[Relay] Introduce arguments limit to FuseOps pass (#15137)

* [Relay] Introduce arguments limit to FuseOps pass

In PR #8313 a parameter `max_function_args` was introduced. It leads to
limit number of function argument and in case when this value is
exceeded then concatenation layer is split to a several concat
operations.

I faced a problem on Adreno GPU that for kernel with big number of
arguments the enqueueNDRange was crashed without any errors. The
problem appeared because of the huge number of arguments. But in this
case not only concat layer was a root cause of the problem. Also after
fusing several operations the final functions had a big number of
arguments.

As it was discussed in #8313, adding a limitation on the number of
function arguments to the FuseOps pass might be a good improvement. In
this PR I introduced such mechanism for limitation number of function
arguments for FuseOps pass and add an arguments limit to OpenCL devices
at 128 parameters.

The idea of current approach is calculate the number of arguments for
each node in fusing algorithm and in case then the number of function
arguments exceeds the limit, specified by `max_function_args`, then the
fusing should be stopped. In case when node has several inputs and for
some of the inputs the number of arguments wasn't computed, then we
postpone fusing for this node and will try fuse this node later when
the number of arguments will be computed for all inputs. This approach
with postponed fusing helps to avoid additional computations during
compilation.

Additionally, case of dynamic shapes should be handled.  In case of
dynamic shape, function arguments also included sizes of dynamic
dimension and strides. The number of strides can be computed by
calculating number of tensor dimensions (the number of strides equals
to the rank of the tensor). The number of additional parameters with
sizes of dynamic dimensions can be calculated by computing number of
dynamic dimensions.

* Fix memory_scope order in test

* Apply code review comments

* Apply comments

Author: echuraev

include/tvm/relay/transform.h

@@ -120,9 +120,12 @@ TVM_DLL Pass FoldConstant(bool fold_qnn = false);
 /*!
  * \brief Split function with huge number of arguments to smaller pieces.
  *
+ * \param max_function_args Maximum number of function arguments. If it equals 0 then SplitArgs
+ *                          shouldn't split the function.
+ *
  * \return The pass.
  */
-TVM_DLL Pass SplitArgs(int max_function_args);
+TVM_DLL Pass SplitArgs(uint64_t max_function_args);
 
 /*!
  * \brief Fuse operations into expr into separate functions.

include/tvm/topi/transform.h

@@ -723,7 +723,7 @@ inline te::Tensor dynamic_strided_slice(const te::Tensor& x, const te::Tensor& b
 }
 
 /*!
- * \brief Calcluate the output shape of strided_slice, the entry point for Relay type relation
+ * \brief Calculate the output shape of strided_slice, the entry point for Relay type relation
  *
  * \param ishape The input tensor shape
  * \param begin The indices to begin with in the slicing

python/tvm/relay/op/tensor.py

@@ -23,7 +23,7 @@
 
 from . import _make
 from .dyn import _make as _dyn_make
-from ..expr import Tuple, Expr, Constant
+from ..expr import Tuple, Expr, Constant, Call
 from . import op as reg
 
 
@@ -1141,12 +1141,15 @@ def concatenate(data, axis):
     result: relay.Expr
         The concatenated tensor.
     """
-    data = list(data)
+    if not isinstance(data, Call):
+        data = list(data)
     if not data:
         raise ValueError("relay.concatenate requires data to be non-empty.")
+    if not isinstance(data, Call):
+        data = Tuple(data)
     if not isinstance(axis, int):
         raise ValueError("For now, we only support integer axis")
-    return _make.concatenate(Tuple(data), axis)
+    return _make.concatenate(data, axis)
 
 
 def einsum(data, equation):

python/tvm/relay/transform/transform.py

@@ -1376,10 +1376,17 @@ def ToMixedPrecision(mixed_precision_type="float16", missing_op_mode=1):
 def SplitArgs(max_function_args):
     """Split function with huge number of arguments to smaller pieces.
 
+    Parameters
+    ----------
+    max_function_args: int
+      Maximum number of function arguments. If it equals 0 then SplitArgs
+      shouldn't split the function.
+
+
     Returns
     -------
     ret : tvm.transform.Pass
-        The registered pass for constant folding.
+        The registered pass.
     """
     return _ffi_api.SplitArgs(max_function_args)
 

python/tvm/target/target.py

@@ -194,7 +194,7 @@ def max_shared_memory_per_block(self):
 
     @property
     def max_function_args(self):
-        return int(self.attrs.get("max_function_args", -1))
+        return int(self.attrs.get("max_function_args", 0))
 
     @property
     def vtcm_capacity(self):

src/relay/analysis/graph_partitioner.cc

@@ -169,6 +169,7 @@ void GraphPartitioner::MergeFromTo(Group* child, Group* parent) {
   if (child == parent) return;
   // update the number of nodes of the parent group
   parent->num_nodes += child->num_nodes;
+  parent->args_num += child->args_num;
   child->parent = parent;
   // update anchor ref and pattern
   if (child->anchor_ref != nullptr) {
@@ -180,6 +181,10 @@ void GraphPartitioner::MergeFromTo(Group* child, Group* parent) {
 
 void GraphPartitioner::CommitFuse_(IndexedForwardGraph::Node* src, IndexedForwardGraph::Node* sink,
                                    Group* target) {
+  if (postpone_node_ != nullptr) {
+    postponed_fusing_map_.insert({postpone_node_, src});
+    return;
+  }
   if (src == sink) return;
   if (visited_.count(src)) return;
   visited_.insert(src);
@@ -220,7 +225,113 @@ size_t GraphPartitioner::CountFusedNodesWithNewChild(IndexedForwardGraph::Node*
   return target->FindRoot()->num_nodes + CountNodesUptoSink_(child, dom_parent);
 }
 
+size_t GraphPartitioner::CountAdditionalArgs_(const TensorTypeNode* ttype, bool with_strides) {
+  size_t any_dims = 0;
+  for (const auto& dim : ttype->shape) {
+    if (dim.as<AnyNode>()) {
+      any_dims++;
+    }
+  }
+  if (with_strides && any_dims > 0) any_dims += ttype->shape.size();
+  return any_dims;
+}
+
+size_t GraphPartitioner::CountArgs_(IndexedForwardGraph::Node* src,
+                                    const IndexedForwardGraph& graph, bool update_postpone) {
+  std::unordered_set<Group*> visited_groups;
+  Group* gnode = groups_[src->index];
+  ICHECK(gnode != nullptr);
+  auto sum = gnode->args_num;
+  visited_groups.insert(gnode->FindRoot());
+  auto calc_args_number = [this, src, &graph, &visited_groups,
+                           update_postpone](const relay::Expr& arg) -> size_t {
+    if (arg.as<VarNode>()) return 0;
+    auto* node = graph.node_map.at(arg.get());
+    Group* prev_group = groups_[node->index]->FindRoot();
+    if (visited_groups.count(prev_group) == 0) {
+      visited_groups.insert(prev_group);
+      if (prev_group->args_num > 0) {
+        // Get the number of arguments from the group
+        return prev_group->args_num;
+      } else if (update_postpone) {
+        // Update pointer to the node which should be postponed for deferred fusing
+        postpone_node_ = src;
+      } else {
+        // Calculate the number of arguments for the node which wasn't processed before
+        return CountArgs_(node, graph, update_postpone);
+      }
+    }
+    return 0;
+  };
+  if (auto call_node = GetRef<ObjectRef>(src->ref).as<CallNode>()) {
+    for (auto& it : call_node->args) {
+      sum += calc_args_number(it);
+    }
+  } else if (auto tuple_node = GetRef<ObjectRef>(src->ref).as<TupleNode>()) {
+    for (auto& it : tuple_node->fields) {
+      sum += calc_args_number(it);
+    }
+  }
+  return sum;
+}
+
+size_t GraphPartitioner::CountArgsLimit_(const IndexedForwardGraph::Node* child) {
+  auto* outputs_list = child->outputs.head;
+  size_t output_args = 0;
+  while (outputs_list != nullptr) {
+    output_args++;
+    if (auto call_node = GetRef<ObjectRef>(outputs_list->value.node->ref).as<CallNode>()) {
+      if (const auto* ttype = call_node->checked_type().as<TensorTypeNode>()) {
+        output_args += CountAdditionalArgs_(ttype, false);
+      }
+    }
+    outputs_list = outputs_list->next;
+  }
+  return (max_function_args_ > output_args) ? max_function_args_ - output_args : 0;
+}
+
+size_t GraphPartitioner::CountFusedArgs(const IndexedForwardGraph& graph,
+                                        IndexedForwardGraph::Node* child) {
+  size_t args_num = 0;
+  auto* outputs_list = child->outputs.head;
+  while (outputs_list != nullptr) {
+    args_num = std::max(args_num, CountArgs_(outputs_list->value.node, graph));
+    outputs_list = outputs_list->next;
+  }
+  return args_num;
+}
+
 void GraphPartitioner::InitGroups(const IndexedForwardGraph& graph) {
+  auto args_counter = [this](const tvm::Object* obj) {
+    size_t args_num = 0;
+    if (auto call_node = GetRef<ObjectRef>(obj).as<CallNode>()) {
+      for (auto& it : call_node->args) {
+        if (it.as<VarNode>() || it.as<TupleGetItemNode>()) {
+          args_num++;
+          if (const auto* ttype = it.as<ExprNode>()->checked_type().as<TensorTypeNode>()) {
+            args_num += CountAdditionalArgs_(ttype);
+          }
+        }
+      }
+    } else if (auto tuple_node = GetRef<ObjectRef>(obj).as<TupleNode>()) {
+      for (auto& it : tuple_node->fields) {
+        if (it.as<VarNode>() || it.as<TupleGetItemNode>()) {
+          args_num++;
+          if (const auto* ttype = it.as<ExprNode>()->checked_type().as<TensorTypeNode>()) {
+            args_num += CountAdditionalArgs_(ttype);
+          }
+        }
+      }
+    } else if (GetRef<ObjectRef>(obj).as<VarNode>()) {
+      args_num++;
+      if (const auto* ttype =
+              GetRef<ObjectRef>(obj).as<ExprNode>()->checked_type().as<TensorTypeNode>()) {
+        args_num += CountAdditionalArgs_(ttype);
+      }
+    }
+    return args_num;
+  };
+
   groups_.resize(graph.post_dfs_order.size());
   for (size_t nid = 0; nid < groups_.size(); ++nid) {
     const auto* graph_node = graph.post_dfs_order[nid];
@@ -231,6 +342,7 @@ void GraphPartitioner::InitGroups(const IndexedForwardGraph& graph) {
     if (group_node->pattern == relay::kOutEWiseFusable) {
       group_node->anchor_ref = graph_node->ref;
     }
+    group_node->args_num = args_counter(graph_node->ref);
     groups_[nid] = group_node;
   }
 }
@@ -244,6 +356,21 @@ void GraphPartitioner::RunFuse(const IndexedForwardGraph& graph,    //
     auto* dom_node = post_dom_tree.nodes[nid];
     Group* group_node = groups_[nid];
     ICHECK(group_node != nullptr);
+    postpone_node_ = nullptr;
+    // Check if the fusing of some inputs was postponed
+    if (postponed_fusing_map_.count(graph_node)) {
+      auto range = postponed_fusing_map_.equal_range(graph_node);
+      for (auto it = range.first; it != range.second; ++it) {
+        // If the number of arguments is less than the limit then the input can be fused
+        if (CountArgs_(graph_node, graph, false) <= CountArgsLimit_(graph_node)) {
+          auto* src = it->second;
+          auto* snode = post_dom_tree.nodes[src->index]->parent->gnode;
+          if (groups_[snode->index]->anchor_ref != nullptr) continue;
+          CommitFuse(src, snode);
+        }
+      }
+      postponed_fusing_map_.erase(graph_node);
+    }
     // no actions for opaque nodes
     if (group_node->pattern == kOpaque) continue;
     // no actions needed if the current node have no dominator
@@ -254,6 +381,15 @@ void GraphPartitioner::RunFuse(const IndexedForwardGraph& graph,    //
     // refuse the fusion if too many ops are going to be fused together
     if (CountFusedNodesWithNewChild(graph_node, dom_node->parent->gnode) > max_fuse_depth_)
       continue;
+    // Refuse the fusion if too many arguments are going to be in the fused function
+    if (max_function_args_ > 0) {
+      auto limit = CountArgsLimit_(graph_node);
+      if (limit > 0) {
+        if (CountFusedArgs(graph, graph_node) > limit) {
+          continue;
+        }
+      }
+    }
 
     if (phase == 2) {
       // Fuse injective ops into intermediate tuples, if any

src/relay/analysis/graph_partitioner.h

@@ -78,7 +78,7 @@ class IndexedForwardGraph {
   std::vector<Node*> post_dfs_order;
 
   /*! \brief Dump the graph into string. */
-  void DebugDump() {
+  void DebugDump() const {
     std::ostringstream os;
     for (size_t i = 0; i < post_dfs_order.size(); ++i) {
       Node* node = post_dfs_order[i];
@@ -162,8 +162,12 @@ class DominatorTree {
  */
 class GraphPartitioner {
  public:
-  explicit GraphPartitioner(support::Arena* arena, int opt_level, size_t max_fuse_depth)
-      : arena_(arena), opt_level_(opt_level), max_fuse_depth_(max_fuse_depth) {}
+  explicit GraphPartitioner(support::Arena* arena, int opt_level, size_t max_fuse_depth,
+                            size_t max_function_args)
+      : arena_(arena),
+        opt_level_(opt_level),
+        max_fuse_depth_(max_fuse_depth),
+        max_function_args_(max_function_args) {}
   /*!
    * \brief Group as a union find data structure.
    */
@@ -183,6 +187,10 @@ class GraphPartitioner {
      * \brief The number of nodes belonging to this group
      */
     uint32_t num_nodes{1};
+    /*!
+     * \brief The number of function arguments belonging to this group
+     */
+    size_t args_num{0};
 
     /*! \brief Optional attributes to annotate the grouped function. */
     runtime::Map<runtime::String, ObjectRef> attrs;
@@ -205,10 +213,21 @@ class GraphPartitioner {
   int opt_level_;
   /*! \brief The maximum number of operations in one fused function */
   size_t max_fuse_depth_;
+  /*! \brief The maximum number of arguments in one fused function */
+  size_t max_function_args_;
   /*! \brief The internal groups. */
   std::vector<Group*> groups_;
   /*! \brief internal field used for deduplication */
   std::unordered_set<IndexedForwardGraph::Node*> visited_;
+  /*! \brief The map with nodes which were postponed for fusing. */
+  std::unordered_multimap<const IndexedForwardGraph::Node*, IndexedForwardGraph::Node*>
+      postponed_fusing_map_;
+  /*!
+   * \brief Fusing of this node should be postponed till all child nodes are evaluated.
+   *        It is used to calculate the number of arguments which will be passed to this node in
+   *        the generated function.
+   */
+  const IndexedForwardGraph::Node* postpone_node_{nullptr};
   // Internal implementation of CheckPath
   template <typename F>
   bool CheckPath_(IndexedForwardGraph::Node* src, IndexedForwardGraph::Node* sink, F fcond);
@@ -247,6 +266,23 @@ class GraphPartitioner {
   void CommitFuse(IndexedForwardGraph::Node* src, IndexedForwardGraph::Node* sink);
 
   size_t CountNodesUptoSink_(IndexedForwardGraph::Node* src, IndexedForwardGraph::Node* sink);
+  // Count the number of additional arguments. In the case of dynamic shape,
+  // generated function takes several additional arguments, such as the sizes of
+  // the dynamic dimensions and strides.
+  // This function calculates the number of such additional arguments.
+  size_t CountAdditionalArgs_(const TensorTypeNode* ttype, bool with_strides = true);
+  // Calculate the number of arguments for the node.
+  size_t CountArgs_(IndexedForwardGraph::Node* src, const IndexedForwardGraph& graph,
+                    bool update_postpone = true);
+  // Count the actual limit of arguments for a generated function.
+  // max_function_args_ specifies the number of maximum function arguments. But
+  // usually, output tensors are also passed to the function as arguments.
+  // Additionally, in the case of dynamic shape, it is necessary to take into
+  // account the number of parameters which specifies the sizes of the dynamic
+  // dimensions.
+  // This function computes the maximum number of arguments by the following formula:
+  // limit = max_function_args_ - output_args_count
+  size_t CountArgsLimit_(const IndexedForwardGraph::Node* child);
 
   // Count the number of nodes in a fused subgraph if child is additionally fused.
   // dom_parent is already known to be a part of the subgraph.
@@ -256,6 +292,10 @@ class GraphPartitioner {
   // is important for correct calculation.
   size_t CountFusedNodesWithNewChild(IndexedForwardGraph::Node* child,
                                      IndexedForwardGraph::Node* dom_parent);
+  // Count the number of arguments in a fused subgraph. This function also takes into account the
+  // number of the child's output node argument. It helps to stop fusing before the node when the
+  // limit will be exceeded.
+  size_t CountFusedArgs(const IndexedForwardGraph& graph, IndexedForwardGraph::Node* child);
 
   // Initialize the groups.
   void InitGroups(const IndexedForwardGraph& graph);

src/relay/backend/build_module.cc

@@ -337,7 +337,7 @@ class RelayBuildModule : public runtime::ModuleNode {
     if (config_->optional_homogeneous_target.defined()) {
       // This pass currently only supports the homogeneous case.
       pass_seqs.push_back(transform::SplitArgs(
-          config_->optional_homogeneous_target->GetAttr<Integer>("max_function_args", -1)
+          config_->optional_homogeneous_target->GetAttr<Integer>("max_function_args", 0)
               .value()
               .IntValue()));
     }

src/relay/backend/vm/compiler.cc

@@ -1059,6 +1059,13 @@ IRModule VMCompiler::OptimizeModuleImpl(IRModule mod) {
   // Always plan devices so the remaining passes don't need to distinguish homogeneous vs
   // heterogeneous execution.
   pass_seqs.push_back(transform::PlanDevices(config_));
+  if (config_->optional_homogeneous_target.defined()) {
+    // This pass currently only supports the homogeneous case.
+    pass_seqs.push_back(transform::SplitArgs(
+        config_->optional_homogeneous_target->GetAttr<Integer>("max_function_args", 0)
+            .value()
+            .IntValue()));
+  }
 
   pass_seqs.push_back(transform::FuseOps());