[Relax][OP] More high-level operators (apache#18)

* relax.cumsum

* Legalizer for expand_dims

* relax.trilu

* relax.cast

* Legalizer for batch_norm and flatten

* relax.take

* relax.full

* relax.split

* relax.broadcast_to

* relax.strided_slice

* relax.image.resize2d

* relax.nn.max_pool2d

* relax.nn.adaptive_avg_pool2d

include/tvm/relax/op_attr_types.h

@@ -403,6 +403,171 @@ struct ReduceAttrs : public tvm::AttrsNode<ReduceAttrs> {
   }
 };  // struct ReduceAttrs
 
+/*! \brief Attributes used in cumsum operator */
+struct CumsumAttrs : public tvm::AttrsNode<CumsumAttrs> {
+  Optional<Integer> axis;
+
+  TVM_DECLARE_ATTRS(CumsumAttrs, "relax.attrs.CumsumAttrs") {
+    TVM_ATTR_FIELD(axis).set_default(Optional<Integer>{NullOpt});
+  }
+};  // struct CumsumAttrs
+
+/*! \brief Attributes used in trilu operator */
+struct TriluAttrs : public tvm::AttrsNode<TriluAttrs> {
+  int k;
+  bool is_upper;
+
+  TVM_DECLARE_ATTRS(TriluAttrs, "relax.attrs.TriluAttrs") {
+    TVM_ATTR_FIELD(k).describe(
+        "The number of diagonals above or below the main diagonal to exclude or include.");
+    TVM_ATTR_FIELD(is_upper).set_default(true).describe(
+        "Whether to keep the upper or lower half of the diagonal.");
+  }
+};  // struct TriluAttrs
+
+/*! \brief Attributes used in cast operator */
+struct CastAttrs : public tvm::AttrsNode<CastAttrs> {
+  DataType dtype;
+
+  TVM_DECLARE_ATTRS(CastAttrs, "relax.attrs.CastAttrs") {
+    TVM_ATTR_FIELD(dtype).describe("Target data type");
+  }
+};  // struct CastAttrs.
+
+/*! \brief Attributes used in take operator */
+struct TakeAttrs : public tvm::AttrsNode<TakeAttrs> {
+  Optional<Integer> axis;
+  int batch_dims;
+  String mode;
+
+  TVM_DECLARE_ATTRS(TakeAttrs, "relax.attrs.TakeAttrs") {
+    TVM_ATTR_FIELD(axis)
+        .set_default(Optional<Integer>{NullOpt})
+        .describe("The axis over which to select values.");
+    TVM_ATTR_FIELD(batch_dims)
+        .set_default(0)
+        .describe("The batch_dims over which to select values.");
+    TVM_ATTR_FIELD(mode).set_default("clip").describe(
+        "Specify how out-of-bound indices will behave."
+        "clip - clip to the range (default)"
+        "wrap - wrap around the indices"
+        "fast - no clip or wrap around (user must make sure indices are in-bound)");
+  }
+};  // struct TakeAttrs
+
+/*! \brief Attributes used in full operator */
+struct FullAttrs : public tvm::AttrsNode<FullAttrs> {
+  DataType dtype;
+
+  TVM_DECLARE_ATTRS(FullAttrs, "relax.attrs.FullAttrs") {
+    TVM_ATTR_FIELD(dtype).describe("Target data type.");
+  }
+};  // struct FullAttrs
+
+/*! \brief Attributes used in split operator */
+struct SplitAttrs : public tvm::AttrsNode<SplitAttrs> {
+  ObjectRef indices_or_sections;
+  int axis;
+
+  TVM_DECLARE_ATTRS(SplitAttrs, "relax.attrs.SplitAttrs") {
+    TVM_ATTR_FIELD(indices_or_sections)
+        .describe("The input array of indices or the number of split sections.");
+    TVM_ATTR_FIELD(axis).describe("The axis to be splitted");
+  }
+};  // struct SplitAttrs
+
+/*! \brief Attributes used in strided_slice operator */
+struct StridedSliceAttrs : public tvm::AttrsNode<StridedSliceAttrs> {
+  Array<PrimExpr> begin;
+  Array<PrimExpr> end;
+  Optional<Array<PrimExpr>> strides;
+  Optional<Array<Integer>> axes;
+  String slice_mode;
+
+  TVM_DECLARE_ATTRS(StridedSliceAttrs, "relax.attrs.StridedSliceAttrs") {
+    TVM_ATTR_FIELD(begin).describe("Indices for begin of slice, begin index is also inclusive");
+    TVM_ATTR_FIELD(end).describe("Indices for end of slice, end index is exclusive");
+    TVM_ATTR_FIELD(strides).describe(
+        "Stride values of the slice, a stride can be negative, which causes a reverse slice.");
+    TVM_ATTR_FIELD(axes).describe(
+        "Axes along which slicing is applied. When it is specified, the length of begin, end, "
+        "strides, and axes must be equal.");
+    TVM_ATTR_FIELD(slice_mode)
+        .set_default("end")
+        .describe(
+            "The slice mode [end, size]."
+            "end - The default slice mode, ending indices for the slice."
+            "size - The input strides will be ignored, input end in this mode indicates the size"
+            "of a slice starting at the location specified by begin. If end[i] is -1,"
+            "all remaining elements in that dimension are included in the slice");
+  }
+};  // struct StridedSliceAttrs
+
+/*! \brief Attributes used in image resize2d operator */
+struct Resize2DAttrs : public tvm::AttrsNode<Resize2DAttrs> {
+  Array<PrimExpr> size;
+  Array<FloatImm> roi;
+  String layout;
+  String method;
+  String coordinate_transformation_mode;
+  String rounding_method;
+  double cubic_alpha;
+  int cubic_exclude;
+  double extrapolation_value;
+
+  TVM_DECLARE_ATTRS(Resize2DAttrs, "relax.attrs.Resize2DAttrs") {
+    TVM_ATTR_FIELD(size).describe("Output image size.");
+    TVM_ATTR_FIELD(roi).describe(
+        "Region of Interest for coordinate transformation mode 'tf_crop_and_resize'");
+    TVM_ATTR_FIELD(layout).set_default("NCHW").describe(
+        "Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+        "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+        "dimensions respectively. Resize is applied on the 'H' and"
+        "'W' dimensions.");
+    TVM_ATTR_FIELD(method).set_default("linear").describe(
+        "Specify the mode to use for scaling."
+        "nearest_neighbor -  Nearest Neighbor"
+        "linear - Bilinear Interpolation"
+        "cubic - Bicubic Interpolation");
+    TVM_ATTR_FIELD(coordinate_transformation_mode)
+        .set_default("half_pixel")
+        .describe(
+            "Describes how to transform the coordinate in the resized tensor"
+            "to the coordinate in the original tensor."
+            "Refer to the ONNX Resize operator specification for details"
+            "Available options are half_pixel, align_corners and asymmetric");
+    TVM_ATTR_FIELD(rounding_method)
+        .set_default("round")
+        .describe(
+            "indicates how to find the \"nearest\" pixel in nearest_neighbor method"
+            "Available options are round, floor, and ceil.");
+    TVM_ATTR_FIELD(cubic_alpha)
+        .set_default(-0.5)
+        .describe("Spline Coefficient for Bicubic Interpolation");
+    TVM_ATTR_FIELD(cubic_exclude)
+        .set_default(0)
+        .describe("Flag to exclude exterior of the image during bicubic interpolation");
+    TVM_ATTR_FIELD(extrapolation_value)
+        .set_default(0.0)
+        .describe("Value to return when roi is outside of the image");
+  }
+};  // struct Resize2dAttrs
+
+/*! \brief Attributes for 2d adaptive pool operator */
+struct AdaptivePool2DAttrs : public tvm::AttrsNode<AdaptivePool2DAttrs> {
+  Optional<Array<PrimExpr>> output_size;
+  String layout;
+
+  TVM_DECLARE_ATTRS(AdaptivePool2DAttrs, "relax.attrs.AdaptivePool2DAttrs") {
+    TVM_ATTR_FIELD(output_size).describe("Output height and width.");
+    TVM_ATTR_FIELD(layout).describe(
+        "Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+        "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+        "dimensions respectively. Pooling is applied on the 'H' and"
+        "'W' dimensions.");
+  }
+};  // struct AdaptivePool2DAttrs
+
 }  // namespace relax
 }  // namespace tvm
 #endif  // TVM_RELAX_OP_ATTR_TYPES_H_

python/tvm/relax/block_builder.py

@@ -213,7 +213,9 @@ def _convert_te_arg_helper(arg):
                     ), "emit_te only supports dict with string as the key currently"
                 return {k: _convert_te_arg_helper(arg[k]) for k in arg}
             elif (
-                isinstance(arg, (int, float, str, tir.IntImm, tvm.ir.Type, tvm.ir.Attrs))
+                isinstance(
+                    arg, (int, float, str, tir.IntImm, tir.FloatImm, tvm.ir.Type, tvm.ir.Attrs)
+                )
                 or arg is None
             ):
                 return arg