[LANG] Generalize compute to tensor region (apache#1476)

include/tvm/expr.h

@@ -108,6 +108,8 @@ class Range : public HalideIR::IR::Range {
   TVM_DLL static Range make_by_min_extent(Expr min, Expr extent);
 };
 
+using Region = Array<Range>;
+
 /*!
  * \brief Type of iteration variable.
  *  Each IterVar have a specific type.

include/tvm/operation.h

@@ -49,7 +49,7 @@ class OperationNode : public FunctionBaseNode {
   }
   /*!
    * \return The list of iteration variable at root
-   * \note root_iter_vars dedides the shape of the outputs.
+   * \note root_iter_vars decides the shape of the outputs.
    */
   virtual Array<IterVar> root_iter_vars() const = 0;
   /*!
@@ -239,6 +239,74 @@ class TVM_DLL ComputeOpNode : public OperationNode {
   TVM_DECLARE_NODE_TYPE_INFO(ComputeOpNode, OperationNode);
 };
 
+/*!
+ * \brief A TenorCompute op that compute a tensor with an tensor intrinsic.
+ */
+class TensorComputeOpNode : public OperationNode {
+ public:
+  /*! \brief IterVar on each axis */
+  Array<IterVar> axis;
+  /*! \brief IterVar on each reduction axis, if the intrin will use the reduce axis */
+  Array<IterVar> reduce_axis;
+  /*! \brief number of axes that can be scheduled */
+  int schedulable_ndim;
+  /*! \brief TensorIntrin used to compute */
+  TensorIntrin intrin;
+  /*! \brief input tensors of intrin */
+  Array<Tensor> inputs;
+  /*! \brief region of input tensors */
+  Array<Region> input_regions;
+  /*! \brief constructor */
+  TensorComputeOpNode() {}
+  // override functions
+  int num_outputs() const final;
+  Array<IterVar> root_iter_vars() const final;
+  Type output_dtype(size_t i) const final;
+  Array<Expr> output_shape(size_t i) const final;
+  Array<Tensor> InputTensors() const final;
+  Operation ReplaceInputs(
+      const Operation& self,
+      const std::unordered_map<Tensor, Tensor>& rmap) const final;
+  void PropBoundToInputs(
+      const Operation& self,
+      const std::unordered_map<const Variable*, IntSet>& dom_map,
+      std::unordered_map<Tensor, TensorDom>* out_dom_map) const final;
+  void GatherBound(
+      const Operation& self,
+      const std::unordered_map<Tensor, TensorDom>& tensor_dom,
+      std::unordered_map<IterVar, Range>* out_dom_map) const final;
+  Stmt BuildRealize(
+      const Stage& stage,
+      const std::unordered_map<IterVar, Range>& realize_map,
+      const Stmt& body) const final;
+  Stmt BuildProvide(
+      const Stage& stage,
+      const std::unordered_map<IterVar, Range>& dom_map,
+      bool debug_keep_trivial_loop) const final;
+
+  void VisitAttrs(AttrVisitor* v) final {
+    v->Visit("name", &name);
+    v->Visit("tag", &tag);
+    v->Visit("axis", &axis);
+    v->Visit("reduce_axis", &reduce_axis);
+    v->Visit("schedulable_ndim", &schedulable_ndim);
+    v->Visit("intrin", &intrin);
+    v->Visit("inputs", &inputs);
+    v->Visit("input_regions", &input_regions);
+  }
+  static Operation make(std::string name,
+                        std::string tag,
+                        Array<IterVar> axis,
+                        Array<IterVar> reduce_axis,
+                        int schedulable_ndim,
+                        TensorIntrin intrin,
+                        Array<Tensor> tensors,
+                        Array<Region> regions);
+
+  static constexpr const char* _type_key = "TensorComputeOp";
+  TVM_DECLARE_NODE_TYPE_INFO(TensorComputeOpNode, OperationNode);
+};
+
 /*!
  * \brief Symbolic scan.
  */
@@ -326,7 +394,7 @@ class ExternOpNode : public OperationNode {
  public:
   /*! \brief The input tensors */
   Array<Tensor> inputs;
-  /*! \brief Symbolic placeholder representationinputs */
+  /*! \brief Symbolic placeholder representation of inputs */
   Array<Buffer> input_placeholders;
   /*! \brief Symbolic placeholder representation of outputs */
   Array<Buffer> output_placeholders;

include/tvm/tensor_intrin.h

@@ -89,5 +89,58 @@ class TensorIntrinNode : public Node {
 inline const TensorIntrinNode* TensorIntrin::operator->() const {
   return static_cast<const TensorIntrinNode*>(node_.get());
 }
+
+
+// Internal node container of tensor intrinsic calling.
+class TensorIntrinCallNode;
+
+/*! \brief Tensor intrinsic calling node. */
+class TensorIntrinCall : public NodeRef {
+ public:
+  TensorIntrinCall() {}
+  explicit TensorIntrinCall(NodePtr<Node> n) : NodeRef(n) {}
+  /*!
+   * \brief access the internal node container
+   * \return the pointer to the internal node container
+   */
+  inline const TensorIntrinCallNode* operator->() const;
+
+  /*! \brief specify container node */
+  using ContainerType = TensorIntrinCallNode;
+};
+
+class TensorIntrinCallNode : public Node {
+ public:
+  /*! \brief the tensor intrinsic */
+  TensorIntrin intrin;
+  /*! \brief input tensors of the intrinsic */
+  Array<Tensor> tensors;
+  /*! \brief regions of input tensors */
+  Array<Region> regions;
+  /*!
+   * \brief IterVar on each reduction axis, if the
+   * intrin will use the reduce axis
+   */
+  Array<IterVar> reduce_axis;
+
+  void VisitAttrs(AttrVisitor* v) final {
+    v->Visit("intrin", &intrin);
+    v->Visit("tensors", &tensors);
+    v->Visit("regions", &regions);
+    v->Visit("reduce_axis", &reduce_axis);
+  }
+  static TensorIntrinCall make(TensorIntrin intrin,
+                               Array<Tensor> tensors,
+                               Array<Region> regions,
+                               Array<IterVar> reduce_axis);
+
+  static constexpr const char* _type_key = "TensorIntrinCall";
+  TVM_DECLARE_NODE_TYPE_INFO(TensorIntrinCallNode, Node);
+};
+
+inline const TensorIntrinCallNode* TensorIntrinCall::operator->() const {
+  return static_cast<const TensorIntrinCallNode*>(node_.get());
+}
+
 }  // namespace tvm
 #endif  // TVM_TENSOR_INTRIN_H_

python/tvm/api.py

@@ -243,24 +243,43 @@ def compute(shape, fcompute, name="compute", tag="", attrs=None):
             raise ValueError("nested tag is not allowed for now")
         tag = _tag.TagScope.get_current().tag
     shape = (shape,) if isinstance(shape, _expr.Expr) else shape
+    # for python3
+    shape = tuple([int(s) if isinstance(s, float) else s for s in shape])
     ndim = len(shape)
     code = fcompute.__code__
 
-    if fcompute.__code__.co_argcount == 0:
+    out_ndim = ndim
+    if code.co_argcount == 0:
         arg_names = ["i%d" % i for i in range(ndim)]
     else:
         arg_names = code.co_varnames[:code.co_argcount]
+        out_ndim = code.co_argcount
 
-    if ndim != len(arg_names):
+    if out_ndim != len(arg_names):
         raise ValueError("fcompute do not match dimension, ndim=%d" % ndim)
 
-    dim_var = [_IterVar((0, s), x, 0) for x, s in zip(arg_names, shape)]
+    dim_var = [_IterVar((0, s), x, 0) for x, s in zip(arg_names, shape[:out_ndim])]
     body = fcompute(*[v.var for v in dim_var])
-    if not isinstance(body, (list, tuple)):
-        body = [body]
-    body = convert(body)
-    op_node = _api_internal._ComputeOp(
-        name, tag, attrs, dim_var, body)
+
+    if isinstance(body, _tensor.TensorIntrinCall):
+        for i, s in enumerate(shape[out_ndim:]):
+            var_name = "ax" + str(i)
+            dim_var.append(_IterVar((0, s), var_name, 4))
+        op_node = _api_internal._TensorComputeOp(name,
+                                                 tag,
+                                                 dim_var,
+                                                 body.reduce_axis,
+                                                 out_ndim,
+                                                 body.intrin,
+                                                 body.tensors,
+                                                 body.regions)
+    else:
+        if not isinstance(body, (list, tuple)):
+            body = [body]
+        body = convert(body)
+        op_node = _api_internal._ComputeOp(
+            name, tag, attrs, dim_var, body)
+
     num = op_node.num_outputs
     outputs = tuple(op_node.output(i) for i in range(num))
     return outputs[0] if num == 1 else outputs
@@ -529,14 +548,14 @@ def decl_buffer(shape,
     dtype = float32 if dtype is None else dtype
     strides = () if strides is None else strides
     if offset_factor != 0 and elem_offset is None:
-        elem_offset = var('%s_elem_offset' % name, shape[0].dtype)
+        shape_dtype = shape[0].dtype if hasattr(shape[0], "dtype") else "int32"
+        elem_offset = var('%s_elem_offset' % name, shape_dtype)
     if data is None:
         data = var(name, "handle")
     return _api_internal._Buffer(
         data, dtype, shape, strides, elem_offset, name, scope,
         data_alignment, offset_factor)
 
-
 def _IterVar(dom, name, iter_type, thread_tag=''):
     """Internal function to create IterVar
 

python/tvm/tensor.py

@@ -30,6 +30,11 @@ def dtype(self):
         """Data content of the tensor."""
         return self.tensor.dtype
 
+@register_node
+class TensorIntrinCall(NodeBase):
+    """Intermediate structure for calling a tensor intrinsic."""
+    pass
+
 
 itervar_cls = None
 
@@ -106,6 +111,7 @@ def name(self):
         return "%s.v%d" % (op.name, self.value_index)
 
 
+
 class Operation(NodeBase):
     """Represent an operation that generate a tensor"""
 
@@ -155,6 +161,12 @@ def reduce_axis(self):
         return self.__getattr__("reduce_axis")
 
 
+@register_node
+class TensorComputeOp(Operation):
+    """Tensor operation."""
+    pass
+
+
 @register_node
 class ScanOp(Operation):
     """Scan operation."""

python/tvm/tensor_intrin.py

@@ -6,9 +6,25 @@
 from . import stmt as _stmt
 from . import make as _make
 from . import tensor as _tensor
+from . import schedule as _schedule
 from .build_module import current_build_config
 from ._ffi.node import NodeBase, register_node
 
+
+def _get_region(tslice):
+    region = []
+    for idx in tslice.indices:
+        if isinstance(idx, slice):
+            assert idx.step is None
+            region.append(_api.Range(idx.start, idx.stop))
+        else:
+            if isinstance(idx, _schedule.IterVar):
+                begin = idx.var
+            else:
+                begin = idx
+            region.append(_make.range_by_min_extent(begin, 1))
+    return region
+
 @register_node
 class TensorIntrin(NodeBase):
     """Tensor intrinsic functions for certain computation.
@@ -17,8 +33,16 @@ class TensorIntrin(NodeBase):
     --------
     decl_tensor_intrin: Construct a TensorIntrin
     """
-    pass
-
+    def __call__(self, *args, **kwargs):
+        tensors = [x.tensor for x in args]
+        regions = [_get_region(x) for x in args]
+        reduce_axis = []
+        if "reduce_axis" in kwargs:
+            reduce_axis = kwargs["reduce_axis"]
+            if not isinstance(reduce_axis, (list, tuple)):
+                reduce_axis = [reduce_axis]
+            reduce_axis = _api.convert(reduce_axis)
+        return _api_internal._TensorIntrinCall(self, tensors, regions, reduce_axis)
 
 def decl_tensor_intrin(op,
                        fcompute,

src/api/api_lang.cc

@@ -239,6 +239,14 @@ TVM_REGISTER_API("_TensorIntrin")
                                   args[6]);
   });
 
+TVM_REGISTER_API("_TensorIntrinCall")
+.set_body([](TVMArgs args,  TVMRetValue* ret) {
+    *ret = TensorIntrinCallNode::make(args[0],
+                                      args[1],
+                                      args[2],
+                                      args[3]);
+  });
+
 TVM_REGISTER_API("_TensorEqual")
 .set_body([](TVMArgs args,  TVMRetValue* ret) {
     *ret = args[0].operator Tensor() == args[1].operator Tensor();
@@ -278,6 +286,18 @@ TVM_REGISTER_API("_ScanOp")
                             args[7]);
   });
 
+TVM_REGISTER_API("_TensorComputeOp")
+.set_body([](TVMArgs args,  TVMRetValue* ret) {
+    *ret = TensorComputeOpNode::make(args[0],
+                                     args[1],
+                                     args[2],
+                                     args[3],
+                                     args[4],
+                                     args[5],
+                                     args[6],
+                                     args[7]);
+  });
+
 TVM_REGISTER_API("_ExternOp")
 .set_body([](TVMArgs args,  TVMRetValue* ret) {
     *ret = ExternOpNode::make(args[0],