[NNVM] Enhance operator fusion for more element wise patterns (#1548)

nnvm/src/compiler/graph_fuse.cc

@@ -161,6 +161,103 @@ nnvm::Graph GraphFusePartition(nnvm::Graph g) {
       }
     }
   }
+
+  /*
+     Above algorithm will not fuse a node whose output is fed to more than one
+     child node. This is because in general, it does not make sense to fuse multiple
+     children branches with their parent, as in the following example.
+
+            conv2d
+            /  |  \
+           /   |   \
+         op    op   op
+          |    |    |
+          |    |    |
+
+     However, when all children branches meet at a certain node, there is a possibility for
+     further operator fusion. For example, all nodes in the following subgraph can be fused
+     into a single node, if three 'in-between' nodes and the bottom node are all element wise
+     operation.
+
+            conv2d
+            /  |  \
+           /   |   \
+         op    op   op
+          \    |    /
+           \   |   /
+          elemwise add
+               |
+
+     This pattern is not uncommon. For example, it arises when conv2d op is followed by exponential
+     linear unit. If bias add and batch normalization are also present, they can be fused as well.
+
+     In fact, above fusion algorithm already fuses three in-between nodes and the element wise
+     add node in the figure above. The following code fuses the conv2d node with the already
+     fused children nodes. The following patterns are supported.
+
+     * Any number of child nodes from the top node
+     * The path from the top node to bottom node can contain any number of element wise ops.
+
+     The only restriction is that in-between nodes cannot have more than one child.
+
+     The overview of the algorithm below is as follows:
+
+     1. Check if all children nodes are fused into a single op by the existing fusion algorithm
+     2. Fuse the parent node to children nodes, and update its group id to be the children's group id
+     3. If the parent node originally belongs to another group (for example, conv + batch norm),
+        propagate the new group id to a grand parent and upward
+  */
+  if (opt_level >= 1) {
+    std::vector<std::vector<uint32_t> > children_group_ids(idx.num_nodes());
+    std::vector<std::vector<uint32_t> > node_ids_per_group(idx.num_nodes());
+    for (uint32_t nid = idx.num_nodes() - 1; nid != 0; --nid) {
+      const auto& inode = idx[nid];
+      if (inode.source->is_variable()) continue;
+      CHECK_NE(group_vec[nid], -1);
+      node_ids_per_group[group_vec[nid]].push_back(nid);
+      if (inode.inputs.size() != 1) continue;
+      const uint32_t parent_nid = inode.inputs[0].node_id;
+      // if parent node has more than one child, record each child's group id.
+      if (ref_count[parent_nid] > 1) children_group_ids[parent_nid].push_back(group_vec[nid]);
+    }
+
+    std::vector<int> new_group_id(idx.num_nodes(), -1);
+    for (uint32_t nid = idx.num_nodes() - 1; nid != 0; --nid) {
+      if (new_group_id[group_vec[nid]] != -1) {
+        // propagate new group id from child
+        group_vec[nid] = new_group_id[group_vec[nid]];
+      }
+      TOpPattern pt = op_pattern.get(idx[nid].source->op(), kOpaque);
+      if (pt == kOpaque) continue;
+      const auto& group_ids = children_group_ids[nid];
+      if (group_ids.size() <= 1) continue;
+      const uint32_t child_group_id = group_ids[0];
+      const auto& children_node_ids = node_ids_per_group[child_group_id];
+
+      auto is_same_group_id = [child_group_id](uint32_t id) {
+          return id == child_group_id;
+      };
+      auto is_fusible_pattern = [&idx](uint32_t child_nid) {
+        TOpPattern child_pt = op_pattern.get(idx[child_nid].source->op(), kOpaque);
+        return child_pt  <= kBroadcast;
+      };
+      // fuse this node with children if
+      // all children belong to the same group and
+      // all nodes in the group are element wise or broadcast op.
+      const bool can_be_fused = std::all_of(group_ids.begin(), group_ids.end(), is_same_group_id) &&
+        std::all_of(children_node_ids.begin(), children_node_ids.end(), is_fusible_pattern);
+
+      if (can_be_fused) {
+        new_group_id[group_vec[nid]] = child_group_id;
+        group_vec[nid] = child_group_id;
+        for (uint32_t nid2 : node_ids_per_group[child_group_id]) {
+          pattern_vec[nid2] = pattern_vec[nid];
+          master_vec[nid2] = master_vec[nid];
+        }
+      }
+    }
+  }
+
   g.attrs["group_root"] = std::make_shared<any>(std::move(group_vec));
   g.attrs["group_master"] = std::make_shared<any>(std::move(master_vec));
   g.attrs["pattern"] = std::make_shared<any>(std::move(pattern_vec));

nnvm/tests/python/compiler/test_op_fusion.py

@@ -5,7 +5,7 @@ import topi.testing
 from tvm.contrib import graph_runtime
 from nnvm import symbol as sym
 from nnvm.compiler import graph_util, graph_attr
-from nnvm.testing import ctx_list
+from nnvm.testing import ctx_list, utils
 
 def test_ewise_injective():
     x = sym.Variable("x")
@@ -77,7 +77,49 @@ def test_injective_reduce_injective():
         np.testing.assert_allclose(out.asnumpy(), c_np, rtol=1e-5)
 
 
+def build_and_run(sym, params, data, out_shape, target, ctx, opt_level=2):
+    with nnvm.compiler.build_config(opt_level=opt_level):
+        graph, lib, params = nnvm.compiler.build(sym, target, shape={"data":data.shape}, params=params)
+    module = graph_runtime.create(graph, lib, ctx)
+    module.set_input(**params)
+    module.set_input("data", data)
+    module.run()
+    out =  module.get_output(0, tvm.nd.empty(out_shape))
+    return out.asnumpy(), graph
+
+
+def test_fuse_conv2d_elu():
+    def elu(data):
+        return -0.5 * sym.relu(1 - sym.exp(data)) + sym.relu(data)
+
+    def get_sym(out_channel):
+        data = sym.Variable(name="data")
+        data = sym.conv2d(data=data, kernel_size=(3,3), channels=out_channel, padding=(1, 1),
+                          layout="NCHW", kernel_layout="OIHW", use_bias=True)
+        data = sym.batch_norm(data)
+        data = elu(data)
+        return data
+
+    in_channel = 8
+    out_channel = 16
+    size = 64
+    dshape = (1, in_channel, size, size)
+    oshape = (1, out_channel, size, size)
+    data = np.random.uniform(-1, 1, dshape).astype(np.float32)
+
+    for target, ctx in ctx_list():
+        sym1 = get_sym(out_channel)
+        sym2 = get_sym(out_channel)
+        _, params1 = utils.create_workload(sym1, 1, dshape[1:], seed=0)
+        _, params2 = utils.create_workload(sym2, 1, dshape[1:], seed=0)
+        output1, g1 = build_and_run(sym1, params1, data, oshape, target, ctx, opt_level=2)
+        output2, g2 = build_and_run(sym2, params2, data, oshape, target, ctx, opt_level=0)
+        np.testing.assert_allclose(output1, output2, rtol=1e-5, atol=1e-5)
+        # data, conv weight, bias, batch norm gamma, batch norm beta, conv op
+        assert g1.index.num_nodes == 6
+
 if __name__ == "__main__":
     test_injective_reduce_injective()
     test_ewise_injective()
     test_conv_ewise_injective()
+    test_fuse_conv2d_elu()

topi/python/topi/arm_cpu/conv2d.py

@@ -39,11 +39,10 @@ def decl_spatial_pack(cfg, data, kernel, strides, padding, layout, out_dtype):
 def schedule_conv2d_nchw_arm_cpu(cfg, outs):
     """TOPI schedule callback"""
     s = tvm.create_schedule([x.op for x in outs])
-    scheduled_ops = []
 
     def _callback(op):
         # schedule conv2d
-        if 'spatial_conv_output' in op.tag and op not in scheduled_ops:
+        if 'spatial_conv_output' in op.tag:
             output = op.output(0)
             conv = op.input_tensors[0]
 
@@ -65,8 +64,6 @@ def schedule_conv2d_nchw_arm_cpu(cfg, outs):
             output = op.output(0)
             _schedule_winograd(cfg, s, output, outs[0])
 
-        scheduled_ops.append(op)
-
     traverse_inline(s, outs[0].op, _callback)
     return s
 

topi/python/topi/util.py

@@ -5,26 +5,34 @@ import tvm
 
 from . import tag
 
-def traverse_inline(s, op, callback):
+def traverse_inline(s, final_op, callback):
     """Traverse computation graph and do auto inline
 
     Parameters
     ----------
     s: schedule
         The schedule
-    op: Operation
+    final_op: Operation
         The final output operator.
     callback: callable
         The callback function on each op
     """
+    visited = set()
+
+    def _traverse(op):
+        if op in visited:
+            return
+        visited.add(op)
         if tag.is_injective(op.tag):
             if op not in s.outputs:
                 s[op].compute_inline()
             for tensor in op.input_tensors:
                 if tensor.op.input_tensors:
-                traverse_inline(s, tensor.op, callback)
+                    _traverse(tensor.op)
         callback(op)
 
+    _traverse(final_op)
+
 
 def prod(x):
     """Get the product of every items in the tuple.