[Relay/TOPI][OP] Add arange op in Relay and TOPI (apache#2621)

* Add arange op

* Update docs

* Fix bug

* add sanity check in relay and mxnet frontend mapping

* lint

* nits

* pylint

* don't allow empty output from arange

* Remove empty test for arange

* Fix bug and update doc

docs/api/python/topi.rst

@@ -67,6 +67,7 @@ List of operators
    topi.not_equal
    topi.greater_equal
    topi.less_equal
+   topi.arange
    topi.image.resize
 
 
@@ -123,6 +124,7 @@ topi
 .. autofunction:: topi.power
 .. autofunction:: topi.greater
 .. autofunction:: topi.less
+.. autofunction:: topi.arange
 
 topi.nn
 ~~~~~~~

docs/langref/relay_op.rst

@@ -95,6 +95,7 @@ This level enables additional math and transform operators.
    tvm.relay.full_like
    tvm.relay.cast
    tvm.relay.split
+   tvm.relay.arange
 
 
 **Level 4: Broadcast and Reductions**
@@ -216,6 +217,7 @@ Level 3 Definitions
 .. autofunction:: tvm.relay.full_like
 .. autofunction:: tvm.relay.cast
 .. autofunction:: tvm.relay.split
+.. autofunction:: tvm.relay.arange
 
 
 Level 4 Definitions

include/tvm/relay/attrs/transform.h

@@ -96,6 +96,25 @@ struct InitOpAttrs : public tvm::AttrsNode<InitOpAttrs> {
   }
 };  // struct InitOpAttrs
 
+/*! \brief Attributes used in arange operators */
+struct ArangeAttrs : public tvm::AttrsNode<ArangeAttrs> {
+  tvm::Expr start;
+  tvm::Expr stop;
+  tvm::Expr step;
+  DataType dtype;
+
+  TVM_DECLARE_ATTRS(ArangeAttrs, "relay.attrs.ArangeAttrs") {
+    TVM_ATTR_FIELD(start).set_default(make_const(Float(32), 0))
+        .describe("Start of interval. The interval includes this value.");
+    TVM_ATTR_FIELD(stop)
+        .describe("Stop of interval. The interval does not include this value.");
+    TVM_ATTR_FIELD(step).set_default(make_const(Float(32), 1))
+        .describe("Spacing between values.");
+    TVM_ATTR_FIELD(dtype).set_default(NullValue<DataType>())
+        .describe("Target data type.");
+  }
+};  // struct ArangeAttrs
+
 /*! \brief Attributes used in squeeze operators */
 struct SqueezeAttrs : public tvm::AttrsNode<SqueezeAttrs> {
   // use axis to make the name numpy compatible.

python/tvm/relay/frontend/mxnet.py

@@ -268,6 +268,18 @@ def _mx_multibox_detection(inputs, attrs):
     return _op.vision.nms(ret[0], ret[1], **new_attrs1)
 
 
+def _mx_arange(inputs, attrs):
+    assert len(inputs) == 0
+    if attrs.get_int("repeat", 1) != 1:
+        raise RuntimeError("arange doesn't support repeat")
+    new_attrs = {}
+    new_attrs["start"] = attrs.get_float("start", 0)
+    new_attrs["stop"] = attrs.get_float("stop")
+    new_attrs["step"] = attrs.get_float("step", 1)
+    new_attrs["dtype"] = attrs.get_str("dtype", "float32")
+    return _op.arange(**new_attrs)
+
+
 def _mx_roi_align(inputs, attrs):
     new_attrs = {}
     new_attrs["pooled_size"] = attrs.get_int_tuple("pooled_size")
@@ -362,6 +374,7 @@ def _mx_roi_align(inputs, attrs):
     "Concat"        : _mx_concat,
     "concat"        : _mx_concat,
     "LeakyReLU"     : _mx_leaky_relu,
+    "_arange"       : _mx_arange,
     "SoftmaxOutput" : _mx_softmax_output,
     "SoftmaxActivation" : _mx_softmax_activation,
     # vision

python/tvm/relay/op/_transform.py

@@ -19,6 +19,7 @@
 _reg.register_schedule("reshape_like", schedule_injective)
 _reg.register_schedule("full", schedule_injective)
 _reg.register_schedule("full_like", schedule_injective)
+_reg.register_schedule("arange", schedule_injective)
 _reg.register_schedule("cast", schedule_injective)
 _reg.register_schedule("strided_slice", schedule_injective)
 _reg.register_schedule("slice_like", schedule_injective)

python/tvm/relay/op/transform.py

@@ -166,8 +166,9 @@ def reshape_like(data, shape_like):
     """Reshapes the input array by the size of another array.
     For an input array with shape ``(d1, d2, ..., dk)``, `reshape_like` operation reshapes
     the input array into an output array with the same shape as the second input array.
+
     .. note::
-    Sizes for both array should be compatible.
+        Sizes for both array should be compatible.
 
     Parameters
     ----------
@@ -249,10 +250,57 @@ def full_like(data, fill_value):
     return _make.full_like(data, fill_value)
 
 
+def arange(start, stop=None, step=1, dtype="float32"):
+    """Return evenly spaced values within a given interval.
+
+    .. note::
+        Similar to ``numpy.arange``, when only one argument is given, it is used
+        as `stop` instead of `start` while `start` takes default value 0.
+
+        Warning: Undefined behavior when dtype is incompatible with start/stop/step.
+        It could lead to different results compared to numpy, MXNet, pytorch, etc.
+
+    Parameters
+    ----------
+    start : tvm.Expr, optional
+        Start of interval. The interval includes this value. The default start
+        value is 0.
+
+    stop : tvm.Expr
+        Stop of interval. The interval does not include this value.
+
+    step : tvm.Expr, optional
+        Spacing between values. The default step size is 1.
+
+    dtype : str, optional
+        The target data type.
+
+    Returns
+    -------
+    result : relay.Expr
+        The resulting tensor.
+
+    Examples
+    --------
+    .. code-block:: python
+
+        relay.arange(5) = [0, 1, 2, 3, 4]
+        relay.arange(1, 5) = [1, 2, 3, 4]
+        relay.arange(1, 5, 1.5) = [1, 2.5, 4]
+    """
+    if stop is None:
+        stop = start
+        start = 0
+    return _make.arange(start, stop, step, dtype)
+
+
 def where(condition, x, y):
     """Selecting elements from either x or y depending on the value of the
     condition.
 
+    .. note::
+        The shape of condition, x, and y needs to be the same.
+
     Parameters
     ----------
     condition : relay.Expr
@@ -282,8 +330,6 @@ def where(condition, x, y):
 
         condition = [1, 0]
         relay.where(conditon, x, y) = [[1, 2], [7, 8]]
-
-    Note that the shape of condition, x, and y needs to be the same.
     """
     return _make.where(condition, x, y)
 

src/relay/op/tensor/transform.cc

@@ -880,6 +880,63 @@ and type as the input array.
 .set_attr<FTVMCompute>("FTVMCompute", FullLikeCompute)
 .set_attr<TOpPattern>("TOpPattern", kElemWise);
 
+// arange operator
+TVM_REGISTER_NODE_TYPE(ArangeAttrs);
+
+bool ArangeRel(const Array<Type>& types,
+               int num_inputs,
+               const Attrs& attrs,
+               const TypeReporter& reporter) {
+  CHECK_EQ(types.size(), 1);
+  const ArangeAttrs* param = attrs.as<ArangeAttrs>();
+  IndexExpr num_elem = tvm::cast(tvm::Int(32), tvm::ceil(
+      tvm::cast(tvm::Float(32), param->stop - param->start) / param->step));
+  if (const tvm::ir::IntImm* val = num_elem.as<tvm::ir::IntImm>()) {
+    CHECK_GT(val->value, 0)
+        << "Invalid arange attributes (start, stop, step): " << param->start
+        << ", " << param->stop << ", " << param->step;
+  }
+  reporter->Assign(types[0], TensorTypeNode::make({num_elem}, param->dtype));
+  return true;
+}
+
+Array<Tensor> ArangeCompute(const Attrs& attrs,
+                            const Array<Tensor>& inputs,
+                            const Type& out_type,
+                            const Target& target) {
+  const ArangeAttrs* param = attrs.as<ArangeAttrs>();
+  return { topi::arange(param->start, param->stop, param->step, param->dtype) };
+}
+
+Expr MakeArange(tvm::Expr start,
+                tvm::Expr stop,
+                tvm::Expr step,
+                DataType dtype) {
+  auto attrs = make_node<ArangeAttrs>();
+  attrs->start = std::move(start);
+  attrs->stop = std::move(stop);
+  attrs->step = std::move(step);
+  attrs->dtype = std::move(dtype);
+  static const Op& op = Op::Get("arange");
+  return CallNode::make(op, {}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_API("relay.op._make.arange")
+.set_body([](const TVMArgs& args, TVMRetValue* rv) {
+    runtime::detail::unpack_call<Expr, 4>(MakeArange, args, rv);
+});
+
+RELAY_REGISTER_OP("arange")
+.describe(R"code(Returns evenly spaced values within a given interval.
+
+)code" TVM_ADD_FILELINE)
+.set_attrs_type_key("relay.attrs.ArangeAttrs")
+.set_num_inputs(0)
+.set_support_level(3)
+.add_type_rel("Arange", ArangeRel)
+.set_attr<FTVMCompute>("FTVMCompute", ArangeCompute)
+.set_attr<TOpPattern>("TOpPattern", kInjective);
+
 // where operator
 bool WhereRel(const Array<Type>& types,
               int num_inputs,

tests/python/frontend/mxnet/test_forward.py

@@ -203,30 +203,51 @@ def test_forward_where():
     mx_cond = mx.nd.array(np_cond)
     mx_x = mx.nd.array(np_x)
     mx_y = mx.nd.array(np_y)
+    shapes = {'cond': dshape, 'x': dshape, 'y': dshape}
     mod = mx.mod.Module(mx_sym, label_names=None, data_names=['cond', 'x', 'y'])
-    mod.bind(data_shapes=[('cond', dshape), ('x', dshape), ('y', dshape)], for_training=False)
+    mod.bind(data_shapes=shapes.items(), for_training=False)
     mod.init_params()
     args, auxs = mod.get_params()
     mx_out = mx.nd.where(mx_cond, mx_x, mx_y).asnumpy()
-    out_shape = dshape
-    shape_dict = {'cond': dshape, 'x': dshape, 'y': dshape}
-    new_sym, params = relay.frontend.from_mxnet(mx_sym,
-                                                shape_dict,
-                                                arg_params=args,
-                                                aux_params=auxs)
+
+    new_sym, _ = relay.frontend.from_mxnet(mx_sym, shapes, args, auxs)
     for target, ctx in ctx_list():
-        with relay.build_config(opt_level=3):
-            graph, lib, params = relay.build(new_sym, target, params=params)
-        m = graph_runtime.create(graph, lib, ctx)
-        # set inputs
-        m.set_input("cond", tvm.nd.array(np_cond))
-        m.set_input("x", tvm.nd.array(np_x))
-        m.set_input("y", tvm.nd.array(np_y))
-        m.set_input(**params)
-        m.run()
-        # get outputs
-        tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).asnumpy()
-        tvm.testing.assert_allclose(mx_out, tvm_out, rtol=1e-5, atol=1e-5)
+        for kind in ["graph", "debug"]:
+            intrp = relay.create_executor(kind, ctx=ctx, target=target)
+            op_res = intrp.evaluate(new_sym)(np_cond, np_x, np_y)
+            tvm.testing.assert_allclose(op_res.asnumpy(), mx_out)
+
+
+def test_forward_arange():
+    def _mx_symbol(F, start, stop, step):
+        if start is None and step is None:
+            sym = F.arange(stop)
+        elif start is None:
+            sym = F.arange(stop, step=step)
+        elif step is None:
+            sym = F.arange(start, stop)
+        else:
+            sym = F.arange(start, stop, step)
+        return sym
+
+    def verify(start, stop, step):
+        ref_res = _mx_symbol(mx.nd, start, stop, step).asnumpy()
+        mx_sym = _mx_symbol(mx.sym, start, stop, step)
+        new_sym, _ = relay.frontend.from_mxnet(mx_sym, {})
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, ctx=ctx, target=target)
+                op_res = intrp.evaluate(new_sym)()
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res)
+    verify(0, 20, None)
+    verify(0, 20, 2)
+    verify(1, 20, None)
+    verify(1, 20, 2)
+    verify(1, 20, 1.5)
+    verify(1, 20.5, None)
+    verify(1, 20, 3)
+    verify(20, 1, -1)
+    verify(20, 1, -1.5)
 
 
 if __name__ == '__main__':
@@ -251,3 +272,4 @@ def test_forward_where():
     test_forward_argmax()
     test_forward_argmin()
     test_forward_where()
+    test_forward_arange()

tests/python/relay/test_op_level3.py

@@ -457,6 +457,40 @@ def test_infer_type_prelu():
     verify_infer_type_prelu((1, 3, 2, 2), None, 1, (1, 3, 2, 2))
     verify_infer_type_prelu((1, 2, 2, 3), None, 3, (1, 2, 2, 3))
 
+
+def test_arange():
+    def verify_arange(start, stop, step):
+        dtype = "float32"
+        if start is None and step is None:
+            x = relay.arange(stop)
+            ref_res = np.arange(stop)
+        elif start is None:
+            x = relay.arange(stop, step=step)
+            ref_res = np.arange(stop, step=step)
+        elif step is None:
+            x = relay.arange(start, stop)
+            ref_res = np.arange(start, stop)
+        else:
+            x = relay.arange(start, stop, step)
+            ref_res = np.arange(start, stop, step)
+
+        func = relay.Function([], x)
+        for target, ctx in ctx_list():
+            for kind in ["graph", "debug"]:
+                intrp = relay.create_executor(kind, ctx=ctx, target=target)
+                op_res = intrp.evaluate(func)()
+                tvm.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=1e-5)
+    verify_arange(None, 20, None)
+    verify_arange(None, 20, 2)
+    verify_arange(1, 20, None)
+    verify_arange(1, 20, 2)
+    verify_arange(1, 20, 1.5)
+    verify_arange(1, 20.5, None)
+    verify_arange(1, 20, 3)
+    verify_arange(20, 1, -1)
+    verify_arange(20, 1, -1.5)
+
+
 if __name__ == "__main__":
     test_cast()
     test_zeros_ones()
@@ -480,3 +514,4 @@ def test_infer_type_prelu():
     test_squeeze_infer_type()
     test_squeeze_bad_axes_infer_type()
     test_split_infer_type()
+    test_arange()

topi/include/topi/transform.h

@@ -868,6 +868,19 @@ inline Tensor tensordot(const Tensor& A,
   return compute(output_shape, func, name, tag);
 }
 
+inline Tensor arange(const Expr start,
+                     const Expr stop,
+                     const Expr step,
+                     Type dtype,
+                     std::string name = "tensor",
+                     std::string tag = kInjective) {
+  Expr num_elem = tvm::cast(tvm::Int(32), tvm::ceil(
+      tvm::cast(tvm::Float(32), stop - start) / step));
+  Array<Expr> shape;
+  return compute({num_elem}, [&](const Array<Var>& indices) {
+    return tvm::cast(dtype, start + step * indices[0]);
+  }, name, tag);
+}
 
 }  // namespace topi
 #endif  // TOPI_TRANSFORM_H_