[Perf] Accelerate block_compute when all nodes are invoked. (dmlc#434)

* refactor.

* accelerate update_all in nodeflow.

* fix.

* refactor.

* fix lint.

* fix lint.

* reorganize.

* reorg.

* remove.

* add doc.

* impl block_incidence_matrix

* fix lint.

* fix.

* simple fix.

* fix test.

* fix interface.

* fix eid.

* fix comments.

include/dgl/immutable_graph.h

@@ -534,23 +534,27 @@ class ImmutableGraph: public GraphInterface {
     return edge_list_;
   }
 
- protected:
-  DGLIdIters GetInEdgeIdRef(dgl_id_t src, dgl_id_t dst) const;
-  DGLIdIters GetOutEdgeIdRef(dgl_id_t src, dgl_id_t dst) const;
-
   /*!
    * \brief Get the CSR array that represents the in-edges.
    * This method copies data from std::vector to IdArray.
+   * \param start the first row to copy.
+   * \param end the last row to copy (exclusive).
    * \return the CSR array.
    */
-  CSRArray GetInCSRArray() const;
+  CSRArray GetInCSRArray(size_t start, size_t end) const;
 
   /*!
    * \brief Get the CSR array that represents the out-edges.
    * This method copies data from std::vector to IdArray.
+   * \param start the first row to copy.
+   * \param end the last row to copy (exclusive).
    * \return the CSR array.
    */
-  CSRArray GetOutCSRArray() const;
+  CSRArray GetOutCSRArray(size_t start, size_t end) const;
+
+ protected:
+  DGLIdIters GetInEdgeIdRef(dgl_id_t src, dgl_id_t dst) const;
+  DGLIdIters GetOutEdgeIdRef(dgl_id_t src, dgl_id_t dst) const;
 
   /*!
    * \brief Compact a subgraph.

include/dgl/nodeflow.h

@@ -0,0 +1,86 @@
+/*!
+ *  Copyright (c) 2019 by Contributors
+ * \file dgl/nodeflow.h
+ * \brief DGL NodeFlow class.
+ */
+#ifndef DGL_NODEFLOW_H_
+#define DGL_NODEFLOW_H_
+
+#include <vector>
+#include <string>
+
+#include "graph_interface.h"
+
+namespace dgl {
+
+class ImmutableGraph;
+
+/*!
+ * \brief A NodeFlow graph stores the sampling results for a sampler that samples
+ * nodes/edges in layers.
+ *
+ * We store multiple layers of the sampling results in a single graph, which results
+ * in a more compact format. We store extra information,
+ * such as the node and edge mapping from the NodeFlow graph to the parent graph.
+ */
+struct NodeFlow {
+  /*! \brief The graph. */
+  GraphPtr graph;
+  /*!
+   * \brief the offsets of each layer.
+   */
+  IdArray layer_offsets;
+  /*!
+   * \brief the offsets of each flow.
+   */
+  IdArray flow_offsets;
+  /*!
+   * \brief The node mapping from the NodeFlow graph to the parent graph.
+   */
+  IdArray node_mapping;
+  /*!
+   * \brief The edge mapping from the NodeFlow graph to the parent graph.
+   */
+  IdArray edge_mapping;
+};
+
+/*!
+ * \brief Get a slice on a graph that represents a NodeFlow.
+ *
+ * The entire block has to be taken as a slice. Users have to specify the
+ * correct starting and ending location of a layer.
+ *
+ * If remap is false, the returned arrays can be viewed as a sub-matrix slice
+ * of the adjmat of the input graph. Let the adjmat of the input graph be A,
+ * then the slice is equal to (in numpy syntax):
+ *   A[layer1_start:layer1_end, layer0_start:layer0_end]
+ *
+ * If remap is true,  the returned arrays represents an adjacency matrix
+ * of shape NxM, where N is the number of nodes in layer1 and M is
+ * the number of nodes in layer0. Nodes in layer0 will be remapped to
+ * [0, M) and nodes in layer1 will be remapped to [0, N).
+ *
+ * A row of the returned adjacency matrix represents the destination
+ * of an edge and the column represents the source.
+ *
+ * If fmt == "csr", the function returns three arrays: indptr, indices, eid.
+ * If fmt == "coo", the function returns two arrays: idx, eid. Here, the idx array
+ *   is the concatenation of src and dst node id arrays.
+ *
+ * \param graph An immutable graph.
+ * \param fmt the format of the returned adjacency matrix.
+ * \param layer0_size the size of the first layer in the block.
+ * \param layer1_start the location where the second layer starts.
+ * \param layer1_end the location where the secnd layer ends.
+ * \param remap Indicates to remap all vertex ids and edge Ids to local Id
+ * space.
+ * \return a vector of IdArrays.
+ */
+std::vector<IdArray> GetNodeFlowSlice(const ImmutableGraph &graph, const std::string &fmt,
+                                      size_t layer0_size, size_t layer1_start,
+                                      size_t layer1_end, bool remap);
+
+}  // namespace dgl
+
+#endif  // DGL_NODEFLOW_H_
+

include/dgl/sampler.h

@@ -9,40 +9,12 @@
 #include <vector>
 #include <string>
 #include "graph_interface.h"
+#include "nodeflow.h"
 
 namespace dgl {
 
 class ImmutableGraph;
 
-/*!
- * \brief A NodeFlow graph stores the sampling results for a sampler that samples
- * nodes/edges in layers.
- *
- * We store multiple layers of the sampling results in a single graph, which results
- * in a more compact format. We store extra information,
- * such as the node and edge mapping from the NodeFlow graph to the parent graph.
- */
-struct NodeFlow {
-  /*! \brief The graph. */
-  GraphPtr graph;
-  /*!
-   * \brief the offsets of each layer.
-   */
-  IdArray layer_offsets;
-  /*!
-   * \brief the offsets of each flow.
-   */
-  IdArray flow_offsets;
-  /*!
-   * \brief The node mapping from the NodeFlow graph to the parent graph.
-   */
-  IdArray node_mapping;
-  /*!
-   * \brief The edge mapping from the NodeFlow graph to the parent graph.
-   */
-  IdArray edge_mapping;
-};
-
 class SamplerOp {
  public:
   /*!

python/dgl/nodeflow.py

@@ -395,6 +395,161 @@ def block_parent_eid(self, block_id):
         assert F.asnumpy(F.sum(ret == -1, 0)) == 0, "The eid in the parent graph is invalid."
         return ret
 
+    def block_edges(self, block_id):
+        """Return the edges in a block.
+
+        Parameters
+        ----------
+        block_id : int
+            The specified block to return the edges.
+
+        Returns
+        -------
+        Tensor
+            The src nodes.
+        Tensor
+            The dst nodes.
+        Tensor
+            The edge ids.
+        """
+        layer0_size = self._layer_offsets[block_id + 1] - self._layer_offsets[block_id]
+        rst = _CAPI_NodeFlowGetBlockAdj(self._graph._handle, "coo", layer0_size,
+                                        self._layer_offsets[block_id + 1],
+                                        self._layer_offsets[block_id + 2])
+        idx = utils.toindex(rst(0)).tousertensor()
+        eid = utils.toindex(rst(1))
+        num_edges = int(len(idx) / 2)
+        assert len(eid) == num_edges
+        return idx[num_edges:len(idx)], idx[0:num_edges], eid.tousertensor()
+
+    def block_adjacency_matrix(self, block_id, ctx):
+        """Return the adjacency matrix representation for a specific block in a NodeFlow.
+
+        A row of the returned adjacency matrix represents the destination
+        of an edge and the column represents the source.
+
+        Parameters
+        ----------
+        block_id : int
+            The specified block to return the adjacency matrix.
+        ctx : context
+            The context of the returned matrix.
+
+        Returns
+        -------
+        SparseTensor
+            The adjacency matrix.
+        Tensor
+            A index for data shuffling due to sparse format change. Return None
+            if shuffle is not required.
+        """
+        fmt = F.get_preferred_sparse_format()
+        # We need to extract two layers.
+        layer0_size = self._layer_offsets[block_id + 1] - self._layer_offsets[block_id]
+        rst = _CAPI_NodeFlowGetBlockAdj(self._graph._handle, fmt, layer0_size,
+                                        self._layer_offsets[block_id + 1],
+                                        self._layer_offsets[block_id + 2])
+        num_rows = self.layer_size(block_id + 1)
+        num_cols = self.layer_size(block_id)
+
+        if fmt == "csr":
+            indptr = F.copy_to(utils.toindex(rst(0)).tousertensor(), ctx)
+            indices = F.copy_to(utils.toindex(rst(1)).tousertensor(), ctx)
+            shuffle = utils.toindex(rst(2))
+            dat = F.ones(indices.shape, dtype=F.float32, ctx=ctx)
+            return F.sparse_matrix(dat, ('csr', indices, indptr),
+                                   (num_rows, num_cols))[0], shuffle.tousertensor()
+        elif fmt == "coo":
+            ## FIXME(minjie): data type
+            idx = F.copy_to(utils.toindex(rst(0)).tousertensor(), ctx)
+            m = self.block_size(block_id)
+            idx = F.reshape(idx, (2, m))
+            dat = F.ones((m,), dtype=F.float32, ctx=ctx)
+            adj, shuffle_idx = F.sparse_matrix(dat, ('coo', idx), (num_rows, num_cols))
+            return adj, shuffle_idx
+        else:
+            raise Exception("unknown format")
+
+    def block_incidence_matrix(self, block_id, typestr, ctx):
+        """Return the incidence matrix representation of the block.
+
+        An incidence matrix is an n x m sparse matrix, where n is
+        the number of nodes and m is the number of edges. Each nnz
+        value indicating whether the edge is incident to the node
+        or not.
+
+        There are three types of an incidence matrix `I`:
+        * "in":
+          - I[v, e] = 1 if e is the in-edge of v (or v is the dst node of e);
+          - I[v, e] = 0 otherwise.
+        * "out":
+          - I[v, e] = 1 if e is the out-edge of v (or v is the src node of e);
+          - I[v, e] = 0 otherwise.
+        * "both":
+          - I[v, e] = 1 if e is the in-edge of v;
+          - I[v, e] = -1 if e is the out-edge of v;
+          - I[v, e] = 0 otherwise (including self-loop).
+
+        Parameters
+        ----------
+        block_id : int
+            The specified block to return the incidence matrix.
+        typestr : str
+            Can be either "in", "out" or "both"
+        ctx : context
+            The context of returned incidence matrix.
+
+        Returns
+        -------
+        SparseTensor
+            The incidence matrix.
+        Tensor
+            A index for data shuffling due to sparse format change. Return None
+            if shuffle is not required.
+        """
+        src, dst, eid = self.block_edges(block_id)
+        src = F.copy_to(src, ctx)  # the index of the ctx will be cached
+        dst = F.copy_to(dst, ctx)  # the index of the ctx will be cached
+        eid = F.copy_to(eid, ctx)  # the index of the ctx will be cached
+        if typestr == 'in':
+            n = self.layer_size(block_id + 1)
+            m = self.block_size(block_id)
+            row = F.unsqueeze(dst, 0)
+            col = F.unsqueeze(eid, 0)
+            idx = F.cat([row, col], dim=0)
+            # FIXME(minjie): data type
+            dat = F.ones((m,), dtype=F.float32, ctx=ctx)
+            inc, shuffle_idx = F.sparse_matrix(dat, ('coo', idx), (n, m))
+        elif typestr == 'out':
+            n = self.layer_size(block_id)
+            m = self.block_size(block_id)
+            row = F.unsqueeze(src, 0)
+            col = F.unsqueeze(eid, 0)
+            idx = F.cat([row, col], dim=0)
+            # FIXME(minjie): data type
+            dat = F.ones((m,), dtype=F.float32, ctx=ctx)
+            inc, shuffle_idx = F.sparse_matrix(dat, ('coo', idx), (n, m))
+        elif typestr == 'both':
+            # TODO does it work for bipartite graph?
+            # first remove entries for self loops
+            mask = F.logical_not(F.equal(src, dst))
+            src = F.boolean_mask(src, mask)
+            dst = F.boolean_mask(dst, mask)
+            eid = F.boolean_mask(eid, mask)
+            n_entries = F.shape(src)[0]
+            # create index
+            row = F.unsqueeze(F.cat([src, dst], dim=0), 0)
+            col = F.unsqueeze(F.cat([eid, eid], dim=0), 0)
+            idx = F.cat([row, col], dim=0)
+            # FIXME(minjie): data type
+            x = -F.ones((n_entries,), dtype=F.float32, ctx=ctx)
+            y = F.ones((n_entries,), dtype=F.float32, ctx=ctx)
+            dat = F.cat([x, y], dim=0)
+            inc, shuffle_idx = F.sparse_matrix(dat, ('coo', idx), (n, m))
+        else:
+            raise DGLError('Invalid incidence matrix type: %s' % str(typestr))
+        return inc, shuffle_idx
+
     def set_n_initializer(self, initializer, layer_id=ALL, field=None):
         """Set the initializer for empty node features.
 
@@ -651,12 +806,13 @@ def block_compute(self, block_id, message_func="default", reduce_func="default",
         assert reduce_func is not None
 
         if is_all(v):
-            dest_nodes = utils.toindex(self.layer_nid(block_id + 1))
-            u, v, _ = self._graph.in_edges(dest_nodes)
-            u = utils.toindex(self._glb2lcl_nid(u.tousertensor(), block_id))
-            v = utils.toindex(self._glb2lcl_nid(v.tousertensor(), block_id + 1))
-            dest_nodes = utils.toindex(F.arange(0, self.layer_size(block_id + 1)))
-            eid = utils.toindex(F.arange(0, self.block_size(block_id)))
+            with ir.prog() as prog:
+                scheduler.schedule_nodeflow_update_all(graph=self,
+                                                       block_id=block_id,
+                                                       message_func=message_func,
+                                                       reduce_func=reduce_func,
+                                                       apply_func=apply_node_func)
+                Runtime.run(prog)
         else:
             dest_nodes = utils.toindex(v)
             u, v, eid = self._graph.in_edges(dest_nodes)
@@ -667,18 +823,18 @@ def block_compute(self, block_id, message_func="default", reduce_func="default",
                                                          block_id + 1))
             eid = utils.toindex(self._glb2lcl_eid(eid.tousertensor(), block_id))
 
-        with ir.prog() as prog:
-            scheduler.schedule_nodeflow_compute(graph=self,
-                                                block_id=block_id,
-                                                u=u,
-                                                v=v,
-                                                eid=eid,
-                                                dest_nodes=dest_nodes,
-                                                message_func=message_func,
-                                                reduce_func=reduce_func,
-                                                apply_func=apply_node_func,
-                                                inplace=inplace)
-            Runtime.run(prog)
+            with ir.prog() as prog:
+                scheduler.schedule_nodeflow_compute(graph=self,
+                                                    block_id=block_id,
+                                                    u=u,
+                                                    v=v,
+                                                    eid=eid,
+                                                    dest_nodes=dest_nodes,
+                                                    message_func=message_func,
+                                                    reduce_func=reduce_func,
+                                                    apply_func=apply_node_func,
+                                                    inplace=inplace)
+                Runtime.run(prog)
 
     def prop_flow(self, message_funcs="default", reduce_funcs="default",
                   apply_node_funcs="default", flow_range=ALL, inplace=False):