[SCHEDULE] Mutate dataflow in schedule, refactor Stage

include/tvm/operation.h

@@ -136,7 +136,7 @@ using FCompute = std::function<Expr (const Array<Var>& i)>;
  * \param dtype the data type of the tensor.
  * \param name The name of the Tensor.
  */
-Tensor Placeholder(Array<Expr> shape,
+Tensor placeholder(Array<Expr> shape,
                    Type dtype = Float(32),
                    std::string name = "placeholder");
 
@@ -147,7 +147,7 @@ Tensor Placeholder(Array<Expr> shape,
  * \param fcompute The compute function to create the tensor.
  * \param name The optional name of the tensor.
  */
-Tensor Compute(Array<Expr> shape, FCompute fcompute, std::string name = "tensor");
+Tensor compute(Array<Expr> shape, FCompute fcompute, std::string name = "tensor");
 
 /*!
  * \brief Construct new tensors by scan over scan_axis.
@@ -158,36 +158,36 @@ Tensor Compute(Array<Expr> shape, FCompute fcompute, std::string name = "tensor"
  * \param state_placeholder The placeholder for the states.
  * \param name The optional name of the tensor.
  */
-Array<Tensor> Scan(IterVar scan_axis,
+Array<Tensor> scan(IterVar scan_axis,
                    Array<Tensor> init,
                    Array<Tensor> update,
                    Array<Tensor> state_placeholder,
                    std::string name = "scan");
 
 // same as compute, specialized for different fcompute function
-inline Tensor Compute(Array<Expr> shape,
+inline Tensor compute(Array<Expr> shape,
                       std::function<Expr(Var)> f,
                       std::string name = "tensor") {
   FCompute fc = [f] (const Array<Var>& i) { return f(i[0]); };
-  return Compute(shape, fc, name);
+  return compute(shape, fc, name);
 }
-inline Tensor Compute(Array<Expr> shape,
+inline Tensor compute(Array<Expr> shape,
                       std::function<Expr(Var, Var)> f,
                       std::string name = "tensor") {
   FCompute fc = [f] (const Array<Var>& i) { return f(i[0], i[1]); };
-  return Compute(shape, fc, name);
+  return compute(shape, fc, name);
 }
-inline Tensor Compute(Array<Expr> shape,
+inline Tensor compute(Array<Expr> shape,
                       std::function<Expr(Var, Var, Var)> f,
                       std::string name = "tensor") {
   FCompute fc = [f] (const Array<Var>& i) { return f(i[0], i[1], i[2]); };
-  return  Compute(shape, fc, name);
+  return  compute(shape, fc, name);
 }
-inline Tensor Compute(Array<Expr> shape,
+inline Tensor compute(Array<Expr> shape,
                       std::function<Expr(Var, Var, Var, Var)> f,
                       std::string name = "tensor") {
   FCompute fc = [f] (const Array<Var>& i) { return f(i[0], i[1], i[2], i[3]); };
-  return Compute(shape, fc, name);
+  return compute(shape, fc, name);
 }
 
 }  // namespace tvm

include/tvm/schedule.h

@@ -131,6 +131,13 @@ class Stage : public NodeRef {
               IterVar* p_x_outer, IterVar* p_y_outer,
               IterVar* p_x_inner, IterVar* p_y_inner,
               Expr x_factor, Expr y_factor);
+  /*!
+   * \brief Specify thread launching group in
+   *  outer most scope of the stage.
+   *  This is only valid for composite operators.
+   * \param threads The threads to be launched.
+   */
+  Stage& outermost_threads(Array<IterVar> threads);
   /*!
    * \brief Vectorize iteration.
    * \param var The axis to be vectorized.
@@ -179,6 +186,28 @@ class Schedule : public NodeRef {
   Stage operator[](const Tensor& tensor) {
     return this->operator[](tensor->op);
   }
+  /*!
+   * \brief create a cache read of original tensor for readers.
+   *  This will mutate the body of the readers.
+   *  A new stage will be created for the tensor.
+   * \param tensor The tensor cached.
+   * \param scope The scope of the cache.
+   * \param readers The readers to redirect to the tensor.
+   * \return The created tensor.
+   */
+  Tensor cache_read(const Tensor& tensor,
+                    const std::string& scope,
+                    const Array<Operation>& readers);
+  /*!
+   * \brief Create a cache write tensor for producing tensor.
+   *  The the tensor will take over body of original tensor op.
+   *  The original tensor's body will be changed to an identity read
+   *  from the corresponding cache.
+   * \param tensor The tensor to be produced.
+   * \param scope The scope of the storage.
+   * \return The created tensor.
+   */
+  Tensor cache_write(const Tensor& tensor, const std::string& scope);
   /*!
    * \brief Normalize the schedule.
    *  This is needed before bound inference.
@@ -193,6 +222,11 @@ class Schedule : public NodeRef {
    * \return the pointer to the internal node container
    */
   inline const ScheduleNode* operator->() const;
+  /*!
+   * \brief access the internal node container
+   * \return the pointer to the internal node container
+   */
+  inline ScheduleNode* operator->();
   // declare container type
   using ContainerType = ScheduleNode;
 };
@@ -244,17 +278,28 @@ class IterVarAttr : public NodeRef {
  */
 class StageNode : public Node {
  public:
-  /*! \brief The operation to be scheduled */
-  Operation op;
   /*! \brief The thread scope level of the stage */
   std::string scope;
+  /*! \brief The operation of stage, can be different from original op. */
+  Operation op;
+  /*!
+   * \brief The original operator.
+   *  The op field can change during schedule to alternate the dataflow,
+   *  while origin_op remains fixed.
+   */
+  Operation origin_op;
   /*! \brief All the nodes in the iter var */
   Array<IterVar> all_iter_vars;
   /*!
    * \brief The current leafs in the schedule.
    *  Operations can only be performed in leaves.
    */
   Array<IterVar> leaf_iter_vars;
+  /*!
+   * \brief Specify threads to be launched at the stage.
+   *  This is only valid for composite ops such as Scan.
+   */
+  Array<IterVar> outermost_threads;
   /*! \brief The relation bwteen of IterVars */
   Array<IterVarRelation> relations;
   /*! \brief additional attributes about iter var. */
@@ -265,17 +310,22 @@ class StageNode : public Node {
   IterVar attach_ivar;
   /*! \brief The stage this node attaches to */
   Stage attach_stage;
+  /*! \brief Whether this is an output stage */
+  bool is_output{false};
 
   void VisitAttrs(AttrVisitor* v) final {
     v->Visit("scope", &scope);
     v->Visit("op", &op);
+    v->Visit("origin_op", &origin_op);
     v->Visit("all_iter_vars", &all_iter_vars);
     v->Visit("leaf_iter_vars", &leaf_iter_vars);
+    v->Visit("outermost_threads", &outermost_threads);
     v->Visit("relations", &relations);
     v->Visit("iter_var_attrs", &iter_var_attrs);
     v->Visit("attach_type", &attach_type);
     v->Visit("attach_ivar", &attach_ivar);
     v->Visit("attach_stage", &attach_stage);
+    v->Visit("is_output", &is_output);
   }
 
   static constexpr const char* _type_key = "Stage";
@@ -285,18 +335,18 @@ class StageNode : public Node {
 /*! \brief node container for schedule */
 class ScheduleNode : public Node {
  public:
-  /*! \brief The root operations */
-  Array<Operation> roots;
+  /*! \brief The output operations in original data flow graph */
+  Array<Operation> outputs;
   /*!
-   * \brief list of all stages for non-placeholder ops
-   *  The stage are ordered in PostDFS order of their op.
+   * \brief list of all stages for non-placeholder ops.
+   * The stages are sorted in dependency order.
    */
   Array<Stage> stages;
   /*! \brief map of operation to the stages */
   Map<Operation, Stage> stage_map;
 
   void VisitAttrs(AttrVisitor* v) final {
-    v->Visit("roots", &roots);
+    v->Visit("outputs", &outputs);
     v->Visit("stages", &stages);
     v->Visit("stage_map", &stage_map);
   }
@@ -412,12 +462,16 @@ inline StageNode* Stage::operator->() {
 
 inline bool Stage::is_scheduled() const {
   const StageNode* n = operator->();
-  return !(n->relations.empty() && n->attach_type == kNone);
+  return !(n->relations.empty() && n->attach_type == kNone &&
+           n->all_iter_vars.same_as(n->leaf_iter_vars));
 }
 
 inline const ScheduleNode* Schedule::operator->() const {
   return static_cast<const ScheduleNode*>(node_.get());
 }
+inline ScheduleNode* Schedule::operator->() {
+  return static_cast<ScheduleNode*>(node_.get());
+}
 
 inline const IterVarRelationNode* IterVarRelation::operator->() const {
   return static_cast<const IterVarRelationNode*>(node_.get());

python/tvm/build.py

@@ -63,7 +63,6 @@ def build(sch,
             arg_list.append(x)
         else:
             raise ValueError("args must be Tensor, Buffer or Var")
-
     # lowering
     bounds = schedule.InferBound(sch)
     stmt = schedule.ScheduleOps(sch, bounds)

python/tvm/schedule.py

@@ -4,6 +4,7 @@
 from ._ctypes._node import NodeBase, register_node
 from . import _api_internal
 from . import tensor as _tensor
+from . import collections as _collections
 
 @register_node
 class Buffer(NodeBase):
@@ -41,6 +42,53 @@ def normalize(self):
         """
         _api_internal._ScheduleNormalize(self)
 
+    def cache_read(self, tensor, scope, readers):
+        """Create a cache read of original tensor for readers.
+
+        This will mutate the body of the readers.
+        A new cache stage will be created for the tensor.
+        Call this before doing any split/fuse schedule.
+
+        Parameters
+        ----------
+        tensor : Tensor
+            The tensor to be cached.
+        scope : str
+            The scope of cached
+        readers : list of Tensor or Operation
+            The readers to read the cache.
+
+        Returns
+        -------
+        cache : Tensor
+            The created cache tensor.
+        """
+        if isinstance(readers, (_tensor.Tensor, _tensor.Operation)):
+            readers = [readers]
+        readers = [t.op if isinstance(t, _tensor.Tensor) else t for t in readers]
+        return _api_internal._ScheduleCacheRead(self, tensor, scope, readers)
+
+    def cache_write(self, tensor, scope):
+        """Create a cache write of original tensor, before storing into tensor.
+
+        This will mutate the body of the tensor.
+        A new cache stage will created before feed into the tensor.
+
+        Parameters
+        ----------
+        tensor : Tensor
+            The tensor to be feed to.
+        scope : str
+            The scope of cached
+
+        Returns
+        -------
+        cache : Tensor
+            The created cache tensor.
+        """
+        return _api_internal._ScheduleCacheWrite(self, tensor, scope)
+
+
 @register_node
 class Stage(NodeBase):
     """A Stage represents schedule for one operation."""
@@ -104,6 +152,18 @@ def set_scope(self, scope):
         """
         return _api_internal._StageSetScope(self, scope)
 
+    def outermost_threads(self, threads):
+        """Force launch threads at outermost scope of the stage.
+
+        Parameters
+        ----------
+        threads : list of threads
+            The threads to be launched.
+        """
+        if isinstance(threads, _collections.IterVar):
+            threads = [threads]
+        _api_internal._StageOutermostThreads(self, threads)
+
     def compute_at(self, parent, scope):
         """Attach the stage at parent's scope
 

src/api/api_lang.cc

@@ -161,7 +161,7 @@ TVM_REGISTER_API(_TensorHash)
 
 TVM_REGISTER_API(_Placeholder)
 .set_body([](TVMArgs args,  TVMRetValue* ret) {
-    *ret = Placeholder(args[0],
+    *ret = placeholder(args[0],
                        args[1],
                        args[2]);
   });
@@ -262,6 +262,12 @@ TVM_REGISTER_API(_StageTile)
     *ret = Array<IterVar>({x_outer, y_outer, x_inner, y_inner});
   });
 
+TVM_REGISTER_API(_StageOutermostThreads)
+  .set_body([](TVMArgs args, TVMRetValue* ret) {
+    args[0].operator Stage()
+        .outermost_threads(args[1]);
+  });
+
 TVM_REGISTER_API(_StageUnroll)
   .set_body([](TVMArgs args, TVMRetValue* ret) {
     args[0].operator Stage()
@@ -280,4 +286,16 @@ TVM_REGISTER_API(_ScheduleNormalize)
         .normalize();
   });
 
+TVM_REGISTER_API(_ScheduleCacheRead)
+.set_body([](TVMArgs args, TVMRetValue* ret) {
+    *ret = args[0].operator Schedule()
+        .cache_read(args[1], args[2], args[3]);
+  });
+
+TVM_REGISTER_API(_ScheduleCacheWrite)
+.set_body([](TVMArgs args, TVMRetValue* ret) {
+    *ret = args[0].operator Schedule()
+        .cache_write(args[1], args[2]);
+  });
+
 }  // namespace tvm

src/lang/operation.cc

@@ -53,7 +53,7 @@ Operation PlaceholderOpNode::make(std::string name,
 
 
 
-Tensor Placeholder(Array<Expr> shape, Type dtype, std::string name) {
+Tensor placeholder(Array<Expr> shape, Type dtype, std::string name) {
   return PlaceholderOpNode::make(name, shape, dtype).output(0);
 }
 
@@ -82,7 +82,7 @@ Array<Expr> ComputeOpNode::output_shape(size_t i) const {
   return Array<Expr>(shape);
 }
 
-Tensor Compute(Array<Expr> shape, FCompute fcompute, std::string name) {
+Tensor compute(Array<Expr> shape, FCompute fcompute, std::string name) {
   auto op_node = std::make_shared<ComputeOpNode>();
   // compute dimension.
   size_t ndim = shape.size();
@@ -188,7 +188,7 @@ Operation ScanOpNode::make(std::string name,
   return Operation(n);
 }
 
-Array<Tensor> Scan(IterVar scan_axis,
+Array<Tensor> scan(IterVar scan_axis,
                    Array<Tensor> init,
                    Array<Tensor> update,
                    Array<Tensor> state_placeholder,