[Relay/TOPI] Added dilation_value attribute to dilate operator.

include/tvm/relay/attrs/nn.h

@@ -596,11 +596,13 @@ struct Conv2DTransposeAttrs : public tvm::AttrsNode<Conv2DTransposeAttrs> {
 /*! \brief Attributes used in dilate operator */
 struct DilateAttrs : public tvm::AttrsNode<DilateAttrs> {
   Array<IndexExpr> strides;
+  double dilation_value;
 
   TVM_DECLARE_ATTRS(DilateAttrs, "relay.attrs.DilateAttrs") {
     TVM_ATTR_FIELD(strides)
         .set_default(Array<IndexExpr>({1, 1}))
         .describe("Dilation stride on each dimension, 1 means no dilation.");
+    TVM_ATTR_FIELD(dilation_value).set_default(0.0).describe("Value used to dilate the input.");
   }
 };
 

include/tvm/topi/nn/dilate.h

@@ -55,19 +55,20 @@ PrimExpr all(Array<PrimExpr> args) {
 }
 
 /*!
- * \brief Dilate data with zeros
+ * \brief Dilate data with given dilation value (0 by default).
  *
  * \param x The input tensor, this can have any number of
  * dimensions and any layout.
  * \param strides Dilation stride for each dimension. Stride 1
  * means no dilation.
+ * \param dilation_value Value used to dilate the input.
  * \param name The name of the operation
  * \param tag The tag to mark the operation
  *
  * \return The output tensor.
  */
-inline Tensor dilate(const Tensor& x, Array<PrimExpr> strides, std::string name = "tensor",
-                     std::string tag = kInjective) {
+inline Tensor dilate(const Tensor& x, Array<PrimExpr> strides, double dilation_value,
+                     std::string name = "tensor", std::string tag = kInjective) {
   auto n = x->shape.size();
   CHECK_EQ(n, strides.size()) << "strides size (" << strides.size()
                               << ") must match dimension of x (" << n << ")";
@@ -94,7 +95,8 @@ inline Tensor dilate(const Tensor& x, Array<PrimExpr> strides, std::string name
         }
         if (not_zero.size() > 0) {
           auto all_not_zero = all(not_zero);
-          return tvm::if_then_else(all_not_zero, x(index_tuple), make_const(x->dtype, 0));
+          return tvm::if_then_else(all_not_zero, x(index_tuple),
+                                   make_const(x->dtype, dilation_value));
         }
         return x(index_tuple);
       },

python/tvm/relay/op/nn/_nn.py

@@ -668,7 +668,7 @@ def compute_cross_entropy(attrs, inputs, out_dtype):
 # dilate
 @reg.register_compute("nn.dilate")
 def compute_dilate(attrs, inputs, out_dtype):
-    return [topi.nn.dilate(inputs[0], attrs.strides)]
+    return [topi.nn.dilate(inputs[0], attrs.strides, attrs.dilation_value)]
 
 
 reg.register_broadcast_schedule("nn.dilate")

python/tvm/relay/op/nn/nn.py

@@ -1549,23 +1549,26 @@ def pad(data, pad_width, pad_value=0, pad_mode="constant"):
     return _make.pad(data, pad_width, pad_value, pad_mode)
 
 
-def dilate(data, strides):
-    """Dilate data with zeros.
+def dilate(data, strides, dilation_value=0.0):
+    """Dilate data with given dilation value (0 by default).
 
     Parameters
     ----------
     data : tvm.relay.Expr
         n-D, can be any layout.
 
-    strides : <tuple of <int>
+    strides : tuple of <int>
         Dilation stride on each dimension, 1 means no dilation.
 
+    dilation_value : int/float, optional
+        Value used to dilate the input.
+
     Returns
     -------
     Output : tvm.relay.Expr
         The computed result
     """
-    return _make.dilate(data, strides)
+    return _make.dilate(data, strides, dilation_value)
 
 
 def mirror_pad(data, pad_width, mode="SYMMETRIC"):

python/tvm/topi/nn/dilate.py

@@ -23,8 +23,8 @@
 
 
 @te.tag_scope(tag=tag.INJECTIVE + ",dilate")
-def dilate(data, strides, name="DilatedInput"):
-    """Dilate data with zeros.
+def dilate(data, strides, dilation_value=0.0, name="DilatedInput"):
+    """Dilate data with given dilation value (0 by default).
 
     Parameters
     ----------
@@ -34,6 +34,9 @@ def dilate(data, strides, name="DilatedInput"):
     strides : list / tuple of n ints
         Dilation stride on each dimension, 1 means no dilation.
 
+    dilation_value : int/float, optional
+        Value used to dilate the input.
+
     name : str, optional
         The name prefix operators generated
 
@@ -62,7 +65,7 @@ def _dilate(*indices):
         if not_zero:
             not_zero = tvm.tir.all(*not_zero)
             return tvm.tir.if_then_else(
-                not_zero, data(*index_tuple), tvm.tir.const(0.0, data.dtype)
+                not_zero, data(*index_tuple), tvm.tir.const(dilation_value, data.dtype)
             )
         return data(*index_tuple)
 

python/tvm/topi/testing/dilate_python.py

@@ -19,7 +19,7 @@
 import numpy as np
 
 
-def dilate_python(input_np, strides):
+def dilate_python(input_np, strides, dilation_value=0.0):
     """Dilate operation.
 
     Parameters
@@ -30,6 +30,9 @@ def dilate_python(input_np, strides):
     strides : list / tuple of n ints
         Dilation stride on each dimension, 1 means no dilation.
 
+    dilation_value : int/float, optional
+        Value used to dilate the input.
+
     Returns
     -------
     output_np : numpy.ndarray
@@ -45,7 +48,8 @@ def dilate_python(input_np, strides):
     for i in range(n):
         output_size += ((input_np.shape[i] - 1) * strides[i] + 1,)
         no_zero += ((range(0, output_size[i], strides[i])),)
-    output_np = np.zeros(shape=output_size)
+    output_np = np.ones(shape=output_size)
+    output_np = dilation_value * output_np
     output_np[np.ix_(*no_zero)] = input_np
 
     return output_np

src/relay/op/nn/nn.cc

@@ -961,9 +961,10 @@ bool DilateRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
 }
 
 // Positional relay function to create dilate operator used by frontend FFI.
-Expr MakeDilate(Expr data, Array<IndexExpr> strides) {
+Expr MakeDilate(Expr data, Array<IndexExpr> strides, double dilation_value = 0.0) {
   auto attrs = make_object<DilateAttrs>();
   attrs->strides = std::move(strides);
+  attrs->dilation_value = std::move(dilation_value);
   static const Op& op = Op::Get("nn.dilate");
   return Call(op, {data}, Attrs(attrs), {});
 }
@@ -972,7 +973,7 @@ TVM_REGISTER_GLOBAL("relay.op.nn._make.dilate").set_body_typed(MakeDilate);
 
 RELAY_REGISTER_OP("nn.dilate")
     .describe(R"code(
-Dilate data with zeros.
+Dilate data with given dilation value (0 by default).
 )code" TVM_ADD_FILELINE)
     .set_num_inputs(1)
     .add_argument("x", "1D Tensor", "Data to dilate.")

src/topi/nn.cc

@@ -75,7 +75,7 @@ TVM_REGISTER_GLOBAL("topi.nn.batch_matmul").set_body([](TVMArgs args, TVMRetValu
 
 /* Ops from nn/dilate.h */
 TVM_REGISTER_GLOBAL("topi.nn.dilate").set_body([](TVMArgs args, TVMRetValue* rv) {
-  *rv = nn::dilate(args[0], args[1]);
+  *rv = nn::dilate(args[0], args[1], args[2]);
 });
 
 /* Ops from nn/flatten.h */

tests/python/relay/test_any.py

@@ -740,18 +740,24 @@ def test_any_pad():
     verify_any_pad(any_dims(4), ((1, 0), (1, 3), (0, 2), (9, 0)), (13, 11, 3, 1))
 
 
-def verify_any_dilate(data_shape, strides, static_data_shape):
+def verify_any_dilate(data_shape, strides, static_data_shape, dilation_value=None):
     assert len(data_shape) == len(strides)
     mod = tvm.IRModule()
     dtype = "float32"
     data = relay.var("data", shape=data_shape, dtype=dtype)
-    y = relay.nn.dilate(data, strides)
+    if dilation_value is None:
+        y = relay.nn.dilate(data, strides)
+    else:
+        y = relay.nn.dilate(data, strides, dilation_value)
     mod["main"] = relay.Function([data], y)
     data_np = np.random.uniform(size=static_data_shape).astype(dtype)
     ref_shape = tuple(
         (static_data_shape[i] - 1) * strides[i] + 1 for i in range(len(static_data_shape))
     )
-    ref_out = np.zeros(shape=ref_shape, dtype=dtype)
+    if dilation_value is None:
+        dilation_value = 0.0
+    ref_out = np.ones(shape=ref_shape, dtype=dtype)
+    ref_out = dilation_value * ref_out
     ref_out[tuple(slice(None, None, strides[i]) for i in range(len(data_shape)))] = data_np
     check_result([data_np], mod, ref_out)
 
@@ -766,6 +772,7 @@ def test_any_dilate():
     verify_any_dilate(any_dims(3), (1, 1, 5), (1, 2, 3))
     verify_any_dilate(any_dims(3), (3, 7, 5), (1, 2, 3))
     verify_any_dilate(any_dims(4), (3, 7, 1, 5), (1, 2, 3, 4))
+    verify_any_dilate(any_dims(4), (3, 7, 1, 5), (1, 2, 3, 4), 1.0)
 
 
 def verify_any_softmax(data_shape, axis, static_data_shape, ref_out_shape):

tests/python/topi/python/test_topi_dilate.py

@@ -26,12 +26,18 @@ def test_dilate():
     target = "llvm"
     ctx = tvm.cpu(0)
 
-    def _test_dilate(input_size, strides):
+    def _test_dilate(input_size, strides, dilation_value=None):
         Input = te.placeholder((input_size))
-        Output = topi.nn.dilate(Input, strides)
+        if dilation_value is None:
+            Output = topi.nn.dilate(Input, strides)
+        else:
+            Output = topi.nn.dilate(Input, strides, dilation_value)
         schedule = te.create_schedule(Output.op)
         input_np = np.random.uniform(size=input_size).astype(Input.dtype)
-        output_np = tvm.topi.testing.dilate_python(input_np, strides)
+        if dilation_value is None:
+            output_np = tvm.topi.testing.dilate_python(input_np, strides)
+        else:
+            output_np = tvm.topi.testing.dilate_python(input_np, strides, dilation_value)
         input_tvm = tvm.nd.array(input_np, ctx=ctx)
         output_size = topi.util.get_const_tuple(Output.shape)
         output_tvm = tvm.nd.array(np.zeros(shape=output_size).astype(Output.dtype), ctx=ctx)
@@ -47,6 +53,7 @@ def _test_dilate(input_size, strides):
     _test_dilate((1, 32, 32, 3, 3), (2, 2, 2, 2, 2))
     _test_dilate((1, 32, 32, 32, 3, 3), (1, 1, 1, 2, 2, 2))
     _test_dilate((1, 32, 32, 32, 3, 3), (2, 2, 2, 1, 1, 1))
+    _test_dilate((1, 32, 32, 32, 3, 3), (2, 2, 2, 1, 1, 1), 1.0)
 
 
 if __name__ == "__main__":