[Relay] Alter Op Layout

include/tvm/relay/attrs/nn.h

@@ -105,6 +105,7 @@ struct Conv2DTransposeAttrs : public tvm::AttrsNode<Conv2DTransposeAttrs> {
   int groups;
   std::string data_layout;
   std::string weight_layout;
+  std::string out_layout;
   DataType out_dtype;
 
   TVM_DECLARE_ATTRS(Conv2DTransposeAttrs, "relay.attrs.Conv2DTransposeAttrs") {
@@ -139,6 +140,10 @@ struct Conv2DTransposeAttrs : public tvm::AttrsNode<Conv2DTransposeAttrs> {
       .describe("Dimension ordering of data and weight. Can be 'OIHW', 'OIHW16o16i', etc."
                 "'O', 'I', 'H', 'W' stands for num_filter, input_channel, height, and width"
                 "dimensions respectively.");
+    TVM_ATTR_FIELD(out_layout).set_default("")
+        .describe("Dimension ordering of output. Can be 'NCHW', 'NHWC', etc."
+                      "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                      "dimensions respectively. Default to be same as input layout.");
     TVM_ATTR_FIELD(out_dtype)
         .set_default(NullValue<DataType>())
         .describe("Output data type, set to explicit type under mixed precision setting");

include/tvm/relay/attrs/transform.h

@@ -164,6 +164,19 @@ struct ClipAttrs : public tvm::AttrsNode<ClipAttrs> {
   }
 };
 
+
+struct LayoutTransformAttrs : public tvm::AttrsNode<LayoutTransformAttrs> {
+  std::string src_layout;
+  std::string dst_layout;
+
+  TVM_DECLARE_ATTRS(LayoutTransformAttrs, "relay.attrs.LayoutTransformAttrs") {
+    TVM_ATTR_FIELD(src_layout)
+        .describe("The source layout of the tensor. (e.g. NCHW)");
+    TVM_ATTR_FIELD(dst_layout)
+        .describe("The destination layout of the tensor. (e.g. NCHW16c)");
+  }
+};
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_TRANSFORM_H_

include/tvm/relay/expr.h

@@ -459,7 +459,7 @@ inline const TTypeNode* ExprNode::type_as() const {
   static_assert(std::is_base_of<TypeNode, TTypeNode>::value,
                 "TType must be a special case of type");
   CHECK(checked_type_.defined())
-      << "Type inference for this Expr has not completed";
+      << "Type inference for this Expr has not completed. Try to call infer_type pass.";
   const TTypeNode* node = checked_type_.as<TTypeNode>();
   CHECK(node != nullptr)
       << "Expected type to be " << TTypeNode::_type_key

include/tvm/relay/op_attr_types.h

@@ -86,6 +86,21 @@ using FTVMSchedule = runtime::TypedPackedFunc<
            const Array<Tensor>& outs,
            const Target& target)>;
 
+/*!
+ * \brief Alternate the layout of operators or replace the
+ *  operator with other expressions. This function will be invoked
+ *  in AlterOpLayout pass.
+ * \param attrs The attribute of the original node.
+ * \param inputs The input symbols of the original node.
+ * \param tinfos An array of placeholders, use for getting the inferred shape
+ *               and dtype of the inputs.
+ * \return new_expr The modified expression.
+ */
+using FTVMAlterOpLayout = runtime::TypedPackedFunc<
+  Expr(const Attrs& attrs,
+       const Array<Expr>& args,
+       const Array<Tensor>& tinfos)>;
+
 /*!
  * \brief Forward rewriting rule for a specific op.
  *

include/tvm/relay/pass.h

@@ -8,6 +8,7 @@
 
 #include <tvm/relay/module.h>
 #include <tvm/relay/expr.h>
+#include <tvm/relay/op_attr_types.h>
 #include <string>
 
 namespace tvm {
@@ -173,6 +174,21 @@ Expr ForwardRewrite(const Expr& expr,
                     std::function<NodeRef(const Call&)> fcontext = nullptr,
                     std::function<Expr(const Expr&)> fmulti_ref_trigger = nullptr);
 
+/*!
+ * \brief Apply rewrite rules to rewrite the expr in post DFS order.
+ * \param expr The expression.
+ * \param rewrite_func The rewrite func that will apply to all operators.
+ * \param fcontext Additional callback to provide context argument for each call node.
+ * \param fmulti_ref_trigger Transformation function to be called when
+ *                           an Expr consumed by multiple callers.
+ * \return The rewritten expression.
+ */
+Expr ForwardRewrite(const Expr& expr,
+                    const FForwardRewrite& rewrite_func,
+                    std::function<NodeRef(const Call&)> fcontext = nullptr,
+                    std::function<Expr(const Expr&)> fmulti_ref_trigger = nullptr);
+
+
 /*! \brief A hashing structure in the style of std::hash. */
 struct StructuralHash {
   /*! \brief Hash a Relay type.

python/tvm/__init__.py

@@ -13,6 +13,7 @@
 from . import schedule
 from . import module
 from . import node
+from . import attrs
 from . import ir_builder
 from . import target
 from . import generic

python/tvm/attrs.py

@@ -0,0 +1,40 @@
+""" TVM Attribute module, which is mainly used for defining attributes of operators"""
+from ._ffi.node import NodeBase, register_node as _register_tvm_node
+from ._ffi.function import _init_api
+from . import _api_internal
+
+
+@_register_tvm_node
+class Attrs(NodeBase):
+    """Attribute node, which is mainly use for defining attributes of relay operators.
+
+    Used by function registered in python side, such as compute, schedule and alter_layout.
+    Attrs is passed as the first argument to these functions.
+    """
+    def list_field_info(self):
+        """ Get fields information
+
+        Returns
+        -------
+        infos: list of AttrFieldInfo
+            List of field information
+        """
+        return _api_internal._AttrsListFieldInfo(self)
+
+    def keys(self):
+        """Get list of names in the attribute.
+
+        Returns
+        -------
+        keys : list of str
+            List of keys
+        """
+        fields = self.list_field_info()
+        for field in fields:
+            yield field.name
+
+    def __getitem__(self, item):
+        return self.__getattr__(item)
+
+
+_init_api("tvm.attrs")

python/tvm/relay/base.py

@@ -21,6 +21,20 @@ def register_relay_node(type_key=None):
     return _register_tvm_node(type_key)
 
 
+def register_relay_attr_node(type_key=None):
+    """register relay attribute node
+
+    Parameters
+    ----------
+    type_key : str or cls
+        The type key of the node
+    """
+    if not isinstance(type_key, str):
+        return _register_tvm_node(
+            "relay.attrs." + type_key.__name__)(type_key)
+    return _register_tvm_node(type_key)
+
+
 class RelayNode(NodeBase):
     """Base class of all relay node."""
     def astext(self, show_meta_data=True, annotate=None):

python/tvm/relay/build_module.py

@@ -17,6 +17,7 @@
     "FoldConstant": 2,
     "CombineParallelConv2D": 3,
     "FoldScaleAxis": 3,
+    "AlterOpLayout": 3,
 }
 
 class BuildConfig(object):
@@ -157,6 +158,13 @@ def optimize(func, params=None):
 
     if cfg.pass_enabled("FoldConstant"):
         func = ir_pass.fold_constant(func)
+
+    if cfg.pass_enabled("AlterOpLayout"):
+        func = ir_pass.infer_type(func)
+        func = ir_pass.canonicalize_ops(func)
+        func = ir_pass.infer_type(func)
+        func = ir_pass.alter_op_layout(func)
+
     return func
 
 

python/tvm/relay/ir_pass.py

@@ -191,6 +191,23 @@ def simplify_inference(expr):
     return _ir_pass.simplify_inference(expr)
 
 
+def canonicalize_ops(expr):
+    """ Canonicalize special operators to basic operators.
+    This can simplify latter analysis. (e.g. Expand bias_add to expand_dims and broadcast_add.)
+
+    Parameters
+    ----------
+    e: tvm.relay.Expr
+        The input Expression
+
+    Returns
+    -------
+    result: tvm.relay.Expr
+        An expression without bias_add
+    """
+    return _ir_pass.canonicalize_ops(expr)
+
+
 def dead_code_elimination(expr):
     """ Remove expressions which does not effect the program result (dead code).
 
@@ -321,3 +338,22 @@ def combine_parallel_conv2d(expr):
         Transformed expression
     """
     return _ir_pass.CombineParallelConv2D(expr)
+
+
+def alter_op_layout(expr):
+    """Alternate the layouts of operators or replace primitive operators with
+    other expressions.
+    This pass can be used for computing convolution in custom layouts or
+    other general weight pre-transformation.
+
+    Parameters
+    ----------
+    expr : tvm.relay.Expr
+        The input expression.
+
+    Returns
+    -------
+    transformed_expr : tvm.relay.Expr
+        Transformed expression with alternated layout.
+    """
+    return _ir_pass.AlterOpLayout(expr)

python/tvm/relay/op/__init__.py

@@ -1,7 +1,8 @@
 #pylint: disable=wildcard-import, redefined-builtin
 """Relay core operators."""
 # operator defs
-from .op import get, register, register_schedule, register_compute, Op
+from .op import get, register, register_schedule, register_compute, register_alter_op_layout, \
+    Op
 
 # Operators
 from .reduce import *
@@ -10,6 +11,7 @@
 from . import nn
 from . import image
 from . import vision
+from . import op_attrs
 
 # operator registry
 from . import _tensor

python/tvm/relay/op/_tensor.py

@@ -80,12 +80,3 @@ def clip_compute(attrs, inputs, output_type, target):
     return [topi.clip(inputs[0], attrs.a_min, attrs.a_max)]
 
 register_schedule("clip", schedule_elemwise)
-register_pattern("clip", OpPattern.ELEMWISE)
-
-# concatenate
-@register_compute("concatenate")
-def concatenate_compute(attrs, inputs, output_type, target):
-    return [topi.concatenate(inputs, axis=attrs.axis)]
-
-register_schedule("concatenate", schedule_injective)
-register_pattern("concatenate", OpPattern.INJECTIVE)

python/tvm/relay/op/_transform.py

@@ -1,8 +1,10 @@
 """Backend compiler related feature registration"""
-# pylint: disable=invalid-name
+# pylint: disable=invalid-name,unused-argument
 from __future__ import absolute_import
+import topi
 from . import op as _reg
 from ._reduce import _schedule_reduce
+from .op import schedule_injective, OpPattern
 
 schedule_injective = _reg.schedule_injective
 schedule_broadcast = _reg.schedule_injective
@@ -15,10 +17,22 @@
 _reg.register_schedule("reshape_like", schedule_injective)
 _reg.register_schedule("full", schedule_injective)
 _reg.register_schedule("full_like", schedule_injective)
-_reg.register_schedule("cast", schedule_broadcast)
+_reg.register_schedule("cast", schedule_injective)
 _reg.register_schedule("strided_slice", schedule_injective)
 _reg.register_schedule("slice_like", schedule_injective)
 _reg.register_schedule("split", schedule_injective)
 _reg.register_schedule("take", schedule_injective)
 _reg.register_schedule("transpose", schedule_injective)
 _reg.register_schedule("where", schedule_broadcast)
+
+# layout_transform
+_reg.register_schedule("layout_transform", schedule_injective)
+_reg.register_pattern("layout_transform", OpPattern.INJECTIVE)
+
+# concatenate
+@_reg.register_compute("concatenate")
+def concatenate_compute(attrs, inputs, output_type, target):
+    return [topi.concatenate(inputs, axis=attrs.axis)]
+
+_reg.register_schedule("concatenate", schedule_injective)
+_reg.register_pattern("concatenate", OpPattern.INJECTIVE)

python/tvm/relay/op/op.py

@@ -107,7 +107,7 @@ def register_schedule(op_name, schedule=None, level=10):
     op_name : str
         The name of the op.
 
-    schedule : function
+    schedule : function (attrs: Attrs, outs: List[Tensor], target: Target) -> sch: Schedule
         The schedule function.
 
     level : int
@@ -124,7 +124,8 @@ def register_compute(op_name, compute=None, level=10):
     op_name : str
         The name of the op.
 
-    compute : function
+    compute : function (attrs: Attrs, inputs: List[Tensor], out_type: Type, target:Target)
+                       -> List[Tensor]
         The compute function.
 
     level : int
@@ -133,6 +134,23 @@ def register_compute(op_name, compute=None, level=10):
     return register(op_name, "FTVMCompute", compute, level)
 
 
+def register_alter_op_layout(op_name, alter_layout=None, level=10):
+    """Register alter op layout function for an op
+
+    Parameters
+    ----------
+    op_name : str
+        The name of the operator
+
+    alter_layout: function (attrs: Attrs, inputs: List[Expr]) -> new_expr: Expr
+        The function for changing the layout or replacing the operator
+
+    level : int
+        The priority level
+    """
+    return register(op_name, "FTVMAlterOpLayout", alter_layout, level)
+
+
 def register_pattern(op_name, pattern, level=10):
     """Register operator pattern for an op.
 

python/tvm/relay/op/op_attrs.py

@@ -0,0 +1,14 @@
+"""The attributes node used for Relay operators"""
+
+from ...attrs import Attrs
+from ..base import register_relay_attr_node
+
+@register_relay_attr_node
+class Conv2DAttrs(Attrs):
+    """Attribute of a Convolution Operator"""
+    pass
+
+@register_relay_attr_node
+class GlobalPool2DAttrs(Attrs):
+    """Attribute of a Global 2D Pooling Operator"""
+    pass

python/tvm/relay/op/transform.py

@@ -387,3 +387,25 @@ def slice_like(data, shape_like, axes=None):
         The computed result.
     """
     return _make.slice_like(data, shape_like, axes)
+
+
+def layout_transform(data, src_layout, dst_layout):
+    """Transform the layout of a tensor
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The source tensor to be transformed
+
+    src_layout: str
+        The source layout.  (e.g NCHW)
+
+    dst_layout: str
+        The destination layout.  (e.g. NCHW16c)
+
+    Returns
+    -------
+    ret : relay.Expr
+        The transformed tensor.
+    """
+    return _make.layout_transform(data, src_layout, dst_layout)