntype : etype;
+      DType* out_data = (*list_out)[type].Ptr<DType>();
+      int dim = 1;
+      for (int i = 1; i < (*list_out)[type]->ndim; ++i)
+        dim *= (*list_out)[type]->shape[i];
+      int n = list_feat[dst_ntype]->shape[0];
+      auto *thr_entry = runtime::CUDAThreadEntry::ThreadLocal();
+      const int th_per_row = 32;
+      const int ntx = 128;
+      const int nbx = FindNumBlocks<'x'>((n * th_per_row + ntx - 1) / ntx);
+      const dim3 nblks(nbx);
+      const dim3 nthrs(ntx);
+      CUDA_KERNEL_CALL((UpdateGradMinMaxHeteroKernel<IdType, DType>),
+                       nblks, nthrs, 0, thr_entry->stream,
+                       feat_data, idx_data, idx_type_data,
+                       out_data, n, dim, type);
+    }
+  }
+}
+
 /*!
  * \brief CUDA implementation of backward phase of Segment Reduce with Min/Max reducer.
  * \note math equation: out[arg[i, k], k] = feat[i, k]

src/array/cuda/spmm.cu

@@ -526,7 +526,7 @@ void SpMMCsrHetero(const std::string& op, const std::string& reduce,
     std::vector<DType*> trans_out((*vec_out).size(), NULL);
 
     bool use_legacy_cusparsemm =
-        (CUDART_VERSION < 11000) &&
+        (CUDART_VERSION < 11000) && (reduce == "sum") &&
         // legacy cuSPARSE does not care about NNZ, hence the argument "false".
         ((op == "copy_lhs" && cusparse_available<bits, IdType>(false)) ||
          (op == "mul" && is_scalar_efeat && cusparse_available<bits, IdType>(false)));
@@ -542,7 +542,6 @@ void SpMMCsrHetero(const std::string& op, const std::string& reduce,
         trans_out[ntype] = out;
       }
     }
-
     // Check shape of ufeat for all relation type and compute feature size
     int64_t x_length = 1;
     for (dgl_type_t etype = 0; etype < (ufeat_ntids.size() - 1); ++etype) {
@@ -565,6 +564,30 @@ void SpMMCsrHetero(const std::string& op, const std::string& reduce,
           x_length *= ufeat->shape[i];
       }
     }
+    // TODO(Israt): Can python do the following initializations while creating the tensors?
+    if (reduce == "max" ||  reduce == "min") {
+      const int64_t dim = bcast.out_len;
+      std::vector<bool> updated((*vec_out).size(), false);
+      for (dgl_type_t etype = 0; etype < ufeat_ntids.size(); ++etype) {
+        DType *out_off = (*vec_out)[out_ntids[etype]].Ptr<DType>();
+        if (reduce == "max")
+          _Fill(out_off, vec_csr[etype].num_rows * dim, cuda::reduce::Max<IdType, DType>::zero());
+        else  // min
+          _Fill(out_off, vec_csr[etype].num_rows * dim, cuda::reduce::Min<IdType, DType>::zero());
+        const dgl_type_t dst_id = out_ntids[etype];
+        if (!updated[dst_id]) {
+          updated[dst_id] = true;
+          if (op == "copy_lhs") {
+            IdType *argu_ntype = (*out_aux)[2][dst_id].Ptr<IdType>();
+            _Fill(argu_ntype, vec_csr[etype].num_rows * dim, static_cast<IdType>(-1));
+          }
+          if (op == "copy_rhs") {
+            IdType *arge_etype = (*out_aux)[3][dst_id].Ptr<IdType>();
+            _Fill(arge_etype, vec_csr[etype].num_rows * dim, static_cast<IdType>(-1));
+          }
+        }
+      }
+    }
 
     auto* thr_entry = runtime::CUDAThreadEntry::ThreadLocal();
     for (dgl_type_t etype = 0; etype < ufeat_ntids.size(); ++etype) {
@@ -606,7 +629,28 @@ void SpMMCsrHetero(const std::string& op, const std::string& reduce,
                 bcast, csr, ufeat, efeat, (*vec_out)[dst_id], NullArray(), NullArray());
           });
         }
-      // TODO(Israt): Add support for max/min reducer
+      } else if (reduce == "max") {
+          SWITCH_OP(op, Op, {
+            NDArray ufeat = (vec_ufeat.size() == 0) ?
+                NullArray() : vec_ufeat[src_id];
+            NDArray efeat = (vec_efeat.size() == 0) ?
+                NullArray() : vec_efeat[etype];
+            cuda::SpMMCmpCsrHetero<IdType, DType, Op, cuda::reduce::Max<IdType, DType> >(
+                bcast, csr, ufeat, efeat, (*vec_out)[dst_id], (*out_aux)[0][dst_id],
+                (*out_aux)[1][dst_id], (*out_aux)[2][dst_id], (*out_aux)[3][dst_id],
+                src_id, etype);
+          });
+      } else if (reduce == "min") {
+          SWITCH_OP(op, Op, {
+            NDArray ufeat = (vec_ufeat.size() == 0) ?
+                NullArray() : vec_ufeat[src_id];
+            NDArray efeat = (vec_efeat.size() == 0) ?
+                NullArray() : vec_efeat[etype];
+            cuda::SpMMCmpCsrHetero<IdType, DType, Op, cuda::reduce::Min<IdType, DType> >(
+                bcast, csr, ufeat, efeat, (*vec_out)[dst_id], (*out_aux)[0][dst_id],
+                (*out_aux)[1][dst_id], (*out_aux)[2][dst_id], (*out_aux)[3][dst_id],
+                src_id, etype);
+        });
       } else {
         LOG(FATAL) << "Not implemented";
       }

src/array/cuda/spmm.cuh

@@ -160,17 +160,11 @@ __global__ void SpMMCsrKernel(
         DType out = BinaryOp::Call(uoff + lhs_add, eoff + rhs_add);
         ReduceOp::Call(&local_accum, &local_argu, &local_arge, out, cid, eid);
       }
-
-      // TODO(isratnisa, BarclayII)
-      // The use of += is a quick hack to compute for cross-type reducing
+      // The use of += is to compute cross-type reducing on heterogeneous graph
+      // when reduce op is `sum`.
       //     C = SpMM(SpA, B) + C
-      // To make it work on max-reducer and min-reducer, i.e.
-      //     C = Max(SpMM<BinaryOp, Max>(SpA, B), C)
-      // it requires at least the following:
-      // 1. Initialize the output buffer with ReducerOp::zero.
-      // 2. Record also which edge type has the maximum/minimum in argmax/argmin.
-      //    This requires non-trivial changes in SpMMCsrKernel itself or writing a new kernel.
-      //    So we leave it to future PRs.
+      // Separate kernel `SpMMCmpCsrHeteroKernel` is used for max- and min-reducer. It
+      // does not affect the output on homogeneous graph as `out` is initialized to zero.
       out[ty * out_len + tx] += local_accum;
       if (ReduceOp::require_arg && BinaryOp::use_lhs)
         arg_u[ty * out_len + tx] = local_argu;
@@ -182,6 +176,67 @@ __global__ void SpMMCsrKernel(
   }
 }
 
+/*!
+ * \brief CUDA kernel of SpMM-Min/Max on Csr format.
+ * \note it uses node parallel strategy, different threadblocks (on y-axis)
+ *       is responsible for the computation on different destination nodes.
+ *       Threadblocks on the x-axis are responsible for the computation on
+ *       different positions in feature dimension.
+ */
+template <typename Idx, typename DType,
+          typename BinaryOp, typename ReduceOp,
+          bool UseBcast = false, bool UseIdx = false>
+__global__ void SpMMCmpCsrHeteroKernel(
+  const DType* __restrict__ ufeat,
+  const DType* __restrict__ efeat,
+  DType* __restrict__ out,
+  Idx* __restrict__ arg_u, Idx* __restrict__ arg_e,
+  Idx* __restrict__ arg_u_ntype, Idx* __restrict__ arg_e_etype,
+  const Idx* __restrict__ indptr,
+  const Idx* __restrict__ indices,
+  const Idx* __restrict__ edge_map,
+  int64_t num_rows, int64_t num_cols,
+  const int64_t* __restrict__ ubcast_off,
+  const int64_t* __restrict__ ebcast_off,
+  int64_t ufeat_len, int64_t efeat_len, int64_t out_len,
+  const int src_type, const int etype) {
+  // SPMM with CSR.
+  int ty = blockIdx.y * blockDim.y + threadIdx.y;
+  const Idx stride_y = blockDim.y * gridDim.y;
+  const int stride_x = blockDim.x * gridDim.x;
+  while (ty < num_rows) {
+    int tx = blockIdx.x * blockDim.x + threadIdx.x;
+    while (tx < out_len) {
+      DType new_out = out[ty * out_len + tx];//ReduceOp::zero();
+      Idx local_argu = 0, local_arge = 0;
+      const int lhs_add = UseBcast ? ubcast_off[tx] : tx;
+      const int rhs_add = UseBcast ? ebcast_off[tx] : tx;
+      for (Idx i = indptr[ty]; i < indptr[ty + 1]; ++i) {
+        const Idx eid = UseIdx ? _ldg(edge_map + i) : i;
+        const Idx cid = _ldg(indices + i);
+        const DType* uoff = BinaryOp::use_lhs ? (ufeat + cid * ufeat_len): nullptr;
+        const DType* eoff = BinaryOp::use_rhs ? (efeat + eid * efeat_len): nullptr;
+        DType tmp_out = BinaryOp::Call(uoff + lhs_add, eoff + rhs_add);
+        ReduceOp::Call(&new_out, &local_argu, &local_arge, tmp_out, cid, eid);
+      }
+      // Update output only when max/min values are different that original output
+      if (out[ty * out_len + tx] != new_out) {
+        out[ty * out_len + tx] = new_out;
+        if (ReduceOp::require_arg && BinaryOp::use_lhs) {
+          arg_u[ty * out_len + tx] = local_argu;
+          arg_u_ntype[ty * out_len + tx] = src_type;
+        }
+        if (ReduceOp::require_arg && BinaryOp::use_rhs) {
+          arg_e[ty * out_len + tx] = local_arge;
+          arg_e_etype[ty * out_len + tx] = etype;
+        }
+      }
+      tx += stride_x;
+    }
+    ty += stride_y;
+  }
+}
+
 /*!
  * \brief CUDA implementation of g-SpMM on Coo format.
  * \param bcast Broadcast information.
@@ -316,6 +371,73 @@ void SpMMCsr(
   });
 }
 
+/*!
+ * \brief CUDA kernel of SpMM-Min/Max on Csr format on heterogeneous graph.
+ * \param bcast Broadcast information.
+ * \param csr The Csr matrix.
+ * \param ufeat The feature on source nodes.
+ * \param efeat The feature on edges.
+ * \param out The result feature on destination nodes.
+ * \param argu Arg-Min/Max on source nodes, which refers the source node indices
+ *        correspond to the minimum/maximum values of reduction result on
+ *        destination nodes. It's useful in computing gradients of Min/Max reducer.
+ * \param arge Arg-Min/Max on edges. which refers the source node indices
+ *        correspond to the minimum/maximum values of reduction result on
+ *        destination nodes. It's useful in computing gradients of Min/Max reducer.
+ * \param argu_ntype Node type of the arg-Min/Max on source nodes, which refers the
+ *        source node types correspond to the minimum/maximum values of reduction result
+ *        on destination nodes. It's useful in computing gradients of Min/Max reducer.
+ * \param arge_etype Edge-type of the arg-Min/Max on edges. which refers the source
+ *        node indices correspond to the minimum/maximum values of reduction result on
+ *        destination nodes. It's useful in computing gradients of Min/Max reducer.
+ * \param src_type Node type of the source nodes of an etype
+ * \param etype Edge type
+ */
+template <typename Idx, typename DType,
+          typename BinaryOp, typename ReduceOp>
+void SpMMCmpCsrHetero(
+    const BcastOff& bcast,
+    const CSRMatrix& csr,
+    NDArray ufeat, NDArray efeat,
+    NDArray out, NDArray argu, NDArray arge,
+    NDArray argu_ntype, NDArray arge_etype,
+    const int src_type, const int etype) {
+  const Idx *indptr = csr.indptr.Ptr<Idx>();
+  const Idx *indices = csr.indices.Ptr<Idx>();
+  const Idx *edge_map = csr.data.Ptr<Idx>();
+  const DType *ufeat_data = ufeat.Ptr<DType>();
+  const DType *efeat_data = efeat.Ptr<DType>();
+  DType *out_data = out.Ptr<DType>();
+  Idx* argu_data = argu.Ptr<Idx>();
+  Idx* arge_data = arge.Ptr<Idx>();
+
+  auto* thr_entry = runtime::CUDAThreadEntry::ThreadLocal();
+
+  int64_t *ubcast_off = nullptr, *ebcast_off = nullptr;
+  int64_t len = bcast.out_len,
+          lhs_len = bcast.lhs_len,
+          rhs_len = bcast.rhs_len;
+  const int ntx = FindNumThreads(len);
+  const int nty = CUDA_MAX_NUM_THREADS / ntx;
+  const int nbx = (len + ntx - 1) / ntx;
+  const int nby = FindNumBlocks<'y'>((csr.num_rows + nty - 1) / nty);
+  const dim3 nblks(nbx, nby);
+  const dim3 nthrs(ntx, nty);
+  const bool use_idx = !IsNullArray(csr.data);
+
+  BCAST_IDX_CTX_SWITCH(bcast, use_idx, ufeat->ctx, ubcast_off, ebcast_off, {
+    CUDA_KERNEL_CALL((SpMMCmpCsrHeteroKernel<Idx, DType, BinaryOp, ReduceOp, UseBcast, UseIdx>),
+        nblks, nthrs, 0, thr_entry->stream,
+        ufeat_data, efeat_data, out_data, argu_data, arge_data,
+        static_cast<Idx*>(argu_ntype->data),
+        static_cast<Idx*>(arge_etype->data),
+        indptr, indices, edge_map,
+        csr.num_rows, csr.num_cols,
+        ubcast_off, ebcast_off,
+        lhs_len, rhs_len, len, src_type, etype)
+  });
+}
+
 
 }  // namespace cuda
 }  // namespace aten

tests/compute/test_new_update_all_hetero.py

@@ -13,11 +13,9 @@ import test_utils
 from test_utils import parametrize_dtype, get_cases
 from scipy.sparse import rand
 rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
-fill_value = {'sum': 0, 'max': float("-inf")}
 feat_size = 2
 
 @unittest.skipIf(dgl.backend.backend_name != 'pytorch', reason='Only support PyTorch for now')
-@unittest.skipIf(F._default_context_str == 'gpu', reason="Max/min reducer not supported on GPU yet.")
 
 def create_test_heterograph(idtype):
     # test heterograph from the docstring, plus a user -- wishes -- game relation